Once a company has a uniform forecasting process in place, how does it take its demand planning to the next level of efficiency and effectiveness? That was the challenge facing Wells Lamont Corp. in 2003 when the Chicago-based glove manufacturer began looking for a tool that could move its planners out of the data gathering business and into a more strategic role at the company.
Founded in 1907, Wells Lamont today is the world's largest glovemaker. Its retail division's product can be found at home improvement centers, supermarkets, discount stores and hardware chains, as well as specialty retailers such as outdoor outfitter and sporting goods stores, while the industrial division sells its gloves through safety distributors into the industrial marketplace.
Aside from forecasting on the order of 5,000 stock-keeping units (SKUs) between the two divisions, Wells Lamont faces planning challenges typical of the two sectors in which it operates. On the retail side, the company offers products with high seasonality, while demand for its industrial gloves runs in up-and-down cycles that reflect trends for the various product lines on this side of the business. As a result, the company's planners cannot take a "cookie-cutter" approach to how they look at demand, performance and forecast adjustments for Wells Lamont's different product lines.
Time for a Change
Steve Bartok, a 20-year veteran of the industry and previously supply chain operations manager for Wells Lamont's industrial division, took over as planning manager in 2002. His initial charge was to establish a uniform forecasting process for the glove manufacturer, and by 2003 the company was running a consistent planning process for both its divisions based on data from the company's J.D. Edwards enterprise resource planning (ERP) system. However, Bartok says that even with the new process in place, Wells Lamont realized that it would need to upgrade its planners' toolkit in order to meet the company's goals for reducing its inventory investment, maintaining high customer service levels and increasing purchasing and production efficiencies.
In part, the need for a new forecasting tool stemmed from Wells Lamont's goal of better utilizing demand projections provided to planners by the company's own sales and marketing staff, as well as data supplied by customers, whether directly or through collaborative teams formed with the glovemaker's staff. "A lot of the programs in the product mix for customers change from year to year or season to season, so we can't rely on the statistical models," Bartok explains. "We needed visibility into when we should incorporate the statistical model or when we should use the business intelligence for the active forecast." In addition, by automating some part of the forecasting process, Wells Lamont believed not only that it could increase the throughput of its planning department, allowing them to get more work done faster, but also that the company could move its planners away from a tactical role — collecting and collating data for the forecast — and move them into a more value-adding role in the company — generating more accurate forecasts faster.
Wells Lamont's planning team reviewed their forecasting process to identify the features and functionality that the company would require in any new solution. First and foremost, Bartok says that he was looking for a solution that could serve as a management tool, that would give the company's planners visibility into which customers and which products needed demand planning or forecast adjustments. The planning team also wanted a tool that would allow for the easy integration of customer-, marketing- and management-supplied business intelligence. Finally, Bartok says the company wanted a tool that could capture the net forecast change on a week-to-week basis so that the planning team could give purchasing and production regular updates and, hopefully, heads-up about changes that would impact those departments' plans within the appropriate lead times to maintain scheduling and purchasing efficiencies while avoiding additional expediting costs.