Crowdsourced Manufacturing Cyber Platform and Intelligent Cognitive Assistants for Delivery of Manufacturing as a Service: Review of Fundamental Issues and Outlook

Crowdsourced Manufacturing Cyber Platform and Intelligent Cognitive Assistants for Delivery of Manufacturing as a Service: Review of Fundamental Issues and Outlook | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Crowdsourced Manufacturing Cyber Platform and Intelligent Cognitive Assistants for Delivery of Manufacturing as a Service: Review of Fundamental Issues and Outlook Xuejian Gong, Jianxin Roger Jiao, Amit Jariwala, Beshoy Morkos This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-592199/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Aug, 2021 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 4 You are reading this latest preprint version Abstract Future cyber manufacturing is envisioned to be fulfilled in a crowdsourcing environment that will engage a large population of manufacturer crowds to collaborate with the cyber platform on a shared understanding of the tasks for delivering manufacturing as a service (MaaS). This review paper is motivated towards a symbiosis of crowdsourcing cyber platform technologies to enhance intelligent decision support to manufacturing planning for achieving MaaS operational goals. The paper reviews the fundamental issues of MaaS through crowdsourcing from a model-based systems engineering perspective that is in line with a systematic framework of platform-driven MaaS. Also discussed is the outlook of analytic and model-based approach for crowdsourced manufacturing in order to enable new cyber manufacturing capabilities that represent a significant transformation of the manufacturing sector. The vision is to facilitate transition from the current practice of focusing on automation and manufacturing informatics within individual enterprise to open manufacturing crowds throughout the cloud platform to fulfill MaaS. The cyber platform and intelligent cognitive assistants enhance MaaS fulfilment by adopting computational modeling and decision analytics to exploit the implicit design and manufacturing knowledge that is incorporated in the library of previously executed manufacturing tasks, which in turn facilitates generation of manufacturing process plans by parametric adjustment of process plans for similar tasks. Biotechnology and Bioengineering Crowdsourcing manufacturing as a service crowdsourcing contracting open manufacturing Industry 4.0 platform economics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Manufacturing industries of the future are poised for more disruptive changes that are impacted by sustainability issues, consumer attitudes and behaviors, digitization, 3D printing, emerging automation technologies, etc. These changes in manufacturing will also signal rippling innovations and adaptations in other businesses and industries due to their ties to the manufacturing industries. A significant trend of transforming and expanding the manufacturing sector to facilitate MaaS fulfillment has been observed [ 75 ]. (1) Mass customization and open manufacturing in a circular economy At the frontend of manufacturing, firms are confronted with challenges for satisfying individual customer needs while managing product variety and developing products more efficiently than their competitors [ 4 ]. The extent of market-of-one has been foreseen as a potential driving force for the next transformation of the global economy [ 44 ], leading to a paradigm shift from traditional mass production to mass customization and personalization [ 58 ]. Large variety in the market and product domain propagating to the manufacturing domain implies higher variations of production processes, causing complex technology portfolio [ 11 ] and restorative supply chain operations in a circular economy [ 32 ]. As a result, future manufacturing needs to utilize information sharing to cooperate with a peer of suppliers through a series of outsourced fabrications to finish the final products within the lead time [ 50 ]. Instead of owning excess production capacities to hedge against demand fluctuation, companies are adopting an open business model [ 8 ] by implementing manufacturing capabilities and resources as a scalable and changeable production network to achieve quick response and adjust capacities [ 15 ] through an agile enterprise structure with extensive capability arsenal [ 36 ]. The open business model allows for creation of new complementary links throughout a value chain, which explicitly arranges the stakeholders along with positions of value creation, delivery and capturing [ 12 ]. This openness in manufacturing enables mass cooperation of manufacturers, which leads to a highly interactive manufacturing network. This network relies on the cooperative collaboration mechanism, which eventually empowers easy installation of new technologies and scaling up of manufacturing capabilities. (2) Crowdsourced manufacturing Crowdsourcing has emerged as a promising means of open business operations by transcending organizational boundaries to leverage resources and capabilities across distributed stakeholders [ 35 ]. Different from the conventional strategy of outsourcing in supply chain management that emphasizes how to delegate a task to a designated agent, crowdsourcing utilizes an open call to a crowd for maximally exploiting the external resources [ 5 ]. Among many perspectives of crowdsourcing, we emphasize future manufacturing from a cyber platform-driven perspective in order to peel out the coordinating and negotiating responsibilities from the crowdsourcer while distilling embedding an open manufacturing business model as the foundation of product innovation and development. The crowdsourcing cyber platform facilitates a manufacturer to explore external knowledge and resource by coordinating the activities of design and manufacturing among the stakeholders through a collaborative manufacturing fulfillment network. The impacts of crowdsourced manufacturing on potential industrial and societal benefits are profound. The cutting-edge information and communications technologies (ICT) and industry trends empower manufacturing to be fulfilled over a crowdsourcing cyber platform to transcend the partners’ borders extensively and build up information exchanging networks. Integration of smart sensors and the networked manufacturing systems equips firms with a cyber-physical-human environment, where a synergy of Internet of Things (IoTs), big data analysis, and machine intelligence with legacy manufacturing technologies such as computer integrated manufacturing systems (CIMS), supply chain management (SCM) and production logistics has stimulated a gigantic manufacturing technology advancement for future manufacturing. The ultimate goal is to achieve competitiveness in collaboration across multiple entities towards an enterprise with an open yet virtual architecture in line with Industry 4.0 [ 33 ] that is characterized as decentralization and digitalization [ 53 ]. (3) Manufacturing as a service Future cyber manufacturing is equipped with ubiquitous connectivity in the manufacturing environment, allowing collection of significant volumes of dispersed information to support distributed decision making to fulfill manufacturing tasks [ 42 ]. The new open manufacturing capabilities enabled by crowdsourcing platform will create opportunities for transforming and expanding the manufacturing sector by developing intelligent cognitive assistants to perform as decision support systems to facilitate the fulfillment of MaaS [ 40 ]. The compelling need for accommodating a dynamic and collaborative network of manufacturing services has a broader implication for a service-oriented paradigm to be deployed as X-as-a-service and extended to the entire manufacturing regime to act as service manufacturing [ 37 ]. Another implication is dedicated to social manufacturing [ 23 ] that aims to take advantage of the interactive relationships among the manufacturer crowds to foster a manufacturing service network as an autonomous organizing process. The trend of cloud-based design and manufacturing offers a framework of connecting smart entities across a population of companies, thus enabling a demand-capacity matching mechanism to serve collaborative product realization [ 52 ]. Crowdsourced manufacturing enables fulfillment of MaaS through a cyber platform based on cloud-based design and manufacturing, which is organized as a dynamic resource sharing mechanism among the manufacturer crowds while engaging more manufacturer population in the MaaS network [ 30 ]. The emerging cyber-physical-human production systems will provoke changes in many ways for future manufacturing concerning MaaS fulfillment in the factory of the future. Leading experts expect less basic, repetitive work but more ambitious tasks in collaboration with the crowdsourcing platform, and thus, the factory of the future will not be deserted, but organized as a network of crowdsourcing platform-driven manufacturing services [ 41 ]. It is conceived that the central theme of crowdsourced manufacturing lies in the new manufacturing capabilities empowered by the crowdsourcing cyber platform that performs as intelligent cognitive assistants to engage a large population of manufacturer crowds in an extended MaaS network. This paper is motivated towards a deeper basic understanding of cyber platform-driven MaaS for advancing societal needs in the manufacturing industries. To examine the key methods and techniques of developing crowdsourced manufacturing platforms as computational tools, the paper reviews the fundamental issues and prevailing methods and techniques that have the potential for increasing the access of entrepreneurs to product-business open innovation and manufacturing services, while enabling manufacturers to offer their services more widely and allow mass customization of products and processes. A holistic framework of crowdsourced manufacturing for MaaS is outlined in the next section. Sections 3 – 6 review the common consensus and state-of-the-art techniques regarding such fundamental issues as model-based collaborative crowdsourcing fulfillment, population dynamics modeling of manufacturer crowds, predictive learning for adaptive production planning and control, and optimal crowdsourcing contracting with dynamic task allocation. A case example of crowdsourced orthodontics with 3D printing is briefed in Sect. 7 to illustrate the promise of crowdsourcing for MaaS. The paper concludes in Sect. 8 with outlook for future research opportunities. 