Downstream Data, Harnessed

How to forecast in the face of volatile consumer demand


By Robert F. Byrne

Recent years of economic upheaval have made forecasting demand increasingly difficult for consumer products companies. Facing the largest drop in wealth in decades, consumers became more conservative and searched for ways to save money on household spending. Large purchases were postponed or eliminated, effectively altering traditional demand patterns. Widespread promotions further distorted consumer buying behavior, and the shift to store brands in some cases may change it permanently. This increase in demand volatility and forecast error taxed the supply chain at a time when manufacturers' profits were already being squeezed.

Faced with these challenges, consumer products companies are investing in new ways to predict demand within a volatile environment. Traditional demand planning systems are inefficient at predicting demand in the best of times, but their shortcomings become acute during economic downturns. By definition, seasonal models relying on historical sales fail to accurately predict current demand in volatile markets. Instead, manufacturers need a means to dynamically sense changes in demand before they occur and to automatically feed updates into supply chain management systems.

To mitigate volatility and gain insight into current demand, manufacturers are turning to point-of-sale (POS) data and finding that using it for forecasting is more complex than first anticipated. While POS data provide a picture of recent sales, they are historical in nature, and POS alone does not provide enough information to create a demand plan. Unfortunately, what retailers sold yesterday is not necessarily a good predictor for what they will order tomorrow. While sell-one/ship-one sounds intuitive, experience has shown that this is rarely the case or forecast error would already have been eliminated.

The Limitations of POS

There are two basic approaches to incorporate retailer data into supply chain management systems to predict manufacturer requirements. The "extended" replenishment planning model advocates forecasting at the store level using POS data and then mechanically modeling all the intermediate inventory locations, flows and policies to generate requirements. This approach is sometimes referred to as "flowcasting." There are three major challenges with this approach:

  • Scalability — While starting from the retail location and working backwards sounds instinctive, the huge modeling requirements limit practical scalability. Enterprise-wide deployments encompassing all items at all stores would be prohibitively complex and expensive. For example, many consumer products are stocked at tens of thousands of locations; Wal-Mart alone has more than 3,000 stores in the U.S. Managing forecasts, safety stock, lead time and so on at this level is far more difficult than managing the same data for 10 manufacturer warehouses. Plus, it requires knowing your customers' inventory policies, lead times and ordering strategy.
  • Conflicting information — It is entirely possible to generate a requirement for 500 cases for today, but there is no guarantee that is what the retailer will order. They may well have ordered 300 cases or 700. It's unclear how to reconcile these conflicting data without additional rules and assumptions like forecast consumption. A better choice would be to model the retailers' actions rather than making assumptions.
  • Accuracy — Generating accurate forecasts at the store level is challenging, and counting on all the intermediate actions to happen as modeled is optimistic at best.

This approach could be useful for predicting the next shipment, but it is less likely to be useful to generate an accurate forecast for the entire product supply lead time.

 

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