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Industry 4.0 emerged as the Fourth Industrial Revolution aiming at achieving higher levels of operational efficiency, productivity and automation. In this context, manual assembly systems are still characterized by high flexibility and low productivity, if compared to fully automated systems. Therefore, the purpose of this paper is to propose the design, engineering and testing of a prototypal adaptive automation assembly system, including greater levels of automation to complement the skills and capabilities of human workers.

A lab experimental field-test is presented comparing the assembly process of a full-scale industrial chiller with traditional and adaptive assembly system.

The analysis shows relevant benefits coming from the adoption of the adaptive automation assembly system. In particular, the main findings highlight improvements in the assembly cycle time and productivity, as well as reduction of the operator’s body movements.

The prototype is applied in an Italian mid-size industrial company, confirming its impact in terms of upgrades of the assembly system flexibility and productivity. Thus, the research study proposed in this paper provides valuable knowledge to support companies and industrial practitioners in the shift from traditional to advanced assembly systems matching current industrial and market features.

This paper expands the lacking research on adaptive automation assembly systems design proposing an innovative prototype able to real-time reconfigure its structure according to the product to work, e.g. work cycle, and the operator features.

The kitting feeding policy creates kits with the parts of each product to assemble. Each kit contains elements with heterogeneous physical properties imposing heterogeneous logistic facilities and management solutions for storage and handling. The purpose of this paper is to present and apply a two-step procedure to design the part warehouse layout and to assign locations in case of kitting with high-variety part attributes. The proposed procedure aims at reducing the kitting travelled distance, shortening the picker paths, best positioning the components in the warehouse to enhance the possibility of creating kits through a single corridor access. The saturation of the warehouse and the minimization of the required storage space are also considered.

Starting from part categorization, the proposed two-step procedure, of general applicability, designs the component warehouse, sizing the corridors (Step 1) before clustering the kits in terms of part commonality and best-assigning clusters to corridors (Step 2) with the goal of reducing the travelled distance and saturating the available storage space.

A comparison model considers the traditional versus the proposed warehouse layout highlighting the potential saving in the picker travelled distance. A case study taken from the harvesting machine agricultural sector exemplifies the applicability and the practical implications of this research.

Elements of originality are the warehouse design strategy and the assignment model for parts based on their physical attributes and their occurrence in the assembly kits. Finally, the case study taken from industry, with a high number of components and part categories, adds value to the research making the proposed procedure able to address large-scale industrial problems.

The purpose of this paper is to investigate the autoclave-pump pressured water distribution system. Pressured water is used in many manufacturing processes, as a raw material or as a service fluid for different applications.

The performances and the total installation costs of such systems are strongly related to its design and to its decision variables definition. The authors first identify the independent variables (i.e. the decision variables) and the dependent variables of the system and, second, propose a techno-economic mathematical method able to determine its minimum installation cost with an integrated approach.

The trade-off between the autoclave installation costs versus the pump installation costs is demonstrated. A sensitive analysis of the cost of the system as function of its decision variables has been performed to propose a practical graphical analysis tools to proper design the integrated pump-autoclave pressured water distribution system.

Many previous researches focus only on the pump system optimization or in the tank system optimization without an integrated approach. The wide utilization in industry of the autoclave-pump pressured water distribution system together with the lack of similar contributions in this area enforces the value of this research.

This paper aims to introduce, apply and validate, through a realistic case study, an analytical cost model to support the design of the tow-train feeding system for mixed-model assembly lines managed according to the just-in-time concept. The fleet size and inventory level, minimizing the total annual cost, are the key model goals, while the tow-train shipping capacity and the service level are the decisional variables to set.

The model computes the material handling, inventory and stockout rising costs of the tow-train feeding system and looks for their minimization. It further computes the expected lead time between consecutive round-trips and the Kanban card number, distinguishing among parts and assembly lines, overcoming the simplifying hypothesis assuming a constant lead time for all parts. The model is validated against a dedicated case study stressing its strengths in terms of cost and inventory-level reduction.

The proposed approach is found to be effective if compared to the standard literature in the field of Kanban system design. The 10.76 per cent cost saving is experienced for the considered case study, and the inventory level is closer to the field-experienced profile.

The model adopts a practical perspective, making it easy and applicable to common operative industries.

The literature neglects to consider the differences in the part consumption when estimating the lead time between tow-train round-trips. The proposed model overcomes such limitations and strengthens the model applicability and performances.

To present an innovative approach to the design of a flexible order‐picking systems (OPS). The proposed conceptual framework is the first step to the development of an expert system created in a rapid and innovative manner, i.e. based on the integration of simulation, genetic algorithms and factorial analysis.

The impact of alternative policies and configurations of both picker‐to‐part and part‐to‐picker OPS is quantified, with the aim of designing and optimising robust facilities capable of minimising global costs and maximising their performances in terms of efficiency and customer service quality.

The importance of validation analysis and both virtual and on‐field data collection emerges.

