Business Analytics for Delivery-Promise Optimization in Carbon-Constrained Online Retail Supply Chains
DOI:
https://doi.org/10.63646/IVDM9175Keywords:
Delivery-promise optimization; business analytics; carbon-constrained supply chain; strategic consumers; dynamic pricing; cost-sharing contract; stackelberg equilibriumAbstract
Online retailers increasingly compete on the speed and reliability of their delivery promises while simultaneously facing pressure to decarbonize their operations. This dual demand creates a complex decision problem because consumer purchasing behavior is jointly shaped by retail price, promised delivery time, environmental impact, and the option to delay purchases in anticipation of price reductions. This study develops a unified business-analytics framework for delivery-promise optimization in a two-echelon carbon-constrained online retail supply chain composed of a manufacturer, an online retailer, and strategic consumers. Five progressive analytical scenarios are constructed: a benchmark with myopic consumers, a fixed delivery-time policy with strategic consumers, a dynamic delivery-time policy under low patience, the same policy under high patience, and a cost-sharing contract that aligns supply-chain incentives. Closed-form Stackelberg equilibria are derived under each scenario, validated through second-order conditions and numerical grid search, and translated into actionable analytics dashboards. Numerical experiments calibrated to typical e-commerce parameters show that a dynamic delivery-time policy lifts manufacturer profit by 8.1% and total system profit by 7.7% relative to the fixed-time benchmark, while simultaneously raising the carbon-reduction effort by 8.8%. The proposed cost-sharing contract enlarges the parameter region in which both supply-chain members benefit by 47%. The findings provide quantitative guidance for analytics-enabled delivery-promise management in sustainable e-commerce.
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References
Addo-Tenkorang, R., & Helo, P. T. (2016). Big data applications in operations/supply-chain management: A literature review. Computers & Industrial Engineering, 101, 528–543. https://doi.org/10.1016/j.cie.2016.09.023
Agatz, N. A. H., Campbell, A. M., Fleischmann, M., & Savelsbergh, M. W. P. (2011). Time slot management in attended home delivery. Transportation Science, 45(3), 435–449. https://doi.org/10.1287/trsc.1100.0346
Agatz, N. A. H., Fan, Y., & Stam, D. (2021). The impact of green labels on time slot choice and operational sustainability. Production and Operations Management, 30(7), 2282–2298. https://doi.org/10.1111/poms.13368
Ahi, P., & Searcy, C. (2013). A comparative literature analysis of definitions for green and sustainable supply chain management. Journal of Cleaner Production, 52, 329–341. https://doi.org/10.1016/j.jclepro.2013.02.018
Asdemir, K., Jacob, V. S., & Krishnan, R. (2009). Dynamic pricing of multiple home delivery options. European Journal of Operational Research, 196(1), 246–257. https://doi.org/10.1016/j.ejor.2008.03.005
Atasu, A., Sarvary, M., & Van Wassenhove, L. N. (2008). Remanufacturing as a marketing strategy. Management Science, 54(10), 1731–1746. https://doi.org/10.1287/mnsc.1080.0893
Bakos, Y. (1997). Reducing buyer search costs: Implications for electronic marketplaces. Management Science, 43(12), 1676–1692. https://doi.org/10.1287/mnsc.43.12.1676
Benjaafar, S., Li, Y., & Daskin, M. (2013). Carbon footprint and the management of supply chains: Insights from simple models. IEEE Transactions on Automation Science and Engineering, 10(1), 99–116. https://doi.org/10.1109/TASE.2012.2203304
Bernstein, F., & Federgruen, A. (2005). Decentralized supply chains with competing retailers under demand uncertainty. Management Science, 51(1), 18–29. https://doi.org/10.1287/mnsc.1040.0218
Bitran, G., & Caldentey, R. (2003). An overview of pricing models for revenue management. Manufacturing & Service Operations Management, 5(3), 203–229. https://doi.org/10.1287/msom.5.3.203.16031
Brandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233(2), 299–312. https://doi.org/10.1016/j.ejor.2013.09.032
Buldeo Rai, H., Verlinde, S., & Macharis, C. (2019). The "next day, free delivery" myth unravelled: Possibilities for sustainable last mile transport in an omnichannel environment. International Journal of Retail & Distribution Management, 47(1), 39–54. https://doi.org/10.1108/IJRDM-06-2018-0104
