Referring to the limitations discussed above, in order to “validate” (i.e. increase con-fidence) that the results from simulations hold true in reality, studies with real world data are needed. In essence, this thesis can be considered a preliminary study that is able to predict something of the real world, but needs further prove.
The final model would have been able to produce even more results than presented in this thesis. Now, the ones that felt the most interesting were chosen to be analyzed.
Some of the topics that were excluded now, but can be investigated in the future, were: how different dynamic algorithms for adjusting the workforce size of the maintenance company affect its profitability; and how the customers could make even more profit with different dynamic algorithms for adjusting the maintenance point on
the fly. It is actually argued that having a dynamic maintenance point rather than stat-ic one increases profitability. Proving this was left out of this study, but could be done later. In addition, taking competition between maintenance companies and cus-tomers into account in the model would be of interest in the future. In theoretical re-spect, this would require a literature review on competitive strategy to be done and defining how competition could be modeled on principal level. In technical respect, adding the competitive “layer” to the model would not be that hard a task. Justifying the logic would require more effort.
ABM is so powerful methodology that it is very likely that using it would reveal new insights in maintenance and in industrial management as a whole. It is argued that not many other tools would have been able to construct and simulate the same system built for this study. ABM can go beyond the possibilities of other modeling tech-niques. Thus, it is quite odd that it has been used so little in maintenance and industri-al management studies.
6 SUMMARY
In this Master’s thesis ABM has been used to analyze maintenance strategy related phenomena. The main research question that has been answered was: what does the AB model made for this study tell us about how different maintenance strategy deci-sions affect profitability of equipment owners and maintenance service providers? To answer that question, first, a literature review of maintenance strategy, ABM and maintenance modeling and optimization was conducted. This review provided the basis for the AB model. With the simulation results from the AB model the research question was answered. In simplest terms, the answer to the main research question is that there are possible ways to optimize maintenance strategy in terms of maximizing profitability.
In the beginning of the theoretical part of the thesis maintenance strategy was “de-fined”, or in other words, it was elaborated on what it means in the perspective of this
thesis. Maintenance is part of asset management that aims to manage the whole life cycle of physical assets optimally, including all related aspects. The main objective of maintenance is to preserve the condition of equipment through its operational life cy-cle. It was emphasized that maintenance objectives are related to other business ob-jectives and especially to the manufacturing obob-jectives and strategy. In SMM (strate-gic maintenance management) maintenance is seen as a core business function for overall business survival and success, and therefore it should be managed strategical-ly. This requires taking into account the maintenance strategy decision elements that were presented in the table 3 (chapter 2.1.1). Then, different and the most common strategic options for maintenance were presented. These included, for example, pre-dictive and corrective maintenance (PM / CM), condition-based maintenance (CBM), reliability-centered maintenance (RCM) and total productive maintenance (TPM).
Finally, the process of choosing an optimal maintenance strategy was elaborated. This is an iterative process consisting of identification of objectives and resources, identi-fication of the most important systems, maintenance strategy decision and optimiza-tion, and performance measurement.
Secondly, the core concepts of ABM were covered. The background on what an agent is and what the abstract structure of an AB system is were presented. ABM is basical-ly about modeling the behavior of and interaction between agents – which are, in short, individual and autonomous decision making units set to meet certain objectives – in an environment and over time. ABM is most suitable to studying bottom-up problems: how simple and predictable local patterns can lead to familiar but highly complex and enigmatic global patterns. In other words, ABM is most suitable to studying emergent phenomena.
In building AB models, the key aspects to be specified are: the beliefs agents have, the ongoing interaction between agents and their environment, the goals that agents try to achieve, and the actions agents perform and the effects of these actions. A more accurate protocol for describing AB models was presented in the table 6 (chapter
2.2.2). One key question regarding AB models is: how can we say that the modeled results are believable? To raise the confidence on the results the so called pattern-oriented modeling methodology is applied (see further explanation in the chapter 2.2.2). Finally, the main benefits of using ABM in economic studies were elaborated.
