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Micro, Small, and Medium Enterprises (MSMEs) are strategic sectors in the Indonesian economy, but often face challenges in efficient and data-driven production management. This article highlights the urgency of the Real Work Lecture (KKN) program as a form of student service in assisting MSMEs, especially in optimizing the production of snacks typical of Tebing Syahbandar. This research aims to optimize the production output of the Untir-untir Titik Factory with an Integer Linear Programming (ILP) approach using the Branch and Bound algorithm. Primary data is obtained through interviews and production documentation, including product type, raw material needs, operational costs, selling prices, and profit margins. The initial analysis was carried out using the simplex method using POM QM software to obtain a linear solution, which was then refined with the Branch and Bound algorithm so that the results were in the form of integers. The results of the study showed that the optimal solution was achieved by producing 25 bales of kolong-kounder and not producing other types of snacks, resulting in a profit of Rp1,650,000 per day. These findings show that the ILP approach with Branch and Bound is able to significantly increase the efficiency and profitability of MSMEs. In addition, this method can be used as a basis for quantitative-based production decision-making. This research also emphasizes the strategic role of KKN in technology transfer and real solution-based assistance for MSME actors in the region, thereby supporting the sustainable strengthening of the local economy.

This study aims to optimize the cake production profit at Cake by Cece using the Branch and Bound algorithm. The data used include raw material requirements per batch, daily raw material availability, and selling prices for three types of cakes: Cookies, Brownies, and Cinnamon Roll. The optimization model is formulated as an Integer Linear Programming problem with the objective of maximizing total daily profit. The model is solved using the simplex method followed by the Branch and Bound algorithm to obtain valid integer solutions. The results indicate that the optimal production combination is 2 batches of Cookies, 2 batches of Brownies, and 3 batches of Cinnamon Roll, yielding a maximum profit of IDR 233,000 per day. This solution satisfies all raw material constraints and is feasible for daily operational implementation. This study provides quantitative recommendations to support production decision-making in culinary sector MSMEs.

This study presents a Systematic Literature Review (SLR) focusing on the application of the Branch and Bound (B&B) method in production optimization. In modern industry, achieving operational efficiency and profitability is essential, yet optimization often yields fractional solutions that are unrealistic for indivisible entities. The Branch and Bound method, as part of Integer Programming, has proven effective in addressing these constraints by converting fractional solutions into optimal integer values. This SLR analyzed 12 scientific articles published between 2016 and 2024, sourced from databases such as Google Scholar, Scopus, and SINTA. The analysis reveals that B&B is widely utilized to maximize production profit across various sectors, including spring beds, woven fabrics, bread, and furniture, often formulated as Integer Linear Programming (ILP) problems. Furthermore, this method is also applied to minimize production time and determine the shortest routes. Overall, B&B is a flexible and efficient tool that assists companies in managing resources and achieving maximum profit with realistic integer solutions.

This study analyzes and optimizes production systems in the Industry 4.0 context, examining the fundamental shift from centralized, push-based production models to decentralized, adaptive, pull-based approaches. The research employs a mixed-method approach combining comprehensive literature review and multiple case studies across manufacturing sectors. Findings reveal that integration of Internet of Things (IoT), cyber-physical systems, artificial intelligence, and big data analytics enables real-time communication between production components, product personalization, and faster decision-making. Despite significant benefits in efficiency, flexibility, and competitiveness, implementation challenges persist, including high initial investment, employee resistance, technical expertise limitations, and integration complexity. Optimization approaches such as mixed-integer linear programming, digitally-integrated Lean Six Sigma, and digital twin simulations effectively enhance performance indicators including flexibility, reliability, and energy efficiency. The study concludes that successful production system transformation requires an integrated strategy encompassing process engineering, digital competency development, change management, and continuous evaluation to ensure sustainable optimization in the digital era