Case Study

International Journal of Lean Six Sigma Process improvement in farm equipment sector (FES): a case on Six Sigma adoption Anupama Prashar

Ar t i c l e in f or mat i o n:

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To cite this th is document: Anupama Prashar , (2014),"Process (2014),"Proces s improvement in farm equipment eq uipment sector (FES): a case on Six Sigma adoption", International Journal of Lean Six Sigma, Vol. 5 Iss 1 pp. 62 - 88 Permanent link to this document: http://dx.doi.org/10.1108/IJLSS-08-2013-0049

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Received 14 March 2013 Revised 13 June 2013 18 August 2013 Accepted 18 August 2013

Process improvement in farm equipment sector (FES): a case on Six Sigma adoption Anupama Prashar Operations, IILM School of Higher Education, Gurgaon, India Abstract The pur purpo pose se of thi thiss st study udy is to ex exhib hibit it how a le lead ading ing co compa mpany ny ope opera rati ting ng in fa farm rm Purpose – The equipment sector (FES) in India utilized Six Sigma statistical tools to reduce field failures of tractor assembly assem bly and there thereby by improve improved d custom customer er satisf satisfaction. action. The st study udy ado adopte pted d Six Sig Sigma ma defi definene-mea measu sure re-an -analy alyse se-Design/methodology/approach – The improve-contr improve -control ol (DMAIC (DMAIC)) methodol methodology ogy in order to identif identify y numerou numerouss criti critical cal proces processs improve improvements. ments. completing the define, measure and analyze phase, it was found that the processes processes Findings – After completing of la lapp ppin ing, g, sp spri ring ng sc scra ragg ggin ing, g, an and d as asse semb mbly ly of va valv lvee bo body dy we were re in inca capa pabl ble. e. Th Thee ma majo jorr recommendations made during the improve phase were to change the hydraulic oil; replacement of manual grinder with tool and cutter grinder for flat drill grinding; automation of lapping system; impr im prove oveme ment nt in sp spri ring ng sc scra raggi gging ng sy syst stem, em, ch chang angee in pi pist ston on de desi sign gn,, et etc. c. As a re resu sult lt of th thee implementation of remedies, field failure reduced by 95 percent producing a cost saving of almost INR 4.366 million/ million/annum. annum. Originality/value – This specific case demonstrates the successful application of Six Sigma DMAIC methodology in FES for driving down the field failures and improved customer satisfaction.

Keywords Six Sigma, DMAIC, Process improvement, Tractor, Critical to quality, Farm equipment sector, Failure mode and effect analysis, Statistical tools, Parts per million Paper type Case study

International Journal of Lean Six Sigma Vol. 5 No. 1, 2014 pp. 62-88 q Emerald Group Publishing Limited 2040-4166 DOI 10.1108/IJLSS-08-2013-0049

1. Introduction Problem solving is not an art, it is a science and a sneak-peek at the proven total quality improvement tools is enough to validate the point. Further, when a set of cont co ntinu inuou ouss im impr prove oveme ment nt te tech chni niqu ques es are key keyed ed in into to hu humon mongou gouss cos costt sav savin ings gs in million mil lions, s, the their ir imp importa ortance nce cann cannot ot be over overemp emphas hasized ized.. In the cur current rent cas case, e, a team dedicated to improving a process achieved 99.567 percent reduction in the cost of poor quality which translated in the cost savings of INR 4.366 million/annum. No onecan ch chal alle leng ngee th thee fa fact ct th that at co cost stss in ge gene nera rall andcost of poo poorr qu qual alit ity y in pa part rtic icul ular ar aree th ar thee ev evil ilss an or orga gani nisa sati tion on ne need edss to ch chec eck k if it we were re to br brid idge ge th thee ga gap p be betw twee een n it itss to topp-li line ne and bottom-line. It is also true that one can inbuilt a perpetual cost reduction plan by inculcating a continuous process improvement culture on the shop floor. Therefore, it follow fol lowss fro from m the abo above ve pre premi mises ses tha thatt fue fuell lling inggro growthand wthand sus sustai taini ning ng it in tod today’ ay’ss dyn dynam amic ic business busi ness envi environm ronment ent neces necessaril sarily y requi require re conti continuous nuous proce process ss impr improveme ovements. nts. Some of the well-known frameworks developed to raise the competitiveness of production processes are stati statistic stical al proce process ss contro controll (SPC) (SPC),, ISO 9000, KAIZEN, total quality quality manage management ment (TQM), benchmarking, theory of constraints (TOC), and Six Sigma (Sambhe, 2012). Originated at Motorola in the early 1980s, Six Sigma has been promoted as a new organizational improvement method (Hoerl et al., 2004; Arnheiter and Maleyeff, 2005). Six Sigma developed as an organized and systematic method for strategic process


improvement that relies on statistical and scientific methods to make dramatic reductions in customer-defined defect rates (Linderman etal., 2003; Tjahjono etal., 2010). Not only, has it been widely adapted in manufacturing processes but also have been proved to be successful in marketing, R&D, healthcare, banking, administrative, finance and many other business processes (Antony, 2004; Snee and Hoerl, 2003; Snee, 2010). Despite the apparent popularity of Six Sigma, not many firms (other than the automotive component manufacturers) in India have adopted this methodology for improving their processes. Studies pinpoint lack of financial and human resources and time constraints in data collection and analysis as key challenges in the adoption of Six Sigma by Indian industries (Sambhe and Dalu, 2011). Today, India is in process of mechanization of its agriculture industry, and there are huge prospects for growth for companies operating in tractor manufacturing sector in the country. However, there is a dire need to improve the existing production processes in this sector using cutting edge research and continuous innovation (Pray and Nagarajan, 2012). The current paper is presented in form of a case study illustrating the adoption of Six Sigma define-measure-analyse-improve-control (DMAIC) methodology for process improvement by a leading company operating in tractor manufacturing sector in India. The company was facing the chronic problem of high rate of field failures of its tractor assembly which was adversely impacting customer satisfaction and thereby its market performance. Needless to say that cost of poor quality was tremendously high. Therefore, this study has been documented with specific goals, which are: .

.

to demonstrate the effective application of various Six Sigma tools such as Pareto analysis, process mapping, and cause and affect diagrams, capability analysis, control charts, etc.; and to report preliminary findings, and to examine conditions which contributed to the successful implementation.

The rest of the paper is presented in the following sequence. The first section briefly reviews the cases on Six Sigma implementation in manufacturing operations in the Indian context. The following sections describe the Six Sigma DMAIC framework and process of integrating quality tools to the chosen process improvement project. This is followed by a discussion on the managerial implications of systematic Six Sigma DMAIC implementation in the company.

