QUALITY PAPER Application of Six Sigma DMAIC methodology to reduce the defects in a telecommunication cabinet door manufacturing process

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QUALITY PAPER

Application of Six Sigma DMAIC methodology to reduce the defects

in a telecommunication cabinet door manufacturing process

A case study

Abhilash C.R.

Department of Industrial Engineering and Management, RV College of Engineering, Bangalore, India, and

Jitesh J. Thakkar

Department of Industrial and Systems Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

Abstract

Purpose–The purpose of this paper is to apply the Six Sigma DMAIC methodology in order to reduce the rejections experienced in the manufacturing of the doors belonging to a telecommunication cabinet.

Design/methodology/approach–The process involved joining of sheet metal and hinge using welding operations with the help of a fixture. The methodology used is the structured DMAIC method in order to identify the root cause for the rejections and solve it.

Findings–The paper provides insights about the identification of the root cause for the defects and the solution to overcome it, and also the benefits that were obtained as a result of the application of the solution.

Research limitations/implications–This methodology has been applied to the variation observed in the dimensions of a particular component to be welded with a main part. This approach can be used to find such dimensional variations.

Practical implications–This study has been successfully carried out in a medium-scale industry which has total quality management in practice.

Originality/value–Six Sigma DMAIC was necessary for the identification and reduction of the defects which arose in the sheet metal and welding operations, and had to be resolved in order to increase the bottom-line.

KeywordsSix Sigma, Welding, DMAIC, Cause and effect analysis, Pareto chart, Telecommunication cabinet door

Paper typeCase study

1. Introduction

In this contemporary world, every industrial firm is striving hard to have a dominating share in the market. The strive for domination is present in almost all sectors of the market. For this purpose, the firms are aiming at higher sales, which, in turn, is dependent on the quality of the product. The quality and cost of the products are one of the critical characteristics to increase the sales. For this purpose, be it manufacturing or service, firms are aiming towards improving the quality of their outcome (Desai and Shrivastava, 2008). Hence, from the past many years, quality improvement is the most sought-after concept discussed not only in the big companies, but in the small-scale sectors as well (Panatet al., 2014).

After the success of the Six Sigma in Motorola, the method of DMAIC has become popular among the industries concentrating on quality improvement. DMAIC is a systematic methodology which is an acronym for define, measure, analyse, improve,

International Journal of Quality &

Reliability Management Vol. 36 No. 9, 2019 pp. 1540-1555

DOI 10.1108/IJQRM-12-2018-0344 Received 15 December 2018 Revised 14 March 2019 Accepted 14 April 2019

The current issue and full text archive of this journal is available on Emerald Insight at:

www.emeraldinsight.com/0265-671X.htm

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control, and follows whenever a defect or a cause for a problem is very difficult to identify by regular inspection.

The main focus or the outcome from the method of Six Sigma is the reduction in the number of defects per million opportunities (DPMO). The number of DPMO per increase in the sigma level is given in Table I (Montgomery, 2009).

Six Sigma is used in both manufacturing as well as service sectors (Muraleedharan et al., 2017). The application of this powerful tool has not only reduced the rate of defects, but also in the areas of product development, customer retention analysis, cycle time optimisation, productivity improvement and market share (Ferreira and Lopes, 2010;

Muraleedharanet al., 2017). In the process of the DMAIC methodology, the define phase comprises of the initial observation of the process under study. The process is examined and a potential opportunity for defect reduction is identified. The tools used in this phase are Pareto chart, process flow diagram, supplier input processing output customer (SIPOC) diagram and a project charter. In the measure phase, the data regarding the quality characteristics and variables under interest are collected. Analysis with respect to the measurement system and the present performance is done. The tools used in this phase are gauge R&R study, process capability analysis and data may be represented using graphical aids such as stem and leaf diagram, scatter plot diagram, histogram, etc.

In the analysis phase, the analysis of the data collected in the measured phase is done.

Various causes for the variations are identified which are involved in the defect generation. The tools used in this phase are failure mode and effects analysis (FMEA), control charts, etc. In the improve phase, the causes for the variations are analysed using tools like cause and effect diagram, brainstorming and design of experiments (DOEs). The improvements obtained in the improve phase are implemented and the state of the process is measured in the control phase use relevant quality control tools like control charts (Montgomery, 2009).

