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Developing the framework for coordination in supply chain of SMEs Rajesh K. Singh
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Indian Institute of Foreign Trade (IIFT), New Delhi, India Abstract Purpose – Supply chain management is one of the most important areas for competitiveness and growth of industries. Small and medium enterprises (SMEs) in India and other developing countries face problems in coordinating their supply chain due to lack of resources and improper directions. The purpose of this paper is to develop a framework for improving the coordination in supply chain and development of an index for coordination. Design/methodology/approach – The interpretive structural modelling (ISM) approach has been employed to develop the structural relationship among different factors of coordination and responsiveness in supply chain to take strategic decisions. This framework is also used to evaluate coordination index for an organization. It has been further illustrated with the help of a case study. Findings – In total, 32 enablers for coordination in a supply chain have been identified based on literature review. These are further grouped into six categories such as top management commitment, organizational factors, mutual understanding, flow of information, relationship and decision making and responsiveness. It is observed that all of these factors have strong mutual linkage and top management commitment is a major driver for improving the coordination among these factors. Research limitations/implications – As ISM methodology is based on experts’ opinion, this framework needs further validation with empirical data and detailed case studies. Originality/value – This paper has explored major factors for coordination in supply chain. It will be of great value for SMEs in developing their strategies for coordination in supply chain. The coordination index evaluated in this paper will also help them in benchmarking their performance in terms of different attributes of supply chain. Keywords India, Supply chain management, SMEs, Coordination, Responsiveness, Interpretive structural modeling, Small to medium-sized enterprises Paper type Research paper
1. Introduction Small and medium enterprises (SMEs) are considered backbone of economic growth in all countries. After markets globalization, SMEs are facing a very different scenario compared with the protective environment of the past. Improvements in competitors’ capabilities have shortened product life cycles, elevated product complexity and expanded accessibility to new technical breakthroughs (Gupta and Cawthon, 1996). Today’s intense competition requires that firms excel simultaneously in several areas without trade-off, including innovativeness and responsiveness to their customers. According to Huin et al. (2002), management of manufacturing SMEs requires speed, accurate and intelligent decision making to cope with the complex dynamic enterprise The author is highly obliged to reviewers for their valuable suggestions and comments and acknowledges the support of the Department of Science and Technology, Govt of India, in sponsoring a research project on supply chain management in SMEs.
Business Process Management Journal Vol. 17 No. 4, 2011 pp. 619-638 q Emerald Group Publishing Limited 1463-7154 DOI 10.1108/14637151111149456
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resources and uncertainty from external demands and variables. Recently, firm level competition has been replaced with competition among the supply chains. As SMEs have commonly been categorized to be component manufacturers for larger companies, where they operate in the “make to order” or rather the “engineer to order” approach, it imposes rigid constraints on meeting changes in requirements at short notice (Little and Lee, 1999). Competitiveness of SMEs also depends upon competitiveness of their suppliers and customers. SMEs can no longer compete without effective coordination in their respective supply chain. According to Hong and Jeong (2006), SMEs have significant impacts on supply chain performance, where they may serve the roles of suppliers, distributors, producers and customers. Supply chain is a network of facilities and distribution options that performs the functions of procurement of materials, transformation of these materials into intermediate and finished products and the distribution of these finished products to customers (Kaihara, 2003). According to Lau et al. (2004) and Hervani et al. (2005), supply chain is coordination of independent enterprises in order to improve the performance of the whole supply chain by considering their individual needs. Supply chain activities transform natural resources, raw materials and components into a finished product that is delivered to the end customer. Recent years have seen a growing globalization of markets and the concentration of companies on their core competencies resulting increased coordination in supply chain (Xue et al., 2007). According to Arshinder et al. (2008), supply chain is generally complex and is characterized by numerous activities spread over multiple functions and organizations, which pose interesting challenges for effective supply chain coordination. To meet these challenges, supply chain members must work towards a unified system and coordinate with each other. Coordination means organizing the activities of two or more groups so that they work together efficiently and know what the others are doing. In other words, the groups are responsible for individual activity tasks but work interdependently for common goals (Cao et al., 2008). In this sense, coordination in supply chain is an operational plan to coordinate the operations of individual supply chain