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Responsive Demand Management in the Era of Digitization

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Strategic Outlook for Innovative Work Behaviours

Part of the book series: Contributions to Management Science ((MANAGEMENT SC.))

Abstract

Being responsive to customer demand is key to business success in supply chains, due to intensive competition between companies and uncertainty of dynamic market conditions. Rapid changes in manufacturing and information technology enable companies to serve customers better, while forcing them to be more responsive and precise in demand management. This chapter focuses on understanding, forecasting, and managing customer demand as a competitive edge in business life. Traditional forecasting techniques such as moving averages, exponential smoothing, or trend projection are moved to a next level by gathering as much customer data as possible. By analyzing this big data with the help of advanced techniques such as machine learning and data mining, it may be possible to turn signals from customers expressing their needs, expectations, and complaints into an accurate and precise forecast of future demand.

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References

  • Aksoy, A., Ozturk, N., & Sucky, E. (2012). A decision support system for demand forecasting in the clothing industry. International Journal of Clothing Science and Technology, 24(4), 221–236.

    Article  Google Scholar 

  • Alpaydin, E. (2009). Introduction to machine learning. Massachusetts: MIT Press.

    Google Scholar 

  • Altunışık, R. (2015). Büyük Veri: Fırsatlar Kaynağı mı Yoksa Yeni Sorunlar Yumağı mı. Yildiz Social Science Review, 1(1), 45–76.

    Google Scholar 

  • Balaban, M. E., & Kartal, E. (2015). Veri Madenciliği ve Makine Öğrenmesi Temel Algoritmaları ve R Dili ile Uygulamaları. İstanbul: Çağlayan Kitabevi.

    Google Scholar 

  • Blanchard, D. (2010). Supply chain management best practices (2nd ed., pp. 44–45). Hoboken, NJ: Wiley.

    Google Scholar 

  • Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140–1154.

    Article  Google Scholar 

  • Jacobs, F. R., & Chase, R.B. (2010). Operations and supply management: The Core (Second ed.). New-York: McGraw-Hill Irvın.

    Google Scholar 

  • Chen, F. L., & Ou, T. Y. (2011). Sales forecasting system based on gray extreme learning machine with Taguchi method in retail industry. Expert Systems with Applications, 38(3), 1336–1345.

    Article  Google Scholar 

  • Ching-Chin, C., Ieng, A. I. K., Wu, L.-L., & Ling-Chieh, K. (2010). Designing a decision-support system for new product sales forecasting. Expert Systems with Applications, 37(2), 1654–1665.

    Article  Google Scholar 

  • Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1883.

    Article  Google Scholar 

  • Chong, A. Y. L., Ch’ng, E., Liu, M. J., & Li, B. (2017). Predicting consumer product demands via big data: The roles of online promotional marketing and online reviews. International Journal of Production Research, 55(17), 5142–5156.

    Article  Google Scholar 

  • Chopra, S., & Meindl, P. (2016). Supply chain management. In Strategy, planning and operation (6th ed., pp. 177–182). Noida: Pearson.

    Google Scholar 

  • Claveria, O., & Torra, S. (2014). Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Economic Modelling, 36, 220–228.

    Article  Google Scholar 

  • Flannagan, R. (2019). How important are online customer reviews? Accessed January 10. http://nuancedmedia.com/how-important-are-online-customer-reviews/

  • Görçün, Ö. F. (2016). Tedarik Zinciri Yönetimi. 3.Baskı. İstanbul: Beta Yayınları.

    Google Scholar 

  • Heizer, J., Render, B., & Munson, C. (2017). Operations management. Sustainability and supply chain management (12th ed.). Noida: Pearson Education Limited.

    Google Scholar 

  • Hofmann, E., & Rutschmann, E. (2018). Big data analytics and demand forecasting in supply chains: A conceptual analysis. The International Journal of Logistics Management, 29(2), 739–766.

    Article  Google Scholar 

  • Kache, F., & Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management. International Journal of Operations & Production Management, 37(1), 10–36.

    Article  Google Scholar 

  • Kim, S., & Shin, D. H. (2016). Forecasting short-term air passenger demand using big data from search engine queries. Automation in Construction, 70, 98–108.

    Article  Google Scholar 

  • Krajewski, L. J., Ritzman, L. P., & Malhotra, M. K. (2010). Operations management: Processes and supply chains (9th ed.). Noida: Pearson Publishing.

    Google Scholar 

  • Lee, H., Kim, S. G., Park, H. W., & Kang, P. (2014). Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach. Technological Forecasting and Social Change, 86, 49–64.

    Article  Google Scholar 

  • Li, L., Chi, T., Hao, T., & Yu, T. (2018). Customer demand analysis of the electronic commerce supply chain using big data. Annals of Operations Research, 268(1–2), 113–128.

    Article  Google Scholar 

  • Levis, A. A., & Papageorgiou, L. G. (2005). Customer demand forecasting via support vector regression analysis. Chemical Engineering Research and Design, 83(8), 1009–1018.

    Article  Google Scholar 

  • Li, X., Pan, B., Law, R., & Huang, X. (2017). Forecasting tourism demand with composite search index. Tourism Management, 59, 57–66.

    Article  Google Scholar 

  • Robbins, S. P., DeCenzo, D. A., & Coulter, M. (2017). Fundamentals of management: Management myths debunked! Tenth edition. London: Pearson Education Limited.

    Google Scholar 

  • Sun, S., Wei, Y., Tsui, K. L., & Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1–10.

    Article  Google Scholar 

  • Bartodziej, C. J. (2017). The concept industry 4.0. In The Concept Industry 4.0 (pp. 27–50). Wiesbaden: Springer Gabler.

    Chapter  Google Scholar 

  • Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84.

    Article  Google Scholar 

  • Xie, K., Wu, Y., Xiao, J., & Hu, Q. (2016). Value co-creation between firms and customers: The role of big data-based cooperative assets. Information & Management, 53(8), 1034–1048.

    Article  Google Scholar 

  • Zhao, Y., Xu, X., & Wang, M. (2019). Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews. International Journal of Hospitality Management, 76, 111–121.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Management Information Systems, Konya Food and Agriculture University, Konya, Turkey

    Tuğba Sarı

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  1. Tuğba Sarı
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Correspondence to Tuğba Sarı .

Editor information

Editors and Affiliations

  1. Istanbul Medipol University, Kadiköy, Istanbul, Turkey

    Hasan Dincer

  2. Istanbul Medipol University, Besiktas, Istanbul, Turkey

    Serhat Yüksel

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Sarı, T. (2020). Responsive Demand Management in the Era of Digitization. In: Dincer, H., Yüksel, S. (eds) Strategic Outlook for Innovative Work Behaviours. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-030-50131-0_16

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