2024 Schedule

Asset decision-making is often performed based on the opinions of stakeholders. Some of these stakeholders may be highly influential and considered subject matter experts—and they may be wrong. Data, therefore, is essential to corroborate or refute the stakeholder narrative. Data sources for reliability engineering are often imperfect. Great manual effort is often required to validate data in order to draw meaningful conclusions and drive correct decisions. Without this data, wrong decisions will be made on reliability engineering efforts, capital investment, and so forth. If the data is messy, many will decline to trust it. Accurate reliability data is essential to understand total cost of ownership and prioritize resources to best effect. This presentation will discuss some tactics and processes for validating reliability data sources such as lost production and asset costs, while also decreasing the sources of error to this data. In order to reduce error in the data sources, people change management is often required. People need to understand the value of the data to themselves and to the business as part of the change management process. The most compelling demonstration of data value is to accurately diagnose the greatest causes of downtime, cost, or quality, and then attack these with continuous improvement tools. And so, reliability data accuracy and asset continuous improvement form a virtuous circle. Ownership and involvement with accurate reliability information can help a squeaky-wheel stakeholder or an experienced subject matter expert be a powerful advocate for correct asset decisions.