members and improve system profit. When supply chain members are separate and independent economic entities, this action plan has to include an incentive scheme to allocate the benefits from coordination among them so as to entice their cooperation (Li and Wang, 2007). It has been observed that SMEs in general are not able to implement supply chain management (SCM) to its full extent, mainly because they are managed at arm’s length by larger customers and have to follow the norms stipulated by the buyer (Arend and Wisner, 2005). Other findings suggest that since larger companies consider SMEs as being easy to replace, buyers are reluctant to form partnerships with SMEs (Arend and Wisner, 2005). Systems, tools and methods also represent significant differences between SMEs and larger companies, in relation to adoption of electronic interfaces between actors in the supply chain. For example, larger companies have the resources and technical budgets to implement e-business and e-supply strategies but SMEs continue to be challenged by resource limitations (Wagner et al., 2003). Some studies have also identified that SCM implementation is negatively correlated with SME performance (Arend and Wisner, 2005). Lack of performance among SMEs after the introduction of SCM, as compared with larger companies, can be related to several reasons. Studies focusing on the processes suggest that SMEs and large enterprises implement SCM differently, and apparently
this difference in implementation is significantly associated with SMEs performance (Arend and Wisner, 2005). According to Arshinder et al. (2009), a supply chain consists of disparate but inter-dependent members who are dependent on each other to manage various resources (such as inventory, money and information). The conflicting objectives and lack of coordination between these members may often cause uncertainties in supply and demand. Coordination may help in managing inter-dependencies and reducing uncertainties. According to Sahin and Robinson (2002), a supply chain is fully coordinated when all decisions are aligned to accomplish global system objectives. Lack of coordination occurs when decision makers have incomplete information or incentives that are not compatible with system-wide objectives. Even under conditions of full information availability, the performance of the supply chain can only be sub-optimal if each decision maker optimizes his own individual objective functions. Lack of coordination will result in distortion of demand, i.e. bullwhip effect. It will increase manufacturing cost, inventory cost, replenishment lead time, transportation cost, labor cost, decrease in efficiency, profit, information distortion (Paik and Bagchi, 2007). In view of increasing importance of coordination for success of SCM in SMEs, this study has tried to identify various factors affecting coordination in supply chain. Further, it tries to group these factors into different categories for developing the framework for coordination in supply chain. Organization of remaining part of the paper is as follows. Section 2 discusses the literature review for the identification of major coordination and responsiveness factors. Section 3 discusses interpretive structural modeling (ISM) methodology and classification of enablers on the basis of driving power and dependency. Section 4 discusses evaluation of coordination index. Section 5 illustrates a case study and Section 6 gives results and discussion. Final section provides concluding remarks. 2. Identification of coordination and responsiveness factors In SCM, its members perform different functions or activities like logistics synchronization, inventory management, ordering, collaborative decision making, forecasting and product design, management of flow of goods, information and money. In traditional supply chains, individual members of supply chain have been performing these activities independently. The supply chain members may earn benefits by coordinating these various activities. Logistics has traditionally been defined as the process of planning, implementing and controlling the efficient flow and storage of goods, services and related information as they travel from point of origin to point of consumption. The inventory management includes determination of the order quantity, the timing of order, reorder point and the replenishment of inventory. SCM can be considered as the coordination of distributed decision making of organizations or participants on material flow, information flow, human flow and cash flow in supply chain from systems perspective (Xue et al., 2005). According to Arshinder et al. (2007), collaborative decision making in SCM helps to reduce information asymmetry, reduces inventory cost, improves the customer service and improves the efficiency of replenishment process. Petersen et al. (2005) found that capabilities of suppliers are required in coordinating the product design process with supplier. The coordination at design stage may result in better design and improved financial performance, if the supplier has sufficient knowledge for design of the product. The factors
