A framework for VSM integrated with Fuzzy QFD

The TQM Journal A framework for VSM integrated with Fuzzy QFD R. Mohanraj M. Sakthivel S Vinodh K.E.K. Vimal

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To cite this document: R. Mohanraj M. Sakthivel S Vinodh K.E.K. Vimal , (2015),"A framework for VSM integrated with Fuzzy QFD", The TQM Journal, Vol. 27 Iss 5 pp. 616 - 632 Permanent link to this document: http://dx.doi.org/10.1108/TQM-11-2012-0088 Downloaded on: 14 August 2015, At: 23:53 (PT) References: this document contains references to 38 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 25 times since 2015* Access to this document was granted through an Emerald subscription provided by Token:JournalAuthor:E29A2770-6779-43CB-AA2D-60AB9E8CB5E3:

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A framework for VSM integrated with Fuzzy QFD

616

Department of Mechanical Engineering, Anna University Regional Centre, Coimbatore, India, and

R. Mohanraj and M. Sakthivel Received 7 November 2012 Revised 20 May 2013 12 November 2013 17 March 2014 Accepted 8 May 2014

S. Vinodh and K.E.K. Vimal Department of Production Engineering, National Institute of Technology, Tiruchirappalli, India Abstract


Purpose – The purpose of this paper is to apply a framework for value stream mapping (VSM) integrated with fuzzy quality function deployment (QFD) for enabling scientific prioritization of improvement proposals to improve leanness. Design/methodology/approach – The literature was reviewed from the perspectives of VSM, QFD and fuzzy logic applications. The current state map was developed for the case component; fuzzy QFD was used for prioritizing improvement proposals and prioritized proposals were incorporated in the future state map. Findings – The approach enabled the scientific mapping of wastes with improvement proposals and thereby enabling systematic implementation of improvement proposals. The conducted pilot study resulted in 4 percent reduction in cycle time. As the lean implementation is a continuous process, furthermore improvements are expected in near future. Research limitations/implications – The study was conducted in an Indian camshaft manufacturing organization. The improvements in terms of leanness parameters were quantified. Practical implications – The findings determined from the study has practical relevance. Besides, managerial implications were also discussed. Originality/value – The study presented in this paper was conducted in a real time manufacturing environment. Hence the contributions of study are found to be valuable among academic and practicing communities. Keywords Lean manufacturing, Quality function deployment, Value stream mapping, Fuzzy logic, Automotive component Paper type Case study

The TQM Journal Vol. 27 No. 5, 2015 pp. 616-632 © Emerald Group Publishing Limited 1754-2731 DOI 10.1108/TQM-11-2012-0088

1. Introduction Nowadays, manufacturing firms are in the position to redefine and redesign their manufacturing systems in order to meet competitive demands raised by market challenges. Many organizations identified lean manufacturing as one of the potential solution to eliminate wastes thereby reduction in cost to stay competitive in modern manufacturing environment (Abdulmalek and Rajgopal, 2007). Lean manufacturing and related techniques/tools have been popularized over last two decades since they enable remarkable improvements in all segments of manufacturing system. Lean practices mainly focus on pinpointing source of wastes by using tools and managerial philosophies, such as just in time, total quality management, total productive maintenance, pull flow, value stream mapping (VSM), Kanban, Kaizen, 5S, single minute exchange of dies (SMED), workforce involvement, etc. (Abdulmalek and Rajgopal, 2007). Among these tools, VSM is a primary tool used to map various activities, which the product comes through during transformation of raw materials to final product (Hines and Rich, 1997; Seth and Gupta, 2005).


