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The present study is intended to develop an effective approach to the real-time modeling of general dynamic nonlinear systems based on the multidimensional Taylor network (MTN).

The authors present a detailed explanation for modeling the general discrete nonlinear dynamic system by the MTN. The weight coefficients of the network can be obtained by sampling data learning. Specifically, the least square (LS) method is adopted herein due to its desirable real-time performance and robustness.

Compared with the existing mainstream nonlinear time series analysis methods, the least square method-based multidimensional Taylor network (LSMTN) features its more desirable prediction accuracy and real-time performance. Model metric results confirm the satisfaction of modeling and identification for the generalized nonlinear system. In addition, the MTN is of simpler structure and lower computational complexity than neural networks.

Once models of general nonlinear dynamical systems are formulated based on MTNs and their weight coefficients are identified using the data from the systems of ecosystems, society, organizations, businesses or human behavior, the forecasting, optimizing and controlling of the systems can be further studied by means of the MTN analytical models.

MTNs can be used as controllers, identifiers, filters, predictors, compensators and equation solvers (solving nonlinear differential equations or approximating nonlinear functions) of the systems of ecosystems, society, organizations, businesses or human behavior.

The operating efficiency and benefits of social systems can be prominently enhanced, and their operating costs can be significantly reduced.

Nonlinear systems are typically impacted by a variety of factors, which makes it a challenge to build correct mathematical models for various tasks. As a result, existing modeling approaches necessitate a large number of limitations as preconditions, severely limiting their applicability. The proposed MTN methodology is believed to contribute much to the data-based modeling and identification of the general nonlinear dynamical system with no need for its prior knowledge.

Scarcity of resources, ecological imbalance, global warming, rising energy prices and the ever-changing need for variety have attracted the government and manufacturers for sustainable development of the industries. The integrated sustainable-green-lean-six sigma-agile manufacturing system (ISGLSAMS) provides a solid platform for meeting both the customers’ variety needs and business sustainability requirements. Many organizations opted for ISGLSAMS, but still due to various barriers organizations are not able to fully implement ISGLSAMS. The purpose of this paper is to identify the barriers to the ISGLSAMS, so that a more sustainable industrial manufacturing system and industrial symbiosis can be developed.

A literature review, from the Web of Science and Google Scholar database, has been carried out to identify the various barriers to the implementation of ISGLSAMS in the entire value chain. A total of 168 research papers have been reviewed for identifying the ISGLSAMS barriers.

This paper elaborates the concept of the ISGLSAMS, its attributes and various barriers and contributes to a better understanding and successful implementation of ISGLSAMS to meet business’ sustainability and market performance goals in the entire value chain. The paper also projects the future research framework and directions for the ISGLSAMS, integrated sustainable-green-lean-six sigma-agile (ISGLSA) product and ISGLSA supply and value chain.

The study contributes to a better understanding of ISGLSAMS’ barriers. The government, stakeholders and policymakers may plan the policy, road map and strategies to overcome the ISGLSAMS’ barriers. In-depth knowledge of subclauses of ISGLSAMS’ barriers will help the practitioners to overcome the ISGLSAMS’ barriers strategically. By overcoming the ISGLSAMS barriers, a more sustainable 7 Rs based market focused manufacturing system can be designed. This will also increase the opportunities to enhance the industrial ecology, industrial symbiosis and better recovery of the product, process and supply chain residual value. This will reduce the waste to the ecosystem.

This work has been carried out in search of a more sustainable manufacturing system, i.e. ISGLSAMS (which is 7 Rs based, i.e. 6 Rs of sustainability with 7th R, reconfiguration) to meet the customer variety needs along with sustainability in the ever-changing customer market. This study adds value to the practitioners to identify and prioritize the ISGLSAMS’ industry-specific barriers and design the solution for the more sustainable development of (1) industries, (2) the industrial symbiosis system and (3) the ISGLSA product, process, system and supply value chain with minimum resource consumption and environmental impact. The research also contributes to the (a) ISGLSAMS (b) ISGLSA supply chain (c) reconfigurable, sustainable and modular products and (d) redesign, recovery and refurbishing of the product to increase the product life cycle.

The use of technology in 4th industrial revolution is at its peak. Industries are trying to reduce the consumption of resources by effectively utilizing information and technology to connect different functioning agents of the manufacturing industry. Without digitization “Industry 4.0” will be a virtual reality. The present survey-based study explores the factual status of digital manufacturing in the Northern India.

After an extensive literature review, a questionnaire was designed to gather different viewpoints of Indian industrial practitioners. The first half contains questions related to north Indian demographic factors which may affect digitalization of India. The latter half includes the queries concerned with various operational factors (or drivers) driving the digital revolution without ignoring Indian constraints.

