Supply Chain 2012 Part 19 pot

New Measures for Supply Chain Vulnerability: Characterizing the Issue of Friction in the Modelling and Practice of Procurement 531 costs, respectively. Each point in this surface represents the average friction for n=280 experiments across all factor levels of s 1 and D for given levels of p and e, and this response surface is very closely modeled by the expression z (average friction) = 1.0 +0.086e 2 + 0.002p 2 - 0.001p (r 2 = .843). 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95 0.25 1.5 2.75 1 1.05 1.1 1.15 friction inventory parameter e fixed cost parameter p Figure 3. Response surface formed by average friction levels across environmental factor levels p and e for two-stage test bed. Observation of the three-stage test results suggests similar relationships between these parameters and the broader effect of chain friction. In the case of the three-stage results, the response surface is closely modelled by z (average friction) = 1.0 + 0.351e 2 – 0.182e - 0.011p 2 + 0.043p (r 2 = .879). Furthermore, the percent increase between three-stage chain friction instances and their corresponding two-stage counterparts describes a highly similar response surface with respect to p and e, as shown in Fig. 4. Further insight can be gained by looking at these two environmental parameters in isolation. Fig. 5 shows the distinctly "inflammatory" effect of the inventory parameter e: the higher the levels of this factor, the higher the observed levels of friction. Fig. 5 also indicates the intuitive result that this phenomenon is compounded by the addition of another level of planning, as the disparity between the average friction in the two-stage and three-stage experiments widens with increasing values of e. Supply Chain: Theory and Applications 532 0.05 0.2 0.35 0.5 0.65 0.8 0.95 1 1.75 2.5 0 0.05 0.1 0.15 % increase in friction inventory parameter e fixed cost parameter p Figure 4. Response surface formed by average percent increase in friction levels when comparing three-stage to two-stage experimental instances, across environmental factor levels p and e. 1 1.02 1.04 1.06 1.08 1.1 1.12 1.14 1.16 1.18 0 0.2 0.4 0.6 0.8 1 inventory parameter e friction 2-stage 3-stage Figure 5. Average friction levels across factor level e for the two-stage versus three-stage results (n = 3,360 instances for each data point). Fig. 6 illustrates average two-stage and three-stage response to the fixed cost parameter p, suggesting similar polynomial relationships with the issue of friction, but nonetheless in New Measures for Supply Chain Vulnerability: Characterizing the Issue of Friction in the Modelling and Practice of Procurement 533 contrast with the ratio e. Unlike the inventory parameter e, the results do not support a strictly increasing relationship between parameter p and resulting friction. Rather, the highest average levels of friction witnessed on Fig. 6 are associated with the values of 1.5 and 2.0, and inspection of the data reveals that the absolute highest values of friction, or economic blind spots, are associated with somewhat lesser values of p, as will be discussed in the next section. Likewise, unlike factor e in Fig. 5, the disparity between the two-stage and the three-stage results does not appear to be strictly increasing with the value of fixed cost parameter p. 1 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 0 0.5 1 1.5 2 2.5 3 3.5 fixed cost parameter p friction 2-stage 3-stage Figure 6. Average friction levels across factor level p for the two-stage versus three-stage results (n = 5,320 instances for each data point). 5.4 Anticipating economic blind spots As discussed earlier, economic blind spots are so named because independent supply chain partners could potentially fail to "see" substantial savings achievable through centralized coordination. Interestingly, the findings in the previous section suggest these instances of extreme friction are associated with the same environmental parameters in both the two- stage and the three-stage test bed. In each case, the worst of the economic blind spots are confined to instances in which the following conditions occur simultaneously: x .75  p  1.25 x e  0.9 x 1.33  s 1 /D  2.0 These three conditions hold true for 282 experimental instances in each test bed. In the case of the two-stage test bed, average friction for this sub-group is 1.219 (in contrast to 1.030 for the entire test bed), containing of all instances of friction of at least 1.30. Within the three- stage test bed, average friction of these 282 instances is 1.310 (compare to 1.054 for all three- stage experiments), containing only 17% of the 880 three-stage experiments with chain friction of at least 1.30, but 100% of the 47 instances in which chain friction was at least 1.40. Supply Chain: Theory and Applications 534 6. Observations and conclusions The results of this simulation study strongly suggest that certain inter-organizational supply chain partnerships could prove extremely vulnerable to the inherent inefficiency of decentralized procurement, while others could function quite comfortably in that mode, dependent on environmental