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The purpose of this paper is to propose a relatively simple and rapid method to create a digital human model (DHM) to serve clothing industry.

Human body’s point cloud is divided into hands, foots, head and torso. Then forward modeling method is used to model hands and foots, photo modeling method is used to model head and reverse modeling method is used to model torso. After that, hands, foots, head and torso are integrated together to get a static avatar. Next, virtual skeleton is bound to the avatar. Finally, a lifelike digital human body model is created by the mixed modeling method (MMM).

In allusion to the defect of the three-dimension original data of human body, this paper presented an MMM, with which we can get a realistic digital human body model with accurate body dimensions. The DHM can well meet the needs of fashion industry.

The DHM, which is got by the MMM, can be well applied in the field of virtual try on, virtual fashion design, virtual fashion show and so on.

The originality of the paper lies in the integration of forward modeling, reverse modeling and photo modeling to present a novel method of human body modeling.

To determine the key reverse logistics variables, which the top management should focus so as to improve the productivity and performance of computer hardware supply chains.

In this paper, an interpretive structural modeling (ISM) based approach has been employed to model the reverse logistics variables typically found in computer hardware supply chains. These variables have been categorized under “enablers” and “results”. The enablers are the variables that help boost the reverse logistics variables, while results variables are the outcome of good reverse logistics practices.

A key finding of this modeling is that environmental concern is the primary cause of the initiation of reverse logistics practices in computer hardware supply chains. For better results, top management should focus on improving the high driving power enabler variables such as regulations, environmental concerns, top management commitment, recapturing value from used products, resource reduction, etc.

In this research, an interpretation of reverse logistics variables in terms of their driving and dependence powers has been carried out. Those variables possessing higher driving power in the ISM need to be taken care on a priority basis because there are a few other dependent variables being affected by them. Variables emerging with high dependence contribute to productivity and performance of green supply chain.

Some essential issues about modeling of reverse logistics (RL) systems and product recovery networks include consideration of the qualities of the returned products, taking into account uncertainty and integrating the forward and reverse flows. The purpose of this paper is to develop the integrated RL model, which focuses on the control of inventory and production planning problems in a case of uncertainty in demand, quantities and qualities of returns.

The model involves a forward production route, three alternative recovery routes and a disposal route. Various levels of qualities are considered for returned products. A fuzzy mixed integer programming model (FMIP) is developed to provide a solution for the problems of production planning and inventory control. After maximizing the satisfaction degree, different solutions can have the same maximum. Moreover, policies that use all recovery routes and reduce the overall uncertainty have no chance to be chosen. To tackle these problems, a two-phase approach method is applied.

According to the results of the numerical example, using different and appropriate recovery options based on the quality of returns can significantly decrease the recovery costs. Similarly, it is shown that the two-phase approach can be an effective and efficient method to reach a satisfactory solution for such problems.

In this study, after maximizing the FMIP model, a two-phase approach ‒ as a novel optimization technique in this research ‒ is employed to achieve a desirable solution.

The purpose of this paper is to present a framework to manage product returns for reverse logistics by focusing on estimation of returns for select categories of products in the Indian context.

The paper develops a conceptual model and thereafter an integrated modeling framework borrowing from existing literature and industry practices. It utilizes product ownership data, average life cycle of products, past sales, forecasted demand and likely impact of environmental policy measures for estimating return flows. Informal interviews with 84 stakeholders are carried out to estimate significant parameters. Software packages, decomposition methods and heuristics are utilized for solution.

The integrated framework helps in estimating returns for select categories of products and thereafter taking simultaneous decisions on their disposition, location and capacity of facilities and flows of returned products for a given time horizon under various strategic, operational and customer service‐related constraints.

A “push” system where the volumes of returns drive the decisions. Estimations and optimization have been carried out for select product categories and not brands or original equipment manufacturers (OEMs). No free choice of facility locations.

The insights and learning under different scenarios may be utilized as inputs for decision making by various stakeholders such as OEMs and their consortia, local remanufacturers and third party service providers.

At methodological level, our framework combines descriptive modeling with optimization technique, while at topological level; it provides detailed solutions for network configuration and design.

The purpose of this paper is to explore the impact of the product residual value (PRV) and the loss of value over time of returned products in the reverse supply chain configuration. It also examines whether or not the distinction of Fisher's functional and innovative products holds for the reverse supply chain.

In order to identify the relevance of the Fisher model, the model needs to be recast in terms of PRV, which, in this context, is considered the independent variable in the reverse logistics arena. Products defined as innovative in Fisher's taxonomy correspond to disposed products with high residual value, whereas functional products correspond to disposed products with low residual value. Furthermore, the PRV and the speed at which returned products lose their value are considered in order to determine the configuration of the reverse supply chain that allows for recapturing most of the PRV. These notions have then been tested by analyzing two reverse supply chains with a case study research methodology.

