Manufacturing Service Providers Research Articles

Operational decisions of photovoltaic closed-loop supply chains with industrial distributed photovoltaic subsidy policy in China

Photovoltaic (PV) generation, as a clean and renewable energy technology, aligns with the global needs for energy transition and sustainable development. Due to its industrial distributed PV subsidy support policy, China has witnessed rapid growth in distributed PV industrial projects, leading to the formation of a mature market trading mechanism. This paper, focusing on China’s industrial distributed PV and its user, aims to explore how the PV system manufacturer (PSM) and PV system service provider (PSSP) can optimize the combination of “recycling mode & power structure” to simultaneously achieve multiple performance improvements, including those in economy, society, and environment (ESS) in the photovoltaic closed-loop supply chain (PV-CLSC), in consideration of the government’s PV subsidy policy. Therefore, by using three game-theoretical approaches including Nash game existed between the PSM and the PSSP under equal power and the Stackelberg game respectively led by PSM and PSSP, the optimal decisions of the PSM and PSSP, and the effective government subsidies under different combinations are obtained. Conclusions show that: To achieve individual optimal economic performance, both PSM and PSSP, as recyclers, invariably prefer a dominant position and choose to conduct recycling activities independently. For social welfare and subsidy efficiency, the government consistently tends to choose the PSM recycling mode in which the PSM and PSSP are expected to be in an equal power structure. Both in the PSM recycling and PSSP recycling modes, the government subsidy is smaller when two members are under the equal position than that respective under the dominant position, and in the latter scenario, the one with the advantage can mainly obtain the difference in the increased government subsidy.

Evolution analysis of low-carbon cooperation of service providers based on Moran process in cloud manufacturing.

Low-carbon cooperation among cloud manufacturing service providers is one way to achieve carbon peak and neutrality. Such cooperation is related to the benefits to service providers adopting low-carbon strategies and stochastic factors such as government low-carbon policies, providers' environmental awareness, and demanders' low-carbon preferences. Focusing on the evolutionary process of service providers' low-carbon strategy selection under uncertain factors, a stochastic evolutionary game model is constructed based on the Moran process, and the equilibrium conditions for low-carbon cooperation among providers are analyzed under benefit-dominated and stochastic factor-dominated situations. Through numerical simulation, the effects of the cloud platform's cost-sharing coefficient for low-carbon investment, matching growth rate, carbon trading price, and group size on providers' low-carbon strategy evolution are analyzed. The research results show that increasing the cloud platform's low-carbon cost-sharing, carbon trading price, and group size can promote low-carbon cooperation among service providers. With greater low-carbon investment costs and greater stochastic factor interference, the providers' enthusiasm for low-carbon cooperation decreases. This study fills the research gap in the low-carbon cooperation evolution of cloud manufacturing providers based on the stochastic evolutionary game and provides decision-making suggestions for governments and cloud platforms to encourage provider participation in low-carbon cooperation and for providers to adopt low-carbon strategies.

Exploring optimal outsourcing strategy with and without transfer payment

AbstractThis study investigates an original equipment manufacturer's (OEM's) outsourcing choice between a competing manufacturer (CP) and a non‐competing manufacturer (NP). We develop a benchmark self‐produce strategy and two outsourcing strategies to differentiate two manufacturing service providers, and examine the optimal strategy alongside an analysis of the respective incentives (e.g., a lump‐sum payment) from the two service providers. The optimal strategy depends on the difference in production efficiency, degree of product substitution, and joint effect of the transfer payments. The transfer payments contribute to a greater range of Pareto improvements, increasing the possibility of outsourcing cooperation while highlighting the role of competition intensity on the model and outsourcing cooperation partner. Effect analysis shows that in the absence of transfer payment, the optimal strategy is beneficial to social welfare. With transfer payment, the optimal strategy is changed and the firms will be more profitable, but at the expense of customers' surplus, which may result in worse social welfare. An extended analysis of mixed strategies, in which the OEM produces part of the products and outsources the rest to CP/NP, shows that while the mixed‐CP strategy can be an optimal choice, the mixed‐NP strategy will degenerate to either self‐produce or complete outsourcing to NP under certain conditions.

