Production Planning & Control

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Supporting collaboration in the development and management of lean supply networks

E. D. Adamides a; N. Karacapilidis a; H. Pylarinou a; D. Koumanakos a a Department of Mechanical Engineering and Aeronautics, University of Patras, Greece Online Publication Date: 01 January 2008 To cite this Article: Adamides, E. D., Karacapilidis, N., Pylarinou, H. and Koumanakos, D. (2008) 'Supporting collaboration in the development and management of lean supply networks', Production Planning & Control, 19:1, 35 - 52 To link to this article: DOI: 10.1080/09537280701773955 URL: http://dx.doi.org/10.1080/09537280701773955

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Production Planning & Control Vol. 19, No. 1, January 2008, 35–52

Supporting collaboration in the development and management of lean supply networks E.D. Adamides*, N. Karacapilidis, H. Pylarinou and D. Koumanakos Department of Mechanical Engineering and Aeronautics, University of Patras, Greece (Received 9 August 2006; final version received 15 October 2007) The purpose of this paper is to demonstrate how the appropriate information and communication technology can act as a catalyst in the development and operations management of lean supply networks; not by automating tasks and procedures, but by providing the enabling infrastructure required for structuring difficult problems and issues arising at inter-organisational boundaries and for taming the social complexity of their resolution processes. Towards this end, we present the design rationale and the functionalities of Co-LEAN, which is an integrated suite of software tools developed by the authors for the design and management of lean supply networks. In addition to providing full operational support in the planning and execution of the lean supply network, Co-LEAN supports internet-based collaboration in the specification of value, the identification and optimisation of value-streams, the alignment of supply-chain strategy with the overall operations strategy, and the supply-chain improvement tasks. The paper discusses the knowledge and information management requirements of lean supply networks, and presents the main components and functionalities of Co-LEAN in the context of a use case in a supply network formed around a stainless steel tanks’ manufacturer. Keywords: lean management; supply chain management; collaboration; knowledge and information management

1. Introduction The notion of the lean supply network (Hines 1994) has been a natural extension of the concept of lean manufacturing. Since the mid-1990s, the philosophy and the toolset of the lean approach were extended to cover the full spectrum of activities from the production of raw materials to the end customers’ transactions (Hines and Rich 1997, Rother and Shook 1998, Womack and Jones 2003, Hines et al. 2004). Clearly, the holistic nature of lean ideas require an equally holistic approach to the supply which extends beyond restrictions of time, place and scope, such as (optimised) supplier selection, supply chain sourcing, supplier development, and logistics (Jones et al. 1997, Davis and Spekman 2004). Instead, a defining characteristic of lean supply chains and networks is that they are formed and maintained by proactive, systemwide collaborative relationships among all-tier suppliers and customers. This means that they are practically very close to the ‘external integration’ (Stevens 1989) and ‘market-in’ (Merli 1991) stages in the evolution of supply chains with respect to their organisational structure (Rich and Hines 2000). As a result, the associated problem-solving and decision-making involves issues that can be found throughout the *Corresponding author. Email: [email protected] ISSN 0953–7287 print/ISSN 1366–5871 online ß 2008 Taylor & Francis DOI: 10.1080/09537280701773955 http://www.informaworld.com

entire product lifecycle, across both functional and organisational boundaries, aiming at achieving ‘comakeship’ (Merli 1991). It involves designing, planning and executing across multiple partners to deliver products of the right design and features, in the right quantity, in the right place, at the right time (Lamming 1993). In operational terms, a lean supply network, in addition to lean manufacturing principles, such as 5S, visual factory, takt time (i.e. the production rate that equals the rate of sales), pull, flow, etc. (Womack and Jones 2003), uses rate based planning and execution (RBPE), a way to smooth production and deliveries along the network through capacity planning driven by the end-products’ bills of materials (Reeve 2002). In employing these lean techniques throughout the network, the objective is to decrease redundancy in materials, processing and transportation activities, as well as in information and knowledge supply by providing them dynamically where and when they are required. Towards achieving this objective, a number of obstacles, which include the difficulty to see and manage cause–effect relationships established across company boundaries, and are responsible for waste, the requirement to break walls between normally insulated parties, and the need to practice collaborative decision making by developing a win/win philosophy


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of thinking supported by agreements and trust between partners should be overcome (Sako 1992, Muckstadt et al. 2001, Cox 2004, Hines et al. 2004, Evans and Wolf 2005). In the problem-solving context, these obstacles are expressed as the ‘forces of fragmentation’ (Conklin 2005) which act against collective decisions and actions, and are intensified by geographic distance, different roles, involvement in different industries, etc. The employment of interfaced manufacturing planning and execution systems is suitable only for inter-firm collaboration in issues characterised by clearness and low social complexity. Otherwise, their contribution is marginal, if not negative by intensifying the effects of fragmentation and unsystemic thinking (Davis and Spekman 2004). As the lines between innovation/ product development, manufacturing, sourcing and distribution are blurring, lean supply management needs to move away from arm’s length contractual relationships towards obligational contractual relations (Sako 1992, Kerrin 2002). These require the development of shared understanding and shared commitment, as an antidote to fragmented perspectives in product and process development, in common vision and shared goals, and in supply network design, planning, execution and improvement. As a result of the above, lean supply chain/network implementations are rare, and collaboration in this domain is still at an embryonic stage, mainly being concentrated on efforts directed towards information exchange among participants at the purely operational level. Also in the form of collaborative planning, forecasting and replenishment (CPFR), vendor managed replenishment (VMR) and synchronised supply, employing technologies such as advanced planning systems (APS) and extended decision management (XDM) (Barratt 2004, Chung and Leung 2005, Holweg et al. 2005). This arises from the fact that organisations can integrate their processes at the operational level without great difficulty because the issues are well-structured, and integration is accomplished through codified information exchanges. However, collaborative strategic processes, such as value specification, innovation, strategy development and improvement, which are crucial for the implementation of the lean supply network, are messy, involve social complexity, and require the development of shared understanding through capturing and communicating knowledge, as well as the management of complex interactions and behaviours among the agents that hold it. Collaboration in such issues requires either face-to-face interaction or the employment of advanced information and communication technology to ‘virtualise’ social interaction and support rich knowledge exchanges. The holding of

