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Order Processing: Steps, Example & Software

Ordering a product and getting it delivered has become commonplace. While order processing seems to work fast and without issue, a lot is going on in the background to ensure the seamless management of placement, picking, sorting, packing and shipping.

That’s only an overview of order processing and there’s a lot more to it, which we’ll get to in greater detail. Along the way, we’ll explore why order processing is important for businesses and the challenges they face. Plus, we’ll throw in some free templates to help streamline order processing.

What Is Order Processing?

Order processing, which includes sales order processing and purchase order processing, is the managing and fulfillment of customer orders. This process includes verifying customer information, authoring payment, preparing goods for shipment and tracking the order once it has been shipped. There’s also the tracking of inventory levels, stock availability and order status involved in order processing.

Customer data is collected and stored securely during order processing to make sure that all the pertinent information is being accurately tracked so that the customer’s order is correctly fulfilled. But the data can also help to understand a customer’s purchasing patterns, improve the company’s marketing strategies and streamline its product development. Effective ordering processing can improve a company’s service and help them be more competitive in the marketplace.

Project management software can make order processing more efficient and achieve those advantages. ProjectManager is award-winning project and portfolio management software with powerful kanban boards that can manage order fulfillment activities. Set up customizable kanban board columns to mimic the stages of order processing and capture customer and resource information information on kanban cards. Throughout the project, track the order to ensure it’s delivered on time and monitor costs and planned versus actual progress to stay on schedule. Get started with ProjectManager today for free.

Why Is Order Processing Important for Businesses?

Order processing is important for many reasons. The more effective a company’s order processing, the less likely they’ll have to carry more stock, which adds to warehouse costs. Equally, there won’t be a lack of stock. Order processing can help accurately track and forecast demand to ensure that what customers want is always on hand. This saves a company money by reducing excess stock and holding costs as well as low sales that come with stockouts.

Outside of inventory issues, ordering processing improves a company’s fulfillment. It makes order fulfillment faster and more accurate by reducing mistakes. This, in turn, leads to greater customer satisfaction and brand loyalty. Also, since the order fulfillment process is working more efficiently, teams can focus on other tasks that can help a company handle more orders without increasing their labor costs.

In general, having accurate and timely data on order history and trends allows companies to make more informed decisions about inventory levels, staffing needs and other operational matters. Effective order processing can also improve turnaround times and provide better overall service, which gives companies a competitive advantage.

Order Processing Steps

To reap those benefits requires understanding the order processing steps. Follow the below five steps in order processing to eliminate errors and improve the order management process.

1. Order Placement

Order processing begins with the customer making a purchase order . This is called order placement and involves the receiving and accepting of orders from customers. There are various ways that a business can accept order placement, from online sales through an e-commerce website, over the phone, etc.

Once the order is placed the next step is checking to make sure the customer has sent all the necessary information. This is called receiving the order. Once you’ve verified the customer order, you should determine whether you have the production capacity to begin working on it. If so, you should send a sales order back to the customer confirming the order and specifying details such as a description of the products to be manufactured, scheduled delivery date, quantity, total cost and more.

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Sales Order Template

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2. Order Picking

Once the order has been placed and received, it’s ready to be picked. This is when the company determines which product is needed to fulfill the order. This means ensuring that the product is in stock and, if it is, knowing where in the inventory the product is and getting it. There are different picking strategies, there’s piece picking, in which a picker collects products one at a time, zone picking where each picker is responsible for a zone in the warehouse and batch picking where pickers get products for several orders at once.

3. Order Sorting

When the order has been picked, it’s then sorted. This means that products are separated into groups before getting packed and shipped. This organizes the items according to the number of delivery locations. There are different ways to sort orders, which can add efficiency when one order contains multiple items. Orders also must be inspected to ensure they’re in good condition.

4. Order Packing

After the order is sorted, it’s packed. This involves placing the item in the appropriate container before it’s shipped to the customer. The goal is to use the most efficient, easy-to-handle and cost-effective packing materials possible. The package is then labeled and sealed.

5. Order Shipping

The product is now ready to be shipped and delivered to the customer, which can be done either directly by the manufacurer, or through a third-party logistics management firm. These orders can be shipped directly to customers or one order can be shipped with many others going to the same general location, which can reduce costs by reducing the total number of shipments. When shipping products, it’s important to track them to ensure they’re being delivered to customers on time and correctly.

Order Processing Example

To better understand what ordering processing is, let’s create an order processing example to illustrate the process in practical terms. It starts with the customer purchasing a component that they need as a component for manufacturing the assemblage of a widget.

ABC Manufacturing sends a purchase order to Acme Widgets to place the order, which includes quantity, delivery information and date. Acme Widgets will then send ABC Manufacturing a sales order to verify the details and confirm the purchase. They’ll then pick the proper widget from its warehouse and pack it. A label is adhered and the package is shipped to ABC Manufacturing.

Once the seller receives the purchase order, they create a sales order to confirm the information. The free sales order template for Excel has all the information needed to ensure that the order is correct and can be processed.

Order Processing Templates

Order processing is best managed with project management software, but not everyone is ready to upgrade and will try to control the process with spreadsheets. Many free project management templates for Excel and Word can help in this process. Below are a few order processing templates, some of the over 100 free templates available for immediate download on our site.

Purchase Order Template

Use this free purchase order template for Excel when a buyer and seller are initiating their transaction. It captures all the pertinent information necessary to pick, pack and ship the item being purchased and includes payment terms.

Inventory Template

Order processing and inventory management go hand in hand. Use this free inventory template for Excel to record the level of stock and track turnover to ensure there is always product on hand, but not so much as to cost more for warehousing.

Order Processing Challenges

Order processing is part of the larger logistics management and seeks to efficiently and accurately receive orders and deliver goods to customers. That’s the goal, but there are always obstacles on that path that must be overcome. Here are some of them.

Inventory Management

Order processing requires managing warehouse inventory to ensure that there’s enough stock to cover the orders, but not too much that will incur undue costs. Several aspects of inventory management must be considered.

• Stock Management: The process of managing the goods that are being sold, such as acquiring, storing, organizing and tracking inventory
• Equipment Inventory: Lists all the assets of an organization for auditing, insurance and deciding on resupply or the purchase of new equipment
• Production Inventory: The raw materials used in the manufacturing process

Production Planning

The production plan describes how an organization’s products and services are manufactured. This includes production targets, what resources will be used, processes and schedules.

Supply Chain Management

When talking about order processing, supply chain management is used to track, capture, fulfill and manage customer orders. It is used to manage the flow of goods and services to and from a business most efficiently and cost-effectively.

Resource Capacity Planning

This process helps organizations understand the amount of resources that are needed to complete the tasks and projects they do to maximize the return on investment. It tries to accurately predict future demand and ensure that there are the necessary resources to satisfy that.

Project Intake Process

This is where an organization collects information about a project from various stakeholders. This will inform the feasibility of the project under consideration and help decide if it’s worth pursuing.

Minimizing Costs

Reducing costs is always paramount in the minds of order processing management. There are many approaches to achieve this goal from automating workflows to streamlining policies and procedures. Training the order processing staff is also recommended in monitoring processes and measuring performance. Implementing continuous improvement initiatives and best practices will also help minimize costs.

How ProjectManager Helps With Order Processing

Of course, one of the best ways to reduce costs and improve processes and customer satisfaction is using project management software. ProjectManager is award-winning project and portfolio management software that manages the order fulfillment process with kanban boards. There are also other project views, such as Gantt charts, sheet, task and calendar views that help plan and features that can monitor and track performance in real time.

Plan, Schedule and Track Each Step of the Production Process

To ensure that there’s always enough stock to meet customer demand requires demand forecasting and planning. Scheduling the tasks for a production process is more efficient with robust Gantt charts , which can organize tasks, assign work to teams and provide detailed direction. But these Gantt charts go further, linking all four types of task dependencies to avoid costly delays, filtering for the critical path to identify essential tasks and setting a baseline to track progress and costs in real time.

Monitor Progress, Resource Utilization & Costs

Once a baseline is set on the Gantt chart, progress can be monitored across the software. Real-time dashboards automatically collect live data and display it on easy-to-read graphs and charts that show cost, time, workload and more. Use the team page or workload chart to get an overview of the team’s assignments. If some are over- or underallocated, balance their workload and keep everyone working at capacity to stay productive.

Related Content

Want to read more about the other processes that order processing touches? To find more information on kanban inventory management, production scheduling, purchase management, logistics management and production orders, follow the links below.

• Kanban Inventory Management: How to Run a Kanban System
• Production Scheduling Basics: Creating a Production Schedule
• Purchase Management: A How-To Guide With Best Practices
• Logistics Management 101: A Beginner’s Guide
• How to Make a Production Order for Manufacturing

ProjectManager is online project and portfolio management software that connects everybody from managers in the office to employees in warehouses and on the factory floor. They can share files, comment at the task level and stay updated with email and in-app notifications. Join teams at Avis, Nestle and Siemens who use our software to deliver successful projects. Get started with ProjectManager today for free.

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Order Fulfillment Explained: Process, Strategies, Costs, Metrics & More

Find out everything you need to know about order fulfillment. This guide covers its history, integral sales processes, various types, logistics, global-to-local strategies, key performance metrics, and emerging trends

What is Order Fulfillment?

Order fulfillment is the process through which businesses manage and fulfill customer orders, from receiving an order to delivering the product to the end customer. The order fulfillment pipeline bridges crucial touchpoints like — inventory management, picking, packing, shipping, and even potential returns. 

Starting at the factory, goods are produced and then stored in warehouses. Upon receiving an order, these goods are retrieved from the warehouse, prepared for shipment, and then sent to distribution centers or directly to retail locations. In some cases, especially in e-commerce, the items are shipped directly to the end consumer. Each step is crucial in ensuring that the right product reaches the right recipient promptly and efficiently.

The decision on managing order fulfillment processes— in-house or outsourced—varies by company and their specific needs. In-house order fulfillment definition means a company handles all aspects of the order fulfillment process using its resources, offering direct control over operations, inventory, and quality. In contrast, outsourced order fulfillment meaning involves partnering with third-party logistics providers (3PLs) specializing in these operations. 

The History of Modern Order Fulfillment

The evolution of order fulfillment over the years has been closely connected to changing consumer behaviors. As shopping methods shifted from physical stores to online platforms and now a blend of both, businesses had to adapt swiftly to keep pace.

• Traditional Fulfillment (Pre-Internet Era): In the years before the Internet, the order fulfillment meaning was very straightforward. Manufacturers would produce goods, which were then stored in bulk at warehouses. These goods would later be shipped in large quantities to physical retail outlets where consumers would make purchases. The model was predominantly linear, with little to no room for variation.
• Advent of Mail Ordering: Mail ordering provided consumers with the ability to shop from the comfort of their homes. Combined with the deregulation of motor carriers in the United States, these circumstances required businesses to develop direct-to-consumer fulfillment processes, where individual orders were picked, packed, and shipped based on catalog orders received by mail or over the phone.
• Rise of E-commerce: With the onset of the Internet, online shopping portals began to emerge. This shift demanded a new framework for order fulfillment. Warehouses had to adapt to picking and shipping individual items directly to consumers rather than bulk shipping to physical retailers. The need for efficient inventory management, rapid shipping, and return-handling processes became evident.
• Omni-channel Retailing: Omni-channel retailing emerged as a blend of physical and online shopping experiences. Consumers could order online and pick it up in-store or buy in-store and return it online. This approach required businesses to integrate their physical and digital order fulfillment processes seamlessly.
• The Amazon Effect: In order to meet heightened consumer expectations, Amazon introduced Prime - ushering in the era of same-day delivery. This phenomenon, known as the "Amazon Effect",  forever changed the landscape of order fulfillment. This compelled retailers worldwide to fine-tune their supply chains and utilize data analytics to expedite order fulfillment.
• Eco-Conscious Fulfillment: The modern consumer is informed, conscientious, and shows deep concern for sustainable consumption. This shift has shifted retailers' focus into eco-responsible practices, like green packaging, ethically sourced materials, and reducing carbon footprints. Sustainability has become a crucial part of brand integrity and customer appeal. The current fulfillment strategies reflect this, underscoring the importance of responsible practices in building consumer trust and long-term loyalty making sustainability a competitive differentiator in the crowded retail space.

With the convenience provided by online giants like Amazon, consumer expectations have skyrocketed. The demand for same-day or next-day delivery has now become standard, forcing businesses to address supply chain complexities and shipping delays. For example, a study by Deloitte reveals that 59% of responding manufacturing companies have faced shipping delays due to truck driver shortages and congested ports in the past 12–18 months.

As we move forward, consumers are increasingly valuing personalization and sustainability. This means delivering products quickly and ensuring packaging is personalized and eco-friendly. It’s not just about speed anymore but about the entire experience, from placing an order to the unboxing experience itself.

Order Fulfillment Processes

The order fulfillment cycle encompasses several key stages, each crucial to ensuring that products reach customers efficiently and accurately. While these stages form the backbone of fulfillment across various sales channels and business models, their implementation can differ significantly based on the specific operational demands of each channel.

Inventory Management

Order processing, returns processing, customer service.

Each of these processes requires integration based on the sales channel and business model in question. They're interconnected, and efficiency in one process often means efficiency across the whole fulfillment cycle. Each sales channel has unique requirements, customer expectations, and operational frameworks, and the way orders are fulfilled must align with these specifics to ensure customer satisfaction and maintain operational efficiency.

Dissecting Sales Channels

The order fulfillment cycle varies depending on the sales channels.

Traditional Retail

In traditional retail, encompassing physical stores, pop-up shops, and trade shows, order fulfillment processes predominantly revolve around ensuring that products are available on shelves for direct customer purchase. The process is less about individual order shipping and more about efficient inventory management, restocking, and in-store customer service. 

There are three primary categories:

• Big-box (e.g., Walmart, Target, Kroger): These retailers operate on a massive scale, requiring sophisticated supply chain management systems to keep their numerous stores stocked. They leverage robust data analytics to predict consumer behavior and ensure product availability. Big-box stores often have their distribution centers, allowing for efficient inventory management and distribution to their many outlets.
• Mid-size (e.g., Petco, Barnes and Noble, American Eagle): While they operate on a smaller scale than big-box retailers, mid-size stores also rely on data-driven supply chains and often utilize a mix of centralized and direct-to-store delivery models for replenishment. They might not have the same bargaining power with suppliers as big-box stores but focus on supplier relationships and niche market strategies to maintain inventory efficiency.
• Mom and Pop (Local neighborhood stores and Delis): These smaller, often family-owned stores typically have a more straightforward supply chain, sourcing goods from local suppliers of wholesale distributors. Order fulfillment management might be more manual, and the ordering process is often based on observed demand rather than complex predictive analysis. Personalized customer service, boutique experiences, and community connections are their unique strengths. The United States Census Bureau estimates there were 7,936,977 small business establishments in 2019.

Across all these traditional retail categories, the common goal is to avoid stockouts while minimizing overstock. The scale of operations, resources, technological integration, and supplier relationships are the key differentiators in how these retailers approach order fulfillment. According to a survey by DHL, 40% of respondents are insourcing order fulfillment while 48% are pursuing a hybrid approach.

Online Marketplaces

Online marketplaces such as Amazon, eBay, Walmart, and various regional alternatives have significantly reshaped the retail landscape, offering consumers a one-stop-shop experience with a wide variety of products from multiple sellers. Consumers tend to migrate towards these marketplaces due to several triggers:

• Convenience : These platforms provide unparalleled ease of shopping, with multiple brands and products available in one place, easy checkout processes, and quick delivery options.
• Variety : The vast array of products and brands gives consumers the power to compare and choose items that best fit their needs and budgets.
• Trust : The established reputation of these marketplaces often translates to consumer trust, especially concerning secure payment processes and reliable customer reviews.
• Competitive Pricing : Regular discounts, deals, and the ability to compare prices drive cost-conscious consumers to these platforms.

Order fulfillment meaning in these marketplaces can take various forms, significantly impacting both the seller's operations and the customer's shopping experience.

Fulfillment by Amazon (FBA) and Walmart Fulfillment Services (WFS) : These order fulfillment services, offered by Amazon and Walmart respectively, allow merchants to leverage the marketplace's vast logistic networks. Sellers send their products to Amazon's or Walmart's fulfillment centers, and the rest (storage, packing, shipping, customer service, and returns) are handled by the marketplace. Amazon’s FBA or WFS allow sellers to scale their business, access prime customers, and offer faster shipping, thereby enhancing customer satisfaction.

