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Integrating Intelligent Maintenance Systems and Spare Parts Supply Chains
Proceedings of the 14th IFAC Symposium on Information Control Problems in Manufacturing Bucharest, Romania, May 23-25, 2012
Integrating Intelligent Maintenance Systems and Spare Parts Supply Chains Danúbia Espíndola*, Enzo M. Frazzon**, Bernd Hellingrath***, Carlos E. Pereira**** * Federal University of Rio Grande Rio Grande, Brazil (e-mail: [email protected]). ** Federal University of Santa Catarina Florianópolis, Brazil (e-mail: [email protected]). *** Westfälische Wilhelms-Universität Münster Münster, Germany (e-mail: [email protected]). **** Federal University of Rio Grande do Sul Porto Alegre, Brazil, (e-mail: [email protected]). Abstract: Effective and efficient maintenance is of major importance for the operators of complex production systems. Insufficient maintenance may induce downtimes with significant effects on operators’ costs, profits and customer service perception. However, guaranteeing a high maintenance service-level may lead to immoderate running costs exceeding the intended benefits. Thus, our research aims at balancing the dimensions of an adequate maintenance service level and moderate costs. The proposed concept approaches this issue by integrating intelligent maintenance systems, i.e. technical information about a system’s maintenance needs, with the coordination and efficient management of the respective spare parts supply chain. Keywords: Intelligent Maintenance Systems, Condition-Based Maintenance, Spare Parts Supply Chains, Supply Chain Coordination, Collaborative Planning, Supply Chain Management 1. MOTIVATION AND PROBLEM DESCRIPTION Satisfactory maintenance is crucial for the operation of complex production systems. Insufficient maintenance potentially due to missing spare parts can result in downtimes of suchlike systems and have major economic effects: downtimes cause diminishing profits for system operators possibly coming along with sustainable effects on their customers’ satisfaction and service perception. Thus, the reasonable management of maintenance activities including spare parts provision is of major relevance for the effective and efficient operation of complex technical systems. Basically, the demands for maintenance services and spare parts respectively occur upon system breakdowns. This sporadic demand cannot be forecasted comparable to the demand of finished products. Thus, it is highly important for system operators to be able to estimate the maintenance need of a system and its components. This is fundamental for an efficient planning of respective maintenance activities and spare parts replenishment in order to minimize downtimes of the maintained system. Maintenance activities only in reaction upon occurring system failures imply high costs with respect to spare parts production and replenishment (e.g. costly emergency shipments) and on-site maintenance provision (e.g. overtimes of service personnel). Facing this issue research in the domain of intelligent maintenance systems has led to means for monitoring technical systems’ maintenance condition. Based on sensorial inputs, the maintenance status, i.e. the probability of a technical system to fail, can be estimated and 978-3-902661-98-2/12/$20.00 © 2012 IFAC
hence the need for maintenance as well as respective spare parts can be forecasted. Thus, suchlike technological status information forms the basis for more efficient maintenance services and spare parts replenishment. Summarized, the integrated management of maintenance services and spare parts supply chains simultaneously has to cope for the dimensions of effectiveness (adequate maintenance service level and spare parts availability) and efficiency (low costs for maintenance service and spare parts provision). The right balance is critical for the competitiveness of production system operators and/or respective maintenance service providers. The described challenges can be further specified and set into relation to the peculiar characteristics of maintenance services and spare parts supply chains as follows. First, from a technical point of view, the challenge is to gain information about the maintenance status of a system, i.e. estimations of components’ maintenance needs and alerts upon concrete failures. Issues hence relate to the accessibility, quality, interpretation and integration of potentially distributed maintenance status information. Second, fulfilling the requirements on effective and efficient maintenance services, i.e. a high service-level with low costs, induces contradictory needs on the management and planning of spare parts supply chains. On the one hand, spare parts have to be available at the right time in the right amount and location. On the other hand, the characteristics of spare parts (e.g. high specificity and costs as well as sporadic demand) in combination with efficiency challenges (e.g. low
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inventories) impose special requirements on the actors being involved in spare parts provision. The actors and respective decision domains of spare parts supply chains can be distinguished by their roles of producing, distributing and using a spare part. The producers of spare parts have to account for production, lot-sizing and local inventory holding decisions. They furthermore can fulfill the distribution on their own or rely on logistics service providers. Respective decisions especially affect inventory holding at multiple sites (e.g. central and regional distribution centers or the system operator) and the determination of transports. Furthermore, the usage of a spare part in concrete maintenance activities has to be accounted for either by system operators or third party maintenance service providers. Thus, the structure of spare parts supply chains with multiple involved actors induces the need for a coordination of their respective activities. Consequently, challenges on the integration of these two perspectives arise. This especially affects the provision of technical maintenance status information to the different planning and decision domains of spare parts supply chains, their inclusion into tailored planning methods and the coordination of the multiple involved actors. The described perspectives on the challenges of maintenance services and spare parts supply chains are depicted in Fig. 1.
solution concept aims at developing improved spare parts planning methods integrating the technical maintenance status information and the coordination of the spare parts supply chain’s actors. Basically, our concept in this domain can be split up into two highly interdependent components. On the one hand, spare parts production and distribution planning methods as well as maintenance management concepts explicitly including service personnel shall be enhanced in order to integrate the newly available maintenance status information. On the other hand, these planning methods – especially in the context of inventory and transportation planning – have to be tailored for each supply chain partner’s planning domain and need to be integrated into a supply chain-wide coordination process. This requires the inclusion of the collected maintenance status information into spare parts forecasting methods and the definition of supply chain partner-specific planning concepts, e.g. regarding production, inventory and transportation decisions as well as the management and support of service personnel in on-site maintenance activities. Furthermore, these planning decisions are intended to be inter-connected in order to avoid inefficiencies due to isolated planning. In conclusion the developed concepts should allow for an adequate maintenance service level, as well as reduce supply chain-wide costs and hence increase the competitiveness of maintenance service providers and the respective spare parts supply chains. Summarized, our solution approach consists of the components being depicted in Fig. 2:
Fig. 1: Challenges for Maintenance Services and Spare Parts Supply Chains Fig. 2: Concept Overview and High-Level Components
2. RESEARCH GOALS Facing the challenges in maintenance services and spare parts supply chains, our research intends to develop solutions for an improvement of these systems’ effectiveness and efficiency. Basically, the described technical as well as supply chain management-related challenges are addressed and consolidated in an integrative solution. From the technical perspective, our goal is to provide prototypical solutions that allow for an improved estimation of components’ maintenance needs by developing Intelligent Maintenance Systems (IMS). The use of intelligent maintenance technology will predict future failures in components allowing the operator to react directly on the component degradation. Besides, through techniques of mixed reality integrated into an IMS, we intend to bring relevant data about the components and machines aiding thus the provision of information to operators and personnel. The intended technical solutions lay the foundation for approaching the described challenges in the spare parts supply chain management perspective. Here, our proposed
3. STATE-OF-THE-ART AND ACHIEVED RESULTS 3.1 Intelligent Maintenance Systems IMS focus on the use of software and sensors in machines and equipment in order to allow for an evolution from corrective and preventive maintenance to predictive systems. Most failures don’t occur abruptly but the equipment suffers a gradual degradation process that can be measured and quantified. One way of quantifying this process and thus quantifying the equipment performance is using the concept of Confidence Value (CV), which is a value ranging from zero to one, with one being normal performance and zero being a failure condition (cf. Djurdjanovic et al. (2003)). The IMS’s focus is the understanding of the components degradation process, based on the state and equipment use condition. These techniques allow the identification of which degraded components and will cause a failure notice before it happens. When the maintenance is performed based on this early failure notice, no unnecessary parts substitution occurs.
