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Matt Weller

Former Research Student

Supervised By

Dr Sven Crone

Thesis Title

Forecasting under collaboration and information-sharing: current practice and a framework for improving accuracy

Thesis Outline

The increased volatility and competitiveness of today's market has led firms to engage in collaborative forecasting and information-sharing practices in order to improve forecast accuracy.  However, knowing how and when to collaborate with downstream partners remains a challenge. It seems that the benefits are widely touted though little substantive research exists about how to best use the mass of downstream data available to forecast more accurately.

This study will use empirical evidence, collected through a survey and follow-up interviews at consumer goods manufacturing companies, to address the following open research questions:

  • What forms of collaboration are companies participating in and what data is being shared by their downstream partners?  
  • How are firms using this information, if at all, in their forecasting process?  Through statistical methods, analytics or judgement?  
  • How do firms cope with differing data conditions and forecasting requirements of different customers?

Subsequently, the work seeks to propose a framework for improving forecasting performance and takes a modelling approach to simulate various forecasting algorithms and scenarios of data usage based on real supply chain data.  Using robust error measurement techniques to compare the performance against benchmark methods, a framework for model selection is developed to help managers determine when and how to collaborate in the forecasting task.

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