Machine Scheduling Optimization in MSMEs to Boost the Scalability and Productivity

Amol Nagar¹* Dr S M Patil²

Department of Manufacturing Engineering and Industrial Management

COEP Technological University,

Pune (MH) India 411005

^1*amn21.mfg@coeptech.ac.in, ²smp.mfg@coeptech.ac.in

Abstract

The Micro, Small, and Medium Enterprises (MSMEs) sector plays a critical role in driving economic growth, generating employment, and promoting exports, particularly in developing countries like India. Scheduling is the key aspect for the growth of MSME. Because inefficient scheduling results in machine underutilization, production delays, increased costs, and customer dissatisfaction, which ultimately impacts the competitiveness and profitability.

This study focuses on the importance of machine scheduling and the pressing need for optimization in manufacturing setups, particularly within MSMEs that operate with limited resources. Efficient machine scheduling ensures timely completion of orders, better utilization of machines and labor, cost savings, and the ability to adapt quickly to changing demands. The Job Shop Scheduling Problem, a well-known optimization problem, is especially relevant in MSMEs with diverse job requirements and complex production sequences. It involves allocating multiple jobs, each consisting of a series of operations, to specific machines while adhering to precedence constraints, machine availability, and processing times.

In this study Witness simulation software is used for modeling and analyzing. Witness allows for the dynamic simulation of production environments, facilitating the evaluation of different scheduling strategies and helping identify bottlenecks, idle times, and optimal job sequences.

This research underscores the urgent need for cost-effective, simple, yet robust scheduling solutions tailored to the operational realities of MSMEs. Implementing optimized job shop scheduling models supported by simulation tools like Witness can significantly enhance productivity, ensure timely deliveries, and enable small enterprises to remain competitive in an increasingly demanding global market.

Keywords:

JSSP, MSME, Witness, Optimization, Scheduling

Acknowledgements

Biographies

Amol Nagar is a part time research scholar at Department of Manufacturing Engineering and Industrial Management. He is working in manufacturing industry and having 25 years of experience in manufacturing engineering, vendor development and manufacturing operations and profit and loss account management. His area of research is focused for manufacturing operations using optimization techniques to improve performance by maximizing utilization of resources for MSME’s. He can be contacted at e-mail: amn21.mfg@coeptech.ac.in

Dr Sudhir Madhav Patil received Bachelor of Engineering degree in Mechanical Engineering from the Late B.S.Deore College of Engineering, Dhule under North Maharashtra University, Maharashtra, India and Master’s and Ph.D. degree in Production Engineering from the College of Engineering Pune (COEP) under Savitribai Phule Pune University (SPPU), India. He is currently working as Associate Professor in the Department of Manufacturing Engineering and Industrial Management of COEP Technological University (COEP Tech), Pune, Formerly College of Engineering Pune (COEP), A Unitary Public University of Government of Maharashtra. He is a Member of The Institution of Engineers (India), The American Society of Mechanical Engineers (ASME) and a Life Member of Tribology Society of India (LMTSI). His main research interest includes Production and Industrial Engineering, Manufacturing Automation, Mechatronics, Robotics and Automation, Project Management, and Tribology. He has published several research papers and is a co-inventor for couple of Indian patents. He can be contacted at e-mail: smp.prod@coeptech.ac.in

https://orcid.org/0000-0002-9898-0793

https://scholar.google.com/citations?user=kWjekvYAAAAJ&hl

https://www.scopus.com/authid/detail.uri?authorId=57647289300

https://www.webofscience.com/wos/author/record/296193

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Dr. Sudhir Madhav Patil