Hicl-2006-Global and Flexible Supply Networks

in Blecker, Thorsten and Wolfgang Kersten (Eds.) (2006) Operations and Technology Management II: Complexity Management in Supply Chains. Concepts, Tools and Methods. Erich Schmidt Verlag, Berlin. ISBN: 3 503 09737 6

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GLOBAL AND FLEXIBLE SUPPLY NETWORKS MODELLING AND SIMULATION

Authors

Eduardo Saiz ([email protected])

Juan Manuel Besga ([email protected])

Eduardo Castellano ([email protected])

Iñaki Zugasti ([email protected])

Fernando Eizaguirre ([email protected])

Institution

IKERLAN Technological Research Centre Pº J. M. Arizmendiarrieta, 2

20500 Arrasate-Mondragón (Gipuzkoa) – Spain Tel.: +34 943 71 24 00 Fax: +34 943 79 69 44 http: www.ikerlan.es

ABSTRACT A key issue faced by organisations today is the challenge posed by how to deliver the different products demanded by customers, in different markets, at any given moment in time, and preferably individually customized, as cheaply and as quickly as possible. The well-known mass customisation business strategy tries to provide a solution to this challenge by combining the efficiency of mass production, with the flexibility of differentiation and customisation. Any strategy deployed to manage demand variety-induced complexity affects manufacturing and distribution supply activities, significantly transforming the way to understand supply networks, both in terms of its structural configuration and its management level. The ongoing research presented in this paper attempts to help globalised organisations to identify alternative supply network configurations, and management strategies, in the form of spontaneous demand driven responsive and efficient global supply networks, that will allow them to respond to different demand requirement scenarios, within required cost and time restrictions. In order to facilitate the understanding of the consequences of adding or removing operational units, processes, reallocation of resources, etc, over the global network performance, a computational decision support system (DSS) approach has been taken.

Keywords: Global supply networks, flexible networks, mass customisation, supply networks simulation.


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Table of Abbreviations ABM Agent Based Modelling ATO Assembly To Order CODP Customer Order Decoupling Point CSS Customer Service Strategy DSS Decision Support System ETO Engineer to Order JIT Just In Time KPI Key Performance Indicators MC Mass Customisation MCS Mass Customisation Scenario MRP Material Requirement Planning MTO Manufacturing To Order MTS Manufacturing To Stock PF Product Family PMF Product Market Family PPS Product Process Structure SD System Dynamics SN(s) Supply Network(s) SNT Supply Network Topology SPT Sales Point Type Table of Figures Figures Figure 1: Taxonomy of Customisation (Steger-Jensen and Svensson, 2004) ..................... 4 Figure 2: Supply Network conceptual model diagram...................................................... 12 Figure 3: Product Market Family (PMF) matrix ............................................................... 13 Figure 4: Customer Service Strategies (CSS) allocation scheme...................................... 15 Figure 5: Product Process Structures (PPS) generation alternatives ................................. 16 Figure 6: Node and Arcs general diagram......................................................................... 17 Figure 7: Example of SNTs associated with different CSSs ............................................. 17 Figure 8: Simulation environment design diagram ........................................................... 18 Figure 9: Node element data structure............................................................................... 18 Tables Table 1: Order characteristics and management strategies relationship (Selldin, 2005)...11 Table 2: MCSs types for a bicycle manufacturer .............................................................. 14 Table 3: MCSs factors values for a bicycle manufacturer................................................. 14


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1. INTRODUCTION Market globalisation, world-wide procurement, geographically distributed plants, more sophisticated customer requirements, increases in product variety, the rapid entry of new technologies and greater uncertainty due to the reduction in product life cycles, the emergence of new markets, etc... (Frey, 1994; Christensen, 1997), are hardening global competition in general, creating a new dynamic environment for supply networks (SNs). Spina et al. (1996), based on the study of 600 companies worldwide (IMSS database), found evidence that this new dynamic environment has led to the development of new managerial approaches, due mainly to the transformation of competitive forces, with the appearance of responsiveness to demands, with a greater degree of customisation, as a key factor (Kidd, 1994). Therefore, current production systems require the capability to change their high level performance dimensions in terms of range -total number of states that the system is capable of achieving- and response -the ease with which changes can be conducted within the range- (Slack, 1987; De Toni and Tonchia, 1998). Also, current productive systems must face these new challenges maintaining in turn high levels of performance in traditional competitive aspects such as cost. In the words of Vokurka and Fliedner (1998), what is needed is: “the ability to produce and market successfully a broad range of low cost, high quality products with short lead times in varying lot sizes, which provide enhanced value to individual customers through customisation”. All of which has led to the need for moving to a multi-focus strategy, in which the concept of flexibility, as the ability to respond adequately to change in different environments, takes on special relevance (Gerwin, 1993). The business strategy of mass customisation (Davis, 1987; Pine, 1992) tackles this issue through the design of productive systems in which product differentiation is conducted as late as possible. The main idea is that one-of-a-kind type products are manufactured with high levels of quality and fast delivery, with the low costs of mass production (Anderson and Pine, 1997). This strategy achieves a compromise between the advantages of product customisation -economies of scope (Kidd, 1994)-, guaranteeing fast response times for customized demands, with productivity and low costs associated with economies of scale, through decoupling points (Tseng and Jiao, 1998). The set of changes described affect the manner in which we conceive manufacturing and distribution networks, SNs, both on a configuration level, management level, and performance (Porter, 1986). Globalised markets, for diverse reasons, push networks to globalisation, i.e. the establishment of new plant nearer to local markets to reduce logistic costs and improve responsiveness, the establishment of production in countries with lower labour costs, etc (Cohen and Lee, 1989; Ferdows, 1989; Flaherty, 1996; Cohen and Huchzermeier, 1999). The existence of network constituent elements in different geographical locations, in different countries, likewise increase complexity and uncertainty when it comes to managing it (Bhatnagara and Sohal, 2005). In order to deepen understanding of the consequences of global and flexible SNs design, and strategic policies, over the global network performance, a computational decision support system (DSS) approach has been taken. The high level of interdependence between the elements of these supply networks, their inherent feedback loops, non-linearities, and delays (Forrester, 1961; Sterman, 2000), mean that purely analytical approaches to the problem are not sufficient (Fowler, 1998). Thus come out the need to


