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This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

The Prophet Muhammad (pbuh) laid the foundations of the Islamic state in Medina in 622 A.D. The state expanded gradually during the 10‐year rule of the Prophet (pbuh). The Arabian Peninsula and Southern Palestine were conquered during that period. The state expanded enormously during the reign of Umar (RA). Many countries of the Roman and Persian empires comprising an area of more than 2.2 million were annexed. Historians have noted that this framework played a vital role in integrating these people which in turn strengthened the state.

This paper aims to classify journal papers in the context of hybrid quality function deployment QFD and multi-criteria decision-making (MCDM) methods published during 2004–2021.

A conceptual classification scheme is presented to analyze the hybrid QFD-MCDM methods. Then some recommendations are given to introduce directions for future research.

The results show that among all related areas, the manufacturing application has the most frequency of published papers regarding hybrid QFD-MCDM methods. Moreover, using uncertainty to establish a hybrid QFD-MCDM the relevant papers have been considered during the time interval 2004–2021.

There are various shortcomings in conventional QFD which limit its efficiency and potential applications. Since 2004, when MCDM methods were frequently adopted in the quality management context, increasing attention has been drawn from both practical and academic perspectives. Recently, the integration of MCDM techniques into the QFD model has played an important role in designing new products and services, supplier selection, green manufacturing systems and sustainability topics. Hence, this survey reviewed hybrid QFD-MCDM methods during 2004–2021.

Literature contributions to Lean manufacturing (LM) are fragmented and show some significant limitations. The purpose of this paper is to identify the existing research gaps in LM as well as to group them into respective themes.

A systematic literature review (SLR) of peer reviewed journal articles in LM was carried out by the authors, based on four major publishers, namely, Emerald Online, Science Direct, Springer Link and Taylor & Francis. In total, 120 articles published in 30 journals during 2005–2016 were collected which revealed LM research gaps. A simple affinity diagram was applied in order to group the research gaps into logical themes.

A large number of research gaps are identified in the LM literature and meaningful themes of these gaps are also revealed.

The SLR carried out by the authors is based on only four academic journal publishers and some of other publishers might have been missed out in this search. Excluding articles focusing on an individual Lean principle or tool/technique is also a limitation of the present SLR.

Researchers and practitioners can use the LM research gaps presented in this study for further development of LM methodology.

Presenting LM research gaps analytically and grouping them into meaningful themes, significantly differentiates the present SLR study from those published so far.

The purpose of this paper is to identify the originality value of the lean manufacturing (LM) studies carried out worldwide so far.

Four major publishers, namely, Emerald Online, Science Direct, Springer Link and Taylor and Francis provided the databases for a systematic literature review (SLR) of peer-reviewed journal articles in LM. In total, 211 articles published in 52 journals during 2005-2016 were collected. The affinity diagram was applied to group the originality value statements identified into logical themes.

The plethora of originality value statements identified in the literature are analytically presented. Furthermore, meaningful themes of the originality value of the LM studies are formulated.

Some publishers might have been missed out in this SLR, given that it is based on only four academic publishers.

Identifying the originality value of the existing LM studies and presenting respective meaningful themes can help researchers and practitioners design their future research and implementation plans, respectively.

The originality value of the LM studies is a subject which has not been reviewed in the literature previously.

This study aims to investigate the interrelationships among the Lean manufacturing (LM) adoption barriers in Indian SMEs. This issue has its own importance as LM has become the inescapable requirement for small- and medium-scale enterprises (SMEs) because of the increased concerns about quality, cost, delivery time and rapidly growing competition in the manufacturing sector and in India it is opposed by many factors/barriers. To act for the eradication of these barriers, we need to systematically analyze them.

Based on the available literature and consultation with the experts, the authors identified 16 LM barriers for Indian SMEs. The authors analyzed the interdependencies among the barriers and prioritized them using integrated Grey-decision-making trial and evaluation laboratory (grey-DEMATEL) approach.

The findings show that limited financial resources, fear in adopting new technology, lack of top management commitment and poor leadership quality are the most critical barriers for LM diffusion in Indian SMEs.

The present research is based on the experts’ inputs, which may be subject to individual biases. In developing countries, such as India, geographical influences are also possible, which are neglected in this study.

This study provides significant insights that can help SMEs to focus on critical cause group barriers to accelerate the LM penetration.

The authors have proposed a Grey-DEMATEL-based LM barrier evaluation framework. Here, the authors analyze the interrelationships among the barriers for LM and segregate them in cause and effect groups.

The purpose of the paper is to develop a hybrid method to prioritize risk factors (RFs) of sustainable supply chain (SSC) considering sustainable customer requirements (CRs) and uncertain evaluation.

In the proposed method, fuzzy Kano model (FKM) is applied to prioritize sustainable CRs considering customer satisfaction (CS) and objective weight of each CR, the interval-valued intuitionistic fuzzy (IVIF) set theory is integrated with quality function deployment (QFD) to translate the sustainable CRs into RFs of SSC under uncertain environment and the IVIF cross-entropy is used to conduct objective analysis to prioritize RFs. Finally, a case in air-conditioner-manufacturing company is presented to demonstrate the proposed method.

A case study of SSC risk management, the comparative analysis and associated discussions are conducted to illustrate the feasibility and effectiveness of the proposed method. The results obtained from the case study shows that RF5 (market share reduction) is the most important RF in the SSC. Compared with the existing methods, the proposed method can integrate sustainable CRs into SSC's RFs, handle uncertain information effectively and obtain objective importance of RFs.

Theoretically, the paper develops a customer-oriented model based on the FKM, QFD, IVIF sets and entropy theory to prioritize RFs of SSC under uncertain environment. The model enables to integrate sustainable CRs into RFs managements and is efficient to deal with the subjectivity and conduct objective analysis to prioritize RFs. In practice, the systematic and correct RFs' priorities analysis provides reliable decision support for the managers to take measures to avoid or mitigate the critical RFs.