The focus of this survey was to understand the current level of digital revolution under the ongoing push by the Indian government focused upon digital movement. The analysis included non-parametric testing of the various demographic and functional factors impacting the digital echoes, specifically in Northern India. Findings such as technological upgradations were independent of type of industry, the turnover or the location. About 10 key operational factors were thoughtfully grouped into three major categories—internal Research and Development (R&D), the capability of the supply chain and the capacity to adapt to the market. These factors were then examined to understand how they contribute to digital manufacturing, utilizing an appropriate ordinal logistic regression. The resulting predictive analysis provides seldom-seen insights and valuable suggestions for the most effective deployment of digitalization in Indian industries.

The country-specific Industry 4.0 literature is quite limited. The survey mainly focuses on the National Capital Region. The number of demographic and functional factors can further be incorporated. Moreover, an addition of factors related to ecology, environment and society can make the study more insightful.

The present work provides valuable insights about the current status of digitization and expects to facilitate public or private policymakers to implement digital technologies in India with less efforts and the least resistance. It empowers India towards Industry 4.0 based tools and techniques and creates new socio-economic dimensions for the sustainable development.

The quantitative nature of the study and its statistical predictions (data-based) are novel. The clubbing of similar success factors to avoid inter-collinearity and complexity is seldom seen. The predictive analytics provided in this study is quite elusive as it provides directions with logic. It will help the Indian Government and industrial strategists to plan and perform their interventions accordingly.

Drawing on the integration of the knowledge-based theory and the resource orchestration theory, this study aims to develop a moderated mediation model on how design/manufacturing/administrative advanced manufacturing technology (AMT) influences product innovation performance. The authors hypothesized that the absorption capacity could mediate the AMT-innovation performance link and that design–manufacturing integration (DMI) could positively moderate the mediating effect of the absorption capacity.

To test the hypothesis, the authors conducted a mail survey of equipment manufacturing firms and obtained 302 valid responses for data analysis. Both hierarchical regression and bootstrapping analysis were conducted to empirically test the research model.

It is revealed that the absorption capacity partially mediated the effect of AMT on innovation and that DMI enhanced the mediated effect. Specifically, the mediating effect of the absorption capacity was more substantial and significant when DMI was high. However, the mediating effect of the absorption capacity was weaker and insignificant when DMI was low.

Overall, this study contributes to the AMT theory on innovation by identifying the absorption capacity and DMI as two key factors that elucidate why and under what conditions AMT affects innovation. Moreover, this study advises managers that besides developing AMT, firms should cultivate a strong DMI, which directs the absorption capacity toward converting the valuable knowledge in firms’ capital equipment into increased innovation performance.

Cloud Manufacturing (CM) is the manufacturing version of Cloud Computing and aims to increase flexibility in the provision of manufacturing services. On-demand manufacturing services can be requested by users to the cloud and this enables the concept of Manufacturing-as-a-Service (MaaS). Given the considerable number of prototypes and proofs of concept addressed in literature, this work seeks real CM platforms to study them from a business perspective, in order to discover what MaaS concretely means today and how these platforms are operating.

Since the number of real applications of this paradigm is very limited (if the authors exclude prototypes), the research approach is qualitative. The paper presents a multiple-case analysis of 6 different platforms operating in the manufacturing field today. It is based on empirical data and inductively researches differences among them (e.g. stakeholders, operational flows, capabilities offered and scalability level).

MaaS has come true in some contexts, and today it is following two different deployment models: open or closed to the provider side. The open architecture is inspired by a truly open platform which allows any company to be part of the pool of service providers, while the closed architecture is limited to a single service provider of the manufacturing services, as it happens in most cloud computing services.

The research shoots a picture of what MaaS offers today in term of capabilities, what are the deployment models and finally suggests a framework to assess different levels of development of MaaS platforms.

Modern technologies are seen as an essential component of the fourth industrial revolution (industry 4.0) and their adoption is vital to transform the existing manufacturing system into industry 4.0-based manufacturing system. Therefore, the primary objective of this research explores the barriers of modern technology adoption and their mitigating solutions in order to align with Industry 4.0 objectives.

Barriers to adopting modern technologies and respective mitigating solutions are identified from the available literature. Further, these barriers are ranked with the help of expert opinions by using the BWM method appropriately. The identified solutions are ranked using the combined compromise solution (CoCoSo) method.