physical-presence meetings for exchanging knowledge and information in a structured manner is quite difficult, and distributed decision processes are unproductive if the dialogues and debates are not structured properly (Muckstadt et al. 2001). This is true not only in very fast changing industries with world-wide supply chains (Shi and Gregory 2005), but also in more mature and geographically restricted business activities (Azumah et al. 2005). Advanced information and communication technologies (ICT) operating over the Internet can overcome these obstacles and act as catalysts not only for information exchange, but also for supporting more advanced collaboration modes (Samaddar et al. 2005). Therefore, the employment of advanced ICT in the broader meaning of the term, including use capabilities and procedures, is necessary for supporting the development and execution of lean supply, both vertically (from raw materials to the end customers) and horizontally (from value specification to supply chain execution). Towards this end, in this paper we present the characteristics and the organisational embedment of an integrated suite of internet-based software tools that can effectively support the design and the operations management of a lean supply network. The rationale behind the suite is based on the idea that the holism of lean requires an equally holistic and consistent approach in making the transformation process and its implementation more productive and more effective. As a result, two recent technologies, namely computersupported argumentation and automatic plan co-ordination, are employed to support collaboration among partners in a supply network at three levels of capability development and deployment (Collis 1994). A knowledge management system (KMS) supports collaborative knowledge-intensive strategic processes, such as value specification, value stream mapping, and supply-network strategy development, in a consistent way. A similar system, employing the same logic in dialoguing and decision-making, supports collaborative supply-network improvements, where a module built around a database using AI techniques addresses in a more automatic fashion previously structured issues and facilitates robust rate-based planning and execution (operational level). At every level, the engagement of Co-LEAN is not for automating tasks and procedures, but to provide the enabling knowledge and social infrastructure for the implementation of lean supply in a network of organisations. Following, in Section 2, we review the activities involved in lean supply network management and identify the ICT challenges that must be met for actively supporting them. In Section 3, we present our suite of tools explaining each one’s functionalities in the framework


Production Planning & Control of the operational environment of the suite’s pilot application. Finally, in Section 4, we draw our conclusions.

2. Operational requirements and technological challenges of lean supply networks As has already been mentioned, collaboration and process integration are the two principal constituent parts of lean supply network management. Merli (1991) has argued that the strategic integration of companies involved in a supply chain/network is a function of the level of ‘trust’ created between the strategists of the organisations and the level of integration of information systems. Sharing information and creating common knowledge in argumentative discourses contributes to shared understanding and trust development, and, in turn, enhances collaborative behaviour (Chesn˜evar et al. 2000, Hardy et al. 2005). In addition structured argumentation facilitates learning as it increases the coherence of organisational mental models by assuring their rationality, logical consistency, and by eliminating any internal contradictions (Rescher 1970), thus contributing to increased overall supply chain/network performance (Hult et al. 2004). Moreover, by providing structured procedures, it encourages individuals and organisations to enter the cyclic learning process which involves a combination of experience, reflection, concept formation and experimentation (Bessant 2004), and facilitates knowledge management (Raccah 1990) and decision-making (Macoubrie 2003). Similarly, as it creates trust and power relations (Bachmann 2001), argumentation has been proved to be an efficient co-ordination mechanism (Malone and Crowston 1990). Therefore, in addition to responding to the requirement for intelligent planning (Vollmann et al. 1997) for addressing purely operational requirements, the employment of ICT that supports argumentation-based knowledge management (argumentation as explanation (van Eemeren et al. 1996)) can provide a consistent platform that integrates strategic and improvement processes across the lean supply network. Lean operations management concentrates on five areas which are associated to different but interrelated design and operational tasks. First, the management of the value stream starts with the specification of value from the customer point of view. In the context of supply network management, value is defined not only by the end (external) customers but also by the internal ones, and is related to product and service characteristics, as well as to the information that

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accompanies them. The collaboration of partners of supply networks for accomplishing incremental and radical innovations through learning is a highly sought activity (Bessant 2004, Chapman and Corso 2005). Undoubtedly, value of long-term significance is created when innovative offerings are developed to best meet the needs of customers. In operational terms, however, innovation is a process where knowledgeable and creative people and organisations frame problems, and select, integrate, and augment information to create understandings and answers (Teece 2001). In this process, the role of information technology is not only to organise data into useful information, but also to enable the transformation of personal information into newly-created organisational knowledge by structuring the problem space and by coordinating activities and actors to tame the social complexity of the problem resolution process (the process of knowing). These are the main tasks of computer-based knowledge management systems (CKMS), which can be defined as systems intended to provide a corporate memory, i.e. an explicit, disembodied persistent representation of the knowledge and information (a sort of knowledge base) in an organisation or a network or related organisation and mechanisms that improve the sharing and dissemination of knowledge by facilitating interaction and collaboration (Taylor 2004). Once value has been specified in the form of product (or/and service) and market characteristics, on the way towards lean operations the supply-network’s structure and behaviour necessary for seizing this value has to be determined. This is the subject of supplychain/network strategy formulation, whose content has been addressed both as a separate entity (Fisher 1997, Childerhouse and Towill 2000), as well as within the framework of manufacturing/operations strategy (e.g. Slack and Lewis 2002, Appelqvist 2003). In the framework of the first approach, Fisher (1997) used a configurational approach to derive normative conditions for the objectives and the behaviour of supply chains suitable for functional (stable demand, low margins, long life cycles) and innovative (changing demand, high margins, short life cycles) products. He suggested that the former should be supported by efficient (low cost) supply chains, whereas the latter by responsive ones (no inventories and stock-outs in sharp demand fluctuations). In a similar logic, Childerhouse and Towill (2000) proposed efficient supply chains for functional products and the employment of the concept of ‘leagility’ for innovative ones. The leagile (lean and agile) supply chain has an efficient (lean) and a responsive (agile) part separated by a decoupling (strategic) inventory.


