Abstract
Humans play a critical part in supply chain management. A lapse by one human or nonconformance to information system recommendations may create ripple effects across the supply chain. There are also instances where human intuition and ingenuity has led to more profitable outcomes that information systems have failed to deliver. The value of humans in the supply chain should thus never be discounted as it is difficult to imagine a supply chain without human intervention in the foreseeable future. These realities call for more thorough research on behavioral supply chain management (BSCM) with a view to understand human behavior that consequently aids with more effective decision-making that benefits all stakeholders. BSCM research studies how humans can be empowered to better contribute to supply chain practice, both individually and collectively, without compromising their own welfare. This chapter provides a brief introduction into BSCM research and identifies the key research subdomains and popular research methods.
References
Aloysius, J., Deck, C., Hao, L., & French, R. (2016). An experimental investigation of procurement auctions with asymmetric sellers. Production and Operations Management, 25(10), 1763–1777. https://doi.org/10.1111/poms.12576
Al-Ubaydli, O., & List, J. A. (2015). Do natural field experiments afford researchers more or less control than laboratory experiments? The American Economic Review, 105(5), 462–466.
Armstrong, J. S. (2006). Findings from evidence-based forecasting: Methods for reducing forecast error. International Journal of Forecasting, 22(3), 583–598. https://doi.org/10.1016/j.ijforecast.2006.04.006
Aruchunarasa, B., & Perera, H. N. (2022). Mitigating the proclivity towards multiple adjustments through innovative forecasting support systems. In N. Subramanian, S. G. Ponnambalam, & M. Janardhanan (Eds.), Innovation analytics: Tools for competitive advantage. World Scientific. https://doi.org/10.1142/q0293
Arvan, M., Fahimnia, B., Reisi, M., & Siemsen, E. (2019). Integrating human judgement into quantitative forecasting methods: A. Omega, 86, 237–252. https://doi.org/10.1016/j.omega.2018.07.012
Becker-Peth, M., & Thonemann, U. W. (2018). Behavioral inventory decisions: The newsvendor and other inventory settings. In K. Donohue, S. Leider, & E. Katok (Eds.), The handbook of behavioral operations (1st ed.). Wiley.
Becker-Peth, M., Katok, E., & Thonemann, U. W. (2013). Designing buyback contracts for irrational but predictable newsvendors. Management Science, 59(8), 1800–1816.
Becker-Peth, M., Hoberg, K., & Protopappa-Sieke, M. (2020). Multiperiod inventory management with budget cycles: Rational and behavioral decision-making. Production and Operations Management, 23(3), 643–663. https://doi.org/10.1111/poms.13123
Bendoly, E. (2016). Fit, bias, and enacted sensemaking in data visualization: Frameworks for continuous development in operations and supply chain management analytics. Journal of Business Logistics. https://doi.org/10.1111/jbl.12113
Bloomfield, R. J., & Kulp, S. L. (2013). Durability, transit lags, and optimality of inventory management decisions. Production and Operations Management, 22(4), 826–842. https://doi.org/10.1111/poms.12017
Bolger, F., & Wright, G. (2011). Improving the Delphi process: Lessons from social psychological research. Technological Forecasting and Social Change, 78(9), 1500–1513. https://doi.org/10.1016/j.techfore.2011.07.007
Bolton, G. E., & Katok, E. (2008). Learning-by-doing in the newsvendor problem: A laboratory investigation. Manufacturing & Service Operations Management, 10(3), 519–538.
Bolton, G. E., Ockenfels, A., & Thonemann, U. W. (2012). Managers and students as newsvendors. Management Science, 58(12), 2225–2233. https://doi.org/10.1287/mnsc.1120.1550
Boylan, J. E., & Syntetos, A. A. (2010). Spare parts management: A review of forecasting research and extensions. IMA Journal of Management Mathematics, 21(3), 227–237. https://doi.org/10.1093/imaman/dpp016
Brinkhoff, A., Özer, Ö., & Sargut, G. (2015). All you need is trust? An examination of inter-organizational supply chain projects. Production and Operations Management, 24(2), 181–200. https://doi.org/10.1111/poms.12234
Cachon, G. P., & Lariviere, M. A. (2005). Supply chain coordination with revenue-sharing contracts: Strengths and limitations. Management Science, 51(1), 30–44. https://doi.org/10.1287/mnsc.1040.0215
Caniato, F., Kalchschmidt, M., & Ronchi, S. (2011). Integrating quantitative and qualitative forecasting approaches: Organizational learning in an action research case. Journal of the Operational Research Society, 62(3), 413–424. https://doi.org/10.1057/jors.2010.142
Cantor, D. E., Blackhurst, J. V., & Cortes, J. D. (2014). The clock is ticking: The role of uncertainty, regulatory focus, and level of risk on supply chain disruption decision making behavior. Transportation Research Part E: Logistics and Transportation Review, 72, 159–172. https://doi.org/10.1016/j.tre.2014.10.007
Card, D., Della Vigna, S., & Malmendier, U. (2011). The role of theory in field experiments. Journal of Economic Perspectives, 25(3), 39–62.
