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.

Purpose: The purpose of this paper is to propose an artificial intelligence-based framework to support decision making in wholesale distribution, with the aim to limit wholesaler out-of-stocks (OOSs) by jointly formulating price policies and forecasting retailer’s demand. Design/methodology/approach: The framework is based on the cascade implementation of two artificial neural networks (ANNs) connected in series. The first ANN is used to derive the selling price of the products offered by the wholesaler. This represents one of the inputs of the second ANN that is used to anticipate the retailer’s demand. Both the ANNs make use of several other input parameters and are trained and tested on a real wholesale supply chain. Findings: The application of the ANN framework to a real wholesale supply chain shows that the proposed methodology has the potential to decrease economic loss due to OOS occurrence by more than 56 percent. Originality/value: The combined use of ANNs is a novelty in supply chain operation management. Moreover, this approach provides wholesalers with an effective tool to issue purchase orders according to more dependable demand forecasts.

Purpose: The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.Design/methodology/approach: In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.Findings: The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.Research limitations/implications: A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.Practical implications: The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.Originality/value: The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

Maintenance of equipment is a crucial issue in almost all industrial sectors as it impacts the quality, safety, and productivity of any manufacturing system. Additionally, frequent production rescheduling due to unplanned and unintended interruptions can be very time consuming, especially in the case of centrally controlled systems. Therefore, the ability to estimate the likelihood that a monitored machine will successfully complete a predefined workload, taking into account both historical data from the machine’s sensors and the impending workload, may be essential in supporting a new approach to scheduling activities in an Industry 4.0 production system. This study proposes a novel approach for integrating machine workload information into a well-established PHM algorithm for Industry 4.0, with the aim of improving maintenance strategies in the manufacturing process. The proposed approach utilises a logistic regression model to assess the health condition of equipment and a neural network computational model to estimate its failure probability according to the scheduled workloads. Results from a prototypal case study showed that this approach leads to an improvement in the prediction of the likelihood of completing a scheduled job, resulting in improved autonomy of CPSs in accepting or declining scheduled jobs based on their forecasted health state, and a reduction in maintenance costs while maximising the utilisation of production resources. In conclusion, this study is beneficial for the present research community as it extends the traditional condition-based maintenance diagnostic approach by introducing prognostic capabilities at the plant shop floor, fully leveraging the key enabling technologies of Industry 4.0.

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.