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The decision-making to outsource and select the most suitable global manufacturing outsourcing partner (MOP) is complex and uncertain due to multiple conflicting qualitative and quantitative criteria as well as multiple alternatives. Vagueness and variability exist in ratings of criteria and alternatives by group of decision-makers (DMs). The paper provides a novel Stochastic Fuzzy (SF) method for evaluation and selection of agile and sustainable global MOP in uncertain and volatile business environment.

Four main selection criteria for global MOP selection were identified such as economic, agile, environmental and social criteria. Total 16 sub-criteria were selected. To consider the vagueness and variability in ratings by group of DMs, SF method using t-distribution or z-distribution was adopted. The criteria weights were determined using the Stochastic Fuzzy-CRiteria Importance Through Intercriteria Correlation (SF-CRITIC), while MOP selection was carried out using Stochastic Fuzzy-VIseKriterijumskaOptimizacija I KompromisnoResenje (SF-VIKOR) in the case study of footwear industry. Sensitivity analysis was performed to test the robustness of the proposed model. A comparative analysis of SF-VIKOR and VIKOR was made.

The worker’s wages and welfare, product price, product quality, green manufacturing process and collaboration with partners are the most important criteria for MOP selection. The MOP3 was found to be the best agile and sustainable global MOP for the footwear company. In sensitivity analysis, significance level is found to have important role in MOP ranking. Hence, the study concluded that integrated SF-CRITIC and SF-VIKOR is an improved method for MOP selection problem.

In a group decision-making, ambiguity, impreciseness and variability are found in relative ratings. Fuzzy variant Multi-Criteria Decision-Making methods cover impreciseness in ratings but not the variability. On the other hand, deterministic models do not cover either. Hence, the stochastic method based on the probability theory combining fuzzy theory is proposed to deal with decision-making problems in imprecise and uncertain environments. Most notably, the proposed model has novelty as it captures and reveals both the stochastic perspective and the fuzziness perspective in rating by group of DMs.

The proposed multi-criteria group decision-making model contributes to the sustainable and agile footwear supply chain management and will help the policymakers in selecting the best global MOP.

To the best of the authors’ knowledge, SF method has not been used to select MOP in the existing literature. For the first time, integrated SF-CRITIC and SF-VIKOR method were applied to select the best agile and sustainable MOP under uncertainty. Unlike other studies, this study considered agile criteria along with triple bottom line sustainable criteria for MOP selection. The novel method of SF assessment contributes to the literature and put forward the managerial implication for improving agility and sustainability of global manufacturing outsourcing in footwear industry.

This study aims to understand how the texture shape, number of textures and addition of nanoparticle additives in lubricants impact the dynamic characteristics of journal bearing by comparing six different texture shapes like triangle, chevron, arc, circle, rectangle and elliptical applied in pressure-increasing region under various geometrical and operating conditions.

The finite element method approach has been employed to solve governing Reynold’s equation, assuming iso-viscous Newtonian fluid, for computation of performance parameters like stiffness and damping coefficient, threshold speed, etc. By using a regression model, the impact of adding nanoparticles Al₂O₃ and CuO to the base lubricant on viscosity variation is calculated for selected temperature ranges and weight fractions of nanoparticles.

The arc-shaped texture with an area density of 28.27%, eccentricity ratio of 0.2 and texture depth of 0.6 exhibited 35.22% higher direct stiffness and 41.4% higher damping coefficient compared to the lowest value in the circle-shaped texture. Increasing the number of arc-shaped textures on the bearing surface with low area density led to declining stiffness and damping parameters. However, with nanoparticle additives, the arc-shaped texture further showed 10.75% and 8.11% improvement in stiffness and 9.99% and 4.87% enhancement in damping coefficient for Al₂O₃ and CuO, respectively, at 90 °C temperature and 0.5% weight fraction.

By understanding the influence of texture shapes on the dynamic characteristics, engineers can design bearings that exhibit improved stability and enhance overall performance.

The purpose of this paper is to provide an analytical model for low carbon supplier development. This study is focused on the level of investment and collaboration decisions pertaining to emission reduction.

The authors’ model includes a fuzzy c-means (FCM) clustering algorithm and a fuzzy formal concept analysis. First, a set of suppliers were classified according to their carbon performances through the FCM clustering algorithm. Then, the fuzzy formal concepts were derived from a set of fuzzy formal contexts through an intersection-based method. These fuzzy formal concepts provide the relative level of investments and collaboration decisions for each identified supplier cluster. A case from the Indian renewable energy sector was used for illustration of the proposed analytical model.