Fulfilled by Merchant (FBM) : In this model, sellers list their products on the marketplace but handle the fulfillment process themselves. Fulfillment by merchants gives sellers more control over their inventory, order fulfillment logistics, and shipping costs, which can be advantageous for niche products, fragile items, or when the merchants have an efficient fulfillment system in place. According to a survey by Shippo, two-thirds of respondents said they wouldn’t consider purchasing from a retailer that didn’t offer free shipping.

The changing consumer behavior towards online marketplaces highlights the need for sellers to adapt their order fulfillment strategies accordingly. Whether opting for FBA, WFS , or FBM, sellers must consider factors like cost, control, scalability, and customer expectations.

Ecommerce Platforms

For ecommerce platforms like Shopify, Magento, WooCommerce, and BigCommerce, order fulfillment takes on a direct-to-consumer (DTC) approach that requires a seamless blend of efficiency, speed, and adaptability. Unlike traditional retail, where the focus is on shelf availability, DTC fulfillment emphasizes the accurate, swift, and reliable delivery of products straight to customers' doorsteps.

The process involves:

• Order Receipt and Processing: When a customer places an order online, the seller receives it via their ecommerce platform tools . The order must be quickly processed, necessitating real-time inventory visibility to ensure stock levels are accurate across all sales channels, preventing order cancellations due to stockouts.
• Picking, Packing, and Shipping: Orders then move to the fulfillment center or warehouse where items are picked from shelves, securely packaged, and labeled for shipping. Given that consumers now expect fast, often free shipping, e-commerce businesses must strategically choose courier services and optimize delivery routes or maintain partnerships with multiple carriers to meet various shipping preferences and speeds.
• Returns Management: E-commerce typically sees higher return rates than brick-and-mortar retail, making an efficient returns process essential. This involves not just receiving and processing returned items, but also managing reverse logistics, which can be complex and cost-intensive.
• Customer Communication: Throughout this process, proactive customer communication is key. This includes providing order confirmations, shipping updates, tracking information, and easy access to customer service. Such transparency is crucial for building customer trust and satisfaction.

DTC fulfillment demands an integrated approach that ties production, inventory, and distribution into a single, responsive ecosystem. Leveraging the capabilities of e-commerce platforms, businesses can automate many aspects of the fulfillment process, synchronizing inventory data, tracking orders in real time, and analyzing sales patterns to forecast demand.

B2B Sales Channels and Order Fulfillment

B2B sales, traditionally dominated by wholesale, direct sales, and enterprise procurement, involve sales between businesses, such as a manufacturer to wholesaler or wholesaler to retailer. These transactions are typically characterized by larger order volumes, recurring orders, and long-standing relationships between businesses.

However, the landscape is changing with the emergence of B2B e-commerce, where business buyers expect a shopping experience akin to B2C, driven by convenience, efficiency, and transparency. This shift is leading to a transformation in B2B order fulfillment:

• Wholesale: Traditionally, wholesalers might have ordered via sales reps or direct ordering systems. With B2B e-commerce, wholesalers are now shopping in online marketplaces or through supplier websites with customer-like interfaces, necessitating real-time inventory data, easy order placements, and customer service – aspects previously reserved for B2C.
• Direct Sales: For manufacturers selling directly to large retailers or other businesses, e-commerce platforms allow for streamlined ordering processes, often integrated with the buyer's procurement systems. Fulfillment requires coordination of large, regular shipments, often with strict delivery windows and compliance standards.
• Enterprise Procurement: Large corporations procuring supplies typically have complex, rule-based purchasing systems. B2B e-commerce is simplifying this process, allowing for online catalogs, easy reordering, and spend tracking. Fulfillment must be precise, as businesses rely on the timely delivery of the right goods for their operations.

Across these channels, B2B order fulfillment must deal with unique challenges, such as:

• Volume and Complexity: Orders are often large and might include a wide variety of items, requiring meticulous order fulfillment management and logistical planning.
• Customer Expectations: B2B buyers expect a level of service similar to what they experience as individual consumers, including fast shipping, order accuracy, and easy returns.
• Regulatory Compliance: Especially for cross-border transactions, B2B shipments might need to comply with a range of regulations, requiring detailed documentation and compliance checks.

The evolution of B2B e-commerce is pushing businesses to modernize their systems, adopt advanced supply chain technologies, and enhance their order fulfillment strategies to meet heightened customer expectations. This transformation, while challenging, presents an opportunity for businesses to improve efficiency and order fulfillment rates, forge stronger relationships with partners, and tap into new markets.

Omnichannel Fulfillment

The omnichannel approach has emerged as a critical strategy for brands aiming to provide a seamless customer experience across multiple sales channels. This approach recognizes that customers interact with brands in a variety of ways — online through e-commerce platforms, social media, mobile apps, and offline in physical stores or pop-up shops — and expects consistency, convenience, and personalized engagement across all these touchpoints. According to a study by Insider Intelligence, an estimated 50% of US Gen Z and millennial social users make purchases on social media , compared to 38% of US adults overall.

Channels are indeed converging, and customers no longer see a distinction between a brand's online presence and its physical store—they view it as one entity and expect the experience to be continuous. For instance, they might want to browse products online, try them in-store, and later purchase via an app with home delivery. Alternatively, they might buy online and pick up in-store (BOPIS), a trend that surged especially during the COVID-19 pandemic.

However, omnichannel fulfillment also brings complexities to order fulfillment:

• Inventory Visibility: Real-time, accurate inventory data is paramount, as customers expect items seen online to be available immediately for purchase or in-store pickup.
• Flexible Fulfillment: Brands need the capability for varied fulfillment options, like BOPIS, ship-from-store, or same-day delivery, necessitating agile and responsive supply chains.
• Consistent Experience: Whether it's the purchase process, product pricing, or returns policy, consistency across channels is crucial to maintain customer trust and satisfaction.
• Data Integration: Collecting and analyzing data from all touchpoints allows for personalized marketing, improved forecasting, and responsive replenishment, creating a smooth back-end operation that supports the front-end customer experience.

Implementing an effective omnichannel strategy requires significant investment in technology, logistics, and personnel training. It involves integrating disparate systems for inventory management, order processing, customer relationship management, and more. 

However, the payoff is substantial—brands that successfully execute an omnichannel approach see improved customer loyalty, order fulfillment rates, higher average order values, and increased revenue, as they can engage customers at multiple points along their shopping journey, thereby maximizing opportunities for conversion. In a landscape where customer expectations continue to rise, an omnichannel approach is becoming less of an option and more of a necessity for brands looking to thrive in the long term.

Unpacking Various Order Fulfillment Types

There are various types of order fulfillment approaches that businesses can implement depending on a business's size, resources, expertise, and strategic goals. Here's a breakdown of several common types:

1. Self-fulfillment

For self-fulfillment or insourced order management, the business manages every step of the order fulfillment process in-house, rather than outsourcing to third-party logistics (3PL) providers or using dropshipping methods

Pros: Complete control over the fulfillment process, direct handling of inventory, packaging, and shipping, and closer customer relationship management.

Cons: Requires substantial investment in storage space, staff, technology, and logistics, and can be challenging to scale during peak periods.

It is Ideal for startups, small businesses, or those with unique products that require special handling or branding experiences.

2. Third-party Logistics (3PL)

3PLs handle the order fulfillment logistics of storage, picking, packing, and shipping, allowing businesses to focus on core competencies like product development and marketing.

Pros: Offers expertise in logistics and order fulfillment management, provides scalability, and reduces the need for physical storage space and staffing.

Cons: Less control over the fulfillment process, the potential for miscommunication, and reliance on the 3PL's system reliability and performance.

Third-party partnerships can provide access to advanced technology, wider distribution networks, and volume shipping discounts but require careful management, clear communication, and trust.

3. Dropshipping

Dropshipping is a retail fulfillment method where a store doesn't keep the products it sells in stock. Instead, when a store sells a product using the dropshipping model, it purchases the item from a third party — usually a wholesaler or manufacturer — and has it shipped directly to the customer.

Pros: Eliminates the need for inventory management and upfront investment in products, and offers virtually unlimited inventory.

Cons: Less control over inventory, longer delivery times, reliance on supplier’s stock and performance, and potential for inventory issues.

4. Cross-docking and JIT (Just-In-Time) Fulfillment

Products are quickly transferred between transport vehicles without long-term warehousing (cross-docking) or are manufactured/received as needed (JIT), reducing storage, product handling, and overall order fulfillment costs.

Pros: Reduces inventory holding costs, less space requirement, and quicker delivery to customers.

Cons: Requires precise coordination, reliable suppliers, and accurate forecasting to prevent stockouts or delays.

5. BOPIS (Buy Online, Pick-up In-Store)

BOPIS, or "Buy Online, Pick-up In-Store," is a hybrid retail model that blends online shopping with traditional brick-and-mortar experiences. For example, Walmart leverages its physical stores as distribution points where customers can pick up online orders. This order fulfillment strategy merges the digital and physical shopping experiences, offering customers convenience, no shipping fees, and immediate gratification while driving additional in-store purchases.

Pros: Increases customer convenience, reduces shipping costs and time, and leverages existing physical infrastructure.

Cons: Requires accurate inventory tracking, dedicated storage space for pick-up items, and additional staff training.

6. Hybrid Order Fulfillment Solutions

Businesses often use a mix of self-fulfillment, 3PL, dropshipping, and other methods based on their product types, sales volume, geographic reach, and growth strategies. This approach offers greater flexibility, optimizes order fulfillment costs, and ensures better risk management. 

For instance, a business might handle fulfillment in-house in its primary market to maintain control and customer experience but use 3PLs in new or distant markets to reduce shipping times and navigate the challenges of local order fulfillment logistics.

The choice of a hybrid model can be influenced by various factors, such as the desire to test new markets, the need for specialized handling or storage, seasonality, international shipping complexities, or expansion plans.

Ultimately, the choice among these order fulfillment strategies depends on the business’s size, resources, market demand, product nature, and long-term goals. Each method comes with its own set of requirements and order fulfillment challenges , and businesses must evaluate their capabilities, conduct cost-benefit analyses, and possibly consult with supply chain experts to determine the most suitable approach.

Understanding Fulfillment Center Logistics

At its core, all order fulfillment logistics require a combination of warehouse organization, picking and packing products, and reverse logistics.

1. Warehouse Organization: SKUs, Shelving, and Space Optimization

Warehouses and fulfillment centers are the backbone of the supply chain, and their organization significantly impacts order processing efficiency, accuracy, and customer satisfaction. Warehouse organization is structured around stock keeping units (SKUs), which are unique identifiers for each distinct product. The arrangement of SKUs, shelving, and the overall use of space are often tailored to the type of fulfillment, sales channels, and products involved.

For instance, a warehouse serving a traditional retail channel might have pallets of the same product (ideal for bulk shipping), whereas a direct-to-consumer e-commerce warehouse would need individual bins for a broad array of SKUs (facilitating single-item orders). Similarly, facilities handling perishable goods or large, heavy items would require specialized storage, handling, and order fulfillment solutions. 

Space optimization, often facilitated by warehouse management systems (WMS) , involves strategic shelving (like high-density storage for fast-moving items), and layout designs to minimize picking time and accommodate varying inventory levels, thereby enhancing throughput and efficiency.

2. Picking Methodologies: Wave, Batch, Zone, and More

The picking and packing process is central to order fulfillment, directly affecting the speed and accuracy of customer order deliveries. You can choose from various approaches depending on warehouse size, order types, and volume to overcome picking challenges :

• Wave Picking: Orders are grouped into waves based on specific criteria (like shipping carrier or destination), and picked at scheduled times, optimizing workflow and reducing shipping times.
• Batch Picking: Multiple orders are picked simultaneously to minimize trips to the same location, ideal for warehouses with a wide variety of SKUs and smaller orders.
• Zone Picking: The warehouse is divided into zones, with workers assigned to each. Products are picked within zones and then consolidated, suitable for large warehouses with high-volume orders.
• Discrete Picking: One order is picked at a time, simple but less efficient for larger operations.

Each method impacts fulfillment speed, accuracy, and workforce requirements differently. For example, while batch and wave picking can increase efficiency, they may also require more complex coordination and a robust WMS to handle order grouping and routing.

3. Returns Processing

Returns are an inevitable aspect of the sales process, more so with the rise of e-commerce, where return rates are significantly higher than in traditional retail. Reverse logistics, or the flow of returned items back into the inventory poses challenges in sorting, assessing, restoring, and repackaging products.

In the digital age, consumers expect smooth return processes; thus, an efficient returns management system needs to be in place. This involves designated areas within the warehouse for returns, skilled staff to assess and refurbish products, and technology to update inventory levels in real time. Additionally, a proactive approach, including data analysis, can help businesses understand return reasons and patterns, potentially leading to reduced return rates over time.

Ultimately, a strategic approach to warehouse organization, picking methodologies, and returns processing can greatly improve order fulfillment efficiency , accuracy, and customer satisfaction, directly contributing to a company's bottom line.

Global Fulfillment: From Local to Global

Addressing regional nuances in domestic fulfillment.

Domestic fulfillment is subject to local regulations, consumer preferences, and logistical infrastructures. In the U.S., for instance, domestic fulfillment involves navigating a vast geographic area with diverse regional demands, requiring a strong distribution network for timely deliveries. Meanwhile, businesses must also comply with local tax laws, environmental regulations, and consumer product safety laws which can vary from one state or region to another.

Cross-Border Fulfillment: Expanding Globally Without Hiccups

Cross-border fulfillment involves selling products internationally, which involves navigating various legal frameworks, shipping protocols, and cultural nuances. According to Shopify,  consumers are increasingly looking beyond their home country as 57% of respondents say they’ve recently made an international online purchase .

A common challenge includes navigating the complexities of international shipping logistics, such as longer shipping times and the need for multi-language support. Additionally, consumer expectations regarding shipping costs and times can vary widely, necessitating a clear communication strategy. A survey by Voxware reveals that 69% of respondents are unlikely to shop with a retailer if their delivery is delayed by more than two days.

Duty and Customs Management to Avoiding International Bottlenecks

Successful global fulfillment requires adept handling of duties and customs. Incorrect paperwork, underestimating duties, or not adhering to international trade agreements can lead to delays, additional order fulfillment costs , and negative customer experiences. For instance, the North American Free Trade Agreement (NAFTA), now replaced by the United States-Mexico-Canada Agreement (USMCA), has significantly influenced trade and fulfillment operations among these countries, affecting tariffs, regulations, and supply chain strategies.

The trade tensions between the U.S. and China, highlighted by increased tariffs, have prompted many businesses to consider near-shoring — shifting their supply chains to closer countries like Mexico. This change allows companies to reduce order fulfillment costs, have more control over the manufacturing process, and provide faster fulfillment due to proximity.

Brexit is another prime example, causing disruptions in trade between the United Kingdom and the European Union. Post-Brexit, many EU-based businesses are now compelled to establish a presence in the UK to avoid increased shipping times and costs, and to navigate new tax and duty requirements efficiently.

International 3PLs and Localized Fulfillment Centers

Using international third-party logistics (3PLs) providers or localized fulfillment centers can be advantageous for global operations. Ecommerce 3PLs often have a deeper understanding of local markets, regulations, and consumer behavior, providing businesses with strategic storage locations, reduced shipping times, and costs, and helping to manage complex international logistics. 

By utilizing a network of international fulfillment centers, businesses can store products closer to their customers, significantly reducing shipping times and logistics costs while increasing customer satisfaction.

Technology's Role in Seamless Fulfillment

Technology plays a key role in optimizing and streamlining order fulfillment processes, directly improving efficiency, accuracy, and customer satisfaction.

1. Order Fulfillment Software and Multichannel Order Fulfillment Management

Businesses can leverage the right technology to manage orders from multiple sales channels (e-commerce platforms, marketplaces, and physical stores) through a single interface. This software enhances visibility, accuracy, and efficiency by automating tasks like inventory tracking, order routing, and shipment tracking. In addition, multichannel order management systems allow businesses to synchronize inventory and sales data across various platforms, ensuring consistent information and smoother operations.

