Designing Multi-Type Purchase Order Workflows through Stakeholder Synthesis: A Framework for Requirements Integration in Retail Supply Chains
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I1P112Keywords:
Requirements Engineering, Purchase Orders, Stakeholder Synthesis, Retail Supply Chain, Causal Inference, Staggered Difference-In-Differences, Procurement Workflow, Enterprise SystemsAbstract
large retail supply chains increasingly rely on heterogeneous purchase-order (PO) workflows spanning routine replenishment, promotion, import, drop-ship, cross-dock, direct-store-delivery, and exception purchasing. These workflows cross merchandising, procurement, planning, logistics, finance, supplier operations, and information-technology boundaries, creating a requirements-integration problem that isolated functional elicitation cannot reliably resolve. Existing requirements-engineering and supply-chain integration studies associate stakeholder participation and information integration with superior performance, but these associations do not establish whether stakeholder synthesis itself causes operational improvement. This article develops a primary causal research design estimating the effect of Stakeholder-Synthesized Requirements Integration (SSRI) on workflow-induced PO exceptions. The proposed empirical setting exploits phased adoption of mandatory SSRI governance across procurement operating cells and uses a staggered difference-in-differences design with cohort-time average treatment effects. SSRI requires cross-functional stakeholder coverage, PO-type decomposition, requirement reconciliation, traceability, scenario validation, and joint sign-off. The primary outcome is workflow-induced exceptions per 100 eligible POs; secondary outcomes include first-pass completion, cycle time, manual handoffs, post-release corrections, and requirements-defect escape. The design pre-specifies modern event-study diagnostics, falsification tests, co-intervention restrictions, and sensitivity analysis. Mechanisms operate through requirements completeness, rule coherence, reduced defect escape, and reduced manual rework, with stronger effects predicted under greater workflow complexity, supplier heterogeneity, demand volatility, and system fragmentation. Because retailer microdata were not supplied, no empirical coefficient is fabricated; instead, the paper provides an executable causal protocol and publication-ready results architecture.
References
[1] Abadie, A. (2005). Semiparametric difference-in-differences estimators. The Review of Economic Studies, 72(1), 1–19. https://doi.org/10.1111/0034-6527.00321
[2] Bano, M., & Zowghi, D. (2015). A systematic review on the relationship between user involvement and system success. Information and Software Technology, 58, 148–169. https://doi.org/10.1016/j.infsof.2014.06.011
[3] Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? The Quarterly Journal of Economics, 119(1), 249–275. https://doi.org/10.1162/003355304772839588
[4] Borusyak, K., Jaravel, X., & Spiess, J. (2024). Revisiting event-study designs: Robust and efficient estimation. The Review of Economic Studies, 91(6), 3253–3285. https://doi.org/10.1093/restud/rdae007
[5] Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200–230. https://doi.org/10.1016/j.jeconom.2020.12.001
[6] Daft, R. L., & Lengel, R. H. (1986). Organizational information requirements, media richness and structural design. Management Science, 32(5), 554–571. https://doi.org/10.1287/mnsc.32.5.554
[7] Damian, D., & Chisan, J. (2006). An empirical study of the complex relationships between requirements engineering processes and other processes that lead to payoffs in productivity, quality, and risk management. IEEE Transactions on Software Engineering, 32(7), 433–453. https://doi.org/10.1109/TSE.2006.61
[8] Davila, A., Gupta, M., & Palmer, R. (2003). Moving procurement systems to the internet: The adoption and use of e-procurement technology models. European Management Journal, 21(1), 11–23. https://doi.org/10.1016/S0263-2373(02)00155-X
[9] de Chaisemartin, C., & D'Haultfœuille, X. (2020). Two-way fixed effects estimators with heterogeneous treatment effects. American Economic Review, 110(9), 2964–2996. https://doi.org/10.1257/aer.20181169
[10] Dyer, J. H., & Singh, H. (1998). The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Academy of Management Review, 23(4), 660–679. https://doi.org/10.5465/amr.1998.1255632
[11] Flynn, B. B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management, 28(1), 58–71. https://doi.org/10.1016/j.jom.2009.06.001
[12] Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28–36. https://doi.org/10.1287/inte.4.3.28
[13] Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics, 225(2), 254–277. https://doi.org/10.1016/j.jeconom.2021.03.014
[14] Hofmann, H. F., & Lehner, F. (2001). Requirements engineering as a success factor in software projects. IEEE Software, 18(4), 58–66. https://doi.org/10.1109/MS.2001.936219
[15] International Organization for Standardization, International Electrotechnical Commission, & Institute of Electrical and Electronics Engineers. (2018). ISO/IEC/IEEE 29148:2018 Systems and software engineering—Life cycle processes—Requirements engineering. ISO.
