lean assessment measures 1

Development of index for measuringleanness: study of an Indian autocomponent industry

Bhim Singh, S.K. Garg and S.K. Sharma

Summary

Purpose – The extant literature fails to provide an efficient method to measure leanness of any

manufacturing firm. The purpose of this paper is to discuss the concept of leanness and to provide an

efficient measurement method for measuring leanness.

Design/methodology/approach – Measurement method is based on the judgment and evaluation

given by leanness measurement team (LMT) on various leanness parameters such as supplier’s issues,

investment priorities, Lean practices, and various waste addressed by lean and customers’ issues.

Further fuzzy set theory is introduced to remove the bias of human judgment and finally defuzzification is

done and results are presented in the form of leanness index.

Findings – Leanness indices have been developed and presented separately on 100 points scale for

all parameters of leanness i.e. LISuppliers ¼ 47:98, LIInvestment ¼ 50:66, LIpractices ¼ 58:38, LIWaste ¼ 60:01,

LICustomers ¼ 47:1.

Research limitations/implications – This leanness measurement method used the views of experts

and may contain human judgment error.

Practical implications – It will be helpful to both academician and practitioners as an assessment tool

for evaluation of lean status of any industry utilized.

Originality/value – Leanness measurement method based on judgment of experts is used first time for

evaluation of leanness.

Keywords Lean production, Manufacturing systems, Fuzzy control, Quality management

Paper type Research paper

1. Introduction

In today’s competitive market, manufacturing firms are facing tremendous pressure of

customer’s expectation about product quality, demand responsiveness, reducing cost and

product variety. To meet with such expectations of customers production industry is striving

for modern manufacturing initiatives and lean manufacturing is one of the best initiatives in

that direction. Lean manufacturing as a multi-dimensional approach that encompasses a

wide variety of management practices, including just-in-time, total quality management

(TQM), work teams, cellular manufacturing, Suppliers involvement, etc. in an integrated

system. The main thrust of lean production is that these practices can work synergistically to

create a systematized, high quality system that fulfils the demands of the customers at the

required pace (Shah and Ward, 2003). Many authors (Womack et al., 1990; Taj and Berro,

2006; Motwani, 2003; Comm and Mathaisel, 2005; Russell and Taylor, 1999) and many more

discussed the concept of lean manufacturing and its benefits at manufacturing

organizations but a few attempts are made to precisely define leanness in context to

assessing lean status of any manufacturing firm. Manufacturing leanness is a concept that

unifies the various practices of promoting lean. Since these practices measure different

objects, e.g. inventory size, quality defects, Kaizen and asset reduction (Emiliani, 2000;

Womack et al., 1990) defined manufacturing leanness as a strategy to incur less input to

PAGE 46 j MEASURING BUSINESS EXCELLENCE j VOL. 14 NO. 2 2010, pp. 46-53, Q Emerald Group Publishing Limited, ISSN 1368-3047 DOI 10.1108/13683041011047858

Bhim Singh is Assistant

Professor, based at the

Department of Mechanical

Engineering, Galgotia’s

College of Engineering and

Technology, Greater Noida,

India. S.K. Garg is a

Professor, based at the


and Industrial Engineering,

Delhi Technical University,

Delhi, India. S.K. Sharma is

a Professor, based at the


Engineering, NIT

Kurukshetra, Haryana,

India.

better achieve the organization’s goals through producing better output, where ‘‘input’’

refers to the physical quantity of resources used and their costs, and ‘‘output’’ refers to the

quality and quantity of the products sold and the corresponding customer services.

According to Papadopoulou and Zbayrak (2005) leanness should not be viewed in the

narrow sense of a set of tools, techniques and practices, but rather as a holistic approach

that transcends the boundaries of the shop-floor thus affecting apart from the production

itself almost all the operational aspects, e.g. design, development, quality, maintenance,

etc. as well as the entire organization and management of the company. Swamidass (2007)

used the ratio of total inventory to sales as a general performance index to analyze over

14,000 firm-years of lean practitioners. This ratio is easier to interpret (i.e. the lower the

better), but it focuses merely on inventory-related performance (Wan and Frank, 2008)

proposed a unit-invariant leanness measure with a self-contained benchmark to quantify the

leanness level of manufacturing systems. Evolved from the concept of data envelopment

analysis (DEA), the leanness measure extracts the value-adding investments from a

production process to determine the leanness frontier as a benchmark. Allway and Corbett

(2002) defined leanness in term of efficiency and effectiveness, according to (Bayou and de

Korvin, 2008) leanness is a matter of degree, so a manufacturing system can be described

as lean, leaner or leanest.

