Improving aggregate production planning considering maximum inventory area and service level with demand uncertainty: a nearshoring context in Mexican companies

ABSTRACT

Nearshoring is a phenomenon that has gained prominence in Mexico over the last 2 years, where companies have faced problems in meeting customer demand due to a service level and limited inventory space. Previous studies in aggregate planning did not consider the possibility of allowing rental of extra warehouse space if needed with a concurrent uncertainty in demand. Therefore, to address this gap in the research, an improved optimization model for multi-product and multi-period aggregate planning with uncertainty in demands given an area of inventory space is proposed. A set of cases based on real industry data were solved and compared to a model where no warehouse space is allowed to be rented. The results are improvements related to production costs and customer demand satisfaction. In addition, the model will help managers of manufacturing companies to determine the minimum inventory space required to effectively meet demand.

Graphical abstract

KEYWORDS:

• Aggregate production planning
• stochastic programming
• inventory space area
• nearshoring

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.

Notes on contributors

José Emmanuel Gómez-Rocha

José Emmanuel Gómez-Rocha Is a Mathematical Optimization technologist with background in stochastic programming applied to production planning and facility location models, he was the winner of the best thesis in Mexico in operations research (bachelor degree), an award granted by the Mexican Society for Operations Research (SMIO) and the Center for Mathematical Research (CIMAT) in Mexico. He has published in Q1 JCR journals such as PLOS ONE, his work has been recognized by the company LINDO systems, a world-class solver company. He holds a master´s degree in Engineering Sciences with a minor in Operations Research from Tecnologico de Monterrey and currently, he is a PhD student at the same institution.

Eva Selene Hernández-Gress

Eva Selene Hernández-Gress: Her research topics are industrial engineering combinatorial problems, such as the facility layout, replacement, job shop scheduling, and traveling salesmen problems with mathematical programming. Her studies are in industries using simulation and data sciences. She is a National Researcher Level II (National Council of Science and Technology: 2021–2024). Eva has authored more than 25 research publications on Industrial Engineering Topics. Eva has been the advisor of two graduated PhD students, and five graduated master’s students.

Cipriano Arturo Santos-Borbolla

Cipriano Arturo Santos-Borbolla: Santos is an experienced Mathematical Optimization Technologist with a demonstrated history of technical and organizational leadership. He was the Lead Scientist in mathematical optimization and machine learning at Gurobi Optimization and is a senior technical advisor to the Recruitology machine learning team. Professor Cipriano retired from Hewlett-Packard Laboratories as a Distinguished Technologist. Professor Cipriano has authored 19 refereed research publications on Supply Chain Management, Computing Infrastructure Management, Storage Management, and Workforce Planning. He was an associate editor of the Journal of Heuristics. Dr Santos has more than 30 patent applications filed, with 18 patents awarded. Professor Cipriano has been the co-advisor of four graduated PhD students, and one graduated master’s student.