Cachon, G. P., & Fisher, M. (2000). Supply chain inventory management and the value of shared information. Management Science, 46(8), 1032–1048. https://doi.org/10.1287/mnsc.46.8.1032.12029
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
Cachon, G. P., & Swinney, R. (2009). Purchasing, pricing, and quick response in the presence of strategic consumers. Management Science, 55(3), 497–511. https://doi.org/10.1287/mnsc.1080.0948
Campbell, A. M., & Savelsbergh, M. W. P. (2005). Decision support for consumer direct grocery initiatives. Transportation Science, 39(3), 313–327. https://doi.org/10.1287/trsc.1040.0105
Campbell, A. M., & Savelsbergh, M. W. P. (2006). Incentive schemes for attended home delivery services. Transportation Science, 40(3), 327–341. https://doi.org/10.1287/trsc.1050.0136
Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: Moving toward new theory. International Journal of Physical Distribution & Logistics Management, 38(5), 360–387. https://doi.org/10.1108/09600030810882816
Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management Science, 46(3), 436–443. https://doi.org/10.1287/mnsc.46.3.436.12069
Chen, F., Federgruen, A., & Zheng, Y. S. (2001). Coordination mechanisms for a distribution system with one supplier and multiple retailers. Management Science, 47(5), 693–708. https://doi.org/10.1287/mnsc.47.5.693.10484
Collier, J. E., & Bienstock, C. C. (2006). Measuring service quality in e-retailing. Journal of Service Research, 8(3), 260–275. https://doi.org/10.1177/1094670505278867
Dana, J. D., & Petruzzi, N. C. (2001). Note: The newsvendor model with endogenous demand. Management Science, 47(11), 1488–1497. https://doi.org/10.1287/mnsc.47.11.1488.10252
De Giovanni, P., & Zaccour, G. (2014). A two-period game of a closed-loop supply chain. European Journal of Operational Research, 232(1), 22–40. https://doi.org/10.1016/j.ejor.2013.06.032
Drake, D. F., Kleindorfer, P. R., & Van Wassenhove, L. N. (2016). Technology choice and capacity portfolios under emissions regulation. Production and Operations Management, 25(6), 1006–1025. https://doi.org/10.1111/poms.12523
Ehmke, J. F., & Campbell, A. M. (2014). Customer acceptance mechanisms for home deliveries in metropolitan areas. European Journal of Operational Research, 233(1), 193–207. https://doi.org/10.1016/j.ejor.2013.08.028
Elmaghraby, W., & Keskinocak, P. (2003). Dynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions. Management Science, 49(10), 1287–1309. https://doi.org/10.1287/mnsc.49.10.1287.17315
Eskandarpour, M., Dejax, P., Miemczyk, J., & Péton, O. (2015). Sustainable supply chain network design: An optimization-oriented review. Omega, 54, 11–32. https://doi.org/10.1016/j.omega.2015.01.006
Esper, T. L., Jensen, T. D., Turnipseed, F. L., & Burton, S. (2003). The last mile: An examination of effects of online retail delivery strategies on consumers. Journal of Business Logistics, 24(2), 177–203. https://doi.org/10.1002/j.2158-1592.2003.tb00051.x
Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101–114. https://doi.org/10.1016/j.ijpe.2015.01.003
Forman, C., Ghose, A., & Goldfarb, A. (2009). Competition between local and electronic markets: How the benefit of buying online depends on where you live. Management Science, 55(1), 47–57. https://doi.org/10.1287/mnsc.1080.0932
Gallego, G., & van Ryzin, G. (1994). Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Science, 40(8), 999–1020. https://doi.org/10.1287/mnsc.40.8.999
Gavirneni, S., Kapuscinski, R., & Tayur, S. (1999). Value of information in capacitated supply chains. Management Science, 45(1), 16–24. https://doi.org/10.1287/mnsc.45.1.16
Gevaers, R., Van de Voorde, E., & Vanelslander, T. (2014). Cost modelling and simulation of last-mile characteristics in an innovative B2C supply chain environment with implications on urban areas and cities. Procedia - Social and Behavioral Sciences, 125, 398–411. https://doi.org/10.1016/j.sbspro.2014.01.1483
Ghosh, D., & Shah, J. (2012). A comparative analysis of greening policies across supply chain structures. International Journal of Production Economics, 135(2), 568–583. https://doi.org/10.1016/j.ijpe.2011.05.027
Ghosh, D., & Shah, J. (2015). Supply chain analysis under green sensitive consumer demand and cost sharing contract. International Journal of Production Economics, 164, 319–329. https://doi.org/10.1016/j.ijpe.2014.11.005
Golicic, S. L., & Smith, C. D. (2013). A meta-analysis of environmentally sustainable supply chain management practices and firm performance. Journal of Supply Chain Management, 49(2), 78–95. https://doi.org/10.1111/jscm.12006