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The early identification of a degraded part can also avoid the propagation of the degradation process to other parts of the system. The IMS chosen in our approach has a core component, called Watchdog Agent that evaluates the working state of machines and equipment (cf. Djurdjanovic et al. (2003)). The Watchdog Agent consists of prognostic algorithms embedded into a group of software tools to predict machine failures. This degradation calculation is based on sensor readings which measure the critical properties of the process/machinery. The Watchdog system supplies a MATLAB tool that allows to visualize and to process these data. The tools supplied by Watchdog are: data manipulation, condition monitor, health assessment, prognostics and diagnostics graph files. However, the visualization interfaces must be improved in order to enhance the access to relevant information. In this context, our research is focused on advanced visualization. Nowadays, Virtual/Mixed Reality techniques are used to bring the right information in the right place at the right time (cf. Regenbrecht et al. (2005), Macchiarella and Vincenzi (2004), Multanen et al. (2009), Stefánik (2008)). The possibility of superimposing virtual objects generated by a computer on the real environment in real time is called Mixed Reality, which represents a powerful support tool for industrial productive processes. Besides, mixed scenarios enable ways of integrating information from several systems. Consequently, the use of these visualization techniques facilitates the users’ understanding contemplating a visualization of the information from maintenance systems and representing a way to provide a safe interaction for the operator. The goal of advanced visualization techniques is to generate tangible and intuitive interfaces for the user. Possible are presentations of 2D graphs, 3D models, maintenance text guides during the fulfillment of maintenance tasks through virtual devices such as HMD (Head Mounted Display), Tablet, PDA (Personal Digital Assistant) and so forth. Another kind of interaction is by voice commands specifying a machine’s components of which the respective information shall be visualized. Some experiments were performed to validate the use of mixed reality in the maintenance of actuators (cf. Djurdjanovic et al. (2003)). The information about component degradation and the used visualization techniques to present “where” and “when” the component will fail reduces the downtime and allows for an optimization of the maintenance processes. 3.2 Spare Parts Management in the Supply Chain As discussed above, two main challenges with respect to planning and managing spare part supply chains can be identified. First of all, existing spare parts planning methods have to improve by integrating technical maintenance status information. Second, due to the multitude of autonomous actors being involved in spare parts supply chains, a coordination of these actors is intended. Thus, this chapter provides an overview of the state-of-the-art in spare parts
planning methods and in supply chain coordination approaches. Spare parts planning methods Due to the high costs for spare parts and their sporadic demand, keeping inventories of all parts at all warehouses in the spare parts network are not economical. On the other hand, inventories of spare parts are necessary to ensure a high service level and hence customer satisfaction (cf. Kutanoglu and Mahajan (2009)). Consequently, research in spare parts management has mainly focused on the investigation of different inventory planning methods until now utilizing general forecasting techniques (cf. Martin et al. (2010)). But also a number specific forecasting methods have been developed, which can be separated into parametics and nonparametric approaches. All of these methods forecast the demand without utilizing actual condition information of the technical system (cf. Syntetos et al. 2009, p. 297). Research about the enhancement of forecasting quality through the usage of actual maintenance status information is just in the beginning and only a few researchers relate forecasting methods to this information (cf. Martin et al. (2010)). Existing models use data about historical spare parts demand in relation to the operating machines, so called installed base data, for subsequent planning (cf. Dekker et al. (2010)). However, the varying quality of the generated data is still an open issue (cf. Dekker et al. (2010), p. 2; Jalil et al. (2009), p. 2.). Research in the domain of spare parts inventory management can be divided according to the scope of the network investigated. Works can be found considering the inventory planning in single-echelon networks, inventory allocation in multi-echelon, multi-item distribution networks and inventory sharing and coordination of facilities (cf. Kutanoglu and Mahajan (2009), p. 730; Muckstadt (2005)). The traditional hierarchical network structure (i.e. satisfying the demand from a central warehouse in case of insufficient inventory at the local warehouse) has been replaced in favor of exchanging inventory between warehouses in the same echelon (cf. Kutanoglu and Mahajan (2009), pp. 729-731). . Finally, the usage of maintenance status information has not been brought into inventory control mechanisms yet. In one of the few works, a sensor-driven prognostic model for supporting component replacement and spare parts inventory decision making has been developed (cf. Elwany and Gebraeel (2008)), providing a first step towards effective inventory planning. Summarized, the integration of condition monitoring data, forecasting and inventory planning is in its beginning. As all of the described inventory management multi-echelon models assume full information availability and hierarchical control of all echelons, the applicability is restricted considering a number of real life situations. As spare parts management is focusing onto locally controlled inventory planning the relevant interdependence between production, inventory and transport planning in spare parts management has not been addresses adequately yet. In order to achieve a high service-level to moderate costs, the capacities and objectives of these planning domains have to be coordinated.