2. A Holistic Framework Of Crowdsourced Manufacturing For Maas 2. A Holistic Framework of Crowdsourced Manufacturing for MaaS The importance of crowdsourcing platform-driven fulfillment of MaaS has been well perceived as one of the potential ways to radically transform concepts of manufacturing. It anticipates new abstractions in design and manufacturing, along with a synergy of cloud computing, AI, smart data infrastructure and predictive analytics, to increase the generality and reliability and reduce the expense of manufacturing process and system control. The cyber platform technologies for manufacturing cut across the manufacturing supply chain, the systems engineering lifecycle, and manufacturing control systems, wherever ICT can affect production control, facilitate integration, and influence societal acceptance and demand. Towards this end, a well-defined research roadmap and capabilities responsive to the future manufacturing trends becomes necessary to provide new insights into fulfillment of manufacturing services through a network of manufacturer crowds in the future cyber manufacturing environment. Fundamental issues are related to manufacturer crowd behaviors over open business value chains, enterprise MaaS reference models, MaaS task fulfillment workflows, data-driven inverse problem solving, adaptive process planning, crowdsourcing contracting, and noncooperative game group decisions underlying crowdsourced manufacturing. Figure 1 shows a holistic framework of cyber platform-driven MaaS that aims to address two critical challenges of crowdsourced manufacturing: MaaS cyber platform and intelligent cognitive assistants from a model-based systems engineering perspective. The vision is to enhance MaaS fulfilment through a population of manufacturer crowds by adopting computational modeling and decision analytics to exploit the implicit design and manufacturing knowledge that is incorporated in the library of previously manufactured tasks and generate manufacturing process plans by parametric adjustment of process plans for similar tasks. (1) MaaS cyber platform and intelligent cognitive assistants : As shown in Fig. 1 , the technical framework of crowdsourced manufacturing is centered on two pillars: MaaS cyber platform and intelligent cognitive assistants. The former involves three fundamental issues in relation to the platform, firm and task levels, i.e., manufacturer crowd behaviors over open business value chains, enterprise MaaS reference models, and MaaS tasks fulfillment workflows, respectively. The latter is the critical computational intelligent support to engage a large population of manufacturer crowds to fulfill specific manufacturing tasks cohesively within a coherent product realization value chain. These intelligent cognitive assistants are designated to such fundamental issues as data-driven inverse problem solving, adaptive process planning, crowdsourcing contracting for MaaS task allocation, and noncooperative game group decisions among the manufacturer crowds. Figure 1 illustrates the coherence among these research focuses and connections between various research components. Core research questions and specific research methods are geared towards addressing: model-based collaborative crowdsourcing fulfillment, population dynamics modeling of manufacturer crowds, predictive learning for adaptive production planning and control, and optimal crowdsourcing contracting with dynamic task allocation. The population dynamics model takes input from collaborative crowdsourcing reference models, and provides an input to population dynamics modeling. The adaptive process planning models perform as the basis of optimal crowdsourcing contracting. It is imperative for an MaaS framework to align with a model-based decision analytic perspective with basic research dedicated to creating mathematical and computational models and decision support tools to advance understanding of crowdsourced manufacturing with particular focuses on collaborative crowdsourcing fulfillment, population dynamics modeling of manufacturer crowds, predictive learning for adaptive production planning and control, as well as optimal crowdsourcing contracting with dynamic task allocation. It is important to adopt both qualitative and quantitative methods along with public participation in data collection through industrial case studies to test research hypotheses, validate theoretical models, and assess technical concepts. 3. Enterprise Maas Reference Modeling For Model-based Collaborative Crowdsourcing Fulfillment The very upfront issue is how the product realization process should be deployed to accommodate MaaS through a crowdsourcing cyber platform, namely enterprise MaaS reference modeling. The crowdsourced manufacturing workflow along a value chain explains the fundamental mechanism underlying the fulfillment MaaS, which involves the decision agents, processes, as well as crowdsourcing contracting mechanism. Crowdsourced manufacturing enables MaaS to be enacted among multi-parties of decision agents, including open innovator \(I\) , crowdsourced manufacturing platform \(P\) , and manufacturing agents \(M\) , as shown in Fig. 2 . The crowdsourcing cyber platform acts as the intermediate domain to bridge the frontend open innovation domain and the backend open manufacturing domain. The platform brokers \(P\) are the primary decision agents in charge of specifying tasks and dispatching the tasks to \(I\) , while making contracting decisions with \(M\) . As an intermediate marketplace for open innovators and manufacturers, the platform serves a population of distinctive open innovators to connect a broader market, meanwhile invites extensive manufacturers to expand the capability arsenal and gain the network benefits. This impulse of expansion leads to many value chains going through the platform company, which can be treated as a series of projects. The increasing number of open innovators requires the platform to scale up an appropriate amount of serving modules to serve the manufacturing of the products. The scaling up of the platform company leads a stacking of the serving modules, which transforms the two-dimensional perspective of crowdsourced manufacturing workflow to a three-dimensional holistic framework. The reference modeling of MaaS workflows necessitates a symbolic system to formulate the interaction processes and task fulfillment. It requires to cover the workflow for a crowdsourcing project and explains the scaling-up process to support a large volume of value chains. A holistic reference model will enable further development of information and material flows among diverse decision agents associated with crowdsourced manufacturing. Formal model-based systems engineering tools such as SysML (sysml.org), have great potential owing to the advantages in supporting the specification, analysis, design, verification and validation of a broad range of systems and systems-of-systems. Also rich opportunities exist for the powerful CIMOSA [ 60 ], Purdue Enterprise Reference Architecture [ 61 ], and IDEF (idef.com), which were originated from the initiatives of reference modeling of computer-integrated manufacturing systems (CIMS) in 1990’s. The potential challenge however is associated with the extent of SysML modeling to capture the complex semantics and dynamic workflows of MaaS. It is critical to mitigate this risk by enhancing problem context verification through industrial case studies. To alleviate the overwhelming programming work, it is possible to utilize free and open source software of SysML-compliant modeling tools such as Modelio and Papyrus (SysMLtools.com). 