Artificial intelligence and machine learning on operational decision-making (post-pandemic): a study of multinational company, Abuja, Nigeria

Abstract

Multinational organisations such as Nestlé Nigeria Plc, Abuja, faced immense difficulties in the post-pandemic operating environment, especially in the need to ensure that decision making is accurate and quick to maintain efficiency and competitiveness. This study looked at the influence of Artificial Intelligence and machine learning on operational decision making, specifically on the use of AI driven predictive analytics, and AI based decision support systems. The descriptive research was used and a population of 496 managerial and operational staff who directly participate in decision making was identified while the sample of 269 respondents was obtained using the Yamane's formula with a 20% margin of error. Stratified sampling was used to include production managers, officers of the supply chain, inventory controllers, and logistics coordinators in a representative sample. Structured questionnaires were used for data collection, and expert review was used to validate content and Cronbach's alpha was used to validate reliability with coefficients of 0.87 for predictive analytics, 0.85 for decision support systems, 0.82 for decision accuracy, and 0.80 for decision speed. The results of the regression analysis carried out with SPSS v27 showed that the use of predictive analytics significantly increased the accuracy of the decision (H₀₁ rejected), while the use of AI based decision-support systems significantly improved the speed of the decision (H₀₂ rejected). The study findings showed that the use of AI & ML significantly enhanced decision making for the organisation, Nestlé Nigeria Plc; thus, the researchers recommended that Nestlé Nigeria Plc should continue investing in and implementing the technology of predictive analytics tools and decision support systems to ensure that decisions are made optimally and accurately in the future within the company.