A Data-Driven Educational Journey for Supply Chain Network Analytics

Published Online:https://doi.org/10.1287/ited.2024.0094

References

  • Aksin Z, DeHoratius N (2010) Introduction to the special issue, Part 2: Teaching service and retail operations management. INFORMS Trans. Ed. 11(1):1–2.Google Scholar
  • Awwad M, Kulkarni P, Bapna R, Marathe A (2018) Big data analytics in supply chain: A literature review. Proc. Internat. Conf. Indust. Engrg. Oper. Management (IEOM Society International, Washington, DC), 418–425.Google Scholar
  • Balza-Franco V, Paternina-Arboleda CD, Cantillo V, Macea LF, Ramírez-Ríos DG (2017) A collaborative supply chain model for non-for-profit networks based on cooperative game theory. Internat. J. Logist. Systems Management 26(4):475–496.CrossrefGoogle Scholar
  • Basole RC, Bellamy MA, Park H (2017) Visualization of innovation in global supply chain networks. Decision Sci. 48(2):288–306.CrossrefGoogle Scholar
  • Biswas S, Sen J (2017) A proposed architecture for big data driven supply chain analytics. Preprint, submitted May 14, https://arxiv.org/abs/1705.04958.Google Scholar
  • Camm JD, Cochran JJ, Fry MJ, Ohlmann JW (2022) Data Visualization: Exploring and Explaining with Data (Cengage Learning, Mason OH).Google Scholar
  • Cure-Vellojin LN, Ramirez-Rios DG, Herrera-Hernandez MC, Paternina-Arboleda CD, Miller WA (2011) A Fictitious Play algorithm applied to a retailer’s replenishment decision problem in a two-echelon supply chain. Internat. J. Logist. Systems Management 8(3):247–266.CrossrefGoogle Scholar
  • Dalal J (2022) An exercise for teaching transportation problem using spatial data. INFORMS Trans. Ed. 23(1):12–26.LinkGoogle Scholar
  • Dimitrov NB, Brown G, Carlyle M (2013) A real-world network modeling project. INFORMS Trans. Ed. 14(1):4–12.LinkGoogle Scholar
  • Dubey R, Gunasekaran A (2015) Education and training for successful career in Big Data and Business Analytics. Indust. Commercial Training 47(4):174–181.CrossrefGoogle Scholar
  • Fosso Wamba S, Akter S (2019) Understanding supply chain analytics capabilities and agility for data-rich environments. Internat. J. Oper. Production Management 39(6/7/8):887–912.CrossrefGoogle Scholar
  • Gomez-Jacome N, Garcia-Llinas G, Paternina-Arboleda CD, Jaller-Martelo M (2019) Caribbean ports, inland logistics, and the Panama Canal expansion: A mode and port choice analysis. Paternina-Arboleda C, Voβ S, eds. Computational Logistics. ICCL 2019. Lecture Notes in Computer Science, vol. 11756 (Springer, Cham), 154–170.CrossrefGoogle Scholar
  • Gopal PRC, Rana NP, Krishna TV, Ramkumar M (2024) Impact of big data analytics on supply chain performance: An analysis of influencing factors. Ann Oper Res. 333(2):769–797.CrossrefGoogle Scholar
  • Jubiz-Diaz M, Saltarin-Molino M, Arellana J, Paternina-Arboleda C, Yie-Pinedo R (2021) Effect of infrastructure investment and freight accessibility on gross domestic product: A data-driven geographical approach. J. Adv. Transportation 2021:1–22. CrossrefGoogle Scholar
  • Kulkarni SS, Mai B, Amirkiaee SY, Tarakci H (2019) Dynamic interactive visualizations: Implications of seeing, doing, and playing for quantitative analysis pedagogy. INFORMS Trans. Ed. 19(3):121–142.LinkGoogle Scholar
  • Li L (2020) Education supply chain in the era of Industry 4.0. Systems Res. Behav. Sci. 37(4):579–592.CrossrefGoogle Scholar
  • Mason AJ (2011) OpenSolver—An open-source add-in to solve linear and integer progammes in excel. Oper. Res. Proc. 2011: Selected Papers Internat. Conf. Oper. Res. (Springer Berlin Heidelberg, Berlin, Heidelberg), 401–406.Google Scholar
  • Microsoft (2023) What can copilot’s earliest users teach us about generative AI at work? Work Trend Index Special Report, November 15, 2023. https://www.microsoft.com/en-us/worklab/work-trend-index/copilots-earliest-users-teach-us-about-generative-ai-at-work.Google Scholar