4. Population Dynamics Modeling Of Manufacturer Crowd Behaviors Over Open Business Value Chains One important metric of successful platform-driven MaaS is indicated by an increase in the population size of the manufacturers who are engaged in MaaS, earn businesses through the platform, and in turn do more MaaS businesses through the platform. The manufacturer crowd is naturally divided into various manufacturing clusters according to their technical capabilities and competencies. The manufacturers within one cluster, however, are confronted with a massive impact of competition. Because of the existence of the awarding process by the manufacturing evaluation broker in the platform, only the best-performed manufacturer in each cluster can be selected and awarded with the contract. In the meantime, realization of the value chain expects a broader spectrum of manufacturing competency and a large volume of capacity, entailing multi-party game decisions among the manufacturers through not only competition but also cooperation. It also requires inter-cluster cooperation to increase the fulfillment capabilities that the platform can connect to and the attraction to the open innovators to initiate the value chain. This inner-cluster cooperation indicates the manufacturers need to cooperate with their peers to participate in the bidding to attract more manufacturers in other clusters for future success. Furthermore, there is a robust co-evolutionary relationship in the entire manufacturer population. The decision-making process of participating in a value chain or not is based on the revenue of their peers at the current time point. A promising area is to formulate this fitness-decreasing behavior as an evolutionary puzzle and investigate MaaS decisions based on evolutionary competition-cooperation (ECC) [ 48 ] game-theoretic models. To find the equilibrium of the evolutionary dynamic supply contracting mechanism, the evolutionary game model is widely applied [ 47 , 57 ]. Since the incentive can be utilized as the factor of sustaining a crowdsourcing ecology [ 54 ], it is worthwhile to study the potential of game models for the incentive design in the crowdsourcing for MaaS. The population-based decision support resides with an ECC game mechanism to arrive at an equilibrium among the manufacturers in crowdsourcing. It is important to reveal the trends in the overall population of manufacturers. The population states bringing more profit will be more attractive to the manufacturers. This suggests the necessity of modeling the population growing phenomenon which is better to be governed in replication equations. Research opportunities exist for applying population dynamics theory, which has traditionally been the dominant branch of mathematical biology [ 21 ]. As a refinement of the Nash equilibrium, some of the equilibrium points in the replication equations can be determined as an evolutionary stable state that can survive from the invasion of the population in the long run. A critical research problem is to study how to use the stability evaluation and the phase diagram of the manufacturer clusters to be interpreted as an effective revision protocol to sustain the prosperity of the manufacturer population. Figure 3 shows the phase diagrams of the two-cluster ECC game. To mitigate the uncertainty and potential risk of extending population dynamics theory to the manufacturing field, Monte Carlo simulation models can be developed to verify these revision protocols in light of practical relevance and managerial implications. 5. Predictive Learning For Adaptive Production Planning And Control Process planning information exchange services in crowdsourced manufacturing can be enabled by developing a conceptual MaaS informatics system framework based on cloud computing. Figure 4 shows a five-layer architecture to reveal interactions among decision agents, while providing decision support to fulfillment of adaptive production planning and control. It includes the decision agent, application, decision support, distributed data management and infrastructure layers. Based on this MaaS informatics architecture, the manufacturer crowds basically involve two types of information exchange. The first type is information utilization – exploiting smart sensing and communication technologies such as industrial IoTs and block chains to collect, process and organize manufacturing fulfillment data in a distributive manner. The second type is associated with data retrieval services for dispersed decision agents while providing a proof-of-work to serve the revenue distribution among the stakeholders of the MaaS network. To take advantage of the large transaction data generated and collected in the crowdsourcing cyber platform, machine learning and data analytics can be applied to enable predictive MaaS production planning and control. A promising area of research is to develop a data-driven inverse problem solving method and analytics tools. It has profound implications for data-driven design and manufacturing [ 22 ]. A performance metric is the improved quality of specific domain problems in comparison with the results of the traditional direct/forward problem solving approach. Validation can be achieved through the user experiments and industrial case studies. In addition, the ultimate goal of MaaS is to facilitate mass customization and open manufacturing, for which production planning and control must be adaptive to individual MaaS order fulfillment by reusing previous similar transactions. A noteworthy research topic is case-based machine learning for adaptation of manufacturing processes and quick generation of MaaS process planning. Comparing with traditional parametric adjustment of process plans for similar tasks, case-based reasoning and machine learning [ 64 ] will tremendously enhance categorization, indexing and searching of 3D solid models, process information and supply chain transactions to better exploit the implicit design and manufacturing knowledge with semantics incorporated in the library of previously manufactured parts and MaaS tasks. The potential challenge, however, is the actual applicability of data-driven inverse problem solving and predictive process adaptation for MaaS. In practice, it is important to mitigate the uncertainty and potential risk by enhancing problem context verification through industrial case studies. 6. Optimal Crowdsourcing Contracting With Dynamic Task Allocation The operations of MaaS through the crowdsourcing platform are achieved as transactions using the brokerage and bidding mechanism [ 76 ]. It is similar to, yet different from, supply contracting for outsourcing in supply chain management [ 62 ], which mainly deals with costing and logistics decisions associated with financial transactions. Crowdsourcing contracting, however, not only deals with cost and financial decisions, but also involves engineering assessment of manufacturers’ capabilities with respect to the technical specifications of each MaaS order, in addition to strategic flexibility among manufacturer crowds as well as in the MaaS platform operations [ 43 ]. To leverage both technical and financial analysis in holistic contracting evaluation decisions, it is possible to extend a generic design evaluation metric based on the information content measure [ 26 ], which is originated from axiomatic design theory [ 56 ]. Also promising is to extend real options theory to valuation analysis of MaaS cyber platform economics based on our prior work on strategic flexibility of product platform technical systems [ 24 ]. Specifically, crowdsourcing contracting can be approached from a unique perspective of service-oriented manufacturing, which provides a fair and efficient mechanism for crowdsourcing task decomposition and allocation. It also forges the manufacturing network and provides the logistic service to transport the manufacturing resources and WIP according to the precedence relationship of the production plan. Compelling research issues are associated with the methods and decision tools of ontology and graph grammar-based task reasoning and instantiation, such that the crowdsourcing task handling process acts like an MES/ERP system on a large scale. It handles the manufacturing activities from the derivation of the manufacturing tasks, as well as the planning of the material flow inner and outer of the manufacturer’s shop floor, as shown in Fig. 5 . Moreover, a critical aspect of MaaS decision support is material flow management for engineering logistics of the required materials, WIP, subassemblies, or final products on time. It is challenging to keep a reasonable cost while aligning customers, products, processes, and logistics to deal with the increase of product variety [ 28 ]. From the MaaS platform perspective, a resource platform can collect the information from the manufacturer crowds, formulate the origins and destinations of the service demands, find the common routes in the corresponding transportation service tasks, and synchronize the manufacturing activities to achieve just in time [ 45 ]. A viable approach is to adopt a cross-docking strategy [ 59 , 38 ] in managing the service-oriented logistic networks. Since the solutions generated by the manufacturer crowds are evaluated by the platform, a manufacturing resources re-planning function is needed. It involves searching for sharable capabilities and order re-arranging to allocate the existing orders on the shop floor and determine the acceptance of the orders. After the awarding process with manufacturing supply contracts to select the preferred manufacturers, the resources re-planning serves the management the mix of the actual assigned tasks and current orders on the shop floor. It is of particular importance to consider a multi-party game decision scenario for this dynamic task allocation process. Research opportunities exist for formulating a mathematical model of bilevel game optimization through nested leader-follower joint optimization for optimal allocation of tasks [ 10 ]. The rational of game theoretical optimization lies in seeking for equilibrium solutions, instead of global optima, to arrive at a synergy of various interests of multiple decision makers in a noncooperative game environment [ 23 ]. The research challenges and potential risk lie in how to evaluate the efficiency of MaaS task allocation. Furthermore, rigorous mathematical analyses and intensive computational studies are necessary to verify model assumptions and solution conditions. 7. A Case Example Of Crowdsourced Orthodontics With 3d Printing Crowdsourced manufacturing enables new cyber manufacturing that represents a significant transformation of the manufacturing sector from current practice of focusing on automation and manufacturing informatics within individual enterprise to open manufacturing crowds through the cloud platform to deliver manufacturing as a service. The potential industrial and societal benefits can be exemplified by a case of orthodontics with 3D printing. As shown in Fig. 6 , a crowdsourcing cyber platform makes open innovation, open design and open manufacturing possible, entailing open business value chains. It facilitates the orthodontists, who are entrepreneurs of mass customization and personalization but do not have any design and manufacturing capabilities, to access 3D printing manufacturing services that are delivered by a crowd of 3D printing firms, who also deliver manufacturing services to many other businesses through the platform. This enables manufacturers to offer their services more widely and allow customization of products and processes to be fulfilled more efficiently. 