Search results

Spare parts needs are dependent on the characteristics of the product in question, e.g. its reliability and maintainability, and the characteristics of the environment in which the product is going to be used (e.g. the temperature, humidity, and the user/operator's skills and capabilities), which constitute covariates. The covariates have a significant influence on the system reliability characteristics and consequently on the number of required spare parts. The main objective of this research study is to evaluate the associated risks (i.e. risk of shortage of spare parts) in estimation of the required number of spare parts due to not considering the characteristics of system operating environment.

An event tree is a graphical logic model that identifies and quantifies possible outcomes following an initiating event (non‐considering system operating environment in this case) in spare parts planning. In the present research a risk analysis is performed through a new and non‐standard event tree analysis. It used an event tree analysis in which the states of organization and managerial decisions took place in risk analysis.

In the present study a modified form of event tree analysis was introduced and implemented. In the new version the undesired states were used instead of barriers in combination with events and consequents changes as a safety function in event tree analysis. The output of the event tree analysis shows that there is a considerable operational risk due to losses (production and economical) associated with the non‐consideration of the machine working environment.

In the estimation of the accurate amount of support and spare parts needed for any industrial system/machine, it is strongly recommended to take the product operating environment into account. This can be proved by the event tree risk analysis method used in a modified and non‐standard form in the present research. The results of risk analysis can help managers in making accurate decisions for product support and spare part needs in the future.

Modified event tree analysis is a new approach suggested for visualizing the risk associated with non‐considering of system operating environment in required support/spare parts estimation. Visualization of risk in graphics can facilitate correct decision making in spare parts planning.

To redesign the spare parts (MRO) inventory management at Company XYZ's warehouse, considering the conditions after the COVID-19 pandemic.

To address this research project, the authors integrated three methodologies: action research, Lean Six Sigma (DMAIC) and Cross Industry Standard Process for Data Mining. These methodologies integrated the Lean Six Sigma (LSS) 4.0 framework applied in this project.

The spare parts inventory value was reduced by 15%, and inventory turnover increased by 120% without negatively impacting the internal service level.

Practitioners leading or participating in continuous improvement projects (CIPs) should consider data quality (data available and data trustworthiness), problem-solving approach and target area involvement to achieve CIP goals. Otherwise, the LSS 4.0 could fail or extend its duration by several weeks or months.

This project shows the importance of controlling a target area before deciding to conduct a LSS 4.0 project. To address this problem, the LSS 4.0 team implemented 5S during the measure phase of the DMAIC. Also, this project offers significant practitioner and theoretical contributions to the body of knowledge about LSS 4.0.

The purpose of this research was to study the impacts of adoption of additive manufacturing (AM) for spare parts procurement, specifically in the context of supply chain resilience (SCR) especially regarding efficient spare parts inventory management. Furthermore, key narratives in the adoption of AM toward better SCR are explored.

In-depth interviews with semi-structured open-ended questionnaire were conducted to collect primary qualitative data from 24 supply chain management (SCM) experts. Respondents consisted of experts across various industries. The data were analyzed by thematic content analysis method.

The results indicated that AM could be a suitable tool to reduce dependence on original equipment manufacturers (OEMs) for spare parts procurement. Data analysis also revealed that AM adoption might lead to significant cost and lead time reduction. Designs protected as intellectual properties (IP), substantive post-processing requirements and material compatibility were revealed to be barriers in adoption.

The impacts of utilizing AM for procurement of spare parts on the overall resilience of the supply chain were highlighted. Theoretical analysis of the findings was based on theoretical aspects of SCR. This was especially regarding efficient spare parts inventory management. The study results revealed the factors responsible adoption of the AM technology. A novel approach was undertaken to study the effect of AM adoption on “time-to-market” of newly launched products.

The research provided insights regarding practical applications of AM adoption in spare parts procurement. The study could be beneficial to the early adopters of AM across industries for making managerial decisions. Unfamiliarity of supply chain managers with the AM technology was believed to be a major reason to adopt the technology. The study provided essential inputs regarding challenges and alternate adoption strategies of AM. Thus, the research was believed to be of potential value for creating awareness among supply chain managers regarding AM technology.

The research provided new insights on the impact of AM adoption in the context of SCR toward efficient spare parts inventory management. Various limiting and facilitating factors specific to Indian context were also explored.

