12 Order Fulfillment Along the Supply Chain

CASE 12.3: STANLEY BLACK & DECKER’S COLLABORATIVE SUCCESS WITH LOWE’S AND HOME DEPOT

In today’s retail world, consumers are in the driver’s seat. They expect a “seamless” experience across all the shopping channels that a retailer offers. This has put a strain not only on retailers to the right products to the right channel at the right time but also on every other node in the supply chain, par- ticularly their suppliers. The strain has resulted in a renewed interest on collaboration focused on opti- mizing inventory and order fulfillment.
One of the key technologies supporting this renewed collaboration is store – level DRP. Store- Level Distribution Resource Planning (DRP) is a collaborative approach that utilizes the retail- er’s POS data to produce a model that yields a bottoms-up, time-phased forecast of consumer

sales, shipments, receipts, and inventories at all stores or channels and distribution centers (DCs) for all items usually over a 12-month period. Typically, the forecasts are for daily periods in the near-term (say next 3 months) and weekly for the remaining time. These forecasts are then used by the suppliers (manufacturers) to calculate – not forecast – what should be produced and deliv- ered, where it should be product and delivered, and when it should be produced and delivered. In this way, supply is tied directly to demand at the (real or virtual) shelf. If there are changes in the demand at a store or channel, then the model is used to adjust production and delivery.
The model for a single forecast provides visi- bility across the supply chain with a number of attendant benefits (VICS 2013):

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  Improved availability at the shelf, in-stocks, ser- vice levels, forecast accuracy and productivity.
  
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  Reduced safety-stock, product costs, transpor- tation costs and lead times.
  

In alliance with the superstore retailers Lowe’s and Home Depot, Stanley/Black & Decker has been one of the strongest proponents of DRP. Stanley Black & Decker was built in 2010 from the merger of The Stanley Works with Black & Decker. According to their latest financial reports, today they are a global provider power and hand tools, products and services for industrial applica- tions, mechanical access solutions (e.g. door locks), and electronic security and monitoring sys- tems. Last year their revenues were approximately
$11 billion with close to 50% coming from North America. Within North America two of their larg- est customers are Lowe’s and Home Depot.
A few years back, before the merger, Black and Decker established dedicated demand fore- casting teams for one of their three divisions (Hardware and Home Improvement) that worked directly with Lowe’s and Home Depot in the same cities where the two retailers were located. The focus was on matching supply levels with consumer demand while maintaining the high fill rates and delivery schedules required by the two retailers. The overall process, which rested on spreadsheets, proved too cumbersome, inflexible and time consuming. The result was increased
overtime costs, unfulfilled demand, and problem- atic inventory levels.
After thorough review, the process and system was replaced by a 3rd party software demand forecasting system which provided the means to use POS data from Lowe’s and Home Depot to model item level demand at the store level across time. In this way they had a centralized process for conducting line reviews, as well as determin- ing the impacts of price changes and promotions. Later they added a system for master planning at the plant level and for fulfillment which improved operating efficiency and improved fill rates and optimized the multi-level replenishment process. All of the systems were provided by JDA Software Group, Inc. (jda.com).
In 2010, Stanley Black and Decker and Lowe’s were nominated for the VICS CPFR Implementation Excellence award for their DPR implementation. The nomination noted the joint improvements in fill rates to 98% percent levels, in-stock improvements to 98%, reduction in excess inventory, and a 10% improvement in forecast accu- racy, along with reduced transportation costs.