Leanness can be an assessment parameter to measure the lean status of any organization

and accordingly organization can be designated as lean, leaner and leanest (Table I). This

paper is covered in five sections. In first section of this paper leanness concept is discussed

in detail. In second section leanness measurement method is presented followed by

leanness model formulation and leanness parameter discussion. In third section brief

methodology is discussed how scores awarded by LMT members converted in fuzzy grades

to remove the human judgment error and finally defuzzification is done. In fourth section

computation is done and individual leanness index is obtained for all five parameters. Finally

the paper is concluded in the last section.

2. Leanness measurement method

Chan et al. (2003) introduced innovative performance measurement method for

measurement of supply chain performance using grade awarded by performance

measurement team (PMT), a similar type of method named as leanness measurement

method (LMM) is developed and used to evaluate leanness of manufacturing firm. In this

paper the leanness of an Indian automobile component manufacturing firm situated in

National Capital Region (NCR) is measured using the model shown in Figure 1. A five

members team having expertise in lean implementation is selected for this purpose, every

leanness measurement team member (LMTM) is asked to rate the existing status of the firm

on the bases of five leanness parameters such as suppliers issues, investment priorities,

lean practices, various waste, customers issues and details regarding these parameters is

discussed in next section of this paper.

2.1 Leanness assessment parameters

Five parameters as shown in Figure 1 are selected for the assessment of leanness of the

given automobile firm. These parameters are selected from the extant literature discussed

by many authors as (Saurin and Ferreira, 2009; Taj and Berro, 2006; Comm and Mathaisel,

2005; Simpson and Power, 2005; Barla, 2003; Karlsson and Ahlstrom, 1996) in this section

each parameter is discussed in details regarding its content.

1. Suppliers issues. Every evaluator is asked to rate this parameter as per the relation

between the selected organization and its suppliers on following issues: Commitment of

suppliers to continuously reduce cost, Communication with suppliers, Involvement at

early stage of new product development, Joint (R&D) venture with suppliers, Long term

business partnership, Proximity with suppliers, Quality assured supplies, Reduced

response time of suppliers, Reducing number of suppliers, Sharing of profit with

suppliers, Supplier’s development activities, Visit of supplier at given organization and

Visit of company person’s at supplier’s premises.

VOL. 14 NO. 2 2010 jMEASURING BUSINESS EXCELLENCEj PAGE 47

Table I Brief about measurement models presented in literature

Author, Year Parameters Formula used Remarks

Soriano-Meier andForrester (2002)

Elimination of waste, continuousimprovement, zero defects, jest in timedeliveries, pull of materials,multifunctional team, Decentralization,Integration function and verticalinformation

Hypothesis testing Developed and tested the model thatcan evaluate the degree of leannesspossessed by manufacturing firms.Results have provided to support thethree hypothesis

Bayou and de Korvin(2008)

Just-in-time (JIT), Kaizen, and qualitycontrols

Fuzzy logic: basicconcepts

Compared the leanness of GM andFord production system and foundFord’s system is more than 17 percentleaner than GM’s system over thethree-year period

Borenstein et al. (2004) No of employees, no. of vehicles,investment in training programme, ITinvestments, physical area, total costand infrastructure investment

Data envelop analysis Suitable method was developed tomeasure performance of multi unitservice spread around the country andconcluded that this technique can beapplied to any public or privatecompanies mainly in retail sectors

Brill and Mandelbaum(1990)

Task sets, weights of importance andmachine performance

Mathematical modeling Measures of machine adaptivity aredefined in terms of measures offlexibility, which are relatives to tasksets, their weights of importance andmachine task effectiveness measure

Chan and Qi (2003) Supplying, inbound logistics, coremanufacturing, outbound logistics andmarketing and sales

Fuzzy logic Revealed the key issues in the existingperformance measurement methods,especially in SCM context and alsoproposed a cross- organizationalperformance measurement method

Chan et al. (2003) Customer satisfaction, flexibility,information and material flow, effectiverisk management and supplierperformance

Fuzzy logic Provided a simple and robustmathematical model to calculate aperformance index of a performancemeasure in supply chain.

