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This paper intends to explore and appraise the humanitarian supply chain management enablers (HSCMEs) for efficient and effective humanitarian operations. This research aims to analyze the interaction of enablers for humanitarian supply chain management (HSCM) using a proposed hybrid framework consists of fuzzy Delphi (FD), interpretive structural modeling (ISM)–matriced impacts croises multiplication appliquee a un classement (MICMAC) and revised Simos approach.

This research is deliberate to identify 28 HSCMEs through a literature review and experts' opinions; out of which 20 HSCMEs are selected using FD. ISM is applied to know contextual relationship among the selected HSCMEs for developing a hierarchical model. The MICMAC analysis classifies the HSCMEs based on driving power and dependence power to validate the developed hierarchical ISM structure. The revised Simos technique is used to prioritize the HSCMEs to access its relative significance in humanitarian operations.

The finding of the analysis suggests that government policy and leadership support obtained the highest priority, having high driving power and low dependence power is significantly strategic and emerged as the leading driver for the HSCM implementation.

ISM model presents an insight into interrelationship among HSCMEs, but this cannot quantify the impact of each HSCMEs.

Disaster relief aid agencies and stakeholders may focus on the enablers having high driving power and higher weight in designing and executing an effective and efficient humanitarian supply chain and to improve their activities and strategies of HSCM.

This research helps humanitarian logisticians and humanitarian organizations to make better decisions to improve their operational performance in pre and postdisaster phases.

This paper explores the application of proposed hybrid framework to analyze the HSCMEs that can be considered as the original contribution.

With the growth of unstructured data, opportunities to generate insights into supply chain risks in low cost countries (LCCs) are emerging. Sourcing risk has primarily focused on short-term mitigation. This paper aims to offer an approach that uses newsfeed data to assess regional supply base risk in LCC’s for the apparel sector, which managers can use to plan for future risk on a long-term planning horizon.

This paper demonstrates that the bulk of supplier risk assessments focus on short-term responses to disruptions in developed countries, revealing a gap in assessments of long-term risks for supply base expansion in LCCs. This paper develops an approach for predicting and planning for long-term supply base risk in LCC’s to address this shortfall. A machine-based learning algorithm is developed that uses the analysis of competing hypotheses heuristic to convert data from multiple news feeds into numerical risk scores and visual maps of supply chain risk. This paper demonstrates the approach by converting large amounts of unstructured data into two measures, risk impact and risk probability, leading to visualization of country-level supply base risks for a global apparel company.

This paper produced probability and impact scores for 23 distinct supply base risks across 10 countries in the apparel sector. The results suggest that the most significant long-term risks of supply disruption for apparel in LCC’s are human resource regulatory risks, workplace issues, inflation costs, safety violations and social welfare violations. The results suggest that apparel brands seeking suppliers in the regions of Cambodia, India, Bangladesh, Brazil and Vietnam should be aware of the significant risks in these regions that may require mitigative action.

This approach establishes a novel approach for objectively projecting future global sourcing risk, and yields visually mapped outcomes that can be applied in forecasting and planning for future risks when considering sourcing locations in LCC’s.

The aim of this analysis is to review the Indian manufacturing organizations practicing Lean Six Sigma (LSS) tools/techniques with an objective of monitoring the performance of an organization and to develop recommendation for strategies to benchmark organizational operational efficiency.

This study offers insights of the LSS performance measurement aspects of the Indian manufacturing organizations based on Data envelopment analysis (DEA) approach. The five inputs and two outputs are considered on the basis of literature review and discussed with the practitioners.

In this analysis, the relative efficiency score of 18 Indian manufacturing organizations has been determined in order to assist evaluation of the impact of monetary investment on the outputs. The present analysis not only investigates the optimum level of input variables but also lays down a significant observation that an organization having higher profit and inventory turnover ratio is not necessarily an efficient organization.

The results assist to determine the best practice units, potential source of inefficiency and deliver beneficial data for the consistent enhancement of the operational efficiency. The DEA results assist managers and decision makers to derive appropriate strategies to enhance their performance with reference to the efficient organization and to regard it as their role model.

