Marco Taisch
email: marco.taisch@polimi.it

Bojan Stahl
email: bojan.stahl@polimi.it

Gokan May

Matteo Cocco
email: matteo.cocco@polimi.it

Manufacturing System Design Decomposition for Sustainability

Keywords: sustainability, manufacturing strategy, axiomatic design

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

How firms can turn sustainable manufacturing into a competitive weapon, so as to alleviate upstream and downstream performance improvements along the triple bottom line, while reducing environmental harm and stimulating social solidarity, is a major area for research [START_REF] Kleindorfer | Sustainable Operations Management[END_REF]. The development and deployment of adequate sustainable manufacturing strategies are still open issues and of utmost interest both for research and practice [START_REF] Drake | Sustainable Operations Management: An enduring stream or a passing fancy?[END_REF].

Besides a lack of process deployment as well as trade-off understanding, the definition of a sustainable manufacturing strategy coherent with other functional and business strategies is a practical gap [START_REF] Mirvis | Vision, mission, values: Guideposts to sustainability[END_REF]. Especially the internal consistency of manufacturing strategy, i.e. the translation of competitive priorities into decision categories in order to form, expand or maintain manufacturing capabilities, and the design of appropriate manufacturing improvement programs to enable strategic choices, is a major obstacle for companies nowadays. The development of competitive edge is not led to by the linear adding of pollution prevention projects or by the adoption of certain environmental or social standards or certifications following the motto one size fits it all. Moreover, taking the XPS (company-specific production system) approach as a model for designing firm-tailored production systems [START_REF] Netland | Company-specific production systems and competitive advantage: a resource-based view on the Volvo Production System[END_REF], in the same way firms have to be capable to tailor their strategy to their system, structure, culture and product in order to reach be-yond low hanging fruits benefits. In this article, we propose a framework based on Axiomatic Design (AD) principles which can guide top management and decision-makers in operationalizing their firm's manufacturing strategy.

Background

Manufacturing strategy as a discipline was initiated ca. 40 years ago as it was recognized by US scholars that linking long-term decisions in manufacturing with business strategy may have a positive impact on business performance and create competitive advantage [START_REF] Demeter | Manufacturing strategy and competitiveness[END_REF]. Manufacturing strategy is the deployment and adjustment of manufacturing capabilities by patterns of decisions to efficiently and effectively exploit the manufacturing function by means of its resources for the purpose of maximizing the competitive advantage while maintaining consistency with higher corporate goals and strategies. Based on the pioneering work of Wickham Skinner, two different research streams have emerged in the field: manufacturing strategy content and manufacturing strategy process.

The research on content sets the substance of decisions and its junction into the focus and accompanying changes in the structure and infrastructure of a firm's manufacturing system. The research on process on the other hand enlightens the ways in how strategy is formulated and operationalized. Another popular term used by scholars here is also manufacturing strategy formulation or formation. [START_REF] Leong | Research in the process of manufacturing strategy[END_REF] introduced the predominant theoretical construct in manufacturing strategy content which is composed of competitive priorities, decision categories and manufacturing capabilities [START_REF] Leong | Research in the process of manufacturing strategy[END_REF]. Krajewski and Ritzman define competitive priorities as "the dimensions that a firm's production system must possess to support the demands of the markets that the firm wishes to compete in" [START_REF] Krajewski | Operations Management: Strategy and Analysis[END_REF]. The strategic choices made in manufacturing strategy can be classified into structural and infrastructural decision areas [START_REF] Hayes | Restoring our competitive edge: competing through manufacturing[END_REF]. Decisions in the structural area have influence on the physical resources, while infrastructural decision impact their relationship and activities within operations. Most decisions cannot be ordered along these archetypes, but have a hybrid effect. While competitive priorities are intended capabilities and future models of the strengths of the company, manufacturing capabilities are priorities put in practice or priorities put to work via decision categories [START_REF] Ward | Configurations of manufacturing strategy, business strategy, environment and structure[END_REF]. Manufacturing capabilities refer therefore to a firm's operations performance relative to its competitors, and the alignment of priorities and capabilities, i.e. creating good strategic fit, reflects an efficient and effective allocation of resources for capability development [START_REF] Schoenherr | The fit between capabilities and priorities and its impact on performance improvement: revisiting and extending the theory of production competence[END_REF].

The advancement within the manufacturing strategy process research is rived into macro and micro process views. The macro process view is characterized by a topdown approach, with the aim of emphasizing the link from business strategy to the functional manufacturing strategy, and the focus towards the consistency of hierarchy of strategies. On the other hand, the micro process view is made upon bottom-up or integrative views, emphasizes the link of the manufacturing strategy to the manufacturing function, and the translation function of competitive priorities into strategic manufacturing initiatives [START_REF] Kim | Operationalizing manufacturing strategyan exploratory study of constructs and linkage[END_REF].

The introduction of sustainability into research on operations management has gained large popularity among scholars. Kleindorfer et.al (2005) define sustainable OM as "the set of skills and concepts that allow a company to structure and manage its business processes to obtain a competitive return on its capital assets without sacrificing the legitimate needs of internal and external stakeholders and with due regard for the impact of its operations on people and the environment" [START_REF] Kleindorfer | Sustainable Operations Management[END_REF].

Sustainability in manufacturing strategy as a research endeavor has gained momentum. In the past years research was mainly empirically investigating the introduction of sustainability as a competitive priority in the corporate agenda. Most researchers have focused on the environmental pillar in their studies, while social pillar has received less attention. Findings are contradictory with no or little explanation for differentiations. While some studies refuse the hypothesis of sustainability as a competitive priority [START_REF] Thuerer | Competitive priorities of small manufacturers in Brazil[END_REF], others include it successfully in other manufacturing strategy constructs like the extended sandcone model [START_REF] Avella | Cumulative manufacturing capabilities: an extended model and new empirical evidence[END_REF]. While researchers have mainly focused on scientific inquiries on competitive priorities, the areas of decision categories or the entire spectrum within the manufacturing strategy process field are largely neglected. The aim of the paper is to fill this gap and develop a practical framework to incorporate sustainability into manufacturing strategy content.

Methodology

Past research in the field was largely empirically driven. Although this is needed and relevant, the theoretical, prescriptive, and modern perspective might be lost and leave innovative ideas and concepts aside.

The research endeavour takes over a pragmatist epistemological point of view in a prescriptive research setting. The research question is: how can firms translate the sustainability principle into their manufacturing strategy. Since a pattern or methodological guidance cannot be gathered from its complex environmental situation due to lack of empirical studies and lack of proven approaches, we decided to embed our research in a prescriptive research setting. It enables us to design an approach based on axiomatic instead of empirical reasoning. Through deductive reasoning we can apply and test the approach in a case setting, which in turn can serve later as inductive testing for empirical-based research.

This paper reports research in which axiomatic design methodology is used to model the strategy content of a sustainable manufacturing system which consist of functional requirements, design parameters and process variables. Axiomatic design (AD) as a design framework was created by Professor Suh of the Massachusetts Institute of Technology [START_REF] Suh | Axiomatic Design[END_REF]. Here, design is seen as the interplay between "what we want to achieve" and "how we want to achieve"; thus consistently differentiating between goals and means (or solutions). Typically the design process follows a five step procedure, i.e. identifying customer needs, problem definition (problem to solve for need satisfaction), solution(s) creation, analysis and optimization, solution fit. Using the Axiomatic Design approach by Suh, sustainability goals are purposely translated into functional requirements. The four main concepts around axiomatic design are domains, hierarchies, zigzagging and design axioms.

Typically there are four domains distinguished (see Figure 1). Left domains define what should be achieved while right domains defines how it should be achieved. The customer domain contains customer requirements, needs, expectations, specifications and anticipations. These are then translated into the functional domain as functional requirements (FR). Functional requirements are independent from each other and characterize the minimum solution to a given design problem. The design parameters (DP) are specifications within the solution in the physical domain that are chosen to specify the FRs. In the same way, process variables (PV) characterize the process that satisfies the DPs in the process domain. The third main concept of axiomatic design is zigzagging that describes the process of decomposing the design into hierarchies by alternating between pairs of domains. Hereby, starting from e.g. of the FR 1 of hierarchical layer a, the designer crosses domains and chooses design parameter 1 for the same hierarchical layer and then crosses again domains and goes back to the functional domain to define sub-level requirements of FR1.

The fourth concept is design axioms. Two axioms provide a basis for the best fit selections among different design scenarios. The Independence Axiom states that the independence of the FRs must be maintained. This means that in an acceptable design, a DP can be adjusted to satisfy its corresponding FR without affecting other FRs. And the Information Axiom states that the information content must be minimized. Of the alternative designs that fulfill the Independence Axiom the best design has the minimum content. It can be translated that simpler designs are more preferable to other designs.

The relationship between FRs and DPs is given as follows:

{𝐹𝑅} = [𝐴]{𝐷𝑃} (1)
A is the design matrix from which each element can be expressed as

𝐴 𝑖𝑗 = 𝜕𝐹𝑅 𝑖 /𝜕𝐷𝑃 𝑗 (2)
Each line of the vector stated in (1) can be written as

𝐹𝑅 𝑖 = ∑ 𝐴 𝑖𝑗 𝐷𝑃 𝑗 (3)
Similarly the relationship between DPs and PVs is described by a design matrix B as

{𝐷𝑃} = [𝐵]{𝑃𝑉} (4)
Hence, including ( 4) into (1), the FRs can be defined as

{𝐹𝑅} = [𝐴][𝐵]{𝑃𝑉} (5)
The relationship between FR-DP-PV through the design matrix A and B is important for the satisfaction of the independence axioms.