Several modern technologies and their capabilities are recognised to support the industry 4.0-based manufacturing systems. This study identifies 22 barriers to the effective adoption of modern technologies in manufacturing and 14 solutions to overcome these barriers. Change management, the high initial cost of technology and appropriate support infrastructure are the most significant barriers. The most prominent solutions to overcome the most considerable barriers are ‘supportive research, development and commercialisation environment’, ‘updated policy and effective implementation’ and ‘capacity building through training’ that are the top three solutions that need to be addressed.

The barriers and solutions of modern technology adoption are obtained through a comprehensive literature review, so there is a chance to ignore some significant barriers and their solutions. Furthermore, ranking barriers and solutions is done with expert opinion, which is not free from biases.

This identification and prioritisation of barriers will help managers to understand the barriers so they can better prepare themselves. Furthermore, the suggested solutions to overcome these barriers are helpful for the managers and could be strategically adopted through optimal resource utilisation.

This study proposes a framework to identify and analyse the significant barriers and solutions to adopting modern technologies in the manufacturing system. It might be helpful for manufacturing organisations that are willing to transform their manufacturing system into industry 4.0.

This study aims to address the aspects of product and process innovation strategies and their determining factors to understand their characteristics in clothing manufacturing and contribution for a successful and competitive clothing industry.

This general review is based on literature data of previous studies on innovation that transcend and cover the aspects of innovation applicable in the clothing industry. Although the scope of discussion is theoretically broad, it focusses on the context of innovation strategies in clothing manufacturing and the determinant factors indicating the acquisition and implementation of product and process-related innovation activities, simultaneously exploring and linking their implications for adopting, managing and integrating enterprise activities to the values of desired innovation novel models.

Based on theoretical background and pragmatic generalizations, product and process innovation strategies in clothing manufacturing firms tend to incline more towards computer-integrated technologies and concepts meant to promote product development, process optimization and organizational integration. Industry, technological and R&D factors tend to significantly determine innovation capability of a clothing firm.

This review generates integrated conceptual frameworks for product and process innovation strategies applicable in clothing firms and their determinant factors as prelude to empirical validation.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

With the emergence of the different Industry 4.0 technologies and the interconnectedness between the physical and cyber components within manufacturing systems, the manufacturing environment is becoming more susceptible to unexpected disruptions, and manufacturing systems need to be even more resilient than before. Hence, the purpose of this work is to explore how does incorporating Industry 4.0 into current manufacturing systems affects (positively or negatively) its resiliency.

A Systematic Literature Review (SLR) was performed with a focus on studying the manufacturing system’s resilience when applying Industry 4.0 technologies. The SLR is composed of four phases, which are (1) questions formulation, (2) determining an adequate search strategy, (3) publications filtering and (4) analysis and interpretation.

From the SLR results’ analysis, four potential research opportunities are proposed related to conducting additional research within the research themes in this field, considering less studied Industry 4.0 technologies or more than one technology, investigating the impact of some technologies on manufacturing system’s resilience, exploring more avenues to incorporate resiliency to preserve the state of the system, and suggesting metrics to quantify the resilience of manufacturing systems.

Although there are a number of publications discussing the resiliency of manufacturing systems, none fully investigated this topic when different Industry 4.0 technologies have been considered. In addition to determining the current research state-of-art in this relatively new research area and identifying potential future research opportunities, the main value of this work is in providing insights about this research area across three different perspectives/streams: (1) Industry 4.0 technologies, (2) resiliency and (3) manufacturing systems and their intersections.

Advancements in responsive manufacturing have been supporting companies over the last few decades. However, manufacturers now operate in a context of continuous uncertainty. This research paper explores a mechanism where companies can “elastically” provision and deprovision their production capacity, to enable them in coping with repeated disruptions. Such a mechanism is facilitated by the imitability and substitutability of production resources.

An inductive study was conducted using Gioia methodology for this theory generation research. Respondents from 20 UK manufacturing companies across multiple industrial sectors reflected on their experience during COVID-19. Resource-based view and resource dependence theory were employed to analyse the manufacturers' use of internal and external production resources.

The study identifies elastic responses at four operational levels: production-line, factory, company and supply chain. Elastic responses that imposed variable-costs were particularly well-suited for coping with unforeseen disruptions. Further, the imitability and substitutability of manufacturers helped others produce alternate goods during the crisis.

While uniqueness of production capability helps manufacturers sustain competitive advantage against competitors during stable operations, imitability and substitutability are beneficial during a crisis. Successful manufacturing companies need to combine these two approaches to respond effectively to repeated disruptions in a context of ongoing uncertainties. The theoretical contribution is in characterising responsive manufacturing in terms of resource heterogeneity and resource homogeneity, with elastic resourcing as the underlying mechanism.