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.