Late orders: The tug of war between operations and transportation

Despite the enormous effort to meet customers’ expectations, e-retailers often struggle with late deliveries. News reports indicate that the struggle is real. Sure, consumers expect free and fast delivery (Kumar, 2025; Riedl & Mehta, 2026), but final mile operations are expensive and risky. They represent over 50% of e-fulfillment costs and are a key driver of customer satisfaction (Yerapothina, 2025). Furthermore, 87% of shoppers feel that the retailer is fully responsible for delivery delays, regardless of whether the fault lies with the warehouse or the carrier (Convey, 2018).

To address this, retailers typically pour investments into siloed solutions like faster picking bots, better packing materials, or more aggressive carrier contracts. However, these efforts often overlook the entire process flow and, more importantly, the psychological and operational "tug of war" that occurs when managers and workers face mounting order queues.

Our research, recently published in the Journal of Business Logistics (Masorgo et al., 2026), investigates the intricate relationship among order processing (picking, sorting, packing), order delivery, and ultimate on-time performance. By analyzing a dataset of over 10,000 orders from a major e-retailer and conducting interviews with senior operations managers, we identified three critical levers that dictate whether an order arrives on time or falls through the cracks.

1. Early detection is everything

The first step in winning the tug of war is knowing when you are actually losing. Many managers operate in a “visibility vacuum,” where they can see the length of the queue but cannot identify which specific orders within that queue are jeopardizing the delivery promise.

We found a concave relationship between lateness and Order Processing Time (OPT), defined as the proportion of total planned lead time consumed by picking, sorting, and packing. Our data show that orders enter a “danger zone” as soon as internal processing eats up more than 25% of the e-retailer’s total promised lead time.

When processing exceeds this threshold, the pressure shifts downstream to transportation, often leaving carriers with an impossible window. As one operations manager noted: “During the day, pick stations have long lists, then pack stations also have a long line. There is no way to know which orders are about to be late to process them first. We focus on reducing the lines, but the cart with the late order can be stuck way in the back.”

Deeper dive: Elaborating Theory of Swift Even Flow in E-Fulfillment Operations

To counter this, managers must implement monitoring systems that flag orders the moment they hit the critical OPT mark (e.g., 25% in our study), allowing for surgical precision in taking action before the delay becomes irreversible.

2. The art of strategic deprioritization

As internal processing time climbs, a natural tug of war ensues. Managers initially try to compensate for warehouse delays by expediting transportation. They might switch a standard shipment to a premium courier or authorize overtime for drivers.

However, there is an economic and behavioral limit to this recovery effort. Our research found that when OPT exceeds 58% of the e-retailer’s planned lead time, a shift occurs. Managers begin to deprioritize the order.

This is a behavioral response to lost causes. When an order is likely to be late, managers often choose to protect the system’s overall flow rather than wasting expensive resources on an order that looks like it will miss its window. One manager summarized this pragmatism: “Don’t sacrifice the mass to save the few. If an order is late by a day, does it matter if it is two days late?”

Understanding this critical benchmark is vital. Without it, operations often fall into the trap of “re-picking,” or resending items to the picklist because they haven’t reached the transportation bay in time. This creates ghost inventory and overwhelms the staff. By formalizing this threshold (e.g., 58% in our study), companies can stop chasing doomed orders and redirect those resources to ensure that the other orders in the queue stay on track.

3. The complexity lag: Simplicity as a speed lever

The third lever involves the nature of the order itself. It is well-documented that larger, multi-item baskets are harder to pick, but the impact on lateness is compounded by workers’ and managers’ behavior.

Managers and workers naturally gravitate toward less complex orders, such as those containing multiple units of the same SKU or very few items. These orders allow for consistent, rhythmic movements in picking and packing. We found that these simple orders are delivered approximately 8 hours faster than complex ones.

Managers can use this to their advantage by creating dedicated fast-track workflows for low-complexity orders. By clearing the easy wins quickly, they reduce the total volume of the queue, allowing specialized teams to focus on the high-complexity baskets that are more prone to errors and delays.

