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The purpose of this study was to produce dimensionally accurate and reliable fused layer modeling (FLM) feedstock composed of an impact modified polypropylene matrix, compounded with a cellulose nanofiber (CNF) reinforcement and coupled by a maleic anhydride coupling agent to produce comparable mechanical properties in comparison to the industry-standard method of injection molding (IM).

A spray dried CNF (SDCNF) was compounded with the polymer matrix using a masterbatch method. The composite was diluted with neat polymer and extruded into a filament and then printed into standardized mechanical testing samples. For comparison, the filament was chopped and standardized samples were produced with IM.

A loss in mechanical properties of up to 30% was observed in FLM samples. If normalized to reflect improved density from a part consolidation method, losses are reduced to 15% or show improvements in the neat polymer matrix.

Limited research has been done on producing FLM feedstock, reporting mechanical property results based on standardized testing and comparing the same material with IM.

Selection of resilient suppliers has attracted the attention of researchers in the past one decade. The devastating effect of COVID-19 in emerging economies has provided great impetus to the selection of resilient suppliers. Under volatile and uncertain business scenarios, supplier selection is often done under imprecise and incomplete information, making the traditional decision-making methods ineffective. The purpose of this paper is to demonstrate the application of a fuzzy decision-making method for resilient supplier selection.

A group of three decision makers was considered for evaluating various alternatives (suppliers) based on their performance under different primary, sustainability and resilience criteria. Experts' opinion about each criterion and alternative was captured in linguistic terms and was modelled using fuzzy numbers. Then, an algorithm for solving resilient supplier selection problem based on the trapezoidal intuitionistic fuzzy technique for order preference by similarity to ideal solution (TrIFTOPSIS) was introduced and demonstrated through a case study.

A closeness coefficient was used to rank the suppliers based on their distances from intuitionistic fuzzy positive-ideal solution and intuitionistic fuzzy negative-ideal solution. Finally, the proposed fuzzy decision making model was applied to a real problem of supplier selection in the clothing industry.

The presented TrIFTOPSIS model provides an effective route to prioritise and select resilient suppliers under imprecise and incomplete information. This is the first application of intuitionistic fuzzy multi-criteria decision-making for resilient supplier selection.

The purpose of this paper is to attempt supplier selection considering economic, environmental and social sustainability issues.

Subjective human judgment bears some kind of vagueness and ambiguity; fuzzy set theory has immense potential to overcome this. Owing to the advantage of intuitionistic fuzzy numbers set over classical fuzzy numbers set; three decision-making approaches have been applied here in intuitionistic fuzzy setting (namely, intuitionistic-TOPSIS, intuitionistic-MOORA and intuitionistic-GRA) to facilitate supplier selection in sustainable supply chain.

The stated objective of this research “to verify application potential of different decision support systems (in intuitionistic fuzzy setting) in the context of sustainable supplier selection” has been carried out successfully. A case empirical research has been conducted by applying three different decision-making approaches: intuitionistic fuzzy-TOPSIS, intuitionistic fuzzy-MOORA and intuitionistic fuzzy-GRA to an empirical data set of sustainable supplier selection problem. The ranking orders thus obtained through exploration of aforesaid three approaches have been explored and compared.

As compared to generalized fuzzy numbers, intuitionistic fuzzy numbers exhibit a membership degree, a non-membership degree and the extent of hesitation; a better way to capture inconsistency, incompleteness and imprecision of human judgment. Application potential of aforesaid three decision support approaches has been demonstrated in this reporting for a case sustainable supplier selection.

Green supply chain management (GSCM) has become an important issue with increasing awareness of environmental protection. A firm's environmental approach is not only relevant to its inner efforts, but its suppliers' environmental performance is also important. The aim of this study is to propose an integrated multi‐criteria decision‐making (MCDM) approach based on intuitionistic fuzzy set (IFS) and grey relational analysis (GRA) for green supplier selection.

Green supplier selection is a MCDM process that contains different kinds of uncertainties. Because of the vagueness and imprecision of decision makers' evaluations and subjectivity of the criteria, IFS and GRA are exploited to handle these uncertainties.

A numerical example is presented for the proposed approach. The analyses of the results show that fuzzy set theory and grey theory can be used jointly for green supplier selection problems in uncertain environments.