2. Literature review Before taking up the case explanations, let us briefly review the literature on status of Six Sigma practices in Indian context. Sarkar et al. (2013) applied Lean Six Sigma methodology for claim settlement cycle time reduction in the insurance sector. The study revealed that Lean Six Sigma methodology work very well in the insurance sector for reducing process cycle time by carrying out process changes. Deshmukh and Chavan (2012) reviewed literature on evolution and status of utilization of the Six Sigma philosophy in the development of small to medium-sized enterprises (SMEs). The study also documented illustrious Six Sigma practices in Indian context. The study found that management’s commitment is most important in SME Six Sigma implementation. It was also revealed that the benefits of Six Sigma

Process improvement in FES

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have been enjoyed largely by the bigger industrial units and to a relatively lesser extent by the smaller units, i.e. SMEs. Kaushik et al. (2012) in a study on a small bicycle chain manufacturing unit justified that right implementation of Six Sigma, which is normally presumed to be in the domain of large industries, can benefit the small manufacturing companies equally well. The study demonstrated the application of Six Sigma methodology to improve productivity levels. After applying Six Sigma it was found that the chain manufacturing firm increased its profits by controlling high rejection rate of cycle chain bush and improved its sigma level from 1.40 to 5.46 by reducing the variation of bush diameter in the process of bicycle chain bush manufacturing. Sambhe and Dalu (2011) in a study on evaluation of Six Sigma implementation on medium scale Indian automotive enterprises found that many Indian small manufacturing enterprises (SMEs) were operating at 2-3 sigma quality levels resulting in a need for a good design methodology for Six Sigma implementation. Another finding was that the majority of these enterprises were implementing DMAIC methodology, where almost no practices of design for Six Sigma (DFSS) as well as lean manufacturing were found. Such a finding is important in the light of abundant evidence that majority of such enterprises are not using Six Sigma in new product development and their DMAIC implementation is incomplete until coupled with lean manufacturing principles. Karthi et al. (2011) proposed a model for integrating Lean Six Sigma DMAIC methodology and belt based training infrastructure with ISO 9001:2008 standard based quality management system (QMS). They suggested that the model L6QMS 2008 integrates the Lean Six Sigma requirements with the ISO 9001:2008 standard. The study also illustrated the implementation of integrated model through case studies. Desai and Patel (2009) in a cross-sectional study on benefits of Six Sigma implementation in Indian industries found that the manufacturing sector is at the top in implementing Six Sigma with 69 percent contribution followed by information technology (IT) industries with 15 percent contribution. The study found that all services combined stand at a contribution of mere 8 percent. The study highlighted that the largest benefit drawn from Six Sigma implementation is by large-scale industries in the area of cost reduction. This is probably due to the scale of production that large industries enjoy. On the other hand, the medium scale industries have gained majorly in profitability. Antony and Desai (2009) presented the results from an empirical investigation of Six Sigma status in the Indian industry. The results of empirical study reflected the reasons for application of Six Sigma by Indian organizations, the most and least commonly used tools and techniques, critical success factors (CSFs) for the implementation of Six Sigma, and common impediments in the implementation. Prabhushankar et al. (2008) presented the results of a survey conducted on Six Sigma implementation in Indian automotive component industry. The findings of the survey indicated that financial constraints and non-existence of any statutory requirement as the major barriers for implementing Six Sigma. The study highlighted the need for an exclusive model of QMS which would link the standards, innovation practices and Six Sigma for enabling the automobile manufacturing sector not only in India, but also in other developing countries to help them achieve global competitiveness.


Kaushik and Khanduja (2008) applied Six Sigma DMAIC methodology to a specific case of thermal power plant for the conservation of energy. They implemented Six Sigma project recommendations to reduce the consumption of demineralized (DM) make-up water from 0.90 to 0.54 percent of maximum continuous rating (MCR) resulting in annual comprehensive energy saving of INR 30.477 million. Kumar and Sosnoski (2009) highlighted the potential of DMAIC Six Sigma in realizing the cost savings and improved quality by using the case study of a leading manufacturer of tooling. The study examined one of the chronic quality issues on shop floor by utilizing Six Sigma tools. The study showed that DMAIC Six Sigma process is an effective and novel approach for the machining and fabrication industries to improve the quality of their processes and products and ensuring profitability by driving down manufacturing costs. Sujar et al. (2008) examined the CSFs for the implementation and operation of Six Sigma and for studying the level of quality characteristics in Indian software industries implementing Six Sigma. The study identified that top management support as most important factor whereas linking Six Sigma to human resources (HRs) as the least important factor in the ranking of CSFs. The study also found that the software industries need to improve the level of two operational performance indicators, which are product attributes and return on quality. Pandey (2007) in a case study on multi-national corporation (MNC) banks in Indian context examined the utility of Six Sigma interventions as a performance measure. The study explored the applicability of Six Sigma interventions in HR function in general and in training function in particular, for making the training design and delivery operationallyefficient and strategically effective.The study found that SixSigma approach is helpful for better alignment of training function with the organizational requirements and it enables to change the role of the HR function from cure towards pro-action. Desai (2006) presented a real life case where Six Sigma has been successfully applied at one of the Indian small-scale units to improve one of their core processes. The studies reflected that SSI’s is constantly on the alert to gain a competitive edge, using many tools and techniques that have long been flaunted as a way to beat the competition.

3. Case study The study is conducted at tractor assembly plant of a leading company operating in Tractor Manufacturing Sector located in Northern India. The company produces the leading tractor brand in India, since 1983 and possesses a strong commitment on the part of the top management to make the production system more efficient. The company was facing the challenge of high rate of field failures of one of its tractors models and this was adversely impacting company’s market performance. The intensity of problem was obvious upon studying the numbers. The warranty claim data for a period of one year (December 2011 to January 2012) showed that a total of 451 tractors had experienced relief valve assembly failure in field and were subsequently replaced. This caused rising repair costs on the part of the organisation, let alone the beating it had on the product’s reputation. A relief valve is responsible for the efficiency of hydraulic system of a tractor. The hydraulic system is an import assembly of tractor which helps in operating the tractor for agricultural purposes. The hydraulic system has a cylinder fitted with


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a safety valve called relief valve. A relief valve is an assembly, which comprises a relief valve body, piston, spring, and plug. The relief valve protects the cylinder from bursting in case of unwanted high pressures. Figure 1 shows the relief valve assembly and its components. The division initiated a project in January 2012 with the aim of reducing the rate of field failures. A project team was formed, comprising of members of functions impacted by the project implementation. The team adopted project by project approach using Six Sigma DMAIC methodology for solving the problem.

3.1 Methodology: Six Sigma framework The project team followed the DMAIC process within a Six Sigma framework. The five phases (define-measure-analyze-improve-control) and key tools used in each phase are listed in Table I. These phases are discussed in detail in the following sections. 3.1.1 Define. In this phase, a project charter was created, customers of the project and their needs and requirements (critical to quality (CTQs)) were identified and a high level process map supplier-input-process-output-customer (SIPOC) was created (Benbow and Kubiak, 2010). Project charter: the project started with developing a project charter which included the business case, problem statement, mission statement, project goals, process boundaries, project team composition, and the project milestones.