In this context, this paper case study reports a conducted in a camshaft component manufacturing organization, where VSM tool and fuzzy quality function deployment (QFD) was used. After the construction of current state map, possible proposals for streamlining processes were identified. In general, it is infeasible to implement all possible proposals concurrently. In order to scientifically prioritize available proposals, Fuzzy QFD was used (Mohanraj et al., 2011). QFD was selected over approaches like Pugh selection matrix because fuzzy logic can be integrated with QFD to eliminate vagueness and inconsistency associated with crisp values. The study presented in the paper was conducted in a real time manufacturing environment. The novelty of the proposed approach is that it integrates fuzzy QFD with VSM framework for enabling prioritization of improvement techniques to be deployed in industrial scenario. Fuzzy concepts, QFD and VSM-based combined methodology improves the effectiveness of lean tool implementation. Hence the contributions of study are found to be valuable among academicians and practitioners. So, this study has been initiated as a pilot project to enable continuous improvement in case organization. Also, the experience gained by the conduct of this study help practitioners to apply a similar methodology in near future for improving their process efficiency.

VSM integrated with Fuzzy QFD 617

2. Literature review on VSM, QFD and fuzzy QFD The literature review was performed in three perspectives, namely: VSM, QFD and fuzzy logic and QFD. 2.1 Literature review on VSM VSM is a paper and pencil tool, which was created using a predefined set of standardized icons. Three important steps in VSM include identification of product family, mapping current state and developing future state map by incorporating the proposals (Abdulmalek and Rajgopal, 2007; Lasa et al., 2008). VSM was applied as an approach to identify and remove non-value added (NVA) activities (Seth et al., 2008). Hines and Rich (1997) presented the typology of seven mapping tools for various types of wastes in the value chain. The brief description about seven VSM tools is presented in Table I. Mapping tools

Description

Process activity mapping Supply chain response matrix Production variety funnel

Preliminary analysis of process followed by detailed recording of all activities required in each process It provides a graphical overview of constraints associated with various players of the supply chain Production variety funnel allows the planner to understand how the firm or the supply chain operates and also helps to manage accompanying complexity Quality filter mapping Quality filter mapping approach identifies quality problems existing in the supply chain and helps to map three different types of quality defects: product defect, service and internal defect Demand amplification Demand amplification mapping is used to show how demand changes along mapping the supply chain in varying time buckets Decision point analysis The identification of actual demand pull and decision point will be identified. Decision point has been identified using decision point analysis Physical structure Physical structure mapping is useful in understanding overview on supply volume/value chain as an overview

Table I. Seven VSM tools

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Abdulmalek and Rajgopal (2007) presented the case study in which lean principles were applied in a continuous manufacturing sector and scope for applying various lean techniques using VSM tool was tested. With an example, they explained tools appropriate for the particular waste. Lummus et al. (2006) reported a VSM project in medical field. Many case studies were conducted in VSM and some of them were discussed. Seth et al. (2008) identified various VA and NVA activities involved in the value chain of the cottonseed oil industry using techniques like critical observations and further interviews. They discussed various obstacles to productivity improvement. McDonald et al. (2002) described an application of VSM in an engineer-to-order motion control products manufacturing plant. They described application of both current state and future state of the product line, as well as the analysis and results obtained from simulation. Hines et al. (1999) described the application of VSM to the development of a supplier network. The resulting supplier association program involved around 50 key suppliers across eight product category areas. The above illustration showed that VSM was diversely applied in various fields like cottonseed oil industry, textile industries and equipment replacement problems. Still, the application of VSM in automobile component manufacturing shop floor has been found to be less (Belokar et al., 2012). VSM can provide a better result in auto-component manufacturing industries (Priyavrat, 2010; Singh and Singh, 2013). With the implementation of VSM, Singh and Singh (2013) reduced cycle time by 69.41 percent, and WIP by 18.26 percent. But, the authors mentioned that, the above result cannot be expected for all the cases or products. The main factors that cause NVA in machine shop include delayed deliveries, long queues and high work in process inventories, improper utilization. These problems increase overall cost of production. Belokar et al. (2012) used VSM to eliminate these waste in shop floor. The study revealed that there is an improvement in the takt time by implementing the proposed changes. Rahani and Al-Ashraf (2012) demonstrated the application of VSM for a product line as a part of lean production implementation. A current state map was drawn to document current activities on the production floor. Then an implementation plan with the details of the steps was developed. With that, future state map was developed to enable lean process flow by eliminating the root causes through process improvements. Sheth et al. (2014) used VSM in an automobile industry and reduced the NVA time by 25 percent. Saranya and Nithyanath (2012) applied VSM methodology in a two wheeler manufacturing organization by mapping the flow of information from customers back to manufacturing. These review shows that VSM has wide range of applications and opportunity in machine shop and automotive manufacturing organizations. But, the prioritization of improvement proposals is a critical activity which needs to be scientifically ranked (Mohanraj et al., 2011). The authors have used QFD for this purpose. 2.2 Literature review on QFD applications QFD is a powerful tool for improving product design and quality, and enabling a customer-driven quality system (Kahraman et al., 2006). Since the inception of QFD in the early 1970s, it has met with varying degrees of success. QFD is a technique extensively used for this purpose, when the voice of the customer needs to be accurately translated into technical languages (Vinodh and Chintha, 2011a). Chen and Ko (2010) used QFD to maximize customer satisfaction in new product development. Vinodh et al. (2011) illustrated the application of QFD in the supply chain domain to select the best supplier by considering various supplier demands. The results showed


