Artificial intelligence for supply chain resilience: the case of Zimbabwe

Author

Chirenje, Tinashe

Title

Artificial intelligence for supply chain resilience: the case of Zimbabwe

Abstract

The study aimed to assess the potential of Artificial Intelligence for enhancing the resilience of supply chains in Zimbabwe. Its main focus was to establish a relationship between adoption of Artificial Intelligence and the resilience of supply chains in Zimbabwe. The study's objectives were to explore the specific vulnerabilities faced by Zimbabwe's supply chains, identifying key vulnerabilities in the supply chains of Zimbabwe, evaluating the potential of Artificial Intelligence powered solutions to address identified supply chain vulnerabilities, investigating the key barriers for successful Artificial Intelligence adoption in Zimbabwe's supply chain ecosystem and assessing the effectiveness of Artificial Intelligence in improving supply chain resilience in Zimbabwe. A descriptive research design was utilised, and 60 participants were selected using stratified random sampling. Primary data was collected using questionnaires with a three-point and five-point Likert scale. The questionnaire's reliability and validity were tested, producing a Cronbach value of 0.817. SPSS version 20 was used to analyse the data, and descriptive statistics, frequency tables, correlation coefficient (r=0.523), and regression with a beta coefficient of 0.753 were used to ascertain the statistical significance of the factors. The study identified several capabilities of Artificial Intelligence that had a significant impact on enhancing the resilience of the supply chain in Zimbabwe which include reducing disruption impact, offering personalised solutions, more efficient resource allocation, demand forecasting and enabling innovation. The findings also revealed that stakeholders in the healthcare sector were are more hesitant to adopt Artificial Intelligence solutions because data sensitivity issues are a significant challenge to them as healthcare often deals with sensitive patient data. Financial incentives and subsidies seem to be less desired compared to support that fosters knowledge, skills, and tailored solutions on Artificial Intelligence. To implement such measures successfully, it is crucial to have efficient systems and processes in place.

Date

June 2024

Publisher

BUSE

Keywords

Artificial Intelligence,

Resilience of supply chains,

Supervisor

NIL