Figure 1. Relief valve assembly

Table I. Six Sigma framework

Phases

Tools used

Define Measure

Project charter, CTQ tree, SIPOC “As is” process map, MSA, data collection plan, baseline sigma calculation, capability analysis Cause and effect diagram, Pareto analysis, hypothesis testing Process failure mode and effect analysis, improvement plan Control plan, control chart

Analyze Improve Control

Business case: it is important that the proposed process improvement project should have strong impact on the strategic business objectives (Kumar et al., 2009). Therefore, the business case of the project was created keeping the strategic objectives of the company in mind is as follows: The company is facing high warranty claims due to field failures of relief valves resulting in higher customer dissatisfaction. Thus, by decreasing field failures at various stages, we can reduce the cost of warranty claims and anticipate an increase in customer satisfaction levels.


67

Problem statement: the problem statement framed, considering the aspects of time-period, specificity and measurability (Snee, 2001), is as follows: ) T P ( 6 1 0 2 l i r p A 4 2 9 3 : 0 0 t A y g o l o n h c e T f o e t u t i t s n I l a n o i t a N a y i v a l a M y b d e d a o l n w o D

Field failure of Relief valve assembly, to the tune of 111,810 PPM, resulting in high warranty claims and dissatisfied customers and cost of poor quality of INR 4.385 million/Annum.

Mission statement: the mission statement was framed as follows: Reduction in field failure problems of Relief Valve up to 95% (from current 111,810 to 478 PPM) level by the end of June 2012.

The details of project goals, process boundaries, project team composition, and project milestones are specified as part of the project charter in Appendix 1. Identification of CTQs factors: since the CTQs tree is the most effective tool for identifying the customer requirements, it was applied. This simple tool helps to move from general needs of the customers to the more specific requirements (Lucas James, 2002). The CTQ tree for the present project is shown in Figure 2. The process parameters identified as CTQs are defined as follows: .

.

.

Holding pressure low (y 1 ). If pressure value after cracking goes below 120 bars in 10 s, such case is considered as “holding pressure low”. Cracking pressure low (y 2 ). If relief valve cracks below 160 bars or above 165 bars, such a case is considered as “low or high cracking pressure”. Fast dropping (y3 ). If pressure level is unable to reach at cracking pressure (160 bars) and pressure gauge displays 0 bar, such a case is considered as “fast dropping”.

SIPOC chart: the high-level process map (SIPOC) gives a pictorial view of the affected process (Desai, 2006). The SIPOC chart is shown in Figure 3. 3.1.2 Measurement . This phase is essentially a data collection phase. It involves mapping the existing process (“as is” process map), analysis of measurement system, data collection plan, calculation of baseline sigma (present sigma level) and capability

Figure 2. The CTQ tree

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Figure 3. High-level process map (SIPOC)

analysis of CTQs (Benbow and Kubiak, 2010). The “as is” flow chart of overhauling the cooling fan assembly is shown in Figure 4. Measurement system analysis (MSA): gauge R&R studies were carried out to test the accuracy of the testing system used to measure the relief valve assembly. It was found that the percentage contribution was 4.10 (which was less than 7.7 percent), study variation was 20.25 percent (which was less than 27.8 percent) and number of distinct categories was six (which was greater than five). Thus, the current measurement system was acceptable. The snapshot of Minitab output of gauge R&R studies are annexed in Appendix 2. Part manufactured by supplier

Transport to plant

Not OK

Receipt inspection (Visual/Passive testing)

OK

No

1

Goods receipt Rejection

Installation of Valve on tractor

Active Testing

OK Installation of Valve on tractor

Line rejection

Field Failure

2 1

Not OK

Rejection store

Customer

Yes

Warranty claim

Line rejection

Manual dispatch to supplier

Figure 4. “As is” process map

Notes : aRed circled activities are non-value adding activities (NVA); blue circled activities are value adding (VA) activities

Data collection plan for CTQs: a data collection plan was formulated depicting the data type, sample size, data collection method and responsibilities. Format used for data collection is illustrated in Table II. Data collected using this plan is given in Appendix 3. Base line sigma level: the base line sigma level for the three CTQs was calculated using the warranty claim data using the formula given below. It is presented in Table III (Benbow and Kubiak, 2010):

DPMO


¼

Number of defects*106 = Number of opportunities*number of units


69

3.1.3 Analyze. During this phase, data was analysed to seek explanations for the data collected during the measure phase. The tools used during phase included Pareto chart, cause and effect diagram and failure mode and effect analysis (FMEA), etc. (Benbow and Kubiak, 2010). Pareto chart: in order to identify the major cause of defects, the team plotted a Pareto chart (Figure 5) using the warranty claim data collected for one year (December 2011 to January 2012). Based on Pareto chart, team made decision to analyze the input variables for “holding pressure low” which amount to 77.4 percent of total failures for further analysis. Cause and effect diagram: the team prepared a cause and effect diagram to narrow down the focus (Figure 6).

Measurement/ X Operational Type of metrics or definition data Y (attribute/ variable)

Process parameters (CTQs) Holding pressure low Cracking pressure low Fast dropping

Data Sample Who? How? Measurement Graphical or source size system statistical tool capable? to be used?

Table II. Data collection plan

Sigma level 2.78 3.85 4.36

Table III. Base line sigma

Figure 5. Pareto chart

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Valve body

Spring Spring length variation w r t time

Burr on face of hole dia 3

Pressure setting not ok

Concentricity of dia. 12 & dia 3 holes

No. of coils

70

Testing

Face parallelism

Change in viscosity of Hyd. Oil wrt temperature

Dust in valve body Chips in Hyd oil Ovality in hold dia. 3

Perpendicularity


Scragging not ok

Concentricity of dia. 7 & dia 3 hole not ok

Assembly method wrong

Variation in bore dia. 12 of body

Test rig faulty

Holding pressure low Piston not seating on hold dia 3

Final assembly Area/inspection testing area

Variation in dia. 12 of piston Perpendicularity 0.01 not ok

R/O of taper end w r t dia. 12 Finish of taper

Lapping time

Lapping speed/RPM

Chips & dust in washing oil

Lapping weight Chips & contamination in relief valve assesmbly

Burr chip off due to hammering Rust on piston

Figure 6. Cause and effect diagram

Piston

Lapping operation

Environment and washing system

Note: aRed circle shows potential cause

Hypothesis testing: in order to test the identified probable cause of failure, hypotheses were formulated and tested using Gemba investigation, design of experiment (DOE) and process capability study. The results of hypothesis testing are listed in Table IV. The details of hypothesis testing are annexed in Appendix 4. So, the team validated the following root causes of failure of relief valve assembly: .

burr on face of hole with dia. 3 (valve body);

.

concentricity of hole dia. 3 w.r.t dia. 12 was not correct;

.

lapping of piston was not correct;

.

spring length variation w.r.t time;

.

pressure setting was not correct;

.

ovality in hole dia. 3;

.

change in viscosity of hydraulic oil w.r.t temperature; and

.

burr chip off due to continuous hammering action of piston.