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Thus, inefficiencies due to for example extra shipments, overhead costs for small production lot sizes and uncoordinated inventory levels could be avoided while simultaneously regarding service-level requirements. Up to now, research dealing with the integration of the different planning domains of maintenance services, production and transport processes is restricted to the impact of maintenance on production planning (e.g. Najid et al. (2011); Nourelfath et al. (2010)). It can be concluded that an, an integrative planning and control of the different processes in spare parts supply chains is still an open issue.. In distinction to the spare parts supply chain, the integrated view of production and transport planning is under recent investigation in the product supply chain (e.g. Böhle et al. (2009); Zesch and Hellingrath (2010); De Matta and Miller (2004); Chen and Vairaktarakis (2005); Pundoor and Chen (2005); Chen and Pundoor (2006); Stecke and Zhao (2007)). These developments are mainly focusing on special application areas (e.g. the automotive industry), further research has to be done to transfer the results to spare parts supply chains. It would therefore be a relevant research approach to consider the applicability of these developments towards spare part supply chains. Furthermore not only the integration but also the coordination between the different planning domains can be seen as a relevant topic, considering the autonomy of the different actors involved Supply Chain Coordination A high division of labor between legally independent participants in general and with respect to spare parts provision in special characterizes the structure of today’s supply chains. The resulting inter-organizational networks consist of several autonomous yet in their actions highly interdependent companies. Since these autonomous companies cannot necessarily be forced to follow decisions or plans of a superordinate unit, the management and hence coordination of such heterarchical supply chains cannot be achieved in the same way as in hierarchical organizations. Coordination mechanisms for hierarchical supply chains have been researched intensely, resulting in the development and practical application of sophisticated methods being implemented in e.g. advanced planning systems (cf. Stadtler and Kilger (2008)). However, these coordination mechanisms cannot be applied to heterarchical structures. Especially, the necessary revelation of potentially sensitive data (e.g. about local cost structures) to one superordinate unit and the abandonment of local decision autonomy are major impediments for an implementation in heterarchical supply chains. Both aspects might result in a competitive advantage for other supply chain partners and hence participation in suchlike coordination mechanisms is not desirable for all supply chain actors (cf. Breiter et al. (2009), pp. 4-5; Holmström et al. (2002), p. 40). Consequently, several approaches in current SCM research address the aspects and peculiarities of heterarchical supply chains in the development of adequate decentralized coordination mechanisms. Especially, approaches in the domain of collaborative planning (CP) promise to meet the requirements imposed by heterarchical SCs with respect to
decision autonomy and privacy of information. CP aims to achieve coordinated behavior by a “joint decision making process for aligning plans of individual SC members […]” (Stadtler (2009), p. 6). This kind of coordination intends to overcome the restrictions of traditional hierarchical planning concepts regarding the practical applicability in today’s supply chains, while simultaneously improving supply chain cost and/or performance. The concrete inter-organizational planning process in CP depends on the object of coordination (e.g. collaborative production planning) and the SC structure, which is intended to be supported (e.g. multi-tier). Consequently, different CP concepts solving specific coordination problems have evolved in recent years.1 However, the applicability of existing CP approaches for coordinating the different autonomous actors in heterarchical spare part supply chains have not yet been investigated Besides these spare part supply chain-related research gaps, coordination by CP is a young research area in general. This is especially visible in the missing common understanding regarding the formal representation and evaluation of CP concepts. With respect to modeling, the current approaches rely on proprietary and different means of representing the underlying structures and processes in CP. Hence, the transferability of existing results to other problem settings (i.e. re-use and extension of coordination mechanisms) is hardly possible. Furthermore, since the developed CP concepts have been mostly evaluated in specific scenarios and with respect to special performance indicators, traceability of results and their portability to other scenarios is limited. These issues are addressed in our current research (cf. Hellingrath and Küppers (2011a)) and shall therefore be regarded in developing and applying CP in the domain of spare parts supply chain coordination. 4. FUTURE RESEARCH TOPICS 4.1 Intelligent Maintenance Systems Future research should investigate on self-maintenance and the use of devices such as Radio-Frequency Identification (RFID), tags, sensors, actuators and mobile phones applied to IMS. The other idea of this work is to propose an integration model to manage the information in industrial maintenance processes. The proposal includes the modeling of a flexible hardware and software infrastructure, which can be applied to any maintenance application. Considering that, the bottleneck of this proposal is the relationship and exchange of data among different systems, an information management model has been developed. The model allows for integration of CAx (CAD, CAE and CAM), IM (Intelligent Maintenance) and 1
Exemplary CP concepts aim at coordinating production lot sizes (e.g. Dudek (2009)) or network capacities (e.g. Hegmanns (2010)), or integrated production and distribution planning (e.g. Böhle (2010)). Reviews and approaches for classifications are provided by, e.g., Breiter et al. (2009) or Stadtler (2009).
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MR (Mixed Reality) data. This approach provides practical and theoretical support for experiments that intend to apply mixed visualization in maintenance activities. The goal is to provide an extensible and generic model for the integration and management of maintenance data. 4.2 Spare Parts Management in the Supply Chain The resulting challenges in research of spare parts management are the maintenance status information availability, the usage of this information in planning and forecasting methods as well as the coordination of the multiple actors of spare parts supply chains. Information generated in maintenance processes is not properly deployed to improve the spare parts supply chain. Relevant data regarding expected maintenance service dates could be mentioned as an example. Providing actual information for transportation processes, logistics, and, in some critical cases, production would improve the capability and shorten the service time of maintaining production systems. Hence, planning and forecasting methods themselves should be improved by including the usage of maintenance status information. Based on the identified specificities of production, inventory management and transport processes within a spare parts supply chain, methods being capable of integrating the actual maintenance status have to be researched in order to improve the quality of the different planning results e.g. in determining inventory levels. Furthermore, informational integration and synchronization across functional and organizational boundaries in supply chains are major challenges. Overcoming them depends on a better understanding of tangible and intangible aspects, as well as on developing proper integration concepts, approaches, and tools (see section 3.2). Indeed, the interfaces between maintenance, production and transportation systems can be improved based on the mutual understanding of differences between connected functions, organizations and contexts (cf. Frazzon (2009)). Local decisions cannot only depend on the efficiency of the individual processes at different locations, but rather have to take into account the behavior of linked decision structures. Specifically for spare parts supply chains, the characteristics of the demand are a major challenge. With respect to the development of the collaborative planning concept for spare parts supply chain coordination, the proposed approach will rely on the results being achieved in our previous research in this context (see section 3.2). These results have been consolidated in the so called Framework for Intelligent Supply Chain Collaboration (FRISCO), which allows for the formal modeling and evaluation of CP concepts (cf. Hellingrath and Küppers (2011a)). The framework has been successfully applied to a – in its complexity and fundamentals to spare parts supply chains comparable – CP approach (cf. Hellingrath and Küppers (2011b)). This proof-of-concept shows that FRISCO on the one hand supports the development of CP and on the other hand allows for the estimation of a CP concept’s benefits in concrete supply chain scenarios. Hence, this
