Planning by Exception

Demand planners at Wells Lamont, the world's largest glove manufacturer, have put their finger on a way to bring new value to the company by leveraging technology that allows them to plan by exception.


Demand planners at Wells Lamont, the world's largest glove manufacturer, have put their finger on a way to bring new value to the company by leveraging technology that allows them to plan by exception.

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. "We wanted to spend less time compiling and identifying areas of the demand plan that needed adjustment action, and we wanted to spend more time making decisions and taking actions based on the data," says Steve Bartok, demand planning manager at Wells Lamont.

Founded in 1907 by W.O. Wells, a 24-year-old buggy whip and hosiery jobber, Wells Lamont today is the world's largest glovemaker. Moving from its original home in Aberdeen, S.D., to Minneapolis before settling in the Chicago suburbs, the company currently operates both retail and industrial divisions. The 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. Global conglomerate The Marmon Group, a $5.6 billion global conglomerate, owns Wells Lamont.

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

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, which runs on an IBM iSeries platform. 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. Oftentimes, the projections supplied from these various sources conflicted with forecasts generated using statistical models based on, for example, past demand for a given SKU. "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.

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