Castañeda, J. A., Brennan, M., & Goentzel, J. (2019). A behavioral investigation of supply chain contracts for a newsvendor problem in a developing economy. International Journal of Production Economics, 210, 72–83. https://doi.org/10.1016/j.ijpe.2018.12.024
Chen, K.-Y., & Wu, D. Y. (2019). Buyer–supplier interactions. In K. Donohue, E. Katok, & S. Leider (Eds.), The handbook of behavioral operations (pp. 459–488). Wiley.
Chen, D. L., Schonger, M., & Wickens, C. (2016). oTree-An open-source platform for laboratory, online, and field experiments. Journal of Behavioral and Experimental Finance, 9, 88–97. https://doi.org/10.1016/j.jbef.2015.12.001
Choi, E. W., Özer, Ö., & Zheng, Y. (2020). Network trust and trust behaviors among executives in supply chain interactions. Management Science, 66(12), 5823–5849. https://doi.org/10.1287/mnsc.2019.3499
Croson, R., Schultz, K. L., Siemsen, E., & Yeo, M. L. (2013). Behavioral operations: The state of the field. Journal of Operations Management, 31(1–2), 1–5. https://doi.org/10.1016/j.jom.2012.12.001
Croson, R., Donohue, K., Katok, E., & Sterman, J. D. (2014). Order stability in supply chains: Coordination risk and the role of coordination stock. Production and Operations Management, 23(2), 176–196. https://doi.org/10.1111/j.1937-5956.2012.01422.x
Cui, Y., Chen, L. G., Chen, J., Gavirneni, S., & Wang, Q. (2013). Chinese perspective on newsvendor bias: An exploratory note. Journal of Operations Management, 31(1–2), 93–97. https://doi.org/10.1016/j.jom.2012.10.001
Davis, A. M., & Hyndman, K. (2019). Multidimensional bargaining and inventory risk in supply chains: An experimental study. Management Science, 65(3), 1286–1304. https://doi.org/10.1287/mnsc.2017.2985
Davis, A. M., & Leider, S. (2018). Contracts and capacity investment in supply chains. Manufacturing & Service Operations Management, 20(3), 403–421. https://doi.org/10.1287/msom.2017.0654
Donohue, K., Katok, E., & Leider, S. (Eds.). (2019). The handbook of behavioral operations. Wiley. https://doi.org/10.1002/9781119138341
Donohue, K., Özer, Ö., & Zheng, Y. (2020). Behavioral operations: Past, present, and future. Manufacturing & Service Operations Management, 22(1), 191–202. https://doi.org/10.1287/msom.2019.0828
Eckerd, S., Hill, J., Boyer, K. K., Donohue, K., & Ward, P. T. (2013). The relative impact of attribute, severity, and timing of psychological contract breach on behavioral and attitudinal outcomes. Journal of Operations Management, 31(7–8), 567–578. https://doi.org/10.1016/j.jom.2013.06.003
Elmaghraby, W., & Katok, E. (2019). Behavioral research in competitive bidding and auction design. In K. Donohue, E. Katok, & S. Leider (Eds.), The handbook of behavioral operations (pp. 525–556). Wiley.