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attend simulation environments that have tested its validity for this purpose (Shapiro, 2001). In the field of supply chain management simulation, both systems dynamics - SD (i.e. Forrester, 1961; Towill, 1996b; Akkermans et al., 1999; Anderson et al., 2000; Gonçalvez, 2002; Sterman, 2000), as well as agent based modelling - ABM (i.e. Parunak et al., 1998; Swaminathan et al., 1998; Sadeh et al., 1999; Lee and Allwood, 2000; Kaihara, 2003), and hybrid approaches of both (i.e. Akkermans, 2001; Schieritz and Größler, 2003), enclose one of the greatest amount of work in this area. The simulation approach will allow us to conduct, for a multitude of scenarios and conditions, systematic testing of the structure and operation of this type of complex SNs, its behavioural patterns and properties, in order to identify, both alternative flexible supply network structures, as well as those strategies, policies, and rules, that are most adequate for their management, both on a local and a global network level, at low cost and risk (Pidd, 1993). And thus, approach the challenge of this on-going research, about how to enable a globalised organisation, identify, based on demand orders, alternative spontaneous SN configurations, and their management strategies, by following a mass customisation business approach, to facilitate respond different customized demand scenarios, in the most efficient, cost and time, possible manner. The outline of this paper is organised as follows. Section 2 presents a literature review of the different issues that provide the context for the study of global and flexible SNs simulation. In section 3, the research problem and research approach are specified. Section 4 is divided in two parts, the first one deals with some demand driven SNs concepts, which shall be used in the following, for the presentation of the conceptual model developed in this on-going research. In Section 5, some insights about the implementation of the conceptual model in the simulation environment are briefly presented. Finally, section 6 shows some preliminary conclusions and the future steps of the on-going research. 2. LITERATURE REVIEW RELATIVE TO GLOBAL AND FLEXIBLE SUPPLY NETWORKS SIMULATION ISSUES 2.1 Global Supply Networks A supply chain is a network that performs the functions of materials procurement, transformation of these materials into intermediate and finished products, and the distribution of those products to the final customers. SNs are composed of production units (manufacturing and assembly processes, and inventories for temporary stocking) and storage points (distribution centers), connected by transportation of goods and by exchange of information, as well as their corresponding planning and control systems (Shapiro, 2001). In the case of Global SNs, each productive unit and storage points mentioned shall be geographically located in different countries, thereby adding an additional difficulty due to the variety of contexts; cost factors, labour factors, government factors, infrastructure factors, business/support services, customer factors, supplier factors, competitor factors, etc, and the uncertainty that all this brings (Bhatnagara and Sohal, 2005). Global SNs have appeared in the last decades due to many different reasons: (1) To be the best in competitive globalised environments, in which clients have sophisticated their demands, and ever shorter technology-product-process life cycles, companies are


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forced to focus on what they know how to do best, and subcontract every other aspects in which they are not excellent (Hamel and Prahalad, 1996); (2) ICTs have reduced transaction costs (partner localisation, negotiation, coordination, etc) facilitating the decentralisation of tasks in collaborative companies and their interdependence (Hagel III and Singer, 1999); (3) And a whole series of other reasons derived from the search for market growth in new markets, the establishment of new plant nearer local markets to reduce logistic costs and response times, the establishment of production facilities in countries with lower labour costs, etc (Porter, 1986; Cohen and Lee, 1989; Ferdows, 1989; Flaherty, 1996; Cohen and Huchzermeier, 1999). 2.2 Competitive Factors, and Productivity vs. Flexibility The evolution over time, of competitive manufacturing factors, can be used as a guide for the changes SNs have been subjected to in order to respond to the market. In this sense, many authors (i.e. De Meyer et al., 1989; Wu, 1994; Ward et al., 1998), reach the conclusion that current global SNs must be competitive in the following areas: low cost, flexibility (mainly volume flexibility and product mix flexibility), delivery time, and quality. At fist sight, these four competitive priorities seem contradictory in their execution, which leads us to believe that a trade-off will be necessary to treat them together. On the one hand there is a need of networks with high levels of productivity - economies of scale – in order to reduce costs, and on the other hand, flexible networks - economies of scope (Kidd, 1994) – to ensure low response times for customised demand. However, some literature also suggest that this trade-off is not necessary, according to authors like Ferdows and De Meyer (1990), Suarez et al. (1996), Grubbström and Olhager (1997), Lamming et al. (2000), amongst others, the key issue lies in establishing if, whether, the operational cost savings, and the revenues, derived from the implementation of a more flexible manufacturing system justify, or not, the investment required. 2.3 The Mass Customisation Strategy The business strategy adopted in this research to bring together, under the same production system, the competitive advantages of product “customisation” (economies of scope), and the efficiencies associated with “mass production” (economies of scale), is Mass Customisation - MC (Davis, 1987; Pine, 1992; Tseng and Jiao, 1998). Quoting Pine (1992): “Mass customisation denotes the ability to provide customised products and services at a comparable price and speed of equivalent standardised offerings”.

