Analyzing the Role of Artificial Intelligence and Machine Learning in Optimizing Supply Chain Processes in Kenya

Aug 3, 2026

Purpose: The study analyzes how artificial intelligence and machine learning contribute to optimizing supply chain processes. Methodology: A desk study design relying on secondary data collected from published studies, online journals, and library resources is used. Findings: AI and ML improve demand forecasting accuracy, support inventory management through replenishment automation, and deliver real-time analytics that reveal bottlenecks and inefficiencies for proactive decisions. Theoretical, practical, and policy implications emphasize technology acceptance and data-driven blockchain adoption with governance on privacy, interoperability, and standards.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Supply chain process optimization in developed economies

Supply chain optimization in developing economies

Abstract

Purpose

Methodology

Findings

Unique contribution to theory, practice, and policy

Analyzing the Role of Artificial Intelligence and Machine Learning in Optimizing Supply Chain Processes in Kenya Jackson Mwangi Analyzing the Role of Artificial Intelligence and Machine Learning in Optimizing Supply Chain Processes in Kenya Jackson Mwangi Article History Received 23rd January 2024 Received in Revised Form 26th January 2024 Accepted 20thFebruary 2024 How to Cite Mwangi, J. (2024). Analyzing the Role of Artificial Intelligence and Machine Learning in Optimizing Supply Chain Processes in Kenya . International Journal of Supply Chain Management, 9(1), 39 – 50. [https://doi.org/10.47604/ijscm.2322](https://doi.org/10.47604/ijscm.2322) Abstract Purpose: The aim of the study was to analyze the role of artificial intelligence and machine learning in optimizing supply chain processes Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Artificial intelligence (AI) and machine learning (ML) play a pivotal role in optimizing supply chain processes by enhancing demand forecasting accuracy through sophisticated algorithms. They streamline inventory management by predicting demand patterns and automating replenishment tasks, leading to reduced stockouts and excess inventory. AIpowered analytics enable real-time insights into supply chain performance, identifying bottlenecks and inefficiencies for proactive decision-making. Unique Contribution to Theory, Practice and Policy: Theory of technology acceptance model (TAM), resource-based view (RBV) theory & dynamic capabilities theory may be used to anchor future studies on analyzing the role of artificial intelligence and machine learning in optimizing supply chain processes. Encourage supply chain stakeholders to adopt blockchain solutions for enhanced transparency, traceability, and efficiency. Advocate for regulatory frameworks that promote the adoption of blockchain technology in supply chains while addressing concerns related to data privacy, interoperability, and standardization. Keywords: Artificial Intelligence, Machine Learning, Optimizing Supply Chain Processes ©2024 by the Authors. This Article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)) INTRODUCTION Optimization of supply chain processes involves streamlining and improving various aspects of the supply chain to maximize efficiency and minimize costs. This includes activities such as inventory management, transportation, warehousing, and procurement. Companies in developed economies like the USA, Japan, and the UK have been at the forefront of implementing advanced technologies and methodologies to optimize their supply chains. For example, in the USA, companies have been increasingly adopting automation and data analytics to improve inventory management and demand forecasting. According to a study by Deloitte, 69% of companies surveyed in the US reported that they have implemented advanced analytics in their supply chain operations, leading to improved decision-making and cost savings (Deloitte, 2017) . Similarly, in Japan, companies have been focusing on lean manufacturing principles and Just-inTime (JIT) inventory systems to optimize their supply chains. Toyota, for instance, is renowned for its efficient supply chain management practices, which have enabled the company to reduce waste and improve productivity. According to data from the Japan External Trade Organization (JETRO), the adoption of lean principles has led to a significant reduction in lead

FAQs

What is the purpose of the study on AI and ML in supply chain optimization?

To analyze the role of artificial intelligence and machine learning in optimizing supply chain processes.

How was the research conducted?

A desk study methodology was used, relying on secondary data from already published studies and accessible online journals and libraries.

What benefits do AI and machine learning provide in supply chains, according to the findings?

They enhance demand forecasting accuracy, streamline inventory management with automated replenishment, and provide real-time analytics to identify bottlenecks and inefficiencies.

Aug 3, 2026

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