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Mass finishing is a commonly employed surface finishing process for improving surface characteristics of aerospace engineering components. Optimization of surface characteristics of such critical components require an explicit computational model that can describe the surface characteristics of the finished component. This paper aims to develop an explicit computational model that can describe the surface roughness as a function of various process parameters which influence the mass finishing process.

In the present work, the authors propose to study the roughness characteristics using a combined evolutionary computing approach based on Multi-Adaptive Regression Splines (MARS) and Genetic Programming (GP) techniques.

The authors conducted sensitivity and parametric analysis to capture the dynamics of surface characteristics by unveiling dominant input variables and hidden non-linear relationships. It is found that by regulating the process time and media size, a greatest variation in surface finish reduction can be achieved in mass finishing process.

To the best of authors knowledge, for the first time a hybrid evolutionary computational technique has been proposed in this work. The authors combined two powerful evolutionary techniques, namely Multi-variate Adaptive Regressive Splines and Genetic Programming approach. The proposed approach was able to capture the dynamics of surface roughness with higher accuracy as comparable to that of the experiments.

The functioning of multi-gene genetic programming (MGGP) algorithm suffers from the problem of difficulty in model selection. During the preliminary analysis, it is observed that there are many models in the population whose performance is better than that of the model selected with a little compromise on training error. Therefore, an ensemble evolutionary (Ensemble-MGGP) approach is proposed and applied to the data obtained from the vibratory finishing process. The paper aims to discuss these issues.

Unlike the standard GP, each model participating in Ensemble-MGGP approach is made by combining the set of genes. Predicted residual sum of squares criterion (PRESS) criterion is integrated to improve its evolutionary search. The parametric analysis and sensitivity analysis (SA) conducted on the proposed model validates its robustness by unveiling dominant input parameters and hidden non-linear relationships.

The results indicate that the proposed Ensemble-MGGP model outperforms the standardized MGGP model. SA and parametric analysis reveals relationships and insights into vibratory finishing process.

Literature emphasises on characterization of vibratory finishing process using the experimental-based-studies. In addition, the issue of difficulty in model selection in genetic programming is addressed. This work proposes a new ensemble evolutionary approach to counter these issues.

The purpose of this research study was to examine the role of Social Networking Technologies (SNT) in developing Public Services Supply Chain (PSSC) during COVID-19 pandemic. Due to lack of sufficient support from corporations and non-government groups, citizens in developing democracies were dependent upon their governments for the delivery of vital public services. During the COVID-19 epidemic, a number of organisations attempted to assist governments in managing the supply of public services. However, organisations often lacked a thorough grasp of how to cultivate social ties for the delivery of efficient and effective public services for citizens. In this research work, to study the delivery of efficient and effective public services to citizens, the authors have proposed a social network viewpoint. This was at a ‘meso-level’ lens so as to examine the intersection between organisational SNT and PSSC. According to the qualitative study conducted in this research work, it became evident that organisational social ties could play a significant role in facilitating homophilic and heterophilic ties. This was specially so for the distribution of public services in pandemic situation like COVID-19. However, the research study findings also found out that these network forces were highly dynamic and dependent upon a set of factors. These factors included tie frequency, the sequencing of interaction with social ties and the prevailing norms. This research study enriched the understanding regarding the role of SNT. This was in the context of developing PSSC during critical crisis situations such as prevalent during COVID-19 pandemic. This research study offered a better understanding of social ties and motivational factors in the social networking environment. The social ties were analysed based on the contact incidents of six participants focusing on the PSSC during the COVID-19. Future research studies could consider a diverse set of participants in complex global PSSC consisting of multiple constraints. Furthermore, this research study had practical implications for managers. The study revealed that homophilic social ties were more supportive as compared to the sequence of interactions starting with heterophilic ties. Therefore, managers needed to develop better PSSC especially as compared to unrelated institutions.

Content analysis is a methodology that has been used in many academic disciplines as a means to extract quantitative measures from textual information. The purpose of this paper is to document the use of content analysis in the supply chain literature. The authors also discuss opportunities for future research.

The authors conduct a literature review of 13 leading supply chain journals to assess the state of the content analysis-based literature and identify opportunities for future research. Additionally, the authors provide a general schema for and illustration of the use of content analysis.

The findings suggest that content analysis for quantitative studies and hypothesis testing purposes has rarely been used in the supply chain discipline. The research also suggests that in order to fully realize the potential of content analysis, future content analysis research should conduct more hypothesis testing, employ diverse data sets, utilize state-of-the-art content analysis software programs, and leverage multi-method research designs.