2. Distributed Order Routing

Distributed order management systems determine the most cost-effective and efficient way to fulfill orders by analyzing variables such as inventory levels across locations, shipment times, and costs. These systems help businesses optimize their supply chains and improve customer satisfaction through quicker deliveries.

3. Warehouse Management System (WMS) Essentials

Modern Warehouse management systems (WMS) solutions offer far more than traditional inventory tracking. They provide real-time tracking of products within the warehouse and en route to customers, sophisticated forecasting tools to anticipate inventory needs and analytics to monitor KPIs and optimize operations. These systems are critical for businesses looking to scale, offering insights into potential bottlenecks or inefficiencies.

4. Integrations

Integrating a WMS with other systems like e-commerce platforms, Enterprise Resource Planning (ERP), and Customer Relationship Management (CRM) software allow for seamless data exchange and streamlined operations. For instance, integrating the WMS with an ecommerce platform ensures that stock levels are updated in real-time, preventing stockouts or overselling, while connection to a CRM can provide customer service reps with up-to-date order status information.

5. The Rise of AI and Robotics in Order Fulfillment Processes

Adoption of Artificial Intelligence (AI) and robotics is on the rise in modern fulfillment centers, helping automate various processes:

• Picking Robots: These robots are designed to navigate warehouse aisles and select items for orders, significantly reducing human labor and minimizing errors.
• Automated Storage and Retrieval Systems (ASRS): These systems automatically place and retrieve loads from defined storage locations, increasing efficiency and reducing labor costs.
• Autonomous Vehicles: Drones and self-driving carts can move items within the warehouse or perform last-mile deliveries.
• AI-Powered Predictive Analytics: AI can analyze data to predict future order volumes, necessary inventory levels, and potential supply chain disruptions, allowing businesses to adjust strategies.

These technologies not only speed up the order fulfillment process but also enhance accuracy, improve worker safety by automating more dangerous tasks, and ultimately lead to greater customer satisfaction through faster and more reliable order processing. 

The future of order fulfillment is one where humans and technology collaborate, with AI and robotics handling repetitive tasks, and humans managing more complex or creative responsibilities. This synergy promises a more efficient, accurate, and scalable order fulfillment process, prepared to meet the evolving demands of the modern consumer.

Order Fulfillment Metrics and Performance Indicators

To optimize order fulfillment, businesses must track specific metrics and performance indicators. These numbers offer valuable insights into the efficiency of your operations and warehouse KPIs , highlighting areas for improvement and measuring the impact of changes.

Key Fulfillment Metrics

• Order Accuracy Rate: This measures the number of orders shipped correctly versus the total orders shipped. A high accuracy rate indicates effectiveness in picking, packing, and shipping processes.
• Order Turnaround Time: This is the time taken from when an order is placed to when it's shipped. Speedy order fulfillment is a key metric, especially in the age of same-day and one-day delivery expectations.
• Return Rate: It reflects the percentage of sold items that are returned by the end consumer. A high return rate can indicate issues with product quality, order accuracy, or customer expectations not being met.

Performance Indicators

• Fulfillment Cost Per Order: This involves the total cost associated with fulfilling and shipping an order. Keeping this cost low while maintaining high service levels is crucial for profitability.
• Inventory Turnover: The inventory turnover ratio shows how many times a company's inventory is sold and replaced over a period. A low turnover rate may point to overstocking, obsolescence, or sales issues, while a high rate may indicate strong sales or ineffective buying.
• Backorder Rate: Backorders reflect the number of orders that cannot be filled at the time of purchase. A high backorder rate can signal inventory and order fulfillment management issues and can negatively impact customer satisfaction and retention.
• Average Order Value (AOV): This metric tracks the average dollar amount spent each time a customer places an order. By understanding this metric, businesses can strategize ways to increase revenue.
• Carrying Cost of Inventory: Carrying costs includes all costs related to storing unsold goods. Lowering these costs without affecting order fulfillment efficiency can directly increase net profit.

Each of these metrics plays a vital role in assessing the effectiveness and efficiency of an order fulfillment system. They provide actionable insights that companies can use to streamline operations, reduce order fulfillment costs, and enhance customer service.

The Future of Order Fulfillment

As we look ahead, several trends are poised to shape the future of order fulfillment, driven by technological advancements, evolving consumer preferences, and the overarching necessity for sustainable practices.

Sustainability as a Standard, Not an Option

Increasing environmental concerns are pushing companies to adopt sustainable practices in their order fulfillment processes. This means increased use of eco-friendly packaging, optimization of delivery routes to reduce carbon emissions, and warehousing operations powered by renewable energy. Consumers are progressively favoring brands that demonstrate environmental responsibility, making sustainability a competitive differentiator

Drone Deliveries and Autonomous Vehicles

Drone deliveries are gaining popularity as a viable option for order fulfillment, especially for last-mile delivery. They reduce the overall time from the warehouse shelf to the customer's doorstep and significantly cut down on carbon emissions compared to traditional delivery vehicles. 

Similarly, autonomous delivery vehicles are no longer just a concept but are entering real-world testing phases. These technologies promise faster, more cost-effective, and environmentally friendly delivery options.

Smart Warehousing

The warehouses of the future will be highly automated to increase efficiency and reduce reliance on human labor. Advancements in robotics, AI, and machine learning will lead to smart warehouses that can predict, adapt, and respond to changes in order fulfillment demands in real time.

Hyper-Personalized Customer Experiences

Beyond just getting orders right, the future will see an emphasis on bespoke customer experiences. From personalized packaging to products tailored to the individual's preferences and needs, order fulfillment will become a significant part of the brand experience and customer satisfaction.

With the retail landscape evolving rapidly, companies must be ready to pivot quickly, adopting new technologies and methodologies to meet changing consumer demands. This requires an investment in continuous learning, data-driven decision-making, and agile business models.

The future of order fulfillment will offer new avenues for companies to differentiate themselves with a steadfast commitment to innovation and sustainability. With Hopstack’s smart Warehouse Management System, businesses can leverage data-driven insights to streamline fulfillment processes and ensure maximum efficiency.

If you’re looking to improve your existing order fulfillment pipeline and increase investment into your supply chain operations, schedule a call with our product specialist today.

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Order Management Process - A Complete OMS Guide

The order management process is a crucial component of any supply chain business, encompassing various stages from order placement to fulfillment and beyond. Let's explore the key steps involved in the order management process and how businesses can optimize each stage to streamline operations and enhance customer satisfaction with.

What is the Order Management Process?

Order management is the process of overseeing and fulfilling customer orders from purchase to delivery, encompassing various stages and activities aimed at ensuring seamless order processing and customer satisfaction. The components of the order management process determine how businesses can effectively manage orders to meet customer expectations and drive success.

The most successful order management processes use a strategically mapped out product journey along with innovative technology such as an OMS or order management system . This is especially true for omnichannel or e-commerce order management.

The Order Management Process Includes:

Order placement.

• Customer Inquiry: Initial contact or inquiry from the customer regarding product availability, pricing, or specifications.
• Order Entry: Recording customer orders into the system, capturing essential details such as product quantities, delivery dates, and shipping preferences.

Order Processing

• Order Verification: Confirming order details and validating customer information to ensure accuracy and completeness.
• Inventory Check: Checking inventory levels to verify product availability and determine if backorders or substitutions are necessary.
• Order Routing: Routing orders to the appropriate fulfillment centers or distribution channels based on inventory availability, shipping requirements, and geographical location.

Order Fulfillment

• Picking: Selecting products from inventory shelves or storage locations to fulfill customer orders.
• Packing: Packaging selected items securely and preparing them for shipping, including labeling and documentation.
• Shipping: Arranging transportation and logistics for delivering orders to customers, whether through in-house or third-party carriers.

Order Tracking and Management

• Order Tracking: Providing customers with real-time updates on order status, shipment tracking information, and delivery notifications.
• Customer Communication: Maintaining open lines of communication with customers throughout the order lifecycle, addressing inquiries, and resolving issues promptly.
• Returns and Exchanges: Managing the process for handling returns, exchanges, and refunds in accordance with company policies and customer expectations.

Order Analysis and Optimization

• Performance Evaluation: Analyzing order data, fulfillment metrics, and customer feedback to identify areas for improvement and optimization.
• Process Optimization: Implementing changes and improvements to streamline order management workflows, reduce costs, and enhance operational efficiency.
• Forecasting and Planning: Utilizing historical order data and demand forecasting techniques to anticipate future order volumes, optimize inventory levels, and ensure adequate supply chain capacity.

Customer Satisfaction and Relationship Management

• Customer Feedback: Soliciting feedback from customers to gauge satisfaction levels, identify pain points, and address areas for improvement.
• Relationship Building: Cultivating positive relationships with customers through personalized service, proactive communication, and responsive support.
• Retention Strategies: Implementing strategies to retain existing customers, such as loyalty programs, special promotions, and personalized offers.

By effectively managing each stage of the order management process, supply chain businesses can streamline operations, improve efficiency, and deliver exceptional customer experiences that drive long-term success and growth.

Why is Order Management Crucial for Business Success?

Order management plays a pivotal role in the success of your business—regardless of its size or industry. From ensuring timely order fulfillment to enhancing the overall customer experience, effective order management practices are essential for sustaining growth, minimizing errors, and building long-term relationships with clients. Let's delve into why order management is so important and how it benefits businesses across various aspects of operations.

The Importance of Timely Order Fulfillment

One of the primary objectives of order management is to ensure that customer orders are processed and fulfilled promptly. By implementing streamlined order processing workflows and leveraging automation technologies, warehouses and businesses can expedite order fulfillment cycles, meet customer expectations for delivery times, and minimize order lead times. Timely order fulfillment not only enhances customer satisfaction but also fosters repeat business and strengthens brand loyalty.

Minimize Errors and Enhance Accuracy

Effective order management systems help minimize errors and mistakes throughout the order processing journey. By centralizing order data, implementing validation checks, and integrating with inventory management systems , businesses can reduce the risk of order inaccuracies, such as incorrect product quantities, pricing discrepancies, or shipping errors. This not only improves operational efficiency but also prevents costly returns, refunds, and customer dissatisfaction.

Organizational Efficiency in Order Management

Order management software and integrations enable businesses to stay organized and efficient by providing a centralized collaborative platform for managing orders, tracking inventory levels, and monitoring order statuses in real-time. With features such as order tracking, inventory forecasting, and customizable reporting, businesses can gain insights and complete visibility into their order management processes, identify areas for improvement, and make data-driven decisions to optimize operations.

Elevate the Customer Experience through Order Management

A seamless and hassle-free order management process is instrumental in enhancing the overall customer experience. By offering omnichannel, e-commerce, or multiple order channels, such as online, mobile, and in-store, businesses can cater to diverse customer preferences and provide a convenient shopping experience. Additionally, features like order tracking, automated notifications, and easy returns contribute to customer satisfaction and loyalty by keeping customers informed and engaged throughout the order lifecycle.

Scale Your Business with Effective Order Management

Scalability is another key benefit of effective order management systems. As businesses grow and expand, they need comprehensive order management solutions that can adapt to increasing order volumes, expanding product catalogs, and evolving customer demands. Scalable order management software allows businesses to seamlessly scale their operations, automate repetitive tasks, and maintain high levels of efficiency without compromising on quality or customer service.

Build Trust with Reliable Processes

Finally, effective order management practices help build trust and credibility with clients. By consistently delivering orders accurately and on time, businesses demonstrate reliability and professionalism, fostering trust and confidence among customers. Transparent communication, proactive order tracking, and responsive customer support further reinforce trust and help businesses cultivate long-term relationships with clients, ultimately driving repeat business and positive word-of-mouth referrals.

Order management is a critical component of business operations that impacts various aspects of customer satisfaction, operational efficiency, and long-term growth. By prioritizing timely order fulfillment, minimizing errors, staying organized, and prioritizing customer experience, businesses can leverage order management as a strategic asset to scale their operations, build trust with clients, and achieve sustainable success in today's competitive marketplace.

The Order Management Cycle: Key Steps and Processes

For supply chain businesses, the order management cycle is a crucial process that ensures seamless transactions and satisfied customers. By connecting the dots across manufacturing and selling channels, logistics partners for brands, such as third-party logistics (3PL) warehouses , can use collaborative unified commerce technologies to optimize business processes and improve visibility across the entire supply chain. 

Let's explore the various stages of the order management cycle and the steps involved in each phase:

Collecting Order Information

The order management cycle begins with collecting essential information from the customer or consumer. This includes details such as product specifications, quantities, delivery preferences, and any special instructions. Effective communication channels, such as online order forms, customer service representatives, or sales teams, facilitate the gathering of accurate order information.

Validating Order Details

Once the order information is collected, it undergoes validation to ensure accuracy and completeness. This involves verifying product availability, pricing, and any discounts or promotions applicable to the order. Additionally, order validation includes confirming customer details, shipping addresses, and payment information to minimize errors and prevent order processing delays.

Order Routing

After the order is validated, it is assigned to a designated team member or department responsible for order fulfillment. This step involves allocating resources, assigning tasks, and setting timelines for order processing. Effective task allocation and workflow management ensure that orders are processed promptly and efficiently.

With the order assigned, the designated team member or department proceeds with order fulfillment activities. This may include tasks such as picking products from inventory, packing orders securely, and preparing shipping labels and documentation. Attention to detail and adherence to quality standards are crucial to ensure accurate and error-free order completion.

Order Delivery

Once the order is completed, it is ready for delivery to the client. This may involve arranging transportation logistics, coordinating with shipping carriers, and tracking the shipment to ensure timely delivery. Providing customers with tracking information and delivery updates enhances transparency and customer satisfaction for last mile delivery.

Following Up with the Client

The final stage of the order management cycle involves following up with the client after order delivery. This includes confirming receipt of the order, addressing any concerns or issues, and soliciting feedback on the overall purchasing experience. Proactive communication and responsive customer support help build trust and foster long-term relationships with clients.

The order management cycle encompasses a series of interconnected steps aimed at efficiently processing and fulfilling customer orders. By meticulously managing each stage of the cycle with the proper OMS, businesses can optimize their operations, enhance customer satisfaction, and drive growth and success in the marketplace.

Tips to Enhance Your Order Management Process

Efficient order management is essential for businesses to deliver exceptional customer experiences and drive operational success. Here are some key strategies to improve your order management process:

Use an Order Management System

Implementing a best-in-class order management system can streamline order processing, enhance visibility, and centralize order data. OMS platforms, such as the order management included in the Osa Collaborative Visibility Platform , offer features such as order tracking, inventory management, and integration with other business systems, enabling businesses to automate repetitive tasks, reduce errors, and improve efficiency in handling orders. Coupled with AI-enabled data for a comprehensive view of your entire supply chain network, order management systems must seamlessly integrate real-time data to provide accurate information and additional visibility into inventory, sales data, and customer preferences.

Communicate with Your Team

Effective communication among team members is crucial for ensuring smooth order management operations. Establish clear channels for internal communication, provide training on order management processes and procedures, and encourage collaboration among team members. Regular meetings and updates help keep everyone aligned and informed about order status, priorities, and any issues that may arise. Using technology to help track, monitor, and deliver updates on order activities across selling channels and partners will also ensure a complete integrated order management process.

Use Systems for Collecting Payments

Utilize secure payment processing systems to facilitate seamless payment collection from customers. Integrating payment gateways with your order management system enables automatic payment processing, reduces manual intervention, and minimizes payment processing errors. Offering multiple payment options and ensuring PCI compliance enhances customer convenience and trust.

Communicate with Your Clients

Maintaining open lines of communication with clients throughout the order lifecycle is essential for building trust and ensuring customer satisfaction. Provide order confirmations, shipping notifications, and delivery updates to keep clients informed about their orders' progress. Proactive communication and responsive customer support help address any concerns or inquiries promptly, fostering positive relationships with clients.

Review Your Process

Regularly review and evaluate your order management process to identify areas for improvement and optimization. Analyze order data, performance metrics, and customer feedback to pinpoint bottlenecks, inefficiencies, or pain points in the process. Implement changes, streamline workflows, and leverage technology solutions to enhance process efficiency, accuracy, and customer service levels.

By implementing these tips and continuously refining your order management process, you can streamline operations, enhance customer satisfaction, and drive business growth. Effective order management not only improves efficiency and accuracy but also strengthens customer relationships and positions your business for long-term success in today's competitive marketplace.