framework is intended to assist the above described development of a CP concept for spare parts supply chains. 4.3 Evaluation of the Integrated Concept As motivated above, the integration of intelligent maintenance systems with the management of the spare parts supply chain is the fundament of our approach. Hence, an evaluation of the integrated concept is aspired. The evaluation of the collaborative planning approach for coordinating the spare parts supply chain will rely on the previously described FRISCO framework. Besides supporting CP development, FRISCO’s evaluation environment provides means to execute the CP concept’s underlying processes in a testing environment in order to assess its expected benefits. Thus, applying FRISCO in this context allows for evaluating the collaborative spare parts planning concepts, their execution in an information system and hence is a step for their implementation in real supply chains. The challenge of integrating intelligent maintenance systems with spare parts supply chains will therefore be analyzed using simulation-based computational experiments and is intended to be later evaluated in real-world case studies. 5. CONCLUSION With this paper a first conceptualization of integrating IMS and spare parts supply chains planning and control has been presented, targeting at the effectiveness (guaranteeing an adequate maintenance service level) and efficiency (reduction of supply chain-wide costs) of spare parts supply chains. This issue will be achieved on the one hand by providing a model for the integration and management of maintenance data, which are gained by an IMS. On the other hand spare parts planning methods are extended by integrating the technical maintenance status information. Furthermore the transfer of CP concepts to spare parts supply chains supporting the coordination of the spare parts supply chain actor’s is considered. For evaluating the integrated concept with the described FRISCO framework and simulation-based computational experiments, exemplary numerical scenario data are needed to assess the expected benefits of the concept and to evaluate the application in real-world scenarios. Future work has to focus onto the detailed specification of the concept and the development of quantitative models for supporting the execution and the assessment of the concept. REFERENCES Böhle, C., Dangelmaier, W., Hellingrath, B. (2009). A Lot Sizing Model with Integrated Tour Planning. In: Information Control Problems in Manufacturing. Volume 13, Part 1. Böhle, C. (2010). Eine theoretische und praktische Herleitung eines Verfahrens für die kostenminimale Koordination von Lieferanten und Logistikdienstleistern zur Belieferung lieferantengesteuerter Lager. Paderborn. Breiter, A., Hegmanns, T., Hellingrath, B., Spinler, S. (2009). Coordination in Supply Chain Management - Review and Identification of Directions for Future Research. In:
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Voss, S., Pahl, J., Schwarze, S. (eds) Logistik Management. Physica-Verlag, Heidelberg. Chen, Z.-L., Pundoor, G. (2006). Order assignment and scheduling in a supply chain. In: Operations Research, 54:555-572. Chen, Z.-L., Vairaktarakis, G.L. (2005). Integrated scheduling of production and distribution operations. In: Management Science, 51:614-628. De Matta, R., Miller, T. (2004). Production and inter-facility transportation scheduling for a process industry. In: European Journal of Operational Research, 158:72-88. Dekker, R., Pinçe, Ç., Zuidwijk, R., Jalil, M.N. (2010). On the use of installed base information for spare parts logistics: a review of ideas and industry practice. Report Econometric Institute EI2010 - 73:1-22. Djurdjanovic, D., Lee, J., Ni, J. (2003). Watchdog Agent, an infotronics-based prognostics approach for product performance degradation assessment and prediction. In Advanced Engineering Informatics 17(3-4):109–125. Dudek, G. (2009). Collaborative Planning in Supply Chains: A Negotiation-Based Approach. Springer. Elwany, A.H., Gebraeel, N.Z. (2008). Sensor-driven prodnostic models for equipment replacement and spare parts inventory. In: IIE Transactions 40(7):629-639. Frazzon, E.M. (2009). Sustainability and Effectiveness in Global Logistic Systems. GITO-Verlag, Berlin. Hegmanns, T. (2010). Dezentrales Planungs- und Prozesskonzept für ein kollaboratives Bedarfs- und Kapazitätsmanagement in Produktionsnetzwerken. Verl. Praxiswissen, Dortmund. Hellingrath, B., Küppers, P. (2011a) Model-Driven Development of Multi-Agent Based Collaborative Planning Concepts for Heterarchical Supply Chains. In: Marík, V., Vrba, P., and Leitão, P. (eds) Holonic and Multi-Agent Systems for Manufacturing. Springer. Hellingrath, B., Küppers, P. (2011b) Multi-Agent Based Evaluation of Collaborative Planning Concepts in Heterarchical Supply Chains. In: Sucky, E., Asdecker, B., Dobhan, A., Haas, S., and Wiese, J. (eds) Logistikmanagement: Herausforderungen, Chancen & Lösungen; Band III. Holmström, J., Framling, K., Tuomi, J., Karkkainen, M., and Ala-Risku, T. (2002). Intelligent product agents: the key to implementing collaboration process networks? In: International Journal of Logistics Management 13(2):39–50. Jalil, M.N., Zuidwijk, R.A., Fleischmann, M., van Nunen, J.A.E.E. (2009), Spare parts logistics and installed base information. ERIM Report Series Research in Management ERS-2009-002-LIS:1-27. Kutanoglu, E., Mahajan, M. (2009). An inventory sharing and allocation method for a multi-location service parts logistics network with time-based service levels. In: European Journal of Operational Research 194(3):728742. Macchiarella, M. and Vincenzi, D. (2004). Augmented Reality In A Learning Paradigm For Flight And Aerospace Maintenance Training. In: Digital Avionics Systems Conference, 2004. DASC 04. The 23rd IEEE, pp. 5.D.1- 5.1-9.Martin, H., Symtetos, A.A., Parodi, A.,
Polychronakis, Y.E., Pntelon, L. (2010). Integrating the spare parts supply chain. An inter-discilinary account. In: Lournal of Manufacturing Technology Management 21(2):226-245. Multanen, P., Mäkiranta, A., Nuutinen, P., Leino, S-P., Helin, K., Lind S. (2009). Adaptation of Virtual Tools to Support Cost-effective Maintenance of Machines, In COMADEM200922th International Congress Condition Monitoring and Diagnostic Engineering Management. Muckstadt, J.A. (2005). Analysis and Algorithms for Service Parts Supply Chains. Springer, New York. Najid, N.M.A., Alaoui-Selsouli, M.B., Mohafid, A.C. (2011). An integrated production and maintenance planning model with time windows and shortage cost. In: International Journal of Production Research, 49(8):2265-2283. Nourelfath, M., Fitouhi, M.-C., and Machani, M. (2010). An integrated model for production and preventive maintenance planning in multi-state systems. In: IEEE Transactions on Reliability 59(3):496-506. Pundoor, G., Chen, Z.L. (2005). Scheduling a productiondistribution system to optimize the trade-off between delivery tardiness and distribution cost. In: Naval Research Logistics, 52:571-589. Regenbrecht, H., Baratoff, G., and Wilke, W. (2005). Augmented Reality Projects in the Automotive and Aerospace Industries. In: Computer Graphics and Applications, IEEE 25(6):48-56. Stadtler, H., Kilger, C. (eds) (2008) Supply Chain Management and Advanced Planning. Springer. Stadtler, H. (2009) A framework for collaborative planning and state-of-the-art. In: OR Spectrum 31:5‐30. Stecke, K.E., Zhao, X. (2007). Production and Transportation Integration for a Make-to-Order Manufacturing Company with a Commit-to-Delivery Business Mode. In: Manufacturing & Service Operations Management, 9 (2):206-224. Stefánik, A., Gregor, M., Furmann, R., Skorík, P. (2008) Virtual Manufacturing in Research & Industry. In: IMS'08: 9th IFAC Workshop on Intelligent Manufacturing Systems October 9-10, 2008, Szczecin, Poland. Syntetos, A.A., Keyes, M., Babai, M.Z. (2009). Demand categorisation in a European spare parts logistics network. In: International Journal of Operations & Production Management. 29(3):292-316. Zesch, F., Hellingrath, B. (2010) Integrated ProductionDistribution Planning: An optimization model for mixedmodel assembly lines. In: Proceedings of the 22nd Annual NOFOMA Conference. Kolding.