3.1.4 Improve. This phase involves identification of possible solutions, their implementation and verification of workability of the solutions (Benbow and Kubiak, 2010). The team implemented and validated the following solutions. To order to avoid burr on face of hole dia. 3 (valve body) the following improvements were made:

S. no.


Probable cause

Validation method

Conclusion

1

Variation in dia. 12 of piston

Process capability study

2

Excess R/O of dia. 12 of piston w.r.t tape


3

Burr on face of hole dia. 3 (valve body)


4


5

Concentricity of hole dia. 3 w.r.t dia. 12 not correct Variation in bore dia. 12 of body

6

Lapping of piston not correct

7

Spring length variation w.r.t time

Process capability study and DOE Regression analysis

8

Assembly method wrong

DOE Shinein

9

Test rig faulty


10

Pressure setting not correct


11

Ovality in hole dia. 3

Gemba investigation

12

Chips/contamination in relief valve assembly Gemba investigation

13

Change in viscosity of hydraulic oil w.r.t Regression analysis temperature Burr chip off due to continuous hammering Gemba investigation action of piston Piston not seating on hole dia. 3 (sticking at Gemba investigation first groove during full opening)

Hypothesis invalid Hypothesis invalid Hypothesis valid Hypothesis valid Hypothesis invalid Hypothesis valid Hypothesis valid Hypothesis invalid Hypothesis invalid Hypothesis valid Hypothesis valid Hypothesis invalid Hypothesis valid Hypothesis valid Hypothesis valid

14 15

.

.


flat drill re-sharpening on tool and cutter grinding machine instead of manual re-sharpening was introduced; and re-sharpening frequency was established (after every 25th piece based on results of trials).

To check the concentricity of hole dia. 3 w.r.t dia. 12, the following corrective actions were taken: .

overhauling of machine was done;

.

old stoppers were replaced with new ones;

.

turret station play was rectified by changing guide bush;

.

taper shank drill was introduced instead of parallel shank drill; and

.

all worn out drill guiding bushes were replaced with the new ones.

To prevent the under or over lapping, the lapping system was automated and optimum factor levels (lapping speed, time and weight) were determined (Plate 1).


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Table IV. Hypothesis testing

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Plate 1. Automated lapping system

To avert the spring length variations due to continuous working of spring, manual scragging was replaced with fully automated scragging system (Plate 2) and number of scragging cycles increased from 50 to 175 rpm. The problem of inaccurate pressure setting was eliminated by placing separate testing rigs for setting and testing of relief valve (Figure 7). It enabled the detecting of pressure variation at an early stage. To eliminate ovality in hole dia. 3, the assembly process was modified (Figure 8). For preventing the change in viscosity of hydraulic oil (EP-80) with respect to temperature, the hydraulic oil was changed (viscosity of VG-46 between 46 and 52 poise between 40 C and 80 C). The problem of sticking of piston at the first opening groove during full opening was solved by introducing piston design modification of shifting the position of groove by 2 mm (Figure 9). Process failure mode effect analysis (PFMEA): the PFMEA of the process was conducted for critical steps (drilling, grinding, lapping, scragging, and assembly). All the failure modes and its effect were identified, controls were planned and risk priority number (RPN) of before and after controls were analysed and the effectiveness after implementing the improvements was assessed. It wasobserved that the RPNfor allthe keyprocess activities was significantly reduced after the implementation of the solutions. The PFMEA is annexed in Appendix 5. The implementation of remedies reduced the COPQ from INR 4.385 million to INR 19,000 (field failures from 111,810 to 478 ppm). The sigma level enhanced from 8

8


73


Plate 2. Automated spring scragging system

Figure 7. Testing rigs for setting and testing relief valve

2.32 to 4.8. The rolled throughput yield (RTY) at supplier end improved from 0.49 to 0.99 percent. All this ultimately contributed to improvedcustomer satisfaction (Figure 10). 3.1.5 Control . Once the problem is solved and results have begun to show up, it is very typical to stop at improve phase. However, effective problem solving strategy requires the team to ensure long-term retention of gains. Control phase covers steps to hold gains and ensure that benefits of improvement continue in the future (Benbow and Kubiak, 2010). The control plan included the following: (1) Establishing supplier audit plan on weekly/monthly basis. (2) Monitoring mechanism of rejection at supplier end and its analysis on weekly basis. (3) Inspection was introduced at the following machines:

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“To be” Process (For elimination of ovality problem) Blank of R.V Body from supplier

74

Reamer dia 12

Blank of Piston from supplier


B.O Inspection

Drill dia. 11.5

Drill dia. 2.5

Lapping of Piston & Body Flat Drill 11.5

B.O Inspection

Spring from supplier

B.O Inspection

Cap from supplier

B.O Inspection

hole mill Dia. 2.8 & Reamer Dia. 3

Drill dia. 2.5

Grinding of Dia. 12

*

Operation to be deleted

Threading M18 x 1.5

Washing of all Components

Taper Grinding

Assembly of Relief Valve

Scragging of springs

Pressure Setting

Figure 8. Modified assembly process

Hole Mill Dia. 11.8

Pressure Testing

Cap Locking

Packing & Dispatch

Operation to be added

Groove position change

Piston sticking at first groove in R.V.Body

Figure 9. Piston design modification

Before .

turret lathe;

.

scragging machine; and

.

lapping machine.

After

(4) Introduction of new standard operating procedures (SOPs) for the following processes: .

process sequence change;

.

flat drill re-sharpening;


75


Figure 10. Improvements

.

spring scragging;

.

lapping system;

.

hydraulic oil change;

.

pressure setting; and

.

pressure testing.

(5) Check sheet for concentricity with 100 percent inspection introduced. (6) Pressure setting and testing rig calibration frequency established both at receipt stage and at supplier end. (7) Material codification and SOPs displayed at supplier end as well as at plant receipt stage.

