Abstract

Supply chains face many challenges in today’s business environment. This paper examines how advanced analytics can be used to improve supply chain efficiency and resilience with a particular focus on demand forecasting and late delivery analysis. The thesis focuses on two areas, which are forecasting demand and analyzing late deliveries. With the help of data-driven models for forecasting demand and pinpointing reasons behind delayed shipments, establishments can take appropriate measures that will reduce risks and enhance operational efficiency. A complete analytic framework is suggested that integrates sophisticated analytics into supply chain management so that organizations can optimize all their operations in a holistic manner. Through literature review and studies aims at finding out different ways through which predictive modeling coupled with machine learning among other analytical techniques may be utilized to enhance performance across the entire supply chain. This study provides valuable information for industries looking forward towards building resilient yet efficient supply chains under dynamic business settings.

Library of Congress Subject Headings

Supply chain management--Management; Demand (Economic theory)--Forecasting; Machine learning

Publication Date

Spring 2024

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ehsan Warriach

Campus

RIT Dubai

Plan Codes

PROFST-MS

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