the practical feasibility of application of QFD. Mohanraj et al. (2011b) presented the case study where QFD was used to scientifically prioritize wastes and techniques for waste elimination. Using VSM, wastes were identified and appropriate proposals were derived to eliminate those wastes. Then using QFD, prioritization was done. Vinodh et al. (2007) proposed Innovative Total Quality Functional Deployment (ITQFD), which facilitates the spontaneous involvement of team members for delivering innovation out of customers’ voices. The implementation study conducted in electronics switches manufacturing company was explained. From the results, they indicated that ITQFD can be implemented in a real time manufacturing scenario. Almannai et al. (2008) developed an integrated approach using Failure Mode Effects Analysis (FMEA) and QFD. By combining the principles of both, they formed a decision-making tool. QFD was used to select manufacturing process and FMEA was used to check the associated risks. Due to ill-defined and vague indicators which exist in human judgment on advanced manufacturing systems, most measures need to be described subjectively by linguistic terms which are characterized by ambiguity and multi-possibility. These applications showed the flexibility of QFD methodology which also enables prioritization of improvement proposals considering the wastes as customer requirements (CRs). 2.3 Literature review on fuzzy logic According to the principle of incompatibility (Zadeh, 1973), when facing a complex decision, human beings have difficulty in making a precise decision. As an effect, the data of human subjective judgment are usually fuzzy and imprecise in nature (Line et al., 2006). Fuzzy data can be expressed using linguistic terms or in fuzzy numbers (Chen et al., 1992). Thus, the value of fuzzy measures as a linguistic value and then linguistic terms need to be converted to fuzzy numbers (Shyamal and Pal, 2007). Uncertainty can be classified into two types – probabilistic uncertainty and fuzzy uncertainty, though people were not aware of fuzzy uncertainty before mathematical formulation of fuzziness by Zadeh (1965). Fuzziness can be represented in different ways. One of the most useful representations is a membership function (Shyamal and Pal, 2007). There are many forms of fuzzy numbers to represent vague cases. The various types of fuzzy numbers are trapezoidal and triangular (Liou and Wang, 1992). The various operations in fuzzy logic are addition, subtraction and multiplication. These operations are explained as follows: Fuzzy-number addition: (a1, a2, a3) ⊕ (b1, b2, b3) ¼ (a1+b1, a2+b2, a3+b3); Fuzzy-number subtraction: (a1, a2, a3) ⊝ (b1, b2, b3) ¼ (a1−b1, a2−b2, a3−b3); Fuzzy-number multiplication: (a1, a2, a3) ⊗ (b1, b2, b3) ¼ (a1 × b1, a2 × b2, a3 × b3) (Klir and Yuan, 1995; Bowles and Pelaez, 1995; Lin et al., 2006). The concept of linguistic variable was used in association with fuzzy logic theory. A linguistic variable is a variable whose values are words or sentences in a natural or artificial language, for example low, high, etc. Fuzzy logic finds many applications, namely, assessing agility level of organization, leanness level of application, supplier selection. Fuzzy logic has been infused with techniques like ANP, AHP, TOPSIS, VIKOR, etc. for the application of material selection, concept selections, etc. Jia and Bai (2011) proposed an approach for manufacturing strategy development based on fuzzy QFD. They proposed a methodology which integrates fuzzy set theory and QFD to mitigate the inherent impreciseness and vagueness of decision-relevant inputs. Supplier selection is a highly important multi-criteria group decision-making


