Social Manufacturing Research Articles

A modified variable neighborhood search algorithm for manufacturer selection and order acceptance in distributed virtual manufacturing network

In this paper, we investigate a manufacturer selection and composition problem for Distributed Virtual Manufacturing Network (DVMN) with order acceptance and scheduling of deteriorating jobs, where potential manufacturers include proprietary plants and outsourced co-manufacturers. In such a problem, at the beginning of planning horizon, a manufacturing company (MC) receives a series of order requests from its customers. Due to the limited production capacity, the MC is not able to fulfill all the orders requested by the customers on time. To satisfy the order demand as much as possible, one of the MC’s choices is to establish a collaborative manufacturing platform (i.e., DVMN) that leverages the advantages of a social manufacturing network to expand their production capabilities. However, the increase in production capacity does not mean that all orders from the customers are able to be accepted and produced. In fact, standing at the profit-maximizing point of view, the order requests that do not bring profits to the company need to be rejected at the beginning of planning horizon. For the accepted orders, the MC will produce a part of them by its plants in the DVMN, and the remaining orders will be outsourced to other co-manufacturers in the DVMN. Based on some structural properties, we develop a novel Reduced Variable Neighborhood Search (RVNS) algorithm incorporated multiple random mutations neighborhood structures (NSs-MRM) to determine the composition of the DVMN, and further determine which orders should be rejected, which accepted orders should be processed by the company-owned plants in the DVMN, and which accepted orders should be outsourced to the co-manufacturers in the DVMN. In particular, the processing sequence of the orders produced in the company-owned plants are also determined. Finally, a reasonable composition of the DVMN and corresponding order acceptance as well as order scheduling on each member of the DVMN are given. To evaluate the proposed algorithm, a series of computational experiments are conducted, and the experimental results show that the proposed algorithm outperforms other existing baseline algorithms in solving both small-scale and large-scale problem instances.

Makerchain: A blockchain with chemical signature for self-organizing process in social manufacturing

The growth of personalized demands requires socialized resources to timely self-organize themselves with crowd intelligence for co-creating open architecture products. This social manufacturing paradigm drives an increased demand for makers to track the authenticity and quality of products. A new decentralized blockchain-driven model, named Makerchain, is presented to handle the cyber-credit of social manufacturing among various makers. An anti-counterfeiting method composed of chemical signature is proposed to represent unique features of personalized products. Twinning unique signature data to blockchain and other functional databases is realized and anticipated to make manufacturing service transactions among makers more trustworthy. Based on an automated execution mechanism of smart contracts among makers, a decentralized manufacturing network can be enabled for automating transactions among makers, as well as third-party verification of product lifecycle through a trail of historic events. A Makerchain Decentralized Application (DApp) is presented to demonstrate the proposed approach through which clustered makers can self-organizing themselves around personalized demands.

Social Learning Evolution (SLE): Computational Experiment-Based Modeling Framework of Social Manufacturing

As a new form of manufacturing industry in the Internet era, social manufacturing has its inherent “social-cyber” complexity: the source of manufacturing service is social, and such sociality aggravates the diversity, uncertainty, and dynamics of service supply. This poses new challenges to the service matching between supply-side and demand-side. In order to meet this challenge, it is necessary to conduct a complexity analysis of social manufacturing. Traditional researches mainly rely on data statistics and macro analysis, in which there are difficulties in clearly identifying the links between various impact factors and macro evolution phenomena. In order to change such a situation, this paper proposes a modeling framework of social manufacturing from the aspect of social learning evolution (SLE), including individual evolution model, organizational learning model, and social learning model. Based on the SLE framework, the corresponding computational experiment system is built to analyze the complexity of social manufacturing. The performance of several evolution mechanisms in social manufacturing is simulated and compared as a case study to present the application of SLE framework. The results demonstrate that our method has a substantial promise.

Social factory as a production node of social manufacturing

Social factory is served as the production node of social manufacturing communities/network to make manufacturing factories shift to the internet-based ones. The social factory aims to deal with fast-changing production requirements, sharing and competing of product orders, flexible resource configuration, ubiquitous interconnections, and real-time production monitoring and control. To achieve these visions, an extended cyber-physical system-enabled social factory model is proposed by integrating current cyber-physical system with machining equipment, social sensors, and smart workpieces. Within the proposed social factory model, the system framework and runtime logic are presented, and some core concepts such as extended cyber-physical system node, social sensor, and smart workpiece are clarified. Based on that, the social factory model is implemented by developing diverse extended cyber-physical system nodes and then connecting them with humans to form a collaborative production network where humans can access and control the machines anywhere and anytime. To validate the proposed social factory framework, a flexible production line in our lab is regarded as an extended cyber-physical system-enabled social factory to demonstrate the decentralized production interaction and cooperation.

