Skip to main content
SpringerLink
Log in

Conclusion and Advanced Topics

  • Chapter
  • First Online:
Demand Prediction in Retail

Part of the book series: Springer Series in Supply Chain Management ((SSSCM,volume 14))

  • 1133 Accesses

Abstract

This chapter aims to conclude this book by first summarizing all the concepts and methods we covered. We then briefly discuss seven more advanced topics related to demand prediction, such as deep learning methods, transfer learning, and data censoring. For each topic, we provide a number of relevant references for interested readers. We close by discussing several decisions that can be guided by prescriptive analytics tools that rely on demand prediction.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    For a general introduction to deep learning, see Goodfellow et al. (2016). For applying deep learning to demand prediction in retail, see, e.g., Husna et al. (2021).

  2. 2.

    See Pan and Yang (2009).

  3. 3.

    See, e.g., Gaikar and Marakarkandy (2015).

  4. 4.

    Chen et al. (2004).

  5. 5.

    https://machinelearningmastery.com/data-leakage-machine-learning/.

  6. 6.

    See, e.g., Kök and Fisher (2007), Vulcano et al. (2012), and Subramanian and Harsha (2020).

  7. 7.

    For more details, see the seminar work Ben-Akiva and Lerman (2018).

  8. 8.

    See, e.g., Khan (2002), Hu et al. (2019), and Baardman et al. (2017).

  9. 9.

    http://web.vu.lt/mif/a.buteikis/wp-content/uploads/PE_Book/3-7-UnivarPredict.html.

  10. 10.

    https://towardsdatascience.com/endogeneity-the-reason-why-we-should-know-about-data-part-i-80ec33df66ae.

  11. 11.

    See, e.g., Angrist and Pischke (2008), Angrist et al. (2000).

  12. 12.

    See, e.g., Cohen et al. (2017), Cohen et al. (2021), and Ferreira et al. (2016).

References

  • Angrist, J. D., Graddy, K., & Imbens, G. W. 2000. The interpretation of instrumental variables estimators in simultaneous equations models with an application to the demand for fish, The Review of Economic Studies, 67(3), 499–527.

    Article  Google Scholar 

  • Angrist, J. D., Pischke, J. S. 2008. Mostly harmless econometrics: An empiricist’s companion. Princeton University Press.

    Book  Google Scholar 

  • Baardman, L., Levin, I., Perakis, G. and Singhvi, D., 2017. Leveraging comparables for new product sales forecasting. Available at SSRN 3086237.

    Google Scholar 

  • Ben-Akiva, M. and Lerman, S.R., 2018. Discrete choice analysis: theory and application to travel demand. Transportation Studies.

    Google Scholar 

  • Chen, P.Y., Wu, S.Y., Yoon, J., 2004. The impact of online recommendations and consumer feedback on sales. ICIS 2004 Proceedings, p.58.

    Google Scholar 

  • Cohen, M.C., Kalas, J.J. and Perakis, G., 2021. Promotion Optimization for Multiple Items in Supermarkets. Management Science, 67(4): 2340–2364.

    Article  Google Scholar 

  • Cohen, M.C., Leung, N.H.Z., Panchamgam, K., Perakis, G. and Smith, A., 2017. The impact of linear optimization on promotion planning. Operations Research, 65(2): 446–468.

    Article  Google Scholar 

  • Ferreira, K.J., Lee, B.H.A. and Simchi-Levi, D., 2016. Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing & Service Operations Management, 18(1), pp.69–88.

    Google Scholar 

  • Gaikar, D. and Marakarkandy, B., 2015. Product sales prediction based on sentiment analysis using twitter data. International Journal of Computer Science and Information Technologies, 6(3), pp.2303–2313.

    Google Scholar 

  • Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y. 2016. Deep learning (Vol. 1, No. 2). Cambridge: MIT press, Cambridge: MIT press.

    Google Scholar 

  • Hu, K., Acimovic, J., Erize, F., Thomas, D.J. and Van Mieghem, J.A., 2019. Forecasting new product life cycle curves: Practical approach and empirical analysis, Manufacturing & Service Operations Management, 21(1), pp.66–85.

    Google Scholar 

  • Husna, A., Amin, S.H., Shah, B., 2021. Demand Forecasting in Supply Chain Management Using Different Deep Learning Methods. In Demand Forecasting and Order Planning in Supply Chains and Humanitarian Logistics (pp. 140–170). IGI Global.

    Google Scholar 

  • Khan, K.B., 2002. An exploratory investigation of new product forecasting practices. Journal of Product Innovation Management, 19(2), pp.133–143.

    Google Scholar 

  • Kök, A.G. and Fisher, M.L., 2007. Demand estimation and assortment optimization under substitution: Methodology and application. Operations Research, 55(6), pp.1001–1021.

    Google Scholar 

  • Pan, S. J., & Yang, Q. 2009. A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10), 1345–1359.

    Article  Google Scholar 

  • Subramanian, S., Harsha, P., 2020. Demand modeling in the presence of unobserved lost sales, Management Science.

    Google Scholar 

  • Vulcano, G., Van Ryzin, G., Ratliff, R., 2012. Estimating primary demand for substitutable products from sales transaction data. Operations Research, 60(2), pp.313–334

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Desautels Faculty of Management, McGill University, Montreal, QC, Canada

    Maxime C. Cohen

  2. Virtuo Technologies, Paris, France

    Paul-Emile Gras

  3. Boston Consulting Group GAMMA, New York, NY, USA

    Arthur Pentecoste

  4. New York University Shanghai, Shanghai, China

    Renyu Zhang

Authors
  1. Maxime C. Cohen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul-Emile Gras
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arthur Pentecoste
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Renyu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Cohen, M.C., Gras, PE., Pentecoste, A., Zhang, R. (2022). Conclusion and Advanced Topics. In: Demand Prediction in Retail . Springer Series in Supply Chain Management, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-030-85855-1_8

Download citation

Publish with us

Policies and ethics

Search

Navigation