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Total productive maintenance consists of strategies and procedures that aim to guarantee the entire functioning of machines in a production process so that production is not interrupted and no loss of quality in the final product occurs. Planned maintenance is one of the eight pillars of total productive maintenance, a set of tools considered essential to ensure equipment reliability and availability, reduce unplanned stoppage and increase productivity. This study aims to analyze the influence of statistical reliability on the performance of such a pillar.

In this study, we utilized a multi-method approach to rigorously examine the impact of statistical reliability on the planned maintenance pillar within total productive maintenance. Our methodology combined a detailed statistical analysis of maintenance data with advanced reliability modeling, specifically employing Weibull distribution to analyze failure patterns. Additionally, we integrated qualitative insights gathered through semi-structured interviews with the maintenance team, enhancing the depth of our analysis. The case study, conducted in a fertilizer granulation plant, focused on a critical failure in the granulator pillow block bearing, providing a comprehensive perspective on the practical application of statistical reliability within total productive maintenance; and not presupposing statistical reliability is the solution over more effective methods for the case.

Our findings reveal that the integration of statistical reliability within the planned maintenance pillar significantly enhances predictive maintenance capabilities, leading to more accurate forecasts of equipment failure modes. The Weibull analysis of the granulator pillow block bearing indicated a mean time between failures of 191.3 days, providing support for optimizing maintenance schedules. Moreover, the qualitative insights from the maintenance team highlighted the operational benefits of our approach, such as improved resource allocation and the need for specialized training. These results demonstrate the practical impact of statistical reliability in preventing unplanned downtimes and informing strategic decisions in maintenance planning, thereby emphasizing the importance of your work in the field.

In terms of the originality and practicality of this study, we emphasize the significant findings that underscore the positive influence of using statistical reliability in conjunction with the planned maintenance pillar. This approach can be instrumental in designing and enhancing component preventive maintenance plans. Furthermore, it can effectively manage equipment failure modes and monitor their useful life, providing valuable insights for professionals in total productive maintenance.

The goal of this chapter is to introduce the network paradigm for analyzing relational phenomena and organizing knowledge in higher education research. This introduction is presented by example: it analyzes knowledge on inter-organizational relationships of higher education institutions. The formation, maintenance, and impact of relationships are implicitly relational, although they have largely been understood as a consequence of institutional practices. Through the network paradigm, we show that focusing on relations we can develop new and more precise models to understand the antecedents, consequences, and characteristics of these networks.

This paper revisits the debate between environmental determinism and strategic choice. It compares the two theories on their philosophical underpinnings, their view of decision making, and the environment. Although we argue that the theory of strategic choice generally prevails, we recognize that there are factors that restrict the choice and discretion of managers. Here we look at three types of factors—industry related factors (extent of regulation and stage of life cycle), organization related factors (characteristics of top management and organization size), and time related factors and explain how each of these factors either inhibit or enhance managerial discretion. Implications for theory and practice are discussed.

Environmental volatility is a central construct in strategy studies. This paper argues that three factors confound the literature on volatility: asymmetry in conceptualization, asymmetry in operationalization, and lack of attention to level of analysis. These limitations inhibit the development of the concept and make much of the research on volatility non‐additive. However, environments do matter and to make better sense of it we need a meta‐conceptualization. To do this, the paper presents a process‐based resources‐oriented view of volatility that argues that the volatility experienced by the firm is largely a function of the resources it has available to meet the demands made of it. It is proposed that volatility originates from four basic resource configurations: managerial‐human resources configuration, physical resources‐conversion configuration, intangible resources configuration, and positional configuration. Propositions consistent with prior theories and incorporating the new resources‐oriented viewpoint are presented and discussed.

The purpose of this paper is to increase productivity through smart maintenance planning by including productivity as one of the objectives of the maintenance organization. Therefore, the goals of the paper are to investigate existing machine criticality assessment and identify components of the criticality assessment tool to increase productivity.