Steger-Jensen and Svensson (2004) have classified different generic levels of Mass Customisation (MC), in relation with different MC approaches, strategies, stages, and types of customisation, these are: (1) Standardisation; (2) Usage; (3) Package and distribution; (4) Additional services; (5) Additional custom work; (6) Assembly; (7) Fabrication; (8) Design. These eight generic levels of MC occupy an asymmetric position within the mass production – individual product manufacturing (one-of-a-kind) axis, and can be associated with either Engineer to Order (ETO), Manufacture to Order (MTO), Assemble to Order (ATO), and Manufacture to Stock (MTS) strategies (see Figure 1).


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Figure 1: Taxonomy of MC that embrace both, the variety of products, and the stability of processes. Source: Steger-Jensen and Svensson (2004)

The correct implementation of a MC strategy must take into account: (1) The drastic increase in product variety, and therefore product structure; (2) Operational aspects, i.e. multiple types of products manufactured simultaneously in small lot sizes (Miyashyita and Russel, 1994), dynamic product combinations to respond to random orders with different delivery dates, maturity times not affected by transitory incidents with fabrication processes, etc., and; (3) On a supply network level, the configuration of spontaneous supply chains around customised demand orders, using agile manufacturing systems and dynamic networks of operative units, which respond as quickly and as cheaply as possible (Anderson y Pine, 1997). The flexibility aspect becomes therefore a key aspect for the production system to respond adequately.

2.4 Management of Flexibility and Supply Chain Flexibility The management of flexibility, which deals with the ability of a system to cope with changing internal and external environmental conditions, is a line of research that has been widely followed during the past decades (Slack, 1988, 1990; Gupta and Buzacott, 1989; Gerwin, 1993; Upton, 1994; Koste and Malhotra, 1999; Olhager and West, 2002). Amongst authors, who have investigated the relationship between the marketplace competitive priorities and manufacturing flexibility, it is worth highlighting the work of Chambers (1992), Chen et al. (1992), Upton (1994), Sanchez (1995), Suarez et al. (1996), and Olhager and West (2002). Although there is a lot of literature on the subject, this is very fragmented and confusing, not existing a coherent systematic definition of types and categories, as well as common metrics for the term. Sethi and Sethi (1990) where amongst the first authors to try the definition of an unification criteria for the different typologies and facets of the concept of flexibility. They identified in their survey more than fifty types of manufacturing flexibility. Other authors who have made an important effort in this direction are De Toni and Tonchia (1998), Shewchuk and Moodie (1998), Koste and Malhotra (1999), as well as D’Souza and Williams (2000). According to the results of the study by De Toni and Tonchia


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(1998), the main reasons for the need of flexibility are; the environmental uncertainty, and the variability of products and processes. Therefore, contemporary companies are required to be capable of changing their high level performance dimensions in terms of response and range. Said changes are due as much to product properties (i.e. product variety, customisation level, rate of new product introduction…), as to production itself, i.e. the capability of manufacturing systems to adapt new products in production, or the ability to change production schedules in terms of demand or product mix (Helo, 2001). The literature on supply chain flexibility is more recent and smaller that that relating to manufacturing flexibility, despite it being, in the words of Fine (1998), Beamon (1999) and Duclos et al. (2000), vital for developing new supply chain competitive advantages, in areas such as delivery and innovation, from the moment they operate in an ever more dynamic environment with greater uncertainty. As with manufacturing flexibility, there are a number of different types of supply chain flexibility defined in the literature. Three of them are listed as examples: (1) Beamon (1999) uses four types of supply chain flexibility – volume flexibility, delivery flexibility, mix flexibility, and new product flexibility; (2) Vickery et al. (1999), in their literature review, identify five main types – product flexibility, volume flexibility, new product flexibility, distribution flexibility, and responsiveness flexibility; (3) Duclos et al. (2000), in turn, identify six types of supply chain flexibility – production systems flexibility, market flexibility, logistics flexibility, supply flexibility, organisational flexibility, and information systems flexibility. It has been suggested that the absence of consensus is due, on the one hand, to the fact that flexibility, in many instances, can not be dealt as an individual concept, but rather can be interpreted as a second-order competitive criterion which needs to be applied to other dimensions and objectives (Slack, 1990; Chambers, 1992), in addition to having multiple distinct levels of the productive system as its sphere of application. Regards to this last aspect, following the flexibility literature meta-framework developed by Koste and Malhotra (1999), and going from the production perspective to the strategic perspective, we can identify the following levels: (1) Machine flexibility, labour flexibility and material handling flexibility; (2) Operation flexibility and routing flexibility; (3) Volume flexibility, mix flexibility, new product flexibility, expansion flexibility, modification flexibility; (4) Supply Chain Flexibility Level (Duclos et al., 2000) – manufacturing flexibility, organizational flexibility, system flexibility, R&D flexibility, marketing flexibility, and; (4) Strategic flexibility. In line with the mentioned flexibility meta-framework, Suarez et al. (1991) highlight the need to bear in mind the unit of analysis, therefore, if the unit is the firm, the key aspect, they believe, should be how to obtain output flexibility for volume flexibility, mix flexibility, and new product flexibility. The case of the mix flexibility of the firm refers to the capacity of the manufacturing system to alter the relative production amount between products in a product mix, which, for example, could be conducted, by using a multi-product plant, or through a certain number of single-product plants. In this last case, each single-product plant would have a low grade of mix flexibility being able to develop high productivity ratios, and would at the same time form part of a superior structure and strategy leading to mix flexibility on a firm level (Vereecke and Van Dierdonk, 1999). The multi plant option, multi-plant networks, is also very interesting for creating flexible production capacity, transferring production volume from overloaded plants to others that at that moment in time have excess capacity (Rudberg et


