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The purpose of this paper is to develop a preliminary conceptual scale for the measurement of distributed manufacturing (DM) capacity of manufacturing companies operating in rubber and plastic sectors.

A two-step research methodology is employed. In first step, the dimensions of DM and different levels of each dimension have been defined. In second step, an empirical analysis (cluster analysis) of database firms is performed by collecting the data of 38 firms operating in Italian mould manufacturing sector. Application case studies are then analyzed to show the use of the proposed DM conceptual scale.

A hyperspace, composed of five dimensions of DM, i.e. manufacturing localization; manufacturing technologies; customization and personalization; digitalization; and democratization of design, is developed and a hierarchy is defined by listing the levels of each dimension in an ascending order. Based on this hyperspace, a conceptual scale is proposed to measure the positioning of a generic company in the DM continuum.

The empirical data are collected from Italian mould manufacturing companies operating in rubber and plastic sectors. It cannot be assumed that the industrial sectors in different parts of the world are operating under similar operational, regulatory and economic conditions. The results, therefore, might not be generalized to manufacturing companies operating in different countries (particularly developing countries) under different circumstances.

This is first preliminary scale of its kind to evaluate the positioning of companies with respect to their DM capacity. This scale is helpful for companies to compare their capacity with standard profiles and for decision making to convert the existing manufacturing operations into distributed operations.

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.

The manufacturing paradigm is shifting from being a central system to a distributed system and is increasingly becoming dependent on wide area networks for the management of information services. The information subsystem employed in a manufacturing setting is susceptible to getting congested when used in a wide area network setting during peak hours. The issue of information and control subsystem integration and the characterization of network congestion have been researched in this paper using faded information field architecture.

A wide area network was simulated with all its associated components like routers, switches, gateways, etc. by programming a simulation engine. The simulation engine mimicked a wide area network employed in a manufacturing enterprise.

The architecture holds the promise to address the integration and congestion issues in a manufacturing system by employing autonomous decentralized and autonomous information fading techniques. Results show the effectiveness of the proposed technique in addressing the issue of network congestion in a distributed wide area network.

The issue of information and control subsystems of a manufacturing system has been reported earlier. However, the network‐centric problems like congestion during peak hours of operation have not been addressed in these emerging paradigms. The research reported in this paper focuses on the congestion issue and could be useful to wide area networked manufacturing system designers and managers.

3D printing has been warmly welcomed by clothing enterprises for its customization capacity in recent years. However, such clothing enterprises have to face the digital transformation challenges brought by 3D printing. Since the business model is a competitive weapon for modern enterprises, there is a research gap between business model innovation and digital transformation challenges for 3D-printing garment enterprises. The aim of the paper is to innovate a new business model for 3D-printing garment enterprises in digital transformation.

A business model innovation canvas (BMIC), a new method for business model innovation, is used to innovate a new 3D-printing clothing enterprises business model in the context of digital transformation. The business model canvas (BMC) method is adopted to illustrate the new business model. The business model ecosystem is used to design the operating architecture and mechanism of the new business model.

First, 3D-printing clothing enterprises are facing digital transformation, and they urgently need to innovate new business models. Second, mass customization and distributed manufacturing are important ways of solving the business model problems faced by 3D-printing clothing enterprises in the process of digital transformation. Third, BMIC has proven to be an effective tool for business model innovation.

The new mass deep customization-distributed manufacturing (MDC-DM) business model is universal. As such, it can provide an important theoretical reference for other scholars to study similar problems. The digital transformation background is taken into account in the process of business model innovation. Therefore, this is the first hybrid research that has been focused on 3D printing, garment enterprises, digital transformation and business model innovation. On the other hand, business model innovation is a type of exploratory research, which means that the MDC-DM business model’s application effect cannot be immediately observed and requires further verification in the future.

The new business model MDC-DM is not only applicable to 3D-printing garment enterprises but also to some other enterprises that are either using or will use 3D printing to enhance their core competitiveness.

A new business model, MDC-DM, is created through BMIC, which allows 3D-printing garment enterprises to meet the challenges of digital transformation. In addition, the original canvas of the MDC-DM business model is designed using BMC. Moreover, the ecosystem of the MDC-DM business model is constructed, and its operation mechanisms are comprehensively designed.

The emergence of distributed manufacturing (DM) is examined as a new form of localised production, distinct from previous manifestations of multi-domestic and indigenous production.

Supply network (SN) configuration and infrastructural provisioning perspectives were used to examine the literature on established localised production models as well as DM. A multiple case study was then undertaken to describe and explore the DM model further. A maximum variation sampling procedure was used to select five exemplar cases.

Three main contributions emerge from this study. First, the research uniquely brings together two bodies of literature, namely SN configuration and infrastructure provisioning to explore the DM context. Second, the research applies these theoretical lenses to establish the distinctive nature of DM across seven dimensions of analysis. Third, emerging DM design rules are identified and compared with the more established models of localised production, drawing on both literature and DM case evidence.

This study provides a rich SN configuration and infrastructural provisioning view on DM leading to a set of design rules for DM adoption, thus supporting practitioners in their efforts to develop viable DM implementation plans.

The authors contribute to the intra- and inter-organisational requirements for the emerging DM context by providing new perspectives through the combined lenses of SN configuration and infrastructural provisioning approaches.

