| dc.description.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mechanisms and outdated ones.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. | en_US |