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al., 1998). The potential configurations of multi-plant networks can become, therefore, a key aspect in obtaining, via very different means, the levels of flexibility desired on a firm level. Thereby, for each case being analysed one must consider what type of flexibility is desired for each unit of analysis, and define the particular manner in which it shall be developed to obtain the objectives as efficiently as possible for the system as a whole (Cohen and Huchzermeier, 1999). 2.5 Supply Networks Simulation As mentioned in the introduction, the high level of interdependence between the supply network constituent elements, their inherent feedback loops, non-linearities, and delays (Forrester, 1961; Sterman, 2000), convert simulation in an ideal tool for approaching the study of configuration, dynamic behaviour, and supply network performance, in different conditions and environments (Towill, 1996a; Shapiro, 2001; Terzi and Cavalieri, 2004). Amongst the main approaches to supply chain simulation, the developments in systems dynamics (SD), and agent based modelling (ABM), stand out. 2.5.1 System Dynamics Approach to Supply Networks Simulation Simulation models based on SD have an approach based on non-lineal feedback loops and temporal delays of different processes, and company decision making systems, which define the structure of the system, to thereby comprehend how the system generates endogenously dynamic behavioural modes, that are many times counterintuitive. It is a continuous simulation based on ordinary differential equations, with an aggregate variable vision, for identifying leverage points of the predefined structure and those policies which improve the performance of the system (Forrester, 1961; Sterman and Morecroft, 1994; Coyle, 1997; Sterman, 2000). Supply chain modelling and simulation is an application area of SD as old as the technique itself. Jay W. Forrester, founder of SD, was the first to apply it in 1958 to study of the effect of changes in market demand on a four-level downstrean supply chain (Forrester, 1958). In that work, Forrester dealt with the issue of how feedback loops, and physical as well as information and decision delays inherent in supply chains, condition their dynamic, i.e. demand amplification, inventory oscillatory behaviour, the effect of advertising policies on production variation, decentralized control, or the impact of the use of information technology on the supply chain management process, finding that the nature of the managerial policies could modify significantly these resulting instabilities. The developments in the area of supply network simulation have occurred since then with applications that deal with its diverse dimensions: Mass (1975), on the interaction between inventory and production policies; Morecroft (1983), on the impact of material requirements planning over supply chain performance; Sterman (1989), on misperceptions of feedbacks in supply chain dynamic decision-making and the bull-whip effect; Naim and Towill (1994), on a framework for designing supply chains; Towill (1996b), on supply chain re-engineering and time compression; Akkermans et al. (1999), on enablers and inhibitors of effective international supply chain management; Anderson et al. (2000), on demand amplification along capital equipment supply chains; Gonçalvez (2002), on the impact of shortages on push-pull production systems; Akkermans and Vos (2003), on the demand amplification effect in service supply chains. In particular it is worth highlighting the work being conducted, in the last decades, by John D. Sterman and


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collaborators, at the MIT-Sloan School of Management, as well as Denis R. Towill and collaborators, at The Logistics System Dynamics Group at Cardiff University. 2.5.2 Agent Based Modelling Approach to Supply Networks Simulation ABM is more focussed on how interactions, between intelligent individual agents based on simple real rules, generate system behaviour in an evolving manner. It is an event based simulation, with an emergent vision (bottom-up) and evolving structures. One of its functionalities is to locate the leverage points in said individual interaction rules to obtain the desired systemic behaviour (Epstein and Axtell, 1996; Axelrod, 1997; Gilbert and Troitzsch, 1999; Tesfatsion, 2003). In the case of ABM simulations for supply networks, the agents are the companies and their decision policies, whilst interactions are represented by the flow of materials and information exchanged within the supply network – for a complete description see Choi et al. (2001). Amongst the most interesting work regarding the application of ABM and simulation over supply chain management are the following: Parunak et al. (1998), about dynamic analysis of supply chains; Strader et al. (1998), on the simulation of order fulfillment in divergent assembly supply chains; Swaminathan et al. (1998), on a generic multiagent approach for the modeling of supply chain dynamics; Sadeh et al. (1999), about MASCOT, an agent-based architecture for coordinated mixed-initiative supply chain planning and scheduling; Chang and Harrington (2000), about the issue of centralization vs. decentralization management in a retail chain; Lee and Allwood (2000), about a multi-agent based simulation approach for supply chain network dynamics analysis; Qing and Renchu (2001), about the modelling and simulation of a supply chain distribution system; Kaihara (2003), about the resource allocation problem in the supply chain for dynamic environments, and; Sadeh et al. (2003), about an agent-based e-supply chain decision support system. 2.5.3 An Integrated Systems Dynamics - Agent Based Modelling Approach Both techniques for supply chain simulation are highly complementary; SD provides a vision on an aggregates level, whilst ABM provides the bottom-up vision of the system. They both share the common objective however, which is to discover the leverage points in the system in order to redirect its behaviour to achieve superior performance levels: ABM – through the rules for individual agents; SD – through changes on decision making parameters and system feedback structures (Scholl, 2001). The integration of both simulation paradigms offers the possibility of combining the strengths of each, producing an ideal tool for modelling and simulating alternative designs, and decision making systems, at different supply network levels (Akkermans, 2001; Schieritz and Größler, 2003). Following the work of Akkermans (2001), and Schieritz and Größler (2003), the simulation platform that will be used to implement the conceptual model developed in this on-going research (section 4.2), is Anylogic (2006). Anylogic is a multi-paradigm simulation platform, which allows SD and ABM simulation to be combined in the same simulation environment. This will provide greater flexibility when dealing with different approaches for the design and management of global and flexible SNs, and facilitate its study with different levels of detail.


