SOP 7200 Module 1 Operations Strategy Problems Example

Six and a Half Orders an Hour and a 3 p.m. Cutoff: Operations Strategy Problems for an E-Commerce Fulfillment Center

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Master of Business Administration, Thomas Edison State University

SOP-7200: Strategic Operations Management

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Six and a Half Orders an Hour and a 3 p.m. Cutoff: Operations Strategy Problems for an E-Commerce Fulfillment Center

These problems apply Module 1 of Krajewski et al. (2016) to Port Newark Fulfillment, a composite third-party fulfillment center in Elizabeth, New Jersey, that stores products and ships online orders for about forty consumer brands. The general manager wants to understand how productivity has changed, which competitive priorities matter most to clients, which picking process fits the center's order profile and how fully its capacity is used. Each problem is answered with the formula, the inputs and a decision.

Problem 1: Labor Productivity

Labor productivity is output divided by labor input. In the first quarter of 2025 the center shipped 186,000 orders with 31,000 labor hours, or 6.00 orders per labor hour. In the first quarter of 2026 it shipped 214,000 orders with 32,800 labor hours, or 6.52 orders per labor hour. Labor productivity therefore rose about 8.7 percent. The general manager attributes most of the gain to new handheld scanners and a reorganized packing line.

Problem 2: Multifactor Productivity

Krajewski et al. (2016) caution that single-factor measures can mislead, since labor productivity can rise simply because spending shifts to other inputs. Multifactor productivity divides output by several inputs, valued at constant prices. Valuing each order at its 2025 average fee of $7.90, output was $1,469,400 in the first quarter of 2025 and $1,690,600 in 2026. Inputs combine labor at $24 an hour, packaging materials at $0.62 per order and overhead of $410,000 and $425,000, for totals of $1,269,320 and $1,344,880.

Multifactor productivity rose from 1.158 to 1.257, an increase of about 8.5 percent, close to the labor gain. That confirms the improvement is real rather than a shift of cost from labor into equipment or overhead.

Problem 3: Competitive Priorities, Order Winners and Qualifiers

The textbook describes competitive priorities in terms of cost, quality, time and flexibility, and distinguishes order qualifiers, which a firm must meet to be considered, from order winners, which lead customers to choose it. Ward et al. (1998) developed the measures of cost, quality, delivery and flexibility that underlie this framework, and Boyer and Lewis (2002) examined whether operations must still trade these priorities off against each other, finding that the question depends on the firm's situation rather than having a single answer.

Interviews with client brands show that shipping accuracy above 99.5 percent is a qualifier: a center below it is not considered. The order winner is the same-day shipping cutoff. Brands that sell on major marketplaces need orders placed by mid-afternoon to ship the same day, and the center's current 1 p.m. cutoff loses bids to a competitor offering 3 p.m. Price matters but mainly as a qualifier within a narrow range. The center's operations strategy should therefore protect accuracy and invest in moving the cutoff later.

Problem 4: Process Choice

The center's orders are small, averaging 1.8 items, drawn from about 22,000 different products, with volume concentrated in afternoons. Krajewski et al. (2016) link process choice to volume and variety. Single-order picking, in which one picker walks the building for one order, is flexible but wastes travel time at this volume. Zone batch picking, in which pickers stay in assigned zones and pick many orders at once for later sorting, suits high volumes of small orders across many products. The recommendation is to adopt zone batch picking for the afternoon peak while keeping single-order picking for large or rush orders, which also supports a later cutoff by shortening the time from order release to packing.

Problem 5: Utilization and Efficiency

Design capacity is the maximum output under ideal conditions, 14,000 orders a day; effective capacity, allowing for breaks, changeovers and maintenance, is 12,500 orders a day; actual output averages 11,200. Utilization, actual output divided by design capacity, is 80 percent; efficiency, actual output divided by effective capacity, is 89.6 percent. The gap between design and effective capacity, 1,500 orders a day, points to scheduling losses that the process change could recover, and the remaining headroom of about 1,300 orders a day against effective capacity gives the center room to absorb volume from a later cutoff.

Conclusion

The center's productivity gains are genuine, its clients treat accuracy as a qualifier and the shipping cutoff as the order winner, and zone batch picking fits its order profile while supporting a later cutoff. Capacity measures show both the room to grow and the scheduling losses worth recovering. Together, the problems point to an operations strategy built on accuracy and speed, with investment directed first to the afternoon pick and pack process.

References

Boyer, K. K., & Lewis, M. W. (2002). Competitive priorities: Investigating the need for trade-offs in operations strategy. Production and Operations Management, 11(1), 9-20. https://doi.org/10.1111/j.1937-5956.2002.tb00181.x

Krajewski, L. J., Malhotra, M. K., & Ritzman, L. P. (2016). Operations management: Processes and supply chains (11th ed.). Pearson.

Ward, P. T., McCreery, J. K., Ritzman, L. P., & Sharma, D. (1998). Competitive priorities in operations management. Decision Sciences, 29(4), 1035-1046. https://doi.org/10.1111/j.1540-5915.1998.tb00886.x