Cost and Profit Analyzing Mathematical Model: A Strategic Tool for Garment Manufacturers

Abstract

Utilizing data-driven analysis leads to higher-quality decisions. Analytical approaches allow the manufacturers of ready-made garment (RMG) factories to examine various factors and consider multiple scenarios. Manufacturers often determine profit margins based on their personnel's experience rather than using data-driven analysis. This approach will help manufacturers determine the appropriate profit margin for pre-costing through cost minimization, which they can then incorporate into the total product cost for final pricing. The first step identifies and describes the variables required to develop a model. We will develop a manufacturing cost optimization model in the second stage using mixed-integer linear programming (MILP) techniques. By integrating various factors, such as production capacity, demand variations, raw material sourcing, and pricing strategies, the model aims to provide a strategic tool for garment manufacturers to enhance their profitability. We use a mathematical programming language (AMPL) to solve the formulated mixed integer linear program. This model can determine the profit by changing one parameter and keeping all other parameters unchanged. Using this concept, the manufacturer can assess the impact of a change in any parameter on profit. Manufacturers put their manufacturing costs in the data file of this model and can determine whether they generate profit or loss. Garment manufacturers can use this idea to calculate the percentage of profit margin they need to add to the total production cost to achieve profitability. We have framed the issue and presented some simulations to test our model. Based on collected data, the model estimates that increasing the cost value by 10% decreased the profit margin by 4% to maintain the same selling price.