Qualification: 
Ph.D
pg_saleeshya@cb.amrita.edu

Dr. Saleeshya P. G. received both her M. Tech. and Ph. D. in Industrial Engineering and Operations Research from IIT Bombay. Her dissertation was titled, "Agile Manufacturing Systems --- An Investigative Study." She has numerous publications at national and international levels, and a total of 20 years experience in industry and academia. Her research is mainly focused on the area of operations strategy: agile manufacturing (especially agility in automotive industries), lean manufacturing in continuous manufacturing systems, responsive supply chains and lean 6-sigma.

Qualifications

  • B. Tech., Mechanical Stream Industrial Engineering, College of Engineering Trivandrum , University of Kerala, 1988.
  • M. Tech., Industrial Engineering and Operations Research, IIT-Bombay, 1999.
  • PhD, IIT-Bombay, 2005, Topic- A Study on Agile Manufacturing Systems, Guide- Prof. A.Subash Babu, Professor, Department of Mechanical Engineering, IIT-Bombay.

Experience (Total: 27 Yrs, 5 Months)

  • Teaching

    • 16 Years in Mumbai University Engineering College, in different capacities as: Lecturer, Senior Lecturer, and Assistant Professor, (1991-2006)
    • Associate Professor in Mechanical Engineering- Amrita School of Engineering, Coimbatore. October 2006 to January 2011
    • Professor in Mechanical Engineering- Amrita School of Engineering, Coimbatore, from 1-1-2011 (Current position)
  • Industrial Experience

    • Around 2.5 years-Central Railways (1989-1991)
  • Research Experience

    • 7 yrs IIT Bombay (1999-2005)

Subjects Taught

Lean Manufacturing (PG-Level, PhD)
Supply Chain Management (PG-Level, PhD)
Quality Engineering (PhD)
Concepts for Competitive Manufacturing (PG-Level)
Principles of Economics and Management (PG-Level)
Green Manufacturing (PG-Level, PhD)
Production and Operations Management (PG-Level, PhD)
E-Commerce & Industrial Finance (UG Level)
Industrial Engineering & and ERP (UG Level)
Total Quality Management (UG Level)
Enterprise Management (UG Level)
Project Management (UG Level)
Manufacturing Engineering (UG Level)
Production Processes (UG Level)
Machine Tools & Machining Processes (UG Level)
Metrology & Instrumentation (UG Level)
Engineering Drawing (UG Level)
Machine Drawing (UG Level)
Financial Management (UG Level)
Operations Research (UG Level)
Principles of Management (UG Level)

Areas of Interest 

  • Agile Manufacturing
  • Lean Manufacturing
  • Cellular Manufacturing
  • Reconfigurable Manufacturing
  • Supply Chain Management/ Responsive supply chain
  • Quality Management

Current Research Interest

  • Agility in Automobile Industries
  • Assessment of agility in Textile industries
  • Assessment of leanness of manufacturing systems
  • Sustainable leanness
  • Rural supply chain management

Publications

Publication Type: Conference Paper

Year of Conference Publication Type Title

2017

Conference Paper

Priya Harikumar and Dr. Saleeshya P.G., “Integrating FMEA, QFD and Lean for risk management in hospitals”, in COSMAR-16, Indian Institute of Science (IISc) Bangalore, 2017.

2016

Conference Paper

Jithin Jose and Dr. Saleeshya P.G., “Lean Green manufacturing practices in Cement Industries”, in COSMAR-16, Indian Institute of Science (IISc) Bangalore, 2016.

Publication Type: Journal Article

Year of Conference Publication Type Title

2017

Journal Article

Tharun Thomas, Dr. Saleeshya P.G., and Priya Harikumar, “A combined AHP and ISM based model to assess the leanness of a manufacturing company”, International Journal of Business Performance Management, vol. 18, pp. 403 - 426, 2017.[Abstract]


Our prime objective in the present study was to explore the unidentified lean enablers of manufacturing companies and form a suitable model to assess the leanness of a manufacturing company. A comparative study was also conducted between lean followed in industries and actual lean practice which revealed a fact that, a wide gap persists between the lean followed in industries and actual lean practice. Suitability of the model for leanness assessment was tested with the help of an industrial study using analytical hierarchy process. Percentage wise results of lean enablers were obtained as output of the study, which helped in lean assessment. For further study, interpretive structural modelling (ISM) was used to identify different wastes in a lean manufacturing company. The study also helps to discover how ISM tool can be used to find the interconnectivity between the different wastes in a manufacturing company.