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Integrating Intelligent Maintenance Systems and Spare Parts Supply Chains Danúbia Espíndola*, Enzo M. Frazzon**, Bernd Hellingrath***, Carlos E. Pereira**** * Federal University of Rio Grande Rio Grande, Brazil (e-mail: [email protected]). ** Federal University of Santa Catarina Florianópolis, Brazil (e-mail: [email protected]). *** Westfälische Wilhelms-Universität Münster Münster, Germany (e-mail: [email protected]). **** Federal University of Rio Grande do Sul Porto Alegre, Brazil, (e-mail: [email protected]). Abstract: Effective and efficient maintenance is of major importance for the operators of complex production systems. Insufficient maintenance may induce downtimes with significant effects on operators’ costs, profits and customer service perception. However, guaranteeing a high maintenance service-level may lead to immoderate running costs exceeding the intended benefits. Thus, our research aims at balancing the dimensions of an adequate maintenance service level and moderate costs. The proposed concept approaches this issue by integrating intelligent maintenance systems, i.e. technical information about a system’s maintenance needs, with the coordination and efficient management of the respective spare parts supply chain. Keywords: Intelligent Maintenance Systems, Condition-Based Maintenance, Spare Parts Supply Chains, Supply Chain Coordination, Collaborative Planning, Supply Chain Management 1. MOTIVATION AND PROBLEM DESCRIPTION Satisfactory maintenance is crucial for the operation of complex production systems. Insufficient maintenance potentially due to missing spare parts can result in downtimes of suchlike systems and have major economic effects: downtimes cause diminishing profits for system operators possibly coming along with sustainable effects on their customers’ satisfaction and service perception. Thus, the reasonable management of maintenance activities including spare parts provision is of major relevance for the effective and efficient operation of complex technical systems. Basically, the demands for maintenance services and spare parts respectively occur upon system breakdowns. This sporadic demand cannot be forecasted comparable to the demand of finished products. Thus, it is highly important for system operators to be able to estimate the maintenance need of a system and its components. This is fundamental for an efficient planning of respective maintenance activities and spare parts replenishment in order to minimize downtimes of the maintained system. Maintenance activities only in reaction upon occurring system failures imply high costs with respect to spare parts production and replenishment (e.g. costly emergency shipments) and on-site maintenance provision (e.g. overtimes of service personnel). Facing this issue research in the domain of intelligent maintenance systems has led to means for monitoring technical systems’ maintenance condition. Based on sensorial inputs, the maintenance status, i.e. the probability of a technical system to fail, can be estimated and 978-3-902661-98-2/12/$20.00 © 2012 IFAC
hence the need for maintenance as well as respective spare parts can be forecasted. Thus, suchlike technological status information forms the basis for more efficient maintenance services and spare parts replenishment. Summarized, the integrated management of maintenance services and spare parts supply chains simultaneously has to cope for the dimensions of effectiveness (adequate maintenance service level and spare parts availability) and efficiency (low costs for maintenance service and spare parts provision). The right balance is critical for the competitiveness of production system operators and/or respective maintenance service providers. The described challenges can be further specified and set into relation to the peculiar characteristics of maintenance services and spare parts supply chains as follows. First, from a technical point of view, the challenge is to gain information about the maintenance status of a system, i.e. estimations of components’ maintenance needs and alerts upon concrete failures. Issues hence relate to the accessibility, quality, interpretation and integration of potentially distributed maintenance status information. Second, fulfilling the requirements on effective and efficient maintenance services, i.e. a high service-level with low costs, induces contradictory needs on the management and planning of spare parts supply chains. On the one hand, spare parts have to be available at the right time in the right amount and location. On the other hand, the characteristics of spare parts (e.g. high specificity and costs as well as sporadic demand) in combination with efficiency challenges (e.g. low
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inventories) impose special requirements on the actors being involved in spare parts provision. The actors and respective decision domains of spare parts supply chains can be distinguished by their roles of producing, distributing and using a spare part. The producers of spare parts have to account for production, lot-sizing and local inventory holding decisions. They furthermore can fulfill the distribution on their own or rely on logistics service providers. Respective decisions especially affect inventory holding at multiple sites (e.g. central and regional distribution centers or the system operator) and the determination of transports. Furthermore, the usage of a spare part in concrete maintenance activities has to be accounted for either by system operators or third party maintenance service providers. Thus, the structure of spare parts supply chains with multiple involved actors induces the need for a coordination of their respective activities. Consequently, challenges on the integration of these two perspectives arise. This especially affects the provision of technical maintenance status information to the different planning and decision domains of spare parts supply chains, their inclusion into tailored planning methods and the coordination of the multiple involved actors. The described perspectives on the challenges of maintenance services and spare parts supply chains are depicted in Fig. 1.