This analysis renders a DEA based framework of LSS practicing Indian manufacturing organizations. The framework is unique in terms of its input-outputs variable selection and measurement procedure.

Gives a bibliographical review of the finite element methods (FEMs) applied for the linear and nonlinear, static and dynamic analyses of basic structural elements from the theoretical as well as practical points of view. The range of applications of FEMs in this area is wide and cannot be presented in a single paper; therefore aims to give the reader an encyclopaedic view on the subject. The bibliography at the end of the paper contains 2,025 references to papers, conference proceedings and theses/dissertations dealing with the analysis of beams, columns, rods, bars, cables, discs, blades, shafts, membranes, plates and shells that were published in 1992‐1995.

The purpose of this paper is to present research work based on the authors’ conceptual framework reported in the VRAP Conference 2013. It is related with an efficient method to obtain an optimal part build orientation for additive manufacturing (AM) by using AM features with associated AM production knowledge and multi-attribute decision-making (MADM). The paper also emphasizes the importance of AM feature and the implied AM knowledge in AM process planning.

To solve the orientation problem in AM, two sub-tasks, the generation of a set of alternative orientations and the identification of an optimal one within the generated list, should be accomplished. In this paper, AM feature is defined and associated with AM production knowledge to be used for generating a set of alternative orientations. Key attributes for the decision-making of the orientation problem are then identified and used to represent those generated orientations. Finally, an integrated MADM model is adopted to find out the optimal orientation among the generated alternative orientations.

The proposed method to find out an optimal part build orientation for those parts with simple or medium complex geometric shapes is reasonable and efficient. It also has the potential to deal with more complex parts with cellular or porous structures in a short time by using high-performance computers.

The proposed method is a proof-of-concept. There is a need to investigate AM feature types and the association with related AM production knowledge further so as to suite the context of orientating parts with more complex geometric features. There are also research opportunities for developing more advanced algorithms to recognize AM features and generate alternative orientations and refine alternative orientations.

AM feature is defined and introduced to the orientation problem in AM for generating the alternative orientations. It is also used as one of the key attributes for decision-making so as to help express production requirements on specific geometric features of a desired part.

The purpose of this paper is to identify, analyze and orient the enablers of Six Sigma to enhance supplier capability for an Indian manufacturing supply chain (SC).

In total, nine enablers of Six Sigma were identified through an extensive literature review and discussion held with managers/senior managers in different Indian manufacturing companies. The interpretative structural modeling (ISM) approach is applied to the Indian auto ancillary company for developing and analyzing the structural framework of enablers to enhance the supplier capability.

The enablers such as top management commitment and leadership, supply chain management, standardization, training and education, human resource management and project selection and execution methodology of Six Sigma related to supplier capability have emerged as the prominent enablers, which are driving force in the system for the Indian manufacturing SC.

This study is restricted to only one Indian manufacturing company. Therefore, the outcomes of the study should not be generalized. Further studies may be carried out for several Indian manufacturing industries to get a more comprehensive implementation approach, their validity and their variation across the different industries.

The simplicity and clarity of the proposed structural framework of Six Sigma helps in the identification and orientation of enablers for the successful implementation of Six Sigma in the SC. The proposed structural framework can be applied to different manufacturing SCs by allowing managers to structure the enablers considering their unique implementation constraints, which can reflect their priority considerations.

The study goes beyond the conceptual discussion of supplier capability issues. The supplier capability cannot be seen as a standalone approach irrespective of the constraints from the supplier domain as it is in synchronization with the entire SC performance. The enablers and their orientation with respect to the SC are providing a unique contribution toward supplier management planning. The outcomes from the proposed structural framework are used for developing action plans for organization “A” or other organizations to build suitable supplier capability in the SC.

Apparel manufacturers' achievement of green manufacturing (GM) goal remains low. This paper aims to identify and prioritise the barriers to GM implementation in apparel companies.