This paper involves the application of DMAIC to reduce the defects in the manufacturing of the door panel of a telecommunication cabinet. The industry under consideration is a small-scale industry which houses the manufacturing process of the door panel using sheet metal. Other than the door panel for the telecommunication cabinet, the industry also manufactures other types of sheet metal products based on the customers’design requirements. The main objective of this application of DMAIC for this process is to reduce the number of rejections or defects of the door panels. This helps in reducing the investment made on reworking of the panels and the loss due to scrap. The process of manufacturing of the door panel is explained further in Section 3. The rest of the paper is structured as follows. Section 2 provides a brief description about the various cases across the quality engineering community about the application of DMAIC. Section 3 explains about the case under consideration and the application of DMAIC, phasewise, in detail. Section 4 briefs the results, elaborating the outcomes of the improvement done on the process. Section 5 discusses the future scope that can be made possible leading to further improvements and implications.

Sigma level Percentage inside the specification limit (%) DPMO

1 30.23 697,700

2 69.13 608,700

3 93.32 66,810

4 99.379 6,210

5 99.9767 233

6 99.99966 3.4

Table I.

DPMO per increase in sigma level

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2. Literature review

Prior to the beginning of the undertaken project, a brief literature review was done which showcases the projects undertaken by different personnel in different domains. The list of the literatures reviewed, the domain of application and the key methodologies with several aids are listed in Table II.

Desai and Shrivastava (2008) performed a Six Sigma implementation project in a large-scale industry and observed the SAW boom machine process. The scope was to improve the yield of SAW A101 boom machine. Among the other machines, the A101 machine had the lowest yield of 42.3 per cent. The utilisation of this machine process was 61.8 per cent, which translated to a sigma level of 1.8. The main reason for the low yield was found out using a Pareto chart and it was the lack of work which depends on job scheduling. The team discussed, conducted FMEA, cause and effect analysis and developed process which minimised the existing problem. The process yield improved to 90 per cent translating to a sigma level of 2.78. The cost to poor quality reduced to`220,000 from`1,420,000.

Junankar and Shende (2011) executed a study on the implementation of Six Sigma in order to reduce the number of defects in a belt manufacturing industry. The defects in this

Paper Study domain Tools and methodologies

Desai and Shrivastava (2008)

Welding DMAIC, project charter, SIPOC, FMEA, matrix diagram

Junankar and Shende (2011)

Leather belt manufacturing DMAIC, total quality management, project charter, SIPOC, Process capability analysis, cause and effect diagram, Pareto chart, FMEA

Hung and Sung (2011) Custard bun production DMAIC, Pareto chart, time series plot, measurement system analysis, process capability analysis, cause and effect diagram, FMEA, DoE, ANOVA

Liu (2011) Forging DMAIC, SIPOC, performance measurement, gauge

R&R, cause and effect diagram, Pareto chart Jirasukprasertet al.

(2012)

Gloves manufacturing DMAIC, two-way ANOVA, fish bone diagram, Pareto chart, voice of customer, boxplot

Singh and Kumar (2014)

CO2laser machining Process mapping, SIPOC diagram, gauge R&R, process capability study, cause and effect diagram, two samplet-test, factorial experiments, control chart Vijayakumaret al.

(2013)

Auto component manufacturing

DMAIC, SIPOC, process capability analysis, cause and effect diagram, gauge R&R study, failure mode and effects analysis

Panatet al.(2014) Manufacturing in R&D DMAIC, SIPOC, voice of customer, FMEA, time series plot

Kumaret al.(2015) Pump castings DMAIC, cause and effect analysis Indrawati and

Ridwansyah (2015)

Iron ore manufacturing DMAIC, process capability analysis, failure mode and effect analysis

Drabet al.(2015) Electrical power steering manufacturing

Regression analysis, Pareto chart, design of experiments

Yadav and Sukhwani (2016)

Clutch manufacturing DMAIC, run chart, cause and effect diagram, brainstorming

Singh and Lal (2016) Automobile muffler manufacturing

DMAIC, brainstorming Srinivasanet al.(2016) Furnace nozzle

manufacturing

DMAIC, TQM, Pareto chart, cause and effect analysis, design of experiments

Costaet al.(2017) Extrusion of tire semi-products

DMAIC, project charter, Gantt chart, SIPOC, Pareto chart, cause and effect diagram

Krotov and Mathrani (2017)

Quality management in nutritional products manufacturing

DMAIC, SIPOC, project charter, Gantt chart, Measurement assessment tree, Pareto chart, fishbone diagram, design of experiments

Table II.

List of cases involving application of DMAIC

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type of industry came in the form of rework orders. The aim of this project was to reduce the rework quantity of leather belts, especially dealing with fabric rough and hence reduction in defects. As per this study, due to the increase in the rework, the production increased and the time reduced. During the duration of study, the main reason for the rework orders were issues related to fabric roughness. At the time of study, the industry was producing 167,484 belts with an average rejection rate of 5.93 per cent which led to 15,832 belts to be reworked. The belt processing time was 140.97 min and the existed sigma level was 2.7. The total cost for the reworking of 15,832 belts was`1,209,374, and this was huge as per the company standards. After doing using a Pareto chart and FMEA, the causes for the reworking was insufficient penetration of the cutter, inadequate operator attention, insufficient cutting angle and bluntness of the cutter overtime.

