PurposeThe after-sale service industry is estimated to contribute over 8 percent to the US GDP. For use in this considerably large service management industry, this article provides verification in the application of decision tree-based machine learning algorithms for optimal maintenance decision-making. The motivation for this research arose from discussions held with a large agricultural equipment manufacturing company interested in increasing the uptime of their expensive machinery and in helping their dealer network.Design/methodology/approachWe propose a general strategy for the design of predictive maintenance systems using machine learning techniques. Then, we present a case study where multiple machine learning algorithms are applied to a particular example situation for an illustration of the proposed strategy and evaluation of its performance.FindingsWe found progressive improvements using such machine learning techniques in terms of accuracy in predictions of failure, demonstrating that the proposed strategy is successful.Research limitations/implicationsThis approach is scalable to a wide variety of applications to aid in failure prediction. These approaches are generalizable to many systems irrespective of the underlying physics. Even though we focus on decision tree-based machine learning techniques in this study, the general design strategy proposed can be used with all other supervised learning techniques like neural networks, boosting algorithms, support vector machines, and statistical methods.Practical implicationsThis approach is applicable to many different types of systems that require maintenance and repair decision-making. A case is provided for a cloud data storage provider. The methods described in the case can be used in any number of systems and industrial applications, making this a very scalable case for industry practitioners. This scalability is possible as the machine learning techniques learn the correspondence between machine conditions and outcome state irrespective of the underlying physics governing the systems.Social implicationsSustainable systems and operations require allocating and utilizing resources efficiently and effectively. This approach can help asset managers decide how to sustainably allocate resources by increasing uptime and utilization for expensive equipment.Originality/valueThis is a novel application and case study for decision tree-based machine learning that will aid researchers in developing tools and techniques in this area as well as those working in the artificial intelligence and service management space.

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This paper presents a neural network clustering method for the part-machine grouping problem in group technology. Among the several neural networks, a Carpenter-Grossberg network isselecteddue to the fact that this clustering method utilizesbinary-valued inputs and it can be trained without supervision. It is shown that this adaptive leader algorithm offersthe capability of handling large, industrysizedata sets due to the computational efficiency. The algorithm was tested on three data sets from prior literature, and solutions obtained werefound to result in block diagonal forms. Some solutions were also found to be identical to solutions presented by others. Experimentson larger data sets, involving 10000 parts by 100 machine types,revealedthat the method results in the identificationof clusters with fast execution times. If a block diagonal structure existed in the input data, it was identifiedto a good degreeof perfection. It was also found to be efficient with some imperfections in the data.

This study addresses the problem of identifying families of parts having a similar sequence of operations. This is a prerequisite for the implementation of cellular manufacturing, group technology, just-in-time manufacturing systems, and for streamlining material¯ows in general. A pattern recognition approach based on arti® cial neural networks is proposed, and it is shown that the Fuzzy ART neural network can be e ectively utilized for this application. First, a representation scheme for operation sequences is developed, followed by an illustrative example. A more comprehensive experimental veri® cation, based on the mixture-model approach is then performed to evaluate its performance. The experimental factors include size of the part± machine matrix, proportion of voids, proportion of exceptional elements, and vigilance threshold. It is shown that this neural network is e ective in identifying good clustering solutions, consistently and with relatively fast execution times.

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