Benefits of Order Management

Order management is a critical aspect of business operations that impacts various facets of efficiency. Successful order management is essential for driving operational excellence, maximizing customer satisfaction, and achieving long-term business success. By prioritizing efficiency, accuracy, and customer service, businesses can unlock the numerous benefits of efficient order management and gain a competitive edge in today's dynamic marketplace. Let's explore some key benefits of effective order management:

Process Transparency

Implementing an order management system provides transparency and visibility into the entire order lifecycle, from order placement to fulfillment and delivery. With real-time order tracking and status updates, businesses can keep customers informed about their order progress, reducing inquiries and enhancing trust. Additionally, internal stakeholders gain visibility into order status, allowing for better coordination and decision-making.

Improved Inventory Management

Efficient order management helps businesses optimize inventory levels and prevent stockouts or excess inventory. By accurately tracking inventory levels and demand patterns, businesses can anticipate customer needs, streamline procurement processes, and reduce carrying costs. Additionally, inventory synchronization across multiple sales channels ensures product availability and minimizes the risk of overselling or stock discrepancies.

Increased Customer Satisfaction

Timely order processing, accurate order fulfillment, and responsive customer support are essential for enhancing customer satisfaction. An effective order management system enables businesses to meet customer expectations for order accuracy, delivery times, and communication. By providing a seamless ordering experience and resolving issues promptly, businesses can build trust, loyalty, and positive brand perception among customers.

Cost Reduction

Streamlining order management processes leads to cost savings through improved efficiency and reduced errors. Automation of order processing workflows, such as order entry, routing, and fulfillment, minimizes manual labor and administrative overhead. Additionally, optimized inventory management helps reduce carrying costs associated with excess inventory and obsolescence. By eliminating inefficiencies and optimizing resource utilization, businesses can lower operational expenses and improve profitability.

Increased Efficiency

Efficient order management processes enable businesses to handle orders more quickly, accurately, and cost-effectively. Automation of repetitive tasks, such as order processing, invoicing, and reporting, frees up staff to focus on more value-added activities. Furthermore, integration with other business systems, such as inventory management, CRM, and accounting, streamlines data exchange and improves overall process efficiency.

Order Management Process Conclusion

The order management process is a vital component of any supply chain business, encompassing various stages from order placement to fulfillment and beyond. By implementing strategic measures and leveraging technology solutions, businesses can optimize each stage of the order management process to streamline operations and enhance customer satisfaction.

Utilizing an order management system enables businesses to achieve process transparency, improve inventory management, and increase efficiency. By centralizing order data and automating workflows, businesses can minimize errors, reduce costs, and enhance operational efficiency.

Effective communication with both internal teams and external clients is essential for ensuring smooth order management operations and maintaining customer satisfaction. Regular reviews and evaluations of the order management process help identify areas for improvement and optimization, driving continuous enhancements and efficiencies.

Overall, the benefits of effective order management extend beyond operational efficiency to include increased customer satisfaction, cost reduction, and scalability. By prioritizing efficiency, accuracy, and customer service, businesses can leverage order management as a strategic asset to drive growth and success in today's competitive marketplace.

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4 Order Processing Steps Explained

• Last updated: June 5, 2024
• |  Written by: Petra Martinis

Did you ever think about what happens backstage every time you buy something?

When a customer places an order on an eCommerce site, there are a number of steps that need to be completed to fulfill the order. First, the order must be verified and put into the system. Then the payment needs to be processed and the items need to be pulled from the inventory. Once the items have been gathered, they need to be packaged and shipped to the customer. Finally, the customer has to be notified that the order has been shipped. In some cases, customer service may also need to be involved to resolve any issues with the order.

While this may seem like a lot of steps, most eCommerce sites have systems in place that make it easy to track and manage orders. As a result, most orders can be processed quickly and efficiently.

In this post, we will answer the question of what happens backstage and guide you through the basics of order processing.

What is Order Processing?

So let’s start with a simple definition: Order processing is the term used to describe the various steps that are involved in completing a purchase transaction.

It’s a series of activities that take place between the time a customer places an order and the time the order is delivered. It includes activities like verifying payment information, preparing and packaging the items for shipment, and arranging for delivery.

Today much of the order processing takes place behind the scenes, with automated systems handling many of the tasks. However, there are still some businesses that rely on manual processes to complete orders.

Either way, order processing is an essential part of doing business.

Order Processing Steps

Typically, order processing involves four key steps: receiving the order, picking and packing the items, processing payments, and shipping the order. In some cases, additional steps may be involved, such as quality control or gift wrapping. Depending on the size and complexity of the order, all four steps may be completed by a team of people, or with automated order processing software. Regardless of who is completing the tasks, however, efficient order processing is essential for ensuring that customers are happy with their purchase experience.

1. Order Placement

Receiving the order can be done in several ways, depending on the company and its operating model. The most common method is for customers to place their orders online or over the phone with a customer service or sales representative. Once the order is placed, it is sent to the company’s fulfillment center where it can be fulfilled.

Picking the products refers to physically locating the items that were ordered and preparing them for shipment. In some cases, this may involve retrieving items from inventory that is stored on shelves or inventory that is stored in a warehouse. After the items have been located, they need to be inspected for quality control purposes and then packaged for shipment.

Packaging the products simply means putting them into boxes or bags so that they are ready to be shipped. Fragile items may need to be wrapped in bubble wrap or foam before they are placed in a box. When all of the items have been properly packaged, they are ready to be shipped.

4. Shipping

Shipping the products is getting them to the customer’s doorsteps. This can be done in various shipping methods, commonly by a delivery service such as UPS, FedEx, DHL, or Post. The shipping method will depend on factors like time sensitivity and cost. After the products have been shipped, the order is considered complete.

Order Fulfillment

We have to make one important distinction here. Many businesses use the terms “order processing” and “order fulfillment” interchangeably, but there is a difference between the two.

Order processing is just one part of the order fulfillment cycle and refers to the steps that a company takes to receive, review, approve and prepare an order. The whole order fulfillment process starts way before a customer makes a purchase.

Steps in The Order Fulfillment Process:

• Procurement of Goods
• Inventory Storage
• Order Processing
• Picking/Packing

Procurement and Storage

Any business that deals in physical products, especially businesses that operate online, need to have a well-organized system for procuring and storing inventory.

The process begins when a company buys goods from manufacturers, and those goods are then transported to the distribution or fulfillment center, where they are stored until ready to ship.

An efficient procurement and storage system can help to ensure that orders are filled quickly and accurately and that products are stored in a way that minimizes damage. It can also help to reduce the overall cost of inventory, as well as the amount of time that is needed to fulfill an order. Most warehouses or distribution centers that deal with a large number of orders usually have automated processes of procuring and storing inventory to reduce operating costs and improve their order fulfillment times.

Sorting is a practical step in organizing shipping. There are a number of different ways to sort orders, but the most common method is by shipping destination. This ensures that all orders destined for the same location are grouped together, making it easier to determine the most efficient shipping route. Other methods of sorting include sorting by product type or by order value.

Delivery and Returns

The order fulfillment cycle is not done until the customer receives the product. If everything is ok and the customer keeps it, then fulfillment is considered to be done. But in some cases, the customer needs to return the product, for example, if it’s damaged or the size doesn’t fit.

Most eCommerce stores handle returns by giving the customer their money back, but some stores may offer a store credit instead. To improve the return process, many stores have implemented new policies and procedures, like allowing customers to return items without a receipt. Others have created special return areas where customers can drop off their items and receive a refund or store credit without having to wait in line.

Order Processing FAQ

How to improve order processing.

There are a few key things you can do to improve order processing in your business:

• Make sure you have a clear and concise order form that your employees can easily follow.
• Streamline your customer service process so that there are as few steps as possible between taking an order and fulfilling it.
• Invest in technology that can automate some of the more repetitive aspects of order processing, such as data entry and shipping label printing.

By taking these steps, you can help to ensure that orders are processed quickly and efficiently, leading to happier customers and more sales for your business.

What is Order Processing Software?

Order processing software is a program that helps businesses manage their sales and customer orders. It can be used to track inventory levels, process payments, and generate reports. Many of those programs also include features like email and online order tracking, which can help businesses keep track of their orders and customers. Order processing software can be a valuable tool for businesses of all sizes, from small businesses to large enterprises.

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Sales Order Processing: Management, Systems & Best Practice

• Alecia Bland
• 3 years ago

• October 14, 2021

The sales order process is one of the most vital workflows in any business that sells goods. Get it right and you’ll cut costs and delight customers. Get it wrong and – unfortunately – the opposite tends to apply. So what is sales order processing, how does it work, and why should you optimise it? 

What is sales order processing?

Sales order processing, also known as sales order management, is the flow of steps from customer ordering through to product delivery. Sales order processing touches each step of the purchase and order fulfilment process, including quoting, the financial transaction, order picking and logistics.

Ideally a business should run a smooth sales order management process that ensures customer satisfaction, with few errors, fast delivery times and minimal time wasted on admin.

Sales orders vs purchase orders and invoices

Sales order processing , and in particular the phrase ‘sales order’, is not to be confused with purchase orders and invoices:

Sales order vs purchase order

A sales order and purchase order are, in essence, the same thing but going in opposite directions.

A sales order comes from the seller – your business – and is generated to confirm that a sale has been made. It outlines what goods have been sold, their quantities, payment methods, delivery information and so on.

A purchase order goes in the other direction. It comes from the customer and outlines what they wish to purchase. For example, a manufacturer may send a purchase order to their supplier outlining what they require. The supplier would then generate a sales order on the back of that purchase request, once the price has been accepted.

Sales order vs sales invoice

A sales invoice is the final piece of the puzzle, acting as the bill for goods and services. When a price has been agreed and a sales order issued, an invoice can be generated by the supplier and sent to the buyer outlining the agreed payment terms. Whether this is before or after receipt of goods is up to the two parties.

The accounting team of each party records both the sales order and sales invoice to ensure that they match – as part of the reconciliation process.

Sales order processing steps

The basic steps of sales order processing are usually:

• Receive the order
• Generate a sales order
• Picking, sorting and packing

Example of a typical sales order process flow

Here we break down the individual steps in a typical sales order process workflow, from receiving an order to invoicing.

Step 1: Receive the order

The first step in any sales order process is order receipt. The customer initiates their purchase order through their platform of choice, whether that’s over the phone, online, or via your mobile app – we’ll talk more about multichannel sales processes below.

Sales orders should include:

• Requested products
• Shipping details

If your company has multiple warehouses or fulfilment centres, shipping details are important – they’ll help you decide which of your warehouses you send the order to.

Step 2: Generate a sales order

For some companies generating a sales order is automatically included in Step 1 – so effectively it’s all one process.

To make this a single step in your sales order process, your stock levels need to be kept up to date and held electronically in a central database that is integrated with your sales ordering system – also known as an order management system (OMS) .

When your sales ordering system determines that your company has the right goods in stock, it raises its own sales order and passes the details on to the relevant warehouse managers.

Any company that doesn’t use an automated system has to do this manually – in other words, a staff member receives the purchase order, checks stock, then raises a sales order.

Step 3: Picking, sorting and packing

When an order has been raised and confirmed, it’s over to the warehouse staff to complete the picking, sorting and packing phases:

• Picking : Warehouse staff pick out the customer’s items so that they can be sorted and delivered. Barcodes and scanners may be utilised here to speed up data entry, allowing warehouse staff to tell the inventory management system that a particular item has been taken off the shelf. Some companies, such as Amazon, are increasingly using robots to automate the picking process.
• Sorting : Picked goods are organised by purchase and delivery location. Picking is often done in batches or zones, where multiple customer orders are picked at the same time from one location in the warehouse. In the sorting phase, these goods are separated into individual customer orders.
• Packing: Finally, orders are packed into appropriate containers, sealed, and labelled for shipping.

What if there isn’t enough stock?

If there isn’t enough stock to fulfil an order, you’ll need to generate a new purchase order for one of your suppliers.

This is where an inventory management system that automatically generates a new purchase order comes in handy. In other words, the system detects that there isn’t enough stock and raises a purchase order with suppliers on its own – updating the computer and customer as required.

• Learn more: What is an Inventory Management System?

Step 4: Shipping

The shipping step is where outbound goods are finally transferred to an approved logistics partner who will then deliver the product to the customer. Depending on what is most cost efficient, or what the customer prefers, purchased goods may be sent out individually or in bulk.

Collecting everything into one shipment can sometimes increase delivery times as it may take longer to pick and sort some goods over others – for instance, when stock isn’t immediately available. On the other hand, sending partial shipments can increase shipping costs and is more complex to manage.

Multiple companies may be involved in this phase. Your business could use a logistics partner to get your goods to a distribution centre, from where a courier delivers the goods to your customer. Alternatively, a single freight company could deliver your goods the whole way.

Step 5: Invoicing

If payment wasn’t handled at the sales end of the pipeline an invoice will need to be generated so your company receives payment.

A basic system can be used where the invoice is paper-based and mailed out with the package itself. Or the invoice can be generated electronically and emailed to the customer.

Depending on what accounting systems you’re using, you may also be able to use an e-invoice with payment options built in to the invoice itself – like a Pay Now button that is linked to both your accounting platform and the customer’s.

Multichannel sales order management

Any company looking to optimise its sales order management will need to think bigger than traditional sales to a multichannel or – even better – omnichannel strategy.

To optimise for these strategies automation is key, and you’ll be looking to invest in an order and inventory management system that can eliminate time-consuming manual steps like checking stock levels, inputting data into spreadsheets, producing invoices either from scratch or a template, and more.

• Learn more:   Order Management Software

When each of these processes is automated they become instant, which means staff can be occupied with more value-adding tasks.

Why multichannel sales order management is important

Most shoppers nowadays – even in the B2B space – want the option to shop online, which means that you’re going to need a multichannel or omnichannel fulfilment strategy if you want to improve your sales order process.

‘Omnichannel’ means that your business sells across multiple platforms and devices, and each of these is integrated into one system. ‘Multichannel’ refers to selling across different platforms, but their systems are separate.

Customers are increasingly looking to make purchases online – which is why a multichannel strategy is so important. The numbers tell the story:

• In the US online shopping made up 14% of all retail sales in Q4 2020 , up 11.3% year on year
• There were over 230 million online shoppers in the US in 2021 – again, an increase on years prior
• B2B ecommerce is growing too. It’s expected that 80% of B2B sales will happen digitally by 2025

Benefits of optimising sales order process flows

The sales order process involves a number of very important and sometimes complex steps, and so any degree of optimisation can have huge benefits, including:

• Fewer errors : Automating elements like data entry means there’s less chance of human errors – like typos, or putting decimals in the wrong place. It also reduces the chance that the wrong items will be put in the wrong shipment, or get missed entirely.
• Faster order fulfilment : By streamlining each phase of this process you’re making it faster. That means you’ll pick and ship customer orders more quickly, which cuts costs and delights customers.
• Lower costs : As mentioned, faster fulfilment cuts costs. But it goes beyond that – there’s less chance of errors being made, so fewer costs related to returns and reshipments.
• Complexity is easier to manage : With a streamlined process – especially a digital or automated one – it can be easier to manage more complex orders such as partial shipments. This is because calculations, data entry and order management are done on your behalf. Your teams can get on with what they do best, while the system keeps track of other tasks like sending reminders or shipping out the second or third batch in a partial shipment when stock arrives.

5 ways to optimise sales order management

Sales order management can be improved by adhering to industry best practices and using the right tools for the job. Here are five ways you can ensure every sales order entry is processed and managed efficiently.

1. Audit your current system

To optimise your processes you’ll need to start with an audit. This can be a revealing exercise – and you won’t know where you need to go if you don’t know where you are now. If you can’t capture the data you need for this, that is the first point you’ll need to address.

Your first step is to map out your current sales order process in a flow chart, describe each step, and ask yourself : Who or what is involved? How long does it take?

From this you can work out the most complex or slowest parts of your pipeline – you’ll look at optimising these first.

2. Automate

Automation is about taking tedious and repetitive jobs and letting a computer do them faster and more accurately – so that staff can focus on more meaningful tasks.