After taking precautionary methods and improvising the practices, the defects quantity came down to 37,480 DPMO. The defects contribution percentage reduced to 2.7 per cent, which accounted for `415,203 for the reworking cost for the belts. The sigma level improved to 3.2.

Hung and Sung (2011) carried out a project on Six Sigma to improve the process and defect reduction in a Taiwanese food industry which manufactured buns. The team identified the type of bun which had a greater number of defects and it was found out to be pork bun. Using a Pareto chart of the number of customer complaints received, the main type of defect was identified as the shrinkage of bun overtime. The product defective rate was 0.8533 per cent for buns and shrinkage contributed to 26 per cent of it. The 32 g custard bun was considered among the list of buns to reduce the shrinkage defects. After brainstorming and cause and effect analysis, three types of causing factors were categorised as major, medium and minor. Later, FMEA was done and many factors were considered for a DOE which included factors of stuffing temperature, ferment type, steaming time, volume, steaming time as significant. After the improvement, the defect rate was reduced by 70 per cent, and the net defect of shrinkage and buns in particular decreased from 0.405 to 0.141 per cent.

Liu (2011) described a case of a forging industry. A forging part which was a sub-part of a main part was the focus of the study. In the forging dies, there existed dents on the corners. A customer raised a complaint of overheating and accused that the dents were the main reason.

After conducting gauge R&R (repeatability and reproducibility) study and detailed observation, the dent occurrence was 98 per cent, which was a serious threat. As a result of cause and effect analysis and cause and effect matrix, weak liquidity and lack of expansion volume were the prime causes. This had to be corrected by increasing the hardness and was done by coating TiCN (Titanium Cyanide). As a result, the dent occurrence rate dropped from 98 to 0.015 per cent. The savings for every month increased to $250,000.

Jirasukprasertet al.(2012) undertook an investigation of a rubber gloves manufacturing process. The scope was narrowed down to reduce the defects of holes and stain in the rubber gloves. According to the Pareto chart, the hole was the major contributor for defects. Due to the holes in the gloves, the leaking defect was observed which contributed to a defect of 19.51 per cent, 4,495 defects in number and 60 per cent of the total defects due to various other reasons. The total defects as per the DPMO count came up to 195,095 with a sigma level of 2.4. In the improvement phase, using DOEs and two-way analysis of variance, it was found that the optimum level of oven temperature as 230°C and a conveyer speed of 650 revolutions per minute. After implementing, the defect contribution reduced to 8.38 per cent. The process’ DPMO quantity came down to 83,750 and the sigma level increased to 2.9 with a reduction in the defect rate by 50 per cent.

Singh and Kumar (2014) undertook a Six Sigma project in a small-scale industry manufacturing CO₂laser machine unit. The foremost reason for commencing this study was that there was a high rejection rate in laser nozzle head due to diameter variation.

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The measurement system was under the extreme limit in this process. After a brief study of processes involving the factors of operator, drill regrinding, drill replacement and job holding mechanisms, drill regrinding and job holding mechanisms were found to be the primary causes for the defects. For the exploration of optimal conditions, the factors were varied at two levels and experiments were run. After the application of optimal levels, the sigma level of the process improved from 2.21 to 5.64.

Vijayakumaret al. (2013) undertook a process improvement project in an auto part manufacturing cell. The focus of study was decided as the manufacturing of links which was a continuous process. From the older records, it was showed that 80 per cent of the defects were from links. A total of 11 key quality characters of the link were measured, and 5 characters had lowZ-bench values. Among the 11 characteristic parameters, the outer diameter of the link had a low capability and a shift in the mean. After an analysis of cause and effect, FMEA, gauge R&R and brainstorming, it was observed that the cutting speed of the tool was not matching with the prescribed speed provided by the manufacturer of the inserts. The CNC used for machining had low cutting speed than prescribed. So, an insert suitable for the cutting speed of the CNC machine was chosen. Another important root cause was the slider accountable for X axis movement.

The slider had loss of tercite material and lubrication failure. The technicians corrected this after a week. As a result, the outer diameter’s process capability increased from 0.85 to 1.95. The centring efficiency improved by 143 per cent, and capability increased from 0.66 to 1.61.