8. Summary Future cyber manufacturing is envisioned to be fulfilled in a crowdsourcing environment that will be populated by manufacturer crowds to collaborate with the cyber platform on a shared understanding of the tasks for delivering manufacturing as a service (MaaS). This review paper advocates a symbiosis of crowdsourcing cyber platform technologies to enhance intelligent decision support to manufacturing planning for achieving MaaS operational goals. Consistent with a systematic framework of platform-driven MaaS, the focus on the cyber platform and intelligent cognitive assistants entails a model-based systems engineering perspective. This technical concept aims to enhance MaaS fulfilment through a population of manufacturer crowds by adopting computational modeling and decision analytics to exploit the implicit design and manufacturing knowledge that is incorporated in the library of previously executed manufacturing tasks, which in turn facilitates generation of manufacturing process plans by parametric adjustment of process plans for similar tasks. The analytic and model-based approach to crowdsourced manufacturing enables new cyber manufacturing capabilities that represent a significant transformation of the manufacturing sector. This will facilitate transition from the current practice of focusing on automation and manufacturing informatics within individual enterprise to open manufacturing crowds throughout the cloud platform to fulfill MaaS. The prevailing efforts of cloud-based design and manufacturing are geared towards computing platform technologies and ICT support. This review recognizes one imperative research issue of cyber manufacturing is how to better understand and formulate the domain problems, namely enterprise MaaS reference modeling. Formal enterprise MaaS reference modeling helps enhance understanding and formulation of the domain problems. Advances of research on SysML modeling of collaborative crowdsourcing fulfillment of MaaS contribute to a deeper fundamental understanding of crowdsourced manufacturing as a new manufacturing paradigm. Population dynamics modeling of manufacturer crowd behaviors over open business value chains reveals fundamental differences of cyber platform-driven MaaS fulfillment from traditional individual enterprise operations. The data-driven inverse problem solving and case-based machine learning methods have great potential for predictive adaptation of manufacturing processes of MaaS production planning and control, while exploiting the large transaction data generated and collected in the crowdsourcing cyber platform. Crowdsourcing contracting coincides with a multi-party noncooperative game-theoretic optimization problem. It empowers dynamic MaaS task allocation that will improve the traditional supply contracting models to deal with not only cost and financial decisions, but also to excel in engineering assessment of manufacturers’ capabilities with respect to the technical specifications of each MaaS order, in addition to valuation of strategic flexibility among manufacturer crowds involved in the MaaS platform operations. Declarations a. Funding: This material is based upon work partially supported by the Renewable Bioproducts Institute at Georgia Tech (Gong and Jiao) and the National Science Foundation NSF Future of Work at the Human-Technology Frontier Big Idea under Grant No. 1928313 (Jiao). b. Conflicts of interest: The authors declare no competing interests. c. Availability of data and material (data transparency): Not applicable d. Code availability (software application or custom code): Not applicable e. Ethics approval (include appropriate approvals or waivers): Not applicable f. 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Manage Sci 51(3):452–466 Zhang LL, 2007, Process platform-based production configuration for mass customization Zhou F, Jiao RJ, Chen S, Zhang D (2011) A Case-Driven Ambient Intelligence System for Elderly In-Home Assistance Applications. IEEE Transactions on Systems Man Cybernetics - Part C: Applications Reviews 41(2):179–189 Mertens DM (2014) Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods. Sage publications Jiao RJ, Zhou F, Gebraeel NZ, Duffy D (2020) Towards Augmenting Cyber-Physical-Human Collaborative Cognition for Human-Automation Interaction in Complex Manufacturing and Operational Environments. International Journal of Production Research, In press Htet Hein P, Voris N, Morkos B (2017) Investigating Requirement Change Propagation Through the Physical and Functional Domain, Research in Engineering Design, DOI: 10.1007/s00163-017-0271-6 Htet Hein P, Morkos B, Sen C (2017) Utilizing Node Interference Method and Complex Network Centrality Metrics to Explore Requirement Change Propagation, ASME International Design Engineering Technical Conferences, Cleveland, OH, DETC2017-67930 Hein PH (2015) Predicting Requirement Change Propagation through Investigation of Physical and Functional Domain. Florida Institute of Technology Voris N (2017) Investigating the Influence of Functional and Non-Functional Requirements on Change and Change Propagation. Florida Institute of Technology Prabaharan Graceraj P, Jacquelyn K, Nagel; Christopher S, Rose; Ramana M. Pidaparti. 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DOI: 10.1109/TEM.2020.298355 Cite Share Download PDF Status: Published Journal Publication published 12 Aug, 2021 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 18 Jul, 2021 Reviews received at journal 07 Jun, 2021 Editor assigned by journal 06 Jun, 2021 First submitted to journal 04 Jun, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-592199","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":31924678,"identity":"5998288c-a7cb-4861-a949-05e0a07c75e7","order_by":0,"name":"Xuejian Gong","email":"","orcid":"","institution":"Georgia Institute of Technol","correspondingAuthor":false,"prefix":"","firstName":"Xuejian","middleName":"","lastName":"Gong","suffix":""},{"id":31924679,"identity":"3d7290ba-9a2a-4f16-a489-97981a654857","order_by":1,"name":"Jianxin Roger Jiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYFCCgw0MDBVgFhsQMxOr5QwDAw8JWoCAsY0ULeaNhxs/fJxXK2/P3vzsAUOFdWIDIS0yBw42S87cdtywh+eYuQHDmXTCWiSAfpHm3XaMsUcih02Cse0wUVqaf/POOWbfI/8GqOUfcVrapHkbahJ7JHiAWhqI1GI549iB5J4zaWYSCcfSjQlrkTj++MaHmjrb9vbDzyQ+1FjLEtTCIHEARB6GcBIIKgcBfrCpdUSpHQWjYBSMghEKAEwwP/hzAZoxAAAAAElFTkSuQmCC","orcid":"","institution":"Georgia Institute of Technol","correspondingAuthor":true,"prefix":"","firstName":"Jianxin","middleName":"Roger","lastName":"Jiao","suffix":""},{"id":31924680,"identity":"7fe6fda2-4bd6-4039-b2d1-541ba35b002c","order_by":2,"name":"Amit Jariwala","email":"","orcid":"","institution":"Georgia Institute of Technol","correspondingAuthor":false,"prefix":"","firstName":"Amit","middleName":"","lastName":"Jariwala","suffix":""},{"id":31924681,"identity":"7a3c93d4-332a-434d-97f2-3e0573f4fc9a","order_by":3,"name":"Beshoy Morkos","email":"","orcid":"","institution":"University of Georgia","correspondingAuthor":false,"prefix":"","firstName":"Beshoy","middleName":"","lastName":"Morkos","suffix":""}],"badges":[],"createdAt":"2021-06-04 19:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-592199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-592199/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-021-07789-7","type":"published","date":"2021-08-12T15:08:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":10208571,"identity":"d04f3827-2a5c-4d39-9426-ef14f05d29fb","added_by":"auto","created_at":"2021-06-10 14:13:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":322888,"visible":true,"origin":"","legend":"Technical framework of crowdsourced manufacturing for manufacturing as a 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manufacturer crowds","description":"","filename":"Onlinefloatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-592199/v1/3c6368f7f6f2e552a70c4bb5.jpg"},{"id":10208137,"identity":"a567b9c9-3562-4236-8739-7570f243ca11","added_by":"auto","created_at":"2021-06-10 14:10:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":101566,"visible":true,"origin":"","legend":"Informatics architecture of MaaS production planning \u0026 control","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-592199/v1/8e4322bca30be984b8e127ac.png"},{"id":10208572,"identity":"2c40c531-2987-48ec-9473-4cf798c5f09e","added_by":"auto","created_at":"2021-06-10 14:13:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":176304,"visible":true,"origin":"","legend":"MaaS task derivation, reasoning and instantiation across technical domains of crowdsourced manufacturing","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-592199/v1/f405db844fc8a7d7b1cdb092.png"},{"id":10209011,"identity":"8b2354dc-61ba-4236-9f41-80b42741e885","added_by":"auto","created_at":"2021-06-10 14:16:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":294806,"visible":true,"origin":"","legend":"Crowdsourced manufacturing makes 3D printing orthodontics possible by delivering manufacturing as a service","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-592199/v1/46515dd31008aff8b8922500.png"}],"financialInterests":"","formattedTitle":"Crowdsourced Manufacturing Cyber Platform and Intelligent Cognitive Assistants for Delivery of Manufacturing as a Service: Review of Fundamental Issues and Outlook","fulltext":[{"header":"1. Introduction","content":" \u003cp\u003eManufacturing industries of the future are poised for more disruptive changes that are impacted by sustainability issues, consumer attitudes and