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2017

Journal Article

Dr. Saleeshya P.G. and G. Veda Vyass, “Assessment and quantification of leanness in manufacturing systems - an investigative study”, Int. Journal of Business and Systems Research (IJBSR), vol. 11, pp. 309-324, 2017.[Abstract]


The concept of lean is ever-expanding and has continuously been researched upon. But, the question of how lean a system is has been frequently overlooked. In this study, we have researched upon the capability of a multi-level model to calculate the leanness of a system based on studies conducted in various manufacturing industries. The major issue studied and addressed in the problem is the evaluation of wastes in the system through the inhibitors of lean manufacturing in an industry. The different levels of the assessment model deal with the lean dimensions in the first and topmost level, the eight major wastes in lean manufacturing in the second level and the inhibiting factors affecting lean behaviour in the final level. Leanness as a metric is then calculated through the implementation of variability source mapping and data envelopment analysis techniques on the specific data collected through our research instrument. The obtained results have been assessed through plotting all dimensions simultaneously on a radar chart for evaluation. The results have been presented and the model's validity has been assessed. Inferences were drawn from findings of the study.

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2017

Journal Article

Dr. Saleeshya P.G., J., D. Krishna.C., and U., M. Krishna, “Study and Analysis of Sea Port Operations and Productivity Improvement by Optimized Berth Utilization”, International Journal of Business Innovation and Research(IJBIR), , vol. 13, no. 4, pp. 403-429, 2017.[Abstract]


The present scenario shows an exponential increase in competition between seaports. Individual ports have to effectively operate cargo handling capacities to meet the requirements of the exporters who set an international benchmark. Ports should ensure maximum utilisation of infrastructural facilities to foster price competitiveness. This paper was aimed at increasing the productivity of the port by optimised berth utilisation. Problems in the existing system were identified through various operational studies. To investigate this hypothesis, regression analysis was done on the data which helped us identify the most significant factor that affected the efficiency of the port. Crew and transport scheduling model was developed to provide an optimum scheduling sequence, which reduced the turn round time of the vessel. Finally, to depict the gross impact, cost comparisons were done. The system was re-instigated with the proposed changes resulting in a significant decrease of the turn round time, hence satisfying the demand.

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2016

Journal Article

P. Raghuram and Dr. Saleeshya P.G., “Assessing the responsiveness of supply chain -structural equation modelling based approach”, International Journal of Logistics Systems and Management, vol. 25, pp. 558-579, 2016.[Abstract]


Companies worldwide have now realised that 'customer focus' is what wins the game. As demands of the customer become more and more volatile and unpredictable, they have to stay ahead of their competitors in fulfilling the needs of the customer. The supply chain as a 'unit' has to compete against other supply chains for reaching out and delighting the customer. Responding fast to the changing customer needs has been an intangible factor underlying a successful business. Thus, responsiveness of the supply chain has become the differentiator between the competing supply chains. In this paper, the end-to-end supply chain factors influencing responsiveness are identified from extant literature and a model of responsiveness of the supply chain has been developed by conducting a survey of 204 pump manufacturers. The developed model has been validated through confirmatory factor analysis using structural equation modelling. These findings can help to identify the areas of improvement for the supply chain and may lead to a focused improvement in these areas thus improving responsiveness and profitability.

