Smart Maintenance - maintenance in digitalised manufacturing

What does digitalised manufacturing entail for maintenance organizations? This is a pressing question for practitioners and scholars within industrial maintenance management who are trying to figure out the best ways for responding to the rapid advancement of digital technologies. As technology moves faster than ever before, this is an urgent matter of uttermost importance. Specifically, in order to secure the success of highly automated, intelligent, connected and responsive production systems, industrial maintenance organizations need to transform to become leading enablers of high performance manufacturing in digitalised environments. In this thesis, this transformation is referred to as “Smart Maintenance”. The purpose of this thesis is to ensure high performance manufacturing in digitalised environments by enabling the adoption of Smart Maintenance. In order to stimulate adoption, a holistic understanding of Smart Maintenance is needed. Therefore, the aim of this thesis is to describe future scenarios for maintenance in digitalised manufacturing as well as to conceptualize and operationalize Smart Maintenance. The holistic understanding has been achieved through a phenomenon-driven research approach consisting of three empirical studies using multiple methods. Potential changes for maintenance organizations in digitalised manufacturing are described in 34 projections for the year 2030. From these projections, eight probable scenarios are developed that describe the most probable future for maintenance organizations. In addition, three wildcard scenarios describe eventualities that are less probable, but which could have large impacts. These scenarios serve as input to the long-term strategic development of maintenance organizations.Smart Maintenance is defined as “an organizational design for managing maintenance of manufacturing plants in environments with pervasive digital technologies” and has four core dimensions: data-driven decision-making, human capital resource, internal integration and external integration. Manufacturing plants adopting Smart Maintenance are likely to face implementation issues related to change, investments and interfaces, but the rewards are improved performance along multiple dimensions when internal and external fit have been achieved. Smart Maintenance is operationalized by means of an empirical measurement instrument. The instrument consists of a set of questionnaire items that measure the four dimensions of Smart Maintenance. It can be used by practitioners to assess, benchmark and longitudinally evaluate Smart Maintenance in their organization, and it can be used by researchers to study how Smart Maintenance impacts performance. This thesis has the potential to have a profound impact on the practice of industrial maintenance management. The scenarios described allow managers to see the bigger picture of digitalisation and consider changes that they might otherwise ignore. The rich, understandable, and action-inspiring conceptualization of Smart Maintenance brings clarity to practitioners and policy-makers, supporting them in developing implementation strategies and initiatives to elevate the use of Smart Maintenance. The measurement instrument makes it possible to measure the adoption of Smart Maintenance in manufacturing plants, which serves to develop evidence-based strategies for successful implementation. Taken together, the holistic understanding achieved in this thesis enables the adoption of Smart Maintenance, thereby ensuring high performance manufacturing in digitalised environments

Adoption patterns and performance implications of Smart Maintenance

To substantiate and extend emergent research on maintenance in digitalized manufacturing, we examine adoption patterns and performance implications of the four dimensions of Smart Maintenance: data-driven decision-making, human capital resource, internal integration, and external integration. Using data collected from 145 Swedish manufacturing plants, we apply a configurational approach to study how emergent patterns of Smart Maintenance are shaped and formed, as well as how the patterns are related to the operating environment and the performance of the manufacturing plant. Cluster analysis was used to develop an empirical taxonomy of Smart Maintenance, revealing four emergent patterns that reflect the strength and balance of the underlying dimensions. Canonical discriminant analysis indicated that the Smart Maintenance patterns are related to operating environments with a higher level of digitalization. The results from ANOVA and NCA showed the importance of a coordinated and joint Smart Maintenance implementation to the maintenance performance and productivity of the manufacturing plant. This study contributes to the literature on industrial maintenance by expanding and strengthening the theoretical and empirical foundation of Smart Maintenance, and it provides managerial advice for making strategic decisions about Smart Maintenance implementation

Building and testing necessity theories in supply chain management

This article contributes to the Emerging Discourse Incubator initiative by presenting how supply chain management scholars can contribute to theory development by means of necessity theories. These are unique theories that inform what level of a concept must be present to achieve a desired level of the outcome. Necessity theories consist of concepts that are necessary but not sufficient conditions for an outcome, where the absence of a single causal concept ensures the absence of the outcome. The theoretical features of necessary conditions have important implications for understanding supply chain management phenomena and providing practical applications. In 2016, Necessary Condition Analysis (NCA) became available for building and testing necessity theories with empirical data. However, NCA has not yet been used for the development of supply chain management theories. Therefore, we explain how necessity theories can be built and tested in a supply chain management context using necessity logic and the empirical methodology of NCA. We intend to inspire scholars to develop novel necessity theories that deepen or renew our understanding of supply chain management phenomena

A Strategy Development Process for Smart Maintenance Implementation

Technological advancements are reshaping the manufacturing industry toward digitalized manufacturing. Despite the importance of top-class maintenance in such systems, many industrial companies lack a clear strategy for maintenance in digitalized manufacturing. The purpose of this paper is to facilitate the implementation of maintenance in digitalized manufacturing by proposing a strategy development process for the Smart Maintenance concept.A process of strategy development for smart maintenance is proposed, including six steps: benchmarking, setting clear goals, setting strategic priority, planning key activities, elevating implementation and follow-up.The proposed process provides industry practitioners with a step-by-step guide for the development of a clear smart maintenance strategy, based on the current state of their maintenance organization. This creates employee engagement and is a new way of developing maintenance strategies

A similarity-based Bayesian mixture-of-experts model

We present a new nonparametric mixture-of-experts model for multivariate regression problems, inspired by the probabilistic $k$-nearest neighbors algorithm. Using a conditionally specified model, predictions for out-of-sample inputs are based on similarities to each observed data point, yielding predictive distributions represented by Gaussian mixtures. Posterior inference is performed on the parameters of the mixture components as well as the distance metric using a mean-field variational Bayes algorithm accompanied with a stochastic gradient-based optimization procedure. The proposed method is especially advantageous in settings where inputs are of relatively high dimension in comparison to the data size, where input--output relationships are complex, and where predictive distributions may be skewed or multimodal. Computational studies on two synthetic datasets and one dataset comprising dose statistics of radiation therapy treatment plans show that our mixture-of-experts method performs similarly or better than a conditional Dirichlet process mixture model both in terms of validation metrics and visual inspection

Hindering Factors in Smart Maintenance Implementation

In today’s industrial environment, innovations and advancements in technology are extremely fast. This development has led to a Fourth Industrial Revolution where industrial companies strive to achieve highly digitalized and resilient production systems. To realize such production systems, the role of maintenance is critical. Industrial companies are anticipated to transform their maintenance organizations towards Smart Maintenance, but they need evidence-based guidance in pursuing such an implementation. Thus, the purpose of this paper is to support industry practitioners in their Smart Maintenance implementation. By means of an empirical case study within energy production, this paper identifies and describes hindering factors that impede the implementation of Smart Maintenance, as well as provides recommendations for overcoming the hindering factors. The recommendations can be used by industry practitioners to increase the likelihood of success in their Smart Maintenance implementation, thereby helping industrial companies in their development of sustainable and resilient production systems