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3. RESEARCH PROBLEM AND RESEARCH APPROACH 3.1 Research Problem The aim of the research is to gain understanding on how to design and manage global SNs in order to make them more flexible and efficient, under changing demand scenarios, by a case study research, and with the help of a DSS simulation environment. An important aspect in obtaining adequate network flexibility and efficiency involves taking into account the precise moment at which customization occurs within the management process of the customer order. The strategy of customising as late as possible means alternative mixed SNs have to be available, whereby one part of the network produces generic products for planned demand, and another part of the network takes care of customisation based on customer order requirements, a concept known as ‘postponement’, which will be put into operation via customer order decoupling points (CODP). The use of a case study during the research allows us to focus this aim in a more specific objective:

To obtain, for a real firm, via a DSS simulation environment, guidelines that can help in the design and management of spontaneous demand driven responsive and efficient global SNs. For which, based on different network conditions (i.e. capacity constraints, suppliers lead times, internal processes lead times, inventory levels, means of transport, supplier location, manufacturing units, distribution centres...), and customised demand scenarios, alternative possible configurations will be identified from their global multi-plant network. These multi-plant network configurations should provide the levels of flexibility required at the firm level, with behaviours and performance levels suited to market demands (response time, cost, quality). A mass customisation strategy, operationalised via multiple intra and inter plant CODP, defined according to different individual demand orders, will be used for this purpose.

3.2 Research Approach The approach adopted can be classified as an engineering approach for performance enhancement of systems (Pritsker, 1997). For developing the research we have based on: (1) Previous studies identified in the literature; (2) A case study research of a bicycle manufacturer, denoted in this paper as GBIKE, with a global multi-plant network, and requirements for flexibility in their response to markets which demand products with a high degree of customisation; (3) A simulation environment which allows to conduct, for diverse conditions and scenarios, systematic testing of the structure and operation of this type of supply network, its behavioural patterns and properties, in order to identify, both, more flexible alternative supply network structures, and the strategies, policies and rules, that are more adequate both on a local and global network level. It is a combination therefore of case study research and simulation modelling research. Case studies are frequently used for exploratory and theory building research (Yin,


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1994). The selected case study, GBIKE, is a bicycle manufacturer with a global multi-plant network and urgent requirements for introducing flexibility in their response to markets demanding products with a high degree of customisation. The study of this company is entirely relevant to the project objective. The simulation modelling research approach is also fundamental to this research due to, as mentioned previously, the high levels of interdependence between constituent elements of these supply networks, their inherent feedback loops, non-linearities, and delays, make network behaviour in the face of market demand variations a dynamic process which generally produces counterintuitive behaviours over time (Forrester, 1961; Sterman, 2000), and therefore makes purely analytical approaches to the problem inadequate due to the fact that they generally require too many assumptions and also lack de la ability of effective communication (Pritsker 1997; Fowler, 1998). The modelling and simulation methodological framework followed in the research is the one of systems dynamics - SD, as its validity has been demonstrated for the design of company simulations and strategic decision-making laboratories– microworlds (Senge, 1991; Warren, 2002). Its main steps, in outline, are the following ones (Forrester, 1961; Coyle, 1997; Sterman, 2000):

1. Elicitate the conceptual model. 2. Implement the conceptual model in the simulation environment. 3. Calibrate and validate the simulated model based on empirical data. 4. Conduct structural and behavioural analysis of the simulated model under

different conditions (i.e. policies, operations...), and scenarios (i.e. customer demand scenarios).

5. Make managerial suggestions based on the knowledge of the model behaviour to improve the real world system performance (i.e. SN; alternative supply network structures, as well as strategies, policies and rules better suited for management both on a local and global network level).

As regards the identified stages, section 4.2 presents the developments made to define the conceptual model corresponding to stage 1 of the methodology. Section 5 includes some first notes about developments regarding the implementation of the conceptual model in the simulation environment. About the remaining stages, they are, at this time, under development. 4. GLOBAL AND FLEXIBLE SUPPLY NETWORK CONCEPTUAL MODEL Before introducing the conceptual model, developed for the design and management of demand driven responsive and efficient global SNs, some specific literature models will be introduced, which place in context the heuristics elicited from the case study experts. Section 4.2 collects the results from the conceptual model elicitation for GBIKE. The conceptual model elicitation, consists of different sub-stages: (i) Identify and define the study problem – implied aspects that are representative of the real system behaviour; (ii) Identify and isolate factors which appear to interact creating the observed symptoms – system limits; (iii) Trace feedback circuits, relationships, which link decisions and actions, and; (iv) Formulate the policies, decision heuristics, frequently used by the organisation. Once these sub-stages have been completed it becomes possible to