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2016

Journal Article

M. Safeer, S. P. Anbuudayasankar, Dr. Saleeshya P.G., and K. Ganesh, “A stochastic planning model for humanitarian relief response logistics”, International Journal of Enterprise Network Management, vol. 7, pp. 250-259, 2016.[Abstract]


During emergencies, decision making to support relief preparedness and response activities are challenging task requiring quick and effective action from responders under uncertainty conditions. This paper proposes a response planning stochastic model for humanitarian transportation operations. The objective of this proposed model is to minimise total transportation duration during emergency situations. This model aims to maximise satisfaction levels through attaining quick response which can help in making decisions on disaster relief logistics. © Copyright 2016 Inderscience Enterprises Ltd.

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2015

Journal Article

Dr. Saleeshya P.G. and Ajai Bhadran, “Productivity improvement through lean initiative in a surgical equipment manufacturing company: a case study”, International Journal of Business and Systems Research (IJBSR), vol. 9, pp. 297–314, 2015.[Abstract]


The paper presents a case study conducted at a surgical equipment manufacturing company, where productivity improvement can be achieved by reducing the lead time required to manufacture their product. Using an important lean tool value stream mapping (VSM) the various wastes that are held up at each stage of manufacturing is identified, from which the most critical waste has been selected using a cost-time profile (CTP). Then a model is developed and solved using regression analysis. Based on the values obtained a cost-time profile is drawn again and compared, which reveals that there is nearly a 50% increase in the overall productivity.

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2015

Journal Article

Dr. Saleeshya P.G. and S Anirudh, “A bi-level approach to frequency optimisation of public transport systems”, International Journal of Business Innovation and Research (IJBIR), vol. 9, pp. 608–630, 2015.[Abstract]


In this study, we concentrate on one of the three major areas of the whole transit network design problem that being the frequency setting. The main problem addressed here is the minimisation of losses borne by a public state transport corporation in India. This is achieved by optimally allocating resources to shared routes already in existence. The bus frequency setting problem is solved using a bi-level methodology. In the first level minimum required fleet size for the routes are found out by considering the routes individually. In the second level with the guarantee of load feasibility, allocation of frequencies is done for the fleet size found in the first level. This is done by also taking into consideration the achievement of minimisation of operational cost of the routes under consideration as added objectives. The relation between the operations cost and the maintained frequencies are found out using artificial neural network (ANN), and the second level is solved using a multi objective genetic algorithm (GA). This methodology has been used in optimising two shared routes and the results are thus presented.

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2015

Journal Article

Dr. Saleeshya P.G. and B. Sachin, “Modeling and analysis of an agile supply chain”, International Journal of Productivity and Quality Management (IJPQM), vol. 15, pp. 486-510, 2015.[Abstract]


This paper is an outcome of a study carried out in five manufacturing industries. Based on the literature review, field study and industry study a multi-level model which constitute the enablers of agile supply chain, the factors which are identified as the sources of driving force of these enablers has been developed. This model also identifies the business performance indicators (BPIs) of a manufacturing system. The objective of this paper is to identify the most important enablers of agile supply chain and the driving forces behind these enablers in an organisational system. By identifying these factors and forces, an organisation can channel its resources in the most effective way in order to enhance the achievement potential of its different BPIs. Multiple regression and Gauss elimination method are identified as suitable tools for analysis in this study. Copyright © 2015 Inderscience Enterprises Ltd.

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2015

Journal Article

Dr. Saleeshya P.G., A. Sneha, C. Karthikeyan, C. Sreenu, and A. K. Rohith, “Lean practices in machinery manufacturing industries - A case study”, International Journal of Logistics Systems and Management (IJLSM), vol. 20, pp. 536-554, 2015.[Abstract]


Lean manufacturing is an emerging concept in the Indian manufacturing scenario. Many small and medium scale enterprises in the country are aiming at becoming lean and thereby improving their productivity. This paper is an outcome of a case study in one such enterprise that deals with the manufacturing and assembly of offset printing machinery in India. Being a vendor-based organisation, part shortage due to delays in the process was a major problem faced. A thorough analysis of the various vendors and processes that the product goes through was done and the process flow was mapped to obtain a clear picture of the entire system, and software modelling helped arrive at quantitative results. Lean tools like Pareto analysis, five-why analysis, source inspection, etc., were used to identify and eliminate the various factors that led to delay. Proper scheduling of jobs is also essential to reduce waiting time, and therefore an algorithm has been developed to help arrive at an optimum scheduling sequence. The results from this case study have proved successful in reducing the lead time of the component under study. Copyright © 2015 Inderscience Enterprises Ltd.