Integrates CAD/CAM, production control and process control in a case study involving manufacturing automation. The manufacturing firm is a distributed cellular system operating in a produce‐to‐order environment. Combines a parametric CAD software with global variables and global relations to achieve a design automation. Results obtained from design automation are transmitted to update the design model and the associative manufacturing model. In addition, scheduling and control are also integrated in the system. Distinguishes three scheduling levels: static scheduling, rescheduling, and real‐time scheduling. For a distributed and effective reason, the distributed shifting bottleneck procedure (DSBP) is adopted as the static scheduler and rescheduler, and the LRPT (longest bottleneck processing time) despatching rule is adopted as the real time despatching rule. FInally, introduces the configuration of the system including the flexible manufacturing cell architecture and the input buffer device. Uses a PLC programming toll that follows the IEC 1131‐3 standard to implement cell controllers. The above implementation aims to easily and effectively execute the CIM system.

Introduces a case‐based management system (CBMS) for decision making such as allocating tasks under a distributed manufacturing environment. The distributed manufacturing environment can be viewed as a multi‐agent system in which each manufacturing plant is an agent to produce the whole or part of the products ordered by the customers. The knowledge is represented as cases in the system which can be reused to produce new solutions. This learn‐by‐experience approach provides a particular strength to case‐based reasoning over most other AI methods. The effectiveness of the proposed concept is demonstrated by applying the CBMS to a task allocation example. By using the CBMS approach, tasks or jobs can be assigned to appropriate agents effectively. Moreover, CBMS can be used locally or globally via WAN or Internet with high‐security protection as a knowledge management system in small and medium enterprises.

In this paper, a three-dimensional (3D) printer-based manufacturing line and supporting system, which supports personalized/customized manufacturing for individual businesses or start-up companies, was studied to evaluate the practicality of using additive manufacturing for personalization/mass customization.

First, factory-as-a-service (FaaS) system, which provides factory as a service to customers, was proposed and designed to manufacture various products within a distributed manufacturing environment. This system includes 3D printer-based material extrusion processes, vapor machine/computer numerical control machines as post-processes and assembly and inspection processes with an automated material handling robot in the factory. Second, a virtualization module for the FaaS factory was developed using a simulation model interfaced with a cloud-based order and production-planning system and an internet-of-things-based control and monitoring system. This is part of the system for manufacturing operations, which is capable of dynamic scheduling in a distributed manufacturing environment. In addition, simulation-based virtual production was conducted to verify and evaluate the FaaS factory for the target production scenario. Main information of the simulation also has been identified and included in the virtualization module. Finally, the established system was applied in a sample production scenario to evaluate its practicality and efficiency.

Additive manufacturing is a reliable, feasible and applicable technology, and it can be a core element in smart manufacturing and the realization of personalization/mass customization.

Various studies on additive manufacturing have been conducted with regard to replacing the existing manufacturing methods or integrating with them, but these studies mostly focused on materials or types of additive manufacturing, with few advanced or applied studies on the establishment of a new manufacturing environment for personalization/mass customization.

Nowadays, companies are increasingly adopting additive manufacturing (AM) technologies due to their flexibility and product customization, combined with non-dramatic increases in per unit cost. Moreover, many companies deploy a plurality of distributed AM centers to enhance flexibility and customer proximity. Although AM centers are characterized by similar equipment and working methods, their production mix and volumes may be variable. The purpose of this paper is to propose a novel methodology to (1) monitor the quality of the production of individual AM centers and (2) perform a benchmarking of different AM centers.

This paper analyzes the quality of the production output of AM centers in terms of compliance with specifications. Quality is assessed through a multivariate statistical analysis of measurement data concerning several geometric quality characteristics. A novel operational methodology is suggested to estimate the fraction nonconforming of each AM center at three different levels: (1) overall production, (2) individual product typologies in the production mix and (3) individual quality characteristics.

The proposed methodology allows performing a benchmark analysis on the quality performance of distributed AM centers during regular production, without requiring any ad hoc experimental test.

This research assesses the capability of distributed AM centers to meet crucial quality requirements. The results can guide production managers toward improving the quality of the production of AM centers, in order to meet customer expectations and enhance business performance.

Progress in theory building in the field of collaborative networks in manufacturing is preponderantly seen in contributions from disciplines outside manufacturing science. Interdisciplinary research is one way of accelerating the development of appropriate theory for this emerging domain where industrial practice has moved beyond the state of the art of scientific knowledge for establishing workable, competitive solutions. The purpose of this paper is to examine to what extent interdisciplinary research has contributed to a better understanding of collaborative (manufacturing) networks.

To find out more about provenances of on‐going studies, to identify clusters of contributions and to provide direction for future work of researchers in this domain, publications of the past 22 years have been evaluated. To retrieve these contributions, a structured literature review has been undertaken by applying keywords to selected databases and using a strictly defined stepwise procedure. In total, 202 publications of all kinds have been evaluated.

From the analysis of the results, it appears that most interdisciplinary contributions to collaborative (manufacturing) networks rely on one original outside discipline for either developing solutions or advancing theoretical insight. Consequently, and after further analysis, it seems that researchers in collaborative networks hardly resort to multi‐disciplinary approaches, unless “natural”; further advances might arrive from stimulating these multi‐disciplinary avenues rather than sticking to more mono‐disciplinary, and less risky, takes on both applications and theoretical insight. A more detailed investigation of the value of contributions reveals that efforts to make interdisciplinary advances are either difficult or limited. Also, the findings indicate that researchers tend to follow a more “technical” approach to decision making by actors in networks rather than searching for a shift in paradigm.

While setting out these directions for future research and guiding research, this first‐of‐its‐kind review introduces the collaboration model as a systematic approach to collaborative (manufacturing) networks. This model might serve as a reference model to integrate disciplines for addressing the characteristics of Collaborative Networks. Its use in the review led to the finding that typical traits of networks, such as changeability, supplementary assets and decentralisation of decision making, are under‐researched.