Pricing decisions of online and offline dual-channel supply chains considering data resource mining

Data resources, a fundamental component in the digital economy, play a vital role for businesses aiming to establish a lasting competitive edge. A company's data resources can uniquely influence the decisions surrounding products and services within the supply chain. The integrated dual-channel supply chain (DCSC) involves direct online channels utilized by manufacturing service providers alongside offline channels, such as physical retail stores, managed by sales service integrators. The DCSC is capable of performing second-stage data mining services in accordance with the sold products and customer services after the product sales are completed. Subsequently, data resource mining can be achieved through a cross-channel approach. As such, considering the exploration of cross-channel data resources, this study strives to formulate a structure for a dual-channel closed-loop supply chain. Furthermore, utilizing concepts from the Stackelberg and Nash equilibrium game theories, it delves into the analysis of pricing decisions and profit allocation. This examination encompasses distinct closed-loop supply chain configurations, viewed through the lenses of both centralized and decentralized decision-making approaches. In addition, the effects of cross-channel data mining and channel consumption preferences on supply chain decisions are analyzed, and the analysis is conducted in combination with numerical examples. As evidenced by the findings in this investigation, cross-channel data resource mining, consumer channel preference, and the data mining value conversion rate can prominently affect the formulation of pricing strategies and the distribution of profits in closed-loop supply chains. The potential value of data resources can lead to the generation of “external incentives” following the strategy of data resource mining. Furthermore, the data resource mining strategy is promising in stimulating the growth of the product and service markets. Finally, the overall profit of the supply chain is increased with the increase in the efficiency of data resource conversion. Enhancements in the efficacy of data resource conversion correspondingly lead to heightened overall profits within the supply chain.

Digital service innovation challenges faced during servitization: a multi-level perspective

PurposeDigital Service innovation (DSI) plays a fundamental role in the successful transition from product manufacturer or traditional service provider to a provider of digitally-enabled service solutions. Multiple impediments make managing this transformation using digital technologies difficult for firms, their customers and wider ecosystems. Extant knowledge of these digital technology impediments requires synthesizing and mapping.Design/methodology/approachA systematic literature review (SLR) was conducted. DSI tools and terminology are synthesized via thematic analysis. Subsequently, impediments to DSI for servitization (covering barriers, challenges and tensions) faced by actors across three key innovation phases: strategic planning, design planning and implementation, and four interaction levels (Micro, Meso, Macro-environment, Macro-ecosystem) are mapped via template analysis.FindingsSix impediment categories (external environmental factors, internal firm factors, capabilities, business models and processes, value creation and interaction) encompassing 28 unique impediment types to DSI during servitization are identified. A framework enabling impediment comparison across innovation phases and ecosystem/network interaction levels, revealing that the majority of barriers can be framed as “challenges” was developed.Originality/valueWhilst literature is emerging relating to digital servitization, there is a lack of research on the role DSI plays in facilitating digital servitization and no comprehensive study of DSI impediments exists. Additionally, consensus around the cross-disciplinary terminologies used is lacking. This study is a structured attempt to map the domain, summarizing the terms, identifying and clarifying impediment categories and providing recommendations for researchers and managers in tackling the latter.

Dynamic cooperative value-added service strategies of the smart manufacturing platform considering the network effect and altruism preference

Smart manufacturing platform has become an important carrier of digital transformation of manufacturing industry. However, due to the capital and technology constraints of manufacturing enterprises, a cross-border cooperation mode with cloud service providers has emerged. To expand users’ scale and enhance competitiveness, the manufacturing enterprise and the cloud service provider have focused on investing in value-added service (VAS) based on their resource endowments. Based on differential game theory and Bellman’s continuous dynamic planning theory, this study investigates dynamic cooperative VAS efforts decisions of the platform considering network effect and altruism preference. The results show that the network effect can stimulate the VAS efforts level of both manufacturer and cloud service provider. Moreover, the manufacturer's altruistic preference weakens the double marginal effect, which is conducive to improve the profit of the platform system, the VAS efforts level and the profit of the cloud service provider. However, the manufacturer's effort level is not affected by the manufacturer's altruistic preference, and it only benefits the manufacturer when the manufacturer’s altruistic preference coefficient is less than a certain threshold. In addition, the manufacturer derives far less profit growth from altruistic preference than the cloud provider does.