To satisfy the axiom designs have to be uncoupled or decoupled, while coupled designs infringe the axiom. A design is uncoupled if the design matrix is diagonal; and a design is decoupled if the design matrix is triangular. In a decoupled design the path dependency becomes critical. Coupled designs are inferior since it is impossible to adjust either DP without affecting other FRs. Hence, the construct of the desired FR can only achieved by a iterative trial-and-error procedure. In the following, a glance at sustainable manufacturing system decomposition is presented.

4

Axiomatic model of sustainable production systems

Viability as firm's objective

In the following we state that the major objective of a sustainable firm is not solely including environmental soundness and social solidarity into their corporate agenda. The major objective is to maximize the viability of the firm. Here, viability is defined according to the systems theory or cybernetics perspective, i.e. maintaining a separate existence. Viability means that a determined configuration of states can be maintained by the organization without time constraints. To emphasize this, from a cybernetic point of view viability is a typical metasystemic measure for the assessment of a system concerning its structural effectiveness. Viability in enterprise context means to maintain a change process due to its environment, its identity, and its primary activities and due to itself [START_REF] Beer | The Heart of Enterprise[END_REF].

First level decomposition

Based on the concept of viability, all organizations pursue it as their ultimate goal. Viability is the central focus of an organizations purpose and all activities within the organization should be allocated to this target. It is assumed that pursuing sustainability in the broader sense enables an organization to fulfill the viability principle in the best way. Figure 2 shows the top FRs, DPs and PVs as well as the FRs of the first level decomposition based on the viability principle for a manufacturing system pursuing sustainable strategy. The technology (FR1) as well as capacity (FR3) asserts emphasis on the product type, i.e. either high volume and undifferentiated or low volume and customer specific. Regarding the vertical integration (FR4) decision-makers ponder developing and producing parts on their own or buying them in. Vertical integration might not only apply to product parts, but to entire departments or product families. The size, capacity and location (FR2, FR3) of facilities are of utmost interest in the manufacturing strategy planning activities. While large factories support economies of scales and high volume production, smaller factories are easier to align to flexibility. The plant location (FR2) may be chosen due to different factors including location of raw material and component suppliers, location of customers, transportation and communication systems, qualification of workforce, legislation and taxes. These structural decisions are added by infrastructural decisions. Production and inventory planning as well as accordant control systems (FR6) are putting two major decisions in place, i.e. centralization and decentralization as well as pull or push systems of the factory. The organizational structure of factories can be based on normality, i.e. hierarchical structure and cost center orientation with clear procedures and low level of decision making, or on autonomy, i.e. viewing the factory as a profit center with enriched degrees of freedom for the middle managers and workforce in making decisions to solve operational problems. Human resource management (FR7) around manufacturing strategy involves areas like payment system, recruitment process, worker-to-job assignment, incentive policies and so on. Quality management (FR5) can be spread from the entire spectrum from merely statistical process control to an entire production philosophy. New product development (FR8) and performance measurement systems (FR9) finalize the infrastructural decision categories that are high level functional requirements for a sustainable manufacturing system. The second level decomposition for FR1-Technology is shown as an example (Figure 3). FR1 is decomposed in four sub-FRs. These sub-FRs are the main requirements that are needed to fulfill the primary FR1 objective. Extracted from an example it can be seen that FR11 and FR13 are tailored towards environmental soundness and FR12 and FR14 serve the competitive priorities cost as well as delivery. This decomposition should be done among all decision criteria. Furthermore, the decomposition should be performed in recursions until saturation and clarity of the objectives and the method to reach them is arrived. While in the second recursion the FRs are still quite abstract, following decompositions can define in detail the requirements, parameters and variables for a sustainable manufacturing system.

Summary and Outlook

The study presented in the paper developed a framework for the design of a sustainable manufacturing systems and the definition of a sustainable manufacturing strategy. The framework is based on Axiomatic Design principles and builds upon decision categories developed in manufacturing strategy. The research serves two major contributions. First, it presents a scientifically motivated hands-on approach for decision makers to define their firm's manufacturing strategy content. Second, it supports the neglected integration of product and production focus in manufacturing strategy which was largely disregarded in the field of operations management.

The research faces two major limitations. First, an application case is mandatory to manifest the value potential of the approach and should be done in future work. Second, other tools in strategic management like scenario technique or SWOT analysis etc. have not been yet considered, hence predictions how the AD approach can be used in combination with other established techniques is not clear.

Future research shall concentrate on overcoming the above stated limitations. Furthermore, and of utmost importance, is to bridge the gap between strategy definition and strategy implementation. While it is among scholars commonly accepted that manufacturing improvement programs are the way to operationalize manufacturing strategy and build up capabilities, it is theoretically and practically unclear how to translate a defined strategy into concrete actions without lining up certain programs and certifications, but heaving the manufacturing system beyond the low hanging fruits.

Fig. 1 .

 1 Fig. 1. Domain perspective in Axiomatic DesignHierarchies are the second main concept in axiomatic design. Hierarchies refer to design architecture of a model. Decomposition is a terminology often used in this junction and refers to the development of functional requirements and design parameters in the next lower level based upon the next higher level FRs and DPs. This process is iterated until concreteness for implementation is reached. At the end the model is composed of different levels of hierarchies.The third main concept of axiomatic design is zigzagging that describes the process of decomposing the design into hierarchies by alternating between pairs of domains. Hereby, starting from e.g. of the FR 1 of hierarchical layer a, the designer crosses domains and chooses design parameter 1 for the same hierarchical layer and then crosses again domains and goes back to the functional domain to define sub-level requirements of FR1.The fourth concept is design axioms. Two axioms provide a basis for the best fit selections among different design scenarios. The Independence Axiom states that the independence of the FRs must be maintained. This means that in an acceptable design, a DP can be adjusted to satisfy its corresponding FR without affecting other FRs. And the Information Axiom states that the information content must be minimized. Of the alternative designs that fulfill the Independence Axiom the best design has the minimum content. It can be translated that simpler designs are more preferable to other designs.The relationship between FRs and DPs is given as follows:

FR0Fig. 2 .

 2 Fig. 2. Organization's top level domain and first decomposition

FR11Fig. 3 .

 3 Fig. 3. Second level decomposition of FR1-Technology


Michael Schenk
email: michael.schenk@fir.rwth-aachen.de

Volker Stich
email: volker.stich@fir.rwth-aachen.de

Managing Supply Chain Disturbances -Review and Synthesis of Existing Contributions

Keywords: Supply Chain Management, Supply Chain Disturbances, Supply Chain Resilience, Supply Chain Event Management, Disturbance Management

Companies suffer the effects of disturbances in supply chains more than ever. Highly dynamical consumer demands and globalized supply chains often lead to unplanned supply chain events through to break-downs causing inability to supply. Consequently, concepts for an efficient supply chain disturbance management are needed, preferably with a real-time reaction to disturbance events. Academic literature comprises many investigations dealing with such approaches. In this paper we examine both a range of methodological contributions to design resilient supply chains and approaches for the flexible coordination of supply chain processes in case of disturbances. After summarizing the major contributions, we point out an approach for a real-time supply chain disturbance management with the target to achieve the required flexibility for coping with supply chain disturbances.

Introduction

The rising global cross-linking of production and sales regions increases steadily the complexity of supply chains. This leads to an increasing sensitivity to disturbances while in the meantime the requirements of the availability, the time of delivery and the security of supplies within the supply chain increases. [START_REF]Deloitte: Supply Chain Resilience: A Risk Intelligent approach to managing global supply chains[END_REF][START_REF]The Business Continuity Institute: Supply Chain Resilience 2011 -An international survey of more than 550 organizations from over 60 countries, which considers the causes and consequences of disruption, the techniques and approaches to identify key supply chains, and methods to gain assurance of resilience capability[END_REF][START_REF] Henke | Supply Chain Agility. Strategische Anpassungsfähigkeit im Supply Chain Management[END_REF] The complexity of this development affects not only strategic considerations of industrial companies, but is also a constant component of their daily business. The companies have to meet high requirements on all levels, from the supply chain design top-down to the supply chain execution. Especially the security of the infrastructure needs to be ensured and complex supply chain processes need to be feasible, despite the high specialisation within the supply chain partners, the low stock and time buffers and the information shortcoming between supply chain partners. The industrial companies currently show obvious deficiencies in managing disturbances within their supply chains. On the one hand the understanding of disturbances and their impacts on the supply chain as well as a suitable integration of a cross-company disturbance management are not developed. On the other hand the applied ICT systems cannot stand up to the increasing requirements, because their implemented set of actions isn't applicable in most cases to counteract occurring disturbances with the required excellence [START_REF] Rice | Building a Secure and Resilient Supply Network[END_REF].

In recent years the research has therefore generated different approaches to deal with the increasing impacts of supply chain disturbances [START_REF] Craighaid | The Severity of Supply Chain Disruptions: Design Characteristics and Mitigation Capabilities[END_REF][START_REF] Kiryazov | Netzwerkorientiertes Supply Chain Controlling und Risikomanagement[END_REF][START_REF] Manuj | Global supply chain risk management strategies[END_REF]. Also, first ICT based approaches like the supply chain event management are developed to reduce the vulnerability of supply chains by providing real-time information of disturbances.