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like communication with manufacturer and a good organizational culture of supplier also play important role while coordinating with manufacturer (Arshinder et al., 2008). In SCM, organizations said to be effective in their coordination put a lot of emphasis on developing their human resources through training of their employees (Gowen and Tallon, 2002). Integration of supply chain activities result in better coordination. Lee (2000) offers concept of supply chain integration, comprising various levels such as information sharing, coordination and organizational linkages. Soroor et al. (2009) and Sawik (2009) has observed that coordination plays a critical role in integrating different departments in an organization along the supply chain to enhance performance. Coordination can be achieved by the integrated scheduling of manufacturing and supplies of raw materials and assembly of finished products. According to Cao et al. (2008), supply chain coordination encompasses every effort of information exchange and integration during the courses of developing, producing and delivering a product or service to end marketplaces. Ryu and Yucesan (2010) have observed that coordination is possible only by allowing returns to all supply chain members. Konijnendijk (1994) examined the coordination process at tactical and operational levels about product specification, volume, mix and lead times between sales and manufacturing in engineer-to-order company. Stank et al. (1999) have observed that inter firm coordination process is characterized by effective communication system, information exchange, partnering and performance monitoring. The main concern of SCM is how to coordinate the independent players to work together as a whole to pursue the common goal of supply chain profitability in changing market conditions. The effectiveness of coordination can be assessed on the basis of quality, innovation and customer satisfaction. This view needs more consideration, because coordination cannot be achieved by focusing on only one or two factors of the supply chain. There are many factors involved in achieving coordination like human, technology, strategies, relationship, rewards, profits, and risks. Apart from these factors, efforts are required to implement initiatives such as sharing of knowledge, scheduling of frequent meetings of stakeholders for conflict resolution, understanding of nature of intermediates and knowledge of supply chain concepts, status or power difference and resistance in following the instructions of other organizations (Grittell and Weiss, 2004). Information technology helps to link the point of production seamlessly with the point of delivery or purchase. It allows planning, tracking and estimating the lead times based on the real time data. Advances in IT (e.g. internet, electronic data interchange, enterprise resource planning, e-business and many more) enable firms to rapidly exchange products, information and funds and utilize collaborative methods to optimize supply chain operations. Sharing of the information between the supply chain members is very important for the effective coordination in supply chain. According to Arshinder et al. (2007), sharing of information between supply chain members helps to substitute information with inventory and lead time, reduces the supply chain costs, reduces the demand variability, enhances responsiveness and improves the service level. Development of relationships should be built on more matured understanding where trust, transparency and faith act as main pillars. According to He et al. (2009), coordination among suppliers and retailers is a very important strategic issue in SCM. Supply chain management (SCM) is a technique that is also linked to the adoption of the lean production system. According to Othman
and Ghani (2008), lean and just-in-time (JIT) practices in SCM helps to improve delivery schedules, eliminate waste, develop close collaboration, rationalization and development of efficient suppliers. In this study, 32 attributes of coordination and responsiveness have been identified from the literature (Table I). To simplify the modeling of various enablers of coordination and responsiveness, these attributes are clubbed into six groups as discussed below.
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(i) Top management commitment According to Ganesan and Saumen (2005), top management support is essential for cross-functional training, integration of departments within the organization and vendor development for a responsive supply chain. Top management commitment is a key enabler for effective SCM (Sandberg and Abrahamsson, 2010). Top management plays key role in developing supply chain strategies. Success of the supply chain depends on the effectiveness of strategies (Sun et al., 2009). Different resources like money, time, technology, manpower, and material are used by the supply chain (Shin et al., 2000). Use of information technology such as internet, intranet, software applications packages and discussion support system can be applied to facilitate the information flow with in the supply chain members (Stanley et al., 2009). (ii) Organizational factors Many automotive companies operate on a JIT system in which parts suppliers deliver parts several times a day to the assembly line. Furthermore, some are applying mass customization based on framework that is similar to those adopted by firms such as Dell Computer Corporation. In some industries, mass customization requires a supplier to provide an original equipment manufacturer with a wide range of variants of a given part. Other organizational factors affecting coordination of supply chain may be lean organization structure (Melton, 2005), organization culture, cross-functional training of employees (Ganesan and Saumen, 2005), etc. (iii) Mutual understanding Trust is a favorable attitude that exists when one supply chain member has confidence in other supply chain member (Anderson and Narus, 1990). Trust is required for flow of information in the supply chain. Risk and reward sharing influence individual supply chain member’s behavior and his interaction with other supply chain members. Conflicts of interest are likely to occur when existing risk and reward sharing maximize individuals benefit in spite of the benefit of all the supply chain members (Cachon and Lariviere, 2005). Trust and commitment are essential for enhancing performance of supply chain in developing countries (Bianchi and Saleh, 2010). Conflicts in vision and goals of supply chain members result in the individuals profit maximization in place of profit maximization of all the supply chain members (Arshinder et al., 2007). (iv) Flow of information Availability of point of sales data is important for a responsive supply chain (Michelino et al., 2008). Cost minimization is the main objective of a supply chain. An appropriate inventory management system at every node of the supply chain minimizes the inventory at supply chain nodes (Marek and Malyszek, 2008). Supply chain inventory management is one of the focal areas of SCM. Coordinating the inventory systems