behaviors, digitization, 3D printing, emerging automation technologies, etc. These changes in manufacturing will also signal rippling innovations and adaptations in other businesses and industries due to their ties to the manufacturing industries. A significant trend of transforming and expanding the manufacturing sector to facilitate MaaS fulfillment has been observed [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cstrong\u003e(1) Mass customization and open manufacturing in a circular economy\u003c/strong\u003e \u003cp\u003eAt the frontend of manufacturing, firms are confronted with challenges for satisfying individual customer needs while managing product variety and developing products more efficiently than their competitors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The extent of market-of-one has been foreseen as a potential driving force for the next transformation of the global economy [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], leading to a paradigm shift from traditional mass production to mass customization and personalization [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Large variety in the market and product domain propagating to the manufacturing domain implies higher variations of production processes, causing complex technology portfolio [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and restorative supply chain operations in a circular economy [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. As a result, future manufacturing needs to utilize information sharing to cooperate with a peer of suppliers through a series of outsourced fabrications to finish the final products within the lead time [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Instead of owning excess production capacities to hedge against demand fluctuation, companies are adopting an open business model [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] by implementing manufacturing capabilities and resources as a scalable and changeable production network to achieve quick response and adjust capacities [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] through an agile enterprise structure with extensive capability arsenal [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The open business model allows for creation of new complementary links throughout a value chain, which explicitly arranges the stakeholders along with positions of value creation, delivery and capturing [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This openness in manufacturing enables mass cooperation of manufacturers, which leads to a highly interactive manufacturing network. This network relies on the cooperative collaboration mechanism, which eventually empowers easy installation of new technologies and scaling up of manufacturing capabilities.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e(2) Crowdsourced manufacturing\u003c/strong\u003e \u003cp\u003eCrowdsourcing has emerged as a promising means of open business operations by transcending organizational boundaries to leverage resources and capabilities across distributed stakeholders [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Different from the conventional strategy of outsourcing in supply chain management that emphasizes how to delegate a task to a designated agent, crowdsourcing utilizes an open call to a crowd for maximally exploiting the external resources [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among many perspectives of crowdsourcing, we emphasize future manufacturing from a cyber platform-driven perspective in order to peel out the coordinating and negotiating responsibilities from the crowdsourcer while distilling embedding an open manufacturing business model as the foundation of product innovation and development. The crowdsourcing cyber platform facilitates a manufacturer to explore external knowledge and resource by coordinating the activities of design and manufacturing among the stakeholders through a collaborative manufacturing fulfillment network.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe impacts of crowdsourced manufacturing on potential industrial and societal benefits are profound. The cutting-edge information and communications technologies (ICT) and industry trends empower manufacturing to be fulfilled over a crowdsourcing cyber platform to transcend the partners\u0026rsquo; borders extensively and build up information exchanging networks. Integration of smart sensors and the networked manufacturing systems equips firms with a cyber-physical-human environment, where a synergy of Internet of Things (IoTs), big data analysis, and machine intelligence with legacy manufacturing technologies such as computer integrated manufacturing systems (CIMS), supply chain management (SCM) and production logistics has stimulated a gigantic manufacturing technology advancement for future manufacturing. The ultimate goal is to achieve competitiveness in collaboration across multiple entities towards an enterprise with an open yet virtual architecture in line with Industry 4.0 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] that is characterized as decentralization and digitalization [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cstrong\u003e(3) Manufacturing as a service\u003c/strong\u003e \u003cp\u003eFuture cyber manufacturing is equipped with ubiquitous connectivity in the manufacturing environment, allowing collection of significant volumes of dispersed information to support distributed decision making to fulfill manufacturing tasks [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The new open manufacturing capabilities enabled by crowdsourcing platform will create opportunities for transforming and expanding the manufacturing sector by developing intelligent cognitive assistants to perform as decision support systems to facilitate the fulfillment of MaaS [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The compelling need for accommodating a dynamic and collaborative network of manufacturing services has a broader implication for a service-oriented paradigm to be deployed as X-as-a-service and extended to the entire manufacturing regime to act as service manufacturing [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Another implication is dedicated to social manufacturing [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] that aims to take advantage of the interactive relationships among the manufacturer crowds to foster a manufacturing service network as an autonomous organizing process. The trend of cloud-based design and manufacturing offers a framework of connecting smart entities across a population of companies, thus enabling a demand-capacity matching mechanism to serve collaborative product realization [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Crowdsourced manufacturing enables fulfillment of MaaS through a cyber platform based on cloud-based design and manufacturing, which is organized as a dynamic resource sharing mechanism among the manufacturer crowds while engaging more manufacturer population in the MaaS network [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe emerging cyber-physical-human production systems will provoke changes in many ways for future manufacturing concerning MaaS fulfillment in the factory of the future. Leading experts expect less basic, repetitive work but more ambitious tasks in collaboration with the crowdsourcing platform, and thus, the factory of the future will not be deserted, but organized as a network of crowdsourcing platform-driven manufacturing services [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. It is conceived that the central theme of crowdsourced manufacturing lies in the new manufacturing capabilities empowered by the crowdsourcing cyber platform that performs as intelligent cognitive assistants to engage a large population of manufacturer crowds in an extended MaaS network.\u003c/p\u003e \u003cp\u003eThis paper is motivated towards a deeper basic understanding of cyber platform-driven MaaS for advancing societal needs in the manufacturing industries. To examine the key methods and techniques of developing crowdsourced manufacturing platforms as computational tools, the paper reviews the fundamental issues and prevailing methods and techniques that have the potential for increasing the access of entrepreneurs to product-business open innovation and manufacturing services, while enabling manufacturers to offer their services more widely and allow mass customization of products and processes. A holistic framework of crowdsourced manufacturing for MaaS is outlined in the next section. Sections \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e6\u003c/span\u003e review the common consensus and state-of-the-art techniques regarding such fundamental issues as model-based collaborative crowdsourcing fulfillment, population dynamics modeling of manufacturer crowds, predictive learning for adaptive production planning and control, and optimal crowdsourcing contracting with dynamic task allocation. A case example of crowdsourced orthodontics with 3D printing is briefed in Sect.\u0026nbsp;\u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e7\u003c/span\u003e to illustrate the promise of crowdsourcing for MaaS. The paper concludes in Sect.\u0026nbsp;\u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e8\u003c/span\u003e with outlook for future research opportunities.