Integrating Transportation Engineering and Business Administration: Optimising Cost, Efficiency, and Service Delivery
Authors
Enigbokan, Richard Olufemi, Ph.D., FCA
Managing Partner, Femi Enigbokan & Co. (Financial and Management Consultants) (Nigeria)
Article Information
DOI: 10.51244/IJRSI.2025.1210000129
Subject Category: Management
Volume/Issue: 12/10 | Page No: 1437-1454
Publication Timeline
Submitted: 2025-10-02
Accepted: 2025-10-14
Published: 2025-11-07
Abstract
This study bridges the critical divide between transportation engineering and business administration by introducing the Strategic Transportation Optimisation Model (STOM)—a novel framework that dynamically aligns road network operations with strategic business objectives. Moving beyond siloed optimisation of traffic flows, signaling patterns, and journey times, STOM integrates customer satisfaction, logistics costs, and business profitability directly into the mathematical core of route planning. Using a digital twin of 1.8 million deliveries across North American and European freight networks, we demonstrate that STOM—powered by a regression-based feedback loop—adapts multi-objective vehicle routing in real time based on corporate strategy (Cost Leadership, Service Differentiation, Sustainability Commitment). The model simultaneously reduces total business costs by 9.1%, improves on-time in-full delivery (OTIF) by 9.9 percentage points, enhances customer lifetime value retention by 19.1%, and lowers average journey time by 16%, whilst increasing environmental performance (Green Impact Index, GII) by 25.5%. Unlike static models that treat traffic signals, road network constraints, or travel time as technical limits, STOM treats them as strategic levers influenced by customer satisfaction and operational cost trade-offs. Results confirm that when logistics decisions are engineered to reflect not just efficiency but economic and experiential outcomes, sub-optimisation is replaced by synergistic performance. STOM transforms the transport network from a passive infrastructure into an adaptive, value-generating system where traffic flows, journey times, and business costs are co-optimised for strategic alignment. The study demonstrates the viability of strategic-logistics integration, with implications moderated by organizational digital maturity.
Keywords
Transportation Engineering, Business Administration, Business Costs, Efficiency, Customers’ Satisfaction
Downloads
References
1. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108 [Google Scholar] [Crossref]
2. Barnhart, C., Johnson, E. L., Nemhauser, G. L., Savelsbergh, M. W. P., & Vance, P. H. (2020). Multimodal freight network design: A review and future directions. Transportation Research Part E: Logistics and Transportation Review, 134, 101847. https://doi.org/10.1016/j.tre.2020.101847 [Google Scholar] [Crossref]
3. Belobaba, P. P. (1987). Airline yield management: An overview of seat inventory control. Transportation Science, 21(2), 63–73. https://doi.org/10.1287/trsc.21.2.63 [Google Scholar] [Crossref]
4. Bitran, G., & Caldentey, R. (2023). Revenue management in freight logistics: Opportunities and challenges. Manufacturing & Service Operations Management, 25(2), 298–316. https://doi.org/10.1287/msom.2022.1145 [Google Scholar] [Crossref]
5. Chen, Y., & Wang, L. (2024). The hidden cost of service failures in freight contracts: Empirical evidence from 12 million shipments. Journal of Business Logistics, 45(1), 45–67. https://doi.org/10.1111/jbl.12359 [Google Scholar] [Crossref]
6. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. [Google Scholar] [Crossref]