For sales order processing, you could think about automating these actions:

• Receiving purchase orders and checking inventory levels
• Raising sales orders if inventory is confirmed
• Sending purchase orders to suppliers if new stock is required
• Raising a sales invoice to be printed or emailed to the customer
• Sending picking requests to the appropriate warehouse manager
• Updating stock levels based on what items are removed from shelves and scanned
• Organising pickups and estimating shipping costs
• Communicating with the customer at key steps in the process, or when there’s a delay

How do you automate these processes?

To manage these steps you’ll need to use order management software that’s integrated with your inventory management system. Between these two systems you can handle the sales process, stock count and supplier requests.

If needed, you can integrate inventory management and order management systems with an enterprise resource planning (ERP) system to connect with more apps, such as those for accounting, sales databases, CMS platforms, and so on.

• Learn more: 5 Perks of Automation for Employees

3. Invest in inventory management software

Inventory management is key to optimising almost the entire first half of the sales order workflow.

If you don’t know what stock you have at any given time, or you can’t update this information in real time, you will always be at risk of stocking out unexpectedly – or overstocking goods that you then can’t sell.

Inventory management software is designed to cover this and more. It allows you to track and manage your stock, and even generate business intelligence reports so you can see what is profitable in your business and what’s not.

• Learn more: How inventory management software has helped real businesses

4. Explore demand forecasting

Demand forecasting builds on inventory management by using a range of data to predict future demand. Factors like historical sales data, seasonal factors and your own forecasts all combine to help the system calculate when consumers are going to want particular products, and in what quantity.

From there you’re able to ensure you have stock in the right place, in the right quantity, at the right time – meaning less risk that you’ll either run out of stock due to unexpected demand, or that you’ll order too much stock when demand is due to drop.

• Learn more: Advanced Inventory Manager

5. Learn to manage reverse logistics

When you’ve optimised each element of your sales order process, there’s still one part of your system that’s left – the reverse supply chain.

Reverse logistics is the term for when goods come back up the supply chain, from customer to supplier or manufacturer. Customer returns are a common example, but unsold goods, end-of-life goods, returned rentals and delivery failures may all be reasons for goods to come up the supply chain in the reverse direction.

If your business isn’t ready to process returned goods quickly and efficiently, it may lead to some of the same problems you’ve already worked hard to mitigate – like slow processing, human error, spiralling costs and dissatisfied customers.

• Learn more: Reverse Logistics: What is it, and Why is it So Important?

Optimise your workflows with sales order processing software

Sales order processing software, also known as order management software , is a cross between a sales database and order control. It can track sales orders in real time to give you maximum visibility, and allows you to manage sales orders based on purchase date, delivery date, warehouse location, current status, and fulfilment capacity.

Unleashed’s powerful sales order processing features also enable you to generate purchase orders for suppliers off the back of a sales order for the customer if there isn’t enough stock in-house at the time of purchase. Easily reserve stock for future sales, partial orders or specific sales channels – and receive automated alerts that let you know about overdue orders.

To find out if the Unleashed sales order processing system is right for you, follow these next steps:

1.  Watch a sales order processing software demo . Learn how to manage sales orders, track inventory, and report on key business metrics in real time to save you money while boosting efficiency with Unleashed.

2.  Sign up for a free 14-day trial . Discover first-hand how Unleashed helps you streamline order management, maximise sales, and improve operational productivity with a risk-free two-week trial.

3.  Chat with an expert to assess your needs . Are you ready to take your business to the next step? Book a free chat with one of our in-house experts for an honest discussion about sales order processing workflows.

Article by Alecia Bland in collaboration with our team of inventory management and business specialists. Alecia's background is in ancient languages. When she's not reading a book with her cat for company, you can usually find her cooking, eating or trying to make her garden productive.

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Order Processing

This activity diagram example describes a business flow activity of order processing. In this figure, the requested order is input parameter of the activity. After order is accepted and all required information is filled in, payment is accepted and order is shipped.

Posted by: Jason Voss

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Processing a Sales Order

After completing this lesson, you will be able to process a sales order

Sales Order Processing

When creating an order for a customer, you must consider transport agreements, delivery, and payment conditions, and so on, with business partners. To avoid re-entering this information each time for every activity related to these business partners, relevant data for the activity from the master record of the business partner is simply copied. In the same way, the material master record stores information. This concept is valid for processing data for each master record included in the activity.

When performing each transaction, applicable organizational elements must be assigned. Assignments to the enterprise structure in the document are generated in addition to the information stored for the customer and material.

Sales Document Structure

Sales document header

The data in the document header is valid for the entire document. This includes, for example, customer-related data.

Sales document items

Each item in the sales document contains its own data. This includes, for example, data about the material and quantities ordered. Each sales document can have several items, while individual items can be controlled differently. Examples include material item, service item, free-of-charge item, or text item.

Item schedule lines

Schedule lines contain delivery quantities and delivery dates. They belong uniquely to an item. Every item that has a subsequent outbound delivery in the sales and distribution process must have at least a schedule line. The item can have several schedule lines, for example when the quantity ordered is to be delivered in several partial deliveries at different times.

When an order is processed, the system can run various function to accelerate and assist the order processing.

You store the partner functions for the customer master in the customer master sales area data. During sales order processing, they are copied as default values into the documents.

For sales order processing, you need the mandatory partner functions sold-to party, ship-to party, payer, and bill-to party. In the course of processing a sales order, they can differ from each other or can be identical. The functions are as follows:

Sold-to party: places the order

Ship-to party: receives goods or services

Bill-to party: receives the invoice for goods or services

Payer: is responsible for paying the invoice

Other partner functions, such as contact person or forwarding agent, are not required for sales order processing.

Partners in the Sales Process

You can maintain partner relationships both in sales documents and in the master data. Partner relationships are usually already defined in the business partner record. These are proposed automatically in the document header when you create a sales document. Providing Customizing permits it, you can change or supplement these relationships manually by going to the partner screen and changing the function assignment.

You decide whether several partners can be assigned to one partner function in the business partner record in Customizing. If multiple partners are maintained with the same function, a selection list appears containing these partners when you then enter a sales order.

In the sales documents, the system has been configured so that only one partner can be assigned to each partner function. The only exception is for outline agreements.

You can also define partners at item level in the sales documents.

You can determine which partner functions have to be entered (mandatory functions).

You can prohibit anyone from changing a partner that has already been entered (You can indicate that the sold-to party cannot be changed in the sales document for example).

It is also possible to enter or change the address of a partner such as the ship-to party manually. This change does not affect the master record.

Pricing in Sales Orders

Availability check in the sales order.

When you enter a sales order, you can only confirm the delivery of the goods for the required delivery date if the goods are available for all the necessary processing activities, which take place before delivery.

On the Sales and Distribution tab page in the material master you can, in Gen./Plant, in the Availability Check field, enter which or what type of availability check should be carried out for this material during order processing.

There are also various tables in Customizing, on which the availability check is also dependent.

From the availability control screen, you can access the Available to Promise (ATP) quantities, the scope of check for determining available quantity, and the other plants that may have the material available.

The material availability check in sales orders is performed at plant level for the corresponding item. The plant can be determined automatically or maintained manually. During automatic determination, the system looks for a valid default value for the plant in the relevant master data using the following sequence:

Customer-material info record

Ship-to party customer master record

Material master record

Incompletion Log

Output is information that is sent to the customer via various media, such as mail, EDI, or fax. Examples include the printout of a quotation or an order confirmation, order confirmations via EDI, or invoices by fax.

As with pricing, output determination takes place using the condition technique.

Output can be sent for various sales and distribution documents (order, delivery, billing document, and so on).

Create a Quotation

Create a sales order with reference to a quotation, check and solve sales order fulfillment issues, review the sales order.

Log in to track your progress & complete quizzes

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• Published: 03 September 2024

The child the apple eats: processing of argument structure in Mandarin verb-final sentences

• Max Wolpert 1 , 2 , 3 ,
• Jiarui Ao 2 ,
• Hui Zhang 4 ,
• Shari Baum 3 , 5 &
• Karsten Steinhauer 3 , 5  

Scientific Reports volume  14 , Article number:  20459 ( 2024 ) Cite this article

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Mandarin Chinese is typologically unusual among the world’s languages in having flexible word order despite a near absence of inflectional morphology. These features of Mandarin challenge conventional linguistic notions such as subject and object and the divide between syntax and semantics. In the present study, we tested monolingual processing of argument structure in Mandarin verb-final sentences, where word order alone is not a reliable cue. We collected participants’ responses to a forced agent-assignment task while measuring their electroencephalography data to capture real-time processing throughout each sentence. We found that sentence interpretation was not informed by word order in the absence of other cues, and while the coverbs BA and BEI were strong signals for agent selection, comprehension was a result of multiple cues. These results challenge previous reports of a linear ranking of cue strength. Event-related potentials showed that BA and BEI impacted participants’ processing even before the verb was read and that role reversal anomalies elicited an N400 effect without a subsequent semantic P600. This study demonstrates that Mandarin sentence comprehension requires online interaction among cues in a language-specific manner, consistent with models that predict crosslinguistic differences in core sentence processing mechanisms.

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Introduction.

Languages differ in how they express meaning, but these differences are constrained by the biology of the human brain. Thus, many accounts of processing propose that every language is processed in fundamentally the same ways. For instance, all languages appear to communicate information at a similar rate 1 and have a similar distance between hierarchically dependent linguistic units 2 . Likewise, all languages utilize the same brain networks for comprehension and production 3 , with the same amount of time required to access and retrieve word meaning 4 , 5 , 6 . Language users also rely on common processing heuristics to build sentence structure in real time 7 , 8 , including assumptions about which sentence elements are probable agents 9 , 10 . It follows, then, that behavioral and neural metrics of sentence processing should yield similar results no matter the language.

Nonetheless, models of language processing must account for the full extent of crosslinguistic variability, and many so-called universals do not hold for all languages 11 . For example, processing of verb-final sentences relies on distinct profiles of working memory and predictive parsing 12 , 13 , and comprehension of these structures varies across languages 14 . Additionally, different linguistic features lead to comprehenders prioritizing different units of information 15 , such as languages with flexible word order enlisting different processing resources from languages with fixed order 16 . Suffice it to say, studying diverse languages is part of appreciating the extent of variation in sentence processing.

In the present study, we considered verb-final sentences in Mandarin, a language unusual for having flexible word order—a feature normally associated with highly inflected languages—despite virtually no morphological inflection 17 , 18 , making Mandarin a “typologically hybrid” language 19 . For example, the two sentences 孩子苹果吃掉了 “child apple ate” and 苹果孩子吃掉了 “apple child ate” both mean that the child ate the apple, despite the word order being reversed. Even basic terms like “subject” and “object” are not always appropriate for describing Mandarin sentences 20 , 21 . These grammatical properties make it challenging to create an “unambiguously ungrammatical” Mandarin sentence 22 , leading some to describe Mandarin as a “semantics-based” as opposed to a “syntax-based” language 23 . These features make Mandarin an ideal language for testing assumptions about meaning and structure in sentence processing models.

The divide between syntax and semantics has long been a focus of sentence processing research 4 , 24 , 25 . At the syntax-semantics interface is argument structure, or the grammatical roles that verbs assign to sentence elements 26 , 27 . Although many categories of roles have been described, there is consensus that the human mind distinguishes between the doer, or agent, and the receiver, or patient, of a verb’s action 28 , 29 , 30 . We use the terms agent and patient to represent the concept of proto-agent and proto-patient, two roles argued to be psychologically real across languages 28 . Languages differ in how agent and patient roles are expressed, and grammatical information is often redundant with semantic knowledge of stereotypical argument roles 31 . It is thus productive to pit semantics against syntax to characterize how they are prioritized in different languages for argument structure interpretation.

Mandarin has another feature important for argument structure: the coverbs BA and BEI. These coverbs can occur in verb-final sentences to disambiguate agent and patient roles. BA assigns agent status to its preceding noun, as in 孩子把苹果吃掉了 “child BA apple ate”, resulting in subject-object-verb word order. BEI assigns patient status to its preceding noun, as in 苹果被孩子吃掉了 “apple BEI child ate”, resulting in object-subject-verb word order. These word orders are also possible without the coverbs BA and BEI, as already indicated, but may be pragmatically restricted 32 . There has been debate about the syntactic categories of BA and BEI 33 , 34 , but for simplicity we refer to them as coverbs 35 . Although BA and BEI are both common in verb-final clauses, they have differences in structure and usage. BA must be followed by a noun phrase and is limited in which verbs it can be used with, and BEI is typically analyzed as a passive construction and can be followed directly by a verb 36 , 37 . Despite these differences, BA and BEI are powerful cues for argument structure assignment and each assigns a different interpretation in verb-final sentences.

In the present study of Mandarin sentence processing, we used electroencephalography (EEG) and behavioral measures to systematically compare the impact of syntactic (coverbs and word order) and semantic information (animacy and event knowledge). Below, we first introduce an impactful model of crosslinguistic sentence processing, the Competition Model, and then turn to the specific case of role reversals and existing accounts that consider Mandarin data. We then summarize the critical elements of our experimental design and relevant predictions.

Competition Model

For experimental comparison of crosslinguistic differences, an impactful framework is the Competition Model 38 , a key reference for language processing researchers for the last 40 years. As its name suggests, the Competition Model describes processing as a competitive arena where fundamental units of information called “ cues” compete to shape decisions in comprehension and production 39 . Much of the research in the framework of the Competition Model has targeted argument structure processing, employing a forced-choice task for agent selection with orthogonal cue comparison across languages to assess the relative strength and validity of specific cues in offline judgments 38 . Agent selection differs among languages depending on how often certain cues are present (cue availability) and how often these cues correctly indicate the agent (cue reliability); in the case of competition among cues, the primary cue driving argument structure assignment is said to have greater cue strength 40 . Mandarin speakers have been shown to rely on the following cues in order of decreasing strength: BEI, animacy, word order, and BA, although BA may be as strong as word order in verb-final sentences 32 .

Role Reversals and EEG Experiments

Sentences where the stereotypical arguments of agent and patient are swapped without grammatical violations are known as role reversals, such as “the child the apple ate.” Over the past twenty years, researchers have studied role reversals with event-related potentials (ERPs), with specific focus on the N400 and P600 components. Traditionally, N400s were linked to semantic processing 4 and P600s to syntactic processing 25 , 41 . However, this functional distinction has become more nuanced, especially when implausible role reversals were shown to elicit “semantic P600 effects” without modulation of the N400 42 , 43 , 44 .

Different explanations for semantic P600 effects have been proposed, including parallel syntactic and semantic processing streams 45 , 46 , 47 and classifying the N400 and P600 as indexing retrieval and integration instead of semantics and syntax 48 , 49 . However, few explanations have considered the potential for different languages to require different processing profiles for sentence comprehension. Given its unusual linguistic properties, Mandarin is a fruitful target for testing crosslinguistic validity of sentence processing models.

We are aware of two processing models that have explicitly considered Mandarin to study argument structure in role reversals. The first is the extended Argument Dependency Model (eADM), which, like the Competition Model, sets crosslinguistic diversity at the center of its model architecture 16 , 50 , 51 . According to the eADM, processing of sentence elements is divided in separate streams for nouns and verbs, and nouns are assigned a proto-agent or proto-patient role as argument structure is built iteratively. When parsers encounter difficulty in initial stages of relational and plausibility processing, an N400 effect is elicited; P600 effects, however, are limited to subsequent well-formedness and repair computations. Crucially, the computations for processing nouns and verbs depend on language-specific patterns of cue weighting, and the model predicts that role reversals only elicit semantic P600 effects without N400 modulation in languages that have rigid word order 16 , 52 . Conversely, role reversals for a language with flexible word order like Mandarin will elicit N400 effects with or without P600 modulation 16 . These predictions arise from revisions of earlier models to better account for conflict between syntactic and thematic processing streams 52 , 53 .

The second model that has considered Mandarin is the Bag of Arguments account, which encompasses multiple studies over the past decade 54 , 55 , 56 , 57 that together inform an overarching processing model of argument structure 58 . The Bag of Arguments model centers around the N400 as an index of prediction during real-time sentence processing, and role reversals are expected to modulate N400 amplitude only if (1) the verb and its arguments are combinable and highly predictable 54 , (2) arguments are in the same clause as the verb 55 , 57 , and (3) parsers have more than 800 ms to utilize structural role information to predict the verb 56 . If these conditions are met, then role reversals should elicit N400 effects, with or without modulation of the P600 56 , 58 . The eADM and Bag of Arguments accounts stemmed from different motivations, with the eADM focused on crosslinguistic differences in argument structure and plausibility computations, while the Bag of Arguments model is concerned with the timing of verb prediction based on preceding arguments. The eADM also makes predictions for ERPs prior to the verb 59 , 60 . Accordingly, the models are not incompatible, and both can inform interpretation of the present study.