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2013

Journal Article

Dr. Saleeshya P.G., D. Austin, and N. Vamsi, “A model to assess the lean capabilities of automotive industries”, International Journal of Productivity and Quality Management (IJPQM) , vol. 11, pp. 195-211, 2013.[Abstract]


Changing customer and technological requirements force manufactures to develop lean capabilities. Many organisations have adopted lean thinking paradigm in their drive to optimise performance and improve competitive position. In this paper, the application of lean production systems is described in an automotive context. The results reported are based on the data collected from a survey covering around 15 manufacturing companies in India using a standard questionnaire. The study was driven by a conceptual model which relates lean practices to competitive objectives. The method of analytic hierarchy process (AHP) was used to analyse and identify which among the various lean production tools and enablers are likely to have greater impact on realising a lean production system. © 2013 Inderscience Enterprises Ltd.

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2012

Journal Article

Dr. Saleeshya P.G. and A Subash Babu, “A combined AHP-and DEA-based approach to measure agility of manufacturing systems”, International Journal of Business and Systems Research (IJBSR), vol. 6, pp. 431–455, 2012.[Abstract]


This paper is related to a real life study carried out in Indian manufacturing organisations to assess the extent to which the agile manufacturing paradigm is recognised and deployed, as this paradigm has been a major objective of many companies. A multi-level framework developed by the authors, along with a questionnaire was used to identify various enabling factors of agility in Indian manufacturing industries. This model helps to determine the measure of agility called ‘Effectiveness Index’ (EI) at the various hierarchical levels of an organisation. In this paper we propose an approach to measure the agility of manufacturing systems by combining the analytical hierarchy process (AHP) and the data envelopment analysis (DEA) methodologies. The method of AHP was used to identify important agility enablers by deriving appropriate weights (Importance Index). DEA was used to compare and find out the priorities of various units of organisations by making use of the values of EI and Importance Index for agility.

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2012

Journal Article

Dr. Saleeshya P.G., A Subash Babu, and A. S. Vishnu, “Agility in Indian Manufacturing Industries: an Empirical Investigation”, International Journal of Indian Culture and Business Management (IJICBM), vol. 5, pp. 208–232, 2012.[Abstract]


This paper reports the salient details of a study carried out with the objectives of identifying what makes an organisation agile, identifying various enablers of agility by field study, developing a modelling framework for assessing agility and assessing agility in Indian manufacturing industries. For this purpose, data were collected from Indian manufacturing industries with the help of a questionnaire. The method of analytic hierarchy process was used to analyse and identify which among these factors are likely to have greater impact on realising an agile organisation. This study revealed a number of interesting observations, which are reported in this paper.

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2012

Journal Article

Dr. Saleeshya P.G., Raghuram, P., and N Vamsi, “Lean manufacturing practices in textile industries–a case study”, International Journal of Collaborative Enterprise (IJCol. Ent), vol. 3, no. 1, pp. 18–37, 2012.[Abstract]


Lean manufacturing is a philosophy of eliminating waste through continuous improvement. This paper is an outcome of a case study conducted in a textile industry in south India. Though the concept of lean manufacturing has shown good results in continuous process industry, it has not been extensively used as compared to discrete manufacturing industries. Process industries, especially textile industries, have automatic machinery which are highly inflexible and have high volume/low variety products. This nature of the textile industry makes implementing lean manufacturing techniques a challenge; hence implementing lean techniques in a textile industry has been taken up as a challenge. We have chosen a combination of value stream mapping (VSM), 5S, kanban, kaizen, poka-yoke, and visual controls to improve the processes. The achievement potential scores before and after lean implementation has been highlighted using radar diagrams. The findings of this study reveal that a thorough analysis of the process, setup, and changeover time (CO), use of colour coding for identification of volume-mix, use of kaizen and quality circles which empower the workforce, are some of the various keys to a successful lean implementation in a textile industry.