There are different kinds of uncertainties in the supplier selection process. The novelty of this study is to use proper uncertainty methods in different steps instead of denoting the whole selection process by the same uncertainty theory. Supplier selection problems occupy wide space in operations research literature. Different criteria are used in different papers. In this study, detailed literature review has been carried out and some criteria among frequently confronted ones proposed for green supplier selection.

This paper recommends a method entitled “SMED 4.0” as a development of conventional single minute exchange of die (SMED) to avoid defect occurrence during production and improve sustainability, besides reducing setup time.

The method builds upon an extensive literature review and in-depth explorative research in SMED and zero defect manufacturing (ZDM). SMED 4.0 incorporates an evolutionary stage that employs predict-prevent strategies using Industry 4.0 technologies including the Internet of Things (IoT) and machine learning (ML) algorithms.

It presents the applicability of the proposed approach in (1) identifying the triple bottom line (TBL) criteria, which are affected by defects; (2) predicting the time of defect occurrence if any; (3) preventing defective products by performing online setting on machines during production as needed; (4) maintaining the desired quality of the product during the production and (5) improving TBL sustainability in manufacturing processes.

The extended view of SMED 4.0 in this research, as well as its analytical approach, helps practitioners develop their SMED approaches in a more holistic way. The practical application of SMED 4.0 is illustrated by implementing it in a real-life manufacturing case.

The purpose of this study is to explore whether five factors drawn from the unified theory of acceptance and use of technology (UTAUT) and UTAUT2 significantly influence customers' intention to use hotel in-room voice assistants (VAs). It further examined culture as a moderator of the relationships between the five factors and customers' intention to use.

The authors collected data from US and Singapore to examine cultural differences in customer acceptance of in-room VAs. All hypotheses were tested via structural equation modeling and multi-group analysis.

The results showed that performance expectancy, social influence and hedonic motivation significantly affected customers' intentions to use in-room VAs, while effort expectancy and facilitating conditions did not. The results confirmed that culture did not play a substantial role in moderating the relationships between these factors and intentions to use.

This study established that the instrument and structural paths in the research model were equivalent across two samples from different countries. The findings may not generalize to other countries as the data arises from customers in the US and Singapore.

The findings provide important implications for hotel operators and vendors seeking to enhance customer acceptance of in-room voice technology.

This study addresses the gaps of extant research by developing and testing a research model to better understand the influential factors of in-room VA adoption within the hotel domain.

‘Being a refugee means learning from your children instead of teaching them yourself’. These are the words of a 43-year-old writer from Turkey, father of three. He is a refugee in Greece, where he works as a tailor. Language, the tool of his trade, is what he can no longer use, even for his own children. In contrast to her father, 12-year-old Boran, who is fluent in both Greek and her mother tongue, sits beside her father and translates what he says, and at the same time explains how frustrated he feels that he cannot communicate with the people around him.

Many projects focus on the reliable operation of the activities in the project. Any failure in the process activities leads to major problems resulting in waste, defects, equipment damage, which has a direct impact on the consumers. In addition, Lean Six Sigma (LSS) is not new to this issue. LSS projects have faced an interruption in the process flow and unforeseen defects. Therefore, the purpose of this paper is to identify the vital failure factors of LSS projects.

Through extant literature review, the researchers found 44 critical failure factors (CFFs) of LSS. Using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) SIMOS approach, the decision makers’ (DMs) rating and weight for each factor were collected. Moreover, the study was conducted in both the manufacturing and service industries to identify the impact of CFFs in LSS projects.

CFFs and their evaluation have received little attention in the literature. Most of the previous studies deal only with the identification of the success factors in general. Therefore, the study identified 44 CFFs and ranked them through DMs. In addition, the TOPSIS SIMOS approach ranked the vital failure factors enabling the management to avert the LSS project from failures.

The study focused on project failures due to CFFs of LSS. Nevertheless, it did not consider other failure factors of project and knowledge management. Further, the sample used to test the approach was considerably small. Therefore, the study can be repeated with significant samples and the vital failure factors compared.

In real-life application, all the parameters in the LSS project need to be understood in a better manner. In such a condition, the practitioner needs to know that the project never fails due to the CFFs and TOPSIS SIMOS approach can prevent the LSS project failures.

The study applied TOPSIS SIMOS approach to rank the CFFs in an LSS project, which is first of its kind and aids the practitioners to make the right decisions in the business environment.