solution concept aims at developing improved spare parts planning methods integrating the technical maintenance status information and the coordination of the spare parts supply chain’s actors. Basically, our concept in this domain can be split up into two highly interdependent components. On the one hand, spare parts production and distribution planning methods as well as maintenance management concepts explicitly including service personnel shall be enhanced in order to integrate the newly available maintenance status information. On the other hand, these planning methods – especially in the context of inventory and transportation planning – have to be tailored for each supply chain partner’s planning domain and need to be integrated into a supply chain-wide coordination process. This requires the inclusion of the collected maintenance status information into spare parts forecasting methods and the definition of supply chain partner-specific planning concepts, e.g. regarding production, inventory and transportation decisions as well as the management and support of service personnel in on-site maintenance activities. Furthermore, these planning decisions are intended to be inter-connected in order to avoid inefficiencies due to isolated planning. In conclusion the developed concepts should allow for an adequate maintenance service level, as well as reduce supply chain-wide costs and hence increase the competitiveness of maintenance service providers and the respective spare parts supply chains. Summarized, our solution approach consists of the components being depicted in Fig. 2:
Fig. 1: Challenges for Maintenance Services and Spare Parts Supply Chains Fig. 2: Concept Overview and High-Level Components
2. RESEARCH GOALS Facing the challenges in maintenance services and spare parts supply chains, our research intends to develop solutions for an improvement of these systems’ effectiveness and efficiency. Basically, the described technical as well as supply chain management-related challenges are addressed and consolidated in an integrative solution. From the technical perspective, our goal is to provide prototypical solutions that allow for an improved estimation of components’ maintenance needs by developing Intelligent Maintenance Systems (IMS). The use of intelligent maintenance technology will predict future failures in components allowing the operator to react directly on the component degradation. Besides, through techniques of mixed reality integrated into an IMS, we intend to bring relevant data about the components and machines aiding thus the provision of information to operators and personnel. The intended technical solutions lay the foundation for approaching the described challenges in the spare parts supply chain management perspective. Here, our proposed
3. STATE-OF-THE-ART AND ACHIEVED RESULTS 3.1 Intelligent Maintenance Systems IMS focus on the use of software and sensors in machines and equipment in order to allow for an evolution from corrective and preventive maintenance to predictive systems. Most failures don’t occur abruptly but the equipment suffers a gradual degradation process that can be measured and quantified. One way of quantifying this process and thus quantifying the equipment performance is using the concept of Confidence Value (CV), which is a value ranging from zero to one, with one being normal performance and zero being a failure condition (cf. Djurdjanovic et al. (2003)). The IMS’s focus is the understanding of the components degradation process, based on the state and equipment use condition. These techniques allow the identification of which degraded components and will cause a failure notice before it happens. When the maintenance is performed based on this early failure notice, no unnecessary parts substitution occurs.
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The early identification of a degraded part can also avoid the propagation of the degradation process to other parts of the system. The IMS chosen in our approach has a core component, called Watchdog Agent that evaluates the working state of machines and equipment (cf. Djurdjanovic et al. (2003)). The Watchdog Agent consists of prognostic algorithms embedded into a group of software tools to predict machine failures. This degradation calculation is based on sensor readings which measure the critical properties of the process/machinery. The Watchdog system supplies a MATLAB tool that allows to visualize and to process these data. The tools supplied by Watchdog are: data manipulation, condition monitor, health assessment, prognostics and diagnostics graph files. However, the visualization interfaces must be improved in order to enhance the access to relevant information. In this context, our research is focused on advanced visualization. Nowadays, Virtual/Mixed Reality techniques are used to bring the right information in the right place at the right time (cf. Regenbrecht et al. (2005), Macchiarella and Vincenzi (2004), Multanen et al. (2009), Stefánik (2008)). The possibility of superimposing virtual objects generated by a computer on the real environment in real time is called Mixed Reality, which represents a powerful support tool for industrial productive processes. Besides, mixed scenarios enable ways of integrating information from several systems. Consequently, the use of these visualization techniques facilitates the users’ understanding contemplating a visualization of the information from maintenance systems and representing a way to provide a safe interaction for the operator. The goal of advanced visualization techniques is to generate tangible and intuitive interfaces for the user. Possible are presentations of 2D graphs, 3D models, maintenance text guides during the fulfillment of maintenance tasks through virtual devices such as HMD (Head Mounted Display), Tablet, PDA (Personal Digital Assistant) and so forth. Another kind of interaction is by voice commands specifying a machine’s components of which the respective information shall be visualized. Some experiments were performed to validate the use of mixed reality in the maintenance of actuators (cf. Djurdjanovic et al. (2003)). The information about component degradation and the used visualization techniques to present “where” and “when” the component will fail reduces the downtime and allows for an optimization of the maintenance processes. 3.2 Spare Parts Management in the Supply Chain As discussed above, two main challenges with respect to planning and managing spare part supply chains can be identified. First of all, existing spare parts planning methods have to improve by integrating technical maintenance status information. Second, due to the multitude of autonomous actors being involved in spare parts supply chains, a coordination of these actors is intended. Thus, this chapter provides an overview of the state-of-the-art in spare parts
planning methods and in supply chain coordination approaches. Spare parts planning methods Due to the high costs for spare parts and their sporadic demand, keeping inventories of all parts at all warehouses in the spare parts network are not economical. On the other hand, inventories of spare parts are necessary to ensure a high service level and hence customer satisfaction (cf. Kutanoglu and Mahajan (2009)). Consequently, research in spare parts management has mainly focused on the investigation of different inventory planning methods until now utilizing general forecasting techniques (cf. Martin et al. (2010)). But also a number specific forecasting methods have been developed, which can be separated into parametics and nonparametric approaches. All of these methods forecast the demand without utilizing actual condition information of the technical system (cf. Syntetos et al. 2009, p. 297). Research about the enhancement of forecasting quality through the usage of actual maintenance status information is just in the beginning and only a few researchers relate forecasting methods to this information (cf. Martin et al. (2010)). Existing models use data about historical spare parts demand in relation to the operating machines, so called installed base data, for subsequent planning (cf. Dekker et al. (2010)). However, the varying quality of the generated data is still an open issue (cf. Dekker et al. (2010), p. 2; Jalil et al. (2009), p. 2.). Research in the domain of spare parts inventory management can be divided according to the scope of the network investigated. Works can be found considering the inventory planning in single-echelon networks, inventory allocation in multi-echelon, multi-item distribution networks and inventory sharing and coordination of facilities (cf. Kutanoglu and Mahajan (2009), p. 730; Muckstadt (2005)). The traditional hierarchical network structure (i.e. satisfying the demand from a central warehouse in case of insufficient inventory at the local warehouse) has been replaced in favor of exchanging inventory between warehouses in the same echelon (cf. Kutanoglu and Mahajan (2009), pp. 729-731). . Finally, the usage of maintenance status information has not been brought into inventory control mechanisms yet. In one of the few works, a sensor-driven prognostic model for supporting component replacement and spare parts inventory decision making has been developed (cf. Elwany and Gebraeel (2008)), providing a first step towards effective inventory planning. Summarized, the integration of condition monitoring data, forecasting and inventory planning is in its beginning. As all of the described inventory management multi-echelon models assume full information availability and hierarchical control of all echelons, the applicability is restricted considering a number of real life situations. As spare parts management is focusing onto locally controlled inventory planning the relevant interdependence between production, inventory and transport planning in spare parts management has not been addresses adequately yet. In order to achieve a high service-level to moderate costs, the capacities and objectives of these planning domains have to be coordinated.