An embedded multiple case study research design was adopted in this paper. Six different cases were chosen from six different production sites operated by three multi-national manufacturing companies. Data collection was carried out in the form of interviews, focus groups and archival records. More than one source of data was collected in each of the cases. The cases included different production layouts such as machining, assembly and foundry, which ensured data variety.

The main finding of the paper is a deeper understanding of how manufacturing companies assess machine criticality and plan maintenance activities. The empirical findings showed that there is a lack of trust regarding existing criticality assessment tools. As a result, necessary changes within the maintenance organizations in order to increase productivity were identified. These are technological advancements, i.e. a dynamic and data-driven approach and organizational changes, i.e. approaching with a systems perspective when performing maintenance prioritization.

Machine criticality assessment studies are rare, especially empirical research. The originality of this paper lies in the empirical research conducted on smart maintenance planning for productivity improvement. In addition, identifying the components for machine criticality assessment is equally important for research and industries to efficient planning of maintenance activities.

In recent years, the mechanical industries began to apply many investments in research and technological development to obtain efficient methods to analyze the integrity of structures and prevent disasters and/or accidents, ensuring people’s lives and preventing economic losses. Any structure, whether mechanical or aeronautical, before being put into use undergoes a structural integrity assessment and testing. In this case, non-destructive evaluations are performed, aiming to estimate the degree of safety and reliability of the structure. For this, there are techniques traditionally used such as ultrasonic inspection, X-ray, acoustic emission tests, among other techniques. The traditional techniques may even have a good instrumental apparatus and be well formulated for structural integrity assessment; however, these techniques cannot meet growing industrial needs, even more so when structures are in motion. The purpose of this paper is to demonstrate artificial immune systems (AISs), ate and strengthen the emergence of an innovative technological tool, the biological immune systems and AISs, and these are presented as computing methods in the field of structural health monitoring (SHM).

The concept of SHM is based on a fault detection mechanism used in industries, and in other applications, involving the observation of a structure or a mechanical system. This observation occurs through the dynamic response of periodic measurements, later related to the statistical analysis, determining the integrity of the system. This study aims to develop a methodology that identifies and classifies a signal in normal signals or in faults, using an algorithm based on artificial immunological systems, being the negative selection algorithm, and later, this algorithm classifies the failures in probabilities of failure and degree of fault severity. The results demonstrate that the proposed SHM is efficient and robust for prognosis and failure detection.

The present study aims to develop different fast access methodologies for the prognosis and detection of failures, classifying and judging the types of failures based on AISs. The authors declare that the present study was neither published in any other vehicle of scientific information nor is under consideration for publication in another scientific journal, and that this paper strictly followed the ethical procedures of research and publication as requested.

This study is original by the fact that conventional structural integrity monitoring methods need improvements, which intelligent computing techniques can satisfy. Intelligent techniques are tools inspired by natural and/or biological processes and belong to the field of computational intelligence. They present good results in problems of pattern recognition and diagnosis and thus can be adapted to solve problems of monitoring and identifying structural failures in mechanical and aeronautical engineering. Thus, the proposal of this study demonstrates and strengthens the emergence of an innovative technological tool, the biological immune system and the AIS, and these are presented as computation methods in the field of SHM in rotating systems – a topic not yet addressed in the literature.

The purpose of this paper is to identify maintenance improvement potentials using an overall equipment effectiveness (OEE) assessment within the manufacturing industry.

The paper assesses empirical OEE data gathered from 98 Swedish companies between 2006 and 2012. Further analysis using Monte-Carlo simulations were performed in order to study how each OEE component impacts the overall OEE.

The paper quantifies the various equipment losses in OEE, as well as the factors availability, utilization, speed, quality, and planned stop time. From the empirical findings, operational efficiency losses are found to have the largest impact on OEE followed by availability losses. Based on the results, improvement potentials and future trends for maintenance are identified, including a systems view and an extended scope of maintenance.

