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

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

The use of engineering tools and methods in maintenance organisations: mapping the current state in the manufacturing industry

Digitalisation is the future of the manufacturing industry, and it will entail production systems that are highly automated, efficient, and flexible. The realisation of such systems will require effective maintenance organisations that adopt engineering approaches, e.g. engineering tools and methods. However, little is known about their actual extent of use in industry. Through a survey study in 70 Swedish manufacturing companies, this study shows to what extent engineering tools and methods are used in maintenance organisations, as well as to what extent companies have maintenance engineers performing work related to engineering tools and methods. Overall, the results indicate a potential for increasing the use of engineering tools and methods in both the operational and the design and development phase. This increase can contribute towards achieving high equipment performance, which is a necessity for the realisation of digital manufacturin

Quantifying the Effects of Maintenance - a Literature Review of Maintenance Models

To secure future competitiveness, manufacturing companies have started a digital transformation where equipment and systems become more complex. To handle the complexity and enable higher levels of automation, maintenance organization is expected to take a key role. However, there are well-known challenges in industry to quantify the effects of maintenance, and thereby argue for maintenance investments. To quantify the effects, researchers have developed several models, but their application is limited in industry. This paper presents a structured literature review of existing maintenance models and discusses how to increase their applicability for practitioners in industry

A Methodology for Continuous Quality Assurance of Production Data

High quality input data is a necessity for successful Discrete Event Simulation (DES) applications, and there are available methodologies for data collection in DES projects. However, in contrast to standalone projects, using DES as a day-to-day engineering tool requires high quality production data to be constantly available. Unfortunately, there are no detailed guidelines that describes how to achieve this. Therefore, this paper presents such a methodology, based on three concurrent engineering projects within the automotive industry. The methodology explains the necessary roles, responsibilities, meetings, and documents to achieve a continuous quality assurance of production data. It also specifies an approach to input data management for DES using the Generic Data Management Tool (GDM-Tool). The expected effects are increased availability of high quality production data and reduced lead time of input data management, especially valuable in manufacturing companies having advanced automated data collection methods and using DES on a daily basis

Handling of Production Disturbances in the Manufacturing Industry

Purpose – A common understanding of what events to regard as production disturbances (PD) are essential for effective handling of PDs. Therefore, the purpose of this paper is to answer the two questions: how are individuals with production or maintenance management positions in industry classifying different PD factors? Which factors are being measured and registered as PDs in the companies monitoring systems? Design/methodology/approach – A longitudinal approach using a repeated cross-sectional survey design was adopted. Empirical data were collected from 80 companies in 2001 using a paper-based questionnaire, and from 71 companies in 2014 using a web-based questionnaire. Findings – A diverging view of 21 proposed PD factors is found between respondents in manufacturing industry, and there is also a lack of correspondence with existing literature. In particular, planned events are not classified and registered to the same extent as downtime losses. Moreover, the respondents are often prone to classify factors as PDs compared to what is actually registered. This diverging view has been consistent for over a decade, and hinders companies to develop systematic and effective strategies for handling of PDs. Originality/value – There has been no in-depth investigation, especially not from a longitudinal perspective, of the personal interpretation of PDs from people who play a central role in achieving high reliability of production systems

Smart Maintenance: a research agenda for industrial maintenance management

How do modernized maintenance operations, often referred to as “Smart Maintenance”, impact the performance of manufacturing plants? This question is a pressing challenge for practitioners and scholars in industrial maintenance management, in direct response to the transition to an industrial environment with pervasive digital technologies. This paper is the second part of a two-paper series. We present an empirically grounded research agenda that reflects the heterogeneity in industrial adoption and performance of Smart Maintenance. Focus groups and interviews with more than 110 experts from over 20 different firms were used to identify contingencies, responses, and performance implications of Smart Maintenance. The findings were transformed into a contingency model, providing the basis for a research agenda consisting of five principal areas: (1) environmental contingencies; (2) institutional isomorphism; (3) implementation issues related to change, investments and interfaces; (4) the four dimensions of Smart Maintenance; and (5) performance implications at the plant and firm level. The agenda can guide the field of industrial maintenance management to move from exploratory work to confirmatory work, studying the validity of the proposed concepts as well as the magnitude and direction of their relationships. This will ultimately help scholars and practitioners answer how Smart Maintenance can impact industrial performance