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SCM coordination issues Top management commitment factors Investment of time and money for resource development Focused communication system Long-term investment motive Commitment to promises Ready to adopt new technology Employees training and empowerment Organizational factors Lean organization structure JIT and lean practices Organization behavior for SC implementation Organization culture for SC implementation Role in SC with respect to other members Integration of departments with in the organization Mutual understanding factors Agreed vision and goals of members of supply chain Trust development in SC members Effective implementation of joint replenishment and forecasting decisions Supply chain risk/reward sharing Flow of information factors Use of information technology (IT) tools and techniques Information sharing/exchange Inventory tracking at SC linkage Sharing of data related to purchasing and supplies Knowledge sharing Design data sharing Relationship and decision-making factors Long-term relationship with suppliers Long-term relationship with customers Collaborative decision making/planning with SC members Logistics synchronization Supply chain integration
Rationalization of suppliers Responsiveness factors Flexibility in production system Delivery on time
Table I. Enablers of coordination and responsiveness in SC
Service reliability Ability to adopt process change
References Shin et al. (2000) Arshinder et al. (2008) Morgan and Hunt (1994) Fisher (1997) Arshinder et al. (2007), Stanley et al. (2009) Gowen and Tallon (2002) Melton (2005), Othman and Ghani (2008) Othman and Ghani (2008) Grittell and Weiss (2004) Grittell and Weiss (2004) Arshinder et al. (2008) Sawik (2009), Soroor et al. (2009) Arshinder et al. (2007) Anderson and Narus (1990), Sahay (2003), Bianchi and Saleh (2010) Aviv (2001), Arshinder et al. (2008) Lee (2000), Cachon and Lariviere (2005), Ryu and Yucesan (2009) Arshinder et al. (2008) Arshinder et al. (2007), Cao et al. (2008), Stanley et al. (2009) Arshinder et al. (2008), Marek and Malyszek (2008) Moinzadeh (2002) Arshinder et al. (2008) Petersen et al. (2005), Arshinder et al. (2008) Olorunniwo and Hartfield (2001), He et al. (2009) Olorunniwo and Hartfield (2001), He et al. (2009) Disney and Towill (2003), Arshinder et al. (2008), Lyu et al. (2010) Arshinder et al. (2008) Lee (2000), Cao et al. (2008), Soroor et al. (2009), Sawik (2009) Petersen et al. (2005), Othman and Ghani (2008) Arshinder et al. (2007) Lee et al. (1997), Cao et al. (2008), Mehrjerdi (2009) Konijnendijk (1994), Cao et al. (2008) Zhao and Wang (2002), Li et al. (2008)
in a supply chain, however, can be challenging because the companies constituting the supply chain are often independent from each other and sometimes even compete against each other. As such, the participants in the supply chain can be reluctant to freely share private cost information, and to let a third party dictate their inventory policies. Information sharing between the supply chain members is essential for a responsive supply chain (Stanley et al., 2009). Information sharing may be of sharing of the inventory data, demand data and product quality data. The traditional communication between the manufacturer and the retailer is made through periodic ordering in large batches. This ordering behavior distorts original demand information because demand variance becomes larger (Ozer, 2003). (v) Relationship and decision making Long-term orientation is expected to have three specific outcomes, i.e. increased relational behavior, decreased conflicts and increased satisfaction. Collaborative decision making by supply chain members result in the better forecasting of demand, trust between the supply chain members and flow of information (Mehrjerdi, 2009). Lyu et al. (2010) studied problem of store-level retailer’s replenishment and proposes three collaborative replenishment mechanism models in the collaborative supplier and store-level retailer environment. (vi) Responsiveness The responsiveness of a supply chain describes how quickly it responds to customer input. Li et al. (2008) have observed that for responsive supply chain, agility in supply chain is an important factor. Conflicts in vision and goals of supply chain members result in the individuals profit maximization in place of profit maximization of whole supply chain (Arshinder et al., 2007). Long-term orientation is expected to have three specific outcomes, i.e. increased relational behavior, decrease conflicts and increased satisfaction. Availability of point of sales data is important for a responsive supply chain (Michelino et al., 2008). Responsive supply chain ensures delivery in time, cost reduction and accurate forecasting of data (Mehrjerdi, 2009). 3. Research methodology For developing the structural framework of coordination in supply chain, interpretive structural modelling (ISM) methodology has been used. Framework developed by ISM is used to evaluate coordination index of a supply chain. Use of this framework is illustrated with the help of case study. 3.1 ISM methodology ISM is an interactive learning process. The method is interpretive in that the group’s judgment decides whether and how items are related, it is structural in that, on the basis of the relationship, an overall structure is extracted from the complex set of items, and it is modeling in that the specific relationships and overall structure are portrayed in a diagraph model. ISM methodology helps to impose order and direction of relationships among elements of a system (Sage, 1977). However, the direct and indirect relationships between the factors describe the situation far more accurately than the individual factor taken in isolation. Therefore, ISM develops insights into collective understandings of these relationships. Jharkharia and Shankar (2005) applied ISM for
