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An agile supply chain is one of the initiatives that nearly all businesses have been trying to adopt in order to remain competitive in the increasingly global market, Moreover, efficient and effective delivery demands an systematic production process to achieve customer satisfaction. In this paper House Of Quality (HOQ) is represented as a practical tool, which allows directly assessing the impact of agile attributes on supply chain components through the relationships matrixes. This approach is based on fuzzy set theory because it has emerged that agility assessment is often dealt with fuzzy logic, due to the imprecise and vague definition of agility indicators. Proposed method provides a structured framework to implement agile strategy for supply chain components. An example is proposed to illustrate the proposed approach. The approach is based on the quality function deployment (QFD) methodology and in particular on the House Of Quality (HOQ), which has been successfully adopted in the new product development fields.
KEY WORDS: Agility, House Of Quality, SCM, Fuzzy logic
Many articles, narrate agility as the capability of companies to react rapidly and efficiently to unforeseen modifications in market demand. Agile production, which begins from the Toyota manufacture system, is one of the first steps that many essential trades around the world have been attempting to choose, so that they can remain competitive in the expanding worldwide commerce. The acceptance of agility doctrines, which appoint more emphasis on leveling the production scheme, causes a positive effect on production scheduling and its outcome. The rest of this paper is organized as follows: A literature review of agility and its application in supply chain management and Fuzzy QFD framework is presented in this section. Proposed method illustrated in material and method. A numerical example used in result and discussion. Finally, the last Section contains Conclusion.
Agile production is one of the first steps that almost all trades have been attempting to choose to be vigorous in the expanding worldwide commerce. Gehani(1995), declares that six actions are needed for the execution of an agile strategy, meaning ''cross- functional team sharing'', ''empowerment for front-line decision making'', ''modular integration of available technologies'', ''delayed design specification'', ''product planning'' and ''enterprise-wide integration of learning''. An extensive classification of agile indexes was recommended by Yusuf et al. (1999), which related 32 indexes describing an agile business, when these attributes were grouped into 10 decision fields. These agile attributes are shown in Table1.
TABLE1. LIST of agile attributes nominated bydecision makers (Yusuf et al., 1999)
Related agile attributes
Concurrent execution of activities
Information accessible to employees
Developed business practice difficult to copy
Empowered individuals working in teams
Cross functional teams
Teams across company borders
Decentralized decision making
Leadership in the use of current technology
Skill and knowledge enhancing technologies
Flexible production technology
Quality over product life
Products with substantial value-addition
First-time right design
Short development cycle times
Culture of change
Rapid partnership formation
Strategic relationship with customers
Close relationship with suppliers
Trust-based relationship with customers/suppliers
New product introduction
Response to changing market requirements
Multi-skilled and flexible people
Workforce skill upgrade
Continuous training and development
Ren et al. (2003) revealed distinctive agile attributes caused different levels of competitive preferences. Womack et al. (1990) employed this word to compare Toyota with the western 'mass production' systems. A key attribute of aforementioned book was that it debated manufacturing procedures and supply chain (Holweg, 2007). In many cases for estimating agility fuzzy logic is employed that is because of the inaccurate explanation of agile indexes. Fuzzy logic allows consideration of the unusual definitions that may be given to the same linguistic word (Dohnal et al., 1993). So, the special contribution of the fuzzy set theory is its capacity to depict vague facts. All in all, many articles on this subject have been published, due to its newness for giving a definition of agility.
1.2. Agility and SCM
Suitable supply chain strategies can be extended contingent on market traits and by concurrently trying to obtain superior standards of customer reactiveness at a lower total cost to a general supply chain (Cox & Chicksand, 2005). Handling supply inputs and on-time distribution of products and services at the lowest cost are impressive practices for having well-kept trade performance (Cox & Chicksand, 2006). Supply chain management merges suppliers, manufacturers, distributors, and customers to satisfy final consumer anticipation effectively.