factors. Thus, it is not surprising how, as discussed in Section 1, much of the existing literature exploring the relative merits of centralized planning and coordination reports distinctly mixed results. Indeed, it now becomes apparent how potentially dangerous it may be to draw conclusions from an average observation of interest in this context- the average loss from decentralized planning across these 127,680 experiments was only 4%, but this summary conceals the presence of distinct economic blind spots ranging as high as 46% increases in system-wide costs. The new measures of link friction and chain friction discussed in Section 2 and the associated conditions of implicit optimization and economic blind spots assist in focusing attention on the relative merits of centralized planning, to rationally weigh these merits against any difficulties present in a given inter-organizational supply chain. Even in the context of the particular simplifying assumptions incorporated into the formulations of Section 3, resulting friction levels showed strong relation to both the environmental factors tested here, and complex interactions of those factors. The powerful influence of the cost factors p and e, both in the creation of instances of implicit optimization and in driving friction upwards, has interesting implications for simulation study design as well as practice. As an example, an earlier study of Simpson (2001) examined centralized versus decentralized procurement across a three level system of substantially greater complexity than the linkages modelled in Section 3, including features such as multiple products, joint order-picking costs, and time variant demand. Nonetheless, a highly centralized scheduling technique outperformed intuitive, pull-style planning by an average of only 1.8% across one group of 900 experiments, and yet this same technique lowered costs by an average of 31.5% within another group of 900 experiments. In hindsight, the only environmental difference between these two groups were the factors identified here as e 1 and e 2 , these values being substantially higher in the latter case. Section 5.4, outlining the environmental factor values most commonly associated with economic blind spots in both test beds, addresses the question posed earlier: when would a substantial effort to restore the operations research perspective of a system make a substantial difference in that system's performance? All three of the conditions identified in Section 5.4 have compelling interpretations. The first two, 0.75  p  1.25 and e  0.9, are indicating those experimental instances in which the fixed replenishment costs and the inventory holding costs of each of the supply chain stages are the most similar to each other. Restated, supply chains linking independent organizations with highly similar cost structures may see the greatest benefits from centralized interventions, or suffer distinct cost increases from independent behaviour. However, to locate the most dramatic blind spots in this simulation study, Section 5.4 coupled the conditions of similar fixed and holding costs with a third condition, 1.33  s 1 /D  2.0. As discussed earlier in Section 5.3.2, the ratio s 1 /D was found to be highly influential on the level of friction within a simulation, with the greatest degree of influence observed when this ratio’s value was at or near a value of 2.0. This condition represents a scenario in which a buyer’s fixed and inventory costs balance such that, when acting independently, this stage would be indifferent or nearly indifferent to recieving lot-for-lot replenishment New Measures for Supply Chain Vulnerability: Characterizing the Issue of Friction in the Modelling and Practice of Procurement 535 versus replenishing two period’s worth of demand requirements with each in-bound shipment. Thus, the presence of this condition of independent indifference to holding one period’s worth of inventory based on cost (it is assumed that the buyer stage would otherwise favor no inventory simply on principle) strongly suggests that effort should be invested in identifying the centralized solution on behalf of system-wide performance. As discussed earlier, there is evidence suggesting that the particular influence of s 1 /D is not likely to hold beyond the conditions of level demand simulated here, in that these moments of indifference must be repeated through time to generate the inefficiency. Arguably, this is not as confining an assumption as it may first appear: supply chains supporting Just-in- Time (JIT) production will likely be supporting level production schedules, resulting in level procument patterns across in-bound partnership links. Furthermore, as pointed out by Gavirneni (2001), much of the recent re-engineering of supply chain partnerships has been in support of JIT inventory management. Thus, the issues of characterizing and identifying those supply chain relationships most vulnerable to decentralized treatment should not be considered simply a promising direction for further research, but a genuine and on-going need in the successful management of the increasingly complex systems observed in the field. 