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Thus, inefficiencies due to for example extra shipments, overhead costs for small production lot sizes and uncoordinated inventory levels could be avoided while simultaneously regarding service-level requirements. Up to now, research dealing with the integration of the different planning domains of maintenance services, production and transport processes is restricted to the impact of maintenance on production planning (e.g. Najid et al. (2011); Nourelfath et al. (2010)). It can be concluded that an, an integrative planning and control of the different processes in spare parts supply chains is still an open issue.. In distinction to the spare parts supply chain, the integrated view of production and transport planning is under recent investigation in the product supply chain (e.g. Böhle et al. (2009); Zesch and Hellingrath (2010); De Matta and Miller (2004); Chen and Vairaktarakis (2005); Pundoor and Chen (2005); Chen and Pundoor (2006); Stecke and Zhao (2007)). These developments are mainly focusing on special application areas (e.g. the automotive industry), further research has to be done to transfer the results to spare parts supply chains. It would therefore be a relevant research approach to consider the applicability of these developments towards spare part supply chains. Furthermore not only the integration but also the coordination between the different planning domains can be seen as a relevant topic, considering the autonomy of the different actors involved Supply Chain Coordination A high division of labor between legally independent participants in general and with respect to spare parts provision in special characterizes the structure of today’s supply chains. The resulting inter-organizational networks consist of several autonomous yet in their actions highly interdependent companies. Since these autonomous companies cannot necessarily be forced to follow decisions or plans of a superordinate unit, the management and hence coordination of such heterarchical supply chains cannot be achieved in the same way as in hierarchical organizations. Coordination mechanisms for hierarchical supply chains have been researched intensely, resulting in the development and practical application of sophisticated methods being implemented in e.g. advanced planning systems (cf. Stadtler and Kilger (2008)). However, these coordination mechanisms cannot be applied to heterarchical structures. Especially, the necessary revelation of potentially sensitive data (e.g. about local cost structures) to one superordinate unit and the abandonment of local decision autonomy are major impediments for an implementation in heterarchical supply chains. Both aspects might result in a competitive advantage for other supply chain partners and hence participation in suchlike coordination mechanisms is not desirable for all supply chain actors (cf. Breiter et al. (2009), pp. 4-5; Holmström et al. (2002), p. 40). Consequently, several approaches in current SCM research address the aspects and peculiarities of heterarchical supply chains in the development of adequate decentralized coordination mechanisms. Especially, approaches in the domain of collaborative planning (CP) promise to meet the requirements imposed by heterarchical SCs with respect to
decision autonomy and privacy of information. CP aims to achieve coordinated behavior by a “joint decision making process for aligning plans of individual SC members […]” (Stadtler (2009), p. 6). This kind of coordination intends to overcome the restrictions of traditional hierarchical planning concepts regarding the practical applicability in today’s supply chains, while simultaneously improving supply chain cost and/or performance. The concrete inter-organizational planning process in CP depends on the object of coordination (e.g. collaborative production planning) and the SC structure, which is intended to be supported (e.g. multi-tier). Consequently, different CP concepts solving specific coordination problems have evolved in recent years.1 However, the applicability of existing CP approaches for coordinating the different autonomous actors in heterarchical spare part supply chains have not yet been investigated Besides these spare part supply chain-related research gaps, coordination by CP is a young research area in general. This is especially visible in the missing common understanding regarding the formal representation and evaluation of CP concepts. With respect to modeling, the current approaches rely on proprietary and different means of representing the underlying structures and processes in CP. Hence, the transferability of existing results to other problem settings (i.e. re-use and extension of coordination mechanisms) is hardly possible. Furthermore, since the developed CP concepts have been mostly evaluated in specific scenarios and with respect to special performance indicators, traceability of results and their portability to other scenarios is limited. These issues are addressed in our current research (cf. Hellingrath and Küppers (2011a)) and shall therefore be regarded in developing and applying CP in the domain of spare parts supply chain coordination. 4. FUTURE RESEARCH TOPICS 4.1 Intelligent Maintenance Systems Future research should investigate on self-maintenance and the use of devices such as Radio-Frequency Identification (RFID), tags, sensors, actuators and mobile phones applied to IMS. The other idea of this work is to propose an integration model to manage the information in industrial maintenance processes. The proposal includes the modeling of a flexible hardware and software infrastructure, which can be applied to any maintenance application. Considering that, the bottleneck of this proposal is the relationship and exchange of data among different systems, an information management model has been developed. The model allows for integration of CAx (CAD, CAE and CAM), IM (Intelligent Maintenance) and 1
Exemplary CP concepts aim at coordinating production lot sizes (e.g. Dudek (2009)) or network capacities (e.g. Hegmanns (2010)), or integrated production and distribution planning (e.g. Böhle (2010)). Reviews and approaches for classifications are provided by, e.g., Breiter et al. (2009) or Stadtler (2009).