The findings show that low PRV is associated with second‐class recovery options (recycling and energy recovery) and that high PRV is associated with first‐class recovery options (reconditioning and remarketing). When the recovery option is recycling, time is not relevant, the primary objective is cost reduction (efficiency), the chain is centralized, and actors and phases of the reverse chain are determined by the specificity of the recycling process. When the recovery option is reconditioning, time is primarily relevant, tradeoffs between costs and time efficiency are necessary, the chain presents a centralized structure, and the presence of other types of actors and phases influences the structure of the reverse supply chain.

The focus is restricted to the industry of electrical and electronic products.

Based on the outcome of the study, managers are able to determine the basic prerequisites for the design of their reverse supply chains.

Previous literature suggests that when the PRV is high, early product differentiation is necessary, and the chain is therefore decentralized. The paper demonstrates that this is not confirmed in the case of low returned volumes and high reconditioning quality standards.

Management education has received an immense amount of attention from both scholars and practitioners in the last few decades. Most research efforts have aimed at improving classroom education processes, programs and outcomes. Only a limited amount of research has focused on extracurricular activities such as mentoring top talented students for international business case competitions. This paper aims to examine the knowledge flow between mentors and strategic management undergraduates while they prepare for international business case competitions. This flow provides the opportunity for the development of phronetic skills, knowledge and leadership potential of undergraduates.

To understand the flow, 55 mentors worldwide were examined to provide evidence of their mentoring practices, perceived development of undergraduates and reverse learning of the business schools' top talents. For the analysis, the study used partial least squares–structural equation modeling.

The results of the study indicate that extracurricular mentoring of business case competitors strongly affects their development and creates recursive effects on the development of mentoring activities.

Given the parsimony of mentors for premier global case study competitions, the findings of this study cannot easily be generalized to mentoring as such.

Despite the importance of the topics, to the best of the authors’ knowledge, a study of this kind has never been conducted before. For the construct of the authors’ model, the study was inspired by the multistage mentoring process theory.

To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA.

The design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected.

The results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased.

(1) Design a closed-loop reverse logistics network for after-sales services; (2) Introduce a multi-objective multi-echelon mixed integer linear programming model; (3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units; (4) Use the GSC factors and DEA method in reverse logistics network.

The purpose of this paper is to provide a theoretical framework for predicting the next period financial behavior of bank mergers within a statistical-oriented setting.

Bank mergers are modeled combining a discrete variant of the Smoluchowski coagulation equation with a reverse engineering method. This new approach allows to compute the correct merging probability values via the construction and solution of a multi-variable matrix equation. The model is tested on real financial data relative to US banks collected from the National Information Centre.

Bank size distributions predicted by the proposed method are much more adherent to real data than those derived from the estimation method. The proposed method provides a valid alternative to estimation approaches while overcoming some of their typical drawbacks.

Bank mergers are interpreted as stochastic processes focusing on two main parameters, that is, number of banks and asset size. Future research could expand the model analyzing the micro-dynamic taking place behind bank mergers. Furthermore, bank demerging and partial bank merging could be considered in order to complete and strengthen the proposed approach.

The implementation of the proposed method assists managers in making informed decisions regarding future merging actions and marketing strategies so as to maximize the benefits of merging actions while reducing the associated potential risks from both a financial and marketing viewpoint.

To the best of the authors’ knowledge, this is the first study where bank merging is analyzed using a dynamic stochastic model and the merging probabilities are determined by a multi-variable matrix equation in place of an estimation procedure.

The purpose of this paper is to explore reciprocal relationships between work/family resources, work/family enrichment (WFE), and work/family satisfaction in a Chinese society.

A longitudinal design was adopted using a three‐wave panel sample. Data were obtained from 310 Taiwanese employees on three occasions, six months apart.

Results of cross‐lagged structural equation modeling analyses offered strong support for the hypothesized reciprocal relationships between the focal constructs. The authors found that while modeling WFE, work resources (supervisory support), WFE and job satisfaction were mutually related to one another over time. While modeling family‐to‐work enrichment (FWE), family resources (family support), FWE and family satisfaction were again mutually related to one another over time.

This is the first longitudinal study on WFE with a non‐Western sample. Basing upon the findings, the authors suggest that the common theoretical models postulating a linear causal chain of work/family antecedents→work/family interaction (WFI)→work/family consequences are inadequate. Instead it is recommended that more elaborate and recursive models including reciprocal relationships need to be formulated to better represent the dynamic and fluid nature of WFI processes.