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The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period order-up-to level ((R, S)) inventory control policy and its different variants such as (R, βS) (R, γO) and (R, γO, βS) proposed by Jakšič and Rusjan, (2008) and Bandyopadhyay and Bhattacharya (2013).

A hybrid forecasting model has been developed by combining the feature of discrete wavelet transformation (DWT) and an intelligence technique, multi-gene genetic programming (MGGP), denoted as DWT-MGGP. Performance of DWT-MGGP model has been verified under (R, S) inventory control policy considering demand from three different manufacturing companies.

A comparison between DWT-MGGP model and autoregressive integrated moving average forecasting model has been done by estimating forecast error and BWE. Further, this study has been extended with analysing the behaviour of BWE considering different variants of (R, S) policy such as (R,βS) (R, γO) and (R,γO,βS) and found that BWE can be moderated by controlling the inventory smoothing (β) and order smoothing parameters (γ).

This study is limited to different variants of (R, S) inventory control policy. However, this study can be further extended to continuous review policy.

The proposed DWT-MGGP model can be used as a suitable demand forecasting model to control the BWE when (R, S), (R,βS) (R,γO) and (R,γO,βS)inventory control policies are followed for replenishment.

This study analyses the behavior of BWE through controlling the inventory smoothing (β) and order smoothing parameters (γ) when demand is predicted using DWT-MGGP forecasting model and order is estimated using (R, S), (R,βS) (R,γO) and (R,γO,βS) inventory control policies.

Robot selection is a critical decision-making task frequently experienced in almost every industries. It has become increasingly complex due to availability of large variety of robotic system in the present market with varying configuration, specification and flexibility. Improper selection may yield loss for the company in terms of potential profit as well as productivity. Hence, selection of an appropriate robot to suit a particular industrial application is definitely a challenging task. The paper aims to discuss these issues.

During robot selection, different criteria-attributes need to be taken under consideration. Criteria may be subjective or objective or a combination of both, depending on the situation. Criteria many be conflicting, in the sense that some criteria may require to be of higher value (higher-is-better), i.e. beneficial; while, others should correspond to lower values (lower-is-better), i.e. adverse or non-beneficial. Hence, the situation can be articulated as a multi-criteria decision-making problem. The specialty of Tomada de Decisión Inerativa Multicritero (TODIM) method is that it explores a global measurement of value calculable by the application of the paradigm of non-linear cumulative prospect theory. The method is based on a description, proved by empirical evidence, of how decision makers’ effectively make decisions in the face of risk.

Hence, the present work has aimed to explore the TODIM approach for industrial robot selection. Assuming all criteria have been quantitative in nature; the paper utilizes two different numeric data sets from available literature resource in perspectives of robot selection. Procedural hierarchy and application potential of the TODIM approach has been illustrated in detail in this reporting.

Variety of tools and techniques have already been documented in literature to solve different kinds of industrial decision-making problems; however, it seems that application of TODIM has got limited usage. Hence, application potential of TODIM has been demonstrated here in light of a robot selection problem.

The recent global market trend is seemed enforcing existing manufacturing organizations (as well as service sectors) to improve existing supply chain systems or to take up/adapt advanced manufacturing strategies for being competitive. The concept of the agile supply chain (ASC) has become increasingly important as a means of achieving a competitive edge in highly turbulent business environments. An ASC is a dynamic alliance of member enterprises, the formation of which is likely to introduce velocity, responsiveness, and flexibility into the manufacturing system. In ASC management, supplier/partner selection is a key strategic concern. Apart from traditional supplier/partner selection criteria; different agility-related criteria/attributes need to be taken under consideration while selecting an appropriate supplier in an ASC. The paper aims to discuss these issues.

Therefore, evaluation and selection of potential supplier in an ASC have become an important multi-criteria decision making problem. Most of the evaluation criteria being subjective in nature; traditional decision-making approaches (mostly dealing with objective data) fail to solve this problem. However, fuzzy set theory appears an important mean to tackle with vague and imprecise data given by the experts. In this work, application potential of the fuzzy multi-level multi-criteria decision making (FMLMCDM) approach proposed by Chu and Velásquez (2009) and Chu and Varma (2012) has been examined and compared to that of Fuzzy-techniques for order preference by similarity to ideal solution (TOPSIS) and Fuzzy-MOORA in the context of supplier selection in ASC.

It has been observed that similar ranking order appears in FMLMCDM as well as Fuzzy-TOPSIS. In Fuzzy-MOORA, the best alternative appears same as in case of FMLMCDM as well as Fuzzy-TOPSIS; but for other alternatives ranking order differs. A comparative analysis has also been made in view of working principles of FMLMCDM, Fuzzy-TOPSIS as well as Fuzzy-MOORA.