In the next chapter, we will give an outline both of recent literature on the design of resilient supply chains and operative management approaches for dealing with disturbances in supply chains. In chapter three we will point out the deficits of the existing approaches and finally identify further need for research, including a first example.

2

Literature Review on Supply Chain Disturbances Management

Designing Resilient Supply Chains

The design of resilient supply chains is one prominent potential to compete with the growing market dynamics and the increasing vulnerability of global supply chains. But due to the recentness of this topic hardly any literature can be found that deals with this subject. According to [START_REF] Rice | Building a Secure and Resilient Supply Network[END_REF] and [START_REF] Sheffi | Building the Resilient Enterprise[END_REF] resilient supply chains possess the ability to compete with unpredicted disturbances and quickly regain their original state or move over to a new, improved state and to adapt to the new constraints. Therefore, [START_REF] Christopher | Building the Resilient Supply Chain[END_REF] defined four fundamental principles. First of all, the business processes need to be reengineered by taking into account both possible disturbances and the aim to reduce complexity. Further principles are the need for intense cooperation within the supply chain, high supply chain agility as well as the setup of a supply chain risk management culture.

[10] identified based on a literature review different management strategies to mitigate the impact of disturbances with all can be assigned to the above mentioned principles. [START_REF] Sheffi | Building a resilient Supply Chain[END_REF] highlighted in his work that from all assigned strategies, two strategies have the highest potential to achieve resilience. It's the raise of flexibility and the integration of redundancies by provision of additional capacities.

Following the strategy of flexibility [START_REF] Kristianto | A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path[END_REF] described in their approach a design method for a reconfigurable supply chain network by optimizing inventory allocation and transportation routing. [START_REF] Singh | Design of global supply chain network with operational risks[END_REF] also designed a model of a multi-stage global supply chain network including rating a set of risk factors. The model shows, that by an interechelon shifting of quantity flows, the overall cost of the supply chain can be minimized in the event of disturbances.

Even though all authors refer in their way to the four principles of [START_REF] Christopher | Building the Resilient Supply Chain[END_REF], some authors indicate that not all of the included strategies are generally adaptive as they have their specific downsides. The strategy of redundant capacities for example is accompanied by high cost and acts opposed to the lean thinking which many companies are following [START_REF] Sheffi | Building a resilient Supply Chain[END_REF]. On the other hand, the strategy to increase flexibility with the effect to react faster to disturbances comes along with significant lower downsides.

[14] developed a method for the evaluation and selection of the preferred strategy to increase the supply chain resilience as preceding to the implementation of any action a cost-benefit analysis is inevitable. [START_REF] Spiegler | A control engineering approach to the assessment of supply chain resilience[END_REF] also developed a method for evaluation based on a control engineering approach. Counter-intuitively their findings imply that optimum solutions for resilience do not create a system that is robust to disturbances affecting the lead time.

However, many fundamental elements of supply chain resilience, e.g. the links between risks and implications for supply chain management as well as their interconnection, are still unknown and there is still a lot of research that has to be done to close these knowledge gaps.

Increasing the Flexibility of Supply Chain Planning and Control Processes

As seen in the preceding chapter, based on the design of resilient supply chains, companies need to seek for new management methods to cope with disturbances on the planning and execution levels in existing supply chains with the target to increase their flexibility. The mayor part of research works on this topic, especially the older ones, is focusing on design models to support the flexibility management in a specific production unit or a particular company and is missing the aspects of cross-company processes between the supply chain partners. [START_REF] Meyer | Logistisches Störungsmanagement in kundenverbrauchsorientierten Wertschöpfungsketten[END_REF] gives a comprehensive overview on such approaches.

In the research project PROLOG ( [START_REF] Prolog | Handlungsanleitung zur Störungsprävention. Ergebnisdokumentation zum Forschungsprojekt PROLOG[END_REF]) concepts for prospective disturbances in supply chains were developed resulting in a guideline for the implementation of a preventive disturbance management by an early identification of internal and external disturbances. The approach is limited to guidelines for the project management of a individually performed disturbance management.

On the other hand [START_REF] Zeller | Möglichkeiten einer maschinellen Verknüpfung von Diagnose und Therapie beim Controlling von Liefernetzen. Teil 11: Diagnose und Therapie[END_REF] analyzed symptom patterns for disturbances in company networks and allocated adequate action propositions. The work, however, concentrates on the strategic to tactical level of the supply chain planning and control and describes many interdependencies only qualitatively. A coordination concept in the form of processes and information flows for the reaction on disturbances is also not subject of the work. Still, both approaches either don't or only marginally examine enterprise-wide interdependencies in supply chain planning associated to the disturbance management. Therefore the following research approaches explicitly aim to improve the coordination of supply chain planning and control tasks.

[19] developed an agent-based concept for cross-system production planning to increase the responsiveness in the supply chain. The focus of the work is the examination of the information exchange and the design of the various planning agents. However, interdependencies of logistical disturbances are not considered in detail.

The aim of the research project IPRONET [START_REF] Ipronet | Internetbasiertes Werkzeug zur unternehmensübergreifenden konfigurierbaren Unterstützung von produzierenden Unternehmen in Netzwerken[END_REF] was the development of an internet based tool for the cross company support of manufacturing companies in supply chains. However, the disturbance management is reduced to the early identification of capacity constraints by an increased demand or reduced facilities and the simulative calculation of action possibilities.

In the research alliance LINET, the aim of a cooperative requirement and capacity planning was pursued for the supply chains in the automotive industry [21]. Its core functionalities include the monitoring of bottleneck capacity, coordination of production programs, the simulation of capacity allocation plans as well as a workflow supported emergency management. The approach from [START_REF] Lloret | Cooperative Supply Chain Rescheduling: The Case of an Engine Supply Chain[END_REF] focused on a collaborative supply chain re-scheduling. It allows cooperative re-scheduling by the selection of the type of transport for sending components from one supply chain partner to another based on the target date and the costs to assemble the final product. Further, [START_REF] Daohai | Study on Supply Disruption Management of Supply Chain Based on Case-Based Reasoning[END_REF] designed in his work a case-based reasoning model for the selection of adequate actions to effectively deal with supply chain disturbances. Another mayor topic to increase the flexibility of supply chain and thus be able to react faster is the enhancement of transparency of information in supply chains [START_REF] Schönsleben | Stand und Entwicklungstendenzen in Europa[END_REF][START_REF] Heusler | Supply Chain Event Management -Grundlagen, Funktionen und potentielle Akteure[END_REF][START_REF] Nissen | Supply Chain Event Management[END_REF][START_REF] Ijioui | Supply Chain Event Management als strategisches Unternehmensführungskonzept[END_REF]. Existing Supply chain event management systems (SCEM systems) are therefore a good leverage point. Based on a target-performance comparison they detect disturbance events and support the monitoring, planning and control of supply chains [START_REF] Ijioui | Supply Chain Event Management als strategisches Unternehmensführungskonzept[END_REF]. Another model for evaluation and control of risks in supply chains regarding both cost and environmental (e.g. traffic, weather) influences can be developed by using available approaches e.g. the complex event processing. Therefore flows of events from the supply chain are aggregated and combined to monitor parameters in real-time [START_REF] Luckham | The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems[END_REF][START_REF] Buchmann | Complex Event Processing[END_REF].

Regarding existing supply chain software [START_REF] Nissen | Supply Chain Event Management[END_REF] underlined the innovation of supply chain event management systems in the functionalities of monitoring and notification as well as in the optimization of supply chain processes using long-term data evaluations. But in general nowadays available SCEM systems are still limited to monitor unplanned events and inform affected business units. To take measures like the replanning of supply chains processes conventional systems as ERP or APS systems are commonly used. SCEM systems still apply as mere pulsing devices which do not give any active decision support [START_REF] Bretzke | Vom Tracking & Tracing zum Supply Chain Event Management -aktueller Stand und Trends[END_REF]. They still lack both in the identification & management and the communication & coordination of disturbances. The limited consideration of logistical interdependences and the insufficient differentiation of disturbances lead to a deficient possibility to derive action propositions and lower the consistency from the identification of disturbances to the adjustment. [START_REF] O'leary | Supporting decisions in real-time enterprises: autonomic supply chain systems[END_REF] stated in his work the importance of incentives and trust among multiple supply chain partners. If supply chain partners lack of trust to each other, then even a basic integration and real-time data flows will be problematic. Also, the whole supply chain will suffer if partners have no incentives to orchestrate their technology with the others.

