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The purpose of this paper is to identify the productivity improvement potentials from maintenance planning practices in manufacturing companies. In particular, the paper aims at understanding the connection between machine criticality assessment and maintenance prioritization in industrial practice, as well as providing the improvement potentials.

An explanatory mixed method research design was used in this study. Data from literature analysis, a web-based questionnaire survey, and semi-structured interviews were gathered and triangulated. Additionally, simulation experimentation was used to evaluate the productivity potential.

The connection between machine criticality and maintenance prioritization is assessed in an industrial set-up. The empirical findings show that maintenance prioritization is not based on machine criticality, as criticality assessment is non-factual, static, and lacks system view. It is with respect to these finding that the ways to increase system productivity and future directions are charted.

In addition to the empirical results showing productivity improvement potentials, the paper emphasizes on the need for a systems view for solving maintenance problems, i.e. solving maintenance problems for the whole factory. This contribution is equally important for both industry and academics, as the maintenance organization needs to solve this problem with the help of the right decision support.

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.

Attempts to develop a model of maintenance decision making using the analytic hierarchy (AHP). Describes problems in maintenance arising from not having clear criteria and not having robust decisions with which to maintain failing equipment. The objective being to develop a dynamic and adaptable maintenance system that utilises existing data and supports decisions accordingly. Proposes a three‐stage system that can handle multiple criteria decision analysis, conflicting objectives, and subjective judgements. Moreover, the methodology facilitates and supports a group decision‐making process. This systematic, and adaptable, approach will determine what specific actions to perform given current working conditions. The first stage involves identifying the criteria upon which engineering personnel wish to formulate a maintenance decision, or action. The second stage is to prioritise the different criteria by implementing a multiple‐criteria evaluation method. Finally, based on different criteria, machines are ranked according to criticality. This is followed by an analysis of failures in a graphical and a hierarchical format.

This paper aims to present a dual-perspective framework for maintenance service delivery that should be used by manufacturing companies to structure and manage their maintenance service delivery process, using aggregated historical and real-time data to improve operational decision-making. The framework, built for continuous improvement, allows the exploitation of maintenance data to improve the knowledge of service processes and machines.

The Dual-perspective, data-based decision-making process for maintenance delivery (D3M) framework development and test followed a qualitative approach based on literature reviews and semi-structured interviews. The pool of companies interviewed was expanded from the development to the test stage to increase its applicability and present additional perspectives.

The interviews confirmed that manufacturing companies are interested in exploiting the data generated in the use phase to improve operational decision-making in maintenance service delivery. Feedback to improve the framework methods and tools was collected, as well as suggestions for the introduction of new ones according to the companies' necessities.

The paper presents a novel framework addressing the data-based decision-making process for maintenance service delivery. The D3M framework can be used by manufacturing companies to structure their maintenance service delivery process and improve their knowledge of machines and service processes.

The artificial intelligence (AI) based total productive maintenance (TPM) condition based maintenance (CBM) approach through Industry 4.0 transformation can well predict the breakdown in advance to eliminate breakdown.

Meeting the customer requirement as per the delivery schedule with the existing resources are always a big challenge in industries. Any catastrophic breakdown in the equipment leads to increase in production loss, damage to machines, repair cost, time and affects delivery. If these breakdowns are predicted in advance, the breakdown can be addressed before its occurrence and the demand supply chain can be met. TPM is one of the essential operational excellence tool used in industries to utilize the existing resources of a plant in a optimal way. The conventional time based maintenance (TBM) and CBM approach of TPM in Industry 3.0 is time consuming and not accurate enough to achieve zero down time.

The proposed AI and IIoT based TPM is achieved in a digitalized data oriented platform to monitor and control the health status of the machine which may reduce the catastrophic breakdown by 95% and also improves the quality rate and machine performance rate. Based on the identified key signature parameters related to major breakdown are measured using the sensors, digitalised by programmable logic controller (PLC) and monitored by supervisory control and data acquisition (SCADA) and predicted in server or cloud.

Long short term memory based deep learning network was developed as a regression forecasting model to predict the remaining useful life RUL of the part or assembly and based on the predictions, corrective action has been implemented before the occurrence of breakdown. The reliability and consistency of the proposed approach are validated and horizontally deployed in similar machines to achieve zero downtime.

The purpose of this study is to propose a hybrid reliability-centered maintenance (RCM) approach for mining transportation machines of a limestone complex, a real case in Esfahan, Iran.

Criteria for selecting critical machines were collected within literature and selected by decision-makers (DCs), and critical machines have been identified using the preference ranking organization method for enrichment of evaluations (PROMETHEE). Also, multi-criteria decision-making (MCDM) methods were used in addition to failure mode, effects and criticality analysis (FMECA) for selecting and prioritizing high-risk failures as well as optimizing the RCM performance. More specifically, the criteria of severity, detectability and frequency of occurrence were selected for risk assessment based on the previous studies, and were weighted using the analytic hierarchy process (AHP) method. Also, the technique for order of preference by similarity to ideal solution (TOPSIS) has been applied to prioritize failures' risk. Finally, the critical failures were inserted in the RCM decision-making worksheet and the required actions were determined for them.

