Ishikawa Diagram, Gray Numbers and Pareto Principle for the Analysis of the Causes of WEEE Production in Cameroon: Case of SMEs Implementing ISO 14001:2015

The issue of Waste from Electrical and Electronic Equipment (WEEE) in Africa lacks a concrete answer at present. This study aimed to provide an integrated approach using qualitative and quantitative research methods based on the 80/20 principle and the grey system theory, in order to address the uncertainty in the existing literature. First, through a qualitative approach, the authors analysed the environment for the management of WEEE by eight companies in Cameroon, through a literature review and observations made in the field under the framework of the ISO 14001:2015 standard. Then, the weights of the selected cause of the WEEE using grey system theory were proposed and applied, combining the findings from both the qualitative and quantitative methods. Based on the data obtained through the analysis, the research results indicate that the assessed Cameroonian companies dealing with WEEE management can implement measures to reduce WEEE

Companies’ E-waste Estimation Based on General Equilibrium Theory Context and Random Forest Regression Algorithm in Cameroon: Case Study of SMEs Implementing ISO 14001:2015

Given the challenge of estimating or calculating quantities of waste electrical and electronic equipment (WEEE) in developing countries, this article focuses on predicting the WEEE generated by Cameroonian small and medium enterprises (SMEs) that are engaged in ISO 14001:2015 initiatives and consume electrical and electronic equipment (EEE) to enhance their performance and profitability. The methodology employed an exploratory approach involving the application of general equilibrium theory (GET) to contextualize the study and generate relevant parameters for deploying the random forest regression learning algorithm for predictions. Machine learning was applied to 80% of the samples for training, while simulation was conducted on the remaining 20% of samples based on quantities of EEE utilized over a specific period, utilization rates, repair rates, and average lifespans. The results demonstrate that the model’s predicted values are significantly close to the actual quantities of generated WEEE, and the model’s performance was evaluated using the mean squared error (MSE) and yielding satisfactory results. Based on this model, both companies and stakeholders can set realistic objectives for managing companies’ WEEE, fostering sustainable socio-environmental practices