Chew et al. (2004) 28 parameters chosen which affectsthe technical evaluation score forcurtain walls and cladding facades

Hypothesis testing Technical evaluation index wasdeveloped and found that it wasattributed to following relativeweightages of 37.8 percent of design,3.6 percent of construction, 14.5percent of customer satisfaction, 41.6percent maintenance and 2.5environment factors

Cousins et al. (2008) Communication performance measure,operational performance measure,socialization mechanism, businessperformance

A structural equationmodel and hypothesizedmodel

Theoretical frame work was supportedwith results indicating that socializationmechanism fully mediate the effects ofsupplier performance measures on thefirms

Kuhnle (2001) Lead time, quality, budget, personnelcapacity, personnel flexibility, andmachine flexibility

No mathematical formula Demonstrated a model that supportschange management by measuring thereconfigurability of manufacturingareas with focus on time consumptionfor reconfigurability

Kumar et al. (1999) Price, quality flexibility, and deliverydependability

Weighted mean Constituted an important tool forcompanies seeking to improve theircompetitiveness through quality. Thequality competitiveness indexdeveloped here will help positionmanufacturing organizations at anappropriate place in the ranking onquality

Sharma and Bhagwat(2007)

Finance, customer, internal businessprocess and learning and growth

Balanced scorecard(BSC) analyticalhierarchy process (AHP)approach

Developed a BSC for SCM evaluationand proposed a method to prioritize thedifferent performance levels in anyorganization using AHP methodology

PAGE 48 jMEASURING BUSINESS EXCELLENCEj VOL. 14 NO. 2 2010

2. Investment priorities. Main investment priorities of the firm on which every LMTM is asked

to evaluate are given as: Research and development, Information Technology,

Automation of processes, Training of employees, Welfare of employees, Market

research, Vendor development, Procurement of new machinery and Advertisement.

3. Lean practices. Evaluation of this parameter is done on the basis of various lean

practices followed by the firm: 5-S, Andons, cellular manufacturing, Kaizen, Kanban,

line stop authority, low cost automation, material resource planning (MRP), Milk run

system, nearby suppliers, Poka yoke, production leveling, production smoothing,

quality circle, single minutes exchange of die (SMED) (low set up time), Six Sigma,

standardization of work, statistical process control (SPC), total productive

maintenance (TPM), total quality management (TQM), value stream mapping (VSM)

and visual control.

4. Various wastes. Any type of waste is always undesirable for any firm. This parameter is

rated on the basis of identification & elimination of various waste by the selected firm.

Various waste addressed by lean are as under: complexity of operation, defects in

products, excessive inventory of finished goods, excessive inventory of raw materials,

excessive inventory of work in process (WIP), excessive inspection, excessive lead time,

excessive movements of man, excessive movements of materials, excessive scrap,

excessive transportation, high rejection, high rework, idleness of workers due to no

electricity, idleness of workers due to no instruction, idleness of workers due to no work,

idleness of workers due to no material, inappropriate processing, machine downtime,

overproduction, poor fund management, poorly planned space, unutilized creativity of

manpower and warranty claim.

5. Customers issues. Customer is the most vital part of any firm. This parameter is rated on

the bases of following customers satisfaction issues present in the organization: Product

varieties, Response time, Cost of product, Market share, product quality, guaranty and

warranty.

3. Methodology

In this section brief methodology of the LMM is discussed.

1. Every performance measurement method have a history and a goal (Chan and Qi,

2003), while measuring a particular leanness parameter, the evaluators of the

leanness measurement team (LMT) considered the planned history and goal, and

then set the measurement scale ranging from just acceptable bottom of the leanness

Figure 1 Leanness measurement model

Leanness Index

Defuzzification

LMTM1

Suppliersissues

Investmentpriorities

Leanpractices

Variouswaste

Customersissues

LMTM2

LMTM5

LMTM3

LMTM4

Normalized weights for eachmeasure using Fuzzy set theory


status to the totally satisfactory lean status. The influence of the associated

environment and lean status in Indian context is being assessed and taken in to

consideration for setting the history and goal. On the basis of the these reason, it is

assumed that this method have a history of 40 points and goal of 80 points on a 100

points scale for all parameters.

