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Fuzzy and Grey Forecasting Techniques and Their Applications in Production Systems

  • Chapter
Production Engineering and Management under Fuzziness

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 252))

Abstract

Forecasting is an important part of decision making as many of our decisions are based on predictions of future unknown events. Forecast is an interesting research topic that has received attention from many researchers in the past several decades. Forecasting has many application areas including but not limited to stock markets, futures markets, enrollments of a school, demand of a product and/or service. Management needs to reduce the risks associated with decision-making, which can be done by anticipating the future more clearly. Accurate forecasts are therefore essential for risk reduction. Forecasting provides critical inputs to various manufacturing-related processes, such as production planning, inventory management, capital budgeting, purchasing, work-force scheduling, resource allocation and other important parts of the production system operation. Accurate forecasts are crucial for successful manufacturing and can lead to considerable savings when implemented efficiently. Forecasting literature contains a large variety of techniques from simple regression to complex metaheuristics such as neural networks and genetic algorithms. Fuzzy set theory is also another useful tool to increase forecast efficiency and effectiveness. This chapter summarizes and classifies forecasting techniques based on crisp logic, fuzzy logic and the grey theory. The chapter also presents numerical examples of fuzzy simple linear regression and grey forecasting methodology.

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering, İstanbul Technical University, 34367, Maçka, İstanbul, Turkey

    Cengiz Kahraman

  2. The Harry F. Byrd, Jr. School of Business, Shenandoah University, Winchester, Virginia, U.S.A.

    Mesut Yavuz

  3. Faculty of Engineering and Architecture, Department of Industrial Engineering, Selçuk University, 42075, Konya, Turkey

    İhsan Kaya

Authors
  1. Cengiz Kahraman
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  2. Mesut Yavuz
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  3. İhsan Kaya
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Editor information

Editors and Affiliations

  1. Management Faculty, Department of Industrial Engineering, Istanbul Technical University, Macka, 34367, Istanbul, Turkey

    Cengiz Kahraman

  2. Harry f. Byrd, Jr. School of Business, Shenandoah University, 1460 University Dr., 22601, Winchester, VA, USA

    Mesut Yavuz

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Kahraman, C., Yavuz, M., Kaya, İ. (2010). Fuzzy and Grey Forecasting Techniques and Their Applications in Production Systems. In: Kahraman, C., Yavuz, M. (eds) Production Engineering and Management under Fuzziness. Studies in Fuzziness and Soft Computing, vol 252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12052-7_1

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  • DOI: https://doi.org/10.1007/978-3-642-12052-7_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12051-0

  • Online ISBN: 978-3-642-12052-7

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