PhD position in “learning and forgetting curves for an ageing workforce and heterogeneous worker...

We are looking for a Ph.D. student who would be interested in the attached project. The student could be placed either at Ryerson University (preferred) or Dalhousie University.

Summary

Learning is a natural human phenomenon where a person performs a task faster and better with repetition. Learning curves (LCs) measure the improvement in performance for individuals, a group of individuals, or organizations. LCs are relevant in human-centred processes with expensive labour costs, where workers perform repetitive tasks. Reducing labour cost increases a firm's competitiveness by lower its product price. It would not be possible to sustain the competitiveness of a firm without understanding how learning occurs within it and what factors affect it.

Many researchers have predicted that learning curve research will become critically important as technology evolves, and workers continuously train to learn new things, and extensively researched learning and its counterpart, forgetting, for decades. Despite those efforts, it is still not known how some factors affect learning and forgetting. One of the main drawbacks of existing works is that researchers have assumed homogeneous rather than heterogeneous learners, i.e., a workforce with the same cognitive and physical abilities. Age, as a workforce characteristic, is strategically important, especially since the median age of the workforce is projected to increase.

This project, therefore, will develop new learning and forgetting curves that account for ageing and model the interactions among a heterogeneous group of workers. This research will advance learning curve theory, modelling and applications by accounting for factors such as memory strength, workers' fatigue and recovery, the physical and cognitive abilities of young and old workers working as a group, job similarity, and continuous changes in work environments. In response, new learning and forgetting curves with strong theoretical and empirical foundations will be developed to represent a heterogeneous rather than a homogenous workforce and will be applied to labour-intensive environments that emphasize workforce cross-training and flexibility where workers are trained to perform functionally different tasks of varying cognitive and physical workloads. The age-oriented learning and forgetting curves (A-OLFCs) to be developed will also be valuable when scheduling jobs and rest-breaks, making resource assignments or deciding on job enlargement, enrichment and rotation policies. The stakeholders that will benefit from the developments of this research include students, researchers, employers, industry associations, operations management professionals, and workers. A-OLFCs have the potential of being applied to functions such as production, purchasing, materials management, warehousing and inventory control, distribution, shipping, and transport logistics. This research will be of benefit to researchers in other fields, e.g., healthcare.