4. Discussion and conclusion The project was a source of learning for the organization in general and for the production team in particular. The key learning was that the corrective actions should never be devised on the basis of perceived causes. Before the application of DMAIC methodology, the team was working on different ways of improving the hydraulic delivery in order to reduce the field failure. The process was based majorly on intuition and problem solving skills of the experts in the production and not on a scientific approach. Therefore, after the validation of causes, it was identified that, the root causes were different. The team learned the importance of selecting the right project. Snee (2001) rightly pointed that project selection is the “Achilles’ heel” of Six Sigma. If projects are not selected properly, the presumptions regarding the efficacy Six Sigma initiatives can be at risk because projects are not able to deliver the expected bottom-line results. This in turn causes frustration in top management and, slowly but surely, the attention and resources are deviated later on other initiatives. In the present case, selection of a chronic

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problem of not meeting delivery commitments and aligning it with the top most business objective, made the impact of project selection for Six Sigma crystal-clear to the firm. Another key learning was the value of involvement of the senior managers in such improvement initiatives. Without top management commitment and devotion, any improvement drives could not have succeeded in its true sense. Thus, by ensuring initiative and active buy in by the top management, this project unfastened the path for a new culture and shared aims in the company. The team managed to involve everyone related to the process in the brainstorming session which resulted in generation of a list of probable causes for field failures. Validation of root causes was done with the approval of senior managers. Some of the problems faced by the team in the execution of project were lack of appropriate documentation, unavailability of technical details and other records. These constraints made the job of data collection for the define phase of the project extensive and prolonged. The goal of Six Sigma is to increase profits by eliminating variability, defects and waste that undermine customer loyalty (Tennant, 2001). The team achieved the goal through this Six Sigma project. References Antony, J. (2004), “Six Sigma in the UK service organizations: results from a pilot survey”, Managerial Auditing Journal , Vol. 19 No. 8, pp. 1006-1013. Antony, J. and Desai, D.A. (2009), “Assessing the status of Six Sigma”, Management Research News, Vol. 32 No. 5, pp. 413-423. Arnheiter, E. and Maleyeff, J. (2005), “The integration of lean management and Six Sigma”, The TQM Magazine, Vol. 17 No. 1, pp. 5-18. Benbow, D.W. and Kubiak, T.M. (2010), The Certified Six Sigma Black Belt, Handbocorrect , Pearson Education, Upper Saddle River, NJ. Desai, D.A. (2006), “Improving customer delivery commitments the Six Sigma way: case study of an Indian small scale industry”, International Journal of Six Sigma and Competitive Advantage, Vol. 2 No. 1, pp. 23-47. Desai, D.A. and Patel, M.B. (2009), “Impact of Six Sigma in a developing economy: analysis on benefits drawn by Indian industries”, Journal of Industrial Engineering and Management , Vol. 2 No. 3, pp. 517-538. Deshmukh, S.V. and Chavan, A. (2012), “Six Sigma and SMEs: a critical review of literature”, International Journal of Lean Six Sigma , Vol. 3 No. 2, pp. 157-167. Hoerl, R., Snee, R., Czarniak, S. and Parr, W. (2004), “The future of Six Sigma”, ASQ Six Sigma Forum Magazine, Vol. 3 No. 4, pp. 38-43. Karthi, S., Devadasan, S.R. and Murugesh, R. (2011), “Integration of Lean Six-Sigma with ISO 9001:2008 standard”, International Journal of Lean Six Sigma , Vol. 2 No. 4, pp. 309-333. Kaushik, P. and Khanduja, D. (2008), “DM make up water reduction in thermal power plants using Six Sigma DMAIC methodology”, Journal of Scientific and Industrial Research , Vol. 67 No. 1, pp. 36-42. Kaushik, P., Khanduja, D., Mittal, K. and Jaglan, P. (2012), “A case study: application of Six Sigma methodology in a small and medium-sized manufacturing enterprise”, The TQM Journal , Vol. 24 No. 1, pp. 4-16.

Kumar, M., Antony, J. and Cho, B.R. (2009), “Project selection and its impact on the successful deployment of Six Sigma”, Business Process Management Journal , Vol. 15 No. 5, pp. 669-686. Kumar, S. and Sosnoski, M. (2009), “Using DMAIC Six Sigma to systematically improve shop floor production quality and costs”, International Journal of Productivity and Performance Management , Vol. 58 No. 3, pp. 254-273. Linderman, K., Schroeder, R., Zaheer, S. and Choo, A. (2003), “Six Sigma: a goal-theoretic perspective”, Journal of Operations Management , Vol. 21 No. 2, pp. 193-203. Lucas James, M. (2002), “The essential Six Sigma”, Quality Progress , January, pp. 27-31. ) T P ( 6 1 0 2 l i r p A 4 2 9 3 : 0 0 t A y g o l o n h c e T f o e t u t i t s n I l a n o i t a N a y i v a l a M y b d e d a o l n w o D

Pandey, A. (2007), “Strategically focused training in Six Sigma way: a case study”, Journal of Europeans Industrial Training , Vol. 31 No. 2, pp. 145-162. Prabhushankar, G.V., Devadasan, S.R. and Shalij, P.R. (2008), “Six Sigma in Indian automotive components sector: a survey”, ICFAI Journal of Operations Management , Vol. 7 No. 3, pp. 18-37. Pray, C.E. and Nagarajan, L. (2012), “Innovation and research by private agribusiness in India”, IFPRI Discussion Paper 01181. Sambhe, R.U. (2012), “Six Sigma practice for quality improvement – a case study of Indian auto ancillary unit”, IOSR Journal of Mechanical and Civil Engineering , Vol. 4 No. 4, pp. 26-42. Sambhe, R.U. and Dalu, R.S. (2011), “An empirical investigation of Six Sigma implementation in medium scale Indian automotive enterprises”, International Journal of Productivity and Quality Management , Vol. 8 No. 4, pp. 480-501. Sarkar, A., Mukhopadhyay, A.R. and Ghosh, S.K. (2013), “Improvement of claim processing cycle time through Lean Six Sigma methodology”, International Journal of Lean Six Sigma , Vol. 4 No. 2, pp. 171-183. Snee, R.D. (2001), “Dealing with the Achilles’ heel of Six Sigma initiative – project selection is a key to success”, Quality Progress , March, pp. 66-72. Snee, R.D. (2010), “Lean Six Sigma – getting better all the time”, International Journal of Lean Six Sigma, Vol. 1 No. 1, pp. 9-29. Snee, R.D. and Hoerl, R.W. (2003), Leading Six Sigma , FT/Prentice-Hall, Englewood Cliffs, NJ. Sujar, Y., Balachandran, P. and Ramasamy, N. (2008), “Six Sigma and the level of quality characteristics – a study on Indian software industries”, AIMS International Journal of Management , Vol. 2 No. 1, pp. 17-27. Tennant, G. (2001), Six Sigma: SPC and TQM in Manufacturing and Services , Ashgate, Aldershot. Tjahjono, B., Ball, P., Vitanov,V.I., Scorzafave, C., Nogueira,J., Calleja,J., Minguet,M., Narasimha, L., Rivas, A.,Srivastava, A., Srivastava, S. andYadav, A. (2010), “Six Sigma: a literature review”, International Journal of Lean Six Sigma , Vol. 1 No. 3, pp. 216-233.

