Action rule mining seeks to generate rules that indicate what changes can be made to move an object from one class (state) to another class. An action rule is composed of the changes, known as actions, that correlate with the change in the class value. The current work in action rule mining focuses on frequent, or highly occurring, action rules. The working assumption is that the end user is interested in class transitions that move a large number of objects from the initial class to the new (final) class with a high degree of confidence. Currently, very little work focuses on rare action rules; that is, classes which occur infrequently. In this paper, we provide a definition for a rare action rule and then propose a consequent-constraint based algorithm for generating these rare action rules.

Optimal inventory management always requires the right number and types of parts to be available at the right time at the right place. However, this has become even more critical in the age of prescriptive maintenance, which seeks to predict an impending failure along with what part(s) are about to fail. To take advantage of these predictions, it is essential that the parts, personnel and time frame are made available in a timely fashion to keep the operation going uninterrupted. Usually, the level of parts maintained in a part warehouse is determined using static thresholding where quantities of each part is determined and is fixed for a given part. In literature, such methods are known as static thresholding. While work has been done to accommodate unpredictability of parts usage in supply chain, there is lack of attention that has been paid to consider the demand of parts in future due to prediction of impending failure. To address this, this paper introduces a noble method and system, entitled dynamic thresholding, to ensure the part inventory adapts to predicted usage while maintaining minimum inventory management cost. The feasibility of the concept and system is demonstrated using simulated datasets.

Detecting anomalies, in many cases, is only the first step. Often, it is merely the first step in a process that seeks to recover from an anomalous situation so that the system is back to a normal state. Manual analysis is a possible approach; however, this can be time consuming and error prone. Ideally, it would be desirable to have an automated means for resolving anomalies, which is invoked whenever one or more anomalies are detected. In this paper, we present a tool, TADS (Transformation of Anomalies in Data Streams), that creates automated recommendations based upon the observed streaming data. This is accomplished by utilizing the recently introduced concept of dynamic action rules with the Massive Online Analysis (MOA) data streaming platform. In experimental results, we demonstrate that TADS is able to correctly generate recommendations 100% of the time for over half the experiments and over 90% for all but two experimental conditions. In addition, only seconds are required to generate the personalized recommendation for each anomaly. Hence, the results indicate that TADS is a viable approach for correcting anomalies within a streaming environment.

The concept of equipment maintenance is as old, if not older, as industrial revolution. However, the mode, medium and timing of maintenance during equipment life cycle have evolved from reactive maintenance to prescriptive maintenance. Reactive maintenance was the main approach practiced until 1980s. The proactive maintenance was introduced in 1990s to insulate customers from equipment failures by using equipment data and fixing problems remotely. Prescriptive maintenance, which incorporates the Internet of Things, digitization and artificial intelligence, especially machine learning, has gained importance as in intelligent approach to equipment maintenance and has the potential to greatly improve upon proactive maintenance. However, existing prescriptive maintenance solutions are piece-meal and lack complete solutions to keep equipment in working conditions most of the time at optimal cost. We propose a Holistic End to End Prescriptive Maintenance (HeePMF) framework resulting in much reduced unplanned equipment downtime at optimal cost. This framework provides solution by integrating maintenance needs analysis, equipment and operational data, predictive technologies with feedback, personnel scheduling, supply chain improvement, part management, process improvement and knowledge management. The working of framework is demonstrated by providing a case study.