The proposed model and case illustration may help manufacturing firms to collaborate with their suppliers for improving their carbon performances.

The study contributes to the low carbon supply chain management literature by identifying the decision criteria of investments toward low carbon supplier development. It also provides an analytical model of collaboration for low carbon supplier development. Though the purpose of the study is to illustrate the proposed analytical model, it would have been better if the model was empirically validated.

Though the earlier studies on green supplier development program evaluation have considered a set of criteria to decide whether or not to invest on suppliers, these are silent on the relative level of investment required for a given set of suppliers. This study aims to fulfill this gap by providing an analytical model that will help a manufacturing firm to invest and collaborate with its suppliers for improving their carbon performance.

This chapter of the book aims to introduce multiobjective linear programming (MLP) as an optimum tool to find the best quality engineering techniques (QET) in the main domains of supply chain management (SCM). The importance of finding the best quality techniques in SCM elements in the shortest possible time and at the least cost allows all organizations to increase the power of experts’ analysis in supply chain network (SCN) data under cost-effective conditions. In other words, this chapter aims to introduce an operations research model by presenting MLP for obtaining the best QET in the main domains of SCM. MLP is one of the most determinative tools in this chapter that can provide a competitive advantage. Under goal and system constraints, the most challenging task for decision-makers (DMs) is to decide which components to fund and at what levels. The definition of a comprehensive target value among the required goals and determining system constraints is the strength of this chapter. Therefore, this chapter can guide the readers to extract the best statistical and non-statistical techniques with the application of an operations research model through MLP in supply chain elements and shows a new innovation of the effective application of operations research approach in this field. The analytic hierarchy process (AHP) is a supplemental tool in this chapter to facilitate the relevant decision-making process.

The paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.

First, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.

The paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.

The paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

The last few years have witnessed massive artificial intelligence (AI) and gaming adoption that has navigated the emerging markets. Moreover, according to the WOG summit (world government summit report, by Nielsen) 2020 reports, AI with gaming mechanisms are expected to enrich marketing services in the coming future in the emerging markets. Countries such as India, China and South Korea contribute significantly to this area, and recent forecasting allows the need to increase in emerging markets. Similarly, these countries have a maximum number of youth gamers and AI-driven technology adopters. The adoption of AI-driven technologies and amplification of gamification in marketing services are new phenomena. Moreover, gaming and AI dynamics are relatively new in emerging countries and need greater attention. Thus, this book chapter proposes a dyad model that would explain users' and companies' perspectives to understand the role of AI and gamification for the emerging markets. The chapter will explain how AI-driven gamification helps the users of emerging markets. The chapter will also illustrate how companies in emerging markets use AI for gamification. Therefore, the dyad model would also comprehend the gap, opportunities and challenges in this area and the subsequent strategies to help all the stakeholders.

During pre-vaccine era, pharmaceutical supplies [self-care essentials (SCEs)] have been proved to be a major deflector, protector and safety guard against novel coronavirus disease (COVID-19). Hence, the objective of the study is to provide a comprehensive socio-technological decision-making framework based on multiple criteria for selecting the suppliers of pharmaceuticals, such as SCEs, by multi-brand enterprises (distributors) in the pandemic environment.

A hybrid methodology of Bayesian best worst method (BWM) and multi-attributive border approximation area comparison (MABAC) method has been applied for carrying out the study. Bayesian BWM has been applied for computing the importance of criteria identified for the selection of SCEs' suppliers during pandemic environment and MABAC method evaluated the suppliers of the SCEs.

In the study, the authors have identified eight criteria such as disinfection and sanitization of vehicles, social conscience of suppliers, brand (Technological recognition) of SCEs and logistics and distribution network, among others, which are critical to the selection of a supplier for the supply of SCEs. The application of the proposed hybrid model revealed that lead time and quality of SCEs are of utmost concern for pharmacies in a pandemic environment. Among the ten suppliers, results showed that Suppliers 2, 4 and 5 have been ranked first for supplying hand wash, hand sanitizer and face mask, respectively.

The proposed model has helped the multi-brand distributors of pharmaceuticals in selecting suppliers during the ongoing crisis of COVID-19. In addition to that, in future the outcomes of the study would be helpful for multi-brand distributors as well as pharmacies and hospitals in selecting the best suppliers. Policy makers will be able to make and revise the policies immediately with the help of the proposed decision-making framework.

The paper makes a novel contribution towards theory with the criteria identified for selecting best suppliers during the pandemic COVID-19. Additionally, the proposed hybrid model helps multi-brand distributors of pharmaceuticals in making decisions that lead to a huge social and economic success in pandemic time.