Maintenance is unjustifiable : an improved inference

Abstract

Research motivesIn general, decisions appear to be encumbered by subjectivity which is problematic for their validation. In this work, however, we do aim for a validation of decisions. A maintenance policy may seem to be a suitable means of validation because it triggers decisions at a high rate and because the abundant policy violations are typically also recorded. These policy violations may therefore give a glimpse into the counterfactual reality that maintenance policy compliance intends to avoid in the first place. This work demonstrates the feasibility of this unconventional approach to maintenance policy validations. It would be naïve to expect a decisive maintenance policy validation, but at least we purport to improve the justifiability of maintenance. ApproachWe take the viewpoint that inference precision follows from the choice of an argument, an operationalisation and a sampling procedure. We develop a number of candidate arguments and samples. Our iterative journey along these candidates leads to an improved inference.Our contribution -We have implemented a maintenance policy validation by a causal argument and a sample from a realistic case study at an improved inference precision; -We have implemented a maintenance policy validation that relies on evidence about policy violations that from a normative decision theoretical perspective appears to be new; -We have implemented alternatives for conventional maintenance performance indicators that enable more precise causal inferences in the case study. ConclusionTo validate a maintenance policy by the proposed approach is very difficult if the only evidence available is from an organisation's recording routines. Therefore, an explicit justification of maintenance cannot easily be obtained. However, the proposed approach showed how to improve the associated inference precision in the specific case study. Practical implicationsThis work reveals that conventional maintenance performance indicators are typically insufficient for capturing the variations which will allow us to learn about the system behaviour. We propose and implement some construction rules for maintenance performance indicators that enable us to reveal prima facie causalities from recording routines. Although these construction rules appear to be straightforwardly implementable, they are often violated in the practice of maintenance performance measurement. We therefore argue that organisations could potentially enhance support of their maintenance policy assessments through recording routines; possibly by validating some formal argument, as we do in this work, or else by simply asking: "Where does this peak come from?".ii SamenvattingOnderzoeksmotieven Besluitvorming is subjectief waardoor een validatie lastig is. Dit werk poogt desondanks besluiten te valideren. Een onderhoudsbeleid kan een geschikte casus voor een dergelijke validatie blijken omdat het besluiten met een hoge frequentie genereert terwijl de veelvuldig voorkomende beleidsovertredingen eveneens worden geregistreerd. Deze ...

Enhanced Data Driven Decision Support

“…Weibull plots). As (life) times are not instantaneously observable, collecting sufficient replications typically requires much time (Abernethy, 2006) and the spatiotemporal proximity of a causality may remain unobserved (Rijsdijk, 2016). As time is merely seen as an uncontrollable associated variable rather than as a cause of cleaning events, inferred (life) time dependencies only enable a decision maker to predict cleaning events.…”

“…The probability value is the probability of obtaining an outcome that is at least as extreme as the outcome being observed. Rijsdijk (2016) proofed, without using some Wald statistic, that this probability value directly follows from:…”

“…( 15). The motivation for Fisher's exact conditional approach has been detailed in (Rijsdijk, 2016).…”