The paper provides detailed insights about the state of equipment effectiveness in terms of OEE in the manufacturing industry. Further, the results show how individual OEE components impact overall productivity and efficiency of the production system. This paper contributes with the identification of improvement potentials that are necessary for both practitioners and academics to understand the new direction in which maintenance needs to move. The authors argue for a service-oriented organization.

The purpose of this study was to assess the readiness of the Swedish manufacturing industry to implement dynamic, data-driven preventive maintenance (PM) by identifying the gap between the state of the art and the state of practice.

An embedded multiple case study was performed in which some of the largest companies in the discrete manufacturing industry, that is, mechanical engineering, were surveyed regarding the design of their PM programmes.

The studied manufacturing companies make limited use of the existing scientific state of the art when designing their PM programmes. They seem to be aware of the possibilities for improvement, but they also see obstacles to changing their practices according to future requirements.

The results of this study will benefit both industry professionals and academicians, setting the initial stage for the development of data-driven, diversified and dynamic PM programmes.

First and foremost, this study maps the current state and practice in PM planning among some of the larger automotive manufacturing industries in Sweden. This work reveals a gap between the state of the art and the state of practice in the design of PM programmes. Insights regarding this gap show large improvement potentials which may prove important for academics as well as practitioners.

The study aims to synthesize findings from over two decades of research, highlighting key trends, progress, innovations, methodologies and challenges in bioclimatic design strategies and their interconnection with building environmental performance across the world.

This systematic review examines advancements in bioclimatic design strategies aimed at enhancing the environmental performance of buildings from 2000 to 2023 (n = 1,069). The methodology/approach involves a comprehensive analysis of literature from the SCOPUS database using bibliometric analysis, identifying trends, thematic evolution, keyword clusters and pivotal strategies such as passive solar design, natural ventilation, green roofs and thermal mass utilization.

The review highlights significant progress in several areas, including improved simulation/modeling tools for passive solar design, advanced computational fluid dynamics models for natural ventilation optimization, and the integration of green roofs with photovoltaic systems for increased building energy efficiency. Additionally, the use of phase change materials and high-performance glazing has reduced heating and cooling loads, while real-time optimization technologies have enhanced building performance and led to energy savings.

The study recognizes limitations where the effectiveness of bioclimatic strategies varies across different climates. For example, passive solar design is highly effective in temperate climates but less so in tropical regions. Global differences in design preferences and building types and practices impact the applicability of bioclimatic strategies and traditional building methods in some cultures may not easily integrate with modern approaches, affecting their implementation and effectiveness. Furthermore, practical implications highlight the potential for reduced reliance on artificial heating, cooling and lighting systems, while social implications underscore the role of bioclimatic design in promoting sustainable construction practices.

Practical implications highlight the potential for reduced reliance on artificial heating, cooling and lighting systems.

Social implications underscore the role of bioclimatic design in promoting sustainable construction practices.

This review offers a detailed analysis of bioclimatic design evolution, highlighting trends such as adaptive building designs and smart materials. This study serves as a crucial resource for architects, engineers and policymakers, advocating for innovative, climate-responsive design solutions to mitigate the environmental impact of the built environment and address challenges related to climate change and urbanization.

This chapter focuses on the relationship between biotechnology start-ups and larger pharmaceutical corporations when they work as partners in innovation strategic alliances. For three decades, these companies have become major players in innovation in the health sector. This means that the development of many products is a result of the cooperation they carry out. However, due to the great differences between these companies, certain problems can often arise. More specifically, our analysis explores the perceptions of the achievement expected by each partner. This is an important aspect to determine the satisfaction of these firms among strategic alliance. The authors follow qualitative methods to address the topic, conducting personal interviews with managers of these companies. Our findings reveal the concrete facts that can prevent reaching the proposed goals of these partners as well as stress the crucial importance of the human aspect to mitigate potential problems.