Inaccurate forecasting impacting profitability and inventory planning
Demand forecasting is often inaccurate because of the inherent complexity of the problem: first, it is not easy to directly forecast a metric that can be highly volatile; second, that metric is affected by multiple parameters whose influence itself varies over time. Producing a demand forecast that addresses either of these two challenges individually is bound to inaccuracies: a straight time series analysis would lose the causality/correlation effects of influential parameters, while a straight AI approach would lose the time dimensionality of the problem. Merging the two is therefore key, but is time consuming to implement ab initio. As a result, lots of the forecast produced by companies are currently created manually by a human analyst. This is both time consuming and prone to errors.