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understanding the barriers in IT-enablement of supply chains. Singh et al. (2007a, b) have used ISM methodology for implementation of AMTs and also for improving SMEs competitiveness, respectively. The application of ISM typically forces managers to reassess perceived priorities and improve their understanding of the linkages among key factors. The various steps involved in the ISM methodology are: (1) Identification of elements, which are relevant to the problem or issues, this could be done by literature review or any group problem solving technique. (2) Establishing a contextual relationship between elements with respect to which pairs of elements will be examined. (3) Developing a structural self-interaction matrix (SSIM) of elements, which indicates pair-wise relationship between elements of the system? (4) Developing a reachability matrix from the SSIM and checking the matrix for transitivity. Transitivity of the contextual relation is a basic assumption in ISM which states that if element A is related to B and B is related to C, then A will be necessarily related to C. (5) Partitioning the reachability matrix into different levels. (6) Based on the relationships given above in the reachability matrix draw a directed graph (DIGRAPH) and remove transitive links. (7) Convert the resultant digraph into an ISM, by replacing element nodes with statements. (8) Review the ISM model to check for conceptual inconsistency and make the necessary modifications. Above described steps, which lead to the development of ISM model, are discussed below. 3.1.1 Structural self-interaction matrix. For analyzing the criteria a contextual relationship of “leads to” is chosen here. For developing contextual relationships among variables, expert opinions based on management technique such as brainstorming was considered. In this exercise, total five experts (four from industry and one from academia) agreed to participate. For expressing the relationship between different critical factors for coordination and responsiveness in supply chain four symbols have been used to denote the direction of relationship between the parameters i and j (here i , j) as given below: V : Factor i will lead to factor j. A : Factor j will lead to factor i. X : Factor i and j will lead to each other. O : Factors i and j are unrelated. The following statements explain the use of symbols V, A, X and O in SSIM: . Variable 1 leads to 5 (V). . Variables 2 and 4 lead each other (X). Considering above notations, SSIM is developed in Table II.
3.1.2 Initial reachability matrix. The SSIM has been converted into a binary matrix, called the initial reachability matrix by substituting V, A, X and O by 1 and 0 as per the case. The substitution of 1s and 0s are as per the following rules: (1) If the (i,j) entry in the SSIM is V, the (i,j) entry in the reachability matrix becomes 1 and the (j,i) entry becomes 0. (2) If the (i,j) entry in the SSIM is A, the (i,j) entry in the reachability matrix becomes 0 and the (j,i) entry becomes 1. (3) If the (i,j) entry in the SSIM is X, the (i,j) entry in the reachability matrix becomes 1 and the (j,i) entry also becomes 1. (4) If the (i,j) entry in the SSIM is O, the (i,j) entry in the reachability matrix becomes 0 and the (j,i) entry also becomes 0 (Table III).
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3.1.3 Final reachability matrix. The final reachability matrix is obtained by incorporating the transitivity as enumerated in Step 4 of the ISM methodology. This is shown in Table IV. In this table, the driving power and dependence of each factor are also shown. The driving power of a particular factor is the total number Sr. no.
Factors
1
2
3
4
5
6
1 2 3 4 5 6
Top management commitment Organizational factors Mutual understanding Flow of information Relationship and decision making Responsiveness
V
V X
V X X
V X X X
V V V V V
Sr. no.
Factors
1
2
3
4
5
6
1 2 3 4 5 6
Top management commitment Organizational factors Mutual understanding Flow of information Relationship and decision making Responsiveness
1 0 0 0 0 0
1 1 1 1 1 0
1 1 1 1 1 0
1 1 1 1 1 0
1 1 1 1 1 0
1 1 1 1 1 1
Sr. no.
Factors
1
2
3
4
5
6
DP
1 2 3 4 5 6
Top management commitment Organizational factors Mutual understanding Flow of information Relationship and decision making Responsiveness Dependence
1 0 0 0 0 0 1
1 1 1 1 1 0 5
1 1 1 1 1 0 5
1 1 1 1 1 0 5
1 1 1 1 1 0 5
1 1 1 1 1 1 6
6 5 5 5 5 1
Note: DP, Driving power
Table II. Self interaction structural matrix
Table III. Initial reachability matrix
Table IV. Final reachability matrix
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Table V. Iteration 1
Table VI. Iteration 2
Table VII. Iteration 3