7. References Aderohunmu, R., A. Mobolurin, N. Bryson. (1997). Joint vendor-buyer policy in JIT manufacturing: Response to Hofmann's comments. Journal of the Operational Research Society, Vol. 48 (5), pp. 547-549. Armistead, C.G and Mapes, J. (1993). The impact of supply chain integration on operating performance. Logistics Information Management, Vol. 6 (4), pp. 9-14. Bowersox, D.J. (1969). Physical distribution development, current status, and potential. Journal of Marketing, Vol. 33 (1), pp. 63-70. Chen, F. (1998). Echelon reorder points, installation reorder points, and the value of centralized demand information. Management Science, Vol. 44 (12), pp. 221-234. Dudek, G. And Stadtler, H. (2007). Negotiation-based collaborative planning in divergent two-tier supply chains. International Journal of Production Research, Vol. 45 (2), pp. 465-484. Gavirneni, S. (2001). Benefits of co-operation in a production environment. European Journal of Operational Research. Vol. 130, pp. 612-622. Gurnani, H. and Y. Gerchak. (2007). Coordination in decentralized assembly systems with uncertain component yields. European Journal of Operational Research. Vol. 176, pp. 1559-1576. Hofmann, C. (1997). Comments on 'Joint vendor-buyer policy in JIT manufacturing'. Journal of the Operational Research Society, Vol. 48 (5), pp. 546 - 547. Huang, G.Q., J.S.K. Lau and K.L. Mak. (2003). The impacts of sharing production information on supply chain dynamics: a review of the literature. International Journal of Production Research, Vol. 41 (7), pp. 1483-1517. Jørgensen, S. and P.M. Kort. (2002). Optimal pricing and inventory policies: Centralized and decentralized decision making. European Journal of Operational Research. Vol. 138, pp. 578-600. Supply Chain: Theory and Applications 536 Lau, J.S., G.Q. Huang, K.L. Mak. (2004). Impact of information sharing on inventory replenishment in divergent supply chains. International Journal of Production Research, Vol. 42 (5), pp. 919-941. Otto, A. and H. Kotzab. (2003). Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain. European Journal of Operational Research. Vol. 144, pp. 306-320. Schwarz, L. and L. Schrage. (1975). Optimal and system myopic policies for multi-echelon production/inventory assembly systems. Management Science, Vol. 21(11), pp. 1285-1294. Simpson, N.C. and S.S. Erenguc. (2001). Modeling the order picking function in supply chain systems: formulation, experimentation and insights. IIE Transactions. Vol. 33(2), pp. 119-130. Simpson, N.C. (2007). Central versus local multiple stage inventory planning: An analysis of solutions. European Journal of Operational Research. Vol. 181(1), pp. 127-138. Stadtler, H. (2005). Supply chain management and advanced planning- basics, overview and challenges. European Journal of Operational Research. Vol. 163(3), pp. 575-588. Wang, H., M. Guo and J. Efstathiou (2004). A game-theoretical cooperative mechanism design for a two-echelon decentralized supply chain. European Journal of Operational Research., Vol. 157, pp. 372-388. Whang, S. (1995). Coordination in operations: A taxonomy. Journal of Operations Management, Vol. 12, pp. 413-422. 26 Competence Based Taxonomy of Supplier Firms in the Automotive Industry Krisztina Demeter , Andrea Gelei and István Jenei Corvinus University of Budapest Hungary 1. Introduction In the last 15-20 years companies went through a series of heavy economic blows in Hungary: first, the paternalistic state disappeared and they had to start to manage themselves and their own capital. Second, the collapse of the Russian industry forced most of them to find new markets for their products in order to survive. Third, the accession to EU brought competition much closer to them than ever before. Although a lot of companies disappeared during these years and even more were founded, we can say, that they have to improve themselves continuously to adapt to the changing conditions in order to remain competitive. Thus competitiveness is and was a focal issue in the Hungarian economy. Today, with the accession to EU, the key to competitiveness for Hungarian companies is to what extent they are able to join European or even global supply chains. How can they discover the requirements of various customers and how can they improve their internal operations to fit these requirements? These are very general questions, but the answers are different company by company. We believe, however, that there must be some general patterns behind the scene, which might be useful for companies to know how to position themselves in the supply chain. Several OEMs have started business in the automotive industry in Hungary and in the neighbouring transition economies providing chance for Hungarian suppliers to join their supply chains. Furthermore, due to the intense global competition and the matured stage in the life cycle in automotive companies supply chain management practices are vital. That is why we selected this industry as the basis of our research. We believe that similarly to portfolio models which segment suppliers, we can build taxonomy on various customer values and supplier