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The concept of sustainable product design (SPD) is gaining significant attention in recent research. However, due to inherent uncertainties associated with new product development and incorporation of multiple qualitative and quantitative criteria; SPD is a complex and challenging task. The purpose of this paper is to introduce a novel approach by integrating quality function deployment (QFD), multi-criteria decision making (MCDM) technique and Six Sigma evaluation for facilitating SPD in the context of Industry 4.0.

The customer requirements are evaluated through the neutrosophic-decision-making trial and evaluation laboratory-analytic network process (DEMATEL-ANP)-based approach followed by utilizing QFD matrix to estimate the weights of the engineering characteristics (EC). The Six Sigma method is then employed to evaluate the alternatives’ design based on the ECs’ values.

The effectiveness of the suggested approach is illustrated through an example. The result indicates that utilization of the neutrosophic MCDM technique with integration of Six Sigma methodology provides a simple, effective and computationally inexpensive method for SPD.

The proposed approach is helpful in upstream evaluation of the product design with limited experimental/numerical data, maintaining a strong competitive position in the market and enhancing customer satisfaction.

This work provides a novel approach to objectively quantify performance of SPD under the paradigm of Industry 4.0 using the integration of QFD-based hybrid MCDM with Six Sigma method.

In order to minimize the impact of variability on performance of the process, proper understanding of factors interdependencies and their impact on process variability (PV) is required. However, with insufficient/incomplete numerical data, it is not possible to carry out in-depth process analysis. This paper presents a qualitative framework for analyzing factors causing PV and estimating their influence on overall performance of the process.

Fuzzy analytic hierarchy process is used to evaluate the weight of each factor and Bayesian network (BN) is utilized to address the uncertainty and conditional dependencies among factors in each step of the process. The weighted values are fed into the BN for evaluating the impact of each factor to the process. A three axiom-based approach is utilized to partially validate the proposed model.

A case study is conducted on fused filament fabrication (FFF) process in order to demonstrate the applicability of the proposed technique. The result analysis indicates that the proposed model can determine the contribution of each factor and identify the critical factor causing variability in the FFF process. It can also helps in estimating the sigma level, one of the crucial performance measures of a process.

The proposed methodology is aimed to predict the process quality qualitatively due to limited historical quantitative data. Hence, the quality metric is required to be updated with the help of empirical/field data of PV over a period of operational time. Since the proposed method is based on qualitative analysis framework, the subjectivities of judgments in estimating factor weights are involved. Though a fuzzy-based approach has been used in this paper to minimize such subjectivity, however more advanced MCDM techniques can be developed for factor weight evaluation.

As the proposed methodology uses qualitative data for analysis, it is extremely beneficial while dealing with the issue of scarcity of experimental data.

The prediction of the process quality and understanding of difference between product target and achieved reliability before the product fabrication will help the process designer in correcting/modifying the processes in advance hence preventing substantial amount of losses that may happen due to rework and scraping of the products at a later stage.

This qualitative analysis will deal with the issue of data unavailability across the industry. It will help the process designer in identifying root cause of the PV problem and improving performance of the process.