Product Service Systems for Social Manufacturing: A new service system with multi-provider

Product-Service System (PSS) business model drives the outputs of the enterprises from products to product-services, in which they can get considerable and stable revenues and their customers can be satisfied totally. However, due to the increasing service complexity and labor cost, isolated enterprises may not be able to provide the life cycle PSS for the customers. For solving this problem, we introduced the idea of social manufacturing (SocialM), which strengthens the socialized manufacturing resources to collaborate for covering the whole stages of product life cycle with aggregated resource capabilities. To integrate the idea with PSS, a novel service paradigm called PSS for SocialM is proposed to realize collaborative and alliance of services. In this paper, the implementing architecture and operational logic of PSS for SocialM are demonstrated. A practical case is analyzed to validate the feasibility and effectiveness of PSS for SocialM.

Moving from mass customization to social manufacturing: a footwear industry case study

ABSTRACTA social manufacturing (SM) system for the footwear industry is proposed to improve customisation accuracy and efficiency through the use of big data and crowdsourcing. SM is a novel manufacturing paradigm for distributed, collaborative, service-oriented, crowdsourcing-based, intelligent, and customised footwear production. First, the definition and process of footwear SM are introduced. Next, key supporting technologies are discussed, including 3D scanning, an evaluation mechanism for supply–demand matching, and optimised allocation of socialised manufacturing resources. Finally, the system development and an application case are described. In SM, a prosumer is defined as someone who is both a producer and consumer at the same time, and can be involved in the full life cycle of a personalised product, including the design, manufacture, service, and consumption processes. This allows the experience of every prosumer to be enhanced through participation, potential demands for personalised product to be released, and the production efficiency of customisation to be improved.

Reinforcement learning based compensation methods for robot manipulators

Smart robotics will be a core feature while migrating from Industry 3.0 (i.e., mass manufacturing) to Industry 4.0 (i.e., customized or social manufacturing). A key characteristic of a smart system is its ability to learn. For smart manufacturing, this means incorporating learning capabilities into the current fixed, repetitive, task-oriented industrial manipulators, thus rendering them ‘smart’. In this paper we introduce two reinforcement learning (RL) based compensation methods. The learned correction signal, which compensates for unmodeled aberrations, is added to the existing nominal input with an objective to enhance the control performance. The proposed learning algorithms are evaluated on a 6-DoF industrial robotic manipulator arm to follow different kinds of reference paths, such as square or a circular path, or to track a trajectory on a three dimensional surface. In an extensive experimental study we compare the performance of our learning-based methods with well-known tracking controllers, namely, proportional-derivative (PD), model predictive control (MPC), and iterative learning control (ILC). The experimental results show a considerable performance improvement thanks to our RL-based methods when compared to PD, MPC, and ILC.

Manufacturing service order allocation in the context of social manufacturing based on Stackelberg game

For adapting the socialization, individuation and servitization in manufacturing industry, a new manufacturing paradigm called social manufacturing has received a lot of attention. Social manufacturing can be seen as a network that enterprises with socialized resources self-organized into communities that provide personalized machining and service capabilities to customers. Since a community of social manufacturing has multiple enterprises and emphasizes on the importance of service, manufacturing service order allocation must be studied from the new perspective considering objectives on service cost and quality of service. The manufacturing service order allocation can be seen as a one-to-many game model with multi-objective. In this article, a Stackelberg game model is proposed to tackle the manufacturing service order allocation problem with considering the payoffs on cost and quality of service. Since this Stackelberg game can be mapped to a multi-objective bi-level programming, a modified multi-objective hierarchical Bird Swarm Algorithm is used to find the Nash equilibrium of the game. Finally, a case from a professional printing firm is analyzed to validate the proposed methodology and model. The objective of this research is to find the Nash equilibrium on the manufacturing service order allocation and provide strategies guidance for customer and small- and medium-sized enterprises with optimal service cost and lead time. According to the game process and Nash equilibrium, some rules are revealed, and they are useful for guiding practical production.

