Blue Slimstock


Robust Forecast

Businesses in the building trade typically rely on a global network of suppliers to satisfy the needs of their customers. Consequently, lengthy lead times of up to several months are the norm for businesses in this sector. As a result, suppliers and distributors of building materials depend upon accurate long-range demand forecasts in order to satisfy future demand. However, given the volatile nature of demand coupled with the influence of seasonality and ever-evolving product lifecycles, developing a robust forecast is often fraught with difficulty.

So what steps can businesses take to improve the accuracy and relevance of their demand forecasts?

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Accurate forecasting is the basis of effective inventory management. Yet for many organisations, forecasting is a complex undertaking which is often prone to error. The problem for many businesses is that a reliable forecast must take into account a whole host of different factors as well as data from across the business. Even determining the most appropriate forecasting methodology in the first place can be a difficult decision, let alone identifying why a forecast is inaccurate and then taking the right steps to rectify the issue.

With this in mind, what can supply chain teams do to improve the way they go about forecasting? How can demand planners account for the influence on product lifecycle, seasonality, and emerging trends of future? How can businesses reduce the margin of forecasting error to get a more accurate picture or future demand?

As part of our “building blocks to a better supply chain” series, we have put together a simple 6 step guide to help you significantly increase the quality your forecasts. Put together by Slimstock’s inventory & forecasting expert, Steven Pauly, this guide is designed to help you overcome the following challenges:

  • Choosing the right forecast procedure
  • Choosing the right forecast procedure
  • Identifying the cause of forecast error
  • Account for evolving Product lifecycles
  • Taming the bullwhip effect
  • Maintaining a responsive forecast

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