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In the second approach, scholars and practitioners of operations/manufacturing strategy that distance themselves from ‘best-practice’/configurational approaches (‘recipes can easily be imitated, thus they are not strategic’) (Hayes et al. 1996, Hill 2000, Slack and Lewis 2002), view supply network strategy under their own subject’s broad umbrella as a set of supplyrelated decisions that must be taken for achieving generic, previously prioritised operational objectives such as cost, flexibility, dependability, speed, quality, innovativeness, service, etc. In practice, the objective is to develop and leverage the appropriate supply-network resources and capabilities which are in long-term correspondence with the environment-induced, but management-prioritised, objectives (Gagnon 1999). Clearly, this is an iterative group decision process involving participants from different functions and different organisations (Riis et al. 2006). Information technology with the characteristics of the aforementioned computerised knowledge management systems can assist and leverage this collaborative effort not only by helping strategists to reach an agreed action plan effectively, but also by augmenting (inter)organisational learning in the strategy process per se (Lane 1994, Karacapilidis et al. 2006). The definition of value and the alignment of operational resources and capabilities with operational objectives act as inputs to the process of defining the value stream which extends along the entire supply network. Mapping, even better modelling and simulating, the value stream facilitates the development of shared understanding and the identification of initiatives for its improvement, e.g. for reducing or eliminating waste (Rother and Shook 1998, Mertins and Jochem 2001, Duggan 2002). In multiple participant/stakeholder settings, collaborative modelling and simulation technology (Miller et al. 2001, Taylor 2001, Umeda and Zhang 2006) enhances the design and/or redesign tasks (including the implementation of ‘flow’ and ‘pull’ objectives) by acting as the transitional object (Papert 1980) for improvement ideas, mind frames and argumentations, thus fully supporting actions towards the ‘continuous search for perfection’ imperative of lean management (Lamming 1994, Middel et al. 2005). Finally, although the ‘single-piece flow’ and ‘pull by the customer’ objectives are associated with the design phase of supply networks (the definition of structural and operational characteristics of the system), they also entail purely operational challenges which are addressed at the information rather than the knowledge layer. So, in addition to the value stream definition task that takes place at design time, the operational model underlying the planning and

execution mechanism for specific value streams and for specific time frames and demand patterns, should guarantee that flow and pull of waste-free activities are executed in an efficient manner. For this task, internet technology (XDM and APS) has been proposed as a better alternative to attempts of legacy system (ERP) integration (Kehoe and Boughton 2001). Nevertheless, since lean is contingent to stable demand and operating conditions, at this level, it is necessary to implement intelligent technologies for assisting in the efficient instantiations/reconfigurations when the assumed conditions in supply network are not present, and when demand or internal operating conditions change significantly (Vollmann et al. 1997). This can be done by exploiting, automatically or semi-automatically, the relations that exist among the activities and resources in the value stream maps within the same company and/or across different companies in the network, as well as those that exist in individual order fulfilment plans. Overall, the ICT requirements for supporting lean supply network management can be summarised in the support for synergy (to tame social complexity), as well as in the consistent integration of knowledge and information across activities in the entire product(s) life-cycle. The Co-LEAN software suite described below is a coherent response to these requirements.

3. The Co-LEAN suite The Co-LEAN suite consists of five interconnected tools that operate over the internet to enable the development and implementation of a lean supply network. As already mentioned, they support knowledge, social, and decision-making processes at three levels: strategic, improvement and operational. At the strategic level, Co-INNOV supports the collaborative innovation and product development processes (value specification) while Co-NET assists in the collaborative development of transparent supply strategies at the individual node and network levels, whereas Co-SISC is used for collaborative simulation-based supply chain/network design. At the supply-network improvement level, Co-SOLVE, in connection with Co-SISC, supports collaborative problem-solving and improvement teams. Finally, at the operational level, CoLEAN-PE, which is the binding core of the suite, provides algorithms for rate-based planning and execution of the supply chains of individual products, groups of product offerings (value streams), and customer orders by exploiting the multi-level relations that exist between operations in different value streams and different order fulfilment paths. Table 1 shows the


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Production Planning & Control correspondence of Co-LEAN modules with the lean management objectives and drivers. Co-LEAN has been developed over the last four years, initially as separate components that were later integrated through Co-LEAN-PE. Figure 1 shows the overall functional structure of Co-LEAN whose operation and individual module details are described below. A use case of a stainless-steel tank manufacturer that accompanies the description demonstrates the features of the suite in an applications context. For the implementation of Co-LEAN, technologies supported by the Microsoft’s.NET platform, such as ADO.NET, XML Web Services, and Visual J#.NET, have been exploited. The Delphi Rapid Applications Development environment was also used. Access to the tools of the Co-LEAN suite is provided through a dedicated Web server. To use the complete range of services provided, users at the focal company, customers and suppliers, require only a Web browser (i.e.

there is no need to download any specific application at the client side). Depending on the tool used each time, users may exploit some built-in templates and customise their working environment according to their profile and collaboration requirements. The interoperability of the suite’s tools is achieved through the exchange of the appropriate XML messages. The communication of the suite’s tools with its proprietary database, model base and knowledge base, as well as with remote databases is regulated through a dedicated SQL server. Connections with remote databases are achieved through the OLEDbControls of .NET platform.

3.1. Developing the lean supply network: Co-INNOV, Co-NET and Co-SISC Co-INNOV and Co-SOLVE are based on Knowledge Breeder, a web based computer-supported

Table 1. The lean imperatives and the response of the Co-LEAN suite. Corresponding tools of the Co-LEAN suite

The lean supply chain agenda Specify value Develop supply chain strategy Map the supply chain, eliminate waste, construct ‘pull’ loops, make value flow Collaborative rate-based planning Collaborative rate-based execution (coordination of plans) Supply chain improvement

Co-INNOV Co-NET Co-SOLVE, Co-SISC Co-SOLVE, Co-LEAN-PE Co-LEAN-PE (Relations Manager) Co-SOLVE

Relations manager

Forecasted demand

Co-LEAN-PE

Co-INNOV

Co-LEAN -PE order management

Co-LEAN -PE planning

Co-NET

Co -SISC Schedules (production and delivery rates) Co-SOLVE

Customers and suppliers databases

Supply network (current state)

Figure 1. The functional structure of Co-LEAN.