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problem, which requires trade-off between multiple criteria exhibiting vagueness and impreciseness with the involvement of a group of experts. Finally, fuzzy-number ranking method has been used for final ranking of suppliers (Dursun and Karsak, 2013). Based on the literature review, fuzzy QFD has been used prioritize improvement proposals for enabling leanness. With the identified proposals, future state map has been developed and implemented in the case organization. 3. Case study This section deals with details about the case organization, product selection, current state map and future state map development. 3.1 About the case company The case company is a camshaft manufacturing company supplying components to a leading automotive organization. The company is dedicated toward the manufacture of piston. The company has about 20 years of expertise in manufacturing products and also with a manufacturing track record of 100 percent quality performance rating. Totally, 100 percent quality performance means the organization is currently following six sigma quality level. Due to the increased competition, the organization is in the position to improve productivity. An expert team was formed to implement lean-oriented VSM production. 3.2 Product selection Camshaft has been chosen as the candidate product for case study. The case organization decided to streamline candidate product line so as to improve productivity. 3.3 Current state mapping The current state map shows the present scenario of the manufacturing system and is shown in Figure 1. 3.3.1 Physical system of the company. Every week material has been procured from supplier at an average of 1,920 units. The process sequence starts with cutting process. The turning centers in the case organization perform machining like turning, grooving and chamfering on the work pieces. This is followed by drilling operation. Milling is done at one face of the work piece using a milling machine. Then heat treatment has been done in batches. Finishing is done in grinding machine. Inspection is carried out at various levels of processes of work pieces to maintain quality. The accepted parts were packed in a plastic tray containing cups to accommodate parts. The packed parts were subjected to shipment to customers. Usually, the customer releases orders to manufacture components belonging to same part family. For the company, customers and suppliers are same, parent organization. The company receives raw materials every week from supplier to meet weekly requirements. Within the company, raw material was released based on a daily requirement for processing. The finished products were shipped daily to customers from stock. Eliminating or converting NVA into VA. Example: Picking of the tool: In case of centralized tool rack, activities for retrieving tool include reaching the centralized tool rack, picking tool and reaching back to machine. Reaching the centralized tool rack and reaching back to machine is considered as NVA activity and picking tool is necessary but non value added activity (NNVA) whereas NNVA activity can be considered as VA activity for the simplification of analysis. Picking of tool activity is associated with two NVA and one VA activities. If centralized tool rack is replaced by point of use cabin located near the machine, these two NVA can be eliminated.

8,100

12

C/T-12 C/O-5 No of oper-1 Uptime-58%

Cutting

5

21.1

Yearly 1920

Suppliers

Rough turning

55


21.1

5

25


Drilling

Monthly orders

21.1

5

Milling

60


105

5

Heat treatment

5


21.1

5

Daily

Daily

Daily

Production supervisor

Production control

105

25

Inspection

7

675

150 C/T-7 C/O-0 No of oper-2 Uptime-100%

ers

Total cycle time – 181 min Total lead time – 165 hrs

15


Super finishing

Yearly ord


2


Packing

Monthly 160

675

Customer


Figure 1. Current state mapping

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3.3.2 Uptime calculation. Uptime ¼

Available operataing time Available production time

The company is operating only one shift per day. The effective working hours in a shift (excluding lunch break and intervals) – eight hours: (1) The cutting operation is done in cutting machine. Production rate in each machine – five number/hour. Setup time is five minutes:


