Sathishkumar V. R. currently serves as Assistant Professor (Sr. Grade) at Department of Mechanical Engineering, School of Engineering, Coimbatore Campus. His areas of research include Operations Management, Manufacturing Engineering, TQM, Supply Chain Management and Data Mining & Optimization.


  • 2011 : M.E. – Manufacturing Engineering
    Karpagam University
  • 1991 : B.E. – Mechanical Engineering
    PSG College of Technology


  • November 14, 2011 - Present :

Assistant Professor – Sr. Grade
School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore

  • January 2009 - November 2011 :

Assistant Professor
Karpagam University

  • June 2006 - December 2008 :

Head – Business Development and Operations
Butterfly Clothing Co.

  • November 2003 - December 2005 :

Logic Version Technologies

  • April 2001 - October 2003 :

Project Manager
eBiz Global Corp.

  • August 1995 - March 2001 :

Programmer Analyst
Logic Version Technologies

  • June 1994 - March 1995 :

Project Officer
Rajshree Sugars and Chemicals Ltd.


Academic Year Semester Name of the Subject Taught Tutorial/ Lab. Assistance/ Other Courses
Theory Practical
2019 - 20 ODD Engineering Drawing Engineering Graphics CAD   Manufacturing Practice Lab  
EVEN Operations Research Computer Aided Drawing    
2018 - 19 ODD Supply Chain Management Engineering Drawing CAD I ProjectReview Computer Integrated Manufacturing Lab  
EVEN Operations Research Engineering Drawing CAD II    
2017 - 18 ODD Engineering Drawing CAD I Workshop A    
EVEN Principles of Management Operations Research Supply Chain Management    
2016 - 17 ODD Engineering Drawing CAD I Workshop A    
EVEN Operations Research Engineering Drawing CAD II Workshop A    
2015 - 16 ODD Operations Research Engineering Drawing CAD I     Workshop A    
EVEN Operations Management Workshop A Engineering Drawing CAD II  
2014 - 15 ODD Engineering Drawing   Workshop A   Machine Drawing
EVEN Operations Research Operations Management Workshop A    
2013 - 14 ODD Operations Research Engineering Drawing Workshop A   Machine Drawing
EVEN Operations Research Workshop A Computer Aided Drawing  
2012-2013 ODD Enterprise Management Mechanics of Solids   Engineering Drawing Workshop A  
EVEN Operations Research Workshop A  
2011-2012 ODD      
EVEN Operations Research Workshop A Computer Aided Drawing  

Student Projects Guided

Year (5 years) Title of the Project/Thesis Number of Students/group Industry Project / In-house Outcome
UG  Level
2017 - 18 1. Forecasting of NIFTY index using Artificial neural network 4 In-house Completed
2. Design and performance measurement of a textile supply chain 4 In-house Completed
2016 - 17 1. Green supplier selection using fuzzy-TOPSIS and similarity analysis method 3 In-house Completed
2015 - 16 1. Demand forecasting of pre-painted galvalume using artificial neural network 3 In-house Completed
2. An application of VSM to identify sources of wastes and opportunities for improvements 4 In-house Completed
3. A fuzzy multi criteria approach for evaluating supplier performance in SCM 5 In-house Completed
2014 -15 1. Productivity enhancement of an SME by using lean techniques 4 In-house Completed
2. Optimization of multi-objective SC model using genetic algorithm for a paper mill   4 In-house Completed
2103 - 14 1. Improve production rate of pipe spooling process using lean principles   4 In-house Completed
2. Development of mathematical model to minimize total cost incorporating green initiatives for state transport corporation 4 In-house Completed
2012-13 1.Performance evaluation of Resistance Spot Welding on steel alloys with dissimilar thickness 5 In-house Completed
2. Optimization of face milling parameters for a EN 36 Steel 4 In-house Completed
2011-12 1.Development of Aluminium Metal matrix composite with fly ash 4 In-house Completed
PG Level
2018 - 19 1. Capacity Enhancement through Value Stream Mapping and Line Balancing Technique in Compressor Assembly Line 1 Industry Completed and Published in journal


Publication Type: Journal Article

Year of Publication Title


Sathishkumar V. R. and S. P. Anbuudayasankar, “Optimization of A Five Echelon Supply Chain Network using Particle Swarm Intelligence Algorithms”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 9, pp. 2015-2022, 2019.[Abstract]

Technology has shrunk the global markets and information is accessible very quickly and effortlessly. Business organizations world over concentrate on their production systems to improve the quality of the end product, well distribute the product and optimize cost of resources. Transportation cost, inventory carrying cost and shortage costs constitute the major costs in cost of distribution. A competent supply chain always strives to manufacture the right quantity of end products and hold a minimum inventory across the entire supply chain. In thecurrent paper, a five echelon supply chain model is developed and it is optimized using particle swarm intelligence algorithm.

