AI Predicted Product Portfolio for Profit Maximization

Abstract

ABSTRACT Enterprises analyze opportunities and threats in the external environment, measure internal strengths and weaknesses, and formulate strategic objectives to stay ahead of their opponents. Product portfolio management (PPM) is a dynamic process by which an enterprise chooses which products to develop, sell, maintain, and remove to achieve strategic objectives, maximize profit, and balance markets for different capabilities. Most product portfolios involve new products only and exclude existing products. This study proposes a product/market portfolio model that considers both old/new products and old/new markets to maximize overall PPM profit, determine which old products should stay in existing markets, which new markets should be considered, or which markets should be abandoned, and develop new products for old markets or to introduce new products to some new markets. This study uses machine learning and deep learning algorithms to establish prediction models to screen the planned products and markets with a high success rate. Mathematical programming is then used to determine which old products should be sold in which old and new markets and which new products should be launched in which new and old markets to maximize profit. A sensitivity analysis is used to determine the effect of changes in the resource and the risk threshold on profit and product/market selection.