Effect of Machine Learning Model on Supply Chain Performance. A Case of Dubai Port (DP) World/Kigali-Rwanda
DOI:
https://doi.org/10.53819/81018102t2311Abstract
The purpose of this study was to analyse the effect of machine learning model on supply chain performance. Despite this, Dubai Port (DP) World, as a warehouse company, currently faces the challenge of a lack of effective AI explain ability through learning machines together with limited employees in modern AI technology, which is crucial for a successful transition into machine learning for a leading provider of smart logistics solutions, enabling the flow of trade across the globe. In conducting this research, three objectives were laid out to effect of machine learning demand forecasting & planning on supply chain performance in Dubai Port World/Kigali-Rwanda, to assess the effect of machine learning optimization practices on supply chain performance in DP World/Kigali-Rwanda and lastly to determine the effect of machine learning automation on supply chain performance in DP World/Kigali-Rwanda. To achieve these objectives, literature was reviewed on the subject matter including definitions of key concepts, conceptual review, theoretical framework, conceptual framework and research gap analysis. This study based on contingency theory, information processing theory, and practice-based view, additionally researcher applied the universal census by selecting all 131 employees from Dubai Port (DP) World/Kigali, Rwanda. Questionnaire, interview guide and documentation were used as tools of data collection. Data was processed through editing, coding and tabulation and the data also was analyzed by using descriptive statistics. The R value of 0.852 indicates a strong relationship between the predictors and the Supply chain performance in DP World/Kigali-Rwanda. The R Square value of 0.725 indicates that approximately 72.5% of the variability in the outcome variable can be explained by the predictors in the model. Machine learning demand forecasting planning has a coefficient of (β= 0.454, t=8.230, p value=0.000), machine learning optimization practices has a coefficient of (β= 0.252 t=4.125, p value=0.000), and machine learning automation has a coefficient of (β= 0.357, t=6.025, p value=0.000). All these coefficients are statistically significant on supply chain performance in DP World/Kigali-Rwanda, as indicated by their associated Sig. Values below 0.05. Therefore, inline of findings researcher recommended that DP World/Kigali, Rwanda should continue to leverage modern technologies such as Robotic Process Automation (RPA) and Intelligent Document Processing (IDP) to automate shop floor and back-office software-driven processes.
Key words: Machine learning model, supply chain performance and DP World/Kigali, Rwanda.
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