Purpose – The purpose of this paper is to propose a risk‐based approach for spare parts demand forecast and spare parts inventory management for effective allocation of limited resources. Design/methodology/approach – To meet the availability target and to reduce downtime, process facilities usually maintain inventory of spare parts. The maintaining of non‐optimized spare parts inventory claims more idle investment. Even if it is optimized, lack of attention towards the critical equipment spares could threaten the availability of the plant. This paper deals with the various facets of spare parts inventory management, mainly risk‐based spare parts criticality ranking, forecasting, and effective risk reduction through strategic procurement policy to ensure spare parts availability. A risk‐based approach is presented that helps managing spare parts requirement effectively considering the criticality of the components. It also helps ensuring the adequacy of spare parts inventory on the basis of equipment criticality and dormant failure without compromising the overall availability of the plant. Findings – The paper proposes a risk‐based approach that used conjugate distribution technique with the capability to incorporate historical failure rate as well as expert judgment to estimate the future spare demand through posterior demand distribution. The approach continuously updates the prior distribution with most recent observation to give posterior demand distribution. Hence the approach is unique in its kind. Practical implications – Appropriate spare parts unavailability could have great impact on process operation and result in costly downtime of the plant. Following proposed approach the availability target can be achieved in process industry having limited maintenance resources, by forecasting spare parts demand precisely and maintaining inventory in good condition. Originality/value – Adopting the approach proposed in the paper, risk level can be minimized and plant availability can be maximized within the financial constraint. The resources are allocated to the most critical components and thereby increased availability, and reduce risk.

The purpose of this paper is to suggest a framework for extracting the sequential patterns of maintenance activities and related spare parts information from historical records of maintenance data with pre-defined support or threshold values.

A data mining approach has been adopted for predicting the maintenance activity along with spare parts. It starts with a collection of spare parts and maintenance data, and then the development of sequential patterns followed by formation of frequent spare part groups, and finally, integration of sequential maintenance activities with the associated spare parts.

This study suggests a framework for extracting the sequential patterns of maintenance activities from historical records of maintenance data with pre-defined support or threshold values. A rule-based approach is proposed in this paper to predict the occurrence of next maintenance activity along with the information of spare parts consumption for that maintenance activity.

Presented model can be extended for analyzing the failure maintenance activities and performing root cause analysis that can give more valuable suggestion to maintenance managers to take corrective actions prior to next occurrence of failures. In addition, the timestamp information can be utilized to prioritize the maintenance activity that is ignored in this study.

The proposed model has a high potential for industrial applications and is validated through a case study. The study suggests that the model gives a better approach for selecting spare parts based on their similarity or correlation, considering their actual occurrence during maintenance activities. Apart from this, the clustering of spare parts also trains maintenance manager to learn about the dependency among the spares for group stocking and maintaining the parts availability during maintenance activities.

This study has used the technique of data mining to find dependent spare parts itemset from the database of the company and developed the model for associated spare parts requirement for subsequent maintenance activity.

Spare parts management plays a relevant role for equipment-intensive companies. An important step of such process is the spare parts classification, enabling properly managing different items by taking into account their peculiarities. The purpose of this paper is to review the state of the art of classification of spare parts for manufacturing equipment by presenting an extensive literature analysis followed by an industrial assessment, with the final aim to identify eventual discrepancies.

Not only is the attention put on the literature about the subject, but also on an on-field analysis, that is presented comprehending an extensive survey and two in-depth exploratory case studies. The copper mining sector was chosen being representative for the case of capital intensive plants where the cost of maintenance has relevant weight on the total operating cost.

The paper highlights the status of the scientific literature on spare parts classification by showing the current situation in the real industrial world. The paper depicts the existing barriers that leave gaps between theory and real practice for the application of an effective multi-criteria spare parts classification.

The paper provides a review of the theory on spare parts classification methods and criteria, as well as empirical evidences especially for what concern current situation and barriers for an effective implementation in the industrial environment. The paper should be of interest to both academics and practitioners, since it provides original insights on the discrepancies between scientific and industrial world.

The purpose of this paper is to investigate the technology, business and intellectual property issues surrounding the production of spare parts through additive manufacturing (AM) from a digital source. It aims to identify challenges to the growth of the AM spares market and propose suitable solutions.

The paper begins with a systematic literature review and theoretical analysis. This is followed by case study research through semi-structured interviews, forming the basis of a triangulated, cross-case analysis of empirical data.

