Wisnu Aji, Dwi Agustina Kurniawati, Noor Saif Muhammad Mussafi

System Modeling to Optimize the Production Quantity of Men's and Women's Jackets and Achieve Maximum Profit: A Case Study at Konveksi ABC Cikarang Using Linear Programming

Introduction

System modeling to optimize the production quantity of men's and women's jackets and achieve maximum profit: a case study at konveksi abc cikarang using linear programming. Optimize jacket production for maximum profit at Konveksi ABC Cikarang using linear programming. This system modeling study determined the ideal mix for IDR 8.1M profit.

Abstract

This study aims to develop an optimal production strategy for Konveksi Jaket ABC Cikarang, a garment manufacturer specializing in men's and women's jackets. The company faces significant challenges in resource allocation, particularly in managing limited material supplies and production capacity. To address this, a Linear Programming approach is applied to determine the most profitable production combination. The model incorporates two decision variables representing the quantities of men's and women's jackets and is constrained by real-world resource limitations. The objective function is designed to maximize profit, and the model is solved using IBM ILOG CPLEX software. The results indicate that the optimal production decision is to produce 125 units of women’s jackets and none of the men’s jackets, resulting in a maximum profit of IDR 8,125,000. These findings demonstrate that mathematical optimization can serve as an effective decision-support tool for improving production planning and efficiency in resource-constrained environments.

Review

This paper presents a focused case study applying Linear Programming (LP) to optimize the production quantities of men's and women's jackets at Konveksi Jaket ABC Cikarang, with the objective of maximizing profit. The authors clearly identify a pertinent real-world problem of resource allocation under limited material supplies and production capacity, common in small to medium-sized garment manufacturers. The methodology chosen, Linear Programming, is an appropriate and well-established technique for such optimization problems. The study's strength lies in its direct application of a quantitative method to yield a concrete, actionable recommendation: producing 125 units of women’s jackets and no men’s jackets, leading to a calculated maximum profit. This demonstrates the practical utility of mathematical optimization as a decision-support tool in a production environment. While the approach is sound, the abstract leaves several critical details unspecified that would enhance the robustness and practical applicability of the findings. The nature of the "real-world resource limitations" (e.g., specific material types, labor hours per process, machine capacity) is not detailed, which is essential for understanding the model's constraints. Furthermore, the abstract does not mention any consideration of demand for either jacket type; an optimal solution producing zero units of men's jackets strongly suggests that men's jackets are either unprofitable or require resources that are better utilized for women's jackets, assuming sufficient demand for women's jackets exists. A sensitivity analysis on key parameters such as raw material costs, selling prices, or resource availability would be crucial to ascertain the stability of the optimal solution, especially one that dictates exclusive production of a single product type. The strategic implications of completely ceasing production of men's jackets, beyond immediate profit, also warrant discussion. Overall, this study offers a valuable demonstration of applying operations research techniques to a practical manufacturing scenario. The clear objective and direct result make it a compelling example for small and medium-sized enterprises grappling with resource optimization. To strengthen the paper further, a more comprehensive discussion of the model's assumptions, a detailed breakdown of the constraints, and an exploration of sensitivity to input parameters would be highly beneficial. Future research could extend this model to incorporate stochastic elements, multi-period planning, or integrate other strategic objectives beyond pure profit maximization, such as market share or brand diversification. Despite these areas for deeper exploration, the paper effectively highlights the potential of mathematical optimization for improving efficiency and profitability in resource-constrained production settings.

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