Behind Every Online Order: New Review Maps the Hidden Decisions of E-Commerce Fulfillment

Every time a shopper taps the “Place Order” button, an invisible industrial ballet begins. Somewhere, an algorithm or a planner must decide which of a retailer’s warehouses will pick the item, how workers or robots will retrieve it from shelves, which carrier will carry the parcel, and what will happen if the customer changes their mind and sends it back. These decisions are not independent chores on a checklist; they form a tightly coupled chain in which a single upstream choice can ripple through costs, workloads, delivery speed and the customer’s entire experience of the brand. A new systematic review published in the Journal of Management Science and Engineering now offers the most structured map to date of how researchers have tackled this end-to-end problem, and where the field is heading next.

The review, conducted by Yuankai Zhang, Wei-Hua Lin, Qiong Tian and Xiangpei Hu, synthesizes nearly two decades of scholarship on business-to-consumer online order fulfillment. The authors systematically searched the literature covering work published from 2007 through May 2026. Their search initially surfaced 1,495 records, which they filtered down to 198 articles selected for detailed analysis. Rather than organizing the literature by academic subfield or methodology, the team anchored their classification to the real-world fulfillment process itself: order assignment, warehouse operations, shipping and delivery, and return operations. This process-oriented structure, the authors argue, mirrors the way decisions actually unfold inside an e-commerce company, making the review useful both as a scholarly reference and as a practical roadmap for operations managers.

The starting point of the chain is order assignment, the moment a retailer decides where each item in an order will be fulfilled. In modern e-commerce, this is rarely a trivial question. Large retailers operate networks with multiple fulfillment locations, including national distribution centers, regional warehouses and, increasingly under omni-channel models, physical stores that double as miniature fulfillment hubs. Assigning an order to the wrong node can mean longer delivery distances, higher shipping costs, split shipments that frustrate customers, or stock imbalances that leave popular items stranded in the wrong part of the network. The review shows that research on this stage has grown steadily more sophisticated as retailers’ networks have grown more distributed and as omni-channel strategies have blurred the traditional boundary between stores and warehouses.

Once an order lands at a fulfillment location, warehouse operations take over. This stage encompasses the physical heart of e-commerce: receiving inventory, storing it, picking the specific items a customer ordered, packing them and staging them for shipment. The review highlights why this stage is uniquely difficult in the online retail context. Unlike traditional retail, where a store sells a curated local assortment, B2C fulfillment must cope with very large product assortments, often spanning hundreds of thousands of stock-keeping units. Compounding the challenge, customer orders are typically personalized multi-item baskets rather than uniform bulk shipments, which means picking operations must handle enormous variety at high speed. The reviewed literature examines how these pressures shape decisions about warehouse layout, picking routing, batching of orders, zoning, and the deployment of automation, all of which interact with the upstream assignment decisions that determine what arrives at the warehouse floor in the first place.

The third stage, shipping and delivery, is where the retailer’s promises meet physical reality. Customers increasingly expect tight delivery time windows, sometimes measured in hours rather than days, and the review identifies these compressed windows as one of the four defining characteristics that make B2C fulfillment especially challenging. The other three are the very large product assortments already noted, the multi-location nature of fulfillment networks, and the personalized, multi-item composition of individual orders. Together, these four features distinguish online retail fulfillment from classical logistics problems and explain why the field has generated such a large and specialized body of research. Decisions about carrier selection, shipment consolidation, routing and delivery scheduling must all be made under these constraints, and each of them is constrained in turn by what happened, or failed to happen, in the warehouse hours earlier.

The final stage, return operations, is often treated as an afterthought in popular discussions of e-commerce, yet the review gives it full status as an integral part of the fulfillment process. Returns are a structural feature of online retail, not an anomaly: customers cannot physically inspect goods before buying, so a certain fraction of orders will always flow backward through the network. Handling those returns involves decisions about reverse logistics, inspection, restocking, refurbishing and disposition, and those decisions feed back into inventory positions and fulfillment capacity. The authors emphasize that the four stages they map are not silos but a connected system, and their review is structured to make those connections visible rather than burying them between disciplinary boundaries.

“B2C order fulfillment is not a collection of separate tasks. Decisions made at one stage can create consequences at another,” says Zhang. “By viewing the process as a connected system, we can better identify where coordination, data and new technologies can improve fulfillment decisions.” This systems perspective is the review’s central intellectual contribution. Much of the earlier literature, the authors find, examined individual stages in isolation: a paper might optimize picking routes without considering how order assignment shaped the pick list, or model delivery scheduling without accounting for warehouse cut-off times. The trend in recent research, and the direction the review charts for the future, is toward models that capture these interdependencies explicitly, coordinating decisions across the entire chain rather than optimizing each link separately.

That trend is closely tied to a second shift the review documents: the move from static planning toward integrated, real-time decision-making. Traditional fulfillment planning often assumed stable demand patterns and relatively long planning horizons, allowing decisions to be made in advance and executed as scheduled. Modern e-commerce operates at a tempo that makes such static assumptions increasingly untenable. Order streams arrive continuously, inventory positions change by the minute, and delivery promises are made dynamically at the moment of purchase. The reviewed literature shows researchers responding with approaches that update decisions in real time as new information arrives, embedding fulfillment logic directly into the customer-facing transaction rather than treating it as a back-office batch process.

Underpinning both shifts is the rapid maturation of data analytics and artificial intelligence. The team examined how data analytics, AI and emerging generative AI can support forecasting, scenario generation and operational decision-making across the fulfillment process. Demand forecasting, long a staple of operations research, is being augmented by machine learning models that can exploit far richer behavioral data, which the review identifies as a promising direction under the heading of behavioral analytics. Generative AI, meanwhile, opens possibilities for scenario generation, allowing planners to stress-test fulfillment networks against synthetic but plausible demand patterns. Importantly, the authors frame these technologies not as replacements for classical operations research. “Rather than replacing optimization methods, these technologies are increasingly viewed as decision-support tools that can work alongside optimization,” Zhang adds. In this hybrid vision, machine learning supplies predictions and scenarios, while optimization translates them into concrete, constraint-respecting operational plans.

The review also points toward several forward-looking research frontiers beyond real-time integration and AI. Decision-making under uncertainty remains a persistent challenge, since fulfillment networks must cope with demand spikes, supply disruptions and weather-related delivery delays that no forecast fully anticipates. Sustainability is flagged as a growing concern, encompassing the emissions footprint of expedited shipping, packaging waste and the energy profile of large-scale warehouse automation. And the authors highlight the potential of combining artificial intelligence with digital twins and optimization: virtual replicas of fulfillment networks that can be continuously updated with real operational data and used to test decisions before they are executed in the physical world. For managers, the practical message of the review is to stop evaluating warehouse, delivery and returns decisions in isolation and instead instrument and optimize the entire chain. For researchers, it is a call to build models that match the connected, data-saturated, time-critical reality of modern online retail. For the millions of shoppers who will click “Place Order” today without a second thought, it is a reminder that behind that single click lies a cascade of interlocking decisions, and a research community working to make that cascade faster, cheaper and more responsive.

Subject of Research: B2C e-commerce order fulfillment decision-making across order assignment, warehouse operations, delivery and returns

Article Title: From click to doorstep: the hidden decisions behind e-commerce orders

Article References: From click to doorstep: the hidden decisions behind e-commerce orders. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: e-commerce, order fulfillment, supply chain, warehouse operations, logistics, omni-channel retail, artificial intelligence, optimization, digital twins, returns management, systematic review, operations research