Panatet al.(2014) focussed on application of Six Sigma to the research and development (R&D) manufacturing environment of Intel. Manufacturing R&D involves development of a process and increasing it to large volume production if found effective. The authors have concentrated mostly on elimination of wastes during the phase of R&D. The scope was to reduce the non-value-added activity and decreasing the idle time of the system. After a process map understanding, the non-value-added activity was quantified and the activities were eliminated to possible extent. The time was quantified and FMEA for important activities was developed. Based on the risk priority number, the actions were taken on whether to eliminate or modify the process. After implementation, the idle time of the system was reduced by 60 per cent.

Kumaret al.(2015) did a study on application of Six Sigma for an industry producing cast iron castings for submersible pumps. The aim was to infer about the process and reduce the number of defects to a possible extent. The finished product consisted of three sub components–housing, motor pulley and mini chaff cutter. Since, the part is made of casting process and after a detailed observation of the process with relevant data, the main cause for the rejections of the pumps was found to be the bow holes. Using the data of four months, the rejection due to blow holes for upper housing, motor pulley and mini chaff cutter was 8.63, 7.63 and 5.18 per cent, respectively. The total number of rejections was 1,539, and the blow hole contributed by 5.83 per cent. The cause for the blow holes were analysed, which involved moisture content of the soil, lack of venting, low permeability, etc. The root cause after the cause and effect analysis was found to be high moisture content in the soil and low permeability. Relevant tests were conducted and the moisture content in the soil was 7.26 per cent and permeability was 122 cc/min. This proved to be poor quality soil and the soil was replaced with a good quality soil. After the improvement, the loss to the company due to this defect improved from`67,230 to`26,082 for upper housing. An improvement in the loss from`35,720 to`9,785 for motor pulley.

The defects in mini chaff cutter reduced from 87,864 to 31,080. The sigma level improved from 2.04 to 5.83.

Indrawati and Ridwansyah (2015) reported a process improvement case in iron ore industry. The selected industry in the case produced only 12 per cent of the targeted

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production capacity. The loss incurred by the company was`588,801.463. The production waste during the process was scouted. The non-value-added activity consisted about 33.67 per cent of the production waste. The production efficiency of drying iron ore was 52 per cent. The ore with Fe (iron) content of less than 51 per cent was defective and the DPMO was 28,750 with sigma level of 2.96. After an FMEA, the main cause was found to be in the rotary kiln drying process. Further, to reduce the amount of waste generated, chute dust collector was redesigned, standard operating procedure of weighing was established, vibrato metre instalment and nitrogen installing was done. As a result, the problems were able to be solved.

Drabet al.(2015) considered an electrical power steering (EPS) manufacturing process to demonstrate a process improvement by 98.9 per cent. The EPS experienced infant mortality failures. As a result of FMEA, electrical over stress was upheld as one of the major causes.

The reason for this type of failure was due to electro static discharge (ESD). To prevent this, the timing of the ESD was recorded using a video camera. After several analyses, information of no grounding was found. As a result, grounding wires were installed. Still further, friction between the hinge elements, pivot and housing caused ESD. Conductive tape between the frame and fixture was placed to prevent ESD. These actions displayed 98.9 per cent effectiveness. The main metric for success in this study was customer satisfaction and display of achievement.

Yadav and Sukhwani (2016) investigated a process which produced clutches for automobile in an automobile plant. After a detailed observation, in the clutch production line, 22 defects were found out of 220 clutches in one shift. The reason for the rejection was the variation in the dimension of keyway in the clutch plate. The total DPMO accounted was 68,181. The sigma level for this process at the current DPMO was 2.99. Out of four keyways in the plate, the keyway number 2 and 4 were not as per specification. The cause for this error was in the die teeth defect. After brainstorming, the decision of die teeth replacement was taken. The process was observed again. The defects count in the production line was 2 out of 220 in a shift. The total DPMO reduced to 9,090.9 and the sigma level improved to 3.86.

Singh and Lal (2016) conducted a study in an automobile manufacturing industry having a specialisation in the production of mufflers. Primarily DMAIC was implemented to understand the process and the defects in the mufflers were studied. The rejection rate in the production of muffler was quantified as 8.21 per cent. After using a cause and effect diagram and having brainstorming sessions, the main reason for the rejection was found be defective MIG and TIG welding. The practices of the welding process were improved for a better weld quality. The rejection rate reduced to 4.81 per cent after the improvement of the weld practices. The sigma level of the company improved from 2.89 to 3.16. The process yield improved from 91.73 to 95.19 per cent with a savings cost to the company accounting for`940,800.

Srinivasanet al. (2016) conducted a case study in an industry manufacturing furnace nozzle. The purpose of this study was to implement Six Sigma for the process and improve it.

