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.


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.

  • Enhance Your Experience.

    When you register for SDCExec.com you stay connected to the pulse of the industry by signing up for topic-based e-newsletters and information. Registering also allows you to quickly comment on content and request more infomation.

Already have an account? Click here to Log in.

Enhance Your Experience.

When you register for SDCExec.com you stay connected to the pulse of the industry by signing up for topic-based e-newsletters and information. Registering also allows you to quickly comment on content and request more infomation.

OR

Complete the registration form.

Required
Required
Required
Required
Required
Required
Required
Required
Required
Required
Required