Engelbrecht-Wiggans, R., & Katok, E. (2008). Regret and feedback information in first-price sealed-bid auctions. Management Science, 54(4), 808–819. https://doi.org/10.1287/mnsc.1070.0806
Fahimnia, B., Pournader, M., Siemsen, E., Bendoly, E., & Wang, C. (2019). Behavioral operations and supply chain management – A review and literature. Decision Sciences, 50(6), 1127–1183. https://doi.org/10.1111/deci.12369
Fahimnia, B., Arvan, M., Tan, T., & Siemsen, E. (2022). A hidden anchor: The influence of service levels on demand forecasts. Journal of Operations Management, 69, 856. https://doi.org/10.1002/joom.1229
Feng, T., & Zhang, Y. (2017). Modeling strategic behavior in the competitive newsvendor problem: An experimental investigation. Production and Operations Management, 26(7), 1383–1398. https://doi.org/10.1111/poms.12683
Fildes, R., Nikolopoulos, K., Crone, S. F., & Syntetos, A. A. (2008). Forecasting and operational research: A review. Journal of the Operational Research Society, 59(9), 1150–1172. https://doi.org/10.1057/palgrave.jors.2602597
Fildes, R., Goodwin, P., & Önkal, D. (2018). Use and misuse of information in supply chain forecasting of promotion effects. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2017.12.006
Forrester, J. W. (1958). Industrial dynamics: A major breakthrough for decision makers. Harvard Business Review, 36(4), 37–66.
Franses, P. H., & Legerstee, R. (2013). Do statistical forecasting models for SKU-level data benefit from including past expert knowledge? International Journal of Forecasting, 29, 80–87.
Fugger, N., Katok, E., & Wambach, A. (2016). Collusion in dynamic buyer-determined reverse auctions. Management Science, 62(2), 518–533. https://doi.org/10.1287/mnsc.2014.2142
Gavirneni, S., & Isen, A. M. (2010). Anatomy of a newsvendor decision: Observations from a verbal protocol analysis. Production and Operations Management, 19(4), 453–462. https://doi.org/10.1111/j.1937-5956.2009.01110.x
Gino, F., & Pisano, G. (2008). Toward a theory of behavioral operations. Manufacturing & Service Operations Management, 10(4), 676–691. https://doi.org/10.1287/msom.1070.0205
Gurnani, H., Ramachandran, K., Ray, S., & Xia, Y. (2014). Ordering behavior under supply risk: An experimental investigation. Manufacturing & Service Operations Management, 16(1), 61–75.
Haruvy, E., & Katok, E. (2013). Increasing revenue by decreasing information in procurement auctions. Production and Operations Management, 22(1), 19–35. https://doi.org/10.1111/j.1937-5956.2012.01356.x
Haruvy, E., Katok, E., & Pavlov, V. (2020). Bargaining process and channel efficiency. Management Science, 66(7), 2845–2860. https://doi.org/10.1287/mnsc.2019.3360
Harvey, N., & Reimers, S. (2013). Trend damping: Under-adjustment, experimental artifact, or adaptation to features of the natural environment? Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(2), 589–607. https://doi.org/10.1037/a0029179
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2010), 61–135. https://doi.org/10.1017/S0140525X0999152X
Hewage, H. C., Perera, H. N., & De Baets, S. (2022). Forecast adjustments during post-promotional periods. European Journal of Operational Research, 300(2), 461–472. https://doi.org/10.1016/j.ejor.2021.07.057
Ho, T.-H., & Zhang, J. (2008). Designing pricing contracts for boundedly rational customers: Does the framing of the fixed fee matter? Management Science, 54(4), 686–700. https://doi.org/10.1287/mnsc.1070.0788
Hora, M., & Klassen, R. D. (2013). Learning from others’ misfortune: Factors influencing knowledge acquisition to reduce operational risk. Journal of Operations Management, 31(1–2), 52–61. https://doi.org/10.1016/j.jom.2012.06.004
Ibanez, M. R., & Staats, B. R. (2019). Behavioral empirics and field experiments. In K. Donohue, S. Leider, & E. Katok (Eds.), The handbook of behavioral operations (1st ed., pp. 121–148). Wiley.
Kalkanci, B., Chen, K. Y., & Erhun, F. (2014). Complexity as a contract design factor: A human-to-human experimental study. Production and Operations Management, 23(2), 269–284. https://doi.org/10.1111/poms.12067
Katok, E. (2019). Designing and conducting laboratory experiments. In K. Donohue, S. Leider, & E. Katok (Eds.), The handbook of behavioral operations (1st ed., pp. 3–34). Wiley.
Katok, E., & Kwasnica, A. M. (2008). Time is money: The effect of clock speed on seller’s revenue in Dutch auctions. Experimental Economics, 11(4), 344–357. https://doi.org/10.1007/s10683-007-9169-x
Kremer, M., & Van Wassenhove, L. N. (2014). Willingness to pay for shifting inventory risk: The role of contractual form. Production and Operations Management, 23(2), 239–252. https://doi.org/10.1111/poms.12179
Lau, N., Hasija, S., & Bearden, J. N. (2014). Newsvendor pull-to-center reconsidered. Decision Support Systems, 58(1), 68–73.
Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4), 546–558. https://doi.org/10.1287/mnsc.43.4.546
Lim, N., & Ho, T. H. (2007). Designing price contracts for boundedly rational customers: Does the number of blocks matter? Marketing Science, 26(3), 312–326. https://doi.org/10.1287/mksc.1070.0271
List, J. A., Sadoff, S., & Wagner, M. (2011). So you want to run an experiment, now what? Some simple rules of thumb for optimal experimental design. Experimental Economics, 14(4), 439–457. https://doi.org/10.1007/s10683-011-9275-7
Loch, C. H., & Wu, Y. (2008). Social preferences and supply chain performance: An experimental study. Management Science, 54(11), 1835–1849.
Nikolopoulos, K. (2021). We need to talk about intermittent demand forecasting. European Journal of Operational Research, 291(2), 549–559. https://doi.org/10.1016/j.ejor.2019.12.046
Önkal, D., Gönül, M. S., & Lawrence, M. (2008). Judgmental adjustments of previously adjusted forecasts. Decision Sciences, 39(2), 213–238. https://doi.org/10.1111/j.1540-5915.2008.00190.x
Önkal, D., Zeynep Sayim, K., & Lawrence, M. (2012). Wisdom of group forecasts: Does role-playing play a role? Omega, 40(6), 693–702. https://doi.org/10.1016/j.omega.2011.01.010
Özer, Ö., & Zheng, Y. (2016). Establishing trust and trustworthiness for supply chain information sharing. In A. Ha & C. Tang (Eds.), The handbook of information exchange in supply chain management. Springer.
Özer, Ö., & Zheng, Y. (2019). Trust and trustworthiness. In K. Donohue, E. Katok, & S. Leider (Eds.), The handbook of behavioral operations (pp. 489–523). Wiley.
Özer, Ö., Zheng, Y., & Ren, Y. (2014). Trust, trustworthiness, and information sharing in supply chains bridging China and the United States. Management Science, 60(10), 2435–2460. https://doi.org/10.1287/mnsc.2014.1905
Perera, H. N., Hurley, J., Fahimnia, B., & Reisi, M. (2019). The human factor in supply chain forecasting: A systematic review. European Journal of Operational Research, 274(2), 574–600. https://doi.org/10.1016/j.ejor.2018.10.028
Perera, H. N., Fahimnia, B., & Tokar, T. (2020). Inventory and ordering decisions: A systematic review on research driven through behavioral experiments. International Journal of Operations & Production Management, 40(7/8), 997–1039. https://doi.org/10.1108/IJOPM-05-2019-0339
Petropoulos, F., & Kourentzes, N. (2015). Forecast combinations for intermittent demand. Journal of the Operational Research Society, 66(6), 914–924. https://doi.org/10.1057/jors.2014.62
Petropoulos, F., & Siemsen, E. (2022). Forecast selection and representativeness. Management Science. https://doi.org/10.1287/mnsc.2022.4485
Rekik, Y., Glock, C. H., & Syntetos, A. A. (2017). Enriching demand forecasts with managerial information to improve inventory replenishment decisions: Exploiting judgment and fostering learning. European Journal of Operational Research, 261, 182–194. https://doi.org/10.1016/j.ejor.2017.02.001
Remus, W., O’Connor, M., & Griggs, K. (1996). Does feedback improve the accuracy of recurrent judgmental forecasts? Organizational Behavior and Human Decision Processes, 66(1), 22–30. https://doi.org/10.1006/obhd.1996.0035
Rungtusanatham, M., Wallin, C., & Eckerd, S. (2011). The vignette in a scenario based role playing experiment. Journal of Supply Chain Management, 47(3), 9–16.
Sanders, N. R., & Graman, G. A. (2016). Impact of bias magnification on supply chain costs: The mitigating role of forecast sharing. Decision Sciences, 47(5), 881–906.
Schweitzer, M. E., & Cachon, G. P. (2000). Decision bias in the newsvendor problem with a known demand distribution: Experimental evidence. Management Science, 46(3), 404–420.
Siemsen, E., & Aloysius, J. (2019). Supply chains analytics and the evolving work of supply chain managers. Chicago. https://doi.org/10.13140/RG.2.2.15396.30081
Siemsen, E., Moritz, B., & Goodwin, P. (2019). Forecast decisions. In K. Donohue, E. Katok, & S. Leider (Eds.), The handbook of behavioral operations (1st ed., pp. 433–458). Wiley.
