Impact of Predictive Analytics of Big Data in Supply Chain Management on Decision-Making

Abstract

The beginning of information technology has led to a burst of data in every sector of operation. Handling huge volume of data to mine useful information to support decision making is one of the current sources of competitive advantage for organizations. However, preceding research literature on predictive analytics has attributed a lack of direct causal influence on predictive analytics in a manner that support Supply Chain Management in utility companies’ performance. This is as a result of huge data posing great challenges to practitioners when incorporating it into their complex decision making which adds business value. The purpose of this study was to introduce predictive analytics in supply chain management framework that enhances decision making in Kenya Power and lighting Company in Kenya. The study was guided by the following research objectives; to assess the existing predictive analytics in Supply Chain Management, to analyse existing supply chain management systems in utility companies in Kenya and to develop an integrated predictive analytics framework for big data in supply chain management for decision making in Kenya Power and lighting Company in Kenya. This research employed the Design Science research design because one of the key outcomes of the research was framework development. The study was carried out in Kenya Power & Lighting Company in Western Region in the republic of Kenya. The target population was 10 regional finance officers, 10 regional procurement officers, 47 county stores in-charges, 47 county project supervisors and 47 county business managers totalling to 161 as the sample size. The main tools for data collection were questionnaires, interview schedules and documentary review.