Most companies recognize the importance of a repeatable and accurate forecasting process.
Accurate forecasts help minimize inventory, maximize production efficiency, streamline purchasing, optimize distribution, maximize customer service and ensure confidence in company projections. However, developing accurate product forecasts at all stages of a product’s life cycle can be very challenging.
Gartner places demand forecasts at the top of their Hierarchy of Supply Chain Metrics to highlight its impact back through the supply chain. After all, a forecast is not simply a projection of future business; it is a request for product and resources that ultimately impacts almost every business decision the company makes across sales, finance, production management, logistics and marketing.
Typically, a variety of forecasting methods are applicable to any particular type of supply chain scenario. Smart supply chain planners use multiple methods tuned to perform well at different phases of the product life cycle, chosen to best exploit the available historical data and degree of market knowledge. The key is to pick the most effective and flexible methods and models, blend their best features, and shift between them as needed to keep forecast accuracy at its peak.
In this paper we take a brief look at the three categories of forecasting models and the eight methods that have produced superior results for Logility’s many clients in a variety of industries and market conditions around the world.
We also discuss how Multi-Variate Demand Signal Management can help you incorporate internal and external demand data to improve forecast quality and uncover insights to make better and faster decisions.