A Framework for Optimizing Pharmacy Inventory Management System Performance Using Cloud Computing and Machine Learning a Case Study of Nairobi County

Abstract

The purpose of this study was to address how pharmacy inventory management systems can be improved using cloud computing and machine learning. The main aim was to enhance efficacy, accuracy and efficiency in inventory management practices within the pharmaceutical sector. The problem identified was about the inefficiencies and challenges present in conventional stock control methods such as manual tracking mechanisms and outdated ones. Because of these inefficiencies, issues such as stock-outs, excesses, and lack of real-time information critical for decision-making processes arise. To overcome this challenge, a quantitative research design was used where data was collected through questionnaires and interviews from a diverse group of pharmacy personnel. The sample included public and private pharmacies in Nairobi County through stratified random sampling. The methodology involves the use of questionnaires for quantitative data collection on ongoing inventory management practices as well as technological readiness. This study expects that by utilizing cloud computing and machine learning algorithms there will be an inclusive framework created for optimizing pharmacy inventory management systems. The results indicated a need for the implementation of the proposed machine learning and cloud computing framework as the respondent indicated a high dissatisfaction it their current inventory management systems which were indicated to have major challenges that contributed to financial losses, customer dissatisfaction among other. Additionally, this research provides practical recommendations for implementing cloud computing platforms or machine learning solutions which could transform the traditional approach to inventory management thereby enhancing patient care outcomes.