Meta-Analysis of Knowledge Assets for Continuous Improvement of Maintenance Cost Controlling
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Meta-analysis of Knowledge Assets for Continuous Improvement of Maintenance Cost Controlling genehmigte DISSERTATION zur Erlangung des Grades eines Doktors der Ingenieurwissenschaften vorgelegt von M.Sc. Fazel Ansari Chaharsoughi eingereicht bei der Naturwissenschaftlich-Technischen Fakultät der Universität Siegen Siegen 2014 gedruckt auf alterungsbeständigem holz- und säurefreiem Papier 1. Gutachter: Prof. Dr.-Ing. Madjid Fathi 2. Gutachter: Prof. Dr. Volker Wulf Tag der mündlichen Prüfung: 20.11.2014 I Meta-analysis of Knowledge Assets for Continuous Improvement of Maintenance Cost Controlling Acknowledgements My work owes its success to a great number of people. Foremost, I would like to acknowledge the contribution of my family. Without the support of my wife and colleague, Marjan, the successful development and completion of my research would not have been possible. Marjan inspires me to endure the usual hardship experienced with such long-term research. I am delighted to dedicate this work to her. My work has also taken me far away from my homeland and, therefore, has prevented me from sharing certain special moments with my parents. I hope to be a better family member and son with regards to involve- ment in family matters in the future. I would like to thank my first Ph.D. supervisor Prof. Dr.-Ing. Madjid Fathi, chair of the Institute of Knowledge-Based Systems and Knowledge Manage- ment (KBS&KM) at the University of Siegen. He has supported me in devel- oping an academic career and provided opportunities for me to become in- volved in both European- and industry-funded research projects. I would also like to thank my second Ph.D. supervisor Prof. Dr. Volker Wulf, chair of the Institute of Information Systems and New Media and Dean of School of Economic Disciplines at the University of Siegen. I am grateful to him for reviewing the dissertation and providing constructive feedback. I am greatly indebted to Prof. Dr. Ulrich Seidenberg, head of the Institute of Production and Logistics Management (PLM) at the University of Siegen. His strong support and priceless feedback have been an inspiration. I thank Prof. Dr.-Ing. Claus-Peter Fritzen, chair of the group of Applied Me- chanics at the University of Siegen, for supporting our collaborative research with his group. I express my thanks to Dr. Achim Hennings, lecturer at the University of Sieg- en, for reviewing the mathematical model, participating in several hours of discussion, and providing invaluable feedback. Not forgetting that an important aspect over the course of this research has been how to apply it in practice, I thank Dr. Uwe Hermann (Elmos Semicon- ductor AG) for assessing Costprove model, and Dr. Ralf Montino (Elmos Semiconductor AG) for supporting the interviews with maintenance profes- sionals. Special gratitude also goes out to Patrick Uhr and Susanne Dienst, my col- leagues at KBS&KM, for their cooperation and significant inputs towards applying the textual meta-analysis in maintenance cost controlling and the mathematical meta-analysis in product improvement, respectively. I thank Dr. Stefan T. Mol, assistant professor at Amsterdam Business School, for his support. Last, but not least, I acknowledge with gratitude all the master, diploma, or bachelor students who contributed to this interdisciplinary research. Siegen, July 2014 Meta-analysis of Knowledge Assets for Continuous Improvement of Maintenance Cost Controlling II Abstract Maintenance is a combination of multilateral and cross-functional activities and processes. Maintenance processes are identified in both strategic manage- ment and operation systems. Managers, engineers, technicians and operators collaboratively contribute in conducting and performing preventive or correc- tive maintenance activities. Maintenance management is to provide the long- term business strategy that ensures capacity of the production, quality of the product, and the best life cycle cost. It is a decision-making activity which has been highly correlated with expertise of maintenance staff and their own prac- tical experience. Maintenance management intends not only to keep the de- sired performance of machinery, but to continuously improve quality and cost effectiveness of the pertained processes. Maintenance cost management (MCM), consisting of cost planning, monitoring and controlling, thereby is an essential part of the sustainable and efficient maintenance management system. MCM is determined as a knowledge-centered and experience-driven process where exploiting existing knowledge and generating new knowledge strongly influences every instance of cost planning. Taking into account the dynamics of knowledge assets, an interdisciplinary