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R. Dinesh, Sathishkumar V. R., and Krishnakumar, M., “Capacity Enhancement through Value Stream Mapping and Line Balancing Technique in Compressor Assembly Line”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 10, pp. 3734-3743, 2019.


Sathishkumar V. R., S. P. Anbuudayasankar, and K. Ramesh Kumar, “Optimizing Bi-objective, Multi-echelon Supply Chain Model using Particle Swarm Intelligence Algorithm”, IOP Conference Series: Materials Science and Engineering, vol. 310, p. 012025, 2018.[Abstract]

In the current globalized scenario, business organizations are more dependent on cost effective supply chain to enhance profitability and better handle competition. Demand uncertainty is an important factor in success or failure of a supply chain. An efficient supply chain limits the stock held at all echelons to the extent of avoiding a stock-out situation. In this paper, a three echelon supply chain model consisting of supplier, manufacturing plant and market is developed and the same is optimized using particle swarm intelligence algorithm.

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Sathishkumar V. R., Dr. Anbuudayasankar S. P., and M. Thennarasu, “Design and Development of Simulation based Model to Rank Job Flow Strategies”, ARPN Journal of Engineering and Applied Sciences, vol. 11, pp. 6082-6086, 2016.[Abstract]

In recent days many business organizations make huge investment in establishing their shop floors, installing most mechanized machines. These mechanized machines ought to operate in tandem with other machines, whose productivity level are usually different, which leads to individual machines working in maximum efficiency and the overall shop floor working in sub-optimal level. A spool shop assembles flanges, valves and nozzles to lengthier pipe, which are used in the construction of power plant, petroleum refinery, and cement plant. Longer cycle time at different work stations, lengthier job queue waiting for processing, high level of work-in-progress are inherent issues in a spool shop. Individual machines operating at maximum efficiency without analyzing the flow metrics in a spool shop leads to bottleneck. Current study, aims at spotting and decongesting the bottle neck at various machines, improve the output of the spool shop and optimize individual machine utilization. Four simulation models are developed using ARENA and each one of them are evaluated on the following metrics: Output from spool shop per time period, utilization of individual machines per time period, value added time per unit of pipe, average queue length at each machine, average waiting time of a pipe and work-in-progress. First model depicts the data captured in the existing spool shop. In second model, high priority is assigned to the jobs that ought to be further processed in shot blasting machine and heat treatment furnace, thus minimizing the wait time. In third model, a modification is suggested to the existing annealing process, where the job is allowed to cool outside the furnace, thus making the furnace available for the next job. Forth model uses the priority rule in the suggested modified model. In all these models, inter-arrival time of job from storage yard to spool shop is maintained constant. Evaluating each model against performance statistics and queue statistics helps rank models based on each metrics. Models with high priority for further processing make use of single piece flow, a proven lean principle technique that has enhanced the overall efficiency. This eventually motivates practicing shop floor manager to incorporate flow metrics in designing the layout and machine capacity for optimal overall utilization. © 2006-2016 Asian Research Publishing Network (ARPN). All rights reserved.

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Publication Type: Conference Proceedings

Year of Publication Title


Dr. R. G. Priyaadharshini, Sathishkumar V. R., and S. Rajlakshmi, A., “Study on Lean Thinking among MSMEs in the Machine Tool Sector in India”, IOP Conference Series: Materials Science and Engineering, vol. 310. Institute of Physics Publishing, 2018.[Abstract]

In the era of stiff competition and customer expectations, manufacturing organizations across the world are struggling hard to minimize their costs and maximise their performance. Micro, Small and Medium enterprises (MSMEs), who are dependent on large corporate for business and support have a tall task of keeping pace quality in processes and output. They are in the constant vigil to adopt new systems and practices so that they can minimise their cost and maximize the productivity. This study has been conducted in the machine tool sector of Coimbatore, India; which houses more than 9000 companies and offers employment to over one lakh employees. They have a tremendous pressure to use scientific processes to increase their product quality and productivity. While Lean manufacturing has been the thrust to improve the competitiveness among MSMEs in India, this study has attempted to understand their attitude towards lean management and understand the extent to which companies practice lean tools and practices. It has been found that most of the organizations in the study possess a culture of lean thinking and possess the support of top management and employees also towards the initiative. It is also seen that the organizations that incorporated lean in their daily operations have been able to scale up their productivity.

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