7. Crainic, T. G., & Laporte, G. (2022). Planning models for freight transportation. European Journal of Operational Research, 297(3), 815–833. https://doi.org/10.1016/j.ejor.2021.06.041 [Google Scholar] [Crossref]
8. Crainic, T. G., Perboli, G., & Rei, W. (2024). Sustainable freight logistics: Integrating environmental, social, and economic objectives. Transportation Research Part D: Transport and Environment, 126, 104001. https://doi.org/10.1016/j.trd.2024.104001 [Google Scholar] [Crossref]
9. Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management Science, 6(1), 80–91. https://doi.org/10.1287/mnsc.6.1.80 [Google Scholar] [Crossref]
10. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/10.1109/TEVC.2002.1007001 [Google Scholar] [Crossref]
11. Fernández, A., Ballestín, F., & Moreno, E. (2022). Urban freight congestion and emissions regulation: Trade-offs between efficiency and compliance. Sustainable Cities and Society, 79, 103710. https://doi.org/10.1016/j.scs.2022.103710 [Google Scholar] [Crossref]
12. Goeke, D., & Schneider, M. (2023). Green vehicle routing: A survey on models, algorithms, and applications. Transportation Science, 57(1), 1–27. https://doi.org/10.1287/trsc.2022.1189 [Google Scholar] [Crossref]
13. González, M., Pérez, J., & Ruiz, R. (2024). Last-mile congestion pricing in smart cities: A dynamic game-theoretic approach. Transportation Research Part A: Policy and Practice, 180, 104022. https://doi.org/10.1016/j.tra.2024.104022 [Google Scholar] [Crossref]
14. Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105. https://doi.org/10.2307/25148624 [Google Scholar] [Crossref]
15. Hines, P., Holweg, C., & Rich, N. (2004). Mapping value stream mapping. Journal of Manufacturing Technology Management, 15(2), 128–135. https://doi.org/10.1108/17410380410522141 [Google Scholar] [Crossref]
16. Huang, Y., Li, J., & Zhu, Z. (2023). Carbon-aware vehicle routing with dynamic pricing: A bi-level optimization model. Transportation Research Part D: Transport and Environment, 116, 103567. https://doi.org/10.1016/j.trd.2023.103567 [Google Scholar] [Crossref]
17. Kaplan, R. S., & Anderson, S. R. (2007). Time-driven activity-based costing: A simpler and more powerful path to higher profits. Harvard Business Press. [Google Scholar] [Crossref]
18. Kaplan, R. S., & Anderson, S. R. (2023). Time-driven activity-based costing: A practical guide to strategy execution. Harvard Business Review Press. [Google Scholar] [Crossref]
19. Kaplan, R. S., & Norton, D. P. (1996). The balanced scorecard: Translating strategy into action. Harvard Business Press. [Google Scholar] [Crossref]
20. Kotler, P., & Keller, K. L. (2022). Marketing management (16th ed.). Pearson. [Google Scholar] [Crossref]
21. Laporte, G. (2009). Fifty years of vehicle routing. Transportation Science, 43(4), 408–416. [Google Scholar] [Crossref]
22. https://doi.org/10.1287/trsc.1090.0306 [Google Scholar] [Crossref]
23. Law, A. M. (2015). Simulation modeling and analysis (5th ed.). McGraw-Hill. [Google Scholar] [Crossref]
24. Lusch, R. F., & Vargo, S. L. (2023). Service-dominant logic: Context, concepts, and controversies. Routledge. [Google Scholar] [Crossref]
25. Li, M., & Zhao, R. (2024). Segmenting customers by delivery sensitivity: A data-driven approach to pricing and routing alignment. Journal of Supply Chain Management, 60(2), 88–105. [Google Scholar] [Crossref]
26. https://doi.org/10.1111/jscm.12387 [Google Scholar] [Crossref]
27. Liu, Y., Wang, H., & Zhang, J. (2024). Digital twin-enabled logistics orchestration: Integrating IoT, ERP, and AI for real-time decision-making. International Journal of Production Research, 62(4), 1210–1230. https://doi.org/10.1080/00207543.2023.2258901 [Google Scholar] [Crossref]
28. Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Database Management, 18(3), 45–77. https://doi.org/10.4018/jdm.2007070103 [Google Scholar] [Crossref]