Breaking the cycle

The friction between warehouse operations and transportation is where the customer experience often falls apart. The battle against lateness cannot be won through brute-force speed; it requires a sophisticated understanding of these operational tipping points.

To move forward, supply chain leaders should:

• Synchronize visibility: Ensure the warehouse management system (WMS) and transportation management system (TMS) share a single lead time clock.
• Empower decisive deprioritization: Document and use critical thresholds to prevent the bullwhip effect of late orders clogging up the system.
• Buffer for complexity: Account for the 8-hour complexity lag when promising delivery windows for multi-item baskets.

By mastering the critical thresholds, e-retailers can stop the internal tug of war and improve on-time performance.

References

Convey. (2018). Last Mile Delivery: What Shoppers Want and How to #SaveRetail. Convey Retrieved from https://www.getconvey.com/press-d-last-mile-delivery-save-retail/

Kumar, N. (2025). Navigating the future of e-commerce logistics: Balancing speed and cost. Supply Chain Management Review https://www.scmr.com/article/navigating-the-future-of-e-commerce-logistics-balancing-speed-and-cost

Masorgo, N., Hoang, T. T. (Jenny), Dobrzykowski, D. D., Bell, J., & Swink, M. (2026). Elaborating Theory of Swift Even Flow in E‐Fulfillment Operations. Journal of Business Logistics, 47(2). 10.1111/jbl.70063

Riedl, P., & Mehta, P. (2026). How autonomous fulfillment is rewriting the rules of supply chain execution. Supply Chain Management Review https://www.scmr.com/article/how-autonomous-fulfillment-is-rewriting-the-rules-of-supply-chain-execution

Yerapothina, S. T. (2025). Unlocking the last mile: A strategic framework for in-store fulfillment. Supply Chain Management Review https://www.scmr.com/article/unlocking-the-last-mile-a-strategic-framework-for-in-store-fulfillment

About the authors

Nicolò Masorgo (Ph.D., University of Arkansas) is an Assistant Professor in the Farmer School of Business at Miami University. Drawing on his order fulfillment and logistics operations industry experience, his main research interest focuses on last-mile delivery operations, service operations, and service supply chain management. His research has been published in several leading journals, including Journal of Business Logistics, International Journal of Physical Distribution and Logistics Management, and Transportation Journal.

Thu Trang Hoang is an empirical supply chain researcher. She graduated from the University of Tennessee, Knoxville, with a PhD in Supply Chain Management. Her research focuses on three main topics: traceability (i.e., food and human trafficking), operational adaptability (i.e., firms’ roles in community relief during disasters), and crowdsourced logistics/e-commerce (i.e., drivers; behaviors under new service/insurance launches, and mathematical modelling).

David D. Dobrzykowski is a Professor of Supply Chain Management and Senior Director of the SCM PhD program at the Walton College of Business, University of Arkansas. His research examines operations and supply chains that feature unique challenges to information processing and the coordination of work processes, such as in healthcare, humanitarian, and sharing economy contexts. He has published in Journal of Business Logistics, Production and Operations Management, Journal of Operations Management, Decision Sciences, Journal of Supply Chain Management, among other leading outlets.

John E. Bell is the Dove Professor of Supply Chain Management at the University of Tennessee.  He holds a doctorate in Management from Auburn University. His research focuses on raw materials, transportation, and sustainable supply chains. He has published over 40 articles in journals such as Journal of Business Logistics, Transportation Journal, and Journal of Operations Management. Prior to joining UT in 2010, Dr. Bell was a career military officer.

Morgan Swink is the Eunice and James L. West Chaired Professor of Supply Chain Management, and Executive Director of the Center for Supply Chain Innovation in the Neeley School of Business, TCU. He teaches and leads research in areas of supply chain management, innovation management, project management, and operations strategy. He has co-authored two supply chain operations text-books, one managerial book on supply chain excellence, and published more than 100 articles in a variety of academic and managerial journals.

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