Application feasibility of FMLMCDM approach has been verified in comparison with Fuzzy-TOPSIS and Fuzzy-MOORA in the context of agile supplier selection.

To develop a new method for estimation of damage configuration in composite laminate structure using acoustic wave propagation signal and a reduction‐prediction neural network to deal with high dimensional spectral data.

A reduction‐prediction network, which is a combination of an independent component analysis (ICA) and a multi‐layer perceptron (MLP) neural network, is proposed to quantify the damage state related to transverse matrix cracking in composite laminates using acoustic wave propagation model. Given the Fourier spectral response of the damaged structure under frequency band‐selective excitation, the problem is posed as a parameter estimation problem. The parameters are the stiffness degradation factors, location and approximate size of the stiffness‐degraded zone. A micro‐mechanics model based on damage evolution criteria is incorporated in a spectral finite element model (SFEM) for beam type structure to study the effect of transverse matrix crack density on the acoustic wave response. Spectral data generated by using this model is used in training and testing the network. The ICA network called as the reduction network, reduces the dimensionality of the broad‐band spectral data for training and testing and sends its output as input to the MLP network. The MLP network, in turn, predicts the damage parameters.

Numerical demonstration shows that the developed network can efficiently handle high dimensional spectral data and estimate the damage state, damage location and size accurately.

Only numerical validation based on a damage model is reported in absence of experimental data. Uncertainties during actual online health monitoring may produce errors in the network output. Fault‐tolerance issues are not attempted. The method needs to be tested using measured spectral data using multiple sensors and wide variety of damages.

The developed network and estimation methodology can be employed in practical structural monitoring system, such as for monitoring critical composite structure components in aircrafts, spacecrafts and marine vehicles.

A new method is reported in the paper, which employs the previous works of the authors on SFEM and neural network. The paper addresses the important problem of high data dimensionality, which is of significant importance from practical engineering application viewpoint.

Recently, in turbulent and highly competitive marketplace, organizational sustainability in the long run necessitates the adaptation of appropriate supply chain (SC) strategies. Hence, traditional SC philosophies are being restructured nowadays to fulfill different business goals. Articulation of lean, agile, green and resilient SC strategies could amply be found in the literature; however, integration of those in various modes may definitely improve overall SC’s performance. Past researchers have focused on the integration of lean, agile and green paradigms together to ensure an efficient SC construct. But the integration of green and resilient paradigm has been rarely reported in the literature. To deal with the unexpected situations/disturbances in the SC management along with embedded environmental consciousness, the purpose of this paper is to integrate the resilient SC and green SC philosophies; thereof to evaluate of an overall SC “g-resilient”/“ecosilient” index for a case automotive company.

A consolidated list consisting of supply chain practices (combining green and resilient performance indices) have been articulated in this study. A decision-making group has been assumed; where, the role of the decision makers is to provide individuals’ judgment (subjective opinion) toward determining the weight and the rating (performance extent) of various performance indices. The overall g-resilient SC performance has been determined by computing a unique ecosilient (g-resilient) index. The concepts of fuzzy performance importance index along with Degree of Similarity (DOS) adapted from fuzzy set theory (FST) have been applied to rank various performance indicators. In addition to that, the interrelationships amongst various g-resilient indices (performance indicators) have also been established through interpretive structural modeling.

By exploring the concept of fuzzy DOS, outlined in the trapezoidal fuzzy numbers set theory, various SC performance indicators have been classified into three distinct performance categories/levels (namely regretful, tolerable, and satisfactory). Such categorization has been found helpful in order to determine ill (poor) performing SC areas, which need future improvement toward boosting up the overall g-resilient index of the company’s SC.

The study bears significant managerial implications. The decision support framework suggested in this paper is found capable enough to determine a unique index known as “ecosilient (g-resilient) index” toward exploring “greenness” as well as “resiliency” for the case automotive company. Application potential of the proposed ecosilient (g-resilient) index evaluation system has been explored in this reporting. The recommended framework enables the managers to cope up with unexpected disruptions and found helpful in order to reduce the environmental impacts.

The purpose of this paper is to develop a multi-level hierarchical framework (evaluation index system) toward evaluating an “appraisement index” from the prospectus of measuring and monitoring resilient performance of the candidate industry.

In this reporting, vagueness, imprecision, as well as inconsistency associated with subjective evaluation information (aligned with ill-defined assessment indices of SC resilience performance), has been tackled by the application of fuzzy theory.