Street furniture in recycled and resignified materials

Seeking to strengthen sustainable construction through recycling and reuse of cardboard tubes as an efficient use of raw materials in the Municipality of Ocaña, this research was carried out as a proposal for the resignification of the cardboard tube as a material, generating technical, low-cost, and sustainable solutions, allowing the production of street furniture, to generate changes to those social dynamics impregnated by a reality of consumption, which needs new construction techniques and social innovation, that allow giving life to and making visible, sustainable urban spaces, friendly with the environment as well, thus contributing to the transfer of knowledge in the application of the technique. For this reason, it is important to analyze the use, utility and impact of the reuse of solid materials such as cardboard tubes, as it allows identifying different routes that articulate new contributions to social construction modifying customs and traditions, and building social manufacturing aiming at technological innovation for sustainable construction.

Reference architectures for smart manufacturing: A critical review

The demand for more personalized, smart, and sustainable products and the rapid growth of the industrial Internet and cyber-physical technologies have led to the development of several new paradigms for manufacturing in recent years. Examples include smart manufacturing, cyber-physical production systems, Industry 4.0, cloud manufacturing, and social manufacturing. This article questions the necessity of this ‘multiplicity’ of definitions and takes a systematic approach to portraying a consistent ‘blueprint’ (i.e., a reference architecture) that rationalizes and encompasses all of these different viewpoints and proposals. Through conceptual analysis of these new paradigms and several emerging reference architectures such as RAMI4.0, IIRA, IBM Industry 4.0, and NIST Smart Manufacturing, service-orientation was identified as the main theme: digitalization and integration of manufacturing resources on the IoT as on-demand services, as the main enabler of value network integration and collaboration as well as smart plug-and-produce shop-floor systems. The hypothesis was recast in terms of six propositions on the characteristics of smart manufacturing and evaluated through interviews with seven experts from industry, government, and standards organizations. The general hypothesis was accepted, and several recommendations were provided to address the identified design and implementation barriers. In conclusion, two high-priority actionable plans were identified: (1) modeling and composition of micro-services, and (2) optimizing the topology of interactions between micro-services, smart objects, and humans.

Evaluating of dynamic service matching strategy for social manufacturing in cloud environment

As a new form of manufacturing industry in cloud environment, social manufacturing has its inherent “social–cyber” complexity: the source of enterprise services is social, and this sociality aggravates the diversity, uncertainty and dynamics of service supply. Researchers have done a lot to improve the adaptability of service matching strategies. However, it is difficult or even impossible to for traditional methods to evaluate the viability of these strategies because of the “social–cyber” complexity. This poses new challenges to how to evaluate and optimize these strategies in a complex social manufacturing environment. Aiming at the problem, this paper proposes a computational experiment-based evaluation framework, which can simulate all kinds of actual scenarios to verify the performance of service matching strategies. This method includes three parts: (1) design of supply & demand matching strategy; (2) construction of computational experiment system; (3) performance evaluation of service strategies in different experiment environments. A case study is given to verify the applicability of our method by means of comparing several adaptive service matching strategies (supply-oriented, demand-oriented, initial supply & demand-oriented, optimized supply & demand-oriented) in two kinds of market environments. The results demonstrate that our method has a substantial promise.

Selecting manufacturing partners in push and pull-type smart collaborative networks

The idea of Collaborative Manufacturing, also known as the Production Networks or Social Manufacturing, has been around for more than 25 years. It is a production concept based on non-hierarchical collaboration among enterprises often referred as Virtual Enterprise (VE). Despite many scientific research and projects with this topic, it is difficult to find an example of fully operational non-hierarchical production network anywhere in the world. However, that fact could be changed very soon. Namely, the new industrial revolution, called Industry 4.0, encourages industrial enterprises to adopt information-communication technology (ICT) and Internet of Things (IoT) into their production systems, thus creating Cyber-Physical Production System (CPPS). From the aspect of production networks, CPPS represents crucial infrastructure, or a missing link between enterprises. Now, with CPPS in place, non-hierarchical networking and collaboration becomes possible through Smart Collaborative Production Networks. In this research, the concept of information system for Smart Collaborative Production Networks was developed and called ‘VENTIS’. Although the idea of the concept is to manage the collaboration inside Virtual Enterprise, in this research, a special focus has been put on manufacturing planning phase in which optimization problem known as the Partner Selection Problem (PSP) occurs. Since the PSP in manufacturing phase is far more complex than partner selection during the collaborative product development phase, new research premises regarding the Virtual Enterprise type have been set. Two types of Virtual Enterprise business models – Push-type and Pull-type – have been defined in this research. If VE is Push-type, HUMANT algorithm is used to solve PSP that occurs in that case. If VE is Pull-type, a special procedure, inspired by phenomenological reduction, has been established in which set of a priori created VEs is compared with theoretically ‘the best’ VE and theoretically ‘the worst’ VE. Enterprises’ data of production network from Dalmatia (Split-Dalmatia County, Croatia) is used as a Case Study to present ‘VENTIS’ concept and to present the procedure for creation of sustainable Virtual Enterprise.