Uptime ¼

ð12Þð5Þ ¼ 58:3% ð12Þ

The uptime calculations for the remaining machines were performed similarly. 3.3.3 Cycle time calculation. (1) Cutting Production rate ¼ 5 numbers/hour/machine. Production is carried by one cutting machines and one operator. Cycle time ¼ (60 × 1)/(5 × 1) ¼ 12 minutes. The cycle time calculations for remaining machines were done similarly. 3.4 Fuzzy QFD QFD finds many applications in the field of product design, strategic planning, renovation of a computer workroom facility and improvement of customer service (Park and Kim, 1998). QFD utilizes house of quality (HOQ) which is a matrix providing understanding of CRs and establishing priorities of design requirements (DRs) to satisfy them. Thus, the choice of relationship rating scheme is critical in QFD applications. An HOQ typically contains information on CRs, relative importance of CRs, DRs for satisfying CRs, relationships between CRs and DRs and correlations between DRs, as shown in Figure 1. Assignment of the relationship ratings between CRs and DRs, and incorporation of correlations between DRs to a decision process for determining appropriate DRs (Kahraman et al., 2006; Temponi et al., 1999; Büyüközkan et al., 2004; Karsak, 2004). In this study, ratings obtained from direct interview of experts were used for construction of QFD. To overcome ambiguity and impreciseness in experts’ data, fuzzy logic was used. In this study, fuzzy QFD was integrated with VSM. The proposed architecture of fuzzy QFD integrated with VSM is shown in Figure 2. As shown in Figure 2, after the construction of current state map, wastes are categorized as over production, over processing, waiting, transportation, defects, inventory and storage. Then the proposals/improvements identified during brainstorming sessions are designated as design attributes. The proposals identified are Kaizen, Kanban, 5S, jig and Fixture Fool proofing (J & F FP), Logistic System Improvement (LSI), Stage wise Inspection (SI). The brief description about proposals and their importance in this study has been details in Table II. Then, fuzzy QFD procedure was used for construction of correlation and interrelationship matrices. Then wastes as well as improvement proposals are prioritized. This prioritization will help to incorporate improvement proposals in future state map. 3.4.1 Fuzzy function matrix. The waste prioritization and methodology selection was done by constructing and deriving an HOQ adopted from QFD procedure.

VSM integrated with Fuzzy QFD

Correlation matrix

Current state map

Wastes

Proposed Strategies


Prioritized Strategies

Improvement proposal Description The main idea of the logistic improvement Logistic system is to transfer from manual system system improvement to IT enabled system. This may largely compress time with an improved tracking mechanism. The incorporation of LSI may largely help to eliminate wastes like waiting 5S 5S is a lean tool which helps to organize and manage manufacturing operations for enabling effective production with less utilization of space, time, effort and fewer resource. 5S is found to be basic for all lean initiatives. The implementation of 5S may help to eliminate unwanted motion, transportation and scrap Kanban Signaling devices to enable pull production. The implementation of Kanban cards may help to reduce over production Stage wise A component rejected at the final stage of inspection (IS) the production cycle is more vulnerable. So, stage wise inspection can be planned to eliminate the effect. Also, immediate identification of defect can also be rectified. The implementation of SI may reduce scrap This may reduce accidents and mistakes. Jigs and The implementation of fool proofing fixture and fool proofing device may help to eliminate over processing and scraps Kaizen

623

Inter-relationship matrix

Future state map

Figure 2. Integration of VSM and fuzzy QFD

Reasons From the current state mapping, it is evident that WIP and time spent to purchase components is 8,100 minutes. To improve efficiency of supply chain, LSI was identified as a potential solution In the process flow, seven individual stages of operations are required to finish the product. From the initial assessment of shop floor, it was found that workplace and operation were not well organized. So, 5S was identified to be effective WIP was high between stages of operation. In order to reduce WIP, Kanban signaling devices are found to be useful As seven different operations were required to complete one part, SI may help to reduce processing on defective components

In drilling operation, positional accuracy is very important. So, jigs and fixture have been identified to be used. Also, the human intervention is very high which favors fool proofing devices It is a continuous improvement tool that For an effective lean production system, helps to improve performance of shop with continuous improvement plays a major role. To enable continuous improvement the identification of scope for activity, Kaizen event has been planned improvement