of factors (including itself), which it may help achieve while the dependence is the total number of factors, which may help achieving it. On the basis of driving power and dependencies, these factors will be classified into four groups of autonomous, dependent, linkage and independent (driver) factors. 3.1.4 Level partitions. From the final reachability matrix, the reachability and antecedent set for each factor are found. The reachability set consists of the element itself and other elements to which it may help achieve, whereas the antecedent set consists of the element itself and the other elements which may help achieving it. Then the intersection of these sets is derived for all elements. The element for which the reachability and intersection sets are same is the top-level element in the ISM hierarchy. The top-level element of the hierarchy would not help achieve any other element above their own. Once the top-level element is identified, it is separated out from the other elements. Then by the same process, the next level of elements is found. These identified levels help in building the diagraph and final model. From Table V, it is seen that the responsiveness is found at level I. Thus, it would be positioned at the top of the ISM hierarchy. This iteration is repeated till the levels of each factor are found out (Tables V-VII). The identified levels aids in building the final model of ISM. 3.1.5 Classification of factors. In this section, the critical success factors described earlier are classified into four clusters (Figure 1). This classification is similar to that made by Mandal and Deshmukh (1994). The first cluster consists of the “autonomous factors” that have weak driving power and weak dependence. These factors are Factors
Reachability set
Antecedent set
Intersection set
Level I 1 2 3 4 5 6
1,2,3,4,5,6 2,3,4,5,6 2,3,4,5,6 2,3,4,5,6 2,3,4,5,6 6
1 1,2,3,4,5 1,2,3,4,5 1,2,3,4,5 1,2,3,4,5 1,2,3,4,5,6
1 2,3,4,5 2,3,4,5 2,3,4,5 2,3,4,5 6
Factors
Reachability set
Antecedent set
Intersection set
Level II 1 2 3 4 5
1,2,3,4,5,6 2,3,4,5 2,3,4,5 2,3,4,5 2,3,4,5
1 1,2,3,4,5 1,2,3,4,5 1,2,3,4,5 1,2,3,4,5
1 2,3,4,5 2,3,4,5 2,3,4,5 2,3,4,5
Factors
Reachability set
Antecedent set
Intersection set
Level III 1
1,2,3,4,5,6
1
1
6
2, 3, 4, 5
5 Driving power
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Linkage
Drivers
4
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3 Dependent
Autonomous
2 1
6 1
2
3 4 Dependence
5
6
relatively disconnected from the system, with which they have only few links, which may not be strong. The “dependent factors” constitute the second cluster which has weak driving power but strong dependence. Third cluster has the “linkage factors” that have strong driving power and strong dependence. These factors are unstable due to the fact that any change occurring to them will have an effect on others and also a feedback on themselves. Fourth cluster includes the “independent factors” having strong driving power but weak dependence. The driving power and dependence of each of these factors are shown in Table IV. In this table, an entry of “1” added along the columns and rows indicates the dependence and driving power, respectively. Subsequently, the driver power-dependence diagram is constructed as shown in Figure 1. For illustration, the factor six having a driving power of 1 and dependence of 6 is positioned at a place corresponding to driving power of 1 and dependency of 6 in the Figure 1. Similarly all other factors considered in this study are positioned on different quadrants depending on their driving power and dependency. 3.1.6 Formation of ISM-based model. From the final reachability matrix (Table IV), the structural model is generated by means of vertices or nodes and lines of edges. If there is a relationship between the coordination factors i and j, this is shown by an arrow which points from i to j. This graph is called a DIGRAPH or digraph. After removing the transitivity as described in ISM methodology, the digraph is finally converted into ISM as shown in Figure 2. 4. Evaluation of coordination index For computing coordination index, different factors of coordination in supply chain such as top management commitment, mutual understanding, flow of information, relationship and decision making, responsiveness and organizational factors are considered. The framework of Cleveland et al. (1989) for production competence is extended to compute coordination index. Singh (2008) have also used this model to evaluate competitiveness index of an organization. On the basis of Cleveland et al. (1989) model, coordination index is given as: Cj ¼ {Wi Log Ki}
Figure 1. Driving power and dependence diagram
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Organizational factors (C2)
Figure 2. ISM-based model for enablers of coordination and responsiveness in supply chain
Mutual understanding (C3)
Flow of information (C4)
Relationship and decision making (C5)
Top management commitment (C1)
Where: Cj ¼ Coordination index for company j. I
¼ Coordination factor.
R ¼ Rank of coordination factor. Ki ¼ Inverse Rank (If R ¼ 1, K ¼ 6, if R ¼ 2, K ¼ 5). Wi ¼ Weight assigned to particular coordination factor. For assigning weight to different factors of coordination, the highest and lowest values of five-point Likert scale, i.e. 5 and 1 are mapped 100 and 0 per cent, respectively. For each of the six factors of coordination, a weight is assigned. The criteria for weight (Wi) is as under: Wi ¼ þ 1 (strength), when percentage score . 60 per cent (mean value . 3). Wi ¼ 0 (neutral), when percentage score is between 40 and 60 per cent (mean value between 2 and 3). Wi ¼ 2 1 (weakness), when percentage score , 40 per cent (mean value , 2).
For illustration, an example of computation of weight is given below. Say, the mean score for processes ¼ 4.2 on a scale of 1 to 5. Using two-point equation percentage may be calculated. It comes out be 4.2/5 ¼ 0.84; therefore, it is assigned weight of þ 1. Further, this methodology is illustrated with the help of a case in next section.