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elaborate a conceptual model, which contains all the elements necessary to understand what is happening in the system, being able to represent and describe the feedback loops that exist in it, the type of relationships that exists between implicated variables. To conduct this first stage, interviews and meetings have been held with experts at GBIKE, mainly following the methodology of Group Model Building (Vennix, 1996). The result of these sessions has been completed by literature studies related to the problem. 4.1 Model Considerations: Demand Driven Supply Networks Concepts Selldin (2005), in his work Supply chain design - Conceptual models and empirical analyses, deals with the issue of alignment between the triad: “product structure – process structure – supply chain structure”. In his work, he argues that depending on the characteristics of product orders, i.e volume, number of variants, uncertainty in demand, product life cycle length, lead-time accepted, etc, there are different models of supply network that are more adequate (Fisher, 1997), different types of manufacturing process (Hayes and Wheelwright, 1979), as well as planning and control systems (Berry and Hill, 1992). These three models, Hayes and Wheelwright (1979) - Berry and Hill (1992) – Fisher (1997), which Selldin (2005) structures for his work, are very significant as background to place in context the development of the conceptual model of this on going investigation. We next move on to describe briefly each of them, along with a final note about the decoupling point concept, which allows us to relate the customer orders characteristics, and operations management, to the mass customisation strategy. 4.1.1 Customer Orders Characteristics and Supply Network Structure Regarding the relationship between the characteristics of orders and supply network models, Fisher (1997), based on the cases of Campbell Soup and Sport Obermeyer, distinguishes between functional products with foreseeable demand, and innovative products with unpredictable demand. For those belonging to the first group, the author assigns a physically efficient type of supply network, the aim of which is to maximise efficiency at the lowest cost possible, high levels of manufacturing level resources, a strategy of inventory minimisation and a reduction in lead times. As regards the second group, innovative products, Fisher (1997) recommends market responsive type supply networks. The aim of these ones is to respond quickly to a demand with a high degree of uncertainty for minimising stock-outs and obsolete inventories. For this, excess manufacturing capacity is required, as well as product parts and finished products broad buffers, aggressive investment in lead time reduction, supplier selection based on their speed, quality and flexibility, and a modular type design strategy which facilitates the postponement of customisation of the product as late as possible. 4.1.2 Customer Orders Characteristics and Processes As regards the relationship between product order characteristics and manufacturing process type, the product-process matrix of Hayes and Wheelwright (1979), establishes two axes. The first one contains product characteristics, from products with a high number of variations and low volumes of orders (unique and one-of-a-kind products), to products with a low number of variations and high volume of orders (standarized products). The second axis in the matrix, which refers to manufacturing processes, assigns, to each one of the previous types, manufacturing process of the type; project manufacturing for unique products, job shop type processes for one-of-a-kind products,


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flow shop for products with many variants and low volume, line flow for few major products with high volume (i.e. automobile), and finally, continuous manufacturing processes for standardised products with high volumes. This model has been empirically validated in different works (Spencer and Cox, 1995; Safizadeh et al., 1996; McDermott et al., 1997). 4.1.3 Customer Orders Characteristics and Operations Management On the management level, manufacturing operations, inventories and transport at each production unit in the supply chain are planned on different hierarchical levels. Depending on the level of aggregation and planning horizon can be distinguished: Sales and operations planning; Master scheduling; Materials planning, and; Production activity control (Selldin, 2005). As has been said previously, as a function of market order characteristics, i.e. volume, number of variants, uncertainty in demand, product life cycle length, lead-time accepted, etc, at each of these hierarchical management levels different approximations may be adopted (Berry and Hill, 1992; Hill, 2000; Olhager and Rudberg, 2002; Vollman et al., 2005). See Table 1:

Product Orders Characteristics Product type Product range Individual Product volume per period

Standard Narrow

High

Customised Wide Low

Management Strategies

Sales and operations planning level

Level strategy… when via inventories or the backlog of customer orders a stable rate of production can be maintained

Chase strategy… when production follows fluctuations in demand

Master scheduling level

Make-to-stock (MTS)… when production is based on forecasts

Assemble-to-order (ATO)… build-to-order, configure-to-order, and finish-to-order

Make-to-order (MTO)… when production is based on customer orders

Materials planning level

Rate-based planning… when a production rate is set and harmonised through the manufacturing processes.

Time-phased planning… when the different activities in the manufacturing processes are planned to start at different points in time depending on the product structure, production lead-times and due-date.

Production activity control level

Just-in-time type (JIT)… when shortages of material in the manufacturing process triggers a signal for just-in-time replenishment

Material requirement planning type (MRP)… when MRP is often used to plan the activities based on a time-phased planning

Table 1: Order characteristics and management strategies relationship. Source: Selldin

(2005) 4.1.4 Decopupling Points The dividing line between orders manufactured based on forecasts (MTS) and those that are assembled, manufactured, or designed, according to customer orders (ATO, MTO, ETO), determines a very important point in manufacturing processes and SN (Pagh and Cooper, 1998; Mason-Jones et al., 2000; Olhager, 2003), kown as the Customer Order Decoupling Point – CODP (Sharman, 1984; Hoekstra and Romme, 1992). Upstream the CODP there are few product variants and volumes are high, downstream the CODP each product may be unique and customised for a specific customer. Therefore, the CODP marks the point at which an order becomes attached to a specific customer,


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conditioning therefore the capacity of a SN to provide different grades of customised product, at a particular cost and delivery schedule. In a SN there may be multiple CODP, even within the same operation unit certain products may be manufactured MTS and others MTO. 4.2 Conceptual Model: Building Blocks and Relationships The special characteristics of customised orders such as the degree of product adaptation to customer needs, delivery times, order volumes, require strategies that provide agile and efficient solutions at all times. The product structure, processes, and SN, need to have adequate flexibility in order to adapt to diverse situations. The level of complexity associated with this type of decisions, which includes aspects such as range and response, level of customisation, delivery times, price, production costs and product transportation costs for each market, localisation, production centre capacity and workload, supplier and sub-contractor location, etc., suggest the need for models which allow the problem to be represented and which can serve as a basis for constructing simulation and optimisation models which facilitate decision making. The conceptual model developed through GBIKE case study, and described below, attempts to provide a solution to this need. 4.2.1 General Structure – Building Blocks Relationships The conceptual model associated with the SN developed to provide mass customisation environments, has been structured into five conceptual blocks, as illustrated in Figure 2.

