Assessing the Robustness of a Factory Amid the COVID-19 Pandemic: A Fuzzy Collaborative Intelligence Approach

Abstract

The COVID-19 pandemic has affected the operations of factories worldwide. However, the impact of the COVID-19 pandemic on different factories is not the same. In other words, the robustness of factories to the COVID-19 pandemic varies. To explore this topic, this study proposes a fuzzy collaborative intelligence approach to assess the robustness of a factory to the COVID-19 pandemic. In the proposed methodology, first, a number of experts apply a fuzzy collaborative intelligence approach to jointly evaluate the relative priorities of factors that affect the robustness of a factory to the COVID-19 pandemic. Subsequently, based on the evaluated relative priorities, a fuzzy weighted average method is applied to assess the robustness of a factory to the COVID-19 pandemic. The assessment result can be compared with that of another factory using a fuzzy technique for order preference by similarity to ideal solution. The proposed methodology has been applied to assess the robustness of a wafer fabrication factory in Taiwan to the COVID-19 pandemic.

Analyzing the Impact of Vaccine Availability on Alternative Supplier Selection Amid the COVID-19 Pandemic: A cFGM-FTOPSIS-FWI Approach

“…In addition, amid the COVID-19 pandemic, manufacturers (including suppliers and alternative suppliers) must remain robust to the pandemic to ensure their operations [ 17 , 41 ]. Factors that are critical to the robustness of a manufacturer to the COVID-19 pandemic include pandemic containment performance, pandemic severity, vaccine acquisition speed, demand shrinkage, supplier impact, and infection risk [ 43 ]. Some of these factors should also be taken into account when choosing an alternative supplier amid the COVID-19 pandemic.…”

“…Chen and Lin [ 17 ] proposed a FAHP method for comparing various smart and automation technology applications to ensure the long-term operation of a factory amid the COVID-19 pandemic. Chen et al [ 43 ] proposed a fuzzy collaborative intelligence method to evaluate the robustness of a factory to the COVID-19 pandemic. Fuzzy intersection and partial-consensus fuzzy intersection were applied to aggregate experts’ evaluation results, depending on the number of experts who have reached a consensus.…”

“…After that, the fuzzy ideal (zenith) point and the fuzzy anti-ideal (nadir) point are specified, respectively, for each expert as The fuzzy distance from each alternative supplier to the two points are calculated, respectively, as: where reflects the sensitivity of expert k to any change in the overall performance of an alternative: from 1 (very sensitive) to 5 (very insensitive) [ 43 ]. In traditional FTOPSIS, is usually set to 2, i.e., the Euclidean distance.…”

“…If experts do not specify their unequal authority levels, then the authority level of each expert is automatically set to a value proportional to the consistency of his/her pairwise comparison results [ 43 ]: There are other ways to determine the authority level of an expert. For example, To get an absolute ranking, the fuzzy closeness of an alternative supplier can be defuzzified using the center-of-gravity (COG) method [ 63 ]: …”

“…The following factors were considered critical to the performance of an alternative supplier amid the COVID-19 pandemic: level of buyer–supplier cooperation [ 65 , 66 ], delivery speed [ 37 , 38 , 39 , 40 ], company reputation [ 37 , 38 , 39 , 40 ], pandemic containment performance [ 43 ], and pandemic severity [ 43 ]. …”