The detailed observation of the process was ended with a conclusion in the number of defects which were translated into a Pareto chart. The Pareto chart revealed that 19.72 per cent of the defective parts were from drilling. Also, 13.09 per cent of the drilling defects had diameter variation which was the main concern for the authors and industry. This defect caused improper supply of oil to the oil-fired furnace. After a process of brainstorming, the causes were analysed. A DOE was carried out with varying levels of drilling parameters. The optimal conditions for the drilling were inferred as 1,800 rpm and a feed rate of 0.3 mm/revolution with a radial drilling machine. As a result of the improvement, the DPMO was reduced to 15,000 from 35,000, and the defects parentage was reduced to 6 per cent from 14 per cent with a savings of`125,000. The sigma level of the process was raised to 3.67 from 3.31.

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Costaet al.(2017) did a project to improve the extrusion process of a tire production process. The defect during the extrusion is called work-off. In total, 13 per cent of the tread extrusion and 24 per cent of sidewall extrusion was work-off entities. One of the main reasons for this was due to the feeding failure of product mixture and the extruding machine. For the improvement, replacement of air blower treadmill was done. The pit depth was increased. After this, the work-off was reduced by 0.89 per cent with an annual savings of€165,194.

Krotov and Mathrani (2017) documented a case of nutritional product manufacturing company. The study focussed on the variation of ingredients, contamination, presence of foreign matter in the nutrition formulae and resulting in late delivery rates. Out of 196 batches produced, 61 were rejected and late deliveries had been up to 47 per cent of the output. In this line, an important observation made was lack of standard batches and no practice of measurement of waiting time. This increased the human error. As a result of cause and effect analysis, the main sources for errors were blending time, waiting time, blender load, blender type, raw material chosen and cleaning process. As per the FMEA, the waiting time and blender load were the significant factors. To reduce the waiting time, the personnel from mixing division was made responsible for packaging as well which resulted in higher cleanliness. To improvise the blender load, the optimum loads were decided using DOEs. Partial hopping was done for the ingredients. These improvements reduced the defects and decreased the late delivery rate from 47 to 23 per cent.

The above review of the cases available in the literatures give a brief insight on the methodologies followed for the different types of processes. Also, the review provides knowledge on the type of tools that can be used for different phases and different types of problems. All the literatures considered for the review are selected such that they involve manufacturing processes and processes similar to the current process of manufacturing door panel for a telecommunication cabinet door so that the understanding of application of DMAIC with respect to the current process can be similar. Also, from the above literatures, it can be inferred that the method of DMAIC can be extended beyond the manufacturing operations and applicable to service operations as well. Further cases and analysis for better overview on the research in this topic can be studied in Shokri (2017).

3. Case study

The selected case study to be analysed here is the process of manufacturing door panel of a telecommunication cabinet. The study was conducted in an industry which produced a variety of sheet metal products among which the door panel was one. Some of the other products manufactured in the selected industry were sheet metal casings, padlocks, fixtures, designer metal furniture and other engineered products which involved sheet metal. The door panel whose process is under study is shown in Plate 1. The industry followed a pull system where in the production is run based on the customer orders.

The door panel for the telecommunication cabinet was potentially one of the most profitable products for the industry. Also, the greatest number of rejections occurred in this product and the cost of reworking was also high. Hence, this process was considered in order to reduce the number of defects using DMAIC.

The major challenge faced by the industry in the manufacturing of the telecommunication cabinet’s door panel was to maintain the quality level and reduce the rejection rates. The processes of punching, bending, spot welding, TIG welding, TIG welding, sanding and buffing are involved in the manufacturing of the door panel. But the main concern was the positioning of the hinges, padlocks and studs on the surface of the door panel which are welded. Quite often, the rejections are due to the variation in the dimensions of hinge, padlock and stud positions, which resulted in improper alignment of the door to the cabinet and eventually rejections.

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So, to improve the current process by reducing the number of defects and therefore increase the sigma level, the DMAIC phases of Six Sigma is adopted. Using the DMAIC methodology, the process was understood and the problems were identified.

3.1 DMAIC phases

In this case study, in the define phase, the process was observed, understood and scope of the project was decided. In the measure phase, the data of the current process were collected.

In analyse phase, the current performance was analysed using the data from measure phase.

During improve phase, certain solutions are developed with the aim to solve the problem and implemented. During the control phase, the implemented solution is checked and performance was measured to check the improvement level.

3.1.1 Define phase. In this phase, the observation of the process was done. Since the industry manufactured many products, one single product was considered for the study which was the door panel due to the reasons explained earlier (Figure 1).

The telecommunication cabinet is originally intended to house many network cables and corresponding cooling apparatus. The door panel consists of many louvering on the surface and many threaded studs on the inner surface of the door panel. The studs were implemented in order to serve for many utility purposes for the wire harness inside the cabinet. These cabinets serve in many corporate offices and industrial network operations.