Actual orders

Collaboration database


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argumentation system that implements the G-MoBSA (group model building by selection and argumentation) participative problem solving methodology (Adamides and Karacapilidis 2005, Adamides and Karacapilidis 2006). Knowledge Breeder uses a systemic problem-knowledge representation scheme and an evolutionary problem-resolution methodology which enables the ‘breeding’ of the knowledge necessary for the resolution of a specific organisational issue. Co-SOLVE is a stand-alone environment used as a platform for argumentation-based resolution of specific lean supply chain improvement issues, also enabling evolutionary decision making. Co-INNOV, on the other hand, implements a more refined and domain-specific version of Co-SOLVE by supporting the value specification, innovation and product development process in its entirety, enabling the gradual ‘breeding’ of innovation concepts. In Co-INNOV, the value specification and/or the innovation process is/are viewed as a sequence (not necessarily being executed in linear fashion) of issue/ problem resolutions/solutions (Leonard and Sensiper 2003), in which cause-effect-like models are used to represent individual and organisational cognitive schemata of related issues and their possible resolution(s). If the value specification task involves an already existing product, Co-INNOV allows for the ‘deconstruction’ of the final offering to identify the principal sources of value for the customers. The models proposed have a generic structure of the following form:

external organisation, market and supply network environment. All models are opportunistic representations of the problem and solution spaces and are subject to discussion, argumentation, and eventually, modification by the same or other actors as more information is crystallised in the form of organisational knowledge. In this way, Co-INNOV, as well as the rest of Co-LEAN’s modules, fully supports the empirically observed opportunity-driven design and problemsolving process (Guindon 1990). In the argumentation process, conflict resolution is through formal argumentation (logic) rules. The defensibility of each model (the assessment of its coherence as a collectively-owned knowledge item) is determined as an indication of its validity, which acts as suggestion for its eventual collective selection or rejection. On the basis of their perception of the issue, participants can construct and propose models of the problem and their solutions using the problem-solution modelling formalism. As they have access to the whole set of models proposed, they may then adjust their own models and/or contribute to the construction of other models accordingly. Both a model per se (completeness) and/or its defensibility (fact-supported argumentation) can change a participant’s perception of the issue. As a result, mental models (beliefs) are converging around specific models which attract more attention concentrating the argumentation on them. Normally, the model that is best supported by facts and attracts the majority of favourable views of the group is selected.

because symptoms S1 ,S2 ,S3 . . . are observed

Symptoms of the issue

observations supported by arguments AS1,1 ,AS1,2 ,. . . AS2,1 ,AS2,2 . . . , problem is what characterises the issue,

Problem identification

caused by R1 ,R2 ,R3 ,. . . , Roots of the problem causes are supported by arguments AR1,1 ,AR1,2 ,. . . AR2,1 ,AR2,2 . . . , solution is the most appropriate

Proposed solution

because actions A1,A2,A3,. . . which are implied in the solution eliminate R1 ,R2 ,R3 ,. . . ,

Justification of solution

and is feasible and effective solution because actions result in ðobservable effectsÞ E1,E2,E3,. . . Through Co-INNOV, all the participants involved in the value specification process are able to provide their own complete interpretations and proposals of the issues concerned in the form of the logic-based cause-effect models given above. Clearly, models are the result of individual perceptions of the internal and

The argumentation system of Co-INNOV, as well as those of the other collaboration supporting modules of Co-LEAN, relies on the system (set of rules for argument placement and conflict resolution) of logical propedeutic of the Erlangen school (van Eemeren et al. 1996). Complete arguments are represented by means


Production Planning & Control of simple statements related by logical connectives (operators). The logical connectives used are confined to: AND (conjunction), OR (disjunction), IF . . . THEN (implication) and NOT (negation). The argumentation schema provides the rules for conducting the dialogue among participants and for resolving conflicts, i.e. it indicates which argument or clause holds and which is defeated. Structurally, the kernel of Co-INNOV consists of its knowledge base that stores models under consideration, as well as models of already closed discussions. Any form of electronic information (text, spreadsheets, hypertext, drawings, photographs, etc.), as well as direct links to individual tools (e.g. to tools supporting the engineering design phase of product development) can be attached to the model(s) and transmitted to other participants. Models are stored and selected hierarchically using a meta-model of the issues addressed (context definition). Users can upload the current issues under consideration in which they wish to be involved, see the current state of the dialogue and contribute accordingly. They can then move into other issues through the navigating meta-model. By taking into account the structure of the model, the arguments placed and the argumentation rules, the inference engine of the computer-assisted argumentation module determines the defensibility of each model-proposal of problem-solution. The interface of Co-INNOV (as well as those of Co-SOLVE and Co-NET) is in hypertextual form with menus associated to the features provided, and diverse functionalities related to visualisation (folding/unfolding of model components, view/ hide of inputs, creators, dates, etc.). Use case 1. A proposal for new value definition in the industrial equipment sector Stainless Steel Tanks (SST) SA is based in Greece and is one of the largest manufacturers of stainless steel industrial equipment in south eastern Europe. (As the case description inevitably contains information of strategic nature, the real name of the company and other identifying details are protected.) The company specialises in the production of stainless steel tank-like equipment for the food (oil, dairy) and alcoholic beverages (wine, beer) industries, i.e. fermenters, stabilisers, vinificators, oil and milk vats, etc. In addition to a standard product range, which consists of about 50 products, the company undertakes the development and production of customised products and turn-key projects for the above industries, as well as for other more demanding ones, including pharmaceuticals. The main suppliers of SST are a steel mill, various suppliers of motors and electro-mechanical

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components such as valves, shakers, sensors, etc., as well as suppliers of materials for the construction of heating and cooling jackets. The custom products and turn-key projects (mainly breweries and micro-breweries) are built to order and their components are forwarded and installed under SST’s own responsibility. At the time of the study, the core production facility of SST was located at a distance of about 300 km from Athens, and the company’s downstream supply network included a distribution centre/warehouse for supporting sales in the Athens metropolitan region, four regional distributors, as well as retailers all over Greece, and agents/resellers in five other nearby countries. In the process of adoption of a lean operations management style, SST agreed to host the pilot application of the Co-LEAN suite. The software was installed at the company’s headquarters and, after a short training and familiarisation period, access was granted to its main suppliers and customers (distributors and retailers). Although Co-INNOV was not the first module of which actual use was made, for the continuity of the presentation Figure 2 shows a screenshot of the use of this module for collaboratively identifying with customers new sources of value. In the instance of the argumentative dialogue shown, which as it can be clearly observed adheres to the syntax outlined above, the market of used equipment as a new source of value is discussed. A complete model was submitted by a manager of a SST customer company (George). In the instance shown there is only a counter argument on the complete position raised by a SST manager (Thanos) which concerns the need for consulting activities if his company enters the used equipment market. In reality, the debate on entering this market lasted for a couple of months and attracted all the major suppliers and customers that were given access to the software. The final decision taken was to enter this market whose organisation proved to be a significant source of value for both the company (intermediary), as well as for large and smaller customers, acting as sellers and buyers, respectively, within a lean supply network operation (see below). Co-NET is a customised version of Co-MASS which is a computerised knowledge management system for assisting operations managers in the formulation of manufacturing and operations strategy (Karacapilidis et al. 2003). It supports the social and knowledge processes of collaborative manufacturing strategy development by integrating a domain-specific modelling formalism based on the resource-based theory of competitive advantage (Wernerfelt 1984, Gagnon 1999), an associated structured dialogue scheme, again an argumentation-enabling mechanism,


