A secure supply of raw materials plays a vital role in the rapid growth of emerging technologies. The criticality assessment has been introduced to evaluate the supply risk of materials from economic, social and environmental aspects. From business perspective, multiple critical metrics should be involved, so that the decision makers can focus on the different metrics and put efforts to minimize the corresponding risks. However, due to the complexity of assessment and uncertainties in data sources, some metrics cannot be evaluated quantitatively, but qualitatively. This paper introduces a fuzzy linguistic approach to evaluate multiple risk metrics for material criticality assessment. The risk levels and the importance weight of metrics, expressed in linguistic terms, are modeled by triangular or trapezoid membership functions. We apply this method to evaluate the criticality of three materials: Cobalt, Tungsten, and Yttrium. We use matrix operation to aggregate the MFs of the multiple metrics in order to represent the overall criticality. As a result, the three materials are ranked according to their critical levels. The proposed fuzzy linguistic approach shows the advantage to evaluate the criticality with multi-criteria when only qualitative data is available. The membership function is an appropriate way to represent linguistic terms with imprecision, which are commonly used to interpret risk terms. The definition of multiple metrics provides flexibility for the users to choose risk categories they are interested in, and the aggregation of the metrics supports them to compare the criticality of materials. Further study may focus on how to justify importance weight judgments to make tradeoff decision, or integrate temporal factor to predict the future critical risk for business purpose.
Most data-based studies require significant amounts of data to support their decision-making process. Apart from increasing data quantity, scientists tend to be aware of the quality of data that influences the robustness of the results. A Pedigree matrix method is presented to characterize the data quality aspects and quantify the quality rating. Five quality aspects (reliability, completeness, temporal, geographical and technological representativeness) are defined as the characteristics to describe how well the reference data is fit for the underlying study. Reference rules are made subjectively for allocating the quality rating, which enable the computer to select appropriate data effectively from among different data sources. The overall data quality rating is calculated reflecting the quality level and converted to the four-parameter Beta probability distribution for uncertainty quantification. This is complemented by the Monte Carlo simulation that identifies uncertainty hotspots, to further improve the quality of identified data. This study provides an effective way to identify the data of good quality through the definition of reference rules. Making such rules can help the users to effectively capture the descriptive information regarding the data quality, further assess the quality levels consistently. The four-parameter Beta distribution is used for quantitative transformation, since it is appropriate to represent expert judgement. Therefore, the definition of distribution parameters is flexible depending on the expert understanding of uncertainty. This strength extends the application of the method to different data systems. Further research can focus on the development of reference rules for different quality aspects, as well the integration of the Pedigree matrix in various data systems.
Facing issues related to innovative production and public requirement in sustainability, companies expect to develop an effective tool to integrate environmental aspects into their business strategies at product design stage. Although life cycle assessment is commonly used to evaluate the environmental impacts of products or services, it is time consuming, expensive and may produce irrelevant information for business decision making. Eco-design approach, as alternative, requires less efforts for data acquisition and evaluation, and utilises a wide range of indicators that meet business demand. This study develops a matrix-based tool to capture environmental information related to business according to industry engagement. This life cycle thinking-based approach focuses on more relevant environmental information, and provides effectively data to support business strategy. In addition, this approach is practical and flexible to be used at the early design stage where data capture is generally difficult. Finally, it helps the managers to identify data gaps, so that it stimulates further investments in searching more targeted data.