Creating a Real-Time Supply Chain Disturbance Management

The literature review gives an overview, how today's methods and solutions from the supply chain management encounter the challenges of an increasing vulnerability to supply chain disturbances. As the review on the design approaches for resilient supply chains has shown, one mayor potential lies in the strategy of more flexible supply chains including a faster reaction to disturbances and an intense cooperation between the supply chain partners. But as seen, the existing approaches for the operational disturbance management on the supply chain level are still insufficiently designed. In this area, on the one hand the disturbance management is not considered entirely with all its facets, and on the other hand the shown approaches do not focus the real-time reaction to disturbance events and their impacts on the supply chain.This problem is extended by the lack of a comprehensive ICT-based support by supply chain event management systems. Based on the preceding literature review on existing approaches and the summarization of arisen potentials, different requirements can be derived to achieve the target of a real-time supply chain disturbance management. Besides the basic requirements for a holistic consideration of the supply chain disturbance management, including the cross-company coordination between supply chain partners and an increased transparency by the availability of real-time information on disturbances in the supply chain, also aspects of a sociotechnical integration of human and technics, the recursive design of the system and the sufficient interconnection between the different planning and execution levels need to be ensured. As an example how to lever these potentials, the authors want to highlight the on-going research project SMART LOGISTIC GRIDS. The focus of the research project is the design of an ICT-based system to provide adequate action propositions to encounter disturbance events and consequently increase the efficiency and robustness of the supply chain. This requires a flawless integration of different actors along the supply chain, as well as an increased information transparency and availability of disturbance events. Focal issues are the development of a supply chain monitoring and control unit and the provision of global supply chain events by a cloud service. The information about supply chain events is provided by the cloud service, which gathers and aggregates it to complex events. In the supply chain control unit this information is compiled and visualized. The unit enables an optimized execution and control of the supply chain based on the aggregated real-time information of the supply chain. The system provides different, evaluated action propositions to optimize the supply chain both by economical and resource efficiency considerations. Thus additional transport costs due to problems in the supply chain can be reduced or entirely avoided and an increased deliver capacity of the supply chain can be ensured.

Conclusion

The increasing dynamics in consumer demand patterns and the vulnerability of globalized supply chains leads to an increased requirements for coping with supply chain disturbances. As the literature review shows, different works already approach the topic of designing resilient supply chains and managing disturbances in a supply chain. Further, existing ICT solutions like supply chain event management systems partly support to handle supply chain disturbances. But despite the amount of ongoing research and the flurry of supply chain improvement programs, the competitive advantage and distinguish characteristic of future supply chains will be their flexibility, in particular to react on supply chain disturbances in real-time. Therefore, realtime information, real-time coordination between supply chain partners, and optimized action propositions are needed. The presented research project Smart Logistic Grids focuses such an approach by developing an event cloud and a supply chain control unit. Furthermore, a closer collaboration between industry and research units is need, to develop, implement and evaluate effective actions to supply chain disturbances. Thus, best practices can be gathered and standards be developed.

Fig. 1 .

 1 Fig. 1. Target system of the research project Smart Logistic Grids

Table 1 .

 1 Overview of major contributions to the management of supply chain disturbances

Acknowledgement

This paper as well as the project SMART LOGISTIC GRIDS are funded by the Federal Ministry for Economic Affairs and Energy (BMWi; funding code: 19 G 13002C).


Nadine Kafa
email: n.kafa@iut.univ-paris8.fr

Yasmina Hani
email: y.hani@iut.univ-paris8.fr

Abederrahman El Mhamedi
email: a.elmhamedi@iut.univ-paris8.fr

A fuzzy multi criteria approach for evaluating sustainability performance of third -party reverse logistics providers

Keywords: Sustainable supply chain, reverse logistics, third-party provider selection, fuzzy AHP, fuzzy PROMETHEE

Due to the complexity and specificity of reverse logistics system, some organizations outsource all or part of the reverse logistics process to third -party provider (3PRLP). The selection of the most efficient 3PRLP is a crucial task in which it is important to take into account environmental and social criteria as well as economic criteria owing to economic interests, stakeholder pressures, and environmental legislations. The tie between all three aspects of sustainability in 3PRLP selection problem has been almost ignored. This research work deals with this issue and develops a new integrated approach for selecting the best sustainable 3PRLP. A hybrid multi-criteria making decision model is structured to assign the priority weights of decision criteria using fuzzy analytic hierarchy process (FAHP) and to get the final ranking of providers using fuzzy preference ranking organization method for enrichment evaluation (F-PROMETHEE). A numerical example is also presented to illustrate the proposed approach.

Introduction

Reverse logistics (RL) is defined as a set of elements (collection, sorting, treatment, information system and distribution system) [START_REF] Riopel | Ing nierie et gestion de la logistique inverse vers des r seau durables[END_REF] deals with product returns in order to retrieve sustainable values. In today's competitive environment, different organizations take into consideration the management of reverse flows in their supply chain system. According to Kannan et al. [START_REF] Kannan | A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider[END_REF] dealing with returns is a complicated issue because of the need of specialized infrastructures, the lack of experience, and considerable uncertainties regarding to delivery, quality and quantity of the returned products. Therefore, many organizations decide to outsource reverse logistics function to third-party providers (3PRLPs). On the other hand, the introduction of corporate social responsibility principles and sustainability issues in RL system is a means of developing a complete sustainable performance model [START_REF] Nikolaou | A reverse logistics social responsibility evaluation framework based on the triple bottom line approach[END_REF].

Several studies have discussed sustainability issues and highlighted the importance of achieving the triple bottom line of economic, social, and environmental goals in conventional supply chain management [START_REF] Baumann | Modèles d' valuation des performances conomique, environnementale et sociale dans les chaînes logistiques[END_REF], green supply chain management [START_REF] Kafa | Sustainability Performance Measurement for Green Supply Chain Management[END_REF], reverse logistics [START_REF] Nikolaou | A reverse logistics social responsibility evaluation framework based on the triple bottom line approach[END_REF], and supplier selection [START_REF] Govindan | A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach[END_REF] , while researches consider all three dimensions of sustainable development in 3PRLP selection are rather limited till now. Within supply chain management, the implementation of sustainable initiatives and green practices is difficult without cooperation with all partners in the network. Therefore, selecting right 3PRLPs based on integrated sustainability criteria can assist organizations to improve their sustainability performance. Some of the 3PRLP selection related papers have started paying more attention to this issue. Wang and Zhu [START_REF] Wang | Research on Third-party Reverse Logistics Provider Selection Based on Fuzzy Clustering in Perspective of Low-carbon Economy[END_REF] proposed a model to solve the problem of selecting an appropriate 3PRLP in environmental viewpoint of low-carbon economy. Tajik et al. [START_REF] Tajik | A Hybrid Fuzzy MC M Approach for Sustainable Third-Party Reverse Logistics Provider Selection[END_REF]developed a hybrid fuzzy AHP-TOPSIS approach for evaluating sustainable 3PRLPs. The framework they proposed is highly stylized. However, there is a real need for research on 3PRLPs selection problem with sustainability concerns because the majority of the present models focuses on economic and slightly on environmental factors. This paper is among the first research works that shed light on this issue. Hence, the main contribution of this paper is to propose a new model for multi-criteria 3PRLP selection problem based on fuzzy AHP-PROMETHEE approach involving sustainability criteria.

The next section presents the hybrid fuzzy AHP-PROMETHEE approach to assess sustainability performance of 3PRLPs. Third section presents an illustrative example and results analysis. Finally, the paper ends with conclusions.

Proposed model for 3PRL provider selection

Due to 3PRLP selection problem is a complicated MCDM problem and time consuming assignment, a clear process should be required to resolve it. The proposed model is structured to allocate the priority weights of decision criteria by resolving FAHP algorithm and to rank 3PRL providers by resolving F-PROMETHEE algorithm, as illustrated in Fig. [START_REF] Riopel | Ing nierie et gestion de la logistique inverse vers des r seau durables[END_REF].

Sustainability decision criteria

The key criteria for selecting suitable 3PRLP can typically include cost, quality, and financial performance. Furthermore, indexes of low-carbon and social indicators can play a crucial role in selecting 3PRLPs as the need to coordinate and integrate all the business functions with sustainability considerations. Reverse logistics outsourcing should clearly be economically, environmentally and socially applied. The sustainability criteria and sub-criteria have been defined in detail based on specific literature review in the area of 3PRL provider selection by Kafa et al. [START_REF] Kafa | Sustainable approach for third-party reverse logistics provider selection[END_REF].

Fuzzy AHP algorithm

The basic concept of AHP method [START_REF] Saaty | The analytic hierarchy process[END_REF] is to model a general decision problem as a hierarchical structure including sub-problems that can be easily evaluated in order to determine the priorities via pair-wise comparison of the elements at each level of the decision hierarchy. This method has gained popularity observing the amount of studies that have utilized it including supply chain management. As providers' selection criteria always contain ambiguity and diversity of meaning, the fuzzy AHP algorithm proposed by Kwong and Bai [START_REF] Kwong | A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment[END_REF] was employed for estimating the weights of selected criteria as follows.

Step 1: Construct the fuzzy judgment matrices (FCMs) for each level. Fuzzy theory set [START_REF] Zadeh | Fuzzy sets[END_REF] is incorporated with pair-wise comparison in AHP to get more benefit of human reasoning that is approximate rather than precise. The decision maker (DM) preference is represented in form of fuzzy pair-wise comparison matrix (FCM) using the linguistic fuzzy scale represents in terms of triangular fuzzy numbers (TFN) (equally= , moderately= , strongly= , very strongly= , extremely= ).

                1 .... ~.... ..... 1 ~..... 1 3 2 1 2 23 21 1 13 12 ) ( ~n n n n n ij a a a a a a a a a a FCM A                             j i 9 , 7 , 5 , 3 , 1 ~ j i 9 , 7 , 5 , 3 , 1 ~ j i 1 1 1 1 1 1 : ij a
Step 2: Determine the priorities of different decision elements. The prioritization of the elements of each matrix is done by solving fuzzy eigenvalues and eigenvectors. A fuzzy eigenvalue  ~is a fuzzy number solution to:

x x A   (1)
:

x ~is a non-zero n  1 fuzzy eigenvector containing fuzzy numbers.

