New Odette Guidelines for Collaborative Forecasting

For many years the automotive industry has suffered from deficiencies in the holistic forecasting and planning process that is used to determine the demand profile for parts and assemblies required from the upstream supply chain. The impact of inaccurate forecasts has come more sharply into focus as a result of difficulties in the business climate coupled with greater expectations from customers.

Creating more "stable" business conditions is a prerequisite to improving overall supply chain performance but inaccurate demand forecasts are leading to significant levels of waste in our industry which need to be eliminated.

In January 2012 Odette published its Forecast Accuracy Measurement recommendation which defined a best-practice method for measuring the quality of demand forecasts using a Forecast Accuracy Index (FAI). The newly-released Odette Best Practice Recommendation entitled 'Guidelines for Collaborative Forecasting' is a complementary document providing a practical guide to the implementation and use of FAI to continuously improve forecast quality.

The benefits of using a common FAI methodology are clear – providing a unique, standard, state of the art process within our industry allows every stakeholder to "speak the same language".

Manufacturing operations need to be flexible in order to cope with real time changes in customer driven market demand. However, organisations should not be required to develop a costly "over-flexibility" within their facility simply to counter and cover up inefficiencies originating from other business processes. We are convinced that implementing FAI, and undertaking the collaborative forecasting continuous improvement activities within this new Odette Best Practice Recommendation will bring about the process improvements that will lead to a more efficient and effective supply chain. To download an example of FAI and WTS measurement, please click here

Access the Guidelines for Collaborative Forecasting.

Also read A practical approach to achieving reliable demand forecasts

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