Double Picking: Significant Savings from a Simple Change

Editor’s Note: Every year, 40 or so students in the MIT Center for Transportation & Logistics' (MIT CTL) Master of Supply Chain Management (SCM) program complete one-year thesis research projects. The students are early-career business professionals from multiple countries with 2 to 10 years of experience in the industry. The research projects are sponsored by and carried out in collaboration with multinational corporations. Joint teams of company people, MIT SCM students, and MIT CTL faculty work on real-world problems chosen by sponsor companies. In this series, we summarize a selection of the latest SCM research. The researchers for the project described below, Stephanie H. Chen and Eunji Han, investigated a cost-saving order picking practice for their MIT Supply Chain Management Program master's thesis. The sponsor was a major retailer, and the project was supervised by Dr Bruce Arntzen, Executive Director, MIT Supply Chain Management Program. For more information on the program, visit http://scm.mit.edu/program.

For many distribution centers (DCs), order-picking is manual, time consuming and accounts for over 50% of operating expenses. Projects to improve DC efficiency usually focus on optimal storage assignment policies, warehouse layouts, picker routing algorithms, order-batching, and wave- or zone-picking.

Working with a large US retailer, researchers at MIT's Center for Transportation and Logistics have modeled a very simple method to increase pickers' efficiency and reduce picking cost for manual piece-picking.

Two for the Price of One

For this retailer, a typical store carries about 20,000 SKUs almost all of which are slow-moving items with relatively low value. The huge majority of weekly replenishment shipments to a store for any SKU are for either 0 or 1 unit. In each replenishment cycle, the forecasting system generates an order for each store which becomes the pick list for that store in the DC. Pickers walk to the various bins and pick the exact number of pieces ordered for delivery to that store, one store at a time.

While the retailer has implemented several traditional improvement schemes, it is seeking further enhancements. Instead of picking in “eaches” (as few as one unit at a time), the researchers tested the idea of having the pickers pick each SKU in multiples of two, rounding all odd-numbered orders up to the next even quantity. All the orders for 1 unit are then picked as 2 units.

This approach increases the picking efficiency in the DC but shifts an extra unit of inventory to the store. However, the forecasting system (which sees store inventory levels) recognizes that an extra unit has been placed in the store and will then typically skip the next replenishment order since it is not yet needed. Since the slow demand for most SKUs results in replenishment shipments of one unit, this method of picking two units reduces the number of bin visits for each store by approximately 20% if the change is implemented for all SKUs.

However there is an increase in store inventory. How big is this increase? What is the cost? And how does it compare to the huge labor cost savings in the DCs?

Simple But Effective

The researchers created a computer simulation for five stores served by one of the retailer's DCs. Different SKUs have different costs and different replenishment patterns throughout the year. The objective was to identify which SKUs could generate the largest picking cost savings in the DC for the least amount of inventory gain in the stores.

The researchers modeled SKUs with different characteristics in order to determine the ideal characteristics for the items. Since the retailer's forecasting and inventory management systems see inventory levels in both the DCs and the stores simultaneously, the shift of one unit of inventory from the DC to the stores will not usually trigger a significant increase in DC replenishment. Hence the company-wide inventory does not increase significantly.

By applying the proposed method only to the SKUs that carry the “ideal characteristics,” the retailer can attain large picking cost savings without significantly increasing its stores' inventories – and with nearly zero impact on its company inventory. The simulation model showed that if the new strategy is implemented only on the ideal SKUs the picking efficiency increased by 0.6~0.7 units/line, the number of annual picks for each store decreased by 11%~13%, and each store's average inventory increased by up to 2.8%. Given the daily inventory fluctuation in a store it is unlikely that this increase would be noticed.

The scheme does not appear to be covered in current order-picking literature. However, it offers a simplistic way to optimize picking. It is highly applicable to retailers who have been accustomed to picking exact pieces, offering them the opportunity to implement a “picking quantity multiple” that generates great savings with little negative repercussions.

For further information on the SCM thesis authored by Chen and Han titled Gaining an Operational Edge: Piece-Picking Process Optimization contact Dr. Bruce Arntzen, Executive Director, MIT Supply Chain Management Program, at [email protected]. For more information on the SCM program visit http://scm.mit.edu/program.

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