Maintenance is a combination of multilateral and cross-functional activities and processes. Maintenance processes are identified in both strategic management and operation systems. Managers, engineers, technicians and operators collaboratively contribute in conducting and performing preventive or corrective 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 practical experience. Maintenance management intends not only to keep the desired 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 continuous improvement of the maintenance cost planning and controlling. Learning 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, represented, 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 evidence for decision-makers (e.g. CMO) to discover hidden improvement potentials 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 period, 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.