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The present paper proposes a modified version of the Manufacturing cost deployment (MCD) method to analyse engineer-to-order (ETO) production systems. The novel approach, named Project cost deployment (PCD), introduces two substantial and innovative modifications. To begin with, the concept of manual assembly macro-activity replaces the traditional concept of station. Then, a brand-new structure for classifying and analysing losses is introduced, that is specifically defined to deal with the inefficiencies of the manual assembly tasks. The validity of the approach is proved by a real-world industrial application. The obtained results demonstrate that the PCD method allows the analyst to identify the hidden losses and to quantify the wastes from an economical point of view. In addition, PCD permits to estimate the impacts of potential (lean) improvement activities and projects in terms of both efficiency and effectiveness.

Cross-docking is a logistic strategy widely adopted both in manufacturing and distribution contexts. Despite the huge number of researches on this topic, most of them remain rather theoretical as they do not pay enough attention to operational aspects and daily functioning patterns of cross-dockings. This article aims at introducing a new focus for cross-docking research by proposing a visual planning solution to manage daily operation of a manufacturing cross-docking. By avoiding complex optimization algorithms and by adopting concepts belonging to the lean management theory, the article shows the potential of visual planning techniques to improve performance of cross-dockings. Starting from a specific industrial context, the shipping warehouse of an Italian company of the oil andand gas sector, a visual management methodology has been proposed to support the operation of cross-dockings. Although developed in a particular context, the proposed methodology and its underlying principles are to be considered general and potentially applicable to a wide range of cross-dockings. The methodology fully exploits an analytical tool, namely, the Safety Margin coefficientthat could be used for capacity planning purposes and to evaluate quantitatively the system performance at a given time instant.

Purpose The purpose of this paper is to present a new operational approach to equipment maintenance designed to exploit the complementary nature of total productive maintenance (TPM) and reliability-centered maintenance (RCM). Design/methodology/approach This paper focuses on the operational aspect of the maintenance practice, proposing an equipment maintenance methodology that can be suitably adopted to define a new maintenance plan or check and improve an existing one. It integrates principles and methods of TPM and RCM and uses different tools to support decision-makers and operators. It proposes several worksheets which facilitate planning and implementing maintenance activities, that range from autonomous maintenance tasks to maintainability improvement actions. Findings This paper exploits the complementary nature of TPM and RCM to design or improve maintenance plans for a generic system. The tools and worksheets presented can be helpful to practitioners and operators. Originality/value While the integration of TPM and RCM is evidently a central issue in the maintenance management context, it has received limited attention in literature. To the best of the authors’ knowledge, it lacks a structured practical equipment maintenance methodology that exploits the complementary nature of TPM and RCM. Therefore, the approach presented in this paper would fill the current gap.

This paper presents an extension of the Joint-Replenishment Problem under the class of cyclic policies. The developed model includes stochastic demands, backorders-lost sales mixtures, and controllable lead times. With the objective of minimizing total system cost, we propose two heuristics. Numerical experiments investigate the algorithms performance and the model sensitivity.

In the present paper, a novel Single-Minute Exchange of Die (SMED) set-up reduction approach, fully integrated with a 5-Whys Analysis, is presented. It permits to highlight not-optimized conditions from the changeover perspective, with the aim of reducing the effort and the cost of the SMED activity. Indeed, all set-up losses are investigated in a structural manner to identify their root causes and all the possible interactions, thus suggesting better—in terms of efficiency—interventions. In this way, the analyst is supported and facilitated during the definition of the actions required to reduce the set-up times. In particular, the implementation of the ‘why-why analysis’ is supported and integrated with (i) a general schematization of the generic workstation and (ii) an operational worksheet that leads the analyst through the investigation of the machine and its set-up process. The proposed approach is applied to a real industrial context, and the outcomes of such application are presented in the provided case study.

The single-machine jobs-planned maintenance tasks joint scheduling problem is faced.The machine is subjected to failures with a Weibull distributed time-to-failure.The optimum number of planned maintenance tasks is computed.A specific metaheuristic based on the harmony search algorithm is presented. This paper focuses on the single machine scheduling problem, with sequence dependent setup times. Both processing and setup times are deterministic and the objective is to minimize total earliness and tardiness penalties. The novelty of the model can be traced in the fact that the single machine is subjected to breakdowns and that, in order to increase its availability, planned maintenance tasks are also performed. Hence, jobs and maintenance tasks are jointly considered to find the optimal schedule. These features make the problem NP-hard and so, a quasi-optimal solution is searched using a recent metaheuristic, which integrates harmony search and genetic algorithms. In order to validate the proposed metaheuristic, a comprehensive set of scheduling problems was fully investigated. Obtained results, compared with those of exhaustive (for small problems) and standard metaheuristics, confirm both the robustness and the speed of the proposed approach.