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Design and Analysis of Robust Kanban System in an Uncertain Environment

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Design and Analysis of Robust Kanban System in an Uncertain Environment Design and Analysis of Robust Kanban System in an Uncertain Environment Zur Erlangung des akademischen Grades eines Doktors der Ingenieurwissenschaften der Fakultät für Maschinenbau des Karlsruher Instituts für Technologie (KIT) genehmigte Dissertation von M.Sc. Zhe Li Tag der mündlichen Prüfung: 18. Feb. 2013 Hauptreferent: Prof. Dr.-Ing. Kai Furmans Korreferent: Prof. Dr. Stefan Nickel Vorwort Ich möchte mich bei meiner größten Quelle der Unterstützung bedanken: Prof. Dr.-Ing. Kai Furmans, meinem Doktorvater. Ohne seine Unterstützung und Anleitung wäre diese Arbeit nicht möglich gewesen. Ich bin sehr dankbar, dass er dies sowohl akademisch als auch persönlich zeigte. Er hatte immer ein offenes Ohr, gab mir wertvolle Ratschläge, hat meine Arbeit im Detail gelesen, und mir geholfen die Schwierigkeiten im Leben einer ausländischen Studentin zu bewältigen. Ich möchte Prof. Dr. Stefan Nickel, meinem zweiten Gutachter, für seine Anregun- gen und Kommentare zu dieser Dissertation und seiner Bereitschaft, das Koreferat zu übernehmen danken. Ich möchte auch Prof. Dr.-Ing. Gisela Lanza danken, sie ist die Vorsitzende meiner Mündlichprüfung, und gab mir viele hilfreiche Ratschläge. Ich danke zutiefst meiner liebsten Freundin und Kollegin Judith Weiblen. Sie hat mir eine unschätzbare Hilfe und Freundschaft gegeben, seit meinem ersten Arbeitstag am IFL. Ich bin ihr dankbar für alles. Ohne Judith und ihre Familie, hätte ich nicht so schöne Erfahrungen im wunderbaren Deutschland gehabt. Mein tiefer Dank geht auch an meine Freunde und IFL Kollegen Melanie Schwab, Martin Veit, Christoph Nobbe, und Eda Özden. Sie boten immer großzügige Hilfe und warme Ermutigungen, sowohl in meinem akademischen als auch persönlichen Leben. Ich möchte auch meinen anderen IFL Kollegen für die gute Arbeitsatmosphäre danken. Meine chinesischen Freunde Yan Xiaoxing, Yang Qian, und meine Cousine Sun Jia, haben mir in dieser schweren Zeit sehr geholfen. Sie waren immer da, um mich aufzu- muntern und mir Mut zu machen. Ich möchte auch meiner Deutschlehrerin Carmen Reck danken. Ihre Freundlichkeit und Beratung waren mir eine wertvolle Hilfe die deutsche Kultur zu erlernen. Ich bedanke mich bei dem Chinese Scholarship Council (CSC) und dem Karlsruhe House of Young Scientists (KHYS) für die finanzielle Unterstützung während meiner Pro- motion am IFL. Mein letzter und herzlicher Dank geht an meine Eltern, die immer für mich da waren, und für alles. Karlsruhe, Februar 2013 Zhe Li i ii Kurzfassung Zhe Li Entwicklung und Analyse eines robusten Kanban-Systems in einem unsicheren Umfeld Das traditionelle Supply Chain Management hat das Ziel System billiger und schlanker zu gestalten. Die Kanban-Steuerung ist dafür eine unterstützende Methode. Jedoch hat die Kanban-Steuerung auch Beschränkungen. Einerseits reduziert sie die Kosten der Operation für die Supply Chain, andererseits erhöht sie aber auch die Verletzlichkeit des Systems. Insbesondere wenn die Umgebung nicht stabil sondern volatil ist, ist diese Beschränkung offensichtlich. Moderne Supply Chain Operationen müssen verschiedene Risiken eines unsicheren Umfeldes überstehen. Deshalb ist es sinnvoll eine robuste Supply Chain, die mit den Unsicherheiten der Umwelt umgehen kann, zu entwickeln. Da dieses Thema von großem Interesse ist, haben sich in den letzen Jahren viele Forscher und Praktiker damit beschäftigt. In dieser Arbeit wird eine robustes Kanban Modell vorgestellt. Das Modell zielt da- rauf ab, das Supply Chain System mit einem Mechanismus für unterschiedliche Risiken auszustatten und eine gute Leistung in einem unsicheren Umfeld zu erhalten. Die An- wendung des hier entwickelten robusten Kanban Systems sind praktisch, und das Modell kann leicht an ein breites Anwendungsspektrum angepasst werden. Die Arbeit besteht aus vier Hauptteilen. Zuerst, wird ein konzeptionelles Modell des robusten Kanban-Systems entwickelt. Dann wird ein Simulationsmodell erstellt, um das konzeptionelle Modell umzusetzen. In diesem Modell wird der Mechanismus zur Reaktion auf Risiken des unsicheren Um- feldes bereits berücksichtigt. Durch Änderung der System-Konfigurationsparameter auf drei Ebenen, kann das System schnell und effektiv auf unterschiedliche Risikosituatio- nen reagieren. Die Änderung der Parameter können systematisch von über die operative Ebene (z.B. Änderung der Kanban-Anzahl), zur taktischen Ebene (z. B. Änderung der Maschinenbetriebszeit), bis hin zur strategischen Ebene (z. B. einen Ersatz-Anbieter oder einen neuen Server auswählen) eskaliert werden, um die Risiko-Auswirkungen zu mindern und die Störungen des Systems zu vermeiden. Im zweiten Teil, werden Methoden der Parametrierung entwickelt, um geeignete Pa- rameterwerte