According to the obtained values from PROMEHTEE method, the machine with code 739-7 was selected as the first priority and the most critical equipment. Further, based on results of TOPSIS method, the failure mode of “Lubrication hole clogging in crankpin bearing due poor quality oil,” “Deformation of main bearing due to overwork” and “The piston ring hotness due to unusual increase in the temperature of cylinder” have the highest risks among failure modes, respectively.

RCM has been deployed in various studies. However, in the current study, a hybrid MCDM-FMECA has been proposed to cope with high-risk failures. Besides, transportation machineries are one of the most critical equipment in the mining industry. Due to noticeable costs of this equipment, effective and continuous usage of this fleet requires the implementation of proper maintenance strategy. To the best of our knowledge, there is no research which has used RCM for transportation systems in the mining sector, and therefore, the innovation of this research is employment of the proposed hybrid approach for transportation machineries in the mining industry.

Engineering service (ES) development, particularly with supplier co-creation, is nontrivial, and the literature has acknowledged the high relevance of uncertainty in this context. This study aims to investigate the relationship between different supplier co-creation modes (operationally independent [OI] and operationally dependent [OD]) and uncertainty criticality arising during ES development.

This study develops a conceptual framework of five uncertainty types by synthesizing the relevant literature from service management and new product development. This framework guided the empirical work of two in-depth case studies, describing uncertainty criticality in OI and OD supplier co-creation.

The findings show that environmental and organizational uncertainty were generally of high criticality for ES development independently of the supplier co-creation mode. Moreover, uncertainty criticality varied between the two cases, with higher criticality of technical and relational uncertainty as well as less resource uncertainty experienced by the focal organization in the OD case. This suggests that supplier co-creation constitutes an uncertainty reallocation.

Further research is needed to test the generalizability of the qualitative results through quantitative studies.

This research contributes to the service management literature by showing the varying uncertainty profiles manufacturing organizations face when engaging in different supplier co-creation modes. Furthermore, this research provides novel insights on ES development to the broader discussion on ES management.

The purpose of this paper is to primarily focus on labor in maintenance areas, addressing human rights issues, labor standards and safety standards. The main issue is to investigate how these factors are considered to drive the prioritization of maintenance interventions within maintenance plans. In particular, a method for criticality analysis of production equipment is proposed considering specific labor issues like age and gender, which can be useful to steer maintenance plans toward a more social perspective.

The authors focus on the two main social issues of SA 8000 norms, age and gender, exploring how these issues may drive the selection of maintenance policies and the relative maintenance plans. The research is conducted through fuzzy analytical hierarchy process (AHP) implemented within a failure mode effects analysis (FMEA).

The research is conducted through fuzzy AHP implemented within a FMEA. The maintenance plans resulting from the FMEA driven by social issues are evaluated by a benchmark of three different scenarios. The results obtained allowed the firm to evaluate maintenance plans, considering the impact on workers’ health and safety, the environment, social issues like gender and age.

One of the main limitation of this research is that it should also encompass maintenance costs under social and safety perspective. The method developed should be extended by further study of maintenance planning decisions subject to budget constraints. Moreover, it would be worth evaluating the effect of adopting more proactive maintenance policies aimed at improving plant maintainability in view of what emerged during the test case in the presence of an aged workforce and the subsequent need to prevent and/or protect people from hidden risks.

With reference to the results obtained from the two models of this scenario, the authors observed an increase of equipment criticality, from B class to the A class, and similarly from C class to B class. No equipment has reduced its criticality. This depends on the particular context and the relative weights of drivers indicated in its AHP matrixes.

The paper addressed the main social implication as well as other social issues represented by age and gender factors, which are normally neglected. The Action Research (AR) proved the effects resulted from considering either gender factor or gender and age factors at the same time for maintenance policy selection. All in all, an increase of criticality is evident even if “people” is a driver with less importance than “environment” and “structures.”

The present work focussed on a new definition of a criticality ranking model to assign a maintenance policy to each component based on workers’ know-how and on their status. The approach is conceived by the application of a fuzzy logic structure and AHP to overcome uncertainties, which can rise during a decision process when there is a need to evaluate many criteria, ranging from economic to environmental and social dimensions.

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.

Human error data in the form of human error probabilities should ideally form the corner‐stone of human reliability theory and practice. In the history of human reliability assessment, however, the collection and generation of valid and usable data have been remarkably elusive. In part the problem appears to extend from the requirement for a technique to assemble the data into meaningful assessments. There have been attempts to achieve this, THERP being one workable example of a (quasi) database which enables the data to be used meaningfully. However, in recent years more attention has been focused on the PerformanceShaping Factors (PSF) associated with human reliability. A “database for today” should therefore be developed in terms of PSF, as well as task/ behavioural descriptors, and possibly even psychological error mechanisms. However, this presumes that data on incidents and accidents are collected and categorised in terms of the PSF contributing to the incident, and such classification systems in practice are rare. The collection and generation of a small working database, based on incident records are outlined. This has been possible because the incident‐recording system at BNFL Sellafield does give information on PSF. Furthermore, the data have been integrated into the Human Reliability Management System which is a PSF‐based human reliability assessment system. Some of the data generated are presented, as well as the PSF associated with them, and an outline of the incident collection system is given. Lastly, aspects of human common mode failure or human dependent failures, particularly at the lower human error probability range, are discussed, as these are unlikely to be elicited from data collection studies, yet are important in human reliability assessment. One possible approach to the treatment of human dependent failures, the utilisation of human performance‐limiting values, is described.