2. As mentioned, a team of five experts in lean implementation is selected for the study and

asked to rate the said parameters according to the goal and history of the measurement

method. Assumed history of this method is 40 points and goal is 80 points, so each

evaluator awarded score between 40 points and 80 points i.e. (40, 80).

3. On the basis of experience and expertise in the selected area, the scores of the

evaluators are different for same parameter and to remove this bias in the individual

judgment, some weights are assigned to each evaluator. i.e. WT¼(0.15, 0.1, 0.20, 0.3,

0.25). These weights are assigned on the basis of a formal discussion with all the

evaluators on the subject knowledge and predictability of any judgment, which is being

assessed with a series of questions asked from them. This is a relative rating and sum of

all the weights is equal to unity.

4. Score awarded by all LMTM’s are certainly having some vagueness or fuzziness in it. So

judgment of each evaluator is mapped on triangular fuzzy function with six grades shown

in Figure 2 such as �A ¼ (80,100,100), �B ¼ (60,80,100), �C ¼ (40,60,80), �D ¼ (20,40,60),�E ¼ (0,20,40), �F ¼ 0,0,20) and membership function m ¼ ð0; 1Þ and presented in the form

of LIðmÞ

5. Grades obtained after mapping on triangular fuzzy function are multiplied by weights

assigned to each evaluator.

6. Finally defuzzification is done as per Chan et al. (2003) and fuzzy grades are converted in

to crisp number in the form of leanness index.

LIðmÞ ¼LAðmÞ

Aþ LBðmÞ

Bþ LCðmÞ

Cþ LDðmÞ

Dþ LE ðmÞ

Eþ LF ðmÞ

F

4. Computation and results

As discussed in preceding section, all LMTMs award score to different parameters selected

as per the history and goal of the method i.e. (40, 80). All five LMTMs select supplier’s issues

as a parameter and award 43.3, 49.2, 51.2, 63.7 and 73.7 respectively. These scores cannot

be directly mapped on membership function as these scores are from 40 to 80 scale as per

the goal and history of the measurement method, so there is need to convert them in to 0 to

100. The scores are then converted in six grades by using triangular fuzzy function shown in

Figure 2:

Figure 2 Triangular fuzzy function

F1.0

0.00 20 40 60 80 100

Grades

µE D C B A


Leanness score of first evaluator ¼ 43:324080240

* ð100 2 0Þ ¼ 8:25,

Leanness grades for this score are calculated as:

LAð8:25Þ ¼ 0; LBð8:25Þ ¼ 0; LCð8:25Þ ¼ 0; LDð8:25Þ ¼ 0; LE ð8:25Þ ¼ 8:25 2 0

20 2 0

¼ 0:4125; LF ð6:65Þ ¼ 20 2 8:25

20 2 0¼ 0:5875:

So, Leanness grades of first evaluator for suppliers issues are given by:

LGð6:65Þ ¼ 0

Aþ 0

Bþ 0

Cþ 0

Dþ 0:4125

Eþ 0:5875

For LT

1 ðm1Þ ¼ ð0; 0; 0; 0; 0:4125; 0:5875Þ

similarly grades of other evaluators are calculated for this parameter L T2 ðm1Þ¼ (0, 0, 0, 0.15,

0.85, 0), L T3 ðm1Þ¼(0, 0, 0, 0.4, 0.6, 0), L T

4 ðm1Þ¼(0, 0, 0.9625, 0.0375, 0, 0), L T5 ðmqÞ¼(0.2125,

0.7875, 0, 0, 0, 0), these five vectors form fuzzy leanness grade matrix as follows L ðm1Þ ¼½L1ðm1Þ; L2ðm1Þ; L3ðm1Þ; L4ðm1Þ; L5ðm1Þ�

¼

0 0 0 0 0:2125

0 0 0 0 0:7825

0 0 0 0:9625 0

0 0:15 0:4 0:0375 0

0:4125 0:85 0:6 0: 0

0:5875 0 0 0 0

266666666664

377777777775

Now fuzzy leanness grade matrix is multiplied with the relative weights of each evaluator to get

the final grades for this parameter

¼

0 0 0 0 0:2125

0 0 0 0 0:7825

0 0 0 0:9625 0

0 0:15 0:4 0:0375 0

0:4125 0:85 0:6 0 0

0:5875 0 0 0 0

266666666664

377777777775

*

0:15

0:10

0:20

0:30

0:25

266666664

377777775

¼ 0:0531 0:1969 0:2888 0:1063 0:2669 0:0881� �T

From this fuzzy grade vectors Leanness index is generated by defuzzification as

LI ¼ 0:0531

Aþ 0:1969

Bþ 0:2888

Cþ 0:1063

Dþ 0:2669

Eþ 0:881

F

LISuppliers¼ (100*0.0531 þ 80*0.1969 þ 60*0.2888 þ 40*0.1063 þ 20*0.2269 þ

0*0.881) ¼ 47.98, this leanness index is only for single parameter suppliers issues, for other

parameters, Leanness index is calculated by same procedure and given below:

