Demand Forecasting forms an essential component of the supply chain process. It’s one of most difficult aspects of supply chain planning and there are always chances of making blunders in this. Right demand forecasting systems helps significantly in overall profitability of the businesses. Demand is often volatile which makes forecasting both an art and a science.

Demand forecasting is defined as the process by which the historical sales data are used to develop an estimate of the expected forecast of customer demand. Demand planning spans across several aspects with focus on three primary areas.

• Product Management: This area focuses mainly on overall product lifecycle, beginning with the introduction of a new product to its end-of-life planning.
• Statistical Forecasting: Using historical data, statistical forecasting helps in creating advanced predictions about demand. It is important to determine the accuracy of each model, identify outliers, and understand the assumptions which are being made. Seasonal shifts can also be accommodated in the statistical forecasting.
• Marketing Management: Marketing events can significantly influence demands in CPG and FMCG industries. To help a brand connect with a customer, often giveaway, discounts or promotions are run which impacts the demand.

Reason for forecasting the demand is important due to several reasons. If product isn’t available to a customer because of unavailability, business loses on revenue and in prolonged time can lose the customer to a competitor. On the other hand, sitting on unused inventory costs both space and production cost unnecessarily.

Since Demand planning is a multi-step process, it depends on right tools, granular level data, correct information and processes and inputs from stakeholders. Demand forecasting is always going to be wrong, however in long term you should aim for being less wrong each time. Some best practices to keep in mind:

• Implement the right tool: There are plethora of ERPs available for SCM, so choosing the right one for your scenario can be tricky. When considering ERP software, it’s important to examine the ability of the tool to handle forecasting nuances as well as reporting capabilities and reliability of the forecasts produced. It should be customizable to your needs.
• Gather and Prepare Data: Data is the key driver in demand planning. Real time visibility into inventory movement and metrics reports that paint a clear picture can help you in making informed decisions. Data mining can help in identifying areas for improvements.

Advances in machine learning within supply chain is going to help supply chain professionals making it possible to adapt and change the forecasts in real time.