Risk management decision-making models for human-centered 4.0/5.0 assembly/disassembly production

点击翻译按钮获取中文摘要

A sustainable business must place increased emphasis on balancing, indeed optimizing its performance imperatives with the wellbeing of its workers. In a context of sustainable development and unbridled innovation, the reality of our business includes uncertainty and constant adaptation and adjustments. Businesses must be resilient, that is, robust when conditions are foreseeable, and agile in the face of uncertainty. The models available for optimal and integrated management of operational and occupational health and safety (OHS) risks are based on the hypothesis that the business system represented behaves predictably and can be deconstructed entirely into its component parts. These models also aim to confine all system variability within established boundaries. To understand business management systems under real industrial conditions and support decision making, it is crucial that the models used to represent system functioning evolve over time towards integrated systemic and proactive models that allow combining diverse sources of variability and uncertainties. The aim of the proposed research activities is therefore to develop cutting-edge risks management decision-making models for human-centered, resilient and sustainable 4.0/5.0 assembly/disassembly production. We shall use a mixed approach (qualitative and quantitative) based on an integrated design, in realistic case studies drawn from the aeronautical sector and an assembly/disassembly test bench of the Applied Human Factors Lab of École de technologie supérieure. Firstly, a human factors/ergonomics study will be done to consolidate proper data to document twelve realistic case studies of future digitalized work. Secondly, we will design joint industrial engineering (IE)-ergonomics/human factors (HF/E) research protocols for the case studies and lab test each case study to be deployed on the Applied Human Factors lab assembly/disassembly test bench. Thirdly, we will conduct lab experiment for the twelve realistic case studies to produce original data and model systems studied. Finally, we will quantify the models using safe, trustworthy and human-centered artificial intelligence-based algorithms as well as expert elicitation. This proposal will provide cutting-edge decision-making models and knowledge to balance and optimize organizational performance imperatives with the wellbeing of workers on the path to processes digitalization. Scientific advances are expected for one high-priority industrial sector in Canada, namely aeronautics. Assembly/disassembly being an essential activity in manufacturing, among others in the transportation sector, other industrial sectors will benefit from these developments through contextualisation.