Since the part considered is the door panel, it consists of hinges and padlocks.

There existed two padlocks and four hinges in the door panel. The hinges are placed with reference to the previous/upper hinge. The first hinge was placed at a distance of

Plate 1.

Finished door for the telecommunication cabinet

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137.5 mm from the top. Consequently, the second hinge at a distance of 425 mm from the first hinge and the distance between the second and third hinge, 450 mm. The fourth hinge was at a distance of 425 mm from the third hinge. All the measurements were from the centre of the hinges. Similarly, the first padlock was placed at a distance of 163 mm from top and the second hinge was placed at a distance of 188 mm from the bottom. Hence, the accuracy of the placement of the top hinge and has to be improved since the reference point for each hinge and padlock changed. Even though fixtures were used to know the placements of hinges and padlocks, accurate placement was not yet achieved. There were studs which are spot welded on the inner face of the door for customization purposes.

The rejected panels were observed and were quantified with respect to the source for rejection. The main sources for rejections were:

• Code 04: distance between the studs in the top row of the door panel;

• Code 06: distance between the studs on the right vertical column of the door panel;

• Code 07: distance between the studs and the bottom peripheral edge of the door panel;

• Code 08: distance between the studs and the right peripheral edge of the door panel; and

• Code 12: distance between the top peripheral edge and the centre of first hinge.

The defects contribution was analysed using a Pareto chart as shown in Figure 2.

From Figure 2, it can be observed that the majority of the defects were from code 12.

Therefore, the scope of the improvement of this process was decided to reduce the defect due to code 12 measurements. The critical to quality characteristic was the hinge and the padlock distance, distance between the stud and the peripheral edge of the door. The outline process of the manufacturing process is shown in Figure 1.

Considering all the details of the study, a project charter was done as shown in Table III. The charter includes the list of team members, objectives, tools used the characteristics of the data etc. Also, a SIPOC depicting the overall chain from order to supply. The SIPOC diagram is shown in Figure 3. It can be seen from the Pareto chart that, out of a sample of 100 rejected panels, 30 panels are rejected, i.e. 30 per cent, due to the non-conforming of the dimension related to the distance of the top or the first hinge position from the upper peripheral edge of the panel.

3.1.2 Measure phase. In the measure phase, the data of the measurement of Code 12 were collected. The data collected were for a duration of 18 days. According to the industry policy

Arrival of order from the customer

Design and drafting of order using CAD

Transfer of draft with materials to production

Bending of the edges by 90°

MIG welding of 2 padlocks and 4

hinges

MIG welding of the corners of the bent

sheet metal

Punching and louvering operation

on the galvanised iron sheet

Manual deburring

Spot welding of studs on inner surface of panel

Measurement by the quality

inspector

Sanding and gritting of the panel

Finished panel assembled to the

cabinet Figure 1.

Flowchart for the manufacturing process of the door panel

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and lack of human resource, one panel was measured twice by a quality inspector with different approach and orientation. The equipment used by the quality inspectors to measure the data was a Vernier calliper to measure the length of the bent edge of the sheet, a right-angled scale to measure the degree of the bend, a steel tape to measure long distances like the distance of the hinge from the edges and between the hinges. Since the scope of the study was to analyse the measurements of the distance of the hinge from the top peripheral edge of the door panel, concentration was given only on the dimension of code 12. In the industry considered in this case, there are no multiple personnel to measure the parts.

Therefore, the reproducibility error does not exist. Also, repeatability error is considered only when the operator measures the same part repeatedly under initial conditions. But in this case, the operator changes the orientation of the measurement and hence repeatability

Particulars Explanation

Problem objective To improve the process performance involved in manufacturing door panel for a telecommunication cabinet

Problem description Variation in the dimensions of the position of hinges

Metrics Number of rejections

Critical to quality parameter Distance of the first hinge from the top peripheral edge

Tools Pareto chart, SIPOC diagram, cause and effect diagram, brainstorming Team members The authors of this paper

Table III.

Project charter

Supplier GI sheet manufacturer

Input GI Sheet

Processes Punching Deburring Bending Welding Finishing

Output Finished front

door

Customer Telecommuni-

cation company Supplier – Input – Processes – Output – Customer

Figure 3.

SIPOC diagram Pareto chart of dimension code

120

100

80

60

40

20

0

12 7 8 6 4

0 20 40 60 80 100

Dimension code No. of deviations Per cent Cum %

30 28.0 28.0

27 25.2 53.3

22 20.6 73.8

15 14.0 87.9

13 12.1 100.0

Per cent

No. of deviations

Figure 2.