The paper identifies several obstacles to the development of the AM-produced digital spares market. The manufacturing industry will soon be forced to re-think AM as a real manufacturing alternative. Short-term, AM technology has implications for the production of components for legacy systems for which tooling facilities no longer exist. Long-term, AM will be used to produce a wide range of components especially when product and/or service functionality can be increased. To enable companies to navigate current uncertainties in the patent framework (especially the “repair vs make” doctrine), new intellectual property rights strategies could be developed around patenting both complex devices and their individual components, and seeking patent protection for CAD files. Further harmonization of the EU legal framework, the interpretation of claims and the scope of protection offered in the context of spare parts, will also be important.

This study pinpoints key issues that need to be addressed within the European AM business environment and the patent system and proposes recommendations for business and legal frameworks to promote the growth of a stable European digital spare parts market.

For more than ten years, the value of additive manufacturing (AM) for after-sales service logistics has been propagated. Today, however, only few applications are observed in practice. The purpose of this paper is to discuss possible reasons for this discrepancy and to develop a method to simplify the identification of economically valuable and technologically feasible business cases.

The approach is based on the analytic hierarchy process and relies on spare part information, that is easily retrievable from the company databases. This has two advantages: first, the approach can be customized toward specific company characteristics, and second, a very large number of spare parts may be assessed simultaneously. A field study is discussed in order to demonstrate and validate the approach in practice. Furthermore, sensitivity analyses are performed to evaluate the robustness of the method.

Results provide evidence that the method allows a valid prioritization of a large spare part assortment. Also, sensitivity analyses clarify the robustness of the approach and illustrate the flexibility of applying the method in practice. More than 1,000 positive business cases of AM for after-sales service logistics have been identified based on the method.

The developed method enables companies to rank spare parts according to their potential value when produced with AM. As a result, companies can evaluate the most promising spare parts first. This increases the effectiveness and efficiency of identifying business cases and thus may support the adoption of AM in after-sales service supply chains.

Introducing additive manufacturing (AM) in a multinational corporation with a global spare parts operation requires tools for a dynamic supplier selection, considering both cost and delivery performance. In the switchover to AM from conventional manufacturing, the objective of this study is to find situations and ways to improve the spare parts service to end customers.

In this explorative study, the authors develop a procedure – in collaboration with the spare parts operations managers of a case company – for dynamic operational decision-making for the selection of spare parts supply from multiple suppliers. The authors' design proposition is based on a field experiment for the procurement and delivery of 36 problematic spare parts.

The practice intervention verified the intended outcomes of increased cost and delivery performance, yielding improved customer service through a switchover to AM according to situational context. The successful operational integration of dynamic additive and static conventional supply was triggered by the generative mechanisms of highly interactive model-based supplier relationships and insignificant transaction costs.

The dynamic decision-making proposal extends the product-specific make-to-order practice to the general-purpose build-to-model that selects the mode of supply and supplier for individual spare parts at an operational level through model-based interactions with AM suppliers. The successful outcome of the experiment prompted the case company to begin the introduction of AM into the company's spare parts supply chain.

The purpose of this paper is to identify the main reasons for spare parts logistics failures and address logistics distribution design in order to achieve the desired level of after-sales maintenance service.

This research is based on an empirical case study on a large corporation providing worldwide with retail banking hardware, software and services. The case study focuses on the automated teller machine (ATM) part of activities, with a focus on the spare parts distribution and after-sales service network in the Eastern Europe.

The proposed network solution of multiple distribution centers with short-cut distance saving approach will enable the case study company to redesign their spare part logistics architecture in order to achieve short response time. Research findings reveal possible spare parts delivery delays and thus the service-level agreement failures with clients in the case study company.

This research covers a particular supply chain environment and identified research gaps. It discusses a time-based responsive logistics problem and develops a conceptual framework that would help researchers to better understand logistics challenges of installed equipment maintenance and after-sales service.

This case study research shows the “big picture” of spare parts logistics challenges as vital part of installed equipment after-sales and maintenance service network, as well as emphasizes how the unique context of a market like Russian Federation can set-up a distribution network efficiently. Strategies applied to handle such service-level failures, reverse logistics aspects of repairable and non-repairable spare parts to such large ATM after-sales service network based on this longitudinal case offer value for similar scale companies.