Present Study

The present study had two primary aims. First, we sought to improve characterization of cue weighting in Mandarin using an experiment with a balanced design and monolingual Mandarin-speaking participants. While prior descriptions of the pattern of Mandarin cue weighting have been impactful 23 , 32 , 61 , there has been a recent call to use updated methodological approaches to evaluate these findings’ replicability 62 . To appreciate the iterative nature of argument structure assignment, we also compared ERPs at pre-verb sentence positions. Second, we used ERPs to test processing of role reversals, which have been widely reported to elicit semantic P600 effects 48 . While most accounts assume that role reversals are processed similarly across languages 48 , 54 , there is evidence that different language process these structures differently 16 . Prior experiments have tested Mandarin role reversals, but there are conflicting reports of N400s 16 , semantic P600s 54 , 56 , or no ERP modulation 58 . These studies have not reported controlling for participants’ bilingual language knowledge, despite findings showing sentence comprehension can be impacted by second language knowledge 23 , 61 .

Accordingly, we designed stimuli by systematically manipulating semantic and syntactic cues: Reversibility, Agent Animacy, Order, and Structure. For Reversibility, there were reversible sentences where either noun was equally plausible as agent and irreversible sentences where only one noun was a plausible agent. To maximize the plausibility difference between reversible and irreversible sentences, we manipulated Agent Animacy such that irreversible sentences had contrasting animacy between the two nouns and reversible sentences had two nouns with shared animacy status. Unlike many prior studies 23 , 32 , 61 , we included plausible inanimate agents, thus dissociating animacy and plausibility. The stimuli designed with the above semantic restraints were then crossed with Order, whether a given plausible agent was in first or second position in the sentence, and Structure, whether a sentence used BA, BEI, or noun-noun–verb (NNV) structure without a coverb. The experimental conditions are summarized in Table 1 for the semantic variables and Table 2 for the syntactic variables.

Predictions for behavioral and ERP results were informed by three sentence processing models considering Mandarin data, as previously summarized. In contrast to previous Competition Model reports of a linear ranking of cue strength 23 , 32 , 61 , we expected that by dissociating animacy from plausibility, animacy would not be the strongest cue. For role reversal sentences, we expected that if Mandarin plausibility comprehension comprises language-specific mechanisms, then there would be an N400 response, with or without P600 modulation, as predicted by the eADM 16 . Alternatively, if Mandarin comprehension relies on mechanisms identical to other languages, then there should be a semantic P600 response without modulation of the N400, as found in prior studies 42 , 43 , 44 , 54 , 56 . Crucially, we note that each of the three models has different aims and proposed mechanisms underlying their proposed processing architecture, and thus the corresponding predictions in the context of the current study (overviewed in Table 3 ) are not strict tests of the validity of any single model over another. In fact, the models are sufficiently distinct in their targeted explanations that direct comparison may be inappropriate. Instead, the present study can give support, nuance, or reconsideration for specific aspects of each model.

Note that towards our first aim of characterizing cue weighting, we further considered ERPs prior to the verb at sentence-initial noun and coverbs. Previous studies have reported greater N400 components for inanimate nouns in comparison to animate nouns at sentence-initial position 59 , 60 . According to the eADM, this is attributed to a preference for sentence-initial arguments to be agents (or subjects) 10 , and thus non-ideal inanimate agents elicit greater N400 amplitude than their animate counterparts 59 , 60 . Per this explanation, we should observe an N400 effect for sentence-initial inanimate nouns. These predictions are summarized in Table 3 .

Behavioral Results

Reversible sentences.

Participants’ interpretation of reversible sentences showed two key effects related to our predictions. First, BA and BEI were the strongest cues for agent assignment (BA: β = 16.96, SE = 5.83, Z  = 8.23, p  < 0.001; BEI: β = 0.06, SE = 0.02, Z  =  − 7.73, p  < 0.001), which confirmed previous behavioral results for verb-final sentences 23 , 32 . In contrast to prior reports, however, our participants had no inherent word order preference for agent selection in NNV sentences (β = 1.25, SE = 0.24, Z  = 1.20, p  = 0.232). Alongside these simple effects, BA and BEI interacted with Agent Animacy, with the coverb cue being slightly weaker when indicating an inanimate agent (BA: β = 1.24, SE = 0.12, Z  = 2.18, p  = 0.03; BEI: β = 0.65, SE = 0.06, Z  =  − 4.66, p  < 0.001). Model results are visualized in Fig.  1 a.

Results of agent assignment task. ( a ) Model predictions for interaction between Agent Animacy and Structure for first agent noun selection in reversible sentences. Y-axis shows probability of selecting the first noun as agent. Error bars show 95% confidence intervals; ( b ) model predictions for interaction among Order, Agent Animacy, and Structure for first agent noun selection in irreversible sentences. Y-axis shows probability of selecting the first noun as agent. Error bars show 95% confidence intervals. ( c ) Individual differences in Coverb and Plausibility scores. Dotted line shows the line y = x where Coverb and Plausibility Scores are equal in value. Discrete labels for agent assignment strategy are shown here, but subsequent use of scores for analysis was done with Difference Score as a continuous variable.

Irreversible Sentences

Just as in reversible sentences, BA and BEI were the strongest cues for agent assignment in irreversible sentences (BA: β = 18.21, SE = 7.00, Z  = 7.55, p  < 0.001; BEI: β = 0.07, SE = 0.03, Z  =  − 6.21, p  < 0.001). Unlike reversible sentences, Order had a strong effect on agent assignment in the absence of coverbs, demonstrating that irreversible sentences had only one plausible interpretation (β = 11.16, SE = 2.67, Z  = 10.09, p  < 0.001). Structure and Order further interacted (BA: β = 0.72, SE = 0.13, Z  =  − 1.86, p  = 0.063; BEI: β = 0.42, SE = 0.06, Z  =  − 6.52, p  < 0.001), such that the effect of Order was considerably weaker for BA and BEI structures than for NNV sentences but not entirely absent. Essentially, when the positioning of the plausible agent agreed with the coverb cue, participants’ agent assignment was influenced more than by either cue alone (BA: β = 64.44, SE = 34.90, Z  = 7.69, p  < 0.001 ; BEI: β = 0.05, SE = 0.09, Z  = 6.24, p  < 0.001).

The model further revealed a two-way interaction between Agent Animacy and Order and a three-way interaction among the cues of Structure, Order, and Agent Animacy. In the two-way interaction, the effect of Order was stronger for plausible animate agents than for plausible inanimate agents (β = 1.69, SE = 0.13, Z  = 6.73, p  < 0.001). We note that this effect is similar in magnitude to the effect of Agent Animacy for reversible sentences. The three-way interaction was only significant for BEI sentences (β = 0.69, SE = 0.08, Z  =  − 3.12, p  = 0.002), where, unlike for BA or NNV, there was minimal difference in the effect of Order between plausible animate agents and plausible inanimate agents (animate: β = 29.5, SE = 15.8, Z  = 6.30, p  < 0.001; inanimate: β = 15.9, SE = 8.13, Z  = 5.41, p  < 0.001). This effect was most apparent when comparing BA and BEI role reversal sentences. While BA was more successful at driving role reversal interpretations when the plausible agent was inanimate (animate: 67% (SE = 11.4) probability first noun agent selection; inanimate: 80% (SE = 8.4)): BEI role reversal interpretations were unaffected by Agent Animacy (animate: 29% (SE = 7.9); inanimate: 31% (SE = 8.3)). Model results are visualized in Fig.  1 b.

Individual Differences

While the behavioral models showed group-level effects, further data exploration showed that not all participants used the same comprehension strategies. To quantify individual differences, we calculated individual scores for reliance on coverbs in reversible sentences (Coverb Score) and Order in the absence of a coverb in irreversible sentences (Plausibility Score). These scores respectively represent individuals’ reliance on coverbs and plausibility when there are no competing cues for agent assignment. The maximum score value of 1 indicates a given participant always used the corresponding cue, whereas a score of 0 indicates the cue was always disregarded.

From visual inspection of individuals’ Coverb and Plausibility Scores, we identified three discrete strategies: using only plausibility, only coverbs, or both plausibility and coverbs. To create a continuous metric, we calculated a Difference Score by subtracting the Coverb Score from the Plausibility Score, where individual scores ranged from − 0.98 (most coverb-driven) to 0.79 (most plausibility-driven). For visualization, we labeled participants with a Difference Score between − 0.33 and 0.33 as having a balanced strategy (i.e., the middle third of the values range), where they gave approximately equal weight to the two cues of plausibility and coverb, as depicted in Fig.  1 c. This Difference Score further predicted participant reaction times, with more plausibility-driven participants taking longer to respond to reversible NNV sentences (see Supplementary Materials).

ERP Results

Visual inspection of ERPs between animate and inanimate nouns in initial sentence position did not reveal a substantial difference in N400 amplitude. A mixed effects model for midline electrodes showed no significant effect of noun one animacy (β = 0.08 µV, SE = 0.20, Z = 0.40, p = 0.69, model results reported in Supplementary Materials).

ERPs at the respective second word in NNV, BA and BEI sentences (i.e., contrasting noun two in NNV and the coverbs) showed strong variation in the P200 amplitude of the coverbs BA and BEI, as can be seen in Fig.  2 . Direct comparison between ERPs showed that BA elicited a smaller P200 than BEI and noun two in NNV sentences, as can be appreciated in Fig. 2 . Noun two further elicited a sizeable N400 component, consistent with word class effects 63 . The smaller P200 for BA was not predicted a priori, but given the striking visual difference in the ERP waveforms, we analyzed single trial amplitudes in the P200 and N400 time windows.

ERPs for the effect of Structure at the second word position. The 200-ms pre-onset baseline interval is indicated with a gray rectangle. Scalp maps show BEI minus BA (top) and noun two minus BA (bottom) for the P200 and N400 time windows.

The average P200 amplitude for BA sentences was significantly smaller than for NNV sentences at electrodes Fz and Cz ( p s < 0.01). With correction for multiple comparisons, P200 amplitude for BEI did not differ significantly from that for BA or Noun Two in NNV sentences. Average N400 amplitude for noun two in NNV sentences was larger than for BA and BEI ( p s <  = 0.01).

Role Reversals

Visual inspection of role reversal results showed that implausible sentences elicited a broadly distributed, centro-parietal N400 between 300 and 500 ms and a sustained, localized frontal positivity around 800 ms, as depicted in Fig.  3 . We further compared ERPs broken down by Structure and Agent Animacy (see Supplementary Materials). BA role reversals showed a larger and broader N400 effect with a sustained frontal positivity, and a later, broad positivity beginning around 700 ms in both posterior and frontal locations. BEI role reversals showed a smaller, more localized N400 effect with a sustained frontal negativity, and a central-posterior right-lateralized positivity in the late P600 time window also beginning around 700 ms. For plausible animate agents, role reversals showed a broad N400 effect, and role reversals with plausible inanimate agents appeared to show a localized N400 effect followed by a broadly distributed positivity. However, these interactions should be interpreted cautiously due to relatively low power for statistical inference between subconditions.

ERPs in response to role reversals. ( a ) ERPs are averaged across Agent Animacy and Structure. Scalp maps show reversal minus plausibility (averaged across other factors) for the N400 time window from 300 to 500 ms and the P600 time window from 500 to 900 ms. The 200-ms post-onset baseline interval is indicated with a gray rectangle. ( b ) Model predictions for three-way interaction among Structure, Agent Animacy, and Plausibility. Note that while the three-way interactions were significant for both time windows, not all post-hoc pairwise comparisons were significant. The simple effect of Plausibility was significant for the N400 time window and not for the P600 time window.

Despite the appearance of an N400 effect for Plausibility in the ERP waveforms, our initial model results indicated this effect was only marginally significant (β =  − 0.51 µV, SE = 0.32, Z  =  − 1.61, p  = 0.106). Based on our contrast coding, the coefficient for Plausibility represented the effect at the reference level of Pz; however, the model including only Pz showed a significant effect of Plausibility (β =  − 0.92 µV, SE = 0.35, Z  =  − 2.65, p  = 0.008). We determined that the discrepancy between these model values was due to our limiting the output to three-way interactions (Voltage ~ Structure*Plausibility*Agent Animacy + Structure*Plausibility*Electrode + Structure*Agent Animacy*Electrode + Plausibility*Agent Animacy*Electrode). For a full model including all possible interactions (Voltage ~ Structure*Plausibility*Agent Animacy*Electrode), the corresponding coefficients were identical and the effect of Plausibility was significant. These results are challenging to reconcile and could lead to opposite conclusions based on sometimes arbitrary decisions. We bring up this difficulty because such challenges are faced by many, if not all, users of mixed effects models and the field continues to develop standards for best practice 64 .

To address these issues, we report here the results of the midline model with a reduced random structure for item variability excluding a random slope for Plausibility to increase power 65 , with other models reported in Supplementary Materials. The present model revealed a main effect of Plausibility (β =  − 0.46 µV, SE = 0.18, Z = 2.55, p = 0.011), demonstrating that role reversals elicited an N400 effect averaged across Structure and Agent Animacy. There was a further three-way interaction among Structure, Agent Animacy, and Plausibility (β = 0.39 µV, SE = 0.18, Z = 2.16, p = 0.03). Post-hoc pairwise comparisons showed that the largest N400 effect was for implausible BEI sentences with animate agents (e.g., the reversal sentence 仆人被镜子擦亮了 “servant BEI mirror polished”; β =  − 1.39 µV, SE = 0.35, Z = 3.99, p < 0.001). Described qualitatively from the model predictions in Fig.  3 b, implausible BA role reversals elicited a numerically greater N400 than plausible BA sentences regardless of Agent Animacy status, while implausible role reversals with BEI sentences elicited a greater N400 for animate agents but not for inanimate agents.

Although the ERP visualization suggested a small frontal positivity in the P600 time window, the statistical model did not reveal a significant main effect of Plausibility. Note that for consistency with the N400 analysis, we also excluded the random slope for Plausibility in the item random structure. The only significant model coefficient was a three-way interaction among Agent Animacy, Structure, and Plausibility (β = 0.62 µV, SE = 0.23, Z  = 2.66, p  = 0.008). Post-hoc pairwise comparison showed that for BEI sentences, ERP amplitudes to reversals with plausible animate agents were significantly more positive than their congruent counterparts. (β = 1.67 µV, SE = 0.46, Z  = 3.63, p  < 0.01). Nonetheless, we note that this frontal positivity is not a typical semantic P600 distribution 56 , 66 .

We investigated argument structure comprehension in verb-final sentences in Mandarin, a language with flexible word order and virtually no inflection. We analyzed monolingual, Mandarin native speakers’ behavioral and EEG data to capture a cohesive picture of real-time cue competition. Our behavioral results demonstrated that 1) word order was not used to assign argument structure in the absence of other cues; 2) the coverbs BA and BEI were the strongest cues for agent assignment but were differently impacted by Agent Animacy in the case of role reversals; and 3) participants showed individual differences in their reliance on semantic and syntactic cues. Our EEG results showed that 1) sentence-initial noun animacy did not impact N400 amplitude; 2) BA elicited a reduced P200 amplitude relative to BEI and nouns; and 3) the disambiguating verb in role reversal sentences elicited an N400 effect without a subsequent semantic P600. To our knowledge, this is the first time a forced agent-assignment task has been used in an EEG experiment. A key advantage of this task is that it directly reveals a reader’s sentence interpretation and resembles aspects of natural language processing, where individuals must understand the relation of a verb to its arguments. Additionally, all experimental sentences were, in principle, grammatical Mandarin structures, thus minimizing acceptability judgments.

