Planning by Exception

Demand planners at glove manufacturer Wells Lamont 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.

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.

Through a market analysis comparing various forecasting solutions, Bartok's team identified a half-dozen different possible packages and requested that the software vendors come in to demonstrate that their solutions met Wells Lamont's features and functionality requirements. Prior to the demo, the planning team sent out a document to all the vendors outlining Wells Lamont's background, its markets served, demand planning characteristics and the business challenges for each of the company's product lines, as well as a summary process flow for their current demand planning procedure and detailed requirements. In addition, each vendor received sample data to use in running a forecast, data that would bring some of Wells Lamont's business challenges to the surface, and data for which Bartok and his team already had anticipated results.

Ultimately Wells Lamont selected a tool from John Galt Solutions, a Chicago-based software company that offers its Demand Management Engine as a component of its Atlas Planning Suite. "The Galt planning engine really demonstrated everything that we needed to take our demand planning process forward," Bartok says, adding, in particular, that the Galt solution "hit a home run" with its exception reporting capabilities. Bartok explains that the tool allows planners to enter different criteria and multiple demand planning data streams for customers and products, with any range of demand periods, against any kind of metric or target value. The solution, in turn, identifies potential planning problems — a conflict, for example, between the existing forecast for a SKU and the projections provided by a sales rep or a customer collaboration team based on their own business intelligence — and then offers recommendations based on its own statistical models or on customer- or field-supplied opinion lines in the forecast for a given product. "It really leads us to our problems," Bartok says, "to areas where we need to adjust our plan."

Breaking in the New Solution

Wells Lamont selected the Galt solution at the beginning of 2004 and went live with the tool in June in the retail division, following a four-month implementation. The industrial division went live at the beginning of October. As part of the implementation, Galt integrated the Demand Management Engine with Wells Lamont's J.D. Edwards system to automate the process of pulling in demand data for use in the forecast. Bartok says the implementation went smoothly, in part because Wells Lamont's planners already had a culture of pulling in demand data and manipulating it. "We just didn't have all the bells and whistles," he says.

"Our biggest challenge in the implementation," Bartok continues, "was making sure that our data were clean so that we could execute everything correctly, whether it be modeling, reporting or exceptions. We wanted to make sure that our customer codes and attributes that were unique to a vendor, unique to a customer or unique to an item were clean."

In addition, because Wells Lamont was implementing the new forecasting solution at a time of the year when plans were already set for many of its key customer accounts, Bartok's team opted to map the existing forecast data into the Galt engine for the affected SKUs, using those figures as the starting active forecast and making changes, as necessary, to that data immediately after the implementation.

As the process stands now, at the beginning of a new planning period Bartok and his team map sales history updates into the forecasting engine and then run all the exceptions for all their customers and products, giving them visibility into who's trending up and down, based on over- or under-performing sales versus current forecast. Once the planning team has completed running the exceptions, they run the statistical models for all the product lines in the two divisions and then make available to all the appropriate internal decision-makers all the demand data by putting the forecasts up on the company's intranet in the form of Excel pivot tables. Importantly, the system highlights for the decision-makers those items that require action, and the decision-makers can elect to accept the statistical model changes for their SKUs or to interject their own business intelligence or a customer's recommendation. Either way, they provide feedback on the pivot tables to the planners, and the system maps the pivot tables into a simulated scenario so the planners can review the new projections, compare them to the active forecast and, where necessary, question changes, ultimately allowing demand planning to make a decision on which data streams to take as the forecast adjustment.

The demand planners make the forecast adjustments, based on the decision-makers' feedback, throughout the monthly period, but they also update Wells Lamont's ERP system once a week, incorporating all the latest forecast changes. In addition, four or five times a month the planners capture the net unit changes for production and purchasing to make those departments aware of any impact on current plans within the lead time for different SKUs.

Bartok says the company anticipates a one-year payback period on the solution, and adds that Wells Lamont aims to quantify its return on investment by the end of the first quarter of 2005. "We actually expect to see double-digit forecast improvements in some of the product lines," he says. Partly as a result, the company anticipates seeing significant reductions in expediting costs, as well as increases in production and purchasing efficiencies as staff in those departments are fed more accurate forecast data throughout the course of the planning cycle.

Perhaps as important as these gains, Wells Lamont has leveraged the new forecasting engine to take much of the tactical, data entry-type work out of its planners' days and move them into a more strategic, value-adding role at the company. Bartok believes, based on a throughput analysis of the planning department, that the company's planning staff reduced its non-value-adding workload by as much as 70 percent, giving the staff more time to take action on the exceptions identified by the new planning solution. "We're getting the data out there to the decision-makers, we're making visible for them the product lines or programs that need action, and we're getting the results back," Bartok explains. And, finally, Bartok says that the implementation has allowed the company to achieve one other significant goal: "We wanted a process and tool that we would be proud of when sharing data with our customers," he concludes.