The Accuracy Of Demand Forecasting

by | Apr 20, 2018 | Supply Chain Management

In the past, planning for future demand for the ordering of product for retail outlets or for raw materials for manufacturing facilities, was more of an art than a science. Typically, managers or department heads would gather and compare the history of the purchase of raw materials and inventory compared to the sales for the last two or three years. This could be based upon a full year or it may be done by seasons or quarters depending on the business model and type.

While this type of planning for the future was moderately effective for most businesses, it was also largely ineffective at looking at potential current consumer behavior and similar factors to predict what could happen in the future. This is due to the fact this type of demand forecasting only included data from the past.

Technology of Today

With the ability to tap into big data and use large data sets to determine current trends as well as past markets, demand forecasting through advanced software programs has now gone from being an art to being a science.

With the use of data sets for current behavior and future consumer and industry trends, companies can integrate this information with past performance indicators, providing a clear picture of the industry and the business.

Additionally, these systems integrate a range of other data types as well. This can include manufacturing lead times, raw material costs and ordering behaviors as well as potential areas of a slow down in the market.

This allows a business to make informed decisions based on hard data. It also allows different departments to maximize ordering of raw materials and products when prices are low, and future demand is poised to increase. Effective decision making at these times can help to save company money while also allowing for internal decisions for staffing and marketing.

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