AI-Driven Demand Forecasting of Flour Mills in Pakistan

Abstract

The flour milling industry (FMI) in Pakistan plays a critical and indispensable role in the national food supply chain, serving as the primary intermediary between agricultural wheat production and final household food consumption. Wheat flour is a staple commodity for the Pakistani population, making the efficiency, stability, and responsiveness of the flour milling sector directly linked to national food security, price stability, and socio-economic welfare. Despite this strategic importance, the industry largely continues to depend on traditional demand forecasting techniques that are manual, experience-based, or reliant on simple statistical averages. These conventional approaches have become increasingly inadequate in the face of modern market complexities, including volatile wheat prices, inflationary pressures, government policy interventions, climatic uncertainties, population growth, and pronounced seasonal and religious consumption patterns.This Final Year Project critically examines the structural and operational weaknesses of traditional demand forecasting practices within Pakistan’s flour milling industry and proposes an Artificial Intelligence (AI)–driven demand forecasting model as a strategic and technological solution. The study indicates that adoption of Al technologies across its supply chain has led to notable improvements in operational efficiency, cost reduction. The study will add value to the literature on sustainable supply chain management by offering concrete evidence of the transformative power of AI in creating a solution to one of the most pressing issues in the industry. The major suggestions are to implement hybrid forecasting models, incorporating real-time information, and to create collaborative data-sharing models between the partners of the supply chains to achieve the maximum waste reduction.