Further reading Basu, R. (2001), “Six Sigma to fit sigma – what’s next in the evolution of Six Sigma? Agility, efficiency and sustainability integrated across the enterprise”, IIE Solutions , July, pp. 28-33. Brue, G. (2010), Six Sigma for Managers , Tata McGraw-Hill, New Delhi. Pande, P., Neuman, R.P. and Cavanagh, R.R. (2000), The Six Sigma Way , Tata McGraw-Hill, New Delhi.


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Appendix 1. Project charter Project Charter PROJECT CHARTER WORKSHEET

Date: 05/01/12

Project Title: Application of Six Sigma in Tractor Manufacturing Sector

78

Problem Statement: Field failure of Relief valve assembly, to the tune of 111, 810 PPM, resulting in high warranty claims & dissatisfied customer & cost of poor quality of INR 4.385 million/annum. Mission Statement:


Reduction in field failure problems of Relief Valve up to 95% (from current 111, 810 PPM to 478 PPM) level by the end of June 2012. Process Boundaries Start point: Part manufactured by supplier Stop Point: Tractor dispatched to dealer Impacted Functions: Logistics, Production, Quality, Assembly Mentor Name: Team Members

Team Leader: Functional Areas of Team Members

Project Milestones Name of Phase Define Phase/Plan Measure Phase/Do Analyze Phase/Do Improve Phase/Check Control Phase/Act

Target Completion Date

Actual Completion Date

Appendix 2 ) r e l o t / V 2 0 0 8 6 S 2 ( 0 . 0 . 0 . 0 . 5 . 0 . e 4 4 0 0 4 8 c 3 3 6 6 n 1 1 a r e l o t % ) T P ( 6 1 0 2 l i r p A 4 2 9 3 : 0 0 t A y g o l o n h c e T f o e t u t i t s n I l a n o i t a N a y i v a l a M y b d e d a o l n w o D


79

) V S % ( 5 5 0 0 3 0 . 2 2 0 0 9 0 r . . 0 . . . 0 . a v 0 2 0 2 0 7 9 0 1 y d u t s %

) p u o C r a V f o ( 0 0 0 0 0 0 1 0 0 9 0 n 1 . . . . . . o 4 4 0 0 5 0 i t 9 0 u 1 b i r t n o c %

) J D 9 9 0 0 0 1 S 1 1 0 0 0 0 * 1 0 0 9 3 5 1 0 0 0 0 2 0 1 7 0 0 2 4 . 7 . . . . . . 5 ( 1 1 0 0 8 8 . r a v y d u t S

) D S 2 0 0 7 9 3 0 0 6 5 ( 3 p 2 1 1 0 0 1 2 3 0 0 8 6 . 3 9 9 0 0 0 0 3 2 0 0 7 1 v m 0 0 0 0 5 6 3 3 0 0 9 3 e o 1 1 0 0 5 6 3 3 0 0 5 6 d . . . . . . . . . . . . 6 C . r 0 0 0 0 2 2 d 0 0 0 0 1 1 ¼ a t s V S e i r o g e t a 5 c t ¼ c y y e n t c i i i R y t R l l t y i n n n t i t s & i & i o a o i l i 1 i i i r i b i b R l R l d _ _ t t c c e t t r b r a l a f e b u t u t a d o r a d o r o e a i a i g r g r o t t a t p a a p a a a a o - v s e g e o - a r r r r g e e v r e o o s e t e t p p p p c c l l l l e p p e e t t e e t a b r t r t a a a c t m r r u o R R O r u a o u o o R R O a o o o P T P S T P T N S T

Table AI. Gauge R&R studies

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Appendix 3 o r o t / s d l o n o a t l l a a c i c d i t h s e s p i u t a a e r t G s b

y y t y y t i t i l i t i l i l i i l i b b b a a a b a p p p p a a a c s c s c s a c s s i s i s i s i s s s s s s s s e e e e y y y y l l l l c c c c o o o o r a r a r a r a n n n P a P a P a P n a

t n e m e e r ? l m b e u s e t a h t a p s e s e s e s e s e s I m y s a c Y Y Y Y y r i e t e e c g i ? t l r l u e g u t i e d a e m a g n w b t e g o c c e r g e e l a w l c r a o n i i o t o u a l a o H d c M D B C g


e l l h i t w t o c e ? a t h l l a o W c d e l p e m z a i S s

y y t y y t y y t y y t i t i l i t i l i t i l i t i l i l i i l i i l i i l i b b b b b b b a a a a a a a b a p p p p p p p p a a a a a a a c s c s c s c s c s c s c s a c s s i s i s i s i s i s i s i s i s s s s s s s s s s s s s s s s e e e e e e e e y y y y y y y y l l l l l l l l c c c c c c c c o o o o o o o o r a r a r a r a r a r a r a r a n n n n n n n P a P a P a P a P a P a P a P n a

s e s e s e s e s e s e s e s e Y Y Y Y Y Y Y Y e ) l v a i u t s c i l l l u v a a t a l a l a l a r ( u u E u u u u t s s s s s s s s i i e e O i i i i V V D t D V V V V

r r r r o t o t o t o t c c c e e e a r p s p s p s e p n n n I I I O

r r r r r r r o h t o t o t o t o t o o t t c s a a a a a a e e r r r r r r i e p e e e e e g r p s o u p p p p p n O I Y P O O O O O

0 0 1 g n i r u t c a f u n a M





5 1 g n i r u t c a f u n a M

0 2 g n i r u t c a f u n a M

0 0 1

0 0 1

n e c o r i t u a o c s l o a t d a D n a


y l b m e s s A

g i g g i r r i r t t t s s e e s e T T T

a t a d / e ) e f t o l b u a b e i i p r r t y a v t T ( a

e l b a i r a V

e l b a i r a V

e l b a i r a V

e t u b i r t t A

e t u b i r t t A

e t u b i r t t A

e t u b i r t t A

e l b a i r a V

e l b a i r a V

e l b a i r a V

t . f r . f o o 2 2 w y 1 e 1 t 2 2 c . r 1 i . c 1 a 3 a 3 a i i f e . i d p a t . r . . g d a a a i n i i n i a f n d o t d n e d i o o t c e d p r e . t e e r r p s O . o n / r i o r o l o u l o a P R w B C b h B h L

Table AII. Data collection plan

l a n n o i o i t t i a r n e fi p e O d X ro Y X X X X t n e e v l m a e r v u c n f i s o e y r i a l e t s e t e d i o M m P R b

e n g r o u e i n l i s t o t a t i e h r e s s r a n e i v e p m r g y h i n t t u i i 3 t l g t d s . s . n r e a l v i e . e r o O d L w P H

0 0 1

e l b a i r a V e r u s s e r p g n i k c a r C

0 0 1

e l b a i r a V g n i p p o r d t s a F

X X X X X Y Y Y

g n i r p S

g i r g n i t s e T

g i r g n i t s e T


Appendix 4. Hypothesis testing

Validation method: Gemba investigation Ovality in hole dia. 3


Burr on face of hole dia. 3 (valve body)

Burr chip off due to continuous hammering action of piston

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Piston not seating on hole dia. 3 (sticking at first groove during full opening)

Chips between piston & hole dia. 3 hole

Validation method: Process capability studies C omp on en t

O pe rat io na l parameter

Variable/attribute

Dimension

Specification

Instrument used

Least count

Process sigma level (Z)

PPM

Piston

Piston R/O

Variable

NA

0.10 um

Dial Gauge

0.001

5.4

0.0333

Piston dia.