Thakur et al. (2009) used a systematic approach to extend techniques for executing large-scale cereal supply chain traceability in the United States that contains interior and chain traceability. Georgiadis (2005) made a model for information trade between the supply chain actors. The need for comprehensive modeling efforts that entrap the expanded supply chain at a strategic degree has been obviously identified first by industry and recently by academic environment. Furthermore, in today's world of marketing and severe competition for every product along with increasing consumer need, it becomes commanding for companies to explore ways to ameliorate their creativeness regarding performing pliable and standardized technology and accepting verified management doctrines. Christopher (1999) indicated agility has an influential factor in the design of supply chains; this applying to the capability of the supply chain to react rapidly to alterations in customer needs (Christopher, 1999). In low-amount highly volatile supply chains, where customer requirements are often unforeseeable and supplier abilities and changes are uncontrollable, a more agile approach dependent upon innovative products is suitable. In opposite, the agile approach operates better when there are high capacity and predictable demand with supply sureness, as a result of which practical products can be made (Cox & Chicksand, 2005). Managing the supply chain and working closely with suppliers is facilitated by rationalizing the supplier base and focusing on suppliers committed to the ideals of agile production.
1.3. Fuzzy QFD
A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership function, which assigns a grade of membership ranging between 0 and 1 to each object Kahraman and Ertay (2006), presented a fuzzy optimization technique to linear programming (LP) problem with single and multiple objectives. Since then the fuzzy set theory has been applied to formulate and solve the problems in various areas such as image processing and pattern recognition. The HOQ of the QFD methodology, which is greatly used as a powerful tool in successful firms, was originally developed in Japan at the Kobe Ship yards of Mitsubishi Heavy Industries (Hauser & Clausing, 1988). QFD suggesting us organized indication for transforming the customer's needs into characteristics of new products and services. The method involves developing four matrixes, or 'houses', that we enter by degrees as a project for a given product or production process is developed on increasingly specific levels. It is a widely used customer driven design and manufacturing tool and is commonly used in a new product development field to translate customer requirements (whats) into appropriate products engineering characteristics (Hows). The tool that has been used widely in QFD matrix is called the house of quality, which is utilized for the aim of converting market information into product strategies for business. Identifying market and consumer knowledge and retailer involvement are considered as key success factors in product development. Product development needs to be based on consumer needs.
The crucial action in the application of QFD is to build the HOQ precisely; this contains deciding the importance weights of customer demands, the relationship matrix between customer requirements and engineering characteristics, and the correlation matrix among engineering characteristics. The house also helps the design team to set objects for improvements, such as cost reduction (Gandhinathan et al., 2004). The fuzzy logic proved to be useful because the main variables neither quantitatively defined nor attributable to specific sets, were expressed as linguistic variables instead and because the general ''if-then-else'' rules were fundamental tools for linking the input linguistic variables with the system's outputs (Bevilacqua et al., 2009). Sohn and Choi (2001) applied fuzzy-QFD to the supply chain and developed a fuzzy MCDM method to choose a design with an optimal combination of reliability and customer satisfaction. There has been specific consideration to the several personal assessments in the HOQ process and suggested symmetrical triangular fuzzy numbers for removing the ambiguity in people's linguistic assessments. Papers on the use of QFD for the development of products state that the method is a potentially useful tool (Vatthanakul et al., 2010), if adaptations to the method are made and the specific characteristics are taken into account.
2. MATERIAL AND METHOD
QFD suggesting us organized indication for transforming the customer's needs into characteristics of new products and services. The method involves developing four matrixes, or 'houses', that we enter by degrees as a project for a given product or production process is developed on increasingly specific levels. In the present article, our attention focuses on the Planning Matrix, or (HOQ) and the proposed approach is based upon the QFD-Fuzzy and it can be seen that it is achievable to have an agile supply chain management (SCM) with considering HOQ by considering agile attributes. This procedure has been done under fuzzy environment. Correspondingly, the essential concept is Selecting Supply Chain Management components and proposing agility indexes then Surveying relations through House Of Quality can give the outcome. In this model HOQ contains seven different elements, as shown in Fig.1.
Recognizing related agile attributes (AAj, j=1,â€¦,m) according to supply chain components (SCCi, i=1,..,n) Components performances has been done through using HOQ. Therefore SCCs seem as ''whats'' since supply chain components should first recognize and grade proper dimensions to compete together. On the other hand, AAs have the appearance of ''Hows'', since they declare attributes to be increased relying on components importance.