Figure 2: SN conceptual model diagram Orders are classified in the first block as a function of the product family to which they belong, the market to which they are directed (PFM), and the customisation characteristics required (MCS) (1). As a result of this classification, orders are associated with one or more strategies by which they shall be served to customers (CSS) (2). These strategies can be of two types: (i) Global, whereby it is specified, in product and process structures (PPS), what components are to be manufactured, subcontracted or purchased (3), and the form of the SN Topology (SNT) that will take charge of supplying the product (4); (ii) Local, whereby the restocking procedure is established for each SN node (5).


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As a result, a set of key performance indicators (KPI) are obtained for measuring the performance of the SN order transmission process: Inventory level; Compliance with delivery times; Use of resources, etc. Each block is described in greater detail below. 4.2.2 Building Blocks of the Conceptual Model PMF: Product-Market Families The first block allows the competitive position of the company in different markets to be defined (M1, M2, ..., Mm in Figure 3) for different product families (PF1, PF2, ..., PFp in Figure 3) which determine the offer.

Figure 3: Product Market Family (PMF) matrix

The level of detail is increased by specifying, for each market M, the different types of sales points available (SPT1, SPT2, ..., SPTn in Figure 3), i.e. shopping centres, super-mega-stores or specialist shops, and for each product family PF, the different levels of product customisation (MCS1, MCS2, ..., MCSq in Figure 3) that they will offer to their customers, which is described in the next block. For each cell of Figure 3, resulting from the overlap between M, SPT, PF and MCS, the company must establish the sales volume objectives, price and margin, delivery times and the strategies with which it will serve customers. MCS: Mass Customisation Scenarios In this block five scenarios have been established, which identify the different levels of product customisation offered:

MCS1-Standard: The manufacturer has diversified its product adapting it to the different markets and client groups, offering standard products with fixed predefined components which are included in commercial catalogues.

MCS2-Menu: The manufacturer offers the customer the possibility of configuring their product from a set of standard predefined options.

MCS3-Selection: The manufacturer and customer jointly define the product configuration from amongst standard predefined options. No design work is done and this tends to occur in quite high volume orders.


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MCS4-Adaptation: The manufacturer and customer jointly define the product configuration introducing small design adaptations.

MCS5-Pure customisation: The manufacturer customises the product according to the specific needs, requirements, of the customer. Requires design and product industrialisation work, and frequently involves the need for new materials or components.

Table 2 illustrates some of the main characteristics of each customisation scenario in the case of a bicycle manufacturer, in relation to models, decoration and bicycle frame and component sizes.

MCS Models Decoratives Sizes Components

1 Standard Catalogue range Fixed Fixed Prearranged

2 Menu Catalogue range Prearranged

options Prearranged

options Prearranged

options

3 Selection Based on catalogue

Prearranged options

Prearranged options

Based on catalogue

4 Adaptation Based on catalogue

Tailor made Prearranged

options Based on catalogue

5 Pure Customization Based on catalogue

Tailor made Tailor made Based on catalogue

Table 2: MCSs types for a bicycle manufacturer

On the other hand, taking into account other factors such as stock availability of the finished product, habitual order volumes, or delivery schedule requested, each MCSs tends to have a different combination of such factors. Table 3 presents habitual values for the cited factors, and considers separately, initial orders of those which are made to respond to consumption. Additionally, habitual sales types for each MCS are included.

Initial Order Replenishment Order MCS

Stock Order Size

Delivery Time

StockOrder Size

Delivery Time

SPT

1 Standard Yes High Delivery

Schedule Yes Medium Short

Shopping Centres Specialized Superstores

Specialized Shops 2 Menu No Low Short No Specialized Shops

3 Selection No High Delivery

Schedule No Low Short Specialized Superstores

4 Adaptation No High Delivery

Schedule No

Medium-Low

Short Specialized Superstores

5 Pure Customization No Low Short No Specialized Shops

Table 3: MCSs factors values for a bicycle manufacturer

CSS: Customer Service Strategies This block is used to determine the strategy to serve customer orders according to the PFM to which they belong. Orders for standard products, or with a minimum customisation level, which can be fulfilled during distribution phases, can be served


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using a stocking, manufacturing and assembly strategy, to stock (MTS) - type 1 of Figure 4.

Figure 4: Customer Service Strategies (CSS) allocation scheme The greater the degree of customisation required in the product demanded by the customer (types 2 and 3 of Figure 4), greater the level of penetration in the SN of the point at which manufacturing is associated with the customer order (CODP - Customer Order Decoupling Point), limiting therefore the possibilities of producing to stock (MTS) certain components in the structure, and to order (MTO-ATO) the rest. In the borderline case (type 4 of Figure 4), stocking, manufacturing and assembly are conducted specifically by order (MTO-ATO). The increase in the level of penetration, in the SN, conditions the typical decisions associated with product delivery, such as component purchase, or subsets, for suppliers located in different regions or countries, or rather, the subcontracting of certain operations, or the assignment of production phases, to different locations of production plant. Type 4 orders, with short delivery times, may cause manufacturing and assembly to be performed in the same factory, which must be near the delivery point, with suppliers nearby who respond quickly to the specific characteristics of such orders. On the contrary, type 1 orders, with equally short delivery times, allow a greater number of alternatives to be considered for supplying the demanded product. In the conceptual model, this type of decision is called a global policy of the CSSs, given that it affects the global configuration of the SN. In addition to these policies, CSSs include local policies that have less impact on the SN, affecting the existing relationship between pairs of nodes, establishing the procedures by which the material orders made by the customer node are to be supplied from the supplier node. PPS: Product-Process Structures This block represents the different alternatives for generating the product to be delivered to the customer, based on three basic options of process assignment: (i) Self manufacturing-assembly; (ii) Purchase from supplier, or; (iii) Subcontracting. In the example in Figure 5, component C21 can be manufactured internally by the company applying operation 2 to C11, the operation can be subcontracted to another company or can be purchased from an external supplier C’21.