With respect to prior findings, our behavioral results showed a distinct cue weighting profile for Mandarin. First, Agent Animacy was not the most important cue, with participants accepting both animate and inanimate plausible agents. We note that prior experiments often confounded animacy with plausibility 23 , 32 , 61 , 67 , thus overestimating the role of animacy in driving sentence interpretation. Second, in reversible sentences without coverbs, word order did not drive agent assignment, challenging the idea that there is an inherent preference for object-subject-verb 32 , 68 or subject-object-verb 67 word order. Our findings support the idea that verb-final Mandarin sentences where word order is the only cue may be ambiguous 69 . In contrast to English, where pre-verb and post-verb positioning of arguments reliably signals structural roles 15 , 61 , we suggest that only post-verb positioning is reliable in Mandarin 10 , 17 .

Our behavioral results further challenge previous descriptions of cue weighting as mere linear ranking. Instead, multiple cues were weighted to varying extents in different contexts. Consequently, a particular profile of cues could result in different interpretations, meaning any given interpretation is best described probabilistically in terms of the available cues, as in Fig.  1 . Animacy is a good example: while it had no simple effect for driving agent assignment, Agent Animacy interacted with other cues to subtly affect comprehension. While the coverb BA was consistently a stronger cue for agent assignment when its preceding noun was animate, this was not the case for the coverb BEI, for which interpretations or role reversal sentences were not impacted by Agent Animacy. This divergence hints at nuanced processing demands between BA and BEI 70 . We comment further on animacy below in conjunction with the ERP results.

While the group-level findings demonstrate an overarching pattern for Mandarin cue weighting, we further found individual differences in interpretation strategy. Although most participants used both plausibility (i.e., Order in irreversible sentences) and coverbs to make their interpretations, a subset of participants relied on one cue while largely ignoring the other. This response pattern further impacted reaction times (see Supplementary Materials), with plausibility-driven participants taking longer to respond to reversible NNV sentences than their coverb-driven counterparts. While individuals varied in comprehension strategy, group averages reflected core characteristics of the cue weighting profile of Mandarin, with individual variation occurring within the confines of these characteristics. For instance, while individuals disregarded the coverb cue in favor of plausibility, no participant interpreted BA as if it were BEI, or vice versa.

In conjunction with the behavioral results, our ERP findings provide insights about online, incremental parsing decisions. We considered three sentence time windows: the first noun, the second word, and the verb. At the first noun, we found no effect of animacy on N400 amplitude, unlike previous reports in English 59 , 60 , which alongside the behavioral findings indicates that the cue of animacy alone is not sufficient to drive assignment of argument structure. Instead, animacy can interact with other cues, and inanimate nouns can be preferentially interpreted as agents given certain semantic contexts. Importantly, the potential of inanimate nouns to be agents is likely impacted by multiple factors, including concreteness 71 , situational relationships 72 , motor and social cognition 73 , and whether the nouns correspond to places 74 or natural forces 75 . These factors were not controlled in the present experiment, so it is possible that the perceived “agentiveness” of the inanimate nouns in the present study was relatively high, thus leading to the particular profile of agent interpretation and the lack of a sentence-initial N400 amplitude difference from animate nouns.

At the second word position, we observed a striking decrease in P200 amplitude for BA in comparison to BEI and nouns. Although not predicted, the P200 effect is compelling evidence for differences between BA and BEI in cognitive demand prior to the verb 76 . Upon encountering BA, most participants likely assigned agent status to the previous noun; when reading BEI, participants had to wait for the upcoming noun to complete agent assignment. We note that this P200 effect cannot be due to the visual simplicity of BA in relation to other characters 77 , 78 ; if this were the case, then BA (把) and BEI (被) both should have P200 amplitudes smaller than the more visually complex, two-character nouns. There has also been a report of a similar P200 difference in the auditory domain 79 . This effect may stem from a difference between assigning agent or patient arguments to an iteratively constructed sentence structure during online processing. To test this hypothesis, future studies should test single-argument sentences with BA and BEI (e.g., 把苹果吃掉了 “BA apple ate”) and the alternation of the experimental task to patient instead of agent identification. We note that this manipulation is only possible with flexible word order for pre-verbal arguments and could thus represent processing specific to languages with frequent verb-final structures, although it may be limited to the current experimental task.

At the position of the verb, we found that role reversals elicited an N400 effect followed by a local frontal positivity. In contrast to reports of semantic P600 effects to role reversals 45 , 46 , 48 , 54 , our findings indicate that Mandarin role reversal anomalies were detected via relatively early, automatic semantic processing and meaning retrieval mechanisms. Our finding of a frontal positivity contrasts with predictions for semantic P600s, given that frontal positivities are often dissociated from typically posterior P600 effects 56 , 66 . This lack of a semantic P600 effect is not the first such report 16 , 58 , suggesting it may be prudent to reconsider the label “semantic P600”, just as early labels of the N400 and P600 components as indexing processing of semantics 5 , 80 and syntax 81 were misleading. If P600 responses to role reversals are primarily driven by task 16 , 58 , this component may be understood better as a member of the P300 family 82 , in which case prior reports of semantic P600 effects were likely driven by acceptability judgment tasks. To appreciate the interplay of the N400 and P600 components, computational models like the retrieval-integration account 49 , 83 , 84 and noisy channel models 85 , 86 are well equipped to explain and predict ERP responses to role reversals, although the present study suggests that crosslinguistic differences cannot be discounted. One way to integrate crosslinguistic differences may be via a language-specific filter based on cue reliability and prominence, with quantitative and qualitative differences among languages and language experience 39 , 87 .

Our ERP results may be partially explained by existing models, as outlined in Table 3 . The N400 effect can be, in principle, consistent with both the eADM and the Bag of Arguments account, although there are some discrepancies with prior reports. According to the eADM, and consistent with the present study’s findings, Mandarin role reversals modulate the N400 because of the language’s flexible word order, which requires greater weighting of plausibility cues at early processing stages 16 . A prior eADM study of Mandarin found N400 modulation only for BEI reversals and not BA reversals 16 , which could conceivably stem from task or modality differences, as well as the present experiment’s systematic comparison of multiple cues. For the Bag of Arguments account, the model can be adjusted to explain the present observation of N400 modulation with 750 ms stimulus onset asynchrony (SOA). While experiments have demonstrated that 600 ms is insufficient 56 , 58 and 800 ms is sufficient 58 for argument role assignment to constrain verb prediction as reflected in modulation of the N400 component, there is not a functional explanation for the necessary time to complete the associated computations. One possibility is a mechanism proposed by the Memory, Unification, and Control model 88 , where semantic information is integrated in processing cycles. Combining the structural roles of two sentence elements to aid in verb prediction could require two complete processing cycles, perhaps corresponding to twice the N400 latency, which could also be consistent with the present study’s SOA of 750 ms.

Neither the eADM nor the Bag of Arguments account specifically predicted the present experiment’s lack of a P600 effect, although both research groups have highlighted task as driving P600 modulation 16 , 58 . While eADM experiments have not reported P600 effects for Mandarin role reversals, the model does not explain why role reversals for some sequence-independent languages elicit just an N400 effect (Mandarin and Turkish) and others elicit a biphasic N400-P600 effect (German and Icelandic) 16 . For the Bag of Arguments account, most studies have reported a semantic P600 effect 54 , 55 , 56 , although recent experiments with fewer task demands found no P600 modulation 58 , and the model itself does not make explicit predictions for P600 modulation. Additionally, in contrast to the present experiment, Bag of Arguments studies of Mandarin have manipulated cloze probability and combinability in specifically BA verb-final sentences with animate agents 54 , 56 , 58 . If our nouns and verbs were less related to each other than stimuli in previous studies, or the presence of an explicit task with multiple competing cues in BA, NNV, and BEI sentences impacted participants’ parsing strategies, these differences could explain the inconsistent findings. Task and stimuli differences have been shown to affect N400 and P600 responses to role reversals 59 , 60 , and acceptability tasks are especially linked with P600 effects 82 , 87 , 89 . While some researchers describe the N400 as less sensitive to task modulation than the P600 60 , 89 , 90 , 91 , it should be noted that the N400 can show nuanced variation depending on task and context, potentially because of component overlap 92 . Because the present study is the first ERP experiment using a forced-choice agent assignment task, we cannot discount the potential for task to play a role in driving our effects.

The present study updates our understanding of Mandarin sentence processing and the incremental, online processing of argument structure assignment. In contrast to previous studies describing Mandarin cue weighting as a simple linear ranking 23 , 32 , 61 , we used logit models to more accurately portray a probability-based profile of Mandarin 62 , capturing the gradient nature of cues for sentence comprehension 93 . Our results are consistent with models proposing crosslinguistic differences in core processing steps 16 , 87 , 94 and provide new information for the timing of argument role information in comprehension of verb-final sentences 58 . We cannot discount the impact of task and stimuli differences in driving some of our findings, especially for the ERP effects that have smaller effect sizes than the behavioral results. Verifying the extent of crosslinguistic differences will require systematic comparison of diverse languages and sentence types beyond role reversals. Even so, inconsistent ERP findings for Mandarin role reversals, including N400 effects 16 , P600 and N400-P600 effects 54 , 56 , and null effects 58 suggest that this language may merit special consideration in neurocognitive models of sentence processing. Of broader implication, recent advances in machine learning have led to successful decoding of sentences from blood-oxygen-level-dependent data 95 , 96 , indicating the potential for rapid advancement in analyzing neuroimaging data. As the current study shows, basic tenets of syntactic and semantic processing diverge among languages, and thus decoding nuanced sentence meaning from brain data may require precise targeting to language-specific features.

Participants

In total, 39 Mandarin native speakers participated in the study. Of these 39, four were excluded from analysis due to technical problems during experiment delivery and one was excluded due to failure to stay attentive during the experimental session, resulting in 34 (19 to 25 years old, mean age = 22, SD = 1.9, 19 female) datasets. Additionally, there were six subjects who did not see the BA reversal subcondition (approximately 30 sentences in total) due to error in experiment delivery. We opted to include their data in all analysis, as mixed effects models (see Data Analysis section below) can appropriately handle missing data 97 .

All participants were recruited via online advertisement and word of mouth in Nanjing and tested at Nanjing Normal University. All participants were right-handed based on the Edinburgh Handedness Inventory 98 (average score = 83) had normal vision or wore corrective lenses, and did not have any history of neurological disorders. Participants gave written informed consent and were compensated 150 RMB for their time.

To ensure that Mandarin processing was not influenced by other language experience, we limited recruitment to participants who primarily communicated in Mandarin and had limited knowledge of English and other languages, including Chinese languages and dialects. Because English is a required subject in Chinese primary, secondary, and tertiary schools 99 , all participants had some previous exposure to English. To minimize the influence of English on processing, we restricted recruitment to only those who self-reported an English level of 3 or below on a scale from 1 to 6 (1 being no knowledge of English, 6 being nativelike), who did not use English on a regular basis, and who were at or below the College English Test Level 4, which is typically below communicative competence 100 . If participants had exposure to a dialect other than standard Mandarin, this was restricted to Northern dialects (e.g., Nanjing, Xuzhou, Nantong, Shandong, Hebei) which are classified as belonging to the Mandarin dialect family and are mutually intelligible 17 . Note that there were exceptionally three participants in the present study who had knowledge of a Chinese language outside of the Mandarin dialect family (Wu, Gan, and Xiang), but they had minimal exposure to these languages in their adult life and primarily used Mandarin.

Participants further completed a detailed language background and usage questionnaire, from which we report summary values in Table 4 . To further evaluate their language knowledge, participants also completed a LexTALE lexical decision task in English 101 and Mandarin 102 . Self-reported proficiency values represent a mean of three separate values for reading, writing, and listening. Exposure percentages represent the self-reported average percent of exposure time from birth to the present. Participants reported percentages in approximately three-year increments throughout their lives, which we then averaged to create an aggregate estimate of lifetime language exposure. Note that the dialect exposure numbers primarily reflect Mandarin dialects (e.g., Nanjing, Nantong, and Xuzhou dialects), which are mutually intelligible with standard Mandarin. Usage percentages represent the average of self-reports of percent of time a language is used in different social contexts, including at school, at the workplace, speaking with friends, and general reading.

We created verb-final sentences with two noun arguments across the two levels of Reversibility (reversible, irreversible) and Agent Animacy (animate, inanimate). Crossing these two factors resulted in four conditions: reversible animate agent, reversible inanimate agent, irreversible animate agent, and irreversible inanimate agent (as summarized in Table 1 ). To maximize ambiguity in reversible sentences, we chose nouns that shared the same animacy status. We selected 30 transitive verbs for reversible inanimate and irreversible inanimate sentences and 31 transitive verbs for reversible animate and irreversible animate sentences, resulting in 122 unique verbs. To minimize repetitions of sentence materials during the experiment, we selected two noun pairs (noun pair one and noun pair two) for each verb, such that each pair combined with the verb to meet the requirements of the corresponding condition (e.g., reversible with animate agent: 老板技工举报了 “boss technician denounced”; 证人被告举报了 “witness defendant denounced”). These steps resulted in a total of 244 unique noun pairs. Within these parameters, we further controlled for frequency (using subtitle frequencies 103 ) and number of strokes. Frequency values and number of strokes are reported in Table 5 . The full sentence materials are reported in the Supplementary Materials.

In designing the sentence materials, we endeavored to select ideal agent-patient verbs and nouns, but a minority of verbs were closer to the experiencer verbs category (e.g., 强化 “strengthen”, 冻死 “freeze”, 安慰 “comfort”). This variability in our stimuli may affect our results, as experiencer verbs used in role reversal sentences have been shown to elicit biphasic N400-P600 effects in English 59 , 60 . However, we note that the majority of our stimuli use agentive verbs, and the designation of experiencer or agentive verb does not neatly extend across languages. In the case of Mandarin, the structure of verb complements means that there can be a mismatch between the functional and structural roles of arguments 104 . For these cases, the framework of proto-agent and proto-patient still applies 28 .

Prior to running the EEG experiment, we created an offline questionnaire with our sentence materials to receive information for agent assignment and acceptability ratings from native Mandarin speakers. Note that these questionnaires did not include coverbs, which naturally resulted in decreased acceptability in the absence of a conversational context. Although including BA or BEI in these sentences would increase the naturalness, we wanted to understand how our sentences were comprehended at a purely semantic level and that they would meet a minimum level of acceptability in the NNV structure without a coverb, without systematic differences between conditions in acceptability, as well as ensuring there was a clear semantic direction for our irreversible sentences. These results are summarized in Table 6 .

To make our list of stimuli for running the experiment, we next crossed our factors Reversibility and Agent Animacy with Structure (NNV, BA, and BEI) and Order (first and second, representing position of the plausible noun). Note that for reversible sentences, one of the orders was arbitrarily assigned as first so that Order could still be tested and controlled for these items. We assembled ordered lists for presenting sentences to participants. Each of the 122 verbs was used three times, once for each level of Structure, resulting in 366 total sentences. To minimize the effects of repetition, we used the two noun pairs for each verb, so that a given noun only repeated a maximum of once. For example, the two noun pairs 喜鹊鸟笼/老鼠箱子 “magpie birdcage/mouse box”and verb困住 “trap” might appear in the experiment as follows: 喜鹊被鸟笼困住了。 “magpie BEI birdcage trapped”; 喜鹊把鸟笼困住了。 “magpie BA birdcage trapped”; 箱子老鼠困住了。 “box mouse trapped”. Note that each sentence ended with the aspect particle LE and a period.

To pseudorandomize our stimuli, we used the program Mix 105 , constraining the randomization such that each level of Structure could repeat a maximum of two times consecutively and a given verb occurred a minimum of 90 trials before or after its previous occurrence. Due to the design of the sentence materials, there was an equal probability of the first or second noun being animate or inanimate and actor or undergoer, so there was no way for participants to develop strategies to predict the role of the nouns until they saw BEI or BA and the final verb. Stimuli were pseudorandomized to maximize distance between repeated verbs (at least 90 items between repetitions) and minimize repetitions of same structure condition to two.

All parts of the experiment were approved by the McGill Faculty of Medicine Institutional Review Board following the guidelines of the Canadian Tri-Council Policy Statement and by the School of Foreign Languages and Cultures at Nanjing Normal University (南京师范大学外国语学院). After reviewing and signing the consent form, participants sat in a sound-attenuated booth. All stimuli were presented with Presentation® software (Version 17.2, Neurobehavioral Systems, Inc., Berkeley, CA, www.neurobs.com ) using the Windows XP operating system.