Enablers for Adopting Restriction of Hazardous Substances Directives by Electronic Manufacturing Service Providers

The Electronic Manufacturing Service (EMS) industry contributes significantly to toxic waste generation due to its fabrication processes. Notably, adherence to the Restriction of Hazardous Substances (RoHS) Directive varies amongst EMS providers, despite its aim to reduce electronic waste. This study explores the factors influencing EMS providers’ decision to adopt the RoHS directive, utilizing the technology–organization–environment (TOE) and the human–organization–technology (HOT) fit, or the TOE-HOT fit framework. We validated our framework using partial least squares structural equation modeling (PLS-SEM), based on responses from 379 questionnaires from major EMS providers. The results demonstrated that expert resources, adequate resources, perceived industrial pressure, institutional pressure, and costs were positively associated with RoHS adoption. However, innovation, relative advantage, and verification ability were identified as significant barriers. In particular, innovation in the human dimension was the key determinant for RoHS adoption. Therefore, clear policy instruments and regulations may enhance RoHS adoption by EMS providers. These findings can guide environmental policy definitions in governmental laws and strategies, encouraging EMS providers and other firms to adopt RoHS standards.

Towards a general model to predict energy consumption for fused filament fabrication

Additive manufacturing offers the opportunity to manufacture highly complex parts capable of providing improved performance, such as using lattice structures inside parts to reduce part weight. However, further research and development is required to improve the energy efficiency of the machinu85es, given the growing focus on sustainability and sustainable manufacturing. The purpose of this research is to address the interaction between part geometry, layer thickness, and printer type to minimize energy consumption for fused filament fabrication. This experiment was conducted with a Creality Ender 3, Monoprice MP Voxel, and Prusa i3 MK 3S + 3D printers using PLA filament, with two different part geometries and two settings for layer thickness. Active power during printing was recorded during warmup and printing. Energy consumption varied with printer type and layer thickness, while increased part complexity may lead to larger energy consumption. For printer selection, other factors may influence decision making of fused filament fabrication users such as quality and machine cost which have tradeoffs when compared to energy consumption. The first and second energy prediction models evaluated in this study had relatively large mean absolute error of 58 kJ and 29 kJ, when calculated using previously derived empirical coefficients and build time estimates calculated using slicing software, respectively. Our research suggests that improvements in the accuracy of energy prediction models are needed so such models can be applied to a range of printers. This research has implications for additive manufacturing service providers, makerspaces, as well as hobbyists who want to advance the sustainability of additive manufacturing.

To infinity and beyond: A teaching case on Rocket Lab and the emergence of New Zealand's space ecosystem

This teaching case focuses on the relationship between entrepreneurs, their ventures and the broader context—the innovation and entrepreneurship ecosystem (I&EE)—in which entrepreneurial activity occurs. Building on secondary data and informal conversations with industry experts, the case recounts the story of Rocket Lab, an aerospace manufacturer and launch service provider founded in 2006 in New Zealand. The case elaborates on how unique contextual conditions in combination with the founder's vision and skills enabled Rocket Lab to become a dominant force in the space sector, even though the firm was founded in a country with virtually no history in spacefaring and highlights that Rocket Lab's emergence constituted the foundation of a burgeoning space industry in New Zealand. In doing so, the case sensitises students to the importance of self-reinforcing and mutually interdependent relationships in well-functioning I&EEs and the role policymakers play in this context. At the same time, it allows a critical discussion of I&EEs, particularly if they are dominated by a small number of anchor firms.