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Figure 2. A sample dialogue for value specification.

and an efficient algorithm for the evaluation of alternatives. Co-NET helps in deciding on the supply chain related resources and capabilities, which are essential for achieving the prioritised objectives derived from value definition. For example, in the typical operations logic, if it becomes clear that customers value ‘variety’ highly in a product range, once this source of value has been identified, the innovation and product development function comes up with a ‘modular design’ strategy and the operations function puts ‘flexibility’ as the primary objective of production and supply chain initiatives. That is, the resources and capabilities system of production and supply chain should be considered under the objective of flexibility. As a technological artefact, Co-NET can be considered as a web-based application-specific knowledge management system. Compared with other information systems proposed for the collaborative development of strategy, Co-NET takes a more evolutionary perspective based on the aforementioned G-MoBSA methodology. That is, it relies on the assumption that strategy development is a process that begins with the exploration of as many alternatives as possible, followed by the gradual exploitation of the most appropriate of them. Empirical evidence has

shown that in team-based strategy development there is interplay between social and knowledge processes (Schwarz 2003). Groups-within-groups of managers with similar views can emerge at any instance as a result of knowledge exchange and (re)construction. The group with the most persuasive idea, or solution, attracts a critical mass of supporters who argue in its favour. Other groups/views attract less support and more opinions against their proposals for action. As social processes result in the formation of groups, knowledge is clustered around specific ideas, solutions or views. Use case (revisited) 2. Developing supply network strategy One issue of strategic importance related to the supply network of SST was the assessment of the effect of the used industrial equipment market on the overall operations strategy of the company, as well as the determination of the necessary capabilities for this market. Argumentative discussion among supply network participants (managers from different functions, as well as from supplier and customer organisations) included both the determination/prioritisation of strategic objectives and the


Production Planning & Control assessment of the capabilities that are required for achieving them. In particular, the discussion was centred on the importance of quality and dependability as strategic directions. Optimistic (quality will contribute to the enlargement of markets) and pessimistic (quality will increase the reliability and life span of products, and will eventually saturate markets) views were expressed according to the position of the proponent organisations in the topology of the supply network. Suppliers were sceptical about increased quality (and prices) in connection with the development of the used equipment market. Distributors and retailers, on the other hand, were optimistic that the increased quality and the used-equipment market will stimulate demand as a brand name synonymous with quality will be established and more new customers will be approached. The latter views proved more logically sound in the debate as they were supported by stronger arguments and were eventually adopted. Nonetheless, the open argumentative debate of the issue and the logic of the arguments placed built more trust among stakeholders as, in addition to specific positions, ways of thinking were exposed and discussed. In the fringe between strategy and operations, the ‘leaning’ process of a supply network is facilitated by value stream mapping (Rother and Shook 1998). Towards this end, Co-SISC implements a language and dialoguing structure specific to simulationmodelling-based supply chain design (Karacapilidis et al. 2004). Co-SISC is used for mapping the value stream and for collaboratively identifying sources of waste. It deploys a generic systems-oriented language (activities, resources, decisions and their structural and operational parameters), thus enabling different commercial simulation environments to be easily incorporated with. Again, argumentation on model items and their attributes is supported by the system, as is storage and retrieval of discussions/argumentations and simulation models. The construction, validation and verification of simulation models are accomplished by a simulation specialist (technical facilitator) (probably the only external consultant engaged when using Co-LEAN), whilst the rest of decision makers/participants can only see its structure and simulation results, as well as experiment with different parameters. So, in addition to value stream mapping and waste identification, collaborative modelling using Co-SISC augments shared understanding, transparency of individual decisions and trust building along the supply network through the execution of the modelling and simulation process per se.

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Use case (revisited) 3. ‘Leaning’ operations in the supply network (elimination of waste, small batch flow and pulling by the customer) At this stage the actual ‘leaning’ of SST’s supply network was accomplished. The process started with a one-day modelling/mapping workshop, on which members of different organisations that participated in SST’s network were exposed to the features and functionalities of Co-SISC in the context of valuestream mapping. This was carried out by modelling the existing (non-lean) supply network which did not include the collection and selling of used equipment. With the help of an external consultant, the Operations Management division of SST used the model developed in the workshop to determine initiatives towards a leaner supply network. The ‘leaning’ of the network downstream was considered necessary due to the bulkiness of the end products that were difficult and costly to handle at every processing, transportation and storage node. It should be noted that before considering its supply network, an attempt to lean the internal operations by defining product families (mainly around market segmentation: winemaking, brewery, dairy, etc) and developing the associated value stream maps were attempted. However, in view of the new operations to be undertaken, it was thought that only the lean operation of the whole network could provide significant results. As a consequence, SST’s Operations Management function launched an initiative for ‘leaning’ the supply network by forming the project team mentioned before. Using Co-SOLVE and Co-SISC, the team identified three supply chains spanning the network, each having different strategic objectives and corresponding to three value streams: the supply chain of standard products, the supply chain of custom built-to-order products, and that of used equipment. Standard products had periodic but constant demand, hence efficiency was considered as the principal operational requirement. On the other hand, the demand for the second-hand and custom equipment (and projects) was unstable and, therefore, dependability through responsive supply chains was the main requirement. Co-SISC was used for the design of the whole network (all three supply chains). The existing simulation model was modified collaboratively to investigate the effect of two proposed changes towards leaner supply and distribution: the elimination of the Athens warehouse and the introduction of mixed-mode production through level schedules (heijunka) for the standard products (Figure 3). Both issues were debated extensively using the argumentation-supporting functionalities of


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Figure 3. Modelling dialogue and simulation model in Co-SISC.

