How Research in Production and Operations Management May Evolve in the Era of Big Data

Production and Operations Management

Volumn 27, Issue 9, 2018, Pages 1670-1684

  Feng, Qi ^a   Shanthikumar, J George ^a  

^a PURDUE UNIVERSITY   (United States)

Author keywords

Big Data; demand learning and planning; individualization; manufacturing; mass customization

Indexed keywords

EID: 85053211293     PISSN: 10591478     EISSN: 19375956     Source Type: Journal    
DOI: 10.1111/poms.12836     Document Type: Article

References (63)

1. Amato-Mccoy, D. M. 2017. Study: Online shoppers want a personalized experience. Available at https://www.chainstoreage.com/real-estate/german-discount-grocery-chain-heads-east/ (accessed on January 12, 2018).
2. Balci, G., I. B. Cetin. 2010. Market segmentation in container shipping services: A qualitative study. Manage. Res. Rev. 40(10): 1100–1116. https://doi.org/10.1108/MRR-01-2017-0012.
3. Ban, G.-Y., C. Rudin. 2017. The big data newsvendor: Practial insights from machine learning. Working paper.
4. Bauer, H. H., S. J. Barnes, T. Reichardt, M. M. Neumann. 2005. Driving consumer acceptance of mobile marketing: A theoretical framework and empirical study. J. Electron. Commer. Res. 6(3): 181–191.
5. Bloch, P. H. 1995. Seeking the ideal form: Product design and consumer response. J. Market. 59(3): 16–29.
6. Bounckena, R. B., S. Kraus. 2013. Innovation in knowledge-intensive industries: The double-edged sword of coopetition. J. Bus. Res. 66(10): 2060–2070.
7. Brettel, M., N. Friederichsen, M. Keller, M. Rosenberg. 2014. How virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective. Int. J. Inf. Commun. Eng. 8(1): 37–44.
8. Buzacott, J. A., J. G. Shanthikumar. 1992. Stochastic Models of Manufacturing Systems. Prentice Hall, Englewood Cliffs, NJ.
9. Çakanyildirim, M., R. O. Roundy. 2002. SeDFAM: Semiconductor demand forecast accuracy model. IIE Trans. 34(5): 449–465.
10. Carbonneau, R., K. Laframboise, R. Vahidov. 2008. Application of machine learning techniques for supply chain demand forecasting. Eur. J. Oper. Res. 184: 1140–1154.
11. Chandrashekar, G., F. Sahin. 2014. A survey on feature selection methods. Comput. Electr. Eng. 40: 16–28.
12. Chase, C. W. 2013. Using big data to enhance demand-driven forecasting and planning. J. Bus. Forecast. 32 (Summer): 27–32.
13. Choi, H., H. Varian. 2012. Predicting the present with google trends. Econ. Record 88(June): 2–9.
14. Cinlar, E., M. Shaked, J. G. Shanthikumar. 1989. On lifetimes influenced by a common environment. Stoch. Process. Appl. 33: 347–359.
15. Clemen, R. T. 1989. Combining forecasts: A review and annotated bibliography. Int. J. Forecast. 5(4): 559–583.
16. Cui, R., S. Gallino, A. Moreno, D. J. Zhang. 2017. The operational value of social media information. Prod. Oper. Manag. https://doi.org/10.1111/poms.12707.
17. Davidow, W., M. Malone. 1992. The Virtual Corporation. Harper Collins, New York.
18. Dilda, V., L. Mori, O. Noterdaeme, C. Schmitz. 2017. Manufacturing: Analytics unleashes productivity and profitability. Available at https://www.mckinsey.com/business-functions/operations/our-insights/manufacturing-analytics-unleashes-productivity-and-profitability (accessed on January 12, 2018).
19. Dong, L., D. Shi, F. Zhang. 2017. 3D printing vs. traditional flexible technology: Implications for operations strategy. Working paper.
20. Feng, Q., J. G. Shanthikumar. 2017. Data integrated operations management. Working paper.
21. Feng, Q., J. G. Shanthikumar. 2018a. Posted pricing versus bargaining in sequential selling process. Oper. Res. 66(1): 92–103. https://doi.org/10.1287/opre.2017.1651.
22. Feng, Q., J. G. Shanthikumar. 2018b. Supply and demand functions in inventory models. Oper. Res. 66(1): 77–91. https://doi.org/10.1287/opre.2017.1648.
23. Feng, Q., S. Luo, D. Zhang. 2013. Integrating dynamic pricing and replenishment decisions under supply capacity uncertainty. Manuf. Serv. Oper. Manag. 16(1): 149–160.
24. Ferreira, K. J., B. H. A. Lee, D. Simchi-Levi. 2016. Analytics for an online retailer: Demand forecasting and price optimization. Manuf. Serv. Oper. Manag. 18(1): 69–88.
25. Friedman, D. 1991. Evolutionary games in economics. Econometrica 59(3): 637–666.
26. Gardner, E. S. 1985. Exponential smoothing: The state of the art. J. Forecast. 4(1): 1–28.
27. Gardner, E. S. 1990. Evaluating forecast performance in an inventory control system. Management Sci. 36(4): 490–499.
28. Goel, S., J. M. Hofman1, S. Lahaie, D. M. Pennock, D. J. Watts. 2010. Predicting consumer behavior with web search. Proc. Natl Acad. Sci. 107(41): 17486–17490.
29. Guyon, I., S. Gunn, M. Nikravesh, L. A. Zadeh. 2006. Feature Extraction: Foundations and Applications. Springer-Verlag, Berlin, Heidelberg.
