A Model for predictive assessment of cognitive workload - PreKo
The aim of the PreKo project is to support operator well-being and overall system performance in manual assembly by developing predictive assessment models that help ensure a socially sustainable production work system.
In modern manual assembly, operators often work under time pressure to keep up with demands on product quality, work rates and high flexibility, preferably using a good working technique. However, too-high demands on the operator´s physical and cognitive capacity due to physical and information overload often result in unwanted effects for the individual, the manufacturing quality and productivity. Today, requirements for the operator´s assembly skills (regarding both physical and cognitive ability) are often assessed (too) late in the development process of new products when it is difficult to change already decided work plans and manufacturing concepts. The aim is to develop an assessment model for early planning phases of product and production development. Such a model is urgent because crucial decisions are taken in early planning phases of new jobs and work tasks – and at an ever faster rate. Having a model to help predict and avoid potential problems in later manufacturing is expected to reduce the amount of operator-induced quality errors and cognitive load factors, and result in both human and economic benefits.
The goal of the main project is to gather and structure evidence for what this predictive model should include, primarily based on a series of deep interviews with four companies in the vehicle industry. The research will be based on experienced problems among assembly staff, operators and decision makers in design and manufacturing engineering.
Cecilia Berlin (contact)
Associate Professor at Chalmers, Industrial and Materials Science, Production Systems
Maral Babapour Chafi
Researcher at Chalmers, Industrial and Materials Science, Design and Human Factors
Project assistent at Chalmers, Industrial and Materials Science, Design and Human Factors
at Chalmers, Industrial and Materials Science, Production Systems
Full Professor at Chalmers, Industrial and Materials Science, Production Systems
Åsa Camilla Söderström
at Chalmers, Industrial and Materials Science, Design and Human Factors
Funding Chalmers participation during 2018–2021
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