Co-SOLVE. Based on different (stochastic) endcustomer demand patterns, different production rates (capacity bounds) were obtained and assessed, both in technical and economic terms. The interoperability of the three supply chains and the essential (minimum) resource requirements for the whole network were also investigated through simulation modelling. The resulting operational structure of SST’s supply network is described in the following section where the use of Co-LEAN-PE is presented.

3.2. Managing the lean supply chain. Rate based planning and execution using Co-LEAN-PE The Co-LEAN-supported operations management of the lean supply network relies on the appropriate design and development accomplished through Co-INNOV, Co-NET, Co-SISC and Co-SOLVE. The design provides the structure and the fundamental modus operandi on which specific medium- to shortterm operations plans are based on. At the pure operational level, Co-LEAN-PE is the suite’s core module responsible for the rate-based planning and execution of the lean supply chain. It uses forecasted and actual demand data, as well as information from a continuously updated supply database concerning

products, suppliers and supplier relations to accomplish a production and/or delivery schedule (rates) based on the end-customer demand. The principal task of this module is to develop operational plans (schedules) for the different value streams at-large and then to co-ordinate them at the macro (demand smoothing – planning) and micro (order fulfilment – plan execution) levels, respectively. To achieve this, it executes a nested top-down process that starts with the co-ordination of the plans for the different types of offerings (e.g. standardised and custom products), moves down to the coordination of different product value streams, and ends at the execution of individual orders. The whole process of schedule determination and coordination takes place in two phases. At the initial phase (planning), past orders from end customer selling points, after being ‘translated’ into production and delivery rates/capacities, are aggregated, averaged and used in connection with the products’ bills-ofmaterial to broadcast capacity bounds (production and delivery rates) to the nodes (companies) of the supply network. Based on these values, by using takt times, the levelled schedule of the focal company and its basic production sequence within an interval are obtained. In this way, the operational model assumed in the


Production Planning & Control design of the software treats the focal company as the ‘pacemaker’ process which is linked to the other network nodes through supermarket-operated Kanban loops. After their determination, the ratebased plans are co-ordinated by using the Relations Manager (see below). At a second phase (execution), the actual endcustomer orders are treated individually and their fulfilment plans are co-ordinated so that, within the calculated bounds, the best possible use of resources is made. In other words, the Kanban loops are optimised through the co-ordination of individual order execution streams/plans. This co-ordination is also accomplished by the Relations Manager, which is a module that uses conventional (Coloured Timed Petri Nets) and artificial intelligence techniques to represent and manage the relations that exist among the activities and resources in the plans. Using due dates and lead times, the orders are expanded as timed plans which, starting from the outmost-tier supplier activities downstream (e.g. delivery from distributor to retailer), are compared to extract possible relations among them with respect to resource usage. A virtual actor is assigned to each order and takes the responsibility for its most effective fulfilment by extracting at each stage of the plan the previously mentioned relations (Adamides 1996). Once relations are extracted, plan modification operators are employed. The relations defined and used for the co-ordination are both ‘positive’ and ‘negative’ (von Martial 1992, Adamides 1996). Positive relations are the ‘equality’ (an activity is included in the plan of a different (virtual) actor too) and the ‘favour’ relation (a task can be undertaken by a different activity or active resource producing the same outcome). On the other hand, the most common negative relation is ‘conflict’, usually associated with the requirement of two or more activities for the same resource, at the same time. The identification of relations involves the transformation of activity execution times on an absolute time scale for comparison (von Martial 1992). Appropriate parameterised operators are used to handle relations (‘move-timeforward’, ‘move-time-back’, and ‘prioritise’ for conflict, ‘do-favour’ for favour and ‘do-equal’ for equality). The employment of the Relations Manager is of particular importance when the execution plans of the actual orders (execution phase) divert significantly from the rates determined in the first phase, either because of a significant change in demand, or because suppliers declare inability to accomplish schedules. Relations that exist among products (order characteristics) and suppliers (resources) are considered with

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respect to the two principal network node/processor characteristics: capacity and capability. ‘Capacity’ refers to the ability of a different node that has the product in its current schedule (or in its stocks) to supplement the initially selected supplier(s), processors in general, with additional capacity, whereas ‘capability’ refers to the ability of producing the product(s) requiring additional capacity (the product is not in the current production mix and there is no stock). The additional cost burdens that the exploitation of positive relations and the resolution of negative ones imply are then calculated. However, frequently rescheduling and reconfiguration decisions require additional qualitative information and discussion and argumentation using the features of the other modules of Co-LEAN. Information and agreements about available capacities and capabilities are the result of argumentative discussion using the other modules of the Co-LEAN suite. Use case (revisited) 4. The operation of the lean supply network To demonstrate the operational support mechanisms of Co-LEAN-PE, we consider two of SST’s products in vitro: the 60 m3 rotary vinificator (code name RV60) and the 100 m3 milk vat (MV100). A somewhat simplified bill of materials (BOM) for RV60 would contain the steel for the 60 m3 hollow cylinder (supplied by S1), three valves, one thermometer, one shaker (all supplied by S2), one standard motor, one PLC module (all supplied by S3) and materials for the corresponding cooling jacket (supplied by S4). Similarly, the BOM for MV100 consists of the steel for the 100 m3 hollow cylinder (supplied by S1), four valves, one thermometer, one spray ball (all supplied by S2), three sensors (supplied by S3) and materials for the corresponding heating jacket (supplied by S4). The average monthly demand (excluding the seasonal trend for wine-making equipment) for RV60 was determined by Co-LEAN-PE to be 60 units and that of MV100 90 units (Co-LEAN-PE has three different forecasting methods, which can be selected accordingly). On the basis of the calculated levelled schedule, and taking into account the corresponding BOMs, Co-LEAN-PE calculated the daily supplies required for the production of the product range. This means that for producing two units of RV60 and three units of MV100, 2*steel(60 m3), 3*steel(100 m3), 18 valves, five thermometers, three shakers, three sprayballs, two motors, four PLC modules, six sensors, 2*cooling jacket material (60 m3) and 3*heating jacket material (100 m3) have to be delivered to the SST’s facilities daily.


