EMPLOYING MULTIVARIATE VARIABLES PREDICTION ALGORITHMS (MVPAS) IN REDESIGNING THE COST LEADERSHIP STRATEGY TO REDUCE WASTE AND IMPROVE PROFITABILITY: AN EMPIRICAL STUDY IN THE GENERAL COMPANY FOR ELECTRICAL AND ELECTRONIC INDUSTRIES

Abstract

This research aims to explore how multivariate forecasting algorithms (MVPAs) can be used to redesign the application of the cost leadership strategy for a general company in Electrical and Electronic Industries, so that wastes are reduced and profits are increased. The company information includes production and operation data, and a state-of-the-art multivariate data analysis technique and forecasting model are utilized to identify the factors that have the direct and indirect influences on production cost and waste. The results reveal these schemes can provide detailed information about the cost chain fragility and can be used to derive better and more flexible resource management strategies to contain loss. This also resulted in the reorganization of the cost leadership system based on market entity and business performance, and the improvement of income-related performance indicators and the reduction of business expenses.Due to such prospective nature of the advanced forecasting techniques, the research offers a valuable model for other manufacturing companies looking to control their costs and improve their financial performance