Table II. Improvement proposals identification and their description

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The detailed structure of HOQ is shown in Figure 2. The components of HOQ are explained in Table III. In order to overcome vagueness, linguistic variables are used to build HOQ as shown in Figure 2, the importance weights, as well as relationships and correlations are expressed using triangular fuzzy numbers. Relationship matrix Rij (I ¼ 1, …, n, j ¼ 1, …, m) of the HOQ is a matrix whose generic entry (i, j) assess how jth Proposed Methodology (PM) performs in minimizing ith identified wastes (IW). Fuzzy logic was used to deal with ill-defined nature of human judgments. Experts were asked to use linguistic variables of fuzzy logic in order to eliminate ambiguity and impreciseness associated with human judgments. The linguistic terms are shown in Tables IV-VI. The ratings weak, medium, strong, etc. were then converted into fuzzy numbers using relation shown in Tables IV-VI (Bottani, 2009; Vinodh and Chintha, 2011b). For example, if experts provide “strong” rating to define relationships between criteria A and technique B, then using Table IV, it will be converted into (0.7; 1; 1). Similarly for correlation matrix, weight factors will be converted using Tables V-VI. Components of FQFD Description Requirements

Table III. Components of FQFD

Determine the requirements to be achieved from HOQ. In our case, the requirements are identified as wastes to be eliminated Technical descriptors The technical descriptors are the techniques or methods by which the wastes will be eliminated. In our case, technical descriptors are the proposed methodologies Weights Obtain the weight from a group of experts to determine the importance of requirements and technical descriptors Relationship matrix The relationship matrix represents relationship between requirements (wastes to be eliminated) and technical descriptors (proposed methodologies) using the scale shown in Table I Correlation matrix It is the examination of how each of the technical descriptors impact each other

Degree of relationship

Fuzzy number

Strong (•) Table IV. Degree of Medium (∘) relationship with wastes and identified Weak (r) lean concepts

Table V. Linguistic variables for importance weight and corresponding fuzzy number

Importance weight (Wi)

(0.7, 1, 1) (0.3, 0.5, 0.7) (0, 0, 0.3)

Fuzzy number

Very high (VH) (0.7, 1, 1) High (H) (0.5, 0.7, 1) Low (L) (0, 0.3, 0.5) Very low (VL) (0, 0, 0.3)

Description Depicts strong relationship between identified waste and proposed methodology Depicts the medium relationship between identified waste and proposed methodology Depicts no or some relation between identified waste and proposed methodology

Description Indicates identified waste has highest priority with respect to other waste Indicates identified waste has priority with respect to other waste Indicates identified waste has low priority with respect to other waste Indicates identified waste has no priority with respect to other waste

Once the relationship between PMs and IWs were assessed using linguistic variables and then transformed into fuzzy numbers as shown in Table V, relative importance RIj of the jth PM can be computed as a fuzzy cumulative value using Equation (1) (Bottani, 2009; Vinodh and Chintha, 2011b): RI j ¼

n X

W i Rij

j ¼ 1; . . .; m

(1)

i¼1


where Wi is the weighted importance of ith IW and Rij the fuzzy number expressing relationship between jth PM and ith IW: X Scorej ¼ RI j T j0 j RI j0 j ¼ 1; . . .; m (2) Downloaded by Mr vinodh sekar At 23:53 14 August 2015 (PT)

j0 a j

Tjj' of the correlation matrix is assessed using linguistic variables as shown in Table VI has an incremental change of degree of attainment of jth attribute when the attainment of jth one is unitary increased. So, the final scorej of the jth PM can be computed using Equation (2) (Bottani, 2009; Vinodh and Chintha, 2011b). The resulting scorej is also a fuzzy number. In order to rank proposed methodologies, the crisp values are used. The crisp value of a fuzzy triangular number a(l, m, u), is computed using Equation (3) (Bottani, 2009; Vinodh and Chintha, 2011b): Crisp Value ¼

l þ 2m þ u 4

(3)

