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This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

This modeling facilitates the determination of control responses (or possibly reconfiguration) upon such events and the identification of which segments of the pipeline can continue to function uninterrupted. Based on this modeling, an algorithm is presented to implement the control responses and to establish this determination. In this work, the authors propose using Message Queuing Telemetry Transport (MQTT), which is an integrated method to perform the system-wide control based on message exchanging among local node controllers (agents) and the global controller (broker).

Complex manufacturing lines in industrial plants are designed to accomplish an overall task in an incremental mode. This typically consists of a sequence of smaller tasks organized as cascaded processing nodes with local controls, which must be coordinated and aided by a system-wide (global) controller. This work presents a logic modeling technique for such pipelines and a method for using its logic to determine the consequent effects of events where a node halts/fails on the overall operation.

The method uses a protocol for establishing communication of node events and the algorithm to determine the consequences of node events in order to produce global control directives, which are communicated back to node controllers over MQTT. The algorithm is simulated using a complex manufacturing line with arbitrary events to illustrate the sequence of events and the agents–broker message exchanging.

This approach (MQTT) is a relatively new concept in Cyber-Physical Systems. The proposed example of feed-forward is not new; however, for illustration purposes, it was suggested that a feed-forward be used. Future works will consider practical examples that are at the core of the manufacturing processes.

From the perspective of the supply chain risk management (SCRM), this paper addresses the effects of a hybrid production system (make-to-stock and make-to-order) in order to know which risks can impact the production planning process at a large automaker in Brazil. Through the correlation of these themes, the purpose of this paper is to understand the relevant risks to the supply chain (SC).

Before the field research, a theoretical approach was made on two themes. After theoretical analysis of a case study on the automaker and data collection, the work used the Pearson’s product moment correlation (r) and χ² and Kolmogorov–Smirnov tests to assess the risk factors raised by the interviewed professionals, thus characterizing a mixed methodological approach (i.e. qualitative and quantitative).

It was evidenced that many risks are the result of functional failures, such as input of incorrect information in the system, and many are inherent to managerial decisions when procedures and different paths of production are adopted. Additionally, it has been proven that the adoption of a hybrid production planning approach does not increase the risks to the SC and that the identified risks do not necessarily are included within the scope of SCRM.

This study is characterized by an approach which combines SCRM and hybrid production system.

The purpose of this paper is to propose a model for planning and controlling the design process in companies that design, manufacture and assemble prefabricated engineer-to-order (ETO) building systems. This model was devised as an adaptation of the Last Planner® System for ETO multiple-project environments.

Design science research, also known as prescriptive research, was the methodological approach adopted in this research. An empirical study was carried out at the design department of a leading steel fabricator from Brazil, in which the proposed model was implemented in six different design teams.

The main benefits of the proposed model were shielding design work from variability, encouraging collaborative planning, creating opportunities for learning, increasing process transparency, and flexibility according to project status. Two main factors affected the effectiveness of the implementation process commitment and leadership of design managers, and training on design management and project planning and control core concepts and practices.

Some limitations were identified in the implementation process: similarly to some previous studies (Ballard, 2002; Codinhoto and Formoso, 2005), the success of constraint analysis was still limited; some of the metrics produced (e.g. ABI, causes of planning failures) have not been fully used for process improvement; and systematic feedback about project status was not properly implemented and tested.

The main contributions of this study in relation to traditional design planning and control practices are related to the use of two levels of look-ahead planning, the introduction of a decoupling point between conceptual and detail design, the proposition of new metrics for the Last Planner® System, and understanding the potential role of visual management to support planning and control.

This study aims to propose a tailored Lean Six Sigma framework providing an accessible Lean Six Sigma methodology for compound feed manufacturers with the aim of mitigating rising costs and increasingly complex demands from customers.

A Lean Six Sigma framework was designed combining Lean value stream mapping and Six Sigma structured problem-solving with a case study in an Irish compound feed manufacturer.

The study found that the Lean Six Sigma implementation framework provided a simplified approach, which fitted the resource availability within compound feed manufacturing.

The study is limited by the constraints of a sole case study in providing empirical evidence of the effectiveness of the framework. Nevertheless, a conceptual Lean Six Sigma model is proposed, which will assist compound feed manufacturers implementing a continuous improvement approach.

This paper proposes a simplified approach to the implementation of Lean Six Sigma in agricultural compound feed manufacturers and in small and medium-sized organisations. This is the first such study in Ireland and will add to the body of work on Lean in agriculture and aid other agri-businesses and compound feed manufacturers in understanding how Lean Six Sigma can benefit.

To date, the literature has usually assumed that a universal approach to resilience is appropriate in which different resilience capabilities are equally important for all organizations independent of contextual characteristics. In contrast this study investigates if production process characteristics affect resilience capabilities in terms of redundancy, flexibility, agility and collaboration.

An in-depth exploratory multiple case study was carried out in eight companies across different industries. Data were gathered through multiple interviews with key informants in each company.

The authors find differences in, and trade-offs between, resilience capabilities and practices related to redundancy, agility and collaboration induced by the different configurations of production system characteristics: especially between discrete and process industries. Further, a major influential characteristic is the production strategy employed (make-to-stock or make-to-order) which stresses or limits collaboration and redundancy.