LIInvestment ¼ 50:66; LIpractices ¼ 58:38; LIWaste ¼ 60:01; LICustomers ¼ 47:1

5. Conclusion

The concept of leanness is discussed and leanness index is developed for an Indian

automobile company with the help of scores awarded by a five member team on the basis of

various lean parameters prevailing in the company. At first look the results seems

unsatisfactory as index for ‘‘suppliers issues’’ and index for ‘‘customers issues’’ received

less than 50 points on a 100 points scale and only ‘‘waste identified and removed’’

parameter got more than 60 points i.e. 60.01 points which again seems not very satisfactory.

But these indices represent the true picture of lean status in Indian environment and disclose

the fact that Indian industry is still deprived of many lean benefits. The leanness


measurement method discussed in this paper is a very important tool for the practitioners to

assess the lean status of their organization.

References

Allway, M. and Corbett, S. (2002), ‘‘Shifting to lean service: stealing a page from manufacturers’

playbooks’’, Journal of Organizational Excellence, Vol. 21 No. 2, pp. 45-54.

Barla, S.B. (2003), ‘‘A case study of supplier selection for lean supply by using a mathematical model’’,

Logistics Information Management, Vol. 16 No. 6, pp. 451-9.

Bayou, M. and de Korvin, A. (2008), ‘‘Measuring the leanness of manufacturing systems – a case study

of Ford Motor Company and General Motors’’, Journal of Engineering. Technology and Management,

Vol. 25 No. 4, pp. 287-304.

Borenstein, D., Becker, J.L. and Prado, V.J. (2004), ‘‘Measuring the efficiency of Brazilian post office

stores using data envelopment analysis’’, Journal of Operations & Production Management, Vol. 24

No. 10, pp. 1055-78.

Brill, P.H. and Mandelbaum, M. (1990), ‘‘Measurement of adoptability and flexibility in production

system’’, European Journal of Operational Research, Vol. 49 No. 3, pp. 325-32.

Chan, F.T.S. and Qi, H.J. (2003), ‘‘An innovative performance measurement method for supply chain

management’’, Supply Chain Management: An International Journal, Vol. 8 No. 3, pp. 209-33.

Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W. and Lp, R.W.L. (2003), ‘‘A conceptual model of

performance measurement for supply chains’’, Management Decision, Vol. 41 No. 7, pp. 635-42.

Chew, M.Y.L., Tan, S.S. and Kang, K.H. (2004), ‘‘A technical evaluation index for curtain wall and

cladding facades’’, Structural Survey, Vol. 22 No. 4, pp. 210-27.

Comm, C.L. and Mathaisel, D.F.X. (2005), ‘‘A case study in applying lean sustainability concepts to

universities’’, International Journal of Sustainability in Higher Education, Vol. 6 No. 2, pp. 134-46.

Cousins, P.D., Lawson, B. and Squire, B. (2008), ‘‘Performance measurement in strategic buyer-supplier

relationships. The mediating role of socialization mechanisms’’, International Journal of Operations &

Production Management, Vol. 28 No. 3, pp. 238-58.

Emiliani, M.L. (2000), ‘‘Supporting small businesses in their transition to lean production’’, Supply Chain

Management: An International Journal, Vol. 5 No. 2, pp. 66-705.

Karlsson, C. and Ahlstrom, P. (1996), ‘‘Assessing changes towards lean production’’, International

Journal of Operation and Production Management, Vol. 16 No. 2, pp. 24-41.

Kuhnle, H. (2001), ‘‘A state-time model to measure the reconfigurability of manufacturing area – key to

performance’’, Integrated Manufacturing Systems, Vol. 12 No. 7, pp. 493-9.