The interval arithmetic and the α-cuts are introduced to perform fuzzy multiplication and addition. By defining the confidence level α in order to integrate the decision maker (DM) confidence over the judgments [START_REF] Lee | Application of modified fuzzy ahp method to analyze bolting sequence of structural joints[END_REF], the TFN can be illustrated as:

    c b c a a b c a TFN             ) ( , ) ( ,   1 , 0   (2)
For example, with respect to the level  , the lower limit and upper limit of the fuzzy numbers and , can be obtained by applying equation [START_REF] Kannan | A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider[END_REF].

 3 ~ = [1 + 2α, 5 -2α], 1 3 ~  = [1/ (5 -2α), 1/ (1 + 2α)]
The degree of satisfaction for the judgment matrix à is estimated from the DM by the index of optimism. The larger value of the index  is the higher degree of optimism that is defined as a linear convex combination [START_REF] Lee | Application of modified fuzzy ahp method to analyze bolting sequence of structural joints[END_REF]:

  0,1 ) 1 ( ~            ijl iju ij a a a (3)
The eigenvector x ~ is calculated by fixing  and substituting the maximal eigenvalue λmax into the equation (1).

Step 3: Calculate the consistency ratio of a matrix à using the mathematical formula CR= CI/RI as explained by Saaty [START_REF] Saaty | The analytic hierarchy process[END_REF]. A CR of 10% or less is acceptable.

Step 4: Normalize weight vector in order to determine the local and total importance weights (Wi).

Following the above explained steps, the MATLAB package is used to calculate the priority weights of the main aspects, the criteria, and the sub -criteria, (see Table 1.) and then the results are analyzed in the third section.

2.1

Fuzzy PROMETHEE algorithm

The preference ranking organization method for enrichment evaluation (PROMETHEE) is one of the conventional simple outranking methods to resolve MCDM problems which is applied in various areas of research. This method was developed by Brans and Vincke [START_REF] Brans | A preference ranking organization method: the PROMETHEE method for MCDM[END_REF] based on the exploitation of a valued outranking relation via pair-wise comparisons between the alternatives regarding different criteria. The evaluation table that shows the performance level of each potential alternative for each criterion is required as the first point to apply PROMETHEE method. In this paper, all alternatives (3PRLPs) are evaluated using linguistic scale with corresponding fuzzy numbers (TFN) [very good= [START_REF] Wang | Research on Third-party Reverse Logistics Provider Selection Based on Fuzzy Clustering in Perspective of Low-carbon Economy[END_REF][START_REF] Wang | Research on Third-party Reverse Logistics Provider Selection Based on Fuzzy Clustering in Perspective of Low-carbon Economy[END_REF][START_REF] Kafa | Sustainable approach for third-party reverse logistics provider selection[END_REF], good= [START_REF] Kafa | Sustainability Performance Measurement for Green Supply Chain Management[END_REF][START_REF] Wang | Research on Third-party Reverse Logistics Provider Selection Based on Fuzzy Clustering in Perspective of Low-carbon Economy[END_REF][START_REF] Kafa | Sustainable approach for third-party reverse logistics provider selection[END_REF], fair =(3,5,7), poor = (1,3,5), very poor= [START_REF] Riopel | Ing nierie et gestion de la logistique inverse vers des r seau durables[END_REF][START_REF] Riopel | Ing nierie et gestion de la logistique inverse vers des r seau durables[END_REF][START_REF] Nikolaou | A reverse logistics social responsibility evaluation framework based on the triple bottom line approach[END_REF]].The defuzzification of TFNs is done using graded mean integration representation (GMIR) method, proposed by Chen and Hsieh [START_REF] Chen | Representation, ranking, distance, and similarity of L-R type fuzzy number and application[END_REF].Where TFN= (a, b, c) , the GMIR R(TFN) of TFN is:

) 4 ( 6 / 1 ) ( b a c TFN R    (4)
Additional information on priority weights of the criteria is also required which is done using FAHP method in this study, as PROMETHEE method cannot distribute weights to the criteria. The various steps of PROMETHEE can be outlined briefly as follows:

Step 1: Calculate preference function of alternative (a) with regard to alternative (b) in the set of "A" alternatives for each sub-criterion:

    1 , 0 ) , ( P ) , ( ) , ( i    p a b a d f p a P i i i (5)
Where ) , ( b a d i is the difference between the evaluation of "a" and "b" on ith subcriterion. Several basic preference functions are explained by Brans and Vincke [START_REF] Brans | A preference ranking organization method: the PROMETHEE method for MCDM[END_REF] like V-criterion, Gaussian criterion, and U-criterion. In this study, the usual function has been selected which is mostly used with qualitative criteria [START_REF] Gupta | Selection of logistic service provider using fuzzy PROMETHEE for a cement industry[END_REF].

Step 2: Calculate outgoing flow and incoming flow for each alternative using equation [START_REF] Govindan | A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach[END_REF] and equation [START_REF] Wang | Research on Third-party Reverse Logistics Provider Selection Based on Fuzzy Clustering in Perspective of Low-carbon Economy[END_REF] for ranking the alternatives by a partial preorder "PROMETHEE I" technique that introduces the incomparability between alternatives. Then calculate net flow using equation [START_REF] Tajik | A Hybrid Fuzzy MC M Approach for Sustainable Third-Party Reverse Logistics Provider Selection[END_REF] for ranking the alternatives by a total preorder "PROMETHEE II" technique.

  

    A x i i i i x a P W A a ) , ( ) 1 /( 1 ) ( 1  (6)        A x i i i i a x P W A a ) , ( ) 1 /( 1 ) ( 1  (7) ) ( ) ( ) ( a a a        (8)
The Visual PROMETHEE software includes the basic of PROMETHEE method is used to calculate outranking flows and rank the alternatives.

Application and discussion of results

The problem discussed here is related to a manufacturing company wants to implement reverse logistics activities by outsourcing them to 3PRLP. The company desires to consider all the possible key factors which can affect the efficiency of reverse logistics functions. Furthermore, it is important to consider social and environmental as well as economic attributes in 3PRLP selection process in order to achieve sustainable competitive advantage. Currently, the company has four alternatives, namely RLP₁, RLP₂, RLP₃ and RLP₄. To select the right one, the proposed model is applied. The relative importance of each pair of selected criteria is prioritized after asking the global logistics manager as decision maker (DM) in the company concerned to answer a questionnaire including all possible pair-wise comparison. The -cuts fuzzy comparison matrices for all level were obtained by fixing =0.5 and =0.5. Some revisions of judgment with the DM were necessary as some matrices consistency ratios exceeded 0.1. Based on results shown in Table 1., among of all three key aspects the economic factor is the most significant one in selecting a 3PRL provider for this case company as it has the highest value of priority weights W=0.64.Furthermore, under "Economic" aspect, "Overall costs" was considered the most important criterion with the total weight of 0.3267. Then the 3PRLPs should improve the price besides the other elements. "Green level" was determined as the most important sub-criteria under environmental aspect with the total weight of 0.1593. Consequently, the 3PRL providers would be better to achieve reverse logistics activities with minimum environmental impact to surpass their competitors. Moreover, in the social aspect "Customer satisfaction" is the most significant subcriterion that has to be considered by 3PRL providers.

Then the 3PRL providers are evaluated regarding each sub-criterion in order to construct the evaluation table. By using the PROMETHEE application software "Visual PROMETHEE 1.4", the outranking flows of 3PRL providers were obtained, as well as a suitable provider selected. The alternative 3PRLP₁ and 3PRLP₄ are incomparable as per PROMETHEE I but 3PRLP₁ is superior to 3PRLP₄ according to PROMETHEE II that gives the ranking in the preference order of RLP₃≻ RLP₂≻ RLP₁ ≻RLP₄ as shown in Fig. 2.

PROMETHEE I Partial Ranking PROMETHEE II Complete Ranking

Fig. 2. PROMETHEE Ranking

The proposed approach has an advantage regarding the time and the effort of selection process compared to the existing ones.

Conclusions

This paper proposes a new integrated sustainable approach for selecting 3PRLP using AHP and PROMETHEE methods under fuzzy environment. The proposed model provides guidelines to the DM and the results obtained can help not only to select the best sustainable 3PRLP, but also to understand the current state of practice in 3PRLP selection process in the company. In our future research, this will be followed by GAIA plane which provides a descriptive complement vision. Sensitivity analysis of If-what scenarios will be also carried to analyze the impact of changing the criteria weights on alternatives' ranking. Furthermore, the proposed approach can be illustrated by other case study using other MCDM methods like TOPSIS, VIKOR or hybrid methods and the obtained results can be compared with each other in future research.

Fig. 1 .

 1 Fig. 1. Proposed fuzzy AHP-PROMETHEE model for selecting the best 3PRL provider

Table 1 .