\u003c/p\u003e "},{"header":"2. A Holistic Framework Of Crowdsourced Manufacturing For Maas","content":" \u003cdiv class=\"Heading\"\u003e2. A Holistic Framework of Crowdsourced Manufacturing for MaaS\u003c/div\u003e \u003cp\u003eThe importance of crowdsourcing platform-driven fulfillment of MaaS has been well perceived as one of the potential ways to radically transform concepts of manufacturing. It anticipates new abstractions in design and manufacturing, along with a synergy of cloud computing, AI, smart data infrastructure and predictive analytics, to increase the generality and reliability and reduce the expense of manufacturing process and system control. The cyber platform technologies for manufacturing cut across the manufacturing supply chain, the systems engineering lifecycle, and manufacturing control systems, wherever ICT can affect production control, facilitate integration, and influence societal acceptance and demand. Towards this end, a well-defined research roadmap and capabilities responsive to the future manufacturing trends becomes necessary to provide new insights into fulfillment of manufacturing services through a network of manufacturer crowds in the future cyber manufacturing environment. Fundamental issues are related to manufacturer crowd behaviors over open business value chains, enterprise MaaS reference models, MaaS task fulfillment workflows, data-driven inverse problem solving, adaptive process planning, crowdsourcing contracting, and noncooperative game group decisions underlying crowdsourced manufacturing. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a holistic framework of cyber platform-driven MaaS that aims to address two critical challenges of crowdsourced manufacturing: MaaS cyber platform and intelligent cognitive assistants from a model-based systems engineering perspective. The vision is to enhance MaaS fulfilment through a population of manufacturer crowds by adopting computational modeling and decision analytics to exploit the implicit design and manufacturing knowledge that is incorporated in the library of previously manufactured tasks and generate manufacturing process plans by parametric adjustment of process plans for similar tasks.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e(1) MaaS cyber platform and intelligent cognitive assistants\u003c/span\u003e: As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the technical framework of crowdsourced manufacturing is centered on two pillars: MaaS cyber platform and intelligent cognitive assistants. The former involves three fundamental issues in relation to the platform, firm and task levels, i.e., manufacturer crowd behaviors over open business value chains, enterprise MaaS reference models, and MaaS tasks fulfillment workflows, respectively. The latter is the critical computational intelligent support to engage a large population of manufacturer crowds to fulfill specific manufacturing tasks cohesively within a coherent product realization value chain. These intelligent cognitive assistants are designated to such fundamental issues as data-driven inverse problem solving, adaptive process planning, crowdsourcing contracting for MaaS task allocation, and noncooperative game group decisions among the manufacturer crowds. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the coherence among these research focuses and connections between various research components. Core research questions and specific research methods are geared towards addressing: model-based collaborative crowdsourcing fulfillment, population dynamics modeling of manufacturer crowds, predictive learning for adaptive production planning and control, and optimal crowdsourcing contracting with dynamic task allocation. The population dynamics model takes input from collaborative crowdsourcing reference models, and provides an input to population dynamics modeling. The adaptive process planning models perform as the basis of optimal crowdsourcing contracting. It is imperative for an MaaS framework to align with a model-based decision analytic perspective with basic research dedicated to creating mathematical and computational models and decision support tools to advance understanding of crowdsourced manufacturing with particular focuses on collaborative crowdsourcing fulfillment, population dynamics modeling of manufacturer crowds, predictive learning for adaptive production planning and control, as well as optimal crowdsourcing contracting with dynamic task allocation. It is important to adopt both qualitative and quantitative methods along with public participation in data collection through industrial case studies to test research hypotheses, validate theoretical models, and assess technical concepts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"3. Enterprise Maas Reference Modeling For Model-based Collaborative Crowdsourcing Fulfillment","content":" \u003cp\u003eThe very upfront issue is how the product realization process should be deployed to accommodate MaaS through a crowdsourcing cyber platform, namely enterprise MaaS reference modeling. The crowdsourced manufacturing workflow along a value chain explains the fundamental mechanism underlying the fulfillment MaaS, which involves the decision agents, processes, as well as crowdsourcing contracting mechanism. Crowdsourced manufacturing enables MaaS to be enacted among multi-parties of decision agents, including open innovator \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(I\\)\u003c/span\u003e\u003c/span\u003e, crowdsourced manufacturing platform \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(P\\)\u003c/span\u003e\u003c/span\u003e, and manufacturing agents \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(M\\)\u003c/span\u003e\u003c/span\u003e, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The crowdsourcing cyber platform acts as the intermediate domain to bridge the frontend open innovation domain and the backend open manufacturing domain. The platform brokers \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(P\\)\u003c/span\u003e\u003c/span\u003e are the primary decision agents in charge of specifying tasks and dispatching the tasks to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(I\\)\u003c/span\u003e\u003c/span\u003e, while making contracting decisions with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(M\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAs an intermediate marketplace for open innovators and manufacturers, the platform serves a population of distinctive open innovators to connect a broader market, meanwhile invites extensive manufacturers to expand the capability arsenal and gain the network benefits. This impulse of expansion leads to many value chains going through the platform company, which can be treated as a series of projects. The increasing number of open innovators requires the platform to scale up an appropriate amount of serving modules to serve the manufacturing of the products. The scaling up of the platform company leads a stacking of the serving modules, which transforms the two-dimensional perspective of crowdsourced manufacturing workflow to a three-dimensional holistic framework. The reference modeling of MaaS workflows necessitates a symbolic system to formulate the interaction processes and task fulfillment. It requires to cover the workflow for a crowdsourcing project and explains the scaling-up process to support a large volume of value chains. A holistic reference model will enable further development of information and material flows among diverse decision agents associated with crowdsourced manufacturing. Formal model-based systems engineering tools such as SysML (sysml.org), have great potential owing to the advantages in supporting the specification, analysis, design, verification and validation of a broad range of systems and systems-of-systems. Also rich opportunities exist for the powerful CIMOSA [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], Purdue Enterprise Reference Architecture [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], and IDEF (idef.com), which were originated from the initiatives of reference modeling of computer-integrated manufacturing systems (CIMS) in 1990\u0026rsquo;s. The potential challenge however is associated with the extent of SysML modeling to capture the complex semantics and dynamic workflows of MaaS. It is critical to mitigate this risk by enhancing problem context verification through industrial case studies. To alleviate the overwhelming programming work, it is possible to utilize free and open source software of SysML-compliant modeling tools such as Modelio and Papyrus (SysMLtools.com).\u003c/p\u003e "},{"header":"4. Population Dynamics Modeling Of Manufacturer Crowd Behaviors Over Open Business Value Chains","content":" \u003cp\u003eOne important metric of successful platform-driven MaaS is indicated by an increase in the population size of the manufacturers who are engaged in MaaS, earn businesses through the platform, and in turn do more MaaS businesses through the platform. The manufacturer crowd is naturally divided into various manufacturing clusters according to their technical capabilities and competencies. The manufacturers within one cluster, however, are confronted with a massive impact of competition. Because of the existence of the awarding process by the manufacturing evaluation broker in the platform, only the best-performed manufacturer in each cluster can be selected and awarded with the contract. In the meantime, realization of the value chain expects a broader spectrum of manufacturing competency and a large volume of capacity, entailing multi-party game decisions among the manufacturers through not only competition but also cooperation. It also requires inter-cluster cooperation to increase the fulfillment capabilities that the platform can connect to and the attraction to the open innovators to initiate the value chain. This inner-cluster cooperation indicates the manufacturers need to cooperate with their peers to participate in the bidding to attract more manufacturers in other clusters for future success. Furthermore, there is a robust co-evolutionary relationship in the entire manufacturer population. The decision-making process of participating in a value chain or not is based on the revenue of their peers at the current time point. A promising area is to formulate this fitness-decreasing behavior as an evolutionary puzzle and investigate MaaS decisions based on evolutionary competition-cooperation (ECC) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] game-theoretic models. To find the equilibrium of the evolutionary dynamic supply contracting mechanism, the evolutionary game model is widely applied [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Since the incentive can be utilized as the factor of sustaining a crowdsourcing ecology [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], it is worthwhile to study the potential of game models for the incentive design in the crowdsourcing for MaaS.