Variable

12 mm

(–0.08/–0.1)

micrometer

0.01

4.65

1.6597

Body bore dia.

Variable

12 mm

(+0.027/–0.0)

Bore Gauge

0.001

5.97

0.0012

Concentri city of dia 12 & dia 3

Attribute

NA

0.02 um

Concentri city Gauge

1.0898

137,900

Burr on face of hole dia 3

Attribute

NA

No burr on face seat

V is ua l

0. 73 23

2 32 ,0 00

Lapping

Attribute

NA

No lapping marks on piston taper area

V is ua l

1. 06 56

1 43 ,3 00

Ovality in hole dia. 3

Attribute

NA

No ovality

Visual

0.7388

230,000

Relief Valve Body


Validation method: Design of Experiment (DOE) DOE for lapping operation


Std order 8 9 4 3 7 12 6 14 10 11 15 13 1 16 2 5

Run order 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Lapping cycle Lapping RPM 30 60 10 20 30 60 10 60 10 60 30 60 30 20 30 20 30 20 10 60 10 60 10 20 10 20 30 60 30 20 10 20 Factors = 3 Levels = 2 Replicates = 2

Lapping weight 6 2 2 2 6 2 6 6 2 2 6 6 2 6 2 6

Holding pressure 60 85 90 110 120 85 90 100 72 89 86 107 122 96 100 80

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DOE Shinein: Assembly process wrong Response: Holding pressure low Min.120 bars after 120 seconds of cracking Good (BOB) Initial value (as selected) 120 First disassembly and reassembly 124 Second disassembly and reassembly 120 Median 120 Range 4 D 60 12 d 5 D/d ratio

Bad (WOW) 70 50 60 60 20

Conclusion: As D/d is less than equal to 3, thus the components in assembly are reason for the problem, not the assembly process itself

Validation Method: Regression Analysis Spring length variation w.r.t time Regression analysis: Length versus strokes Regression equation is: Length = 27.02 – 0.001948 * Strokes S = 0.0486047 R-Sq = 84.1% R-Sq(adj.) = 83.7% Analysis of Variance Source DF SS MS F P-value Regression 1 0.474338 0.474338 200.78 0.000 Error 38 0.089772 0.002362 Total 39 0.564110 Correlation: Length, Strokes Pearson correlation of Length and Strokes = –0.917 P-value = 0.000 Maximum pressure drop = 20 Kg Length change required for 20 Kg drop = 0.32 mm For a length of 27–0.32 = 26.68 mm Thus strokes required = 175

Change in viscosity of Hydraulic oil w.r.t temperature Experiment: •

6 pieces of relief valve having 135 (+/–1 Bar) taken.

•

6 pieces of relief valve having 125 (+/–1 Bar) taken.

•

Temperature was taken as predictor (Factor)

•

Six levels of factor was chosen (i.e. 50, 60, 70, 80, 90, 100 degree centigrade)

•

Response of each relief valve on different levels was measured.

•

Scatter plot was made and Pearson correlation was calculated for validation.

Pearson correlation of Holding pressure and temperature = –0.834 P-value = 0.000 R-value = –0.834 clearly states that there is a strong negative correlation between temperature & holding pressure. P-value = < 0.05, thus temperature is a significant factor

Appendix 5 n p r d e s i v e R


R 6 1 D 2

6

8 2

8 4 2

8 1 3

6 1 2

4 6 4

2 3 2

6 5 4

6 5 4

1

O 8 S 1

3 2

2 2

8 3

3 2

2 4

8 2

8 2

7 2

7 2

l 0 d d o d 0 e d e e o d c e e 1 n n e c fi c fi . n T a u u d . t r e d e d n e d d n d a d c d d e e e t . o o a e e t n r y t r y i e e fi t fi n d l d s g i d d d i a t t d c c n n n i n c a a u n e e e u a g c i a e h n q fi fi e fi s n r i e n n e e i i i i e k m r r m n n c u u s e a d d d v o o o e t t r e e e i q i q o o o t i l s d t e t t t p n e e n n e e u i i i s u c c n r e f r h h d e g c m n m m e f o p h e e c r a i c n e c c c c p p p P P P p h t d s o a o a s d s d a a h c t r h O r e s O O n n n c n n n a e S p i r A S S S M I a I a M M s P c f f f s o o o d f e o s e d g x g g c e n n n e fi e e o d d i t d c r n c i c e i e n n n n n s s p i e x s n e x s n n n o e a s e a a a s s o o p p p l fi fi e n r l r l e n e i t e e i n l l m r t t a n n c c n t t e a a e e a i i t a a v a a v i t o c o c t i c r a m n h h h h e e h h d v r r n fi r n r n r o o l l i i s c s s c i i c i p p l l e e - t e p c - i t - t - s p - s a a r e t a a e e a t r n e i r n a a n n i n I n M m e n R c a M m R d i R d i M m I i v R fl i 8 6 0 0 0 0 0 2 2 4 1 1 4 2 1 1 7 2 2 R 6 1 1 1 1 2 7 3 3 2 0 0 5 5 1 2 N D 4 1 1 P R 4 3 6 2 4 9 9 6 6 O 5 3 2 8 3 2 8 8 7 7 S 8 y / t i e e e e l l l l l c e g g i g g o o o o o r g u u u u r r r r r u t t t t t t l t a a a a a n n n n n n n g g g g n o g e e o o o e r o g t c g g e g o r c c c c c g g r i n u r s n u u u o l u o o o o u e o o l l o a l C g P b P N N N N C d c N P P e e e e e l S r / l c c y e o u o e t o y l n n l t t i h O l i t e l l h l a a i g n o i o a s a 3 3 o t c . o f n i o 3 n e h w n u h i v m w . o i r . f o o o o d y t a a l l l n g 3 a o y e l m i y i t l n y n n r . g l r i t a l d n d d l t n o a a e d i i i 3 m i e o a e n e 3 c l o i c o o p . t i h r s s e r p p . e t . t e . a a a n d p r a s s n n l r f o o s b l i s t r s . o v f v i i a e r e o i r o r u i p e o o n n P r p h L p b d E O L f L f o C w P i H O d M i l t e l i z n i m s e f t r l r i n b t t t o o o r e i n u a l n n e n o t t f t g l o j u u v l o l o , g i l n i h n o n r n e r b i b i n e n e t e r d o i r d k h l t d e n p l s u y c l l i n l l s y i i i u l u a t a t i o i a r n r t r r l u l a l l a o o u r t e r a C f P s D B B P s D c h t G fl p D e d o m e r u l i a F s s e c o r P e t r a m a P n e n i p O

y t t i . . c 5 r i . e e e . r 2 z z z t a e w i i i z i n 2 s s s i d r e e 1 r r r e c t . s r . e e p e e a . v v a c i v n o r v a O O T f D o O C w r . g 5 g 5 e 2 g 3 8 . . n n 1 n a . 1 i i m 1 i 1 l i l 2 l e l 1 l l l . . . . l d i i l m i a r i a o i i s a r r i e i D f o d D f o d H m d R d D f o t . r . w r e e p a c a T f

y 2 e t c a d i c 1 i . f n r i a a t n d n o r e e r c t . p r r n a o . u T c w B