research raises practical implications in the domain of maintenance. The key aspect of the present work is learning from past experiences for con- tinuous improvement of the maintenance cost planning and controlling. Learn- ing in MCM is an evolutionary and iterative process through which a chief maintenance officer (CMO) compounds and deepens his/her knowledge. CMO analyzes former experiences gained in the past maintenance planning periods, identifies facts or artifacts (i.e. evidence for improving the planning process), and finally enhances planning of the upcoming events by applying the lessons learned. This work principally constitutes a model, Costprove, for meta-analysis of maintenance knowledge assets. The knowledge assets are articulated, repre- sented, and stored in repositories (i.e. explicit knowledge), or remain with (a group of) individuals and need to be extracted, documented, and validated (i.e. implicit knowledge). Meta-analysis is a set of methods for discovering the strength of the relation between certain predefined entities. It provides evi- dence for decision-makers (e.g. CMO) to discover hidden improvement poten- tials in cost planning, and incrementally attain desired company objectives. The main focus of this work is to establish a mathematical meta-analysis for (i) identifying the relation between cost figures (planned, unplanned and total cost), and operation parameter (number of maintenance activities), and (ii) trading-off between planned and unplanned cost. Hence, the model deploys an economic approach for identifying desired cost figures in every planning peri- od, and ultimately defining operation-related parameters. Anticipating the trend of the fourth industrial revolution, the foremost result of this thesis is the development of an integrated and practice-oriented knowledge-based approach to maintenance cost planning and controlling. III Meta-analysis of Knowledge Assets for Continuous Improvement of Maintenance Cost Controlling Zusammenfassung Instandhaltung ist eine Kombination aus multilateralen und bereichsübergreifenden Aktivitäten und Prozessen. Instandhaltungsprozesse bestehen sowohl aus strateg- ischem Management als auch aus operativen Systemen. Manager, Ingenieure, Techniker und Anwender leisten gemeinsam Beiträge zur Administration und Durchf- ührung präventiver oder wiederherstellender Instandhaltungsaktivitäten. Das Instand- haltungsmanagement dient dazu, für die langfristige Unternehmensstrategie zu sor- gen, die die Produktionskapazitäten, die Produktqualität und die niedrigsten Lebens- zykluskosten gewährleistet. Es handelt sich um eine Funktion der Entscheid- ungsfindung, die hochgradig mit der Expertise der Instandhaltungsmitarbeiter und ihren eigenen praktischen Erfahrungen verbunden ist. Das Instandhaltungsmanag- ement sorgt nicht nur dafür, die gewünschte Leistungsfähigkeit des Maschinenparks zu gewährleisten, sondern verbessert kontinuierlich die Qualität und Kosteneffek- tivität der betreffenden Prozesse. Das Instandhaltungskosten-management (MCM), bestehend aus Kostenplanung, -überwachung und -steuerung, ist somit ein wesent- licher Bestandteil eines nachhaltigen und effizienten Instandhaltungsmanagementsy- stems. MCM ist als wissenszentrierter und erfahrungsgetriebener Prozess zu verst- ehen, bei dem die Nutzung vorhandenen und die Generierung neuen Wissens jedes Element der Kostenplanung maßgeblich beeinflusst. Unter Einbeziehung der Dynam- ik der Wissensbestände erhöht ein interdisziplinärer Forschungsansatz die praktische Relevanz im Bereich der Instandhaltung. Der Schlüsselaspekt der vorliegenden Arbeit ist „aus Erfahrungen lernen“ mit dem Ziel einer kontinuierlichen Verbesserung der Instandhaltungskostenplanung und - kontrolle. Lernen im Instandhaltungskostenmanagement ist ein evolutionärer und iterativer Prozess, durch den der „Chief Maintenance Officer“ (CMO) sein Wissen verknüpft und vertieft. Der CMO analysiert bisherige Erkenntnisse, die in den vergan- genen Planungsperioden erworben wurden, identifiziert Fakten oder Hypothesen (z.B. Bestätigungen für die Verbesserung des Planungsprozesses) und verbessert schließl- ich die Planung des zukünftigen Geschehens durch die Umsetzung des Erlernten. In dieser Arbeit wird insbesondere ein Modell, Costprove, für die Meta-Analyse von Instandhaltungswissen entwickelt. Die Wissensbestände sind artikuliert, dargestellt und abgespeichert (explizites Wissen) oder befinden sich bei Personen und müssen extrahiert, dokumentiert und validiert werden (impliziertes Wissen). Meta-Analyse ist ein Methodenpaket zur Entdeckung der Stärke der Beziehung zwischen bestimmten vordefinierten Objekten. Es liefert den Entscheidern (z.B. CMO) Hinweise, verdeckte Verbesserungspotentiale bei der Kostenplanung zu finden und schrittweise die gestec- kten Unternehmensziele zu erreichen. Der Hauptfokus