Pareto analysis for the defects

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error also is difficult to be calculated. So, the metricP/T(precision-to-tolerance) ratio is used to calculate the capability of the measurement system using equation:

P=T¼ ks^Guage

U SLLSL: (1)

From the data obtained by the measurement, it was found thats^Guageis 0.10984.s^Guageis the standard deviation of the observed data. Also, the upper specification limit (USL) and lower specification limit (LSL) are set as 138 and 137 mm, respectively. Considering the constantk value as 6 and substituting theUSLandLSLvalues in (1), theP/Tvalue is 0.659. For a capable measurement system, theP/Tratio should be less than or equal to 0.1 Montgomery (2009).

3.1.3 Analyse phase. Since only one unit was measured by the inspector, in order to check the status of the process, an I-MR chart was made as shown in Figure 4. According to Western Electric Handbook guidelines in Montgomery (2009), the inference from the control chart is as follows.

In the I chart, no points satisfy the Western Electric Guideline to be stated as out of control.

A stratified pattern is observed with a slight peak. In the MR chart, the 15th point is out of 3σ limits. Ten points from the 4th to 13th point are on the same side of the centreline. Among the three consecutive points from 14th to 16th, all are outside 2σlimits. A stratified pattern is observed. So, by observing the pattern and positioning of data points, according to the Western Electric Handbook in Montgomery (2009), the process is out of control.

The results of the measurement system analysis using P/T ratio reveal that the measurement system has to be improved. Further, a process capability analysis was done to examine the capability of the process. Since the measurement system is not accurate, measurement errors also have to be considered. There is chance of considering the process as capable if measuring errors are not considered and is evident in Figure 5. According to Barwal and Anis (2015), the following formula is used to calculate process capability considering the measurement errors and the new process capability is the empirical process capability. Here C^Y_pm and C_pm are process capability indices considering measurement error and not considering, respectively.s²_Y is the total variation,σ²is the variation from the observed data

17 15 13 11 9 7 5 3 1 137.88 137.76 137.64 137.52 137.40

Observation

Individual value

17 15 13 11 9 7 5 3 1 0.4 0.3 0.2 0.1 0.0

Observation

Moving range _ _

MR = 0.0982 UCL = 0.3210

LCL = 0 +2SL = 0.2467

–2SL = 0 +1SL = 0.1725

–1SL = 0.0240 1

I-MR chart of code 12A

X = 137.5792 UCL = 137.8404

LCL = 137.3179 +2SL = 137.7533

–2SL = 137.4050 +1SL = 137.6663

–1SL = 137.4921 _

Figure 4.

I-MR chart for the code 12

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ands²_Gis the gauge variation obtained by constructing a control chart.µis the mean from the observed values andTis the target value Montgomery (2009):

C^Y_pm Cpm¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi s²þðmTÞ² s²_YþðmTÞ² s

¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi s²þðmTÞ² s²þs²_GþðmTÞ² s

: (2)

According to this formula, after substituting the suitable values,C^Y_pmvalue is found to be 0.41. Since this value is less than 1.33, the process is found to be incapable. Also, in extension to this method, the Generalised Confidence Interval method can be used which is explained briefly in Barwal and Anis (2015).

After the measurement and analysis of the data from the process, it is evident that the process is not capable. It was decided to observe the process of positioning of the hinge distance in depth. After several examinations, it was realized that there are several factors to be taken for consideration as the causes for defects.

From the data available from the single day’s production of previous month, the current sigma level of the process was calculated using the DPMO:

No. of panels produced: 5,844.

No. of panels accepted: 5,708.

No. of panels rejected: 136.

DPM O¼ Rejected quantity Produced quantity10⁶ DPM O¼ 136

5;84410⁶¼23;271:73 Process Capability Report for 12

Process Data LSL

Target USL Sample Mean Sample n SD (Overall) SD (Within)

137

* 138 137.579 18 0.0892164 0.107877

LSL USL

Overall Within Overall Capability

Pp PPL PPU Ppk Cpm

1.87 2.16 1.57 1.57

* Potential (Within) Capability

Cp CPL CPU Cpk

1.54 1.79 1.30 1.30

137.10 137.25 137.40 137.55 137.70 137.85 138.00 Performance

Observed Expected Overall Expected Within PPM < LSL

PPM > USL PPM Total

0.00 0.00 0.04

47.89 47.93 1.20

1.20 0.00

0.00

Figure 5.