competences. Our paper discusses this focal question by using a general model of competitiveness for a series of interviews from the Hungarian automotive industry. In this paper we concentrate on the competence side of the model and use interviews as an empirical base. The structure of our paper is the following: first we go through the relevant literature. Next the model of competitiveness and our research method is introduced. Then we describe the cases shortly and analyze the information we got. Finally our taxonomy is developed and conclusions are drawn. Supply Chain: Theory and Applications 538 2. Literature review Firm competitiveness, as defined by Chikán et al (2002) is “the basic capability of perceiving changes in both the external and internal environment and the capability of adapting to these changes in a way that the generated profit flow guarantees the long term operation of the firm”. Firm competitiveness in this broad understanding is basically a function of two factors (Gelei, 2004): customer value and core competences. Customer value is defined from a customer point of view. It includes the aspects that are important for the customer in relation to the supplier. Since customer value is a very broad term, researchers usually split it into different dimensions. The most accepted approach in operations management is to speak about value dimensions as sources of competitiveness (Chase, 2001), such as price, quality, flexibility, reliability or service. Important to emphasize that these dimensions are identified from a customer aspect by the supplier answering the question what the customer wants from me. There are less well-known approaches, however. Their common feature is the supplier aspect they use. Mandják & Durrieu (2000), for example, group value dimensions on transaction, partnership and network levels. They argue that customer values are different when suppliers simply fulfill transactions, when they have to manage their partners or when they have to manage a whole network of companies. Walter et al (2001) speaks about direct and indirect value dimensions. Direct value dimensions are formulated through direct partnerships, while indirect value dimensions are realized beyond the given partnership involving other business partners. For example, volume dimension is a direct value dimension, which refers to the volume generated by the given customer, providing for the supplier to reach the breakeven point. Market dimension, on the other hand, is an indirect value dimension providing reference for the supplier leading to further market opportunities and orders from other customers. Finally, Möller & Törrönen (2003) use the dimensions of efficiency, effectiveness and network. Efficiency relates to the supplier’s financial, profitability aspects, effectiveness relates to customer requirements and satisfaction and network relates to partners and wider stakeholders. In our opinion value dimensions are elements through which value generation for the customer can be realized. Customer values are defined by the customers (consciously or unconsciously) and suppliers have to understand these values in order to provide a product and service package which customer expects and respects. The second factor of firm competitiveness is the sum of resources and capabilities that makes a firm able (capable) to create and deliver what is expected by the customer. The resource based theory of the firm (Penrose, 1959; Wernerfelt, 1984) and, inspired by their views, the resource based strategic management (Hamel – Prahalad, 1990, Grant, 2001) interprets firms as complex sets of resources and capabilities and considers them as the source of firm competitiveness. Although we are aware that there are other approaches to explain competitiveness, such as the industry structure view (Porter, 1980) which considers industrial factors as the bases of competitiveness, or the less known relational view (Dyer & Singh, 1998; Dyer & Nobeoka, 2000), which describes how partnerships can create relational rents which cannot be easily copied by competitors, we stay with the resource based view as we are looking for factors providing competitiveness from inside the companies. In the resource based theory the distinction between the terms “resources” and “capabilities” is fundamental. Grant (2002) defines resources as individual inputs of operations like capital equipment, human resources, intellectual capital, and so on. Teece et al. (1997), on the other Competence Based Taxonomy of Supplier Firms in the Automotive Industry 539 hand, differentiate between factors of production and resources. According to them factors of production are undifferentiated inputs available in disaggregate form in different factor markets. Resources are also different assets of a company, but they have already some firm- specific content. Both interpretations have a basic common feature, namely these resources do not create value on their own. In value creation processes these resources need to be coordinated and managed. Nelson & Winter (1982) emphasize that the permanent and matured patterns of coordination and management