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2012

Journal Article

Dr. Saleeshya P.G., Karthik Suresh Thampy, and P. Raghuram, “A combined AHP and ISM-based model to assess the agility of supply chain-a case study”, International Journal of Integrated Supply Management (IJISM), vol. 7, pp. 167-191, 2012.[Abstract]


An agile supply chain corresponds to the quick responsiveness of an organisation to ever changing market demands without compromising on cost and quality of the product. The textile industry is one such industry having a volatile market demand. This paper is an outcome of a case study conducted in a leading spinning industry in South India. Various enablers and determinants of agility were identified through literature review, field study and industry study. Based on the findings of these studies a multi-level model was developed and the suitability of this model to improve the agility of supply chain was tested. For this, two methodologies - analytical hierarchy process (AHP) and interpretive structural modelling (ISM) were identified as suitable tools. The analysis of the results of AHP and ISM methodologies provide the industry an insight on how the supply chain can be made more agile. Further, the model provides the top management a clear vision of the practices to be implemented and the improvement methods to be adopted by the company for an effective management of supply chains in spinning industries. Copyright © 2012 Inderscience Enterprises Ltd.

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2011

Journal Article

Dr. Saleeshya P.G. and A Subash Babu, “Application of goal programming to manage agility in manufacturing systems”, International Journal of Agile Systems and Management (IJASM) , vol. 4, pp. 222–236, 2011.

2011

Journal Article

Dr. Saleeshya P.G., A. Subash Babu, and A.S. Vishnu, “A model to assess the agility of manufacturing organisations: Systems approach and application”, International Journal of Productivity and Quality Management (IJPQM), vol. 8, pp. 265-295, 2011.[Abstract]


To achieve the status of an agile manufacturer, organisations need to clearly understand the concept of agility related to their industrial and business circumstances and then identify and acquire the appropriate characteristics, which will result in an agile manufacturing organisation. This paper is related to a real-life study carried out in Indian manufacturing organisations to assess the extent to which the agile manufacturing paradigm is recognised and deployed, as this paradigm has been a major objective of many companies. With the help of literature study and a preliminary study of selected industries, a framework was developed which in its schematic form captures and depicts the efforts an organisation may have to put in a concerted manner to enhance its agility. This framework was used to develop a questionnaire with which a survey of Indian manufacturing industries was carried out. The data collected through this questionnaire was analysed by the model developed by the authors. This model helps to determine the measure of agility called effectiveness index for agility at the various hierarchical levels of an organisation. This paper reports the salient details of the model and how it was applied on the data collected from the industry survey. © 2011 Inderscience Enterprises Ltd.

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Publication Type: Conference Proceedings

Year of Conference Publication Type Title

2016

Conference Proceedings

Dr. Saleeshya P.G. and R. Rahul, “Impact of E-Commerce on supply chain management”, Int. Conference on Big Data Analytics for Optimizing Supply Chains, vol. 39. SP Jain Institute of Management and Research (SPJIMR), Bombay, 2016.

2016

Conference Proceedings

Dr. Saleeshya P.G. and .Binu, M., “A Neuro-Fuzzy hybrid model for assessing leanness of manufacturing systems”, int. Conference on Big Data Analytics for Optimizing Supply Chains, SP Jain Institute of Management and Research (SPJIMR). Bombay, p. 124, 2016.

2016

Conference Proceedings

Tharun Thomas, Priya Harikumar, and Dr. Saleeshya P.G., “An ISM based model for finding the root causes of eight forms of wastes in lean manufacturing”, International Conference on E-business and supply chain competitiveness, EBSCC 2016. Indian Institute of Technology, Kharagpur, IITK , p. 439, 2016.

2015

Conference Proceedings

Dr. Saleeshya P.G. and Tharun Thomas, “AHP based study on diversion of lean manufacturing practices in industries”, international Conference on Mechanical and Manufacturing Engineering, (ICMME’2015). SCSVMV University, pp. 052-058, 2015.