Subjective evaluation information (expressed in linguistic term) acquired from the committee of decision makers (called expert group), against different resilience indices/metrics, has been fruitfully explored through the proposed fuzzy-based resilience performance appraisement module. Finally, a case study from an Indian automobile company has been conducted from the perspective of checking effectiveness of the proposed methodology for evaluation of appraisement index indicating SC resilience extent.

This methodology might be successfully applied to help other decision-making problems from the perspective of performance appraisal and benchmarking of candidate alternatives/choices under predefined criteria and subjective evaluation circumstances.

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.

In today’s ever-changing global business environment, successful survival of manufacturing firms/production units depends on the extent of fulfillment of dynamic customers’ demands. Appropriate supply chain strategy is of vital concern in this context. Lean principles correspond to zero inventory level; whereas, agile concepts motivate safety inventory to face and withstand in turbulent market conditions. The leagile paradigm is gaining prime importance in the contemporary scenario which includes salient features of both leanness and agility. While lean strategy affords markets with predictable demand, low variety and long product life cycle; agility performs best in a volatile environment with high variety, mass-customization and short product life cycle. Successful implementation of leagile concept requires evaluation of the total performance metric and development of a route map for integrating lean production and agile supply in the total supply chain. To this end, the purpose of this paper is to propose a leagility evaluation framework using fuzzy logic.

A structured framework consisting of leagile capabilities/attributes as well as criterions has been explored to assess an overall leagility index, for a case enterprise and the data, obtained thereof, has been analyzed. Future opportunities toward improving leagility degree have been identified as well. This paper proposes a Fuzzy Overall Performance Index to assess the combined agility and leanness measure (leagility) of the organizational supply chain.

The proposed method has been found fruitful from managerial implication viewpoint.

This paper aimed to present an integrated fuzzy-based performance appraisement module in an organizational leagile supply chain. This evaluation module helps to assess existing organizational leagility degree; it can be considered as a ready reference to compare performance of different leagile organization (running under similar supply chain architecture) and to benchmark candidate leagile enterprises; so that best practices can be transmitted to the less-performing organizations. Moreover, there is scope to identify ill-performing areas (barriers of leagility) which require special managerial attention for future improvement.

Supplier selection for capital procurement is a major strategic decision for any automobile company. The decision determines the success of the company and must be taken systematically with the utmost transparency. The purpose of this paper is to construct capital procurement decision-making model to optimize supplier selection in the Indian automobile industry.

To achieve the stated objective, a combined approach of fuzzy theory and AHP-DEMATEL is applied. Evaluation parameters are identified through an extensive literature review and criteria validation has been introduced through a Fuzzy Delphi method by using fuzzy linguistic scales to handle the vagueness of information. AHP is employed to find the priority weight of criteria, although an inter-relationship map among criteria is not possible through AHP alone since it considers all criteria as independent. To overcome this, DEMATEL is used to establish cause-effect relationships among criteria.

The results show that the total cost of ownership (TOC) is the first weighted criterion in supplier selection for capital procurement, followed by manufacturing flexibility and maintainability, then conformity with requirement. The cause-effect model shows that supplier profile, TOC, service support and conformity with requirement are in the cause group and are considered to be the most critical factors in selecting the supplier.

The study’s outcome can help the automobile industry to optimize their selection process in selecting their suppliers for capital procurement; the proposed model can provide guidelines and direction in this regard.

The solution of radiative heat transfer problems in participating media is often obtained using the standard discrete ordinates method (SDOM). This method produces anomalies caused by ray effects if radiative boundary conditions have discontinuities or abrupt changes. Ray effects may be mitigated using the modified discrete ordinates method (MDOM), which is based on superposition of the solutions obtained by considering separately radiation from the walls and radiation from the medium. The purpose of this paper is to study the role of ray effects in combined conduction‐radiation problems and investigate the superiority of the MDOM over SDOM.

The MDOM has been used to calculate radiative heat transfer in irregular geometries using body‐fitted coordinates. Here, the blocked‐off region concept, originally developed in computational fluid dynamics, is used along with the finite volume method and SDOM or MDOM to solve combined conduction‐radiation heat transport problems in irregular geometries. Enclosures with an absorbing, emitting and isotropically or anisotropically scattering medium are analyzed.

The results confirm the capability of the MDOM to minimize the anomalies due to ray effects in combined heat transfer problems, and demonstrate that MDOM is more computationally efficient than SDOM.

The paper demonstrates the application of MDOM to combined conduction‐radiation heat transfer problems in irregular geometries using blocked‐off method.