  • Moros-Daza A, Cassandro-De La Hoz D, Jaller-Martelo M, Paternina-Arboleda CD (2019) Using advanced information systems to improve freight efficiency: Results from a Pilot Program in Colombia. Paternina-Arboleda C, Voβ S, eds. Computational Logistics. ICCL 2019. Lecture Notes in Computer Science, vol. 11756 (Springer, Cham), 22–38.CrossrefGoogle Scholar
  • Nguyen T, Zhou L, Spiegler V, Ieromonachou P, Lin Y (2018) Big data analytics in supply chain management: A state-of-the-art literature review. Computers Oper. Res. 98:254–264.CrossrefGoogle Scholar
  • Paternina-Arboleda CD, Das TK (2005) A multi-agent reinforcement learning approach to obtaining dynamic control policies for stochastic lot scheduling problem. Simulation Model. Practice Theory 13(5):389–406. CrossrefGoogle Scholar
  • Ramanathan R, Philpott E, Duan Y, Cao G (2017) Adoption of business analytics and impact on performance: A qualitative study in retail. Production Planning Control 28(11–12):985–998.Google Scholar
  • Ramírez DG, Daza-Escorcia JM, Martinez JV, Paternina-Arboleda CD, Garcia A (2012) The dynamic demand game: A Markov state fictitious play approach to a two-echelon supply chain problem under demand uncertainty. Internat. J. Indust. Systems Eng. 10(3):319–335.Google Scholar
  • Rossman A, Cochran JJ (2018) Interview with James J. Cochran. J. Statist. Ed. 26(2):149–159.CrossrefGoogle Scholar
  • Seyedan M, Mafakheri F (2020) Predictive big data analytics for supply chain demand forecasting: Methods, applications, and research opportunities. J Big Data 7(1):53.CrossrefGoogle Scholar
  • Sheffi Y (2015) The Power of Resilience: How the Best Companies Manage the Unexpected (MITPress, Cambridge, MA).Google Scholar
  • Solano-Charris EL, Paternina-Arboleda CD (2013) Simulation model of the supply chain on a naval shipyard. Internat. J. Indust. Systems Engrg. 13(3):280–297.Google Scholar
  • Sun L, Song G (2018) Current state and future potential of logistics and supply chain education: A literature review. J. Internat. Ed. Bus. 11(2):124–143.CrossrefGoogle Scholar
  • Tatham P, Wu Y, Kovács G, Butcher T (2017) Supply chain management skills to sense and seize opportunities. Internat. J. Logist. Management 28(2):266–289.CrossrefGoogle Scholar
  • Tsai CW, Lai CF, Chao HC, Vasilakos AV (2015) Big data analytics: A survey. J. Big Data 2:21CrossrefGoogle Scholar
  • Van Woensel T, Fisher ML, Fransoo JC (2010) Teaching retail operations in business and engineering schools. INFORMS Trans. Ed. 11(1):29–34.LinkGoogle Scholar
  • Waller MA, Fawcett SE (2013) Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. J. Bus. Logist. 34(2):77–84.CrossrefGoogle Scholar
  • Wang SC, Tsai YT, Ciou YS (2020) A hybrid big data analytical approach for analyzing customer patterns through an integrated supply chain network. J. Ind. Inf. Integr. 20:100177.Google Scholar
  • Wang G, Gunasekaran A, Ngai EW, Papadopoulos T (2016) Big data analytics in logistics and supply chain management: Certain investigations for research and applications. Int. J. Production Econom. 176:98–110.CrossrefGoogle Scholar
  • Woldt J, Prasad S, Ozgur C (2020) Big data and supply chain analytics: Implications for teaching. Oper. Management Ed. Rev. 14:155–176.Google Scholar
  • Woodruff DL, Voβ S (2006) Introduction to the special issue: Integrating OR in teaching supply chain management. INFORMS Trans. Ed. 6(3):1–2.LinkGoogle Scholar
  • Wu Y (2022) Case Article: Integrating network design models for a global supply network. INFORMS Trans. Ed. 22(3):172–175.LinkGoogle Scholar
  • Zheng J, Alzaman C, Diabat A (2023) Big data analytics in flexible supply chain networks. Computers Indust. Engrg. 178:109098.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.