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Considers a class of control systems known as generalized kanban control systems (GKCS) which can be used to implement a pull control mechanism in a manufacturing system. In a GKCS, the production system is decomposed into stages, where each stage consists of a production sub‐system. There are two design parameters per stage: one controls the work‐in‐process in the stage and the other determines the maximum number of finished products of this stage. Investigates the influence of these design parameters on the efficiency of generalized kanban control policies by deriving qualitative properties as well as using experimental results on the behaviour of GKCS.

In this paper, the authors are concerned with a maintenance workshop (MW) centralizing all corrective maintenance activities. The purpose of this paper is to propose a methodology for designing a central maintenance workshop, enabling the evaluation of performance in terms of cost and sojourn time, for a given budget.

The authors propose a modeling framework based on queuing networks. The aim is to maximize operational availability of the production workshop, by reducing the sojourn time of failed equipment in the MW.

The proposed methodology leads to a maintenance decision support tool enabling to give the structure of the MW, performing at a higher level, but at a reasonable configuration cost. Simulation results illustrate the influence of different parameters, such as the number of stations and the level of spare parts in the MW, on the sojourn time of the equipment.

Only corrective maintenance is taken into account and only equipment that can be taken out of the production workshop are considered. The preventive replacement of some equipment items can be taken into account by the repair process by considering them as failed.

The work falls within a more general framework for optimizing maintenance costs, in the context of integration of multi-site services in a distributed context. The paper is concerned with centralized maintenance, and proposes to integrate the so-called repair by replacement technique in a MW, used for a multi-site production workshop.

The purpose of this paper is to propose a methodology for the engineering of E-maintenance platforms that is based on a value-driven approach.

The methodology assumes that a value-driven engineering approach would help foster technological innovation for maintenance management. Indeed, value-driven engineering could be easily adopted at the business level, with subsequent positive effects on the industrial applications of new information and communication technologies solutions.

The methodology combines a value-driven approach with the engineering in the maintenance scope. The methodology is tested in a manufacturing case to prove its potential to support the engineering of E-maintenance solutions. In particular, the case study concerns the investment in E-maintenance solutions developed in the framework of a Supervisory Control and Data Acquisition system originally implemented for production purposes.

Based on literature research, the paper presents a methodology that is implemented considering three different approaches (business theories, value-driven engineering and maintenance management). The combination of these approaches is novel and overcomes the traditional view of maintenance as an issue evaluated from a cost-benefit perspective.

This study aims to analyze the impact of Artificial Intelligence (AI) and Machine Learning (ML) on heritage conservation and preservation, and to identify relevant future research trends, by applying scientometrics.

A total of 1,646 articles, published between 1985 and 2021, concerning research on the application of ML and AI in cultural heritage were collected from the Scopus database and analyzed using bibliometric methodologies.

The findings of this study have shown that although there is a very important increase in academic literature in relation to AI and ML, publications that specifically deal with these issues in relation to cultural heritage and its conservation and preservation are significantly limited.

This study enriches the academic outline by highlighting the limited literature in this context and therefore the need to advance the study of AI and ML as key elements that support heritage researchers and practitioners in conservation and preservation work.

The purpose of this paper is to propose a maturity model to improve warehouse performance.

This paper will follow De Bruin et al’s (2005) suggested six relevant phases: scope, design, populate, test, deploy and maintain in developing the proposed maturity model. This study concentrates on the first five phases.

The proposed warehouse maturity model can be used as descriptive, benchmarking and a prescriptive with a road map for improvement.

The warehouse maturity model was proposed to let warehouse managers evaluate their practices and assess them by maturity level. Then, the proposed warehouse maturity model can be utilized to develop a set of plans for conducting projects to improve the warehouse practices, techniques and tools.

The proposed warehouse maturity model contributes to fill the shortages of maturity model addressing the warehouse environment. In particular, it provides a useful tool to establish the overall maturity level of a warehouse system. The proposed maturity model supports strategic decisions oriented toward improvement capabilities of the warehouse and to compete based on service level provided.