Kumar, A., Motwani, J., Douglas, C. and Das, N. (1999), ‘‘A quality competitiveness index for

benchmarking’’, Benchmarking: An International Journal, Vol. 6 No. 1, pp. 12-21.

Motwani, J. (2003), ‘‘A business process change framwork for examining lean manufacturing: a case

study’’, Industrial Management and data systems, Vol. 103 No. 5, pp. 346-99.

Papadopoulou, T.C. and Zbayrak, M. (2005), ‘‘Leanness: experiences from the journey to date’’, Journal

of Manufacturing Technology Management, Vol. 16 No. 7, pp. 784-807.

Russell, R.S. and Taylor, B.W. (1999), Operations Management, 2nd ed., Prentice-Hall, Upper Saddle

River, NJ.

Saurin, T.A. and Ferreira, C.F. (2009), ‘‘The impacts of lean production on working conditions: a case

study of a harvester assembly line in Brazil’’, International Journal of Industrial Ergonomics, Vol. 39 No. 2,

pp. 403-12.

Shah, R. and Ward, P.T. (2003), ‘‘Lean manufacturing: context, practice bundles, and performance’’,

Journal of Operations Management, Vol. 21 No. 2, pp. 129-49.

Sharma, M.K. and Bhagwat, R. (2007), ‘‘An integrated BSC-AHP approach for supply chain

management evaluation’’, Measuring Business Excellence, Vol. 11 No. 3, pp. 57-68.


Simpson, D.F. and Power, D.J. (2005), ‘‘Use the supply relationship to develop lean and green

suppliers’’, Supply Chain Management: An International Journal, Vol. 10 No. 1, pp. 60-8.

Swamidass, P.M. (2007), ‘‘The effect of TPS on US manufacturing during 1981-1998: inventory

increased or decreased as a function of plant performance’’, International Journal of Production

Research, Vol. 45 No. 16, pp. 3763-78.

Soriano-Meier, H. and Forrester, P.L. (2002), ‘‘A model for evaluating the degree of leanness of

manufacturing firms’’, Integrated Manufacturing Systems, Vol. 13 No. 2, pp. 104-9.

Taj, S. and Berro, L. (2006), ‘‘Application of constrained management and lean manufacturing in

developing best practices for productivity improvement in an auto-assembly plant’’, International

Journal of Productivity and Performance Management, Vol. 55 Nos 3/4, pp. 332-45.

Wan, H.-D. and Frank, C.F. (2008), ‘‘A leanness measure of manufacturing systems for quantifying

impacts of lean initiatives’’, International Journal of Production Research, Vol. 46 No. 23, pp. 6567-84.

Womack, J.P., Jones, D.T. and Roos, D. (1990), The Machine That Changed the World, Macmillan, New

York, NY.

About the authors

Bhim Singh is presently associated with Galgotia’s College of Engineering and Technology,Greater Noida, UP, India, as an Assistant Professor in Mechanical Engineering Department.He holds BTech degree from REC Kurukshetra, and MTech degree from GNDEC, Ludhiana.He is presently pursuing PhD from NIT Kurukshetra on Lean Manufacturing. He has morethan ten years of teaching experience in undergraduate and postgraduate classes. He haspublished papers in international journals and in several national and internationalconferences. He has guided many projects to undergraduate students. His areas of interestare: statistical quality control, operations research, supply chain management, valueengineering and lean manufacturing. Bhim Singh is the corresponding author and can becontacted at: [email protected]

S.K. Garg obtained his PhD from IIT Delhi, India. He is presently associated with theMechanical and Industrial Engineering Department of Delhi College of Engineering, Delhi,as Professor of Industrial Engineering and Operations Research. He has published morethan 50 papers in international journals and conferences. He also authored three books in hisarea of interest. His area of interest includes: lean manufacturing, supply chainmanagement, just in time manufacturing, total quality management, and operationresearch. He is also guiding many research scholars for their PhD degree in his field.

S.K. Sharma, eminent scholar and leader in the field of industrial engineering andentrepreneurship development is currently a Professor in the Department of MechanicalEngineering at National Institute of Technology, Kurukshetra, Haryana, India. He didextensive research in the field of industrial engineering and has guided 16 candidates intheir dissertation for M.Tech degree. He is guiding ten students for their PhD degree in thefield of production and industrial engineering. He has published many papers in nationaland international journals of repute.


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