An investigation on establishing small- and medium-sized enterprises communities under the environment of social manufacturing

Manufacturing industry has become professionalization, socialized, service-oriented, and collaborative, so lots of professional and socialized small- and medium-sized enterprises spring up to provide product–service for satisfying customers’ requirements. Faced with this trend, a new manufacturing mode called social manufacturing has been proposed to aggregate these small- and medium-sized enterprises into communities for mass individualization manufacturing. As the basis of social manufacturing, small- and medium-sized enterprises communities are established by two steps. First, the socialized manufacturing resources of the small- and medium-sized enterprises are described and similar socialized manufacturing resources are clustered into one socialized manufacturing resources community by growing hierarchical self-organizing map algorithm. Suitable socialized manufacturing resources communities are selected and then the socialized manufacturing resources are mapped into small- and medium-sized enterprises. Second, these small- and medium-sized enterprises form a community to complete the order together. Product cost and delivery time serve as the indicator for order allocating in the small- and medium-sized enterprises community. A multi-objective algorithm is proposed to tackle the order allocation problem. A case from a professional printing firm is analyzed to validate the proposed methodology and model. Some rules are revealed and these rules are useful for guiding practical production. The study endorses the establishing small- and medium-sized enterprises communities under the environment of social manufacturing and suggests extensive practical implications.

Contextual self-organizing of manufacturing process for mass individualization: a cyber-physical-social system approach

ABSTRACT Under the tendency of mass individualization demands, social manufacturing has been proposed as a new paradigm. This paper proposes a contextual process configuration model for realizing the mass individualized manufacturing. The Social Internet of Things (SIoT) strategy is introduced to boost sociality and narrow down the contextual computing complexity based on situational awareness in a cyber-physical-social connected space. SIoT monitors spatiotemporal situations in a spontaneous social network of relationships and interactions, and induces relevant smart machines depending on prosumers’ individual demands. Four key enabling techniques of SIoT are detailed, and then a demonstrative implementation together with a case study are presented to address the decentralized intelligence in proactive decision making for mass individualization. It is expected that this study can help readers to gain more understanding on the characteristics and configuration logics of contextual self-organizing of manufacturing process for mass individualization.

Social manufacturing for high-end apparel customization

Social manufacturing (SM), a novel distributed, collaborative and intelligent manufacturing mode, is proposed and developed for high-end apparel customization. The main components of SM cloud are designed, and its research topics are summarized. Then, SM's key technologies are studied. 3D technologies for apparel customization, like 3D modeling, 3D fitting mirror and 3D customization, are developed to improve the customization precision and user experience. Information based collaborative management is realized to share, communicate, and handle the information efficiently among all groups and individuals of SM cloud. Suppliers' evaluation mechanism is designed to support the optimal decisions making. Next, SM cloud is constructed in five layers for high-end apparel customization. By using SM cloud based crowd-sourcing, social resources can be allocated rationally and utilized efficiently, consumer can customize the product in any processes like innovation, design, making, marketing and service, and traditional apparel enterprise can be upgraded into SM mode for keeping it competitive in the future customization markets.

Removing barriers to sustainability research on personal fabrication and social manufacturing

Abstract Since the beginning of the New Millennium, increasingly widespread availability of the Internet and digitally enabled tools have made production processes more accessible to private individuals, introducing new opportunities for personal fabrication and social manufacturing. Yet scant sustainability research has been conducted on this important sector. We argue that research barriers, particularly relating to confusing terminology and lack of individual-centric analytical tools, are largely responsible for this void. The objective of this study is to overcome these barriers by (1) providing an integrating framework that can improve transferability, to other conceptual analyses, of the results of sustainability research conducted from a particular conceptual viewpoint, and (2) suggesting how some firm-centric analytical tools can be modified for effective use in studies of individual-level phenomena. We base our framework on the emerging concept of social manufacturing, first eliciting its main aspects and dimensions with a conceptual literature study, and then discovering its central properties with an empirical case study. We conclude by using the new social manufacturing framework to suggest modifications of three common sustainability analysis tools to make them more applicable to research on individual-level production. By making future investigation in this area more accessible our work contributes to both sustainability research and to the emerging field of research on social manufacturing.

