Demand Planning Comes Of Age

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

Demand Forecasting Reaches The Supermarket Shelves

Most demand planning systems are aimed at manufacturers and wholesalers. But sophisticated, automated demand forecasting systems have even reached the store level. Plano, TX-based Retalix, which provides food retail software systems, for example, introduced DemandAnalytX, which allows retailers to forecast daily demand for each SKU based on the same kinds of factors and calculations incorporated in the demand planning systems used by their suppliers upstream, including sales history, seasonality, pricing, promotions and special events.

The program actually combines forecasting with store-level order development, notes Gil Roth, executive vice president, supply chain development.

"It calculates, based on the forecast, variability in the forecast, and other factors such as shelf life, item cost, interest rate and more, how much product the store should have on hand on any day and how much to order, based on a perpetual inventory, to maintain that level."

For many retailers, the biggest challenge in implementing the system is developing the discipline of maintaining a perpetual inventory at the store.

For the software vendor, applying automated forecasting to the store shelf involved other challenges.

"At the retail level, there is a much higher level of statistical noise compared to the warehouse level, where product is ordered in large quantities and demand patterns are relatively smooth," Roth explains. "If you look at demand for different items at the store level, you will find huge deviations. You may have slow movers that are sold only once or twice a week, or a handful of customers may be responsible for a significant percentage of demand for a particular item."

To deal with this greater level of uncertainty, Retalix developed its own algorithmic tools to allow the system to better differentiate between real patterns and deviations that have no predictive value.

Among the benefits retailers realize from the system, Roth adds, are three that are easy to quantify.

"First is reduction in out of stocks. Our customers typically experience anywhere between a 50-60 percent reduction in out of stock events. This typically translates in to a one-three percent increase in sales. Second is reductions in inventory. These average from 12-20 percent, but in some departments like tobacco are much larger, 25-30 percent."

A third benefit that stores almost don't notice is reduction in perishables lost to spoilage, Roth adds.

"Stores can expect to reduce what they throw out by around 25-50 percent," he says. -C.C.

Extend Forecasting Benefits With Inventory Optimization

Once they've enhanced short-term forecasting through the use of a sophisticated demand planning engine, a growing number of companies are seeking to extend the benefits by integrating a similarly advanced inventory optimization program.

"A major driver of safety stock is forecast error. So once companies have this very accurate forecast, their next question is, how do I monetize that accuracy and take down my safety stock in a structured way that reflects the changes in my forecast error?" comments Robert Byrne, CEO, Terra Technology, Norwalk, CT. "What you're really measuring when you set safety stock levels is variability, so demand forecasting and safety stock calculations are closely related.

"Historically," he adds, "most companies have set inventory targets based on rules of thumb-this is a high volume item, we need about two weeks inventory on hand; this is a low-volume item with lots of variability, better carry three weeks of inventory."

Terra's Multi-Enterprise Inventory Optimization system, introduced last year, systematizes these calculations by measuring forecast error related to lead time on a daily basis.

The system takes as inputs three basic kinds of uncertainty: demand uncertainty, manufacturing uncertainty and transportation, in addition to desired service level to customers and traditional lead times. Combined with historical item movement data, it automatically calculates optimal inventory levels for each SKU, which can be viewed over any time period and compared to actual inventories on hand and in the supply chain.

Similar to sophisticated demand planning systems, Terra's inventory optimizer is designed to deliver its results with minimal human interference, leaving human intelligence free to deal with exceptions.

"The program allows users to do some root cause analysis. If, for example, an item is flagged because last month the safety stock level was 100, and now it's 150, you can drill down into the data to understand what happened, whether it was due to forecast error, a change in lead time, or perhaps schedule compliance fell off a cliff," Byrne points out.

The system also allows users to explore what-if scenarios.

"You can pose questions such as, how would my inventory levels change if I changed my customer service level? Or if I cut my cycle time in half at a certain plant, how would that affect safety stock levels?"

One of the unusual features of Terra's inventory optimizer, Byrne notes, is that it takes into account not just inventory at the warehouse and plant level, but throughout the supply chain, including regional DCs, work-in-progress, and raw materials, in addition to warehouse and plants. -C.C.

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