 1 The priority weights of decision criteria

	Aspects	Crite-	Local	Total	Sub-	Local	Total	Consistency
	Weights	ria	weights	weights	Criteria	weights	weights
	ECO (0,64)	Eco₁ 0,5076	0,3267	R₁ R₂	0,5858 0,4142	0,1914 0,1353	 = 2,06 max CR= 0.00
		Eco₂ 0,4014	0,2583	R₃ R₄ R₅	0,1159 0,5294 0,3547	0,0299 0,1368 0,0916	 = 3.099 max CR= 0.086
		Eco₃ 0,091	0,0585	R₆ R₇ R₈	0,2156 0,6436 0,1409	0,0126 0,0377 0,0082	 =3.099 max CR=0,09
	ENV (0,22)	Env₁ 0,7387	0,1593	R₉ R₁₀	0,7387 0,7387	0,1176 0,0244	 =3.061 max CR=0.053
					R₁₁	0,1081	0,0172
		Env₂ 0,1532	0,0330	R₁₂ R₁₃	0,5092 0,0809	0,0168 0,0027	 = 4.21 max CR= 0.078
					R₁₄	0,2226	0,0074
					R₁₅	0,1872	0,0062
		Env₃ 0,1081	0,0233	R₁₆ R₁₇ R₁₈	0,2654 0,5580 0,1200	0,0062 0,0130 0,0028	 =4.21 max CR=0.078
					R₁₉	0,0566	0,0013
	SOC (0,14)	Soc₁	0,7388	0,1041	R₂₀ R₂₁ R₂₂	0,0769 0,5400 0,3831	0,0080 0,0562 0,0399	 =3.061 max CR=0.053
		Soc₂	0,2612	0,0368	R₂₃ R₂₄	0,8994 0,1006	0,0331 0,0037	 = 2,006 max CR= 0.00


Young-Woo Kim
email: ywkim@mailab.snu.ac.kr

Jinwoo Park

A Lifecycle Data Management System Based on RFID Technology of EPC Class1 Gen2 v2

Keywords: Remanufacturing, Data System, Radio Frequency Identification, Closed-loop Supply Chain Management, EPCC1G2 (EPC Class 1 Generation 2)

To cope with pollution, exhaustion of resources and new regulations extending producer responsibility, the original equipment manufacturers are working hard towards an efficient solution that solves those problems. We propose an RFID (Radio Frequency IDentification) based information system which incorporates large user memory banks of EPCC1G2 v2 standard to follow up lifecycle information about the product. We use hierarchical data models to minimize storage requirement while storing all information about the whole product structure including subassembly and end components. Later we can extract the information to utilize maintenance histories and chemical structure of each component. We also consider applicability of our system in XML hierarchical data structure for the possibility of internet usage.

Introduction

As time goes on, environmental problems such as climate change, destruction of ozone layer and depletion of natural resources are getting worse. For the environmental problems, several movements for saving the environments have been taken place both globally and locally. From a standpoint of manufacturing, product recycling is the way to take part in the environmental movements.

Also the development of brand-new materials as a substitute for natural resources could be the solution for preventing the depletion of natural resources, but it couldn't substitute perfectly. So collecting the products in end-of-life (EOL) phase and recovering the returned products as resources could reduce virgin sourcing obviously. Then the product recycling is considered the most active solution.

Product recycling could give an environmental advantage of extending lifespan of natural resources minded before, as well as an economic advantage from reducing the cost caused by virgin sourcing.

But when the manufacturers deal with a problem of product recycling, quality of returned product may vary significantly according to the age or history and environ-ment of usage. Nowadays many products consist of many components and subassemblies. But they may have all different ages and expected lives. For example, a new component whose expected life is 20 years can be used more than once if it was used as a maintenance part for a product whose remaining expected life is, say, 5 years. In that sense if the life history of components and products are not available, recyclable components in good condition may be disposed after being recycled only once.

According to [START_REF] Fleischmann | Quantitative Models for Reverse Logistics[END_REF] and [START_REF] Thierry | Strategic Issues in Product Recovery Management[END_REF], there are uncertainties in terms of timing, quantity and quality of product return flows [START_REF] Fleischmann | Quantitative Models for Reverse Logistics[END_REF] [START_REF] Thierry | Strategic Issues in Product Recovery Management[END_REF]. And [START_REF] Kulkarni | Networked RFID Systems in Product Recovery Management[END_REF] describes the importance of information that would be helpful to handle the product disposition decisions on the most appropriate recovery option according to the condition of the returned productsthe product can be reused as is, reused after remanufacturing, recycled to recover its material contents or incinerated for thermal energy recoveryand product recovery decisions by inventory visibility [START_REF] Kulkarni | Networked RFID Systems in Product Recovery Management[END_REF].

To handle the uncertainty problems, we propose a framework for the lifecycle data management system which gives a unique identifier to as many components and subassemblies as, and tracks historical events such as repair logs and useful data to solve the product disposition decision by tagging in chemical composition information.

To that end, we focus on Electronic Product Code Class1 Gen2 (EPCC1G2) v2 RFID technology because this new standard is shows the way to be adopted by all standard bodies including ISO and if offers a large user bank memory [START_REF] Kulkarni | Networked RFID Systems in Product Recovery Management[END_REF].

A Framework for the Lifecycle Data Management System

RFID has been considered as an enabler to launch the lifecycle data management system in many researches. [START_REF] Bajic | Auto-ID Mobile Information System for Vehicle Life Cycle Data Management[END_REF] concerned the use of automatic identification devices (Auto-ID tags) as permanent escort memories associated to the car during its whole life cycle. These memories provide an integration frame for vehicle lifecycle information, which are necessary to support the manufacturing, aftersales services and recycling processes [START_REF] Bajic | Auto-ID Mobile Information System for Vehicle Life Cycle Data Management[END_REF]. [START_REF] Jun | A Framework for RFID Applications is Product Lifecycle Management[END_REF] proposed an overall framework for RFID applications in product lifecycle management with a Product Embedded Information Device (PEID) [START_REF] Jun | A Framework for RFID Applications is Product Lifecycle Management[END_REF]. And [START_REF] Parlikad | RFID-based Product Information in End-of-Life Decision Making[END_REF] showed qualitatively that the availability of product information has a positive impact on product recovery decisions, and discuss how RFID-based product identification technologies can be employed to provide the necessary information [START_REF] Parlikad | RFID-based Product Information in End-of-Life Decision Making[END_REF].

RFID also could help operating an efficient supply network. It promises to eliminate manual inventory counting, warehouse mis-picking, and order-numbering mistakes by providing precise data on product location, product characteristics, and product inventory levels [START_REF] Niederman | Examining RFID Applications in Supply Chain Management[END_REF]. Also RFID traceability gives several advantages for enterprises to effectively handle their daily operations with distribution transparency [START_REF] Lee | RFID-based traceability in the supply chain[END_REF]. Networked RFID systems can provide an automated and efficient approach for capturing and delivering complete item level product information in an accurate and timely manner thereby making it possible for high level decisions and process improvements during product recovery stages [START_REF] Kulkarni | Networked RFID Systems in Product Recovery Management[END_REF].

] .

Fig. 1. Events occurred over whole lifecycle

Every data over the lifecycle as shown in Fig. 1 can be stored by combining external databases and the RFID tags attached to a product. The external databases consist of information about every product produced by Original Equipment Manufacturers (OEMs) and every component the OEM produces.

At the beginning-of-life (BOL) phase, products and its components acquire their own identifiers according to the data standard claimed by EPCglobal. EPC is a wellknown universal identification system for any physical object and it is considered as the most important data standard for supply chain applications. So it is widely used in informations systems that need to track or otherwise refer to physical objects with RFID tags.

A product could fail during its MOL (middle-of-life) phase. Product failure is caused by single or multiple component failures. At this point, a product user should decide to repair/substitute failed parts or dispose the product. If the user decide to repair the failed component and use the product continuously, the failed product still remains in the MOL phase. Information about the failed component should be written to the history log in the RFID tag attached to the repaired component. Also the product may still enter again into the MOL phase if the user decide to change failed parts to new ones. For this substitution case, only the failed component enter into EOL phase and collected by a 3rd party reprocessing facility or the OEM who is responsible for extended producer responsibility (EPR). EPR is the preventive conceptual strategy to save environment that was quoted first in the report of Swedish government [START_REF] Lindhqvist | Model for Extended Producer Responsibility: In Ministry of the Environment, From the Cradle to the Gravesix studies of the environmental impacts of products, 7-44[END_REF] and EPR claims that every OEM takes responsibility to collect, recover, and reuse obsolete products and dispose on their own [START_REF] Lindhqvist | Extended Producer Responsibility in Cleaner Production[END_REF].

For the final option to cope with product failure, the user could decide to dispose the failed product if the repair cost is not worth by repairing or changing the failed component(s).

Failed components itself or components from failed products are collected and they could be recycled as follows.

 Reused after treatment  Recovered as resources after dismantle, shredding, and additional treatments  Recovered as thermal energy by incineration A reprocessing facility should make a recycling decisions among the various options described above by considering visual conditions, expert opinions and history logbooks in the attached tag.

All the returned products and components are came into the remanufacturing shop floor and booked into the inventory database of the OEM. Once the returned products and components are booked in, a preliminary sorting is performed to filter out components that do not apparently have much market value. In this step, automated identification and history logs of returned components about repair logs could be helpful to the experts who filter out the returned components in a bad condition.

Fig. 2. Reverse process on the remanufacturing shop floor

Also the OEM can calculate environmental and economical effect both caused by different option taken. It is helpful to make more efficient decision while obeying the environmental regulations.

Also after the recovery process ended, results of recovery option actually taken by OEM's decision will be stored in a external database. From an administrative point of view, it could be an obvious clue for penalties incurred to OEMs who don't obey the regulations or for incentives to OEMs showing excellent recycling performance.