Compared with the studies found in the literature, this article presents an innovative approach with original results that will be of use in the design and control of a flexible OPS.

Recently, material flows have been viewed as an integral part of the overall manufacturing system and a critical factor in SCM. Static approaches and theoretical models are ineffective in considering all variables and constraints involved in complex instances: these often require a lot of computing time and present poor flexibility in terms of model changes. VIS approach is a valid way to support design and management decisions in order to achieve the integrated optimisation of the whole chain, but literature does not discuss difficulties and time required in applying it, or its related costs.

Discrete/continuous hybrid simulation tools are used in order to model and simulate several operating conditions in combination with different system configurations.

The discussion of the industrial cases shows the importance of simulation in supporting decisions concerning the design and management of supply chains in their great complexity and in a stochastic competitive and extended context.

The paper deals with five significant industrial cases, which are simulated in collaboration with important enterprises and belong to different industrial sectors, in order to obtain an original quantitative analysis of time and costs resulting from a simulation optimisation based on the introduction of a set of innovative performance indices.

Modern supply chains collect and deliver products worldwide and link vendors and consumers over thousands of miles. In the food industry, the quality of products is affected by manufacturing/processing and logistics activities, such as transportation and packaging. Specifically, transportation is likely the most critical step throughout the “food journey” from farm to fork because of the potential stresses that affect the products during shipment and storage activities. The purpose of this paper is to present and apply an original assessment of quality, safety and environmental effects due to the international distribution of food products via different container solutions. A case study that examines the shipment of edible oils from Italy to Canada demonstrates that the quality of a product at the place of consumption can be significantly affected by the use of different containers.

A simulation-based quality assessment, combined with a life cycle and environmental analysis, supports the logistic manager in the decision-making process in order to guarantee the highest level of product quality at the place of consumption.

The proposed approach and the illustrated case study demonstrate the importance of conducting safety and quality assessment combined with environmental analyses of sustainable food supply chains.

This paper highlights the interdependency of implications and decisions on food quality and environmental sustainability of supply chain processes and activities.

We studied the relationship between job engagement and systematic problem solving (SPS) among shop-floor employees and how lean production (LP) and Internet of Things (IoT) systems moderate this relationship.

We collected data from a sample of 440 shop floor workers in 101 manufacturing work units across 33 plants. Because our data is nested, we employed a series of multilevel regression models to test the hypotheses. The application of IoT systems within work units was evaluated by our research team through direct observations from on-site visits.

Our findings indicate a positive association between job engagement and SPS. Additionally, we found that the adoption of lean bundles positively moderates this relationship, while, surprisingly, the adoption of IoT systems negatively moderates this relationship. Interestingly, we found that, when the adoption of IoT systems is complemented by a lean management system, workers tend to experience a higher effect on the SPS of their engagement.

One limitation of this research is the reliance on the self-reported data collected from both workers (job engagement, SPS and control variables) and supervisors (lean bundles). Furthermore, our study was conducted in a specific country, Italy, which might have limitations on the generalizability of the results since cross-cultural differences in job engagement and SPS have been documented.

Our findings highlight that employees’ strong engagement in SPS behaviors is shaped by the managerial and technological systems implemented on the shop floor. Specifically, we point out that implementing IoT systems without the appropriate managerial practices can pose challenges to fostering employee engagement and SPS.

This paper provides new insights on how lean and new technologies contribute to the development of learning-to-learn capabilities at the individual level by empirically analyzing the moderating effects of IoT systems and LP on the relationship between job engagement and SPS.

This study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive maintenance. Opportunities and challenges in developing anomaly detection algorithms for predictive maintenance and unexplored areas in this context are also discussed.

For conducting a systematic review on the state-of-the-art algorithms in fault detection for predictive maintenance, review papers from the years 2017–2021 available in the Scopus database were selected. A total of 93 papers were chosen. They are classified under electrical and electronics, civil and constructions, automobile, production and mechanical. In addition to this, the paper provides a detailed discussion of various fault-detection algorithms that can be categorised under supervised, semi-supervised, unsupervised learning and traditional statistical method along with an analysis of various forms of anomalies prevalent across different sectors of industry.

Based on the literature reviewed, seven propositions with a focus on the following areas are presented: need for a uniform framework while scaling the number of sensors; the need for identification of erroneous parameters; why there is a need for new algorithms based on unsupervised and semi-supervised learning; the importance of ensemble learning and data fusion algorithms; the necessity of automatic fault diagnostic systems; concerns about multiple fault detection; and cost-effective fault detection. These propositions shed light on the unsolved issues of predictive maintenance using fault detection algorithms. A novel architecture based on the methodologies and propositions gives more clarity for the reader to further explore in this area.

Papers for this study were selected from the Scopus database for predictive maintenance in the field of fault detection. Review papers published in this area deal only with methods used to detect anomalies, whereas this paper attempts to establish a link between different industrial domains and the methods used in each industry that uses fault detection for predictive maintenance.