für den Risiko-Mechanismus zu finden. Die Parameter sind für die Kon- figuration des robusten Kanban-Systems und die Umsetzung des Risiko-Mechanismus auszuwählen. Ein MINLP (mixed integer nonlinear programming) Modell wurde en- twickelt, um die Basiswerte und die Abgrenzung der 3-Ebene einstellbaren Konfigu- rationsparameter zu wählen (einschließlich der Kanban-Anzahl, Maschinenbetriebszeit, iii Kurzfassung Server-Anzahl und Ersatz-Lieferanten). Einige Ansätze wurden benutzt, um geeignete Parameterwerte des Risiko-Mechanismus zu schätzen. Im dritten Teil, wird ein vergleichendes Experiment mit den Kanban-System Sim- ulationsmodellen vorgenommen. Einige Risikoszenarien werden angenommen, und an- schließend wird die Leistung des hier vorgestellten robusten Kanban-Systems mit den Leistungen von zwei aus der Literatur bekannten Kanban-Systemen (Traditional Toyota Kanban-System, Inventory-based adaptive Kanban-System) verglichen. Basierend auf den Vergleichsergebnissen, bestätigt sich, dass durch das robuste Kanban-System eine Leistungssteigerung in einem unsicheren Umfeld gewonnen werden kann. Im letzten Teil, wird ein Simulations-basiertes facktorielles Experiment durchgeführt, um die Wahl der Basiswerte der Steuerparameter zu verbessern. Zunächst wird ein fak- torielles Experiment entwickelt; dann werden sogenannte Response Surface Methoden angewandt, um die optimalen Parameterwerte (optimal im Sinne des besten NetProfit Ergebnis) herauszufinden. Mit den neuen Parameterwerten kann die Leistung des ro- busten Kanban-System noch weiter verbessert werden. iv Abstract Zhe Li Design and Analysis of Robust Kanban System in an Uncertain Environment Traditional supply chain management focused on making the system cheaper and leaner. In this direction, the Kanban mechanism is a representative control policy pursuing cost- efficient features for the material flow system. However, the adoption of the Kanban mechanism increases the system vulnerability while reducing the cost of operating the supply chain, especially when the environment is not stable. Modern supply chains are subject to various risks in an uncertain environment. Therefore, to build a robust supply network, which can deal with the environment uncertainties, has obtained wide attention among researchers and practitioners in recent years. In this study, we proposed a robust Kanban system model for the supply chain system based on the Kanban mechanism. The model aims at helping the supply chain system deal with various risks in an uncertain environment. The proposed robust Kanban system model and its application methods are practical to use, they can be easily extended to a wide range of applications. The work in this dissertation is composed of four parts. Firstly, a conceptual model of the robust Kanban system is designed, and then a simulation model is built to implement the design ideas. The robust Kanban system uses a risk-response mechanism to handle risks in an uncertain environment. The system can respond quickly and effectively to a variety of risk situations by adjusting a series of system configuration parameters. In order to mitigate the impact of risks and help the system recover from disruption, the response actions are taken systematically from an operational level (e.g. change Kanban number), a tactical level (e.g. adjust machine service time), or a strategic level (e.g. use a backup supplier, start a new server). In the second part, we introduce methods for determining suitable parameter set- tings. The parameters are used for specifying the robust Kanban system configuration and implementing the risk-response mechanism. A mixed integer nonlinear programming (MINLP) model is developed to decide the basic values and ranges of 3-level configuration parameters (including Kanban number, machine service time, server number, and backup supplier supply proportion). And a set of estimation approaches are used to find suitable values of the control parameters that are used in the risk-response mechanism. In the third part, a comparative experiment is carried out based on Kanban system simulation models. Given a variety of risk scenarios, we compare the performance of the proposed robust Kanban system and two other Kanban systems from previous literature (Traditional Toyota Kanban system, Inventory-based adaptive Kanban system). Based v Abstract on comparison results, we confirm the performance improvement made by the robust Kanban system in the uncertain environment. In the last part, we perform another simulation-based experiment to find better set- tings for the control parameters that are used in the risk-response mechanism. We design a factorial experiment, then use response surface methods to determine optimal factor setting (that generates maximum Netprofit in a given region). Using the new parameter setting, the performance of the robust Kanban system
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