\u003c/p\u003e \u003cp\u003eThe population-based decision support resides with an ECC game mechanism to arrive at an equilibrium among the manufacturers in crowdsourcing. It is important to reveal the trends in the overall population of manufacturers. The population states bringing more profit will be more attractive to the manufacturers. This suggests the necessity of modeling the population growing phenomenon which is better to be governed in replication equations. Research opportunities exist for applying population dynamics theory, which has traditionally been the dominant branch of mathematical biology [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As a refinement of the Nash equilibrium, some of the equilibrium points in the replication equations can be determined as an evolutionary stable state that can survive from the invasion of the population in the long run. A critical research problem is to study how to use the stability evaluation and the phase diagram of the manufacturer clusters to be interpreted as an effective revision protocol to sustain the prosperity of the manufacturer population. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the phase diagrams of the two-cluster ECC game. To mitigate the uncertainty and potential risk of extending population dynamics theory to the manufacturing field, Monte Carlo simulation models can be developed to verify these revision protocols in light of practical relevance and managerial implications.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"5. Predictive Learning For Adaptive Production Planning And Control","content":" \u003cp\u003eProcess planning information exchange services in crowdsourced manufacturing can be enabled by developing a conceptual MaaS informatics system framework based on cloud computing. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows a five-layer architecture to reveal interactions among decision agents, while providing decision support to fulfillment of adaptive production planning and control. It includes the decision agent, application, decision support, distributed data management and infrastructure layers. Based on this MaaS informatics architecture, the manufacturer crowds basically involve two types of information exchange. The first type is information utilization \u0026ndash; exploiting smart sensing and communication technologies such as industrial IoTs and block chains to collect, process and organize manufacturing fulfillment data in a distributive manner. The second type is associated with data retrieval services for dispersed decision agents while providing a proof-of-work to serve the revenue distribution among the stakeholders of the MaaS network.\u003c/p\u003e \u003cp\u003eTo take advantage of the large transaction data generated and collected in the crowdsourcing cyber platform, machine learning and data analytics can be applied to enable predictive MaaS production planning and control. A promising area of research is to develop a data-driven inverse problem solving method and analytics tools. It has profound implications for data-driven design and manufacturing [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A performance metric is the improved quality of specific domain problems in comparison with the results of the traditional direct/forward problem solving approach. Validation can be achieved through the user experiments and industrial case studies. In addition, the ultimate goal of MaaS is to facilitate mass customization and open manufacturing, for which production planning and control must be adaptive to individual MaaS order fulfillment by reusing previous similar transactions. A noteworthy research topic is case-based machine learning for adaptation of manufacturing processes and quick generation of MaaS process planning. Comparing with traditional parametric adjustment of process plans for similar tasks, case-based reasoning and machine learning [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] will tremendously enhance categorization, indexing and searching of 3D solid models, process information and supply chain transactions to better exploit the implicit design and manufacturing knowledge with semantics incorporated in the library of previously manufactured parts and MaaS tasks. The potential challenge, however, is the actual applicability of data-driven inverse problem solving and predictive process adaptation for MaaS. In practice, it is important to mitigate the uncertainty and potential risk by enhancing problem context verification through industrial case studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"6. Optimal Crowdsourcing Contracting With Dynamic Task Allocation","content":" \u003cp\u003eThe operations of MaaS through the crowdsourcing platform are achieved as transactions using the brokerage and bidding mechanism [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. It is similar to, yet different from, supply contracting for outsourcing in supply chain management [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], which mainly deals with costing and logistics decisions associated with financial transactions. Crowdsourcing contracting, however, not only deals with cost and financial decisions, but also involves engineering assessment of manufacturers\u0026rsquo; capabilities with respect to the technical specifications of each MaaS order, in addition to strategic flexibility among manufacturer crowds as well as in the MaaS platform operations [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. To leverage both technical and financial analysis in holistic contracting evaluation decisions, it is possible to extend a generic design evaluation metric based on the information content measure [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which is originated from axiomatic design theory [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Also promising is to extend real options theory to valuation analysis of MaaS cyber platform economics based on our prior work on strategic flexibility of product platform technical systems [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSpecifically, crowdsourcing contracting can be approached from a unique perspective of service-oriented manufacturing, which provides a fair and efficient mechanism for crowdsourcing task decomposition and allocation. It also forges the manufacturing network and provides the logistic service to transport the manufacturing resources and WIP according to the precedence relationship of the production plan. Compelling research issues are associated with the methods and decision tools of ontology and graph grammar-based task reasoning and instantiation, such that the crowdsourcing task handling process acts like an MES/ERP system on a large scale. It handles the manufacturing activities from the derivation of the manufacturing tasks, as well as the planning of the material flow inner and outer of the manufacturer\u0026rsquo;s shop floor, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, a critical aspect of MaaS decision support is material flow management for engineering logistics of the required materials, WIP, subassemblies, or final products on time. It is challenging to keep a reasonable cost while aligning customers, products, processes, and logistics to deal with the increase of product variety [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. From the MaaS platform perspective, a resource platform can collect the information from the manufacturer crowds, formulate the origins and destinations of the service demands, find the common routes in the corresponding transportation service tasks, and synchronize the manufacturing activities to achieve just in time [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. A viable approach is to adopt a cross-docking strategy [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] in managing the service-oriented logistic networks. Since the solutions generated by the manufacturer crowds are evaluated by the platform, a manufacturing resources re-planning function is needed. It involves searching for sharable capabilities and order re-arranging to allocate the existing orders on the shop floor and determine the acceptance of the orders. After the awarding process with manufacturing supply contracts to select the preferred manufacturers, the resources re-planning serves the management the mix of the actual assigned tasks and current orders on the shop floor. It is of particular importance to consider a multi-party game decision scenario for this dynamic task allocation process. Research opportunities exist for formulating a mathematical model of bilevel game optimization through nested leader-follower joint optimization for optimal allocation of tasks [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The rational of game theoretical optimization lies in seeking for equilibrium solutions, instead of global optima, to arrive at a synergy of various interests of multiple decision makers in a noncooperative game environment [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The research challenges and potential risk lie in how to evaluate the efficiency of MaaS task allocation. Furthermore, rigorous mathematical analyses and intensive computational studies are necessary to verify model assumptions and solution conditions.