[16] Kotonya, G., & Sommerville, I. (1998). Requirements engineering: Processes and techniques. John Wiley & Sons.
[17] Lim, S. L., Quercia, D., & Finkelstein, A. (2010). StakeNet: Using social networks to analyse the stakeholders of large-scale software projects. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering (pp. 295–304). ACM. https://doi.org/10.1145/1806799.1806844
[18] Méndez Fernández, D., Wagner, S., Kalinowski, M., Felderer, M., Mafra, P., Vetrò, A., Conte, T., Christiansson, M.-T., Greer, D., Lassenius, C., Männistö, T., Nayebi, M., Oivo, M., Penzenstadler, B., Pfahl, D., Prikladnicki, R., Ruhe, G., Schekelmann, A., Sen, S., & Wieringa, R. (2017). Naming the pain in requirements engineering: Contemporary problems, causes, and effects in practice. Empirical Software Engineering, 22(5), 2298–2338. https://doi.org/10.1007/s10664-016-9451-7
[19] Prajogo, D., & Olhager, J. (2012). Supply chain integration and performance: The effects of long-term relationships, information technology and sharing, and logistics integration. International Journal of Production Economics, 135(1), 514–522. https://doi.org/10.1016/j.ijpe.2011.09.001
[20] Rambachan, A., & Roth, J. (2023). A more credible approach to parallel trends. The Review of Economic Studies, 90(5), 2555–2591. https://doi.org/10.1093/restud/rdad018
[21] Roth, J. (2022). Pretest with caution: Event-study estimates after testing for parallel trends. American Economic Review: Insights, 4(3), 305–322. https://doi.org/10.1257/aeri.20210236
[22] Roth, J., Sant'Anna, P. H. C., Bilinski, A., & Poe, J. (2023). What's trending in difference-in-differences? A synthesis of the recent econometrics literature. Journal of Econometrics, 235(2), 2218–2244. https://doi.org/10.1016/j.jeconom.2023.03.008
[23] Sant'Anna, P. H. C., & Zhao, J. (2020). Doubly robust difference-in-differences estimators. Journal of Econometrics, 219(1), 101–122. https://doi.org/10.1016/j.jeconom.2020.06.003
[24] S. K. Gunda, "Analyzing Machine Learning Techniques for Software Defect Prediction: A Comprehensive Performance Comparison," 2024 Asian Conference on Intelligent Technologies (ACOIT), KOLAR, India, 2024, pp. 1-5, https://doi.org/10.1109/ACOIT62457.2024.10939610.
[25] Sharp, H., Finkelstein, A., & Galal, G. (1999). Stakeholder identification in the requirements engineering process. In Proceedings of the 10th International Workshop on Database and Expert Systems Applications (pp. 387–391). IEEE. https://doi.org/10.1109/DEXA.1999.795198
[26] Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175–199. https://doi.org/10.1016/j.jeconom.2020.09.006
[27] Tai, Y.-M., Ho, C.-F., & Wu, W.-H. (2010). the performance impact of implementing web-based e-procurement systems. International Journal of Production Research, 48(18), 5397–5414. https://doi.org/10.1080/00207540903117915
[28] Tushman, M. L., & Nadler, D. A. (1978). Information processing as an integrating concept in organizational design. Academy of Management Review, 3(3), 613–624. https://doi.org/10.5465/amr.1978.4305791
[29] Vaidyanathan, G., & Devaraj, S. (2008). The role of quality in e-procurement performance: An empirical analysis. Journal of Operations Management, 26(3), 407–425. https://doi.org/10.1016/j.jom.2007.08.004
[30] Vickery, S. K., Jayaram, J., Droge, C., & Calantone, R. (2003). The effects of an integrative supply chain strategy on customer service and financial performance: An analysis of direct versus indirect relationships. Journal of Operations Management, 21(5), 523–539. https://doi.org/10.1016/S0272-6963(03)00061-