Based on the crisp values, the PM with highest crisp value is provided more importance. The matrix is shown in Figure 3. After ranking proposals, the identified best improvement proposals are 5S, Inspection stage wise, LSI and J & F FP. As shown in Figure 3, the weights for the IW were obtained from experts. Then the relationship and correlation matrices were constructed after discussing with cross-functional team. As an example, the relationship matrix for LSI proposal was explained. LSI reduces waiting time, inventory, over storage and transportation. This was reflected in the relationship matrix shown in Figure 3. 3.4.2 Model calculation. Using Equation (1), index will be calculated as shown in Figure 3. Degree of correlation

Fuzzy number

Strong positive (++)

(0.3, 0.5, 0.7)

Description

Indicates proposed methodology i will highly support proposed methodology j for the output Positive (+) (0, 0.3, 0.5) Indicates proposed methodology i will support proposed methodology j for the output Negative (−) (−0.5, −0.3, −0) Indicates proposed methodology i will affect proposed methodology j for the output. Strong negative (−−) (−0.7, −0.5, −0.3) Indicates proposed methodology i will strongly affect proposed methodology j

Table VI. Linguistic variable used for correlation matrix and its corresponding fuzzy numbers

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+ + + + +

+

H

Over Production

H

Over Processing

VH

Waiting

VH

Transportation

Figure 3. Fuzzy QFD matrix

Kaizen

JF & FP

IS

Kanban 1.2

1.6

1.0

1.2

3

4

2

5

4

(0.3,0.5,0.7)

1.5

1

(0.6,1,1.4)

3.4

Rank

(0.65,1,1.2)

Crisp Value

Index

(0.78,1.8,2.8)

Score

(0.39,1.2,2.2)

Storage

(0.6,1,1.4)

M

6

(0.65,1.6,2.4)

Inventory

5

(0.5,1.2,1.7)

Defects

VH

4

(0.45,1.4,2.6) (0.45,0.75,1.0)

VH

(0.5,0.75,0.85)

Customer Requirements

3

(1.0,3.3,6.16)


Weight Chart

5S

626

2

LSI

1 Functional Requirements

Sample calculations for LSI were shown as follows: ð0:7; 1; 1Þ ð0:3; 0:5; 0:7Þþ ð0:7; 1; 1Þ ð0:3; 0:5; 0:7Þþ ð0:7; 1; 1Þ ð0:3; 0:5; 0:7Þ þ ð0:5; 0:7; 1Þ ð0:3; 0:5; 0:7Þ ¼ ð0:78; 1:8; 2:8Þ Using Equation (2), Score will be calculated as shown in Figure 3. Sample calculations for LSI proposal are shown as follows: ð1:07; 2:4; 4:4Þþ ð0:45; 0:75; 1:0Þ ð0:3; 0:5; 0:7Þþ ð0:3; 0:5; 0:7Þ ð0:3; 0:5; 0:7Þ ¼ ð1:0; 3:3; 6:16Þ 4. Future state mapping The identified proposals are implemented in the future state map and are shown in Figure 4: (1) Additional efforts like conduct of 5S audit were taken toward the review of 5S policy for activities pertaining to cutting, rough turning, drilling, milling and heat treatment: •

in the above mentioned operations, point of contact was planned to reduce time for searching of tools, standard operating procedure was planned to prepare in order to eliminate human errors and to minimize process variations;

5S

12


Cutting

Yearly 1920

8,100

LSI

Suppliers

21.1

5

IS

55


Rough turning

5S

21.1

5

IS

Monthly orders

25


Drilling

J&F

21.1

5

IS

60


Milling

105

5

21.1

5

IS

15


Super finishing

Yearly orders

Total cycle time – 174 min Total lead time – 163 hrs

5


Heat treatment

5S

Daily

Daily

Daily

Production supervisor

Production control


675

160

5S

2


Inspection

LSI

675

Customer


Figure 4. Future state mapping

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•

the importance of keeping the workplace clean and tidy was explained to the operator; and

•

further, frequent auditing of above mentioned process was proposed to ensure continuous improvement.

(2) Inspection at the end was removed and SI was recommended for cutting, drilling and turning operations: •

this will help to reduce possibility of value addition to defective product; and

•

sampling inspection enables reduced cycle time and also improved capability of the process.