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E.D. Adamides et al. PRODUCE STEEL (2×60+3 ×100 m3) by: midday 1

Required capacity: 15 minutes

Capability and capacity of steel suppliers

Conflict DELIVER STEEL (2×60+ 3×100 m3) by: midday 2

Required capacity: 3 hours


PRODUCE STEEL (~200 m3) by: midday 1

move-time-forward ->midday2

Delivery capability and capacity of steel suppliers


DELIVER STEEL (~200 m3) by: midday 1

do favour

Favour PRODUCE TANKS (2×60+ 3×100 m3) by: end-day 3


DELIVER TANKS (2×60+ 3×100 m3) to D1, by: midday 6


Delivery capability and capacity of SST

DELIVER TANKS (2×60+ 3×100 m3) to R1D1, by: end-day 6

Required capacity: 1 hour

Delivery capability and capacity of distributors

Capability and capacity of SST


PRODUCE TANKS (custom) by: end-day 3


DELIVER TANKS (custom) by: end-day 4

Figure 4. Co-ordination using relations in Co-LEAN-PE.

On average, SST operates 30 shifts per month, which corresponded to small batch daily production of two units of RV60 and three units of MV100. For the sake of the simplicity of presentation, we assume here that SST has only two distributors (D1 and D2), and each of them supplying two retailers. In the absence of actual orders placed by the distributors, dispatches to distributors take place every three days, i.e. a buffer inventory (Kanban supermarket) of up to six RV60 and nine MV100 that is held at SST is emptied. Then, three RV60 are dispatched to D1, three RV60 to D2, four MV100 to D1, and five MV100 to D2. This operation acts as ‘pitch’ for assessing the levelled schedule with respect to the actual orders and for carrying out corrective actions accordingly. Obviously, in the logic of rate-based planning, all the above supplier and customer delivery, as well as production

rates correspond to resource capacities expressed as reserved time slots. This smooth rate-based operational plan of the supply network for the aggregated value chain of standard products had to be co-ordinated with the value streams of customised and used products. Inside the factory, co-ordination took place at the productfamily value stream level. In Co-LEAN-PE value stream co-ordination, at various levels, is accomplished automatically by the Relations Manager as new information representing the state of the supply network is fed to the system’s databases. A case of co-ordination corresponding to the plan for the two standard products of SST with that of custom orders is shown in Figure 4 (only the steel supply chain is shown). The numerical values in the custom-products value stream correspond to the average daily demand


Production Planning & Control

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Figure 5. Co-LEAN-PE: the main screen for constructing the supplier database.

for such products. As it can be seen, co-ordination at this level concerns more the supply part of the network (push part) than the demand satisfaction downstream part controlled by Kanbans (pull part). In this plan, a conflict relation was detected between the activities PRODUCE STEEL for the standard and custom products, respectively because the steel mill was lacking the capacity to accommodate the combined demand within the required time constraints. After detecting this negative relation, the ‘move-timeforward’ operator was engaged for adding to the start time of the activity for the custom products at extra time and displacing its due date to ‘mid-day2’ (instead of ‘mid-date1’). In addition, a positive relation (favour) was detected in the DELIVER STEEL activities which, as long as there was available capacity, could be both moved to only one of the two plans (do-favour operator). Additional, more demanding conflict resolution cases involved the sourcing of steel from a different supplier that had the required capability and capacity, and the use of a different supplier’s transportation medium (e.g. that of S2) to deliver on the initially assigned time. In both cases, these possible favour relations are the result of suppliers’ database consultation (suppliers’ capacities and capabilities). In a similar manner, at the execution phase, Co-LEAN-PE co-ordinated the individual (actual) orders placed by retailers. Here, the co-ordinated plans for the average demand (the pitch) played a

passive role, but they were the medium through which the order fulfilment plans were co-ordinated. Activities belonging to these plans were undertaken or modified by the order fulfilment plans according to latter’s needs. The co-ordination at this phase primarily concerned the Kanban loops from the SST’s Kanban supermarket downstream to the retailers’ deliveries, and the co-ordinated plans for the production and delivery rates played the role of capacity and capability suppliers and distributors for the efficient order execution. The equality relation was detected when the delivery rates (capacity) sufficed for the fulfilment of real orders, the resolution of the conflict relation was through order prioritisation and delaying, whereas the favour relation was exploited for optimised use of resources. For example, instead of returning empty, trucks delivering new equipment were scheduled to collect used tanks. Overall, for SST, as well as for its suppliers and customers, the adoption and use of Co-LEAN did not prove a particularly difficult task to accomplish. However, as was expected, most of the burden for the adoption – especially the initial population of databases – was undertaken by SST. With the exception of the population of databases, customers and suppliers were very willing to co-operate. The manual input of data proved somehow problematic and in full-scale implementations of Co-LEAN this activity certainly requires the use of the appropriate


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‘data-translation’ technology. In total, the usability of Co-LEAN would have been improved should the appropriate interoperability technology existed to connect suppliers’ and customers’ own information systems to the modules of the suite. On the contrary, customers and suppliers were very willing to discuss issues using the Co-LEAN modules: initially driven by curiosity and willingness for experimentation with the new medium, later as a means to put proposals and arguments forward and support them. We sensed that users formed the impression that the argumentation scheme of the suite acted as an independent trusted arbitrator. This was probably the reason for being accustomed to the syntax of Co-INNOV, Co-NET, etc. fairly fast, and for initially using these modules only for issues where consensus was not apparent. Gradually, however, the use of the suite, with the exception of Co-SISC, became almost a routine. The technical complexity of Co-SISC repelled busy executives and it became apparent that only well-planned fully-facilitated workshops could have a significant impact on the definition and analysis of the supply network value streams using the features of this module. A testimonial for the significance of Co-LEAN was given by a SST manager who said that the software helped him better understand the wine-making market. For the first time he actually listened to his customers. Previously, even when he was visiting customers (distributors) to try and find solutions on leaning the supply chain to cut-off SST’s and distributors’ inventories and reduce costs, the emphasis was in just imposing a solution devised in the office, not asking any questions and opinions. The distributors were unprepared to suggest different things, the discussion was just for the sake of discussion, ‘short-termism’ and a shallow approach were prevailing. The installation of the software per se resulted in distributors and suppliers feeling more important and part of a network/team. They realised that SST wanted to really understand them and co-operate for their mutual benefit. In addition, the software was a driver for persuading suppliers and customers to be engaged in lean initiatives. Otherwise, they were reluctant to participate in what they called ‘intangible’ programmes and initiatives that frequently proved to be just fads. The interconnectivity and collaboration were experienced before starting to think in terms of value streams, heijunka, etc. A SST executive that was proficient in lean management tools and techniques provided a first-class learning experience through informal discussion sessions of particular issues. Moreover, the automation of routine tasks in Co-LEAN-PE removed