3

Proposed System for Applying EPCC1G2 v2

EPCC1G2 is the standard working framework proposed by GS1 in 2006 and is widely used for identification in the ultrahigh frequency (UHF) band. It has been a truly unique international protocol with faster read speed and more powerful security. Table 1. Difference between Gen1 and Gen2 [START_REF]EPC Radio-Frequency Identity Protocols Generation-2 UHF RFID Version 2[END_REF] And the latest released air interface protocol, EPCC1G2v2, is an answer by GS1 to the requirements of the EPCglobal user community. The most impressive feature is enhanced user memory for alteration electronic article surveillance and supplementary encodings such as maintenance logging [START_REF] Martin | Efficient ASIC Implementation and Analysis of Two EPC-C1G2 RFID Authentication Protocols[END_REF]. Therefore, EPCC1G2v2 technology seems to be very appropriate for sub-level tagging and launching of the proposed system.

We will discuss a data standard which specifies what is contained in the memory portion of the RFID tags, as well as the format. EPCC1G2 standard, also known as ISO18000-6C specifies the protocols that are used for communication between tags and readers as well as data structure requirements which are compatible with the EPC. Table 2. GRAI-170 EPC scheme [START_REF]EPC Radio-Frequency Identity Protocols Generation-2 UHF RFID Version 2[END_REF] EPCglobal officially announced several data standards for different usage. Using a data standard in existence could pursue interoperability then we could expect enhancing applicability for worldwide usage. We focus on the GRAI (Global Returnable Asset Identifier) standard which is typically used for a specific returnable asset such as reusable shipping containers, pallets because we consider the EOL products and components as resources. Then we select the GRAI-170 EPC tag data standard because memory space of 170 bits seems to be enough space to assign every product and component. Basically we could identify at most 2 112 individual components with same type by using the GRAI-170 EPC scheme. A company that use GRAI applications could take advantage of identifying the returnable asset ownership for the life of the asset wherever it goes, facilitating cross company asset management tracking, guarding against loss [START_REF]GS1, GS1 Identification Key Series -GRAI[END_REF]. And also the standard scheme could be adjustable for memory portion by setting different partition value to reflect characteristics of target product's BOM. If the number of companies associated with a target industry is relatively low and the number of components composed in products is relatively high because the products manufactured are somewhat complex, we could assign smaller space to company prefix while assign larger space to asset type. We develop a data model taking into account of both the IMS(Information Management System) structure of the old IBM database technology [START_REF] Date | An Introduction to Database Systems Volume I 5 th Edition[END_REF] and the XML (eXtensive Markup Language) hierarchical data model for encoding user memory space. XML provides information about the structure and meaning of the data in the Web pages rather than just specifying how the Web pages are formatted for displaying on the screen. So XML has emerged as the standard for structuring and exchanging data over the Web. The XML could represent hierarchical data by possessing relatively smaller space then it could be a solution for tag sizing and reflecting perfectly modular structure because a bill-of-material of any modular product takes the form of hierarchical structure.

Even if history logs and instructions used in recovery process don't have any data structure, XML hierarchical data structure could be a good encoding scheme because it supports writing semi-structured documents. However, it seems to perform better if the data structure for history logs and instructions are well-defined such as the WEEE-vector [START_REF] Luttropp | Improved Recycling with Lifecycle Information Tagged to the Product[END_REF]. The hierarchical model could be transformed tree structure into graph E-R (Entity-Relationship) model if the referential integrity constraint is satisfied. Then we could simply mapping of the hierarchical structure to popular relational model.

Conclusion

We propose the RFID-based modular lifecycle data management system with component-level RF tagging to track and manage event log data of products and its components over whole lifecycle. Obtained lifecycle data could handle the quality uncertainty problem then also it could be helpful to take the most appropriate recovery option to maximize resource utilization by multiple recovery. And it could relieve workload and reduce cost of sorting process because the incremental cost of each scan is very low by automated identification.

From an manufacturers point of view, the optimal production planning with considering remanufactured components could be enabled by increasing inventory visibility. And the proposed system could help environmental administrators by playing the role of correct monitoring system from an administration point of view because real time data gathered couldn't be manipulated.

We consider the advantages when the EPCC1G2v2 standard protocol is applied to the reverse logistics process. EPCC1G2v2 promises more space to store user-defined data in user memory bank. If useful information is provided during recycling process on the shop floor, the workers involved in pre-sorting and grading of returned components could use the information to make the most appropriate decision in order to maximize resource utilization about recovery options immediately without external data access. Also we examine the potentiality of using GRAI-170 EPC scheme as the data standard. Its multidimensional encoding scheme with filter value, partition value and asset type promises enough space to tag every component in the target industry.

Also we introduce the XML hierarchical data model as the solution for RF tag data encoding. It would save the memory space by minimizing memory possession that is the main advantage of using hierarchical structure. It also easily transformed into relational data structure widely used.

Obeying current EPC standards promises great interoperability and we hope this work could be the first step to building a centralized monitoring/management system with EPC discovery service.

Fig. 3 .

 3 Fig. 3. Conceptual design of EPCC1G2v2 tags

Fig. 4 .

 4 Fig. 4. Hierarchical data representation

And structuring of history logs and instructions should be followed up to make perfect structured data-centric XML documents. It would be found out in our following working paper.


Hironori Hibino
email: hibino@rs.tus.ac.jp

Toru Sakuma

Makoto Yamaguchi

Simulation for Sustainable Manufacturing System Considering Productivity and Energy Consumption

Keywords: Sustainable Manufacturing System, Simulation, Productivity, Energy Consumption, Facility State Transition

Managing energy consumption of manufacturing systems has become immediately necessary because electricity consumption has become severely restricted in Japan after the Tohoku earthquake on March 11, 2011. Moreover the energy conservation law has been revised in Japan in April, 2010. This law requires management of the energy consumption and reinforces the regulation to reduce more than one percentage of the previous year's energy consumption per unit of production throughput. Manufacturing system simulations to evaluate productivity have often been used when designing and improving sustainable manufacturing systems. However, manufacturing system simulations to evaluate energy consumption per unit of production throughput and the amount of energy consumption have not been proposed. The purpose of our research is to establish a simulation environment for sustainable manufacturing systems considering the productivity and energy consumption. We also propose an implementation for the simulation environment. A case study for a middle-scale semiconductor manufacturing line is carried out to confirm the efficiency of our proposed simulation environment.

Introduction

Social interest in environmental evaluation, which originates the awareness of global warming and the finite nature of natural resources, has been rising. In industries, finding and evaluating environmental monitoring items in manufacturing systems has become equally important. For example, after the Tohoku earthquake on March 11, 2011, managing energy consumption of the manufacturing systems has become immediately necessary because electricity consumption has become severely restricted in Japan [START_REF]Restriction for electricity consumption[END_REF]. Moreover the energy conservation law has been revised in Japan in April, 2010. This law requires management of energy consumption and reinforces the regulation to reduce more than one percentage of the previous year's energy consumption per unit of production throughput [START_REF]The Energy Conservation Center Japan[END_REF].

On the other hand, industries have also paid attention to increase their productivity [START_REF] Hibino | Emulation in Manufacturing Engineering Processes[END_REF]. Recent manufacturing systems consist of various sub-systems to increase productivity, and each sub-system is complexly related to other sub-systems [START_REF] Hibino | Emulation in Manufacturing Engineering Processes[END_REF].

To cope with these demands, there are great expectations for designing and improving sustainable manufacturing systems with considering both the environmental and productivity aspects [START_REF] Hibino | MSTC Idea Factory, Manufacturing System Evaluation Method Considering Green Factor Using Simulation[END_REF]. There are six main requirements to design and improve the environmental aspect of the sustainable manufacturing systems [START_REF] Hibino | MSTC Idea Factory, Manufacturing System Evaluation Method Considering Green Factor Using Simulation[END_REF]:

1) Establish the prior evaluation of the environmental aspect in a sustainable manufacturing system design and implementation stage. 2) Define the environmental evaluation items.

3) Establish the measuring scale of prior environmental evaluation. 4) Define the data items that information systems must deal with. 5) Establish management methods that take the environmental aspect into account. 6) Enact an international standard.

In this paper, we focuses on requirement 1) of the above list. Especially we deal with the energy consumption as the environmental aspect. Manufacturing system simulation is often used to evaluate productivity when designing and improving manufacturing systems [START_REF] Hibino | Emulation in Manufacturing Engineering Processes[END_REF]. Methods have been proposed to evaluate energy consumption using simulation concerning a single machining [START_REF] Narita | Development of Prediction System of Environmental Burden for Machine Tool Operation : 1st Report, Proposal of Calculation Method of Environmental Burden[END_REF] and a single automated guided vehicle [START_REF] Murayama | Scene Transition Net Simulation in a Distributed Environment[END_REF]. However, concerning sustainable manufacturing systems including a factory and a line, the necessary items and requirements to concurrently evaluate productivity and energy consumption are not clear. It is difficult to evaluate energy consumption using simulation when the sustainable manufacturing systems are designed and improved.

The purpose of our research is to establish a simulation environment for sustainable manufacturing systems considering the productivity and energy consumption. We propose an implementation for the simulation environment. A case study for a middle-scale semiconductor manufacturing line is carried out to confirm the efficiency of our proposed simulation environment.

Proposed Simulation Environment for Productivity and Energy Consumption

We define a formula for the energy consumption per unit of production throughput is as follows:

A = B / C
(1) where: A is energy consumption per unit of production throughput, B is the amount of energy consumption and C is the production throughput.