30. Hoch, S. J., B.-D. Kim, A. L. Montgomery, P. E. Rossi. 1995. Determinants of store-level price elasticity. J. Mark. Res. 32(1): 17–29.
31. Huang, T., J. A. Van Mieghem. 2014. Clickstream data and inventory management: Model and empirical analysis. Prod. Oper. Manag. 23(3) 333–347.
32. Jain, A. K., R. C. Dubes. 1988. Algorithms for Clustering Data. Prentice-Hall, Upper Saddle River, NJ.
33. Kagermann, H., W. Wahlster, J. Helbig. 2013. Securing the future of German manufacturing industry: Recommendations for implementing the strategic initiative industrie 4.0. (April).
34. Khaitan, S. K., J. D. McCalley. 2015. Design techniques and applications of cyberphysical systems: A survey. IEEE Syst. J. 9(2): 350–365.
35. Lee, J., B. Bagheri, H.-A. Kao. 2014. Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia CIRP 16: 3–8.
36. Liu, Y., Z. J. Zhang. 2006. Research note—The benefits of personalized pricing in a channel. Market. Sci. 25(1): 97–105.
37. Lu, M., S. Shen. 2015. Not-for-profit surgery block allocation with cross-subsidization. Working paper.
38. Makridakis, S., R. L. Winkler. 1983. Averages of forecasts: Some empirical results. Management Sci. 29(9): 987–996.
39. Miao, J., L. Niu. 2016. A survey on feature selection. Procedia Comput. Sci. 96: 919–926.
40. Monostori, L. 2014. Cyber-physical production systems: Roots, expectations and r&d challenges. Procedia CIRP 17: 9–13.
41. Mukherjee, U. K., K. K. Sinha. 2017. Product recall decisions in medical device supply chains: A big data analytic approach to evaluating judgment bias. Prod. Oper. Manag. https://doi.org/10.1111/poms.12696.
42. Nurani, R. K., R. Akella, J. G. Shanthikumar. 1996. Optimal monitoring and control of deteriorating processes under two repair options. Working paper.
43. Pang, B., L. Lee. 2008. Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2: 1–135.
44. Parker, P. M. 1994. Aggregate diffusion forecasting models in marketing: A critical review. Int. J. Forecast. 10: 353–380.
45. Petropoulosa, F., S. Makridakisb, V. Assimakopoulosc, K. Nikolopoulosd. 2014. ‘Horses for Courses’ in demand forecasting. Eur. J. Oper. Res. 237(1): 152–163.
46. Pilkington, M. 2016. Blockchain technology: Principles and applications. F. X. Olleros, M. Zhegu, eds. Research Handbook on Digital Transformations. Edward Elgar, Northampton, 225–253.
47. Ross, S. M. 1983. Introduction to Stochastic Dynamic Programming. Academic Press, New York, NY.
48. Shankar, V., M. Kleijnen, S. Ramanathan, R. Rizley, S. Hollande, S. Morrissey. 2016. Mobile shopper marketing: Key issues, current insights, and future research avenues. J. Interactive Market. 34: 37–48.
49. Simchi-Levi, D. 2014. OM forumOM research: From problem-driven to data-driven research. Manuf. Serv. Oper. Manag. 16(1): 2–10.
50. Sloan, T. W., J. G. Shanthikumar. 2000. Combined production and maintenance scheduling for multiple-product single machine production system. Prod. Oper. Manag. 9: 379–399.
51. Song, J.-S., Y. Zhang. 2016. Stock or print? Impact of 3D printing on spare parts logistics. Working paper.
52. Srinivasan, R., M. Swink. 2017. An investigation of visibility and flexibility as complements to supply chain analytics: An organizational information processing theory perspective. Prod. Oper. Manag. (Forthcoming) https://doi.org/10.1111/poms.12746.
53. Srinivasan, D., C. S. Chang, A. C. Liew. 1995. Demand forecasting using fuzzy neural computation, with special emphasis on weekend and public holiday forecasting. IEEE Trans. Power Syst. 10(4): 1897–1903.
54. Talluri, K. T., G. J. Van Ryzin. 2004. The Theory and Practice of Revenue Management. Springer, Berlin.
55. Tian, K. T., W. O. Bearden, G. L. Hunter. 2001. Smart manufacturing: Past research, present findings, and future directions. J. Consum. Res. 28(1): 50–66.
56. Tomlin, B. 2017. Industry 4.0. Presentation at MOSM Supply Chain Management Special Interest Group Conference.
57. Vandermerwe, S., J. Rada. 1989. Servitization of business: adding value by adding services. Eur. Manage. J. 6(4): 314–324.
58. Warnd, M. P., M. C. Jones. 1995. Kernel Smoothing. Champman & Hall/CRC, Baca Raton, FL.
59. West, M., J. Harrison. 1997. Bayesian Forecasting and Dynamic Models. Springer-Verlag, New York.
60. Wolf, C. 2017. Car dealers compete in an online bidding war. Available at https://www.autocheatsheet.com/new-car/car-dealers-compete-online.html (accessed on January 12, 2018).
61. Wu, D., D. W. Rosen, L. Wang, D. Schaefer. 2015. Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation. Comput. Aided Des. 59: 1–14.
62. Yang, Y., B. Pan, H. Song. 2014. Predicting hotel demand using destination marketing organization's web traffic data. J. Travel Res. 53(4): 433–447.
63. Zhang, N., K. Kannan, J. G. Shanthikumar. 2017. A recommender system to nudge customers in a capacity constrained supply chain. Working paper.