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Figure 5: Product Process Structures (PPS) generation alternatives Thereby, PPSs can incorporate the flexibility required so that, as a function of the characteristics, target margins, and delivery times for each type of order, along with the load and localisation situation of the productive installations, the most adequate PPS is assigned for each situation. The criteria used to conduct the assignment in each case constitute another of the elements that configure each CSS. SNT: Supply Network Topology This block is used to define the basic elements for representing material and information flows that occur in the SN. These elements can be one of two types:

Nodes: Basic elements of the topology representing the points of the SN, where transformations or storage occur, to serve the product demanded by the customer. The nodes may correspond to different levels of supplier, production plant, warehouses, distributors, wholesalers, retailers, customers, commercial agents, logistics operators, etc. Generically, the nodes are associated with a physical location (city, region, country, market, etc.), and a set of actions which can be: Reception, to provide entry to components that originate from supplier nodes; Transformation, to perform manufacturing, assembly, and packaging operations; Delivery, to output components to customer nodes. Likewise, nodes conduct managerial tasks, i.e. processing requests (sales orders) from customer nodes, developing planning and schedules, and making orders (purchase orders) to their supplier nodes.

Arcs: Elements of union between two nodes. Can be component arcs when they indicate the transportation of materials from the source node, or supplier, to the destination node, or customer, and information arcs when they indicate the flow of information between nodes.

Figure 6 illustrates the schematic form of a node with its actions and the different input and output arcs.


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Figure 6: Node and Arcs general diagram The SNT is formed from the set of nodes and arcs that may take different forms depending on the CSS established to serve orders and PPS flexibility.

Figure 7: Example of SNTs associated with different CSSs Based on the PPS in Figure 5, Figure 7 presents three different typologies of SN depending on the CSS adopted. N1, N2, N3 y N4 are component suppliers, N5 a subcontractor, N6 the SN focus company, and N7 a customer. SNT1 corresponds to a situation where the focus company conducts all product transformation operations, SNT2 represents the subcontracting to N5 of operation 2 for obtaining component C21 from C11, and SNT3 represents the purchase of component C21. 5. CONCEPTUAL MODEL SIMULATION With respect to the implementation of the conceptual model developed in a simulation environment, a multi-paradigm SD-ABM approach is being used, via the Anylogic (2006) tool. This allows different dimensions identified to be treated in the detail as best suits each one. Figure 8, outlines the simulator being developed for the research.


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Figure 8: Outline of designed simulation environment

As a first step in transferring the conceptual model to the simulation environment, the data structures corresponding to the different concepts included in each blocks, described in section 4.2, have been defined. To do so the Supply Chain Modelling Language of Chatfield et al. (2003) has been used. Figure 9 contains, as an example, the information needed to define the Node type component, for the conceptual model presented. The quantification of these data is a task that is currently being conducted. To elicit said values, especially those that are more qualitative in nature, different elicitation techniques proposed by Sterman and Morecroft (1994) are being used.

Figure 9: Node element data structure


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6. PRELIMINARY CONCLUSIONS AND FUTURE RESEARCH STEPS The proposed conceptual model will serve as a basis for constructing decision support environments, providing, to those responsible of designing and managing SNs, a valuable tool for resolving complex issues as, for instance, the base network configuration, as well as the level of flexibility, and adaptation capability, that their SN should have. This issue is even more pressing if the challenge of product customisation to customer needs is incorporated to the SN design and management dimensions. The modules that comprise the model allow the representation of the different strategies the company can adopt to deal with changing demand orders, or events, that may distort the normal operation of the SN. These strategies can be defined as a function of market characteristics at which the product is aimed, of the customisation requirements for the different types of product, of the alternative processes for serving the demanded product, and of the different possibilities for node assignment phases in the SN. The model elaborated facilitates the definition of spontaneous demand driven responsive and efficient SN, by implementing multiple CODP on intra-company and its inter multiplant network, based on personalized customer orders. Likewise, according to the experience of experts at GBIKE, the presented model can be easily adapted to other companies which operate with the same set of issues. This subjective opinion awaits confirmation via new case studies. As regards future research steps, once the quantification of the data structures has been completed for the different elements of the conceptual model, this will allow us, in the short term, to cover the rest of the indicated objectives:

• Calibration and validation of the model behaviour based on real historical data from GBIKE;

• Evaluation of GBIKE model behaviour under different conditions (i.e. policies, operations...), and scenarios (i.e. customer demand scenarios);

• Identification of efficient global flexible SN designs and management strategies, for GBIKE potential customer demand scenarios.

And lastly, use this knowledge to develop a series of support guidelines to help GBIKE reconstruct, in practice, its SN as a spontaneous demand driven responsive and efficient global SN. ACKNOWLEDMENTS This work was funded by Saiotek REDES-PM of the Basque Country (Grant No. S-PE04IK18) and L-RdSC of the Spanish Government (Grant No. DPI2004-06916-C02-02). The authors thank GBIKE experts for their time and invaluable comments in the development of the conceptual model.


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Hicl-2006-Global and Flexible Supply Networks