Agile Attributes (AAj)
Relationships matrix (Rij)
Importance weight (Wi)
Supply Chain Components ( SCCi)
Relative Importance of AA(Rij)
Ranking of AA (Score j)
Fig.1. Foundation of House Of Quality
In paper the definition of a specific set of SCCs and AAs has not been shown. It is clear that the meaning is related to particular situation of specific company. In this regard, it should be mentioned that, in reality, the relative significance of relations and correlations in HOQ, is decided by human. So, it is obvious that fuzzy logic is proposed to compensate human errors. Because of the indistinctive judgment which has been made in filling HOQ, the linguistic variables were converted in to triangular fuzzy numbers. The degree (weight) of importance for every "what" is based by decision-makers which is shown by fuzzy vector Wi.Wi defines the importance of every part of supply chain. The connections between how the j-th-agile attribute act against i-th supply chain components has been evaluated by HOQ matrix by defining Rij (i=1,â€¦,n, j=1,â€¦,m) for every entry. In traditional methodology of QFD, graphical symbols declare three degrees for relationships (weak, medium, strong), which are translated in a rating scale. When relations between SCCs and AAs have been evaluated, the relative importance RIj of the j-th agile attribute can be defined as a fuzzy weighted average. According to the following formula, is the weight of importance for the i-th supply chain component and Rij is showing the relationship between the j-th AA against the i-th SCC, by defining fuzzy triangular number:
RIj =, j = 1, . . . , m (1)
Yager (1981) suggested crisp value for a fuzzy triangular number F(l,m,n) through below formula for de-fuzzing fuzzy numbers:
The highest score of AAs has significant effect on supply chain components, and should be focused by manufacturers.
3. RESULTS AND DISCUSSION
In this section we use a numerical example, the goal of numerical example in this paper is evaluating benefits of this methodology, In addition to taking its application in to account. Although every company has its own agenda, but overall the fact that every one of them has unique supply chain is unquestionable fact. For shedding light on this purpose, a list of supply chain components (SCCi=1,..,6), has been gathered, namely "design", "supply", "production", "distribution", "services", "customer". They are labeled as "whats". In this study, due to the unclearness of human decisions, graphical symbols which explicating the degree of connection, are converted in to fuzzy triangular numbers. For making the correlations and relationships we used Strong as S, Medium as M and Weak as W with fuzzy numbers (0.7;1;1), (0.3;0.5;0.7) and (0;0;0.3) respectively (Bottani & Rizzi, 2006).
When it comes to reality, relating the pertinent relation between supplies chain components needs face to face interactions with staff's company either in form of interviews or seances. To substantiate the usefulness, an appropriate group, with supervisions of firm's staff and academics was assigned for collecting information and giving feedbacks for improving agile strategies. This procedure was done in some departments such as marketing and production.
For filling out the houses of HOQ, fuzzy linguistics weight were defined, this was extracted from workgroup, then the relative importance of supply chain were assessed by experts. In this example, the pertinent significant Wi of SCCs was based on the work by Ren et al. (2003). Wis(i=1,â€¦6) were recognized by fuzzy linguistic numbers and are Very High (VH), High (H), Low (L) and Very Low (VL) with (0.7;1;1), (0.5;0.7;1), (0;0.3;0.5) and (0;0;0.3) fuzzy numbers, respectively (Bottani & Rizzi, 2006). This information is based upon expert decision makers with considering the strategy of the firm.
In this example, to enhance implementation of this methodology in real cases which, needs evaluating the correlations between "Hows" and demanding 496 comparisons, agile attributes were divided in to 10 decision makers (DDs). These relations are based upon experts judgment and interview by decision makers. For listing related agile attributes, 32 agile attributes were derived from yusuf et al. (1999) in the columns of HOQ. After that, these relations between agile attributes and supply chain's component were recognized by defining the proposed fuzzy numbers and have been written in the center of Fig.2.
In this paper we have proposed an approach based on quality function deployment (QFD) methodology, and particularly of the House Of Quality (HOQ), to enhance agility of supply chain. Some basics of fuzzy logic and fuzzy numbers are required for the application of this method, since fuzzy numbers are adopted to assess both the relationships and the correlations in the HOQs. The benefits associated with having an agile supply chain are various that can be summarized as follows: At First agility indexes are evaluated. The decision-makers can objectively assess each agility index interface and detect when corrective action may be necessary. These correlations have potentials to significantly impact upon the final ranking of agile attributes. Secondly, the model can systematically identify supply chain weaknesses and provide the means for managers to devise a comprehensive improvement plan. Finally, a numerical example was presented to illustrate the proposed method.
Fig.2. Numerical example for House Of Quality