(3) Jig and fixtures used in drilling process were incorporated with fool proofing: •

the jigs ensure precision of holes by acting as foolproof and ensure the reduction in defective product.

(4) Logistics system currently used in the organization was improved for its effectiveness: •

this will reduce time taken for receipt and supply of product; and

•

the ways for improvement will be defined after discussions session with third-party service provider.

5. Results After analyzing the current state map, certain proposals are identified and future state map was proposed. Using Fuzzy QFD, proposals were identified as 5S Jig and LSI, J & F FP and SI has been implemented in FSM. To achieve quick change over, automatic loading system has been introduced; coordinate measuring machine has been used to speed up the inspection rate. Two work cells have been formed by combining machining and face grinding in one cell and by combining inspection and oil dipping in other cell. By following autonomous maintenance policy, NVA time has been reduced. The practical feasibility of the proposed approach was validated with industry decision makers who expressed their opinion that this method enabled scientific prioritization of improvement proposals. During the early stages of implementing lean manufacturing, all improvement proposals cannot be concurrently implemented which consumes significant amount of resource and cost. Also, adequate training needs to be provided for the employees with various tools of lean manufacturing. 5.1 Leanness performance measures In order to quantify improvements from perspective of leanness, values of the parameters before and after implementation of VSM are presented in Table VII. The above results showed that there will be improvement in leanness after implementing future state map. The reduction in lead time is three hours and reduction in cycle time is nine minutes, which may not be as significant. This was the result of initial studies but in near future, additional efforts will be taken to further reduce time. 5.2 Managerial implications The conduct of this study enabled the practicing managers to systematically construct current state map, identify and prioritize proposals using FQFD and implement

Leanness performance measures Improvement On time delivery

Defect rate


Uptime

Reasons

Increased from 75 to 80%

Due to the implementation of LSI: Reduction in lead time Improved communication between machines through signaling devices, namely, ANDON devices. This ensures reduced waiting Reduced to 7% 1. Process capability studies were conducted to improve performance of machines 2. Stage wise inspection Defects were immediately detected and rectified Increased by 3% Elimination of unwanted process and streamlining of shop floor activities

identified proposals in the future state. The managers were provided training on VSM, problems associated with prioritizing proposals, solutions to overcome drawbacks and to deploy improvements in future state map. Also, leanness performance measures have been quantified to illustrate the usefulness of the implementation study. Management commitment, team formation, employee involvement and systematic implementation would facilitate improvement in leanness. 5.3 Practical implications • Based on the conduct of the study, decision makers of the organization understood the theory and practical rationale behind VSM approach. •

The need for prioritization of improvement proposals during VSM implementation has been understood by the decision makers. This prioritization was scientifically done using fuzzy QFD.

•

Several sessions were conducted to decision makers to understand the methodology implemented in practice and to assess improvement.

•

Training being imparted to executives would enable them to conduct further studies on the developed framework. The improvement in leanness measures would facilitate the organization to benchmark with competitors.

•

Also, the knowledge gained by the industrial practitioners will help them to conduct further studies.

6. Conclusions • Lean manufacturing mainly focusses on elimination of seven deadly wastes using various tools/techniques (Vinodh and Chintha, 2011a). •

Leanness is the measure of lean manufacturing performance. In the present study, VSM was the main tool used to identify the opportunities for leanness improvement (Braglia et al., 2006).

•

In this context, this paper reports a case study on the development of fuzzy QFD integrated VSM approach for an automotive component manufacturing organization.


Table VII. Lean performance measures

TQM 27,5


630

•

Various proposals from the perspective of leanness improvement, namely, 5S, J & F FP, LSI and SI were identified using fuzzy QFD and implemented in future state map.

•

After implementing proposals, leanness performance measures like value added time, total cycle time, work in process inventory, on time delivery, defect rate and uptime were observed to improve reasonably.

•

Thus the effectiveness of the VSM tool has been practically validated in a real time manufacturing environment.

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Corresponding author Dr S. Vinodh can be contacted at: [email protected]

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