Demand Planning Comes Of Age

Companies such as Litehouse Foods use demand planning tools to direct their organizations with greater intelligence than ever before.


In addition to providing more accurate input to systems used for planning and managing daily operations, demand planning and forecasting systems are proving highly effective for long-term strategic planning and business intelligence.

At Yuba City, CA-based Sunsweet Growers, for example, use of Zemeter Demand Forecasting from Supply Chain Consultants, Newark, DE, in conjunction with other tools, has enabled the growers cooperative to strategically channel the fruit from its prune harvest each year to the most profitable mosaic of potential product applications, based on forecasts that are updated throughout the year as sales trends develop.

"We use our demand planning process to do a lot of our strategic planning as it relates to product utilization and where sales will go," explains Harold Upton, vice president, strategic business processes at Sunsweet Growers. He says the co-op's beefed up planning process, based around the demand forecasting tool, enables the organization to manage virtually every aspect of its operations better, from working with growers to managing production lines, down to hitting specific targets for limiting overtime in its plants.

"It's hard to pinpoint exactly how much Zemeter itself has contributed, but over the last five years our return to growers-the ultimate measure of our success-has doubled. In the last two years we've set back-to-back record highs in terms of profitability to our members. Installing the system alone didn't cause all this, but clearly one result of using it is that people are making better decisions and that would have been very hard if not impossible without this kind of strategic planning tool," Upton comments.

Fundamentals

Demand planning systems come in a variety of flavors, but combine the same basic elements, notes Sujit Singh, Supply Chain Consultants' chief operating officer.

"There is a statistical engine running historical data to create forecasts; a collaboration engine to take input from the people that are talking to customers; and a demand shaping engine, to help companies model and shape demand through promotions."

"All the programs attempt to merge market information, sales history and collaboration with sales and marketing and through a variety of algorithms, come up with the best single number to drive downstream planning processes," adds Chris Taunton, director of product management and supply chain planning for CDC Software, Atlanta.

In terms of reducing forecast error, their greatest impact tends to be on lower level, or SKU-level forecasting, Singh comments.

"Most companies have a good handle on higher level forecasting. If they sell 500 SKUs divided among 10 product families, they can tell you what their forecast will be at the product family level with a fairly high degree of accuracy, probably within 90-95 percent.

"However, from a production planning and procurement planning perspective, those high level numbers don't mean much. What is really critical for operations is to get forecast numbers at a very detailed level. That's where we provide the most help to companies, in taking their accurate, high level forecasts and extending that accuracy down to the SKU.

"At this highly detailed, SKU level, we've seen many users achieve from 30 to 50 percent improvements in forecast accuracy," Singh says.

Most programs allow users to bring in a wide variety of factors as inputs to forecast calculations. In addition to sales history, which provides the bedrock for all projections, data that may be factored in range from price history and weather predictions or patterns, to syndicated and other POS data, warehouse shipments, competitive activity and "any other causal factor a company might want to incorporate because it has good reason to believe that factor to be leading in nature," Singh comments.

Promotional calendars, of course and promotional histories are also key inputs.

Despite the ability most programs afford users to input their own lift factors tied to specific causal events and to adjust forecasts calculated by the systems, the programs are meant to predict SKU-level demand based largely on sales history with a high degree of autonomy-that is, without user interference.

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