any inconsistencies in the methods used in the different nodes of the network and made this activity far more efficient and consistent. Summarising our experiences from this pilot study, we stress three issues of concern for the adoption of this sort of technology for lean supply chain/network management: (1) The need for technological integration and consistency with the rest of the ICT infrastructure of customers and suppliers. (2) The necessity of a pre-existing positive approach looking towards a co-operative environment – technology itself cannot create this, it can just enhance it. (3) The leading role of the focal company in making clear to its suppliers and customers that the software tool is just an instrument for facilitating the lean management process. It does not guarantee lean performance, and it is really up to the organisations involved to devise the appropriate means and processes for achieving it.

4. Discussion and conclusions Although the successful implementation of lean manufacturing is marginally dependent on information and communication technology (ICT), the geographic and mental distances involved in (lean) supply chains/networks necessitate the use of ICT. Many vendors have identified this need and developed integrated software systems for supporting lean supply chain management. These systems, in addition to providing operational support for lean logistics, concentrate on the management of data required to identify non- or low-value adding activities and to reduce costs. Undoubtedly, these are valuable features but the essence of lean supply network management lies in the systemic coherence of the network which is enabled by collaborative behaviour. In other words, to gain competitive advantage through a lean supply network, prior to leveraging the intellectual capital across the network, a firm needs to build social capital in it (Nahapiet and Ghosal 1998). As a determining attribute of lean management, collaboration depends on the management of human interactions encountered among managers and employees of participating companies. The efficient management of these interactions is synonymous with the elimination of the boundaries that exist between the companies in the supply network. Conventional ICT technology for lean supply network management tries to get control of


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Production Planning & Control the supply network dynamics through standardisation of the interfaces between participating firms. Standardised interfacing facilitates data and information exchange but, in effect, fragments knowledge, the processes of knowing and their creative interplay required for achieving shared understanding and collaboration. Inter-company and functional boundaries continue to exist, and do not only inhibit communication, but most importantly prevent individuals from altering their knowledge in response to events and attitudes occurring in the network, or from acting towards altering the knowledge of other members of the network. Boundary spanners (individuals assigned the specific role of facilitating the meaningful communication between organisations and/or organisational entities) and boundary objects (artefacts like models used to create shared context among different organisations) can overcome the problems of knowledge boundaries and facilitate distributed co-ordination (Carlile 2002). Since boundary spanners are almost impossible to operate in geographically dispersed supply chains, boundary objects seem to be the most appropriate means for coordinating the lean supply chain. In effect, this is where the value of the Co-LEAN suite rests. It is an integrated set of boundary objects that can be employed on-demand according to the specific task or initiative of lean management. The technological homogeneity and integration of the tools and the interoperability of the models used allow the integration of the knowledge produced and used in the different tasks of lean supply chain management. Knowledge integration and creation can take place at two different levels: at the specific task level (e.g. value specification) where different knowledge sources from different participating companies are engaged, and longitudinally across different tasks as knowledge developed in dealing with them can be used in newly arising situations. For instance, the definition of the relations used at the operational level by the Co-LEAN-PE module may be the result of prior discussions, argumentations, and eventually knowledge integration that take place when dealing with issues in the milieus of value specification, supply network strategy, or even in the framework of collaborative improvement programmes. Summarising, the initial application of Co-LEAN presented in the paper revealed the necessity of collaboration at the strategic level and co-ordination at the operational level of lean supply network management. In addition, it showed that information and communication technology has a significant role to play in lean transformation and operation of supply networks, principally not by automating tasks

and procedures, but by providing the enabling knowledge and social infrastructure required to collaboratively structure the issues involved and to manage the social complexity of their resolution.

Notes on contributors Emmanuel D. Adamides is a tenured Assistant Professor of Operations and Technology Management in the Section of Management of the Department of Mechanical Engineering and Aeronautics of the University of Patras, in Greece. Professor Adamides is a graduate of Democritus University of Thrace (Greece), the University of Manchester, and the University of Sussex (UK). Before joining the University of Patras he held academic and professional positions in Switzerland and Greece. He has published extensively in the areas of systems engineering, operations strategy, strategy and knowledge management, and innovation and technology management. His current research interests are in the areas of manufacturing strategy, and innovation and technology management with a special interest in sustainable production technologies. He is particularly interested in the development and use of systems methodologies and tools to support managerial activities in the above areas.

Nikos Karacapilidis holds the position of Professor in Management Information Systems at the Industrial Management and Information Systems Laboratory, Department of Mechanical Engineering and Aeronautics, University of Patras, Greece. His research interests lie in the areas of Intelligent web-based information systems, technologyenhanced learning, e-collaboration, knowledge management systems, group decision support systems, computer-supported argumentation, enterprise information systems and semantic web. He has published in several business and information systems related journals. More detailed information about his publications list, research projects involved and professional activities can be found at http://www.mech.upatras.gr/nikos/

Dimitrios P. Koumanakos is a PhD candidate in the Industrial Management and Information Systems Laboratory of the Department of Mechanical Engineering and Aeronautics of the University of Patras, Greece. His research interests are related to lean supply chain management, inventory management, as well as product


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design and concurrent engineering. His professional experience in the field includes the position of Venue Logistics Manager in the city of Patras during the Olympics Games of Athens 2004.

Charalambia Pylarinou has graduated from the University of Hull (UK) with the degree of Masters of Engineering (Mechanical Engineering) in July 2004. In November 2004 she started her PhD in the Industrial Management and Information Systems Laboratory of the Department of Mechanical Engineering and Aeronautics of the University of Patras. Her research interests are related to the co-ordination of lean supply chain, simulation modelling, and generally production planning and management, as well as knowledge management in decision making.

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