Guide, V. D. R., Jr., & Van Wassenhove, L. N. (2009). The evolution of closed-loop supply chain research. Operations Research, 57(1), 10–18. https://doi.org/10.1287/opre.1080.0628
Gunasekaran, A., & Ngai, E. W. T. (2004). Information systems in supply chain integration and management. European Journal of Operational Research, 159(2), 269–295. https://doi.org/10.1016/j.ejor.2003.08.016
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management. International Journal of Production Economics, 154, 72–80. https://doi.org/10.1016/j.ijpe.2014.04.018
Hofmann, E. (2017). Big data and supply chain decisions: The impact of volume, variety and velocity properties on the bullwhip effect. International Journal of Production Research, 55(17), 5108–5126. https://doi.org/10.1080/00207543.2015.1061222
Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23–34. https://doi.org/10.1016/j.compind.2017.04.002
Hübner, A., Kuhn, H., & Wollenburg, J. (2016). Last mile fulfilment and distribution in omni-channel grocery retailing: A strategic planning framework. International Journal of Retail & Distribution Management, 44(3), 228–247. https://doi.org/10.1108/IJRDM-11-2014-0154
Jiang, L., Yang, Z., & Jun, M. (2013). Measuring consumer perceptions of online shopping convenience. Journal of Service Management, 24(2), 191–214. https://doi.org/10.1108/09564231311323962
Kleindorfer, P. R., Singhal, K., & Van Wassenhove, L. N. (2005). Sustainable operations management. Production and Operations Management, 14(4), 482–492. https://doi.org/10.1111/j.1937-5956.2005.tb00235.x
Kusiak, A. (2018). Smart manufacturing. International Journal of Production Research, 56(1–2), 508–517. https://doi.org/10.1080/00207543.2017.1351644
Lariviere, M. A., & Porteus, E. L. (2001). Selling to the newsvendor: An analysis of price-only contracts. Manufacturing & Service Operations Management, 3(4), 293–305. https://doi.org/10.1287/msom.3.4.293.9971
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, S. F. W. T., Jin, X., & Srai, J. S. (2018). Consumer-driven e-commerce: A literature review, design framework, and research agenda on last-mile logistics models. International Journal of Physical Distribution & Logistics Management, 48(3), 308–332. https://doi.org/10.1108/IJPDLM-02-2017-0081
Linton, J. D., Klassen, R., & Jayaraman, V. (2007). Sustainable supply chains: An introduction. Journal of Operations Management, 25(6), 1075–1082. https://doi.org/10.1016/j.jom.2007.01.012
Mangiaracina, R., Perego, A., Seghezzi, A., & Tumino, A. (2019). Innovative solutions to increase last-mile delivery efficiency in B2C e-commerce: A literature review. International Journal of Physical Distribution & Logistics Management, 49(9), 901–920. https://doi.org/10.1108/IJPDLM-02-2019-0048
McKinnon, A. C. (2010). Product-level carbon auditing of supply chains: Environmental imperative or wasteful distraction? International Journal of Physical Distribution & Logistics Management, 40(1/2), 42–60. https://doi.org/10.1108/09600031011018037
McKinnon, A. C. (2016). The possible impact of 3D printing and drones on last-mile logistics: An exploratory study. Built Environment, 42(4), 617–629. https://doi.org/10.2148/benv.42.4.617
Melacini, M., Perotti, S., Rasini, M., & Tappia, E. (2018). E-fulfilment and distribution in omni-channel retailing: A systematic literature review. International Journal of Physical Distribution & Logistics Management, 48(4), 391–414. https://doi.org/10.1108/IJPDLM-02-2017-0101
Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). Defining supply chain management. Journal of Business Logistics, 22(2), 1–25. https://doi.org/10.1002/j.2158-1592.2001.tb00001.x
Morganti, E., Dablanc, L., & Fortin, F. (2014). Final deliveries for online shopping: The deployment of pickup point networks in urban and suburban areas. Research in Transportation Business & Management, 11, 23–31. https://doi.org/10.1016/j.rtbm.2014.03.002
Pålsson, H., Pettersson, F., & Winslott Hiselius, L. (2017). Energy consumption in e-commerce versus conventional trade channels: Insights into packaging, the last mile, unsold products and product returns. Journal of Cleaner Production, 164, 765–778. https://doi.org/10.1016/j.jclepro.2017.06.242
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275
Plambeck, E. L. (2012). Reducing greenhouse gas emissions through operations and supply chain management. Energy Economics, 34(S1), S64–S74. https://doi.org/10.1016/j.eneco.2012.08.031
