The purpose of demand planning is to improve forecast accuracy and increase supply chain effectiveness. Using current and past forecast performance, demand planners can provide guidance on what adjustments are needed to improve future forecast accuracy. The next step in developing an effective demand planning process is to make the future forecast more forecastable, that is, to take actions that make it more likely that the forecast will be accurate. While there will always be factors outside our control that will influence forecast reliability, there are actions we can take to increase the odds that our forecasts will be accurate. In taking these actions, we move from merely planning demand to managing demand.
Most forecasting approaches are based on the idea that a forecast can be improved if the underlying factors are identified then forecasted separately. It stands to reason, then, that with conventional time series approaches that extract information from historical observations, we may gain insights by isolating and modeling elements like seasonality. In this article, I explore how transforming historical data with multi-cluster time series analysis allows us to gather additional information about seasonality. I discuss a newly introduced modelling approach that combines information from many different clusters of attributes and levels of aggregation for improved seasonal profiles, and consequently allows us to build more robust forecasts.
The second machine age is allowing us to understand and shape our environments using computers and other digital advancements. We’re now seeing unsupervised learning systems that learn faster, require less data, and achieve impressive performance. These supervised and unsupervised intelligent automation techniques can drive automation and enrich their domain experts — not replace them by helping them work more effectively. Intelligent automation driven by Artificial Intelligence (AI) and machine learning (ML) are disrupting the way companies do business. The rapid deployment of automation is helping us set new standards of efficiency, speed, and functionality. Intelligent automation will help demand planners to sense and synthesize vast amounts of information boosting the FVA process guiding demand planners with surgical precision to work smarter.