Quantifying the Return on Investment (ROI) of Prescriptive Optimisation Models In Supply Chain Management: A Simulation and Comparative Analysis
Abstract
The ROI realized through the implementation of the prescriptive models of optimization in supply chain management is comprehensively empirically studied in the present research report. The study analyzed 150 responses of supply chain professionals in eight primary industrial sectors (retail, logistics, healthcare, technology, energy, consumer packaged products, and manufacturing) that participated in it. The conclusion made is that the prescriptive optimisation models have a significant influence on supply chain performance, yielding inconsistent but measurable financial returns. The mean effectiveness rating that organisations report is 5.2 out of 10, which, in turn, indicates the increasing nature of such technologies and the challenges of their implementation and adoption. Some of the major findings of our research are as follows: 1.An approximate 53% of the organisations surveyed have applied prescriptive optimisation models, which are either fully or in pilot application phases. 2.The ROI rates reported by organisations range between 6-15% and more than 50% per year. 3.The principal benefits of the financial are lower costs on inventory (45% with a significant positive impact), lower costs on transportation (49% with a positive impact), and better use of assets. 4.The largest obstacles to implementation are data quality, integration of the legacy system, and the lack of talent, and Intangible rewards such as improved predictability in a business venture, improved decision-making and agility in the organisation are equally important as quantifiable returns. The median investment lies between 500K and 1 M, with the implementation costs that fluctuate over 250K below and 2M above. The implementation strategy, organisational preparedness, change measurement are the key success factors as it has been indicated through the relationship between the cost of implementation and the achieved ROI which indicates that not every increase in the cost of implementation translates to a proportional increase in the achieved ROI.
Keywords
Prescriptive Optimization; Supply Chain Management; Return on Investment; Supply Chain analytics; Inventory Optimization; Transportation Optimization; Technology Adoption; Implementation Barriers.
Citations
IRE Journals:
Anish Yadav, Dr. Komal Malik "Quantifying the Return on Investment (ROI) of Prescriptive Optimisation Models In Supply Chain Management: A Simulation and Comparative Analysis" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 746-754 https://doi.org/10.64388/IREV9I9-1715031
IEEE:
Anish Yadav, Dr. Komal Malik
"Quantifying the Return on Investment (ROI) of Prescriptive Optimisation Models In Supply Chain Management: A Simulation and Comparative Analysis" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1715031
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