The Automotive Industry is characterized by dynamic competition, constantly changing markets and demanding customer requirements. As such, the wide range of products and capital commitment mean that companies need to have a high degree of adaptability, generally resulting in adaptable production systems. The trend in procurement, production and logistics has therefore tended toward developing more flexible supply chains, particularly where companies have to deal with ever more disruptions in their supply chains. Furthermore, the resulting challenges for a company increase dramatically if it is part of several supply chains at the same time.

Customer-supplier relationships in the automotive industry often show relatively pronounced planning deviations in forecasting and release orders, i.e. across all time horizons. Such planning uncertainty and the need for various contingency plans mean high costs and poor performance at different points for OEMs and suppliers. These extra costs – which could be avoided if plans remained on schedule – are known as "turbulence costs".

The main reasons for demand fluctuations include:

• Market developments
• Complexity of the Supply Chain
• Technology, such as changes or quality problems
• Restrictions, such as strikes or legal requirements

The aim of the Forecast Accuracy Measurement recommendation is to define an indicator for forecast reliability which can help to derive an action plan to increase guarantee of supply, and to support cost minimization in supply chains. This means: More stable process chains, more reliable forecasts and release orders, and less rescheduling and fewer additional costs by optimizing forecast accuracy, flexibility and transparency. Evaluating the forecast quality is key to success. Although bilateral customer-supplier contracts include numerous definitions of planning deviation ranges, there is no uniform basis on which to evaluate indicators for forecast quality.

This recommendation therefore proposes the following goals:

• Development of a formula for system-supported evaluation of forecast-quality indicators including stability classification
• Recommendations on the use of these indicators

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