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.

Convinced that they needed a tool to achieve these goals, Wells Lamont's planning team began a comprehensive review of their forecasting process in order 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 — identifying, for example, those products that were over or under the forecast at a given moment, or products that didn't have a forecast but needed one.

The planning team also wanted a tool that would allow for the easy integration of customer-, marketing- and management-supplied business intelligence. This would free the planners from having to rely on statistical models that were not optimal for forecasting the new products that entered the lineup each year or products associated with customers' programs that might change year-to-year or even season-to-season. At the same time, Wells Lamont wanted a solution that incorporated solid statistical modeling to recommend changes based on updates about, for example, how sales were going by different territories or customers.

Finally, Bartok says that 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. In this way, purchasing could avoid buying the wrong materials and ensure that the correct supplies come in on time, while the manufacturing side could adjust its production scheduling as necessary to accommodate changes in customers' programs or fluctuations in demand for certain products.

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.

After reviewing all the demand solutions, Wells Lamont ultimately settled on 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 that 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. That was our biggest task."

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. The other option was to map in the sales histories for each product and allow the engine to generate forecasts, but because the planners also incorporate so much outside business intelligence into their forecast figures, it made more sense to simply go with the existing projections.

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. For example, Wells Lamont's planners run exceptions that identify seasonal customers whose products should be booked at that particular time and exceptions to identify new products without a forecast. They also ran exceptions to identify products that are trending toward obsolescence, or to determine which customer or territory has the highest percentage of ownership for a particular SKU over a 12-month period, which can give the planning team a heads-up to watch a particular customer to detect any trends, especially when key accounts are involved.

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 — those with account or product line responsibility — all the demand data by putting the forecasts up on the company's intranet in the form of Excel pivot tables. The data available to the decision-makers include sales history, current forecast for all items in the customer program or product line, and statistical forecast changes (what the system is recommending), as well as any customer or collaborative data as another opinion line for the decision-makers to consider in making adjustments. Importantly, the system also highlights for the decision-makers those items that require action.

With those data in hand, at some point during the planning period the decision-makers can elect to accept the statistical model changes for their SKUs, or they can 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 that 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 do updates back to 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. The company forecasts out up to 18 months from the middle of the year, going out through the end of the following year for the "rough cuts." Then the planners make changes during the course of the year as necessary.

Currently Wells Lamont has two user licenses for the Galt solution, but the Excel pivot tables generated by the planning team have an audience of about 50 people within the retail division and an equal number on the industrial side of the business, according to Bartok. "We're doing the pivot tables right now, because it's in line with what the sales guys were used to before," the demand planning manager adds. Starting in 2005, Wells Lamont plans to move to the Web form of the John Galt engine, which will allow the planners to share information with decision-makers more quickly, Bartok says.

While Bartok did not discuss Wells Lamont's investment in the new forecasting tool, he says that the company anticipated a one-year payback period on the solution, and he 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. "When you give a vendor or production manager a heads-up saying, 'This is going to be coming on the next plan,' that gives them a few weeks to reroute their purchasing or production plan to improve or maintain the scheduling and purchasing efficiencies," Bartok explains.

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, and more 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 says. 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.