Collaborative contracting for Manufacturing-as-a-Service (MaaS) by information content measurement and decision tree learning

Product realization through Manufacturing-as-a-Service (MaaS) has observed as an emerging trend towards Industry 4.0. It offers new opportunities for reaching external partner’s knowledge and resources while allowing companies to focus on their core competencies. This paper envisions the importance of collaborative contracting for MaaS fulfillments, in which tournament-based crowdsourcing entails evaluation and selection of manufacturing service providers as a best-matching problem of multi-criteria decision making. MaaS collaborative contracting involves numerous evaluation criteria related to not only technical capabilities of the fulfilling agents, but also their business performance involved with MaaS operations in the past history. This paper develops a contracting analysis and evaluation methodology for selection of appropriate MaaS agent selection. The proposed evaluation mechanism utilizes information content measure and decision tree learning for better matching of fulfilling agents to maximize customer satisfaction and effectiveness of multiple stakeholders of MaaS operations. The multi-attribute utility theory is integrated with decision tree learning within a coherent predictive analytics framework to not only synthesize pre-defined functional and cost requirements for customers, but also leverage upon historical data of business performance. A case study of orthodontic brace mass customization through MaaS is reported to illustrate the feasibility and potential of collaborative contracting in MaaS.

Resource Centric Analysis of RSA and ECC Algorithms on FPGA

The electronics industry’s shadow side is counterfeiting, and the doom is growing. Almost every business in the supply chain is impacted by the issue, including component suppliers, distributors, Electronics Manufacturing Services (EMS) providers, Original Design Manufacturers (ODMs), Original Equipment Manufacturers (OEMs), and their clients. In fact, any electronics firm that wishes to benefit from the cheap costs associated with globalization must be aware that someone along the supply chain may be persuaded to acquire fake items and sell them as genuine. A thorough grasp of chip designs, including partitioning and prioritizing data transit and storage, as well as a range of obfuscation techniques and activity monitoring, is necessary to reduce the danger of future hardware breaches. To battle this problem, we need to enforce various security measures at different levels of the supply chain. The recent methods include implementing cryptographic ciphers into the devices. The commonly used ciphers are the hard ciphers. But owing to the advancements and increase in the number of low power and resource constrained devices, there has been a dire need to design ciphers that support such devices. This paper talks about the advantages of lightweight ciphers, aiming to secure low power devices and other embedded devices. This work mainly compares two algorithms, RSA(hard cipher) and ECC(light cipher) in terms of their device utilization and power consumption on a Kintex-7. The presented results are justified from simulations performed on the Vivado design suite.

Additive Manufacturing Service Provider Selection Using a Neutrosophic Best Worst Method

Additive manufacturing (AM) is poised to disrupt the manufacturing industry due to its distinctive (layer-by-layer) approach to part production, as AM comprises a range of fabrication techniques that facilitate the production of customized and highly complex parts. For a particular part selecting the most suitable AM machine and material is an indispensable decision due to several AM machines, their process and material constraints like size, accuracy, mechanical properties etc. This paper demonstrates the multi-criteria framework using Delphi and neutrosophic best-worst method to determine the best suitable AM machine and feasible material from the spool of databases. The Delphi method is employed to determine and validate essential criteria for a selected part, which results in shortlisting the 9 most relevant criteria. Further, the neutrosophic best-worst method is utilized to compute the criteria weights. A rating and normalization approach is deployed to calculate each AM machine and compatible material aggregate score to determine the optimal AM machine, material and ultimately, the AM service provider.

Evolutionary game-based incentive models for sustainable trust enhancement in a blockchained shared manufacturing network

Shared manufacturing (SM) is an advanced manufacturing mode to solve the problems of information asymmetry and resource imbalance in the complex supply and demand relationship under the background of mass personalization. Blockchain can provide technical solutions for trust issues between manufacturing service providers and demanders, but less research has focused on the stable maintenance of a blockchained shared manufacturing network (BSMN). Industrial knowledge graph and cognitive intelligence play an important role in designing incentive mechanisms for BSMN. Based on the evolutionary game theory, this paper designs various incentive models and develops the corresponding smart contracts to encourage different enterprises to participate in the “accounting” operations, to enhance the trust and maintain the operational stability of BSMN. Firstly, the evolution mechanism of BSMN was analyzed. Then, an evolutionary game model between shared manufacturing enterprise nodes was established to deduce the stable state of BSMN. Based on the analysis of the evolutionarily stable state, an incentive model with three strategies to encourage different enterprise nodes to participate in “accounting” operations was determined, and smart contracts of the incentive models were developed. Finally, the effectiveness of the proposed incentive models was verified by simulation experiments.