From mind to products: towards social manufacturing and service

After reviewing the development of industrial manufacturing, a novel concept called social manufacturing (SM) and service are proposed as an innovative manufacturing solution for the coming personalized customization era. SM can realize a customer's requirements of "from mind to products", and fulfill tangible and intangible needs of a prosumer, i.e., producer and consumer at the same time. It represents a manufacturing trend, and is expected to become popular in more and more industries. First, a comparison between mass customization and SM is given out, and the basis and motivation from social network to SM is analyzed. Then, its basic theories and supporting technologies, like Internet of Things (IoT), social networks, cloud computing, 3D printing, and intelligent systems, are introduced and analyzed, and an SM platform prototype is developed. Finally, three transformation modes towards SM and 3D printing are suggested for different user cases.

Smart manufacturing based on cyber-physical systems and beyond

Cyber-physical systems (CPS) have gained an increasing attention recently for their immense potential towards the next generation smart systems that integrate cyber technology into the physical processes. However, CPS did not initiate either smart factories or smart manufacturing, and vice versa. Historically, the smart factory was initially studied with the introduction of the Internet of Things (IoT) in manufacturing, and later became a key part of Industry 4.0. Also emerging are other related models such as cloud manufacturing, social manufacturing and proactive manufacturing with the introduction of cloud computing (broadly, the Internet of Services, IoS), social networking (broadly, the Internet of People, IoP) and big data (broadly, the Internet of Content and Knowledge, IoCK), respectively. At present, there is a lack of a systemic and comprehensive study on the linkages and relations between these terms. Therefore, this study first presents a comprehensive survey and analysis of the CPS treated as a combination of the IoT and the IoS. Then, the paper addresses CPS-based smart manufacturing as an eight tuple of CPS,IoT, IoS and IoCK as elements. Further, the paper extends the eight-tuple CPS-based manufacturing to social-CPS (SCPS)-based manufacturing, termed wisdom manufacturing, which forms a nine tuple with the addition of one more element, the IoP and which is based on the SCPS instead of CPS. Both architectures and characteristics for smart and wisdom manufacturing are addressed. As such, these terms’ linkages are established and relations are clarified with a special discussion. This study thus contributes as a theoretical basis and as a comprehensive framework for emerging manufacturing integration.

Social manufacturing in the fashion sector: New value creation through alternative design strategies?

This paper proposes new perspectives and systemic changes to the linear fashion scheme, which is currently driven by fast, cheap and low quality production that fosters easy disposal or replacement, due to the low product value for the customer/user. The authors seek to open a discussion on new value creation through social manufacturing, specifically facilitated by do-it-yourself (DIY), do-it-together (DIT) and participatory design strategies. Social manufacturing can be seen as a more open and democratic approach to manufacturing, prompting different levels of user participation in the production process. The authors will illustrate how these alternative design strategies, build within the context of social manufacturing, can offer system-level changes by activating and empowering the end user to become value creators, while forming new, more sustainable innovations in design and manufacturing of fashion. The specific questions asked in this paper are: What types of value are created in social manufacturing through opening the design and manufacturing processes with alternative design strategies? Further, can social manufacturing enable sustainable solutions by transitioning the users to manufacturers of their own garments, prompting a new value system and range of business models in the fashion industry? An alternative value framework, developed within this paper, will enable the analysis of empirical data collected in Finland and the U.S. In the discussion, the authors demonstrate strategies to create wider change in the fashion system through social manufacturing, starting at a local level through empowered consumers.

Social manufacturing: When the maker movement meets interfirm production networks

New business models harnessing the power of individuals have already revolutionized service industries and digital content production. In this study, we investigate whether a similar phenomenon is taking place in manufacturing industries. We start by conceptually defining two distinct forms of firm-individual collaboration in manufacturing industries: (1) social cloud manufacturing, in which firms outsource manufacturing to individuals, and (2) social platform manufacturing, in which firms provide manufacturing services to individuals. We then empirically investigate the nature of firm-individual collaboration within these forms, focusing on the role of individuals. We find that the individuals are often makers who view their participation primarily as a hobby and are driven mainly by nonmonetary benefits, that the design process often involves both parties, and that the two forms of collaboration exploit different enabling technologies. Our findings suggest that firms working with individuals can potentially reap multiple benefits, including fresh ideas, broader design support, and quick delivery times. This article contributes to an improved understanding of how firms can build potentially disruptive business models in manufacturing industries by leveraging individuals, thereby adding to the emerging stream of literature on social manufacturing.