Process capability analysis without considering measurement errors

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The formula to convert this into a sigma value is:

Sigma level¼0:8406þ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 29:372:221 lnðDPM OÞ p

Sigma level¼0:8406þ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 29:372:221 ln 23ð ;271:73Þ p

Sigma level¼3:49

For every door panel to be produced, starting from the raw material till the finishing, it costs the`3,540 per completed door. If the door gets rejected, it takes `2,435 for every rejected door to be reworked with proper dimension. So, for 136 defects,`331,160 has to be invested. For 23,271.73 defects, it accounts for`56,666,662.55. The cost for rework is 1.6 per cent of the cost for producing 1m door panels.

3.1.4 Improve phase. After predicting various reasons about the causes for the defects which led to the rejections, a cause and effect diagram was used as shown in Figure 6. to systematically depict the causes for errors and to find a suitable solution. So, a brainstorming session was conducted to decide the main cause for the error. As a result, the fixture was the primary cause for the variation in the code 12. The fixture was designed such that it had a rectangular through pockets where the worker places the hinge and welds it to the door. So, when the fixture was placed on the panel, during welding, slight movements in the fixture caused the position of hinge to be altered. As a solution, it was decided to weld an extra piece of metal sheet to the fixture. This improved the length of the fixture and used to clamp tightly to the panel.

3.1.5 Control phase. In this phase, the solution was implemented and checked for the performance of the process as a result of improvement. The improvement showed that the workers were more comfortable to weld the hinge using the new fixture since the fixture was more stable than the previous fixture. The current level performance is as follows:

No. of panels produced: 4,221.

No. of panels accepted: 4,154.

No. of panels rejected: 67.

DPM O¼ Rejected quantity Produced quantity10⁶

Measurements Material Personnel

Instrument errors Human errors

Less number of sample

Uncleaned surface for welding

Carelessness Lack of skill

Uncomfortable outfit

Fluctuation in voltage Improper welding Outdated machines

Improper hinge position Human movement

Distractions Noise Temperature

Environment Methods Machines

Pre-removal of arc gun

Non-standard procedures Old fixture Improper fixture Figure 6.

Cause and effect diagram

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DPM O¼ 67

4;22110⁶¼15;873:01 The formula to convert this into a sigma value is:

Sigma level¼0:8406þ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 29:372:221 lnðDPM OÞ p

Sigma level¼0:8406þ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 29:372:221 ln 15ð ;873:01Þ p

Sigma level¼3:64

Since the rework cost is`2,435, cost for 82 defects is`199,670. For 15,873.01 DPMO, it is

`38,650,779. It accounts for 1.12 per cent of the production of flawless door panels.

The compared values of before and after the improvement is shown in Table IV.

4. Results and discussion

As shown in the results, the sigma value has been increased from 3.49 to 3.67. The percentage of total rejections in DPMO reduced to 15,873 from 23,271. But the improvement in the sigma level is only 0.18. The number of rejections in the process can be further reduced if the complete design of the fixture is changed. The rework cost has been decreased from

`56,666,662.55 to`38,650,779. This has decreased the cost rework investment percentage from 1.6 to 1.12 per cent with a magnitude of 0.48 per cent per million door panels produced.

Due to the time and investment limitations of the industry, since it is a small-scale industry, the industry cannot afford for costly improvements.

5. Conclusion and recommendation

As mentioned, the magnitude of decrease in the rework cost is 0.48 per cent. Even though this seems to be a small improvement, the practical interpretation of this quantity is a great add-on for the financial ability of the industry. As mentioned previously, since it is a pull system, the investment in costly improvements lasts only till the order finishes and not till the end. During this time, there is a potential threat of reduction in the profit margin as well. Nevertheless, if a DOE is conducted with varying levels of factors such as fixture designs and welding machine voltage level, the performance can still be improved.

Several companies have been striving to improve the quality of their performance. This includes many micro-, small-, medium- and large-scale industries. The pioneers in the quality improvement sector like Motorola, Toyota, AT&T Bell laboratories, etc., have constantly worked on this through DMAIC methodology Yadav and Sukhwani (2016). But, in the case of small-scale companies, it is not possible to hire a dedicated blackbelt personnel as in the big companies. If such thing is done, the industries face a potential incapability of not affording to pay the blackbelts. As studied in many literatures, the process improvement cannot only be done by the Six Sigma black belts but also by people with sufficient knowledge about this. But the level of performance improvement will be slightly less. The level of experience and

Before After

DPMO 23,271.3 15,873.01

Rejection % 2.32 1.58

Rework cost % 1.6 1.12

Sigma level 3.49 3.67

Table IV.

Final result

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knowledge the blackbelts, master blackbelts and the champions possess is what the large-scale companies are looking for an improvement that is very high in magnitude.

Therefore, the DMAIC methodology of the Six Sigma is a powerful technique to improve the process of any industry as shown in this paper.

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