0 2

0 3

0 4

0 5

85

n o h s i n fi r e o o c a P f

5 . 1 1 l t . l a a i l r i F d d

f e e y i v d l e l a o R v b 0 1


0 6

Table AIII. PFMEA

IJLSS 5,1

86

n p r d e s i v e R

R 4 2 D 3 O 8

4 2 3 8

2 1 3 2

8 2 4

2 7 9 8

S 1

2

1

1

n e k a t n o i t c A

1 l e e d e n n n h x i o a l w fi d y d n , p d o e e e g c c i n s s n t y n i u n e a s c d s i a e e l h s e o u d c c s c p q e o p r o s a e t r i x r s r e r fi n i n P d m d f p i

d e h g t n d i d n o w i n a P d i i e t a g d t n O fi e d S p c i i e r t n t u p i d r t c a t o a o u d e s n t e n S i s r m B a m

d e d n e m m n o o i c t e c R a


R

r o g t n a i r n e i p a r O t 2 7

n D 3 p R O 3 S 8 n g i s e d t l n o e r r r t u n C o c

Table AIV.

r o g t n a i r n e i p a r O t 2 7

e c e n o i p i t p c u e p t e s n S i 0 2

e r e o f c e e i b p g g d e n r i n i i t m u t q i a u e l B r c 8

3 3 8

5 2 2

2 1 4

r e t e m o r c i m

r o t c e t o r p l e v e B

d n a e g u a k g c o l l a b i D V

d e e d t a m c u e d y m o l t o l t s t r u u y n F a s i f o n o g i t n a i g g m m e o i n g t t a s r u r A p s c s y s 0 0 3 9 5 8

f o e t r c u e l f i f a E f

r e t e m o r c i M n f i o t 2 y h 1 d g . o i a b t i d n o e e t v s r i o l a P b v

f o e e s r u u l a i a C f

h t t p e o , d n d l e g t e n e s o u c h s e r r f W d w o

e r e u d l i a o F m

/ e e z z i i s s r r e e d v n O u

s s e c o r P

g n i 2 d 1 . n a i i r D g

g n i r e d p n a i r T g

n o t s i P

n o t s i P

g n i r p S

0 1

0 2

0 2

e t r a m a P n . . p o O n

/ y a y t n e l o o l p e c d o o n g h s n b n n s a 3 i e o t . h t c r a s a i a i i e t x l E c w P e s d h e t l p g t e o n d a n g t l g r e n n e u e i p o c h t r f o t r e W o W s p r e d e n z U i s

/ s r r e s e e d p a c n x T e u

) d e u n i t n o c (

l o r t n o c o N . e r m p i t t g r n . . i k w c a r w o C l

/ e e r t f s o o g n e o n n l i o g l i c k c a n n n u g s u t i a g e o i h d r l n t a l c s s c r s t a o i o f e c y e H p n o L s c m p o e t . r m . 2 g d i s e t h s w c 1 t n e u t . . i g d c O a r . x / i r p n e e E R d S l r w g n i g g a r c S

n p r d e s i v e R

R 8 1 D 2 O 9

6 1 2 8

0 4 2 0 1

S 1

1

2

0 8 2 7 5 8

2 e g e i n d P r . O a r r t O o s . o n n S f t f e o y o m r . d l a g d i i e t t l o g i e e a d e c a t a g r l u n fi r a l i n i r a y d s r e fi b a n r i i d r s e i o t e k l e e o t r s o p a a e a h v r r n e n O t m i l e m i c v C c s e r t u s d t s e s n n r e o a o i e r f r t r p o g t g a n o r c a i m f o i n s r fi n i s e i e t r p a g s e r r n e o O t o i r t c v 0 0 5 6 3 3 5 5 7 8 0 9 1

l o r t n o c o N

f o e e s r u u l a i a C f

l o r t n o c o N n o . s s r u k p r n t g i a h o t n m n i a o d i e t l n s i i o r a L p h v r o r r e n a m u H

e g u . f t a k s g n e l e e y t e s e o a r i w e t d u g a y m s e a y d t s r r i k s l e e e r a v y a e P v e S l v e

e r e u d l i a o F m

g s n s i e c p x p a E l

g r n e i d p n p a U l

n e k a t n o i t c A ) T P ( 6 1 0 2 l i r p A 4 2 9 3 : 0 0 t A y g o l o n h c e T f o e t u t i t s n I l a n o i t a N a y i v a l a M y b d e d a o l n w o D

6 3 2 9

d e d n e m m n o o i c t e c R a

/ e g a r o n t d f s f e o s ’ o t r i a m g t o g t n n i a n c e e i y m l o t p r p l t e i s s a u u y p r i a p n F a s l o t

r o g t n a i r n e i p a r O t

5 R 1 3 n D 7 p R O 5 S 9 n g i s e d t l n o e r r r t u n C o c f o e t r c u e l f i f a E f

s s e c o r P

e v g n l o n t i s a v y p i p d p a f d o L o n a b n g i o n t i a p r p a e L p o

e t r a m a P n . . p o O n 0 1

r o g t n a i r n e i p a r O t

g n n i o f i t n c d i l a o o t i g l o i t r a n u m a h i a e e r t s m s a v t s d . u y s u r o r o h N f t p B h y s / r y o t l r r e u a f n a e m g u u a H g e r s u s s e c s x e r E p


87

g n i s i a r t f i w o l l S f o

e r r e u s d s n e r U p

e r g g u s n n i s t d i t e t r s e n P t a e s y l b l a m s n e i F s a 0 3

Table AIV.

IJLSS 5,1

88

About the author Dr Anupama Prashar is a Lean Six Sigma Black Belt certified trainer and has guided over 30 green belt projects. She has 13 years of experience in the area of research, consultancy and management education. Her primary teaching interests are in the area of quantitative analysis and operations management. Her research areas include operational efficiency and business improvement. She has published a number of papers in various international and national journals. She has authored books on industrial safety and environment. Anupama Prashar can be contacted at: [email protected]


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