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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.

Fused filament fabrication (FFF) is a type of additive manufacturing (AM) based on materials extrusion. It is the most widely practiced AM route, especially used for polymer-based rapid prototyping and customized product fabrication in relation to aerospace, automotive, architecture, consumer goods and medical applications. During FFF, part quality (surface finish, dimensional accuracy and static mechanical strength) is greatly influenced by several process parameters. The paper aims to study FFF parametric influence on aforesaid part quality aspects. In addition, dynamic analysis of the FFF part is carried out.

Interpretive structural modelling is attempted to articulate interrelationships that exist amongst FFF parameters. Next, a few specimens are fabricated using acrylonitrile butadiene styrene plastic at varied build orientation and build style. Effects of build orientation and build style on part’s ultimate tensile strength, flexure strength along with width build time are studied. Prototype beams (of different thickness) are fabricated by varying build style. Instrumental impact hammer Modal analysis is performed on the cantilever beams (cantilever support) to obtain the natural frequencies (first mode). Parametric influence on natural frequencies is also studied.

Static mechanical properties (tensile and flexure strength) are greatly influenced by build style and build orientation. Natural frequency (NF) of prototype beams is highly influenced by the build style and beam thickness.

FFF built parts when subjected to application, may have to face a variety of external dynamic loads. If frequency of induced vibration (due to external force) matches with NF of the component part, resonance is incurred. To avoid occurrence of resonance, operational frequency (frequency of externally applied forces) must be lower/ higher than the NF. Because NF depends on mass and stiffness, and boundary conditions, FFF parts produced through varying build style may definitely correspond to varied NF. This aspect is explained in this work.

The purpose of this paper is to develop a decision‐making procedural hierarchy for evaluation as well as selection of third‐party reverse logistics provider (3PL) under fuzzy environment.

Due to uncertainty, vagueness arising from decision makers (DM) subjective judgment towards intangible (qualitative) selection criteria, fuzzy logic has been utilized to facilitate such a decision‐making process for 3PL evaluation and selection.

Evaluating and selecting 3PL providers can be regarded as a multi‐criteria decision making (MCDM) process in which a decision maker chooses, under several selection criteria, the best suited alternative. The present study highlights a case study on evaluation and selection of 3PL service providers for a reputed Indian automobile part manufacturing company. The fuzzy based decision‐making tool applied here has been proved fruitful for its effectiveness.

There are many research issues remaining in the development of this approach. First, the definition of appropriate fuzzy linguistic variables, corresponding membership functions (MFs) and their numbers, and their universe of discourse for a general use in the algorithm. Second, a methodology for accumulating raw data and analyzing the appropriate MFs for the base linguistic variables. Third, the relative importance of every decision maker, the decision‐making environment and structure may affect the decision‐making process. These have been assumed negligible in this study.

The main contributions of this research are: first, an integrated criteria list (followed by sets of sub‐criteria) has been modeled for service quality evaluation and appraisement of 3PL providers. Each sub criteria set has been structured to be preceded by a main criteria. Second, priority weights of various main criteria as well as sub‐criteria; extent of successful performance (rating) of different sub‐criteria have been expressed in fuzzy numbers. It facilitates in accumulating DMs subjective judgments into a unique numerical evaluation score. Third, decision makers risk‐bearing attitude has been estimated and utilized in computing overall evaluation index for alternative candidates. The decision‐making framework presented here can be extended to solve any decision‐making problem designed under a complex and interconnected set of primary criteria followed by sub‐criteria or more extended elaborate criteria hierarchy.

This year, Benchmarking: An International Journal (BIJ) is celebrating its 25th anniversary. The purpose of this paper is to show the historical connections and professional impact of the journal over the years. This is accomplished through a detailed bibliometric analysis (BA) of its content from 1994 to 2018. This paper also identifies leading trends that have affected BIJ during this time frame.

In this paper, the authors employ BA on data retrieved from the Scopus database. The authors used various indicators such as the most productive authors, institutions and countries, as well as the most cited papers, citing journals and articles most cited by BIJ between 1994 and 2018. Additionally, the authors have used the Gephi, VOSviewer software to map journals, keywords and institutions with bibliographic coupling and co-citation analysis.

The journal has improved its impact, productivity and reputation in the period under review. Articles published in BIJ have been cited in other journals more than 15,000 times. The most prominent themes among the articles published in BIJ are benchmarking, supply chain management and performance improvement. Significant contributions have come from authors and institutions around the globe. Co-authorships, though global, have centered around India, the USA and the UK. There is also a thematic similarity between articles from India and Thailand, and the USA and Germany, indicating high academic collaboration or prevalence of similar issues.

This paper provides evidence of the impact of BIJ, and in so doing, it helps authors to decide on the appropriateness of this publication for their research. The reader will also understand leading trends occurring in BIJ over the history of the journal.