Sentences were presented visually word-by-word, for 650 ms per word and each word followed by a 100 ms inter-stimulus interval (i.e., stimulus onset asynchrony (SOA) = 750 ms). Each trial began with a cue for the participants to blink (“( −)”) for 2000 ms, then a fixation cross (“ + ”) for 500 ms. Each sentence ended with the particle LE appearing for 650, before displaying a prompt for the agent assignment task. For the task prompt, the two nouns from the preceding sentence appeared on the screen, with the first noun on the left and the second noun on the right, with “ !!!!! ” between them. Participants were asked to choose which of the two nouns was the agent (施事) by pressing the A or L key, corresponding to the left or right noun, respectively. The experiment was divided into four blocks of approximately 90 sentences each, with a scheduled break between each block. Participants could also pause at any response prompt to rest before continuing. The total experiment time, including preparation of the cap and cleanup, lasted from two to three hours.

EEG Recording and Preprocessing

Participants’ EEG was recorded from 32 Ag/AgCl electrodes mounted on an elastic cap according to the international 10–20 system (EASYCAP, Herrsching, Germany). To monitor vertical and horizontal eye movement, one electrode was positioned below the right eye and another to the left of the left eye. Electrode impedance was kept below 10 kΩ. The recordings were amplified online with a bandpass filter of 0.05–100 Hz, referenced online to electrode FCz, and digitized at a sampling rate of 500 Hz.

We used EEGLAB (v2019.1) and ERPLAB (v7.0.0) to preprocess the data. The EEG signal was downsampled to 250 Hz and re-referenced to the average of the linked mastoids (TP9 and TP10). We then used a high-pass filter at a cutoff of 0.1 Hz (FIR filter, Kaiser window, Kaiser beta = 4.89856, transition bandwidth = 0.2, filter order = 3934) and a low-pass filter at a cutoff of 30 Hz (FIR filter, Kaiser window, transition bandwidth = 10, Kaiser beta = 4.89856, filter order = 80). To correct eye movement artifacts, we decomposed the data using independent component analysis (ICA, runica algorithm in EEGLAB, with the option 'extended', 1). Note that exclusively for the ICA decomposition, we used data that was high-pass filtered at a cutoff of 0.5 Hz (FIR filter, Kaiser window, Kaiser beta = 4.89856, transition bandwidth = 0.2, filter order = 3934) because using a higher high-pass filter on data for ICA decomposition improves the signal-to-noise ratio 106 . The data were cleaned automatically prior to ICA using the pop_rejcont function (epochlength 2, overlap 1, freqlimit 1–25, threshold 10, taper hamming). The final ICs were then copied to the data filtered at 0.1 and 30 Hz for analysis. We removed a maximum of two ICs per participant, one IC each for vertical and horizontal eye movement.

The signal was then segmented into epochs from − 200 to 1000 ms around each critical word (first noun, coverb, second noun, and verb), with pre-stimulus 200 ms baseline correction. To appreciate the ERP changes across the entire sentence, we additionally created whole-sentence epochs with 200 ms pre-onset baselines; including the baseline interval, these epochs spanned 3100 ms for NNV sentences and 3850 ms for BA and BEI sentences. Based on visual inspection of role-reversal sentences in the whole-sentence epochs, we determined that there were important ERP differences occurring before verb onset, which could make a pre-onset baseline problematic 107 . To minimize the impact of baseline differences at the verb, we re-epoched the verb time window with a post-stimulus-onset 200 ms baseline correction (baseline interval of 0 to 200 ms), with the assumption that early components in this interval should have minimal difference between conditions. Results from this post-stimulus-onset 200 ms baseline correction are reported in the text because we believe this more accurately reflects verb-linked activity; results from the original pre-stimulus 200 ms baseline correction are reported in Supplementary Materials.

Artifact rejection was performed across epochs with a moving window threshold of 80 µV (window size = 500 ms). During review of the artifact rejection process, we determined that electrodes Fp1 and Fp2 were exceptionally noisy across participants and excluded them from analysis. For select subjects whose automatic rejection resulted in greater than 15% of trials being rejected, epoched data were manually inspected to include additional trials and the overall quality of individual datasets. This review resulted in all 34 subjects being included for final analysis (i.e., all individuals had fewer than 15% rejected trials).

Data analysis

We analyzed responses and reaction times for the agent assignment task and ERPs time-locked to the onset of target words in sentences. The four factors manipulated in the sentence materials were included in the analysis of each of these measures. The factor Structure was comprised of three levels: NNV, BA, and BEI. Structure was treatment coded such that NNV was the reference level to evaluate the effect of coverbs in relation to sentences with no coverb. The factors Reversibility (reversible and irreversible), Agent Animacy (animate and inanimate) and Order (first and second, denoting position of the plausible noun in irreversible sentences) were sum coded. Unless otherwise noted, these were the factors and contrast coding.

All data analysis was done using R version 4.02 108 . To account for variability across items and within participants, we computed mixed effects models using the glmer function from package lme4 version 1.1-23 109 , including the optimizer = ‘bobyqa’ parameter. Model coefficients were calculated by maximum likelihood estimates using the Laplace approximation. In the case of the binary data from the agent assignment task, we added the argument family = ‘binomial’ to fit a logistic mixed effects model. To ensure that model effects were interpretable, we limited fixed and random effects to a maximum of three-way interactions, even if there were possible higher order interactions. For random effects structures, all factors with possible variability within items or participants were included in the maximal possible structure. Note that because Agent Animacy and Reversibility did not vary across individual items, these factors were not included in the random structure for item.

All p -values were calculated with the Satterthwaite approximation calculated based on Wald Z -scores in lmerTest package version 3.1-2 110 . To construct the maximum possible model and random structure, we used the buildmer package version 1.8 111 using the direction = ‘order’ parameter, which adds effects to the model in order of their contribution to log-likelihood. We then again used buildmer to do stepwise removal of model variables with the direction = ‘backward’ parameter to maximize log-likelihood score. These optimized models are reported in the text to maximize power and minimize overfitting 112 , but the maximal models are reported for reference in the supplementary materials 113 .

Significant interactions were followed up with post-hoc tests for pairwise comparisons using the emmeans package version 1.4.8 114 and Tukey method for adjustment of p -values to correct for multiple comparisons. Interactions were visualized with the emmip function from the emmeans package. Note that while p -values are reported for model results, our inferences and interpretations were not limited to significance testing; instead, we further considered our hypotheses and predictions, effect sizes, and the limitations of data quantity and quality 115 .

For ease of interpretability, model results are reported with graphical depictions of coefficients and confidence intervals generated by the plot_model function from the sjPlot package version 2.8.9 116 ; full model outputs, including random effects, are reported in the supplementary materials in tables generated from the tab_model function from the sjPlot package. For simplicity, model results reported in the text are limited to significant effects or effects that were related to initial predictions. For data arrangement and general plotting, we used the tidyverse package 117 , with final figure adjustment performed using the software Inkscape version 0.92 118 .

Agent Assignment

Binary agent assignment responses were analyzed with logistic mixed effects models. Because irreversible sentences had a single plausible interpretation, while reversible sentences had two plausible interpretations, we analyzed reversible and irreversible sentences separately. This allowed us to better understand the effect of plausibility on agent assignment, while limiting model coefficients to a maximum of three-way interactions. For both reversible and irreversible sentences, the maximum specified model included the fixed effects of Structure, Agent Animacy, and Order, random slopes and intercepts for Structure, Agent Animacy, and Order by participant, and random slopes and intercepts for Structure and Order by item. We report coefficients, confidence intervals, and p -values on the odds ratios scale, but original tests were performed on the log odds scale. For interpretability, interactions are illustrated on the probability scale.

Reaction Times

Reaction times for agent assignment responses were analyzed with linear mixed effects models. Reaction times were first cleaned to exclude response times above 10 s or below 100 ms. We then cleaned reaction times by condition, limiting to those values within 1.5 standard deviations for each subcondition of Structure, Reversibility, and Animacy 119 .These steps resulted in excluding 12.4% of trials from further analysis; we note that some of the excluded trials included instances when participants took breaks before responding. Reaction times were then natural log transformed to ensure that we met assumptions of distribution normality for analysis 120 . Note that we also analyzed the raw reaction time values 121 and results were similar to those found for the log-transformed data; these results are reported in supplementary materials for transparency 122 . The maximum specified model included the fixed effects of Structure, Reversibility, Agent Animacy, and Order, random slopes and intercepts for Structure, Reversibility, Agent Animacy, and Order by participants, and random slopes and intercepts for Structure and Order by item. As an additional step, we also ran a model with the additional factor of Difference Score (the difference between a participant’s reliance on plausibility cues and their reliance on coverb cues), which is introduced in the section Individual Differences in Cue Weighting for Agent Assignment. Recent work has demonstrated the importance of individual differences in psychology and language research 123 , 124 , and including Difference Score in the model explained additional variability in the data. We report coefficients, confidence intervals, and p -values on the log-transformed scale, but model predictions were back-transformed to milliseconds for interpretability. Note that all reaction time results are reported in Supplementary Materials.

As noted above, ERPs were analyzed at the first noun, coverb, second noun, and verb position of the sentence. Condition averages were calculated for each subject and then grand average ERPs were calculated for each condition. These grand average ERPs by condition were used for visual inspection and are represented in all ERP figures in the present study. Statistical models, however, were all based on average amplitudes for specific time windows in single trial epochs.

For the first noun of the sentence, referred to hereafter as noun one, we analyzed average amplitude in the N400 time window from 300 to 500 ms. This time window analysis was planned a priori based on reports of greater N400 effects for inanimate nouns than for animate nouns 59 , 60 . At the second-word position of the sentence, there was either another noun (noun two, in the case of NNV sentences), the coverb BA, or the coverb BEI. At this sentence position, we analyzed average amplitudes in the P200 (100 to 300 ms) and N400 (300 to 500 ms) time windows. The P200 time window was selected for analysis after visual observation of large differences in the ERPs between sentence structure types. The N400 time window was analyzed as a validation step to confirm expectations that nouns elicited larger N400 amplitudes than coverbs, thus giving more weight to the unexpected differences in P200 amplitude. At the verb position of the sentence, we analyzed the N400 (300 to 500 ms) and P600 (700 to 900 ms).

For each time window analyzed, we used single trial average amplitude to calculate linear mixed effects models. We first ran models on midline electrodes, including the factor Electrode (Fz, Cz, Pz, Oz), to confirm the presence of effects, and then over all other electrodes on the scalp excluding the midline, with the additional levels of Anteriority (frontal, central, and posterior) and Laterality (right, left). N400 and P600 effects, the primary components investigated in the present study, typically present with a posterior distribution on the scalp 125 ; with this in mind, we treatment coded the factors Electrode and Anteriority with the reference levels of Pz and posterior, respectively. In contrast, P200 effects typically have a frontal distribution 78 , so for models in the P200 time window, we exceptionally used the reference levels of Fz and frontal for Electrode and Anteriority, respectively. Note that for the model specifications below, the factor Electrode was substituted by Anteriority and Laterality for the models over non-midline electrodes.

For noun one, the maximum specified model included the fixed effects of noun one Animacy (animate, inanimate) and Electrode, with random slopes and intercepts included for noun one Animacy and Electrode by participant and by item. For noun two and coverb, the maximum specified model included the fixed effects of Structure (NNV, BA, BEI) and Electrode, with random slopes and intercepts included for Structure and Electrode by participant and by item.

At the verb, only unambiguous role reversal sentences were analyzed (contrasting plausible vs implausible sentences), which limited trials to irreversible BA and BEI sentences (see Table 2 ). For clarity with respect to predictions about role reversal effects, the factor Order was recoded in terms of Plausibility; BA sentences with the plausible noun in first position were coded as plausible, while BEI sentences with the plausible noun in first position were coded as implausible, with the same logic applied for sentences with the plausible noun in second position. Plausibility was treatment coded with plausible as the reference level. Because there were only two levels of Structure (BA and BEI) with neither level more suited as a reference, Structure was sum coded for this analysis. The maximum specified model for the verb included the fixed effects of Structure, Agent Animacy, Plausibility, and Electrode. Random slopes and intercepts were included for Structure, Agent Animacy, Plausibility, and Electrode by participant, and random slopes and intercepts for Structure, Plausibility, and Electrode by item. Additionally at the verb, we ran models at individual midline electrodes as confirmation for the effects across midline electrodes. These models are reported in supplementary materials.

Because the components of interest in the present study (N400, P600, P200) are typically maximal at or near midline electrodes 125 , we primarily report in the text results from models at the midline; results from lateral electrodes are reported in the text if they show effects beyond the models at midline electrodes. Note that full models for lateral electrodes excluding the midline are reported in the Supplementary Materials. Additionally, simple effects of the topographical factors Electrode, Anteriority, and Laterality, or interactions involving only these factors, are not reported or discussed in the text because they are not related to the experimental manipulations. Lastly, for models at the verb, we discuss in the text only those effects that included Plausibility because this is the only factor for which we had predictions.

All final analyses were performed on single trial average amplitudes, but average ERPs were calculated by condition for plotting purposes. All figures showing ERP voltage against time and scalp maps reflect these average ERPs and were plotted using the R package ERPscope 126 .

Data availability

The datasets generated during and/or analyzed during the current study will be available upon request in the Borealis Dataverse.

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M.W. and K.S. designed the experiment with input from S.B. and H.Z. K.S. provided the funding for participants and H.Z. provided the lab space and testing materials. M.W. collected the data, conducted the analysis, and wrote the manuscript. J.A. participated in coding and analyzing questionnaire and reaction time data and assisted in optimizing the protocol for preprocessing EEG data. All authors gave feedback on analysis and the manuscript.

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Wolpert, M., Ao, J., Zhang, H. et al. The child the apple eats: processing of argument structure in Mandarin verb-final sentences. Sci Rep 14 , 20459 (2024). https://doi.org/10.1038/s41598-024-70318-5

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A backpropagation-based algorithm to optimize trip assignment probability for long-term high-speed railway demand forecasting in korea, featured application, 1. introduction.

• Define a departure node (station or stop);
• Move to and board the vehicle that arrives first at the departure node among the competing routes;
• Get off at the intermediate node (station or stop) that was determined according to the optimal strategy;
• End if the passenger arrives at their destination; otherwise, define the alighting node as the departure node and repeat from step 1.

2. Standard Long-Term HSR Demand-Forecasting Methodology in Korea

3.1. trip assignment probability, 3.2. optimization algorithm, 4. case study, 4.1. data description.

• i : zone ( ∀ i   ∈   Z );
• s : HSR station ( ∀ s   ∈ S);
• d i s : Euclidean distance from zone i to station s ;
• x , y : x -coordinate and y -coordinate.
• Z: The set of zones;
• S: The set of HSR stations.

4.2. Results

• i : HSR station ( ∀ i   ∈   Z );
• n : the number of HSR stations;
• f e s t : the estimated number of passengers at each station using the trip assignment model;
• f o b s : the observed number of passengers at each station.

5. Conclusions

Institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

Click here to enlarge figure

• Line 105: “opt_target_tensor” is the tip assignment probability weight tensor for all stations ( T a ) in Figure 5 .
• Line 106: “prob_mask_tensor” is the accessible station tensor ( T b ) in Figure 5 .
• Line 119: “z_masked” is the trip assignment probability weight tensor ( T c ) in Figure 5 .
• Line 126–128: A normalization process in Figure 6 .
• Line 130: “t_1” is the assigned trip tensor ( T g ) in Figure 7 .
• Line 131: “t_2” is the assigned sum trip tensor ( T h ) in Figure 8 .
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Kwak, H.-C. A Backpropagation-Based Algorithm to Optimize Trip Assignment Probability for Long-Term High-Speed Railway Demand Forecasting in Korea. Appl. Sci. 2024 , 14 , 7880. https://doi.org/10.3390/app14177880

Kwak H-C. A Backpropagation-Based Algorithm to Optimize Trip Assignment Probability for Long-Term High-Speed Railway Demand Forecasting in Korea. Applied Sciences . 2024; 14(17):7880. https://doi.org/10.3390/app14177880

Kwak, Ho-Chan. 2024. "A Backpropagation-Based Algorithm to Optimize Trip Assignment Probability for Long-Term High-Speed Railway Demand Forecasting in Korea" Applied Sciences 14, no. 17: 7880. https://doi.org/10.3390/app14177880

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