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 behaviour, and the shift to store brands, in some cases, even changed 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 at the best of times, but their shortcomings become acute during economic downturns. By definition, models relying on historical sales figures fail to accurately predict current demand in volatile markets. Instead, manufacturers need a way to dynamically sense upcoming changes in demand and to automatically feed new forecasts into supply chain management systems.
