For decades, companies have built supply chains focused on cost optimization, using inventory as a buffer to meet customer service objectives. Yet many companies fail to meet customer service expectations despite having plenty of inventory on hand. Demand forecasting can help reduce inventory, improve on-shelf availability, increase productivity, and minimize waste—all while meeting customer expectations.
Demand forecasting is nothing new. But most current forecasting tools use a one-size-fits-all approach regardless of industry, product lines, and other factors that affect demand. The result is a forecast that can’t adapt as market conditions and consumer behaviors evolve.
A more analytical forecast based on hard data helps promote fact-based tradeoffs and decision making. It also reduces internal conflicts and highlights potential opportunities. Ultimately, better forecasting drives more effective spending, avoids overproduction, eliminates waste and enables a more responsive and flexible supply chain.
What are the differences between the traditional forecasting process and one that helps improve supply chain performance?
Traditional Forecasting Process
Improved Forecasting Process
Manually intensive and high-touch
Statistically driven with little or no manual effort or intervention
One data feed (such as historical shipments)
Multiple, distinct data feeds using strong drivers of demand
Model predicts shipment demand by customer
Model predicts consumer sales and learns order patterns to predict shipments
Static, outdated forecasting algorithms
Model learns over time, adjusting to changes in demand and ordering behaviors to improve accuracy
Forecast accuracy below industry average
Improved demand forecasts bring a variety of benefits:
With greater confidence in demand forecasts, manufacturers and retailers can coordinate promotional and merchandising events and improve the effectiveness of trade spending. Plants can avoid overproduction, improve schedules, and unlock trapped capacity. Production runs can become more frequent and actually tied to consumer demand, which shortens lead times. As last-minute expediting is eliminated, plants can reduce inventory levels and provide fresher products to the consumer.