Above all the benefits, the bottom-up modeling principle makes AB models structur-ally realistic. Real world phenomena are all essentistructur-ally arising from the actions of and interaction between individuals. These individual actions and interactions can be nat-urally modeled with ABM, without imposing exogenous constraints.
Thirdly, after presenting the foundations of maintenance strategy and ABM, model-ing and optimization of maintenance strategies was investigated. The main motives for maintenance modeling are that it allows evaluating and comparing different strat-egies to each other, and that optimization models are much more precise than purely qualitative approaches to finding an optimal strategy. In general, maintenance optimi-zation models cover four basic aspects: the description of a technical system, how it deteriorates over time, what information and options are available for management, and optimization techniques. Every maintenance model incorporates prediction or extrapolation of future performance of a system, whether it is deterministic or proba-bilistic. In reality, the time span a technical system operates is never infinite, but fi-nite. Thus, maintenance optimization models should take the age of equipment into account. Finally, the most common variables that are optimized in maintenance mod-els were presented in the table 7 (chapter 2.3.2). Of these, the profitability was chosen to be optimized in the AB model made for this study.
Before explaining the AB model made for this thesis, previous optimization models were investigated in order to get a view of what has been already done and how. Oth-er than AB methods wOth-ere briefly covOth-ered in the chaptOth-er 2.3.3, and the table 8 summa-rized the basic logics of each of those techniques. The AB model borrows some as-pects from those techniques, such as from Bayesian and Markovian approaches. After introducing these other than AB methods, two actual AB models were more deeply
looked through. Only these two AB simulation models on maintenance optimization were actually found, which points out that ABM has not been used enough in mainte-nance optimization. What comes to taking the maintemainte-nance strategy decision elements into account, the two models were somewhat narrow in their scope. The other one showed only the potential of ABM in maintenance optimization, and the other one utilized ABM in a very narrow and specific application. However, these models pro-vided some basis for the model made for this study.
Finally, the AB model made for this study was explained from defining its purpose and boundaries to dynamic hypothesis, structure, and testing. The final model is that complex so that no revision is given here, and one is encouraged to see the model de-tails in the chapter 3. The simulations gave us answers to the question of how price and pricing logic (time- or value-based) affect the profitability of both the customers and maintenance companies in different maintenance points. And in addition, the re-sults answered how the error in machine condition measurement affects the profitabil-ity in different maintenance points while having a specific pricing logic.
The main and compressed results from the simulations were the following. It is pos-sible to optimize the point when a machine is maintained in order to maximize profits for either the owner of the machine or / and its maintainer. Different pricing logics (time- or value-based) affect the rationale for optimization. In time-based pricing, the owner of the machine wants to minimize the time needed for maintenance, as the maintainer wants to maximize it, leading to a zero-sum game. In value-based pricing, the maintainer wants to maximize the profits of the owner of the machine, as this maximizes the maintainer’s profits. Thus, value-based pricing should be favored compared to time-based pricing, because in value-based pricing both the owner of the machine and the maintainer can operate efficiently, when the maintenance point is optimized from the perspective of the owner of the machine. Finally, it was found that the error in machine condition measurement is a critical parameter in optimizing maintenance point and for profitability. The smaller the errors in condition
measure-ment, the higher the profitability is on average, and the better we are able to optimize the maintenance point, leading to even more increased profitability. Without knowing or with too big errors in condition measurement we cannot really optimize the maintenance point.
The main limitation of this study was the lack of real world data that would have been used as the input and as the reference for the simulation model. The lack of data forced the AB model to be conceptual. However, structural realism of the model was increased by designing the model based on literature findings and feedback from the supervisor and reviewers of this thesis. As a suggestion for future research, the con-clusions from this research should be verified to be true in reality by conducting a study in which real data is available.
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