Examining smart manufacturing challenges in the context of micro, small and medium enterprises

ABSTRACT Smart manufacturing is transforming the industry, and its adoption offers significant benefits to manufacturers, suppliers and customers, such as enhanced organisational performance, competitive advantage, mass customisation, improved sustainability and better collaboration. The objective of this research is to identify the implementation barriers of smart manufacturing and to explore the cause–effect relationship between them using a fuzzy-based Decision Making Trial and Evaluation Laboratory (DEMATEL) approach. Through expert opinions and literature review, 20 barriers to smart manufacturing adoption were identified. The results showed that ‘lack of product digitisation’, ‘lack of ubiquitous design and manufacturing setup’, ‘resistance to change’, ‘lack of data synchronisation’ and ‘lack of high tech infrastructure support’ were the significant barriers in the Indian Micro, Small and Medium Enterprises (MSMEs). The paper intends to guide the managers, governmental organisations and smart manufacturing service providers to formulate effective smart factory adoption strategies in the manufacturing domain.

Integrated configuration design and capacity planning in a dynamic cloud manufacturing system

A cloud manufacturing (CMfg) system is presented as a novel service- and customer-oriented manufacturing paradigm that integrates the distributed manufacturing enterprises to share their manufacturing capabilities or resources and collaborate as an interconnected system in a dynamic environment. Since the high performance of this system depends on the formation of a suitable group of manufacturing service providers, this paper develops an integrated c onfiguration design and capacity planning problem for the CMfg system by considering the dynamic environment of this system. In this regard, dynamic service providers and dynamic demand are considered as two aspects of the dynamic nature of this system. A multi-period multi-objective mathematical model is proposed by maximising the utilities of all three stakeholders of the system. Moreover, three extensions of a discrete multi-objective grey wolf optimiser (DMOGWO) algorithm are devised to solve the medium- and large-scale instances. A comprehensive computational experiment is conducted to assess the performance of the developed meta-heuristic algorithms. Furthermore, by carrying out a sensitivity analysis, some managerial insight is suggested for the managers.

Collaborative Production Task Decomposition and Allocation among Multiple Manufacturing Enterprises in a Big Data Environment

To realize the efficient decomposition and allocation of collaborative production tasks and resources among multiple enterprises, a task decomposition and allocation model for collaborative production among multiple manufacturing enterprises is proposed in a big data environment. The model is designed for the efficient and fast processing of production information using big data technology. This study innovatively applies the 5S management method to conduct data preprocessing for a manufacturing service provider and design the operation process of data cleaning and conversion to improve the efficiency of data processing. A collaborative optimization model, based on a hierarchical model with seven levels and considering time, costs, and services, is established for the task of production to achieve a reasonable match between supply and demand. Finally, the correlation coefficients of manufacturing service providers are configured according to weight order, so that the weight order is symmetrical with that of the manufacturer. The model also engages all manufacturing service providers with different production capabilities in collaborative production. The model is proved to be scientific and effective by using a specific example. In cooperative production activities, the production tasks of small and medium-sized enterprises can be effectively allocated. It can also realize efficient cooperative production among multiple manufacturing enterprises.