This is the first paper that traces the progress of BIJ covering a time span of 25 years.

In today's competitive global marketplace, performance management has been identified as a key strategic consideration towards achieving an efficient supply chain management. The task of estimating supply chain performance extent is seemed a complex problem entitled with multiple subjective performance measures and metrics; subjected to decision-making environment which involves an inherent vagueness, inconsistency and incompleteness associated with decision-makers (DMs) (expert panel) commitment towards assessment of various subjective (quantitative) evaluation indices. Consequently, it becomes difficult towards making a comparative study on performances of alternative supply chains. It is, therefore, indeed essential to conceptualize and develop an efficient appraisement platform helpful for benchmarking of alternative supply chains based on their performance extent. The paper aims to discuss these issues.

The work explores the concept of grey numbers combined with multi-objective optimization by ratio analysis (MOORA) in perceptive to evaluate best alternative from among available alternative supply chains.

The method has been found fruitful to facilitate such a multi-criteria group decision-making (MCGDM) problem under uncertain environment and provides an appropriate compromise ranking order with respect to available possible alternatives.

Supply chain performance appraisement provides necessary means by which an organization can assess whether its supply chain is performing well, whether it has been improved or degraded as compared to the past record. The purpose of this research is to develop and to empirically test a multiple-indices hierarchical appraisement model for benchmarking of supply chain performance and its impact on competitiveness of manufacturing industries.

This paper aims to develop an efficient measurement index evaluation system towards assessing suppliers' green performance practices. Apart from estimating overall performance index, the paper also aims to highlight application of decision‐support tools for selection of appropriate candidate supplier in green supply chain management context.

In order to tackle incompleteness and imprecision arising from assigning appropriateness rating as well as priority weights against subjective performance criteria‐attributes, use of grey numbers was proposed. An efficient grey‐based supplier appraisement platform was established. Application of Grey‐Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and COPRAS‐G method were reported to solve the supplier selection decision‐making problem. The theory of grey numbers was utilized here to facilitate such decision modeling.

Over the last two decades, growing concerns about ecosystem quality have stimulated to a renewed interest in environmentalism. Purchasing professionals should also be concerned and need to rethink purchasing strategies which have traditionally neglected environmental impacts. The “green” purchasing‐packaging in reducing and eliminating waste is a major concern in recent days. In order to help foster environmentally concerned purchasing strategies, this paper presents the findings of supplier evaluation strategy in an enterprise with enhanced degree of awareness and frequent applications of “green” purchasing. Environmental factors are identified that may reshape supplier evaluation decisions. The concept of grey numbers set has been adopted in this work. A case study reflects effectiveness of exploring grey relation theory in the context of green supplier evaluation.

The major contributions of this work have been summarized as follows: development and implementation of an efficient decision‐making tool to support green supplier evaluation; an overall green performance index evaluation platform has been introduced; concept of grey numbers has been efficiently explored to facilitate this decision‐making; the appraisement index system has been extended with the capability to search ill‐performing areas which require future progress; and the proposed appraisement system is capable of reducing the number of green attributes towards computing grey appropriateness index thereby transforming into lesser number of green capabilities, thus, facilitating applying decision‐making tools like grey‐TOPSIS and COPRAS‐grey method for appropriate supplier selection from a set of candidate suppliers.

Decision making is the task of selecting the most appropriate alternative among a finite set of possible alternatives with respect to some attributes. The attributes may be subjective or objective (or combination of both), depending upon the situation; requirements may also be conflicting. In practice, most of the real-world decision-making problems are based on subjective evaluation criteria which are basically ill-defined and vague. Since subjective human judgment bears ambiguity and vagueness in the decision making; application of grey numbers set theory may be proved fruitful in this context. The paper aims to discuss these issues.

Owing to the advantages of grey numbers set theory in tackling subjectivity in decision making; the crisp-TODIM needs to be extended by integrating with grey numbers set theory in order to facilitate decision making consisting of subjective data. Hence, the unified objective of this paper is to propose a grey-based TODIM approach in the context of decision making.

Application potential of grey-TODIM has been demonstrated through a case empirical robot selection problem. Result obtained thereof, has also been compared to that of existing grey-based decision support systems available in literature.

Application potential of grey-based decision support systems (grey-TOPSIS, grey analysis, grey-MOORA) have been highlighted in available literature resource. However, the shortcoming of these approaches is that they do not consider decision-makers’ risk attitude while decision making. TODIM method is derived from the philosophy of Cumulative Prospect Theory (CPT) which considers risk averting attitude of the decision maker in case of gain and risk seeking attitude in case of loss, while comparing dominance between two alternatives with respect to a particular criterion. Hence, this paper contributes a mathematical foundation of TODIM coupled with grey numbers set theory for logical decision making.