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MR (Mixed Reality) data. This approach provides practical and theoretical support for experiments that intend to apply mixed visualization in maintenance activities. The goal is to provide an extensible and generic model for the integration and management of maintenance data. 4.2 Spare Parts Management in the Supply Chain The resulting challenges in research of spare parts management are the maintenance status information availability, the usage of this information in planning and forecasting methods as well as the coordination of the multiple actors of spare parts supply chains. Information generated in maintenance processes is not properly deployed to improve the spare parts supply chain. Relevant data regarding expected maintenance service dates could be mentioned as an example. Providing actual information for transportation processes, logistics, and, in some critical cases, production would improve the capability and shorten the service time of maintaining production systems. Hence, planning and forecasting methods themselves should be improved by including the usage of maintenance status information. Based on the identified specificities of production, inventory management and transport processes within a spare parts supply chain, methods being capable of integrating the actual maintenance status have to be researched in order to improve the quality of the different planning results e.g. in determining inventory levels. Furthermore, informational integration and synchronization across functional and organizational boundaries in supply chains are major challenges. Overcoming them depends on a better understanding of tangible and intangible aspects, as well as on developing proper integration concepts, approaches, and tools (see section 3.2). Indeed, the interfaces between maintenance, production and transportation systems can be improved based on the mutual understanding of differences between connected functions, organizations and contexts (cf. Frazzon (2009)). Local decisions cannot only depend on the efficiency of the individual processes at different locations, but rather have to take into account the behavior of linked decision structures. Specifically for spare parts supply chains, the characteristics of the demand are a major challenge. With respect to the development of the collaborative planning concept for spare parts supply chain coordination, the proposed approach will rely on the results being achieved in our previous research in this context (see section 3.2). These results have been consolidated in the so called Framework for Intelligent Supply Chain Collaboration (FRISCO), which allows for the formal modeling and evaluation of CP concepts (cf. Hellingrath and Küppers (2011a)). The framework has been successfully applied to a – in its complexity and fundamentals to spare parts supply chains comparable – CP approach (cf. Hellingrath and Küppers (2011b)). This proof-of-concept shows that FRISCO on the one hand supports the development of CP and on the other hand allows for the estimation of a CP concept’s benefits in concrete supply chain scenarios. Hence, this
framework is intended to assist the above described development of a CP concept for spare parts supply chains. 4.3 Evaluation of the Integrated Concept As motivated above, the integration of intelligent maintenance systems with the management of the spare parts supply chain is the fundament of our approach. Hence, an evaluation of the integrated concept is aspired. The evaluation of the collaborative planning approach for coordinating the spare parts supply chain will rely on the previously described FRISCO framework. Besides supporting CP development, FRISCO’s evaluation environment provides means to execute the CP concept’s underlying processes in a testing environment in order to assess its expected benefits. Thus, applying FRISCO in this context allows for evaluating the collaborative spare parts planning concepts, their execution in an information system and hence is a step for their implementation in real supply chains. The challenge of integrating intelligent maintenance systems with spare parts supply chains will therefore be analyzed using simulation-based computational experiments and is intended to be later evaluated in real-world case studies. 5. CONCLUSION With this paper a first conceptualization of integrating IMS and spare parts supply chains planning and control has been presented, targeting at the effectiveness (guaranteeing an adequate maintenance service level) and efficiency (reduction of supply chain-wide costs) of spare parts supply chains. This issue will be achieved on the one hand by providing a model for the integration and management of maintenance data, which are gained by an IMS. On the other hand spare parts planning methods are extended by integrating the technical maintenance status information. Furthermore the transfer of CP concepts to spare parts supply chains supporting the coordination of the spare parts supply chain actor’s is considered. For evaluating the integrated concept with the described FRISCO framework and simulation-based computational experiments, exemplary numerical scenario data are needed to assess the expected benefits of the concept and to evaluate the application in real-world scenarios. Future work has to focus onto the detailed specification of the concept and the development of quantitative models for supporting the execution and the assessment of the concept. REFERENCES Böhle, C., Dangelmaier, W., Hellingrath, B. (2009). A Lot Sizing Model with Integrated Tour Planning. In: Information Control Problems in Manufacturing. Volume 13, Part 1. Böhle, C. (2010). Eine theoretische und praktische Herleitung eines Verfahrens für die kostenminimale Koordination von Lieferanten und Logistikdienstleistern zur Belieferung lieferantengesteuerter Lager. Paderborn. Breiter, A., Hegmanns, T., Hellingrath, B., Spinler, S. (2009). Coordination in Supply Chain Management - Review and Identification of Directions for Future Research. In:
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Voss, S., Pahl, J., Schwarze, S. (eds) Logistik Management. Physica-Verlag, Heidelberg. Chen, Z.-L., Pundoor, G. (2006). Order assignment and scheduling in a supply chain. In: Operations Research, 54:555-572. Chen, Z.-L., Vairaktarakis, G.L. (2005). Integrated scheduling of production and distribution operations. In: Management Science, 51:614-628. De Matta, R., Miller, T. (2004). Production and inter-facility transportation scheduling for a process industry. In: European Journal of Operational Research, 158:72-88. Dekker, R., Pinçe, Ç., Zuidwijk, R., Jalil, M.N. (2010). On the use of installed base information for spare parts logistics: a review of ideas and industry practice. Report Econometric Institute EI2010 - 73:1-22. Djurdjanovic, D., Lee, J., Ni, J. (2003). Watchdog Agent, an infotronics-based prognostics approach for product performance degradation assessment and prediction. In Advanced Engineering Informatics 17(3-4):109–125. Dudek, G. (2009). Collaborative Planning in Supply Chains: A Negotiation-Based Approach. Springer. Elwany, A.H., Gebraeel, N.Z. (2008). Sensor-driven prodnostic models for equipment replacement and spare parts inventory. In: IIE Transactions 40(7):629-639. Frazzon, E.M. (2009). Sustainability and Effectiveness in Global Logistic Systems. GITO-Verlag, Berlin. Hegmanns, T. (2010). Dezentrales Planungs- und Prozesskonzept für ein kollaboratives Bedarfs- und Kapazitätsmanagement in Produktionsnetzwerken. Verl. Praxiswissen, Dortmund. Hellingrath, B., Küppers, P. (2011a) Model-Driven Development of Multi-Agent Based Collaborative Planning Concepts for Heterarchical Supply Chains. In: Marík, V., Vrba, P., and Leitão, P. (eds) Holonic and Multi-Agent Systems for Manufacturing. Springer. Hellingrath, B., Küppers, P. (2011b) Multi-Agent Based Evaluation of Collaborative Planning Concepts in Heterarchical Supply Chains. In: Sucky, E., Asdecker, B., Dobhan, A., Haas, S., and Wiese, J. (eds) Logistikmanagement: Herausforderungen, Chancen & Lösungen; Band III. Holmström, J., Framling, K., Tuomi, J., Karkkainen, M., and Ala-Risku, T. (2002). Intelligent product agents: the key to implementing collaboration process networks? In: International Journal of Logistics Management 13(2):39–50. Jalil, M.N., Zuidwijk, R.A., Fleischmann, M., van Nunen, J.A.E.E. (2009), Spare parts logistics and installed base information. ERIM Report Series Research in Management ERS-2009-002-LIS:1-27. Kutanoglu, E., Mahajan, M. (2009). An inventory sharing and allocation method for a multi-location service parts logistics network with time-based service levels. In: European Journal of Operational Research 194(3):728742. Macchiarella, M. and Vincenzi, D. (2004). Augmented Reality In A Learning Paradigm For Flight And Aerospace Maintenance Training. In: Digital Avionics Systems Conference, 2004. DASC 04. The 23rd IEEE, pp. 5.D.1- 5.1-9.Martin, H., Symtetos, A.A., Parodi, A.,
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