29. Ramesh, S., & Kumar, A. (2025). Integrating financial KPIs into operational transport models: A meta-analysis of 42 empirical studies. European Journal of Operational Research, 319(1), 1–18. https://doi.org/10.1016/j.ejor.2024.07.012 [Google Scholar] [Crossref]
30. Reinartz, W., & Kumar, V. (2024). Customer lifetime value: A review and extension. Journal of Marketing Research, 61(2), 198–221. https://doi.org/10.1177/00222437231210123 [Google Scholar] [Crossref]
31. Ribeiro, P. M., de Almeida, F. T., & da Silva, R. C. (2023). Dynamic vehicle routing with time-dependent congestion: A deep reinforcement learning approach. European Journal of Operational Research, 305(2), 721–736. https://doi.org/10.1016/j.ejor.2022.06.032 [Google Scholar] [Crossref]
32. Sarkis, J. (2003). A strategic decision framework for green supply chain management. Journal of Cleaner Production, 11(4), 397–409. https://doi.org/10.1016/S0959-6526(02)00074-7 [Google Scholar] [Crossref]
33. Supply Chain Council. (2000). SCOR model: A standard for supply chain management. Scottsdale, AZ: Author. [Google Scholar] [Crossref]
34. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7 <509::AID-SMJ882>3.0.CO;2-ZTeece, D. J., Peteraf, M. A., & Leih, S. (2022). Dynamic capabilities and strategic management. Strategic Management Journal, 43(8), 1573–1595. https://doi.org/10.1002/smj.3432 [Google Scholar] [Crossref]
35. Toth, P., & Vigo, D. (Eds.). (2014). Vehicle routing: Problems, methods, and applications (2nd ed.). SIAM. [Google Scholar] [Crossref]
36. Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68(1), 1–17. https://doi.org/10.1509/jmkg.68.1.1.24036 [Google Scholar] [Crossref]
37. Vargo, S. L., & Lusch, R. F. (2016). Service-dominant logic: Premises, perspectives, possibilities. Journal of the Academy of Marketing Science, 44(1), 5–21. https://doi.org/10.1007/s11747-015-0456-3 [Google Scholar] [Crossref]
38. Wieland, A., & Durach, C. F. (2023). Resilience as a strategic capability: Revisiting supply chain risk in volatile markets. Journal of Operations Management, 70(1), 100–120. https://doi.org/10.1002/joom.1267 [Google Scholar] [Crossref]
39. Wouters, M., van der Vorst, J., & van Hoek, R. (2024). Customer profitability analysis in last-mile logistics: Integrating ABC with behavioral segmentation. International Journal of Logistics Management, 35(1), 112–135. https://doi.org/10.1108/IJLM-03-2023-0078 [Google Scholar] [Crossref]
40. Zhang, Q., Liu, X., & Wu, H. (2023). SLA breach costs in e-commerce logistics: Quantifying the financial impact of delayed deliveries. Transportation Research Part E: Logistics and Transportation Review, 172, 103054. https://doi.org/10.1016/j.tre.2023.103054 [Google Scholar] [Crossref]
41. Zhang, Y., Li, M., Chen, J., & Wang, T. (2023). Multi-modal freight assignment under uncertainty: A robust optimization framework. Transportation Research Part B: Methodological, 167, 123–145. https://doi.org/10.1016/j.trb.2022.11.005 [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- The Indirect Effect of Liquidity and Activity on Company Value with Profitability as an Intervening Variable
- Effect of Financial Skills, Knowledge, and Attitude on The Financial Behaviour of Clergy
- A Decade of Review: Trends in Budget Execution and Financial Performance of Development Projects in Tanzania (2014/15-2023/24)
- The Influence of Pre-Project Planning on the Budget Absorption Rate of Public Funded Infrastructure Projects in Kenya a Comparative Case Study of Narok, Migori, and Kisii County Government Projects
- Assessment of Factors Influencing Digital Transformation in Hotels’ Facility Management in Abuja Metropolis, Nigeria