The purpose of this paper is to investigate the influence of decision-makers’ (DM) risk bearing attitudes and the effect of the decision-making environment on estimating the overall degree of agility of an organization. The present study explores an extended agility model in a specific organization's hierarchy and reflects how decision-making attitudes alter an organizational agility scenario.

The concept of fuzzy logic has been explored in this paper. Based on DMs’ linguistic judgments, a fuzzy appropriateness rating as well as fuzzy priority weights have been determined for different levels of agile system hierarchy. Using a multi-grade fuzzy approach the overall agility index has been determined. The concept of fuzzy numbers ranking has been explored to show the effect of decision-making attitudes on agility estimations.

Decision-making attributes, e.g. the category of DM (neutral, risk-averse and risk-taking), affect the quantitative evaluation of the overall agility degree, which is correlated with a predefined agility measurement scale.

This study explores a triangular fuzzy membership function to express DMs’ linguistic judgments as fuzzy representations. Apart from triangular fuzzy numbers, trapezoidal and Gaussian fuzzy numbers may also be used for agility evaluation. The model may be used in other agile industries for benchmarking and selection of the best approach.

Selecting the right decision-making group to compute and analyze the agility level for a particular organization is an important managerial decision. In the case of benchmarking of various agile enterprises the decision-making group bearing the same attitude should be utilized.

Agile system modeling and development of agility appraisement platforms have been attempted by previous researchers while the influence of DMs’ risk bearing attitudes, and the effect of the decision-making environment on estimating the overall degree of agility, have rarely been studied. In this context, the authors explore an exhaustive agility model for implementing in a case study and reveal how decision-making attitudes alter organizational agility scenarios.

The purpose of this paper is to develop a comprehensive framework to identify and classify key medical tourism enablers (MTEs) and to study the direct and indirect effects of each enabler on the growth of medical tourism in India.

In this paper, an integrated approach using interpretive structural modeling (ISM) and Fuzzy Matrice d'Impacts Croisés Multiplication Appliquée á un Classement (FMICMAC) analysis has been developed to identify and classify the key MTEs, typically identified by a comprehensive review of literature and expert opinion. The key enablers are also modeled to find their role and mutual influence.

The key finding of this modeling helps to identify and classify the enablers which may be useful for medical tourism decision makers to employ this model for formulating strategies in order to overcome challenges and to become a preferred medical tourism destination. Integrated model reveals enablers such as medicine insurance coverage, international healthcare collaboration, and efficient information system as dependent enablers. No enabler is found to be autonomous enablers. The important enablers like healthcare infrastructure facilities and global competition are found as the linkage enablers. Research in medicine and pharmaceutical science, medical tourism market, transplantation law, top management commitment, national healthcare policy, competent medical and para-medical staffs are found as the independent enablers. Integrated model also establishes the direct and indirect relationship among various enablers.

The research provides an integrated model using ISM and FMICMAC to identify and classify various key enablers of medical tourism in India. In conventional cross-impact matrix multiplication applied to classification analysis, binary relationship of various enablers is considered. FMICMAC analysis helps to establish possibility of relationship among various enablers so that low-key hidden factors can be identified. The low-key hidden factors may initially exhibit marginal influence but they may show significant influence later on during analysis. The uncertainty and fuzziness of relationship among various enablers can be conveniently handled by FMICMAC and expert opinions can easily be captured. This research will help medical tourism decision makers to select right enablers for the growth of medical tourism in India.

The study has been aimed to search an appropriate process environment for simultaneous optimization of quality‐productivity favorably. Various surface roughness parameters (of the machined product) have been considered as product quality characteristics whereas material removal rate (MRR) has been treated as productivity measure for the said machining process.

In this study, three controllable process parameters, cutting speed, feed, and depth of cut, have been considered for optimizing material removal rate (MRR) of the process and multiple surface roughness features for the machined product, based on L₉ orthogonal array experimental design. To avoid assumptions, limitation, uncertainty and imprecision in application of existing multi‐response optimization techniques documented in literature, a fuzzy inference system (FIS) has been proposed to convert such a multi‐objective optimization problem into an equivalent single objective optimization situation by adapting FIS. A multi‐performance characteristic index (MPCI) has been defined based on the FIS output. MPCI has been optimized finally using Taguchi method.

The study demonstrates application feasibility of the proposed approach with satisfactory result of confirmatory test. The proposed procedure is simple, and effective in developing a robust, versatile and flexible mass production process.

In the proposed model it is not required to assign individual response weights; no need to check for response correlation. FIS can efficiently take care of these aspects into its internal hierarchy thereby overcoming various limitations/assumptions of existing optimization approaches.