Rao, S., Griffis, S. E., & Goldsby, T. J. (2011a). Failure to deliver? Linking online order fulfillment glitches with future purchase behavior. Journal of Operations Management, 29(7–8), 692–703. https://doi.org/10.1016/j.jom.2011.04.001
Rao, S., Goldsby, T. J., Griffis, S. E., & Iyengar, D. (2011b). Electronic logistics service quality (e-LSQ): Its impact on the customer’s purchase satisfaction and retention. Journal of Business Logistics, 32(2), 167–179. https://doi.org/10.1111/j.2158-1592.2011.01014.x
Sanders, N. R. (2016). How to use big data to drive your supply chain. California Management Review, 58(3), 26–48. https://doi.org/10.1525/cmr.2016.58.3.26
Sarkis, J., Zhu, Q., & Lai, K. H. (2011). An organizational theoretic review of green supply chain management literature. International Journal of Production Economics, 130(1), 1–15. https://doi.org/10.1016/j.ijpe.2010.11.010
Seuring, S., & Müller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 16(15), 1699–1710. https://doi.org/10.1016/j.jclepro.2008.04.020
Souza, G. C. (2014). Supply chain analytics. Business Horizons, 57(5), 595–605. https://doi.org/10.1016/j.bushor.2014.06.004
Srivastava, S. K. (2007). Green supply-chain management: A state-of-the-art literature review. International Journal of Management Reviews, 9(1), 53–80. https://doi.org/10.1111/j.1468-2370.2007.00202.x
Su, X. (2007). Intertemporal pricing with strategic customer behavior. Management Science, 53(5), 726–741. https://doi.org/10.1287/mnsc.1060.0667
Swami, S., & Shah, J. (2013). Channel coordination in green supply chain management. Journal of the Operational Research Society, 64(3), 336–351. https://doi.org/10.1057/jors.2012.44
Talluri, K. T., & van Ryzin, G. J. (2004). Revenue management under a general discrete choice model of consumer behavior. Management Science, 50(1), 15–33. https://doi.org/10.1287/mnsc.1030.0147
Thøgersen, J., & Nielsen, K. S. (2016). A better carbon footprint label. Journal of Cleaner Production, 125, 86–94. https://doi.org/10.1016/j.jclepro.2016.03.098
Trkman, P., McCormack, K., de Oliveira, M. P. V., & Ladeira, M. B. (2010). The impact of business analytics on supply chain performance. Decision Support Systems, 49(3), 318–327. https://doi.org/10.1016/j.dss.2010.03.007
Tsay, A. A., & Agrawal, N. (2000). Channel dynamics under price and service competition. Manufacturing & Service Operations Management, 2(4), 372–391. https://doi.org/10.1287/msom.2.4.372.12342
Vachon, S., & Klassen, R. D. (2008). Environmental management and manufacturing performance: The role of collaboration in the supply chain. International Journal of Production Economics, 111(2), 299–315. https://doi.org/10.1016/j.ijpe.2006.11.030
Vanclay, J. K., Shortiss, J., Aulsebrook, S., Gillespie, A. M., Howell, B. C., Johanni, R., Maher, M. J., Mitchell, K. M., Stewart, M. D., & Yates, J. (2011). Customer response to carbon labelling of groceries. Journal of Consumer Policy, 34(1), 153–160. https://doi.org/10.1007/s10603-010-9140-7
Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From multi-channel retailing to omni-channel retailing: Introduction to the special issue on multi-channel retailing. Journal of Retailing, 91(2), 174–181. https://doi.org/10.1016/j.jretai.2015.02.005
Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How big data can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246. https://doi.org/10.1016/j.ijpe.2014.12.031
Weber, C. L., & Matthews, H. S. (2008). Food-miles and the relative climate impacts of food choices in the United States. Environmental Science & Technology, 42(10), 3508–3513. https://doi.org/10.1021/es702969f
Whang, S. (1995). Coordination in operations: A taxonomy. Journal of Operations Management, 12(3–4), 413–422. https://doi.org/10.1016/0272-6963(95)00010-P
White, K., Habib, R., & Hardisty, D. J. (2019). How to SHIFT consumer behaviors to be more sustainable: A literature review and guiding framework. Journal of Marketing, 83(3), 22–49. https://doi.org/10.1177/0022242919825649
Yang, X., Strauss, A. K., Currie, C. S. M., & Eglese, R. (2016). Choice-based demand management and vehicle routing in e-fulfillment. Transportation Science, 50(2), 473–488. https://doi.org/10.1287/trsc.2014.0549
Zhu, Q., & Sarkis, J. (2004). Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises. Journal of Operations Management, 22(3), 265–289. https://doi.org/10.1016/j.jom.2004.01.005
