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

Evaluation of methods used for life-cycle assessments in Discrete Event Simulation

The incitements from society for life-cycle assessment (LCA) and credible ecolables are ever-increasing and often important for successful marketing of products. Robust assessment methods are important for comparable, useful and trustworthy LCAs and ecolables. In order to improve the metrics of a product’s ecolable, is it important to fully understand its production system. Discrete Event Simulation (DES) models are able to provide more detailed information than traditional LCA approaches. Therefore, methods used to combining LCA in DES have been developed during the last decade. The combined approaches have matured and the experiences grown. This article compares six previous cases and aims to summarize and discuss their experiences to aid future development. The results show where it is specifically important to make good decisions throughout the modeling methodology, for example goal and scope definition, trustworthy input data for sensitive parts, and communicable impact categories

Constructive Alignment in Simulation Education

Recent and ongoing developments are significantly augmenting both the demand for and the expectations of university simulation education. These developments include increased use of simulation in industry, increased variety of economic segments in which simulation is used, broader variation in demographics of simulation students, and higher expectations of both those students and their eventual employers. To meet the challenges these developments impose, it is vital that simulation educators aggressively and innovatively improve the teaching of simulation. To this end, we explore the application of constructive alignment concepts in simulation education, and compare and contrast its application in the context of two university course offerings. These concepts suggest continuation of some practices and revision of others relative to the learning objectives, learning activities, and assessment tasks in these and other simulation courses

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

Data-driven machine criticality assessment – maintenance decision support for increased productivity

Data-driven decision support for maintenance management is necessary for modern digitalized production systems. The data-driven approach enables analyzing the dynamic production system in realtime. Common problems within maintenance management are that maintenance decisions are experience-driven, narrow-focussed and static. Specifically, machine criticality assessment is a tool that is used in manufacturing companies to plan and prioritize maintenance activities. The maintenance problems are well exemplified by this tool in industrial practice. The tool is not trustworthy, seldomupdated and focuses on individual machines. Therefore, this paper aims at the development and validation of a framework for a data-driven machine criticality assessment tool. The tool supports prioritization and planning of maintenance decisions with a clear goal of increasing productivity. Four empirical cases were studied by employing a multiple case study methodology. The framework provides guidelines for maintenance decision-making by combining the Manufacturing Execution System (MES) and Computerized Maintenance Management System (CMMS) data with a systems perspective. The results show that by employing data-driven decision support within the maintenance organization, it can truly enable modern digitalized production systems to achieve higher levels of productivity

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

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

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

An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study

Recent development in the predictive maintenance field has focused on incorporating artificial intelligence techniques in the monitoring and prognostics of machine health. The current predictive maintenance applications in manufacturing are now more dependent on data-driven Machine Learning algorithms requiring an intelligent and effective analysis of a large amount of historical and real-time data coming from multiple streams (sensors and computer systems) across multiple machines. Therefore, this article addresses issues of data pre-processing that have a significant impact on generalization performance of a Machine Learning algorithm. We present an intelligent approach using unsupervised Machine Learning techniques for data pre-processing and analysis in predictive maintenance to achieve qualified and structured data. We also demonstrate the applicability of the formulated approach by using an industrial case study in manufacturing. Data sets from the manufacturing industry are analyzed to identify data quality problems and detect interesting subsets for hidden information. With the approach formulated, it is possible to get the useful and diagnostic information in a systematic way about component/machine behavior as the basis for decision support and prognostic model development in predictive maintenance

Real-Time data-driven average active period method for bottleneck detection

Prioritising improvement and maintenance activities is an important part of the production management and development process. Companies need to direct their efforts to the production constraints (bottlenecks) to achieve higher productivity. The first step is to identify the bottlenecks in the production system. A majority of the current bottleneck detection techniques can be classified into two categories, based on the methods used to develop the techniques: Analytical and simulation based. Analytical methods are difficult to use in more complex multi-stepped production systems, and simulation-based approaches are time-consuming and less flexible with regard to changes in the production system. This research paper introduces a real-Time, data-driven algorithm, which examines the average active period of the machines (the time when the machine is not waiting) to identify the bottlenecks based on real-Time shop floor data captured by Manufacturing Execution Systems (MES). The method utilises machine state information and the corresponding time stamps of those states as recorded by MES. The algorithm has been tested on a real-Time MES data set from a manufacturing company. The advantage of this algorithm is that it works for all kinds of production systems, including flow-oriented layouts and parallel-systems, and does not require a simulation model of the production system