Proper data availability within a manufacturing enterprise directly drives the ability of industry decision makers to function and operate at optimal effectiveness. The needs of decision makers can vary greatly based on, not only the level at which the decision is being made, but also the perspective and desired effect of that decision. For example, an equipment-level operator needs direct knowledge of that equipment's condition when deciding whether to operate that machine today; a production manager needs to know the number of operational machines when planning system-level operations; a maintenance manager needs knowledge of what maintenance tasks are in the queue and the availability of technicians. Although each decision is related, the information required to support each decision is distinct, dissimilar, and generated from sources that are often independent of one another. The granularity of information needed to make a decision is informed directly by what that decision is and any consequences of that decision. This paper discusses information and data requirements for maintenance decisions in manufacturing from multiple perspectives, including system, equipment, and component -level decisions. These decisions include both structured maintenance (planned and scheduled in advance of failures with long term intervals) and unstructured maintenance (planned and scheduled after a failure) decisions. The goal of this paper is to guide manufacturers to find a minimally optimal set of the correct data to support the decisions they want to make.
Proceedings of 2019 Model-Based Enterprise Summit
April 1-4, 2019
Model-Based Enterprise Summit 2019
Maintenance, Sensing, Data Curation, Operations Management, Decision Support