\u003c/p\u003e "},{"header":"7. A Case Example Of Crowdsourced Orthodontics With 3d Printing","content":" \u003cp\u003eCrowdsourced manufacturing enables new cyber manufacturing that represents a significant transformation of the manufacturing sector from current practice of focusing on automation and manufacturing informatics within individual enterprise to open manufacturing crowds through the cloud platform to deliver manufacturing as a service. The potential industrial and societal benefits can be exemplified by a case of orthodontics with 3D printing. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, a crowdsourcing cyber platform makes open innovation, open design and open manufacturing possible, entailing open business value chains. It facilitates the orthodontists, who are entrepreneurs of mass customization and personalization but do not have any design and manufacturing capabilities, to access 3D printing manufacturing services that are delivered by a crowd of 3D printing firms, who also deliver manufacturing services to many other businesses through the platform. This enables manufacturers to offer their services more widely and allow customization of products and processes to be fulfilled more efficiently.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"8. Summary","content":" \u003cp\u003eFuture cyber manufacturing is envisioned to be fulfilled in a crowdsourcing environment that will be populated by manufacturer crowds to collaborate with the cyber platform on a shared understanding of the tasks for delivering manufacturing as a service (MaaS). This review paper advocates a symbiosis of crowdsourcing cyber platform technologies to enhance intelligent decision support to manufacturing planning for achieving MaaS operational goals. Consistent with a systematic framework of platform-driven MaaS, the focus on the cyber platform and intelligent cognitive assistants entails a model-based systems engineering perspective. This technical concept aims to enhance MaaS fulfilment through a population of manufacturer crowds by adopting computational modeling and decision analytics to exploit the implicit design and manufacturing knowledge that is incorporated in the library of previously executed manufacturing tasks, which in turn facilitates generation of manufacturing process plans by parametric adjustment of process plans for similar tasks. The analytic and model-based approach to crowdsourced manufacturing enables new cyber manufacturing capabilities that represent a significant transformation of the manufacturing sector. This will facilitate transition from the current practice of focusing on automation and manufacturing informatics within individual enterprise to open manufacturing crowds throughout the cloud platform to fulfill MaaS.\u003c/p\u003e \u003cp\u003eThe prevailing efforts of cloud-based design and manufacturing are geared towards computing platform technologies and ICT support. This review recognizes one imperative research issue of cyber manufacturing is how to better understand and formulate the domain problems, namely enterprise MaaS reference modeling. Formal enterprise MaaS reference modeling helps enhance understanding and formulation of the domain problems. Advances of research on SysML modeling of collaborative crowdsourcing fulfillment of MaaS contribute to a deeper fundamental understanding of crowdsourced manufacturing as a new manufacturing paradigm.\u003c/p\u003e \u003cp\u003ePopulation dynamics modeling of manufacturer crowd behaviors over open business value chains reveals fundamental differences of cyber platform-driven MaaS fulfillment from traditional individual enterprise operations. The data-driven inverse problem solving and case-based machine learning methods have great potential for predictive adaptation of manufacturing processes of MaaS production planning and control, while exploiting the large transaction data generated and collected in the crowdsourcing cyber platform. Crowdsourcing contracting coincides with a multi-party noncooperative game-theoretic optimization problem. It empowers dynamic MaaS task allocation that will improve the traditional supply contracting models to deal with not only cost and financial decisions, but also to excel in engineering assessment of manufacturers\u0026rsquo; capabilities with respect to the technical specifications of each MaaS order, in addition to valuation of strategic flexibility among manufacturer crowds involved in the MaaS platform operations.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003ea. Funding: This material is based upon work partially supported by the Renewable Bioproducts Institute at Georgia Tech (Gong and Jiao) and the National Science Foundation NSF Future of Work at the Human-Technology Frontier Big Idea under Grant No. 1928313 (Jiao).\u003c/p\u003e\n\u003cp\u003eb. Conflicts of interest: The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003ec. Availability of data and material (data transparency): Not applicable\u003c/p\u003e\n\u003cp\u003ed. Code availability (software application or custom code): Not applicable \u003c/p\u003e\n\u003cp\u003ee. Ethics approval (include appropriate approvals or waivers): Not applicable \u003c/p\u003e\n\u003cp\u003ef. Consent to participate (include appropriate statements): Not applicable \u003c/p\u003e\n\u003cp\u003eg. Consent for publication (include appropriate statements): All authors consent for publication. \u003c/p\u003e\n\u003cp\u003eh. Authors' contributions: Authors carry equal contributions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlem L, McLean A, Vercoustre AM, 2003, Knowledge sharing technologies to support community participation in natural resource management: a research agenda, In Proceedings of the Australian Conference on Knowledge Management and Intelligent decision Support, Melbourne, Australia, 11\u0026ndash;12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArabani AB, Zandieh M, Ghomi SF (2011) Multi-objective genetic-based algorithms for a cross-docking scheduling problem. Appl Soft Comput 11(8):4954\u0026ndash;4970\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBamberger M, Vaessen J, Raimondo E (2015) Dealing with complexity in development evaluation: A practical approach. 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DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/TEM.2020.298355\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Crowdsourcing, manufacturing as a service, crowdsourcing contracting, open manufacturing, Industry 4.0, platform economics","lastPublishedDoi":"10.21203/rs.3.rs-592199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-592199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFuture cyber manufacturing is envisioned to be fulfilled in a crowdsourcing environment that will engage a large population of manufacturer crowds to collaborate with the cyber platform on a shared understanding of the tasks for delivering manufacturing as a service (MaaS). This review paper is motivated towards a symbiosis of crowdsourcing cyber platform technologies to enhance intelligent decision support to manufacturing planning for achieving MaaS operational goals. The paper reviews the fundamental issues of MaaS through crowdsourcing from a model-based systems engineering perspective that is in line with a systematic framework of platform-driven MaaS. Also discussed is the outlook of analytic and model-based approach for crowdsourced manufacturing in order to enable new cyber manufacturing capabilities that represent a significant transformation of the manufacturing sector. The vision is to facilitate transition from the current practice of focusing on automation and manufacturing informatics within individual enterprise to open manufacturing crowds throughout the cloud platform to fulfill MaaS. The cyber platform and intelligent cognitive assistants enhance MaaS fulfilment by adopting computational modeling and decision analytics to exploit the implicit design and manufacturing knowledge that is incorporated in the library of previously executed manufacturing tasks, which in turn facilitates generation of manufacturing process plans by parametric adjustment of process plans for similar tasks.\u003c/p\u003e","manuscriptTitle":"Crowdsourced Manufacturing Cyber Platform and Intelligent Cognitive Assistants for Delivery of Manufacturing as a Service: Review of Fundamental Issues and Outlook","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-06-10 14:10:25","doi":"10.21203/rs.3.rs-592199/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2021-07-18T11:09:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-06-07T13:32:00+00:00","index":0,"fulltext":""},{"type":"editorAssigned","content":"","date":"2021-06-06T20:53:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2021-06-04T08:41:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5566ae25-34f2-40f6-ae6d-c312df342128","owner":[],"postedDate":"June 10th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":4930521,"name":"Biotechnology and Bioengineering"}],"tags":[],"updatedAt":"2021-08-22T15:28:00+00:00","versionOfRecord":{"articleIdentity":"rs-592199","link":"https://doi.org/10.1007/s00170-021-07789-7","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2021-08-12 15:08:33","publishedOnDateReadable":"August 12th, 2021"},"versionCreatedAt":"2021-06-10 14:10:25","video":"","vorDoi":"10.1007/s00170-021-07789-7","vorDoiUrl":"https://doi.org/10.1007/s00170-021-07789-7","workflowStages":[]},"version":"v1","identity":"rs-592199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-592199","identity":"rs-592199","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}