2014

Conference Proceedings

Dr. Saleeshya P.G. and M. Harikrishnan, “Modeling and analysis of manual assembly plant layout for effective transportation of materials”, International Conference on Advances in Engineering, Technology, and Science (ICAETS 2014) . pp. 132-134, 2014.

2014

Conference Proceedings

Dr. Saleeshya P.G. and Ajai Bhadran, “Productivity improvement through lean initiave:-a case study”, International Conference on Advances in Engineering, Technology, and Science (ICAETS 2014). pp. 55-59, 2014.

2012

Conference Proceedings

Dr. Saleeshya P.G. and Raghuram, P., “Modeling a responsive supply chain through exploratory factor analysis”, Proceedings of The 12th Consortium of Students in Management Research (COSMAR 2012) . Department of Management Studies, Indian Institute of Science, Bangalore , p. 64, 2012.

2006

Conference Proceedings

Dr. Saleeshya P.G. and A. Subash Babu, “Systematic assessment of agility in Indian manufacturing industries”, ICAM. UK , 2006.

2002

Conference Proceedings

Dr. Saleeshya P.G. and A. Subash Babu, “A Multi-level modeling framework to assess the agility of manufacturing systems”, 20th AIMTDR conference BITS Ranchi. pp. 682-690 , 2002.

2002

Conference Proceedings

Dr. Saleeshya P.G. and A Subash Babu, “A multilevel modeling framework to assess the agility in manufacturing systems”, 20th AIMTDR Conference Proceedings, BITS Ranchi. pp. 682-690, 2002.

1999

Conference Proceedings

Dr. Saleeshya P.G., K. N. Nandurker, A. Subash Babu, and N. Rangaraj, “"GT( Group Technology ) in Indian Manufacturing Industries" an investigative study”, 15th International Conference on Production Research (ICPR). Ireland, pp. 1279-1282, 1999.

Publication Type: Book Chapter

Year of Conference Publication Type Title

2014

Book Chapter

Dr. Saleeshya P.G., “Empirical approaches to assess the agility of manufacturing systems”, in (EBAO-1146), Encyclopedia of Business Analytics and Optimization, vol. 2, Montclair State University, USA, jointly with Harvard’s Institute for Quantitative Social Science, 2014, pp. 256-267.

2014

Book Chapter

Dr. Saleeshya P.G., “Optimization approaches to assess the agility of manufacturing systems”, in (EBAO-1145), Encyclopedia of Business Analytics and Optimization, vol. 3, Montclair State University , USA, jointly with Harvard’s Institute for Quantitative Social Science, 2014, pp. 598-607.

Reviewer

  1. Acting as reviewer of International Journal of Manufacturing Systems-Elsevier
  2. International Journal of Business Systems and Research (IJBSR)
  3. European Journal of Management (EJM) Elsevier
  4. European Journal of Operations Research (EJOR) Elsevier
  5. Universal Journal of Marketing and Business Research
  6. Journal of Research in International Business and Management
  7. African Journal of Business Management

Member

  1. Editorial Board Member

    International Journal of DATA sciences, Inderscience

    Currently guiding six PhD’s in Amrita Vishwa Vidyapeetham; areas: PhD AWARDED IN 2018 - 1. Modelling And Analysis Of Supply Chain Responsiveness – P Raghuram

    1. Leanness Assessment In Healthcare System – Priya Hari Kumar
    2. Six Sigma Enabled Lean Manufacturing Systems – Tharun Thomas
    3. Lean In Construction Industries – Isha Sanitha
    4. Sustainable Lean Manufacturing – Vivek Gopi
    5. Leanness In FMCG Industries – Liju Mathew Alexander
    6. Rural Supply Chain Management – Sreejith J
  2. Member - Member Board of Studies

    B. Tech Mechanical Engineering, M. Tech Manufacturing Engineering, Amrita Vishwa Vidyapeetham,

  3. Member - Various Doctoral Committees

    • Examiner and question setter ( PhD, Masters, and Under Graduate Programmes)
      in many Universities such as:
      Usmannia University, Hyderabad, Mumbai University, Pune University, M G University, Kerala, Cochin University, Amrita Vishwa Vidyapeetham, Anna University, Coimbatore…
  4. Membership in Professional Bodies