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5. Case illustration The organization chosen for this case illustration was established in 1979. Earlier, this company was working in very small scale. In recent past, its growth has been generated by a paramount concern to provide result-oriented services to customer’s specific needs. It is an ISO9001, QS 9000 and TS16949 certified company. It has a turnover of Rs 20 crore in 2009. It offers single window service through integrated facilities of advanced design center with several seats of CAD/CAM/CAE, coupled with mold flow analysis, modern tool room with latest CNC machines from Europe and Japan with the capability of making moulds and dies up to 4 tons and injection molding facilities to make in house plastic components as small as 1 gram and up to as big as 4,000 grams. The company is supplier to the automotive, electronics and white goods industries. Those who care for quality, service and JIT delivery are the regular customers of XYZ Ltd Main Customers of the organization are General Motors, Ford, Delphi, Suzuki, Daewoo, Panasonic, Xerox, Canon, Whirlpool, LG Electronics, Gillette, Electrolux. It has export markets in Europe and America. Primary objective of the organization is to produce plastic components of global excellence, precision, durability and elegance conforming to end product requirements. This company is member of Automotive Component Manufacturers Association of India, Plastic export promotion council, Indo-German chamber of commerce, PHD chamber of commerce and industry, Federation of Indian chambers of commerce and industry, India trade promotion organization. The element of flexibility, coupled with fast responses, innovation, quality consciousness and responsibility towards the customers, have enabled the company to always keep its business partners happy and satisfied all over the world. This organization gives top priority for quality along with cost and delivery time. Performance of this organization in terms of some factors in recent past can be stated as below: . Sales growth has increased from 0.07 Rs crore per head in 2006 to 0.14 Rs crore per head in 2009.This has been due to inclusion of new customers and by increasing the range of their products. . Machine hour utilization has increased from 60.53 per cent in 2006 to 87 per cent in 2009. . Break down percentage has decreased from 6.80 per cent in 2006 to 1 per cent in 2009. . Internal PPM rejection has come down from 23,400 in 2006 to 4,000 in 2009. This has been achieved due to implementation of quality management initiatives. . Rework percentage has come down from 1.49 per cent in 2006 to 0.12 per cent in 2009. . Customer satisfaction index has increased from 77 per cent in 2006 to 95 per cent in 2009.This has been possible due to various measures taken such as effective coordination with suppliers and customers.
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Cost of poor quality flash has decreased from Rs 1.87 lakhs in 2006 to 0.5 lakhs in 2009. Sales turn over has increased from 5 crore in 2005 to 20 crore in 2009.This has been due to inclusion of new customers and by increasing the range of their products.
For improving its performance, this organization has taken various measures to improve effectiveness of its supply chain. For analyzing level of coordination in the supply chain of this company, response of the management was taken on different attributes of coordination issues in Likert scale of 1 to 5 (1 – very low, 5 – very high). Score of different attributes for the company are given in Table VIII. For computing coordination index, first of all mean for a particular issue (component) of framework is calculated after taking average of scores for all its key items. After this rank, inverse rank and weight for each issue is decided. Sum of entries of last column (Wi Log Ki), will give coordination index of AB Ltd, i.e. 1.96. Theoretically, coordination index value may range between 2 2.86 and 2.86. Computation of coordination index of the company is illustrated with the help of a worksheet as shown in Table IX. 6. Results and discussion Supply chain management is most important approach for competitiveness and growth of industries in globally integrated economics. A successful supply chain should be well coordinated and responsive to meet these days global competition. To be coordinated and responsive, firms need to know the factors that significantly enable the coordination and responsiveness of that firm. Further these factors should be structured to find out the factors that are prerequisite to success and which facilitate the achievement of others. The analysis of interaction among the factors is provided by ISM in this study. It develops a framework in order to enhance the coordination and responsiveness in the supply chain. The driver power dependence matrix gives some valuable insights about the relative importance and interdependence among factors. The major findings of study are as follows: . The driver-dependence matrix indicates that, there is no autonomous factor for coordination and responsiveness. . Responsiveness is weak driver but strongly dependent on other factors. This variable represent desired objective for any organization and is classified as dependent variable. . Organizational factors, mutual understanding, flow of information and relationship and decision making are linkage variable. It means they have strong driving power as well as strong dependence. So, out of six variables, four are unstable type. It implies that these factors are highly interdependent on each other. . The driver power-dependence diagram indicates that top management commitment has strong driving power. This variable will help organization to achieve their desired objective and is classified as independent variable or driver. Based on response from management on various factors, coordination index for supply chain of the anonymous company was evaluated. Coordination index has been found to
Sr. no.
Coordination factors
Five point likert scale 1 2 3 4 5
1.
Top management commitment
2.
Mutual understanding
3.
Flow of information
4.
Relationship and decision making
5.
Organization factors
6.