This is one of the first studies to explore the effects of production system characteristics as a major contingency factor on the resilience capabilities of an organization. As such it provides valuable insights into the development of a more nuanced contingency approach to how organizations can build resilience and employ specific practices that fit their situation.

This study aims to provide a measurement model, and the underlying constructs and items, for Logistics 4.0 in manufacturing companies. Industry 4.0 technology for logistics processes has been termed Logistics 4.0. Logistics 4.0 and its elements have seen varied conceptualizations in the literature. The literature has mainly focused on conceptual and theoretical studies, which supports the notion that Logistics 4.0 is a relatively young area of research. Refinement of constructs and building consensus perspectives and definitions is necessary for practical and theoretical advances in this area.

Based on a detailed literature review and practitioner focus group interviews, items of Logistics 4.0 for manufacturing enterprises were further validated by using a large-scale survey with practicing experts from organizations located in Central Europe, the Northeastern United States of America and Northern Thailand. Exploratory and confirmatory factor analyses were used to define a measurement model for Logistics 4.0.

Based on 239 responses the exploratory and confirmatory factor analyses resulted in nine items and three factors for the final Logistics 4.0 measurement model. It combines “the leveraging of increased organizational capabilities” (factor 1) with “the rise of interconnection and material flow transparency” (factor 2) and “the setting up of autonomization in logistics processes” (factor 3).

Practitioners can use the proposed measurement model to assess their current level of maturity regarding the implementation of Logistics 4.0 practices. They can map the current state and derive appropriate implementation plans as well as benchmark against best practices across or between industries based on these metrics.

Logistics 4.0 is a relatively young research area, which necessitates greater development through empirical validation. To the best of the authors knowledge, an empirically validated multidimensional construct to measure Logistics 4.0 in manufacturing companies does not exist.

Supply chain (SC) configuration has gained increased acceptance as an important issue when evaluating new customization possibilities and this evidence has contributed to the strengthening of the debate between global vs local production locations. This work contributes in enrichment of this topic by studying how local or global SC location decisions influence performances by considering a SC point of view, in terms of cost and time, in traditional and customized productions.

A discrete event simulation approach, based on experimentation through executable configurations, was used to evaluate different SC scenarios for customized as well as traditional products within the footwear industry.

The results indicated that to identify proper SC locations, existing trade-offs between the time and cost performances should be studied, avoiding the evaluation of a single performance independently and, instead, adopting a complete SC point of view while considering these performances.

This evidence has contributed to the reinforcement of the discussion between far-shore destinations vs near-shore production locations. Further studies are encouraged to adopt the present model, in which addition of other variables such as specific manufacturing competences to differentiate suppliers, both local and global suppliers, or the possibility of realizing special types of product customization required by final consumers can be done.

The paper contributes to the academic and practitioners' debate by proposing a systemic approach to assess SCs’ performances in customized contexts and to compare them to traditional collections. Results indicate that cost and time performance must find a balance that does not necessarily correspond to an exclusively local or global production.

This work contributes to the SC configuration issue by considering the trade-off between efficiency and effectiveness (i.e. SC costs and SC times) for customized productions by reviving and enriching it with an SC perspective in customization contexts.

Lean distributed manufacturing (LDM) is being considered as an enabler of achieving sustainability and resilience in manufacturing and supply chain operations. The purpose of this paper is to enhance the understanding of how LDM characteristics affect the resilience of manufacturing companies by drawing upon the experience of food manufacturing companies operating in the UK.

The paper develops a conceptual model to analyse the impact of LDM on the operational resilience of food manufacturing companies. A triangulation research methodology (secondary data analysis, field observations and structured interviews) is used in this study. In a first step, LDM enablers and resilience elements are identified from literature. In a second step, empirical evidence is collected from six food sub-sectors aimed at identifying LDM enablers being practised in companies.

The analysis reveals that LDM enablers can improve the resilience capabilities of manufacturing companies at different stages of resilience action cycle, whereas the application status of different LDM enablers varies in food manufacturing companies. The findings include the development of a conceptual model (based on literature) and a relationship matrix between LDM enablers and resilience elements.

The developed relationship matrix is helpful for food manufacturing companies to assess their resilience capability in terms of LDM characteristics and then formulate action plans to incorporate relevant LDM enablers to enhance operational resilience.

Based on the literature review, no studies exist that investigate the effects of LDM on factory’s resilience, despite many research studies suggesting distributed manufacturing as an enabler of sustainability and resilience.

This paper investigates how the adoption of additive manufacturing (AM) impacts upstream supply chain (SC) design and considers the influence of drivers and barriers towards the adoption.

Ten case studies investigating AM adoption by Original Equipment Manufacturers (OEMs) in five industries were conducted. This research is driven by a literature-based framework, and the results are discussed according to the theory of transaction cost economics (TCE).

The case studies reveal four patterns of AM adoption that affect upstream SC design (due to changes in supply base or types of buyer–supplier relationships): make, buy, make and buy and vertical integration. A make or buy decision is based on the level of experience with the technology, on the AM application (rapid manufacturing, prototyping or tooling) and on the need of control over production. Other barriers playing a role in the decision are the high initial investments and the lack of skills and knowledge.

This paper shows how different decisions regarding AM adoption result in different SC designs, with a specific focus on the upstream SC and changes in the supply base. This research is among the first to provide empirical evidence on the impact of AM adoption on upstream SCs and to identify drivers of the make or buy decision when adopting AM through the theoretical lens of TCE.