Retail supply chains have grown more and more complex, and companies must have end-to-end supply chain visibility to prepare for unexpected changes in demand. As such, retailers should explore practical, relevant ways to predict demand, while continuing to deliver quality, omni-channel consumer experiences.
CPG companies are increasingly harnessing growing volumes of consumer data through online marketing and direct-to-consumer sales platforms. These companies can enhance their trade promotion activities through artificial intelligence and machine learning, but they must collaborate with retailers to do so. Together, the two can better identify ineffective promotions, forecast more accurately and optimize promotions to generate an optimal sales fit.
In 1956, considerable fluctuations in production, inventories and profit baffled managers in General Electric’s household appliance division. Despite supervisory efforts, the variations endured. Traditionally, managers blamed these types of fluctuations on external causes, like business cycles.
When it comes to demand forecasting, most companies have way too many forecasting mechanisms in play across their organization. Each segment of the business ends up siloed from the others, relying on its own data and analysis, which impacts both efficiency and effectiveness.
Most organizations do a poor job forecasting, with just one in five coming within 5% of forecasts. This statistic is staggering, and it implies that although many organizations understand the value of forecasting, the majority of them are doing it inaccurately.