Sniezek, J. A. (1990). A comparison of techniques for judgmental forecasting by groups with common information. Group & Organization Management, 15(1), 5–19. https://doi.org/10.1177/105960119001500102
Spiliotopoulou, E., & Conte, A. (2021). Fairness ideals in inventory allocation. Decision Sciences, 53, 985–1002. https://doi.org/10.1111/deci.12540
Sroginis, A., Fildes, R., & Kourentzes, N. (2022). Use of contextual and model-based information in adjusting promotional forecasts. European Journal of Operational Research, 307, 1177. https://doi.org/10.1016/j.ejor.2022.10.005
Stangl, T., & Thonemann, U. W. (2017). Equivalent inventory metrics: A behavioral perspective. Manufacturing & Service Operations Management, 19(3), 472–488. https://doi.org/10.1287/msom.2017.0620
Sterman, J. D. (1989). Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management Science, 35(3), 321–339. https://doi.org/10.1287/mnsc.35.3.321
Sterman, J., & Dogan, G. (2015). I’m not hoarding, I’m just stocking up before the hoarders get here. Journal of Operations Management, 39, 6–22.
Strohhecker, J., & Größler, A. (2013). Do personal traits influence inventory management performance? – The case of intelligence, personality, interest and knowledge. International Journal of Production Economics, 142(1), 37–50. https://doi.org/10.1016/j.ijpe.2012.08.005
Syntetos, A. A., Kholidasari, I., & Naim, M. M. (2016). The effects of integrating management judgement into OUT levels: In or out of context? European Journal of Operational Research, 249(3), 1–11. https://doi.org/10.1016/j.ejor.2015.07.021
Tokar, T., Aloysius, J., Williams, B., & Waller, M. (2014). Bracing for demand shocks: An experimental investigation. Journal of Operations Management, 32(4), 205–216. https://doi.org/10.1016/j.jom.2013.08.001
Tokar, T., Aloysius, J. A., Waller, M. A., & Hawkins, D. L. (2016). Exploring framing effects in inventory control decisions: Violations of procedure invariance. Production and Operations Management, 25(2), 306–329.
Trapero, J. R., Pedregal, D. J., Fildes, R., & Kourentzes, N. (2013). Analysis of judgmental adjustments in the presence of promotions. International Journal of Forecasting, 29(2), 234–243. https://doi.org/10.1016/j.ijforecast.2012.10.002
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124
Vickrey, W. (1961). Counterspeculation, auctions, and competitive sealed tenders. The Journal of Finance, 16(1), 8. https://doi.org/10.2307/2977633
von Stackelberg, H. (2010). Market structure and equilibrium (1st ed.). Springer Berlin. https://doi.org/10.1007/978-3-642-12586-7
Wright, G., & Rowe, G. (2011). Group-based judgmental forecasting: An integration of extant knowledge and the development of priorities for a new research agenda. International Journal of Forecasting, 27, 1–13.
Wu, D. Y., & Chen, K.-Y. (2014). Supply chain contract design: Impact of bounded rationality and individual heterogeneity. Production and Operations Management, 23(2), 253–268.
Wu, D. Y., & Katok, E. (2006). Learning, communication, and the bullwhip effect. Journal of Operations Management, 24(6), 839–850. https://doi.org/10.1016/j.jom.2005.08.006
Zhang, Y., & Siemsen, E. (2019). A meta-analysis of newsvendor experiments: Revisiting the pull-to-center asymmetry. Production and Operations Management, 28(1), 140–156. https://doi.org/10.1111/poms.12899
Zhao, Y., Zhao, X., Wang, L., & Chen, Y. (2016). Does elicitation method matter? Behavioral and neuroimaging evidence from capacity allocation game. Production and Operations Management, 25(5), 919–934.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive licence to Springer Nature Switzerland AG
About this entry
Cite this entry
Perera, H.N., Fahimnia, B. (2024). Behavioral Supply Chain Management. In: Sarkis, J. (eds) The Palgrave Handbook of Supply Chain Management. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-89822-9_115-1
Download citation
DOI: https://doi.org/10.1007/978-3-030-89822-9_115-1
Received:
Accepted:
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-030-89822-9
Online ISBN: 978-3-030-89822-9
eBook Packages: Springer Reference Business and ManagementReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences