Empirical Evaluation of an Integrated Forecast to Inventory Planning Tool for Retail Service Levels and Inventory Efficiency

Diwane, Indrajeet Dilip (2025) Empirical Evaluation of an Integrated Forecast to Inventory Planning Tool for Retail Service Levels and Inventory Efficiency. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (694kB) | Preview

Abstract

Retailers are challenged to ensure that they offer high service levels at a minimal inventory cost. The project gives an empirical assessment of an integrated forecast-to-inventory planning tool in a retail setting. The tool is an integrated workflow of a combination of demand forecasting techniques and services-level-based optimization of inventory, executed through a business intelligence dashboard. This project uses the recommendations of the tool and compares them with the current planning practices with a large number of SKU-store combinations using historical sales data and simulation. As the results demonstrate, the combined method can significantly enhance the service levels (cycle service level and fill rate) without the corresponding increase in average stock, which means the inventory efficiency improvement. The number of stockouts was decreased significantly and more so with highly variable products where the current heuristic system under-stocked. Such results indicate that the improved congruence between forecasting and inventory decisions can improve inventory performance in retail environments like the one under analysis. The project will attempt to bridge the gap between analytical work and managerial practice by prototyping an effective tool that will combine demand forecasting, inventory control, and interactive decision support.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Sahni, Vikas UNSPECIFIED
Uncontrolled Keywords:	Demand forecasting; Inventory management; Service level
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Retail Industry
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	07 Sep 2026 10:22
Last Modified:	07 Sep 2026 10:22
URI:	https://norma.ncirl.ie/id/eprint/9855

Actions (login required)

View Item