activities constitute routines. Definite sets of resources and the connected routines are defined as capabilities (Grant, 2002). A third basic term of the resource based approach is competence. Authors of the CLM (Council of Logistics Management) research program called “World class logistics” (1995) interpret “competence” at a high level of abstraction. A company can possess different competences. Core competences are subsets of resources and capabilities that are fundamental to satisfying customer expectations (value dimensions) and consequently firm’s performance (Hamel & Prahalad, 1990. Distinctive competences are those, where firms are particularly good relative to their competitors. In our paper we also consider competence as a higher level building block of firm competitiveness, than resources and capabilities. Thus firm competitiveness is in our understanding a function of two factors: x To what extent a company can identify value dimensions that are important for their customers? x To what extent is a firm able to successfully develop those sets of resources and capabilities (or competences) that make it able to create and deliver the identified important value dimensions? In the following section of our paper we discuss the critical point of connection between these two sides. 3. Model The model is an instrument for analyzing the internal structure of firm competitiveness. This model is summarized in Figure 1 below. On the basis of our model, the fit of customer expectations and core competences will determine the level of competitiveness. If customer expectations in any respect cannot be fulfilled by the supplier then the customer will look for substitution, or alternatively, makes attempt to teach the customer how to provide that value. In any way, the supplier is not competitive at the moment. Also, if the supplier has specific competence not required by its customers, then that competence does not help the company to stay in that particular business. Today in B2B relations the features of delivered products are usually just the preconditions of future business. As customers have different and very detailed requirements and suppliers can provide specific packages, potential partners have to meet and know each other to identify the level of fit. Fairs, exhibitions, customer-supplier meetings, references can serve that purpose. Customers there get to know both the products provided and the providers themselves. Customer audits give further insight into the capabilities of suppliers which make sure the expected value would be provided for customers. However, even if there is a fit at the moment, it can change over time as customers can increase their requirements or suppliers can build new capabilities and thus can provide more valuable packages. Supply Chain: Theory and Applications 540 The purpose of our interview based research is to discover closely related packages of customer value dimensions and core competences that are required to create them. According to our hypothesis automotive suppliers can develop and possess different type of such packages. Along the concrete sets of packages we create taxonomy of automotive suppliers. In this paper we concentrate on the competence part of our model and will discuss customer value issues only as suppliers think about it. Core com p etences Competitiveness Customer value Resources Direct value- dimensions Indirect value- dimensions Capabilities Figure 1. The buildup of firm competitiveness (Gelei, 2004) 4. Case selection The research is based on multiple interviews, twenty one altogether. The automotive industry was selected because supply chain management is the most developed in the automotive industry. Due to its global nature, networking is one of the primary sources of competitiveness (Senter – Flynn, 1999). Actors of Hungarian automotive supply chains are interviewed to capture both expected customer value dimensions by customers and supplier core competences, including their understanding about the required value dimensions. Thus, the unit of analysis is the business unit in Hungary, even if we had to consider the global company background during the analysis. Since our purpose is to find different service packages related to different customer requirements we strived for diversity (Stuart et al., 2002). Our first aspect was to go back to companies which took part in previous research (see Demeter et al, 2004). The reason for this is threefold. First, in that research we went through different levels in two supply chains which can assure the required diversity. Second, we can use additional information about the participating companies from the previous research. Third, we can see the progress these companies made in the last three years, which can help to identify important capabilities. Seven of the 10 companies in our sample belong to this group. The other three companies came into the picture on various ways, but practically randomly: we found one of them on the internet, one of them on a conference and the third one is a supplier of another company in the sample. Four additional companies refused to take part in the research due to the lack of time, to ownership problem, to confidential purposes and to the lack of interest. Depending on company size, the role in the supply chain and the availability of time the number of interviews varied from 1 to 4 by company. The length of interviews also varied between 45 minutes and 3 hours. The interviews were semi-structured and some additional documents were also collected from companies. The positions of interviewees are also diverse. We asked the managing director in case of one interview at a company. [...]