Guest Editorial: Building Trust in Additive Manufacturing, Layer by Layer

Nearly three-quarters (71%) of senior oil and gas professionals have sharpened their focus on digitalization over the past year, according to a 2021 survey by DNV (DNV Outlook). The pandemic has not only increased attention on how digital solutions can make organizations more adaptable and cost efficient, it has also forced companies to discard the normal rules and become more open to change. While data collaboration, cloud-based applications, and remote surveillance top the investment priorities for the year ahead, a growing number of respondents (7%) see additive manufacturing (AM)—the industry equivalent of 3D printing—on their spending list. As an emerging technology, AM uses 3D model data to fabricate parts, enabling, among other benefits, significant cost and time savings in contrast to many traditional manufacturing methods, where the final parts are machined out of a pre-made form. Its purpose is to alleviate and avoid long, expensive production shutdowns and reduce supply chain carbon footprints. Building trust in “printed” parts is key to unlocking this potential. Rapid, Reliable Reproduction The global AM market is expected to reach $350 billion by 2035 (DailyAlts 2021). The technology also has the potential to be enhanced by—or in the other direction, augment—other digital solutions, given it is based on a 3D file. Though accelerating at a slower rate compared to the aerospace and automotive industries, there is increased pressure to shorten the development cycle of components for the oil, gas, and renewables sectors and perform rapid proto-typing and testing of new, more sustain-able concepts. New business models will be developed and a new way of thinking adopted by design engineers to fully utilize its potential (DNV Technology Outlook 2030). To support the digital transformation of the energy industry, DNV, which has been actively investigating the potential of AM since 2014, has unveiled a new service specification document to ensure AM products, assets, and systems are safe, economical, and efficient. DNVGL-SE-0568 “Qualification of Additive Manufacturing Service Providers, Manufacturers, and Parts,” is part of a portfolio of six different AM-related standards and recommended practices (Fig. 1).

Automating the Search and Discovery of Manufacturing Service Providers to Enable a Digital Supply Chain Network

Abstract Uncertainty in manufacturing networks has created barriers to closing the gap between design enterprises and the American industrial base. Uncertainty arises from the lack of transparent access to manufacturer capabilities, the inability to auto-discover service providers who are best capable for a given job request, and the dependence on human word-of-mouth trust network relationships that exist in the manufacturing supply chain. This uncertainty slows down the pace of product development lifecycles from a viewpoint of inefficient forms of supplier assessment, vetting, selection, and compliance, leading to a trust tax tacked onto the final price of products. In times of global crisis such as the coronavirus disease pandemic, this uncertainty also leads to inefficient forms of gathering information on manufacturing capability, available capacity, and registered licenses and assessing compliance. This technical note outlines solution pathways that can help ease the search and discovery process of connecting clients and manufacturing service providers through digitally enabled technologies.

The Priority Order and Sensitivity Analysis of Mechanical System Failure Mode Improvement Based on Computer-aided

Failure mode is an analysis method used to determine potential failure modes and their causes. Specifically, through the implementation of FMEA, the weaknesses of the product can be found before the product design or production process is truly realized, and the product defects can be determined at the prototype stage or before mass production. FMEA is the first set of analysis mode formed by NASA. FMEA is a practical method to solve problems, which can be applied to many engineering fields. At present, many automobile manufacturers and electronic manufacturing service providers (EMS) in the world have adopted this mode to manage and monitor the design and production process. In the product design stage and process design stage, it is a systematic activity to analyze each process one by one, find out all potential failure modes, based on computer analysis of its possible consequences, and take necessary measures to improve product quality and reliability.

Decentralized cloud manufacturing-as-a-service (CMaaS) platform architecture with configurable digital assets

Contemporary Cloud Manufacturing-as-a-Service (CMaaS) platforms now promise customers instant pricing and access to a large capacity of manufacturing nodes. However, many of the CMaaS platforms are centralized with data flowing through an intermediary agent connecting clients with service providers. This paper reports the design, implementation and validation of middleware software architectures which aim to directly connect client users with manufacturing service providers while improving transparency, data integrity, data provenance and retaining data ownership to its creators. In the first middleware, clients have the ability to directly customize and configure parts parametrically, leading to an instant generation of downstream manufacturing process plan codes. In the second middleware, clients can track the data provenance generated in a blockchain based decentralized architecture across a manufacturing system. The design of digital assets across a distributed manufacturing system infrastructure controlled by autonomous smart contracts through Ethereum based ERC-721 non-fungible tokens is proposed to enable communication and collaboration across decentralized CMaaS platform architectures. The performance of the smart contracts was evaluated on three different global Ethereum blockchain test networks with the centrality and dispersion statistics on their performance provided as a reference benchmark for future smart contract implementations.