To evaluate the throughput using a suitable minimum time unit during the design stage, manufacturing system simulation is often used [START_REF] Hibino | Emulation in Manufacturing Engineering Processes[END_REF]. Manufacturing system simulations support creation of suitable manufacturing system conditions by means of simulation models. One of the main purposes of the manufacturing system simulation is to evaluate productivity while analyzing material flow and information flow. A simulation is executed paying attention to particular events of interest which are considered to occur instantaneously. The stagnation phenomena for the material flow in manufacturing systems are often evaluated by simulation.

On the other hand, the total energy consumption in a manufacturing system is calculated by summing the amount of energy consumption for each facility. The energy consumption for each facility is calculated by adding the amount of energy consumption in each facility state (e.g. producing state, idle state, stopping state, etc.). For example, in a machining facility that is one of the most fundamental facilities in a manufacturing system, the method of measuring the amount of electricity consumption is defined by the standards organization in Japan [START_REF]Machine tools -test methods for electric power consumption[END_REF]. The amount of electricity consumption for the machining facility is measured by a procedure that follows a starting state, idle state, producing state, and stopping state sequence. Therefore, with the machining facility, if the facility states and their state transitions and allotted times are clear, the electricity consumption is estimated by the sum of the electricity consumption in response to the periods for each facility state. To evaluate the energy consumption per unit of production throughput and the amount of energy consumption when designing and improving the manufacturing systems, we propose the following requirements:

Requirement 1(R1): Clarifying facility states, their facility state transitions and relationships between each facility state and total energy consumption. Requirement 2(R2): Simulating the state transitions along a time progression and generating information of the state transitions with a timestamp. Requirement 3(R3): Generating information of productivity and energy consumption along a time progression. Requirement 4(R4): Numerically evaluating and visualizing the amount of the energy consumption per unit of production throughput in the minimum time unit.

We propose a simulation environment for productivity and energy consumption. The simulation environment consists of a state transition model for a facility (I1), a simulation system (I2) and a visualization system (I3).

Figure 1 shows an outline of the proposed state transition model for facility (I1), the proposed simulation system (I2) and the proposed visualization system (I3) using the simulation environment for productivity and energy consumption.

Proposed State Transition Model for Facility

The state transition model for facility (I1) has a necessary function 1 (F1) to realize R1. F1 consists of two sub-functions that define the facility states and their state transition (F1.1), and define relationships between the facility state and energy consumption (F1.2).

First, the definition of the facility states and their state transition (F1.1) is explained. On the basis of our analysis of typical facilities in manufacturing systems, facility states and their state transition are analysed and summarized. Typical facilities are machining facilities, industrial robot facilities and solder reflow facilities. Solder reflow facilities are important facilities in semiconductor manufacturing systems. Using the results of our analysis, we propose the definition of the facility states and their state transition (F1.1). Sub-function F1.1 consists of three characteristics. First, the facility states in our proposed model are divided into five categories: the starting state, the idle state, the producing state, the stopping state and the aborting state. Second, with the starting state, the producing state and the stopping state, there are usually sub-states in each facility state. Each facility state in the proposed model can be divided into sub-states. Third, with the state transition in our proposed model, a facility state usually changes to the next facility state when an activity in the facility state is completed. However, the idle state cannot change to the producing state until an event occurs, such as when production parts arrive. The stopping state and the aborting state also change from the producing state only when a facility stoppage occurs. The proposed state transition model for facility (I1) is expressed using a Unified Modelling Language (UML) model.

Furthermore, the definition of relationships between the facility state and energy consumption (F1.2) is explained. A calculation formula for the energy consumption E sn of the facility state S n is as follows:

n n t t Sn Sn dt t P E 1 ) ( (2)
where t n is the finishing time of facility state S n t n -1 is a stated time for facility state S n P sn (t) is the energy consumption in facility state Sn at time t. Using P sn (t), it is possible to calculate the energy consumption of the facility state. However, it is difficult to define P sn (t) for all facilities in a manufacturing system. Therefore, in this study, we use an average P sn from t n -1 to t n .

Proposed Simulation System

The proposed simulation system (I2) has a necessary function 2 (F2) to realize R2 and R3. F2 consists of three sub-functions that simulate the state transition along a time progression (F2.1), generate productivity information including the throughput with a timestamp in the minimum time unit (F2.2) and generate information of the energy consumption with a timestamp in the minimum time unit (F2.3).

First, the simulation of the state transition along a time progression (F2.1) is explained. The simulation supports suitable manufacturing system conditions by means of simulation models. The simulation model consists of facility simulation models that include the proposed state transition model for the facility. The simulation is executed paying attention to particular events of interest which are considered to occur instantaneously. Each event marks a change of the facility state in the simulation. Between consecutive events, the simulation directly jumps in time from one event to the next. Then facility state information with a timestamp and event information for each state transition that occurs are generated by the simulation.

Second, the generation of productivity information including the throughput with a timestamp in the minimum time unit (F2.2) is explained. The production throughput information with a timestamp along a time progression is generated. Then the production throughput information at the place we define, such as a facility level and/or line level, is obtained.

Third, the generation of information of the energy consumption with a timestamp in the minimum time unit (F2.3) is explained. The energy consumption with a timestamp along a time progression is generated. Then, the energy consumption at the place we define, such as a facility level, is obtained.

Proposed Visualization System

The proposed visualization system (I3) has a necessary function 3 (F3) to realize R4. F3 consists of two sub-functions that are used to receive information for the productivity and the energy consumption, to calculate the energy consumption per unit of the production throughput in the minimum time unit (F3.1) and to visualize the energy consumption per unit of production throughput (F3.2).

First, the receipt of information for the productivity and the energy consumption and the calculation of the energy consumption per unit of the production throughput in the minimum time unit (F3.1) are explained. The generated information is a simulation clock, facility numbers, the amount of production throughput, the amount of energy consumption and so on. The generated information with a time stamp is sorted and stored in ascending order. The energy consumption per unit of production throughput for each facility is calculated using the generated information and the formula while considering the minimum time unit.

Second, the created energy consumption per unit of production throughput for each facility is visualized using the spraying figures feature.

Fig. 1

 1 Fig. 1 Our proposed simulation environment for productivity and energy

Implementation of Proposed Simulation Environment for Productivity and Energy Consumption

In this chapter, the implementation for the proposed simulation system (I2) and the proposed visualization system (I3) is proposed.

First, implementation for the proposed simulation system (I2) is described. The proposed simulation system has three sub-functions used to satisfy requirement 2 (R2). The function to simulate the state transition along a time progression (F2.1) is implemented in Witness that is a commercial discrete event simulator. Witness provides a simulation engine function and a simulation programming support function. The simulation engine function controls the simulation along a time progression while paying attention to particular events of interest that occur instantaneously. The simulation programming support function helps engineers to develop extension functions using the Visual Basic (VB) programming language. The facility simulation models that include the proposed state transition model for facility (I1) are implemented in Visual Basic.

Second, implementation for the proposed visualization system (I3) is described. The receipt of information for production throughput and energy consumption and the calculation of the energy consumption per unit of the production throughput in the minimum time unit (F3.1) are implemented in Excel that is a commercial spread sheet. We developed a macro program in Excel. The generated information with a time stamp from the simulation is saved in a text file using the CSV format. The macro program in Excel makes it possible to read the CSV file. The generated information with a time stamp is sorted and stored in ascending order in Excel. The energy consumption per unit of production throughput for each facility is calculated using the generated information.

Case study

The purpose of this case is to evaluate the application of the simulation environment to a middle-scale semiconductor manufacturing line consisting of three facilities, the solder printing facility, the IC mounting facility (mounter facility), and the solder reflow facility. The three facilities were modelled in Witness using our proposed state transition model. Figure 2 shows a simulation model of the middlescale semiconductor manufacturing line and simulation input data. There are four types of products. There are three types of lot sizes as 60, 120, 240. The simulation period in this case study is 40 hours over five days.

Using the above conditions, the simulation was performed. The simulation generated facility state information with a timestamp as well as event information for the state transitions with a timestamp including clock time, facility number, amount of electricity consumption, type of product and product number. The simulation also generated the production throughput with a time stamp.

Using the generated information from the simulation, the dynamic changes in the energy consumption per unit of production throughput along the time axis were visualized in the visualization system. The energy consumption per unit of production throughput was numerically expressed along the time line.

Figure 3 shows transitions of the energy consumption per unit of production throughput in the solder reflow facility. The energy consumption per unit of production throughput was getting smaller when the lot size was getting larger in Figure 3. To analyze their factors, we focused on the amount of no-load running periods in the solder reflow facility. Figure 4 shows histograms of the no-load running periods in the solder reflow facility. S1 in Figure 4 means influence of setting up changes of kinds of products in the mounter facility. S2 in Figure 4 means influence of setting up changes of the reels in the mounter facility. The frequency of S1 was becoming increasing by decreasing the lot size. The more frequency of S1 increased, the longer no-load running periods became because the stagnation phenomena of the material flow occurred. The stagnation phenomena decreased the throughput. Thus the amount of waste energy in the solder reflow facility increased.

It was confirmed the efficiency of the proposed simulation environment through the case study. Histogram of no-load running periods in solder reflow facility

Conclusion

In this paper, we proposed a simulation environment for sustainable manufacturing systems considering the productivity and energy consumption. A state transition model, a simulation system and a visualization system were proposed and implemented considering the necessary items and requirements for the simulation environment. Finally, case studies for a middle-scale semiconductor manufacturing line was conducted to confirm the efficiency of the proposed simulation environment.