First, an extensive literature review is conducted to identify the key barriers to GM implementation. Second, 374 usable questionnaires are collected from apparel manufacturing companies to (a) examine the impact of and (b) rank the barriers. Third, interpretive structural modelling (ISM) is applied to test the relationships among barriers. Finally, structural equation modelling (SEM) is applied to improve the model derived from the ISM.

The results reveal that the independent barriers – lack of eco-literacy among upstream or downstream supply chain members, lack of specific company-level training and monitoring of the progress of GM implementation and inadequate support from regulatory authorities – are the root causes of all the barriers. These three barriers are also at a low level of the ISM model, indicating that they significantly affect the entire system and therefore should be accorded the highest priority in dealing with these barriers.

The results are useful for decision-makers and apparel companies to understand identified barriers, develop potential GM interventions and formulate appropriate strategies to overcome these barriers.

The listed barriers are yet to be comprehensively synthesised in a coherent model and empirically tested in the apparel sector using a combination of the ISM and SEM techniques. The empirically validated model presented in this study is an important step in that direction.

The purpose of this paper is to examine and introduce comprehensive insights into the field of Lean Six Sigma (LSS) by reviewing the existing literature and identifying the research gap. The state of LSS research is assessed by critically examining the field, along with a number of dimensions, including time horizon, year, journal and publisher, university, country, author, geographic analysis, research design, research affairs, research methods, tools/techniques used, focus industries, major research area, benefits gained by LSS, critical success factors and barriers of LSS implementation.

This paper is based on a systematic literature review of 190 articles containing the word LSS in their title, which are published in a well-known database, such as Elsevier ScienceDirect, Taylor and Francis, Emerald Full Text, Springer Link, Wiley InterScience and Inderscience from January 2000 to September 2016.

This analysis reveals 15 significant dimensions to identify the state of LSS research. Authors find a noticeable rise in the attention of LSS research in the available literature. Major findings show that, the empirical research holds greater credibility. Statistics prove that the case study method scores the highest among all the research methods used in the discipline. The largest number of studies have investigated research issues related to implementation and process of LSS. The LSS uses a wide range of tools/techniques/methodologies: the choice of tools is situation-specific. Manufacturing and health-care sectors have been the focus of LSS research, but LSS has also been adopted by other types of industries. The organizations following LSS have improved bottom-line results, improved company profitability and growth and enhanced customer satisfaction. In general the research is more interpretive in nature; there is still a lack of standard in the LSS implementation framework.

This study is limited to reviewing those articles which contain the word LSS appeared in the title.

This study will help understand the current state of research on LSS, various trends in the field, its applicability and future prospects of investigation in the field.

The purpose of this paper is to develop a control chart pattern recognition methodology for monitoring the weekly customer complaints of outsourced information technology-enabled service (ITeS) processes.

A two-step methodology is used to classify the processes as having natural or unnatural variation based on past 20 weeks' customer complaints. The step one is to simulate data on various control chart patterns namely natural variation, upward shift, upward trend, etc. Then a deep learning neural network model consisting of two dense layers is developed to classify the patterns as of natural or unnatural variation.

The validation of the methodology on telecom vertical processes has correctly detected unnatural variations in two terminated processes. The implementation of the methodology on banking and financial vertical processes has detected unnatural variation in one of the processes. This helped the company management to take remedial actions, renegotiate the deal and get it renewed for another period.

This study provides valuable information on controlling information technology-enabled processes using pattern recognition methodology. The methodology gives a lot of flexibility to managers to monitor multiple processes collectively and avoids the manual plotting and interpretation of control charts.

The application of control chart pattern recognition methodology for monitoring service industry processes are rare. This is an application of the methodology for controlling information technology-enabled processes. This study also demonstrates the usefulness of deep learning techniques for process control.

Conventional theories of market entry assume choice availability. This investment assumption is subject to challenges in the power generation market of an emerging economy where the host government controls most key resources and market entry choices. With such constraints, entrants become heavily dependent on their host country partners. This study investigates how the resource dependency frameworks explain better in respect of some US power generation firms that manage to operate electricity facilities in China whereas some have to abort. Using cross‐case analysis, patterns emerged illustrate how two groups of entrants manage key resources differently.