... them have looked to reengineer their organizational processes and business practices and adopt supply chain management best practices An aspect studied by many researchers recently is supply chain sales and operations planning, which deals with the management of client orders through the supply chain Each partner involved must decide quantities and production dates, and allocate resources for each product... challenges of sharing private information between partners Decentralized approaches are now being considered to overcome these problems, giving different partners the responsibility to locally plan their production, using coordination schemes to insure coherent supply chain behavior Agent-based technology provides a natural approach to model supply chain networks and describe specialized planning agents... agent-based supply chain planning, coordination in supply chain, adaptive agent-based planning and learning agents Section 3 presents the proposed framework to design multi-behavior agents, explaining how different planning behaviors can be identified, compared and introduced in an agent-based planning system In Section 4, we give results from an application of the framework to the lumber supply chain Finally,... approaches to more functional approaches Distributed supply chain planning approaches are first reviewed and agent-based planning is presented as a particularly interesting paradigm to manage supply chain planning Next, in order to create a coherent environment, coordination mechanisms used in these approaches are presented, including negotiation between partners Because agent-based planning systems can... Agents for Supply Chain Planning: An Application to the Lumber Industry 553 2.1 Distributed supply chain planning Traditionally, centralized planning systems have been used for production planning in a single company Offering a complete view of the production activities, they usually use optimization algorithms to find the best production planning solutions In a distributed context like supply chains,... research-based tools The platform is designed to simulate supply chain decisions and plan supply chain operations Each agent can be designed with specific planning algorithms and is able to start a planning process at any time, following a change in its environment More details will be given of this platform in section 3 2.2 Coordination in supply chains As discussed previously, distributed planning provides... collection of reusable software components and interfaces needed for any agent involved in a supply chain management system The ABS is geared to handle perturbations caused by stochastic events in a supply chain An interesting simulation is presented using ABS agents to analyze the impact of coordination in supply chains when facing unexpected events Another adaptive agent model is the tribase acquaintance... the supply chain For example, a major mechanical breakdown in a strategic third-tier supplier can reduce supply availability for several days, which can trigger a cascade of perturbations within the supply chain, translating into a delay for the final client Another example is a quick change in demand pattern When such changes happen, every local production plan and demand plan exchanged between partners... optimization tools, the platform increases supply chain reactivity and performance More than a planning tool, this platform can also be used to simulate different supply chain configurations and coordination mechanisms The agent-based architecture presented is based on the functional division of planning domains, inspired by the SCOR model proposed by the Supply Chain Council (Stephens, 2000) Figure 2... two supply chain partners, using a convergence mechanism based on exchange of local associated costs Different agent-based manufacturing systems using negotiation have been proposed (see Shen et al., 2001; Shen et al., 2006) Among them, Jiao (Jiao et al., 2006) presented an agentbased framework that enables multi-contract negotiation and coordination of multiple negotiation processes in a supply chain . point). 5.4 Anticipating economic blind spots As discussed earlier, economic blind spots are so named because independent supply chain partners could potentially fail to "see" substantial. recent re-engineering of supply chain partnerships has been in support of JIT inventory management. Thus, the issues of characterizing and identifying those supply chain relationships most. Research, Vol. 42 (5), pp. 919- 941. Otto, A. and H. Kotzab. (2003). Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain. European Journal