    Life member ISTE, SAE India Western Region, IIT Bombay Alumni Association

Projects Guided

List of B. Tech. Projects guided: 2015 onwards

  1. Assessment and Quantification Of Leanness In Manufacturing Systems- An Investigation Study (2015)
    • Veda Vyass G - CB.EN.U4MEE11521
    • Preetham K - CB.EN.U4MEE11240
    • Raajaganapathi VT - CB.EN.U4MEE11241
    • Madhu Sanjaai L - CB.EN.U4MEE11033
  2. Optimizing The Financial Performance Of The Existing Supply Chain At A Brake Manufacturing Company (2016)
    • Nigil Premkumar - CB.EN.U4MEE12031
    • Pranav Prasad - CB.EN.U4MEE12063
    • Shreejith Shiji Kumar - CB.EN.U4MEE12261
  3. Firefly Enabled Agility Assessment in Manufacturing Systems (2017)
    • Nithesh R - CB.EN.U4MEE13037
    • Sanesh K - CB.EN.U4MEE13053
    • Sai Krishna S - CB.EN.U4MEE13159
  4. Assessing The Sustainability Of Manufacturing System: A Case Study(2018)
    • Pothapragada Chaitanya - CB.EN.U4MEE14030
    • Rohit V - CB.EN.U4MEE14038
    • Kannithi Rahul - CB.EN.U4MEE14226
    • Pagadala Pavan Kalyan - CB.EN.U4MEE14235

List of M.Tech Projects guided: 2015 onwards:

  1. Lean Manufacturing Practices In Agro Machinery Industries And Service Sector Industries: A Case Study (July 2015)
    • Tharun Thomas – CB.EN.P2MFG13023
  2. A Neuro – Fuzzy Hybrid Model For Assessing Leanness Of Manufacturing Systems (July 2016)
    • Binu .M – CB.EN.P2MFG14004
  3. Determination Of Leagility Index Of A Manufacturing Supply Chain Through Fuzzy Approach (July 20016)
    • Rahul R – CB.EN.P2MFG14015
  4. Lean- Green Manufacturing Practices In A Ceement Industry (Nov 2016)Mini Project
  5. Lean – Inventory Management In A Cement Industry(June 2017)Major Project
    • Jithin Jose - CB.EN.P2MFG15007
  6. Manufacturing System Sustainability Through Lean And Agile Initiatives(Nov 2016)Mini Project
  7. Productivity Improvement Through Development Of Sustainability Metrics In A Wire Manufacturing Industry(June 2017) Major Project
    • Vinay Venugopal - CB.EN.P2MFG15023
  8. Application Of Lean Principle In Manufacturing Two Wheeler Safety Gears(Nov 2017)minor project
    • Srinath K.R - CB.EN.P2MFG15007
  9. Productivity Impovement Through Lean Initiative : A Case Study(June 2017 ) Minor Project
  10. Application Of Lean Concepts In Service Sector(Nov 2018) Major Project
    • Akhil Chandran- CB.EN.P2MFG16005
  11. Bottleneck Identification And Process Improvement By Lean Six Sigma DMAIC Method (Nov 2017) Minor Project
  12. Improving The Productivity And Sustainability Of A Conglomerate Process In A Manufacturing Industry(June 2018) Major Project
    • Nikhil Nandakumar – CB.EN.P2MFG16027

Official Address

Department of Mechanical Engineering,
Amrita School of Engineering,
Amrita Vishwa Vidyapeetham,
Coimbatore – 641105
pg_saleesyha@cb.amrita.edu

Address for Communication

C-303, Green Park
L&T Apartments
Sugar cane breeding institute Road
Sheeranaikkam Palayam
Coimbatore-641007

Permanent Address

Parakkal House, Ezhukone. P.O,
Kollam (Distict), Kerala State,
Pin:691505