The current research synthesizes the use of content analysis methods in the supply chain domain and promotes the need to capitalize on the advantages offered by this research methodology. The paper also presents several topics for future research that can benefit from the content analysis method.

The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations.

The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC.

Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations.

This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place.

The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area.

Neo-institutional theories and their constructs have so far only received limited attention in supply chain management literature. As recent supply chain disruptions and their ripple effects affect actors on a broader institutional level, supply chains are confronted with multiple new and emerging, often conflicting, institutional demands. This study aims to unpack the notion of institutional complexity behind supply chain disruptions and present a novel institutional framework to lower supply chain susceptibility and increase supply chain resilience.

The authors identify the patterns of complexity that shape the supply chain susceptibility, namely, distance, diversity and ambiguity, and present three institutional responses to susceptibility to increase supply chain resilience, namely, institutional entrepreneurship, institutional alignment and institutional layering.

This paper analyses the current situational relevance to better understand the various and patterned ways how logics influence both supply chain susceptibility and the supply chain resilience. The authors derive six propositions on how complexity can be reduced for supply chain susceptibility and can be increased for supply chain resilience.

By expanding and extending research on institutional complexity to supply chains, the authors broaden how researchers in supply chain management view supply chain susceptibility, thereby providing managers with theory to think differently about supply chains and its resilience.

Focussing on issues that have impacted textiles, clothing and fast fashion following the COVID-19 outbreak, this study aims to identify agility factors pertinent to retail maturity management, mainly on resilience. These factors were then assessed based on priority.

The authors conducted a strengths, weaknesses, opportunities and threats (SWOT) analysis to identify the main factors. This analysis was integrated with the analytic hierarchy process to rank the internal and external factors. Then, the authors used fuzzy technique for order of preference by similarity to the ideal solution to evaluate the significant strategies from the SWOT analysis based on adaptability and flexibility. Thus, the authors produced a prioritised list of relevant factors and guidelines useful in practical settings.

The authors identified and ranked 14 agility and resilience SWOT factors for maturity management and proposed and prioritised six relevant strategies. These highlighted changes and adaptations must be undertaken by various actors, ranging from the supply/manufacturing side to global retail locations.

The sampling study of experts is limited, and the application is focussed on one area. Future research might investigate other fields, for retail and industry.

The output of this study enables a better understanding of the internal and external factors of maturity management for fast-fashion retailers. Hence, it will further help them go through resilience and agility challenges following the pandemic.

This study significantly contributes to the literature because the results can guide retailers in developing effective decision-making systems for mature business operations following the pandemic outbreak. Here, the authors incorporate internal, external, agility and resilience aspects into one decision-making support system while enhancing existing knowledge on fuzzy multi-criteria decision-making applications. Further, this study discusses critical issues that have affected several aspects of the fashion and textile industry and will likely persist for some time.

Smart manufacturing is the prime gripper for the transformation and upgrading of the manufacturing industry. Smart manufacturing systems (SMSs) largely determine how smart manufacturing evolves in technical and organizational dimensions and how it realizes values in products, production or services. SMSs are growing rapidly and receiving tons of attention from academic research and industrial practice. However, the development of SMSs is still in its fancy, and many issues wait to be identified and solved, such as single point failures, low transparency and ineffective resource sharing. Blockchain, an emerging technology deriving from Bitcoin, is competent to aid SMSs to conquer troubles due to its decentralization, traceability, trackability, disintermediation, auditability and etc. The purpose of this paper is to investigate the blockchain applications in SMSs, seek out the challenges faced by blockchain-enabled SMSs (BSMSs) and provide referable research directions and ideas.

A comprehensive literature review as a survey is conducted in this paper. The survey starts by introducing blockchain concepts, followed by the descriptions of a literature review method and the blockchain applications throughout the product life cycle in SMSs. Then, the key issues and challenges confronting BSMSs are discussed and some possible research directions are also proposed. It finally presents qualitative and quantitative descriptions of BSMSs, along with some conclusions and implications.

The findings of this paper present a deep understanding about the current status and challenges of blockchain adoption in SMSs. Furthermore, this paper provides a brand new thinking for future research.

This paper minutely analyzes the impacts that blockchain exerts on SMSs in view of the product life cycle, and proposes using the complexity science thinking to deal with BSMSs qualitatively and quantitatively, including tackling the current major problems BSMSs face. This research can serve as a foundation for future theoretical studies and enterprise practice.