Responsiveness
Investment of time and money for resource development Focused communication system Long term investment motive Commitment to promises Ready to adopt new technology Employees training and empowerment Agreed vision and goals of members of supply chain Trust development in SC members Effective implementation of joint replenishment forecasting decisions Supply chain risk/reward sharing Use of Information Technology (IT) tools and techniques Information sharing/exchange Inventory tracking at SC linkage Sharing of data related to purchasing and supplies Knowledge sharing Design data sharing Long term relationship with suppliers Long term relationship with customers Collaborative decision making/planning with SC members Logistics synchronization Supply chain integration Rationalization of suppliers Lean organization structure JIT and lean practices Organization behavior for SC implementation Organization culture for SC implementation Role of SC with respect to other members Integration of departments with in the organization Flexibility in production system Delivery on time Service reliability Ability to adopt process change
Mean
3.00
Coordination in supply chain of SMEs 633
2.25
2.67
3.16
3.33
3.50
be 1.96. Maximum value can reach up to 2.86. Presently coordination index of the company is quite high. This approach can be utilized by the organization to benchmark its performance with national and international standards. It can also help in SWOT analysis of the organization. On the basis of SWOT analysis, organizations can develop their supply chain strategies to improve the coordination among different members of supply chain thereby improving the competiveness in global market. It has been observed that this organization is doing quite well in terms of relationship and decision making, organizational factors and responsiveness, however there is need
Table VIII. Score on coordination enablers for AB Ltd
BPMJ 17,4
Sr. no.
Factors of coordination
Rank
Inverse rank (Ki)
Log Ki
Weight (Wi)
3.00 2.25 2.67
4 6 5
3 1 2
0.48 0.00 0.30
0 0 0
0 0.00 0
4 5 6
0.60 0.70 0.78
þ1 þ1 þ1
0.48 0.70 0.78
WiLogKi
1
634 Table IX. Illustration for coordination index of AB Ltd
Top management commitment 2 Mutual understanding 3 Flow of information 4 Relationship and decision making 5 Organizational factors 6 Responsiveness P Coordination index ¼ Cj ¼ {Wi
Mean
3.16 3 3.33 2 3.50 1 Log Ki} ¼ 1:96
for improvement in area of mutual understanding and flow of information in the supply chain. Therefore, this organization should focus on mutual understanding and information flow to improve coordination in supply chain. Improvement in flow of information will help in increasing mutual trust among members of supply chain. In this regard, AB Ltd should invest in various IT tools. Usually most of the SMEs are not very impressive in IT applications due to their resource constraints (Singh et al., 2010 but to compete in global market, SMEs have to overcome on this constraint. 7. Conclusion These days competition is between integrated supply chains rather than individual organizations. To be competitive, a supply chain should be well coordinated. It will help in making supply chain responsive to customer demands. In this study, 32 attributes for coordinated supply chain has been identified. These are grouped into six categories such as top management commitment, organizational factors, mutual understanding, flow of information, relationship, decision making and responsiveness. The ISM approach has been applied to develop the structural relationship among these groups. Findings of this study imply that SMEs should focus on information flow and mutual understanding among the members of supply chain. However, these implications may not be equally true in case of larger organizations, as they are quite strong in terms of IT applications and in efficient flow of information. Thereby, they are able to integrate in the supply chain in more effective manner. This study has also illustrated an approach for evaluating coordination index in a supply chain with a case study. This approach will help SMEs to benchmark their performance in terms of various attributes of supply chains. It will also help them in analyzing their strengths and weaknesses for developing strategies to improve coordination in supply chain. Framework developed in this study will be very useful for developing strategies to improve coordination in supply chain. However, it has got some limitations due to chances of biasing in experts’ opinion. Therefore, findings should not be generalized and as a future scope, these results should be validated with some empirical data and case studies. References Anderson, J.C. and Narus, J.A. (1990), “A model of distributor firm and manufacturer firm working partnerships”, Journal of Marketing, Vol. 54, pp. 42-58.
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About the author Rajesh K. Singh is Associate Professor at the Indian Institute of Foreign Trade, New Delhi, India. His areas of interest include competitiveness, small business management, quality management and supply chain management. He has about 60 papers published in reputed journals and conferences. He has published papers in journals such as Industrial Management & Data Systems, Singapore Management Review, International Journal of Productivity and Performance Management, Benchmarking: An International Journal, Journal of Modelling in Management, Competitiveness Review: An International Business Journal, Global Journal of Flexible Systems and Management, International Journal of Productivity and Quality Management, International Journal of Services and Operations Management, International Journal of Automotive Industry and Management, International Journal of Logistic Systems Management, Business Process and Management Journal, IIMB Management Review and Productivity Promotion. Rajesh K. Singh can be contacted at:
[email protected]
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