Competences-based performance model of multi-skilled workers with learning and forgetting

• Published in: Expert systems with applications 2017-07, Vol.77, p.226-235
• Main Author: Korytkowski, Przemyslaw
• Format: Article
• Language: English
• Subjects: Algorithms; Competence; Forgetting; Graph theory; Learning; Learning curve; Learning curves; Multi-skilled worker; Performance appraisal; Skilled workers; Tasks; Worker performance; Workforce planning
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2017.02.004

•A performance model of multi-skilled workers with learn and forget curve is proposed.•The algorithm relies on a model of required and possessed competences.•The algorithm is provided for estimation of production output or production time. The relationship between performance and experience is non-linear, thus planning models that seek to manage workforce development through task assignment are difficult to solve. This gets even more complicated when taking into account multi-skilled workers that are capable of performing a variety of tasks. In this paper we develop a competences-based analytical model of the performance of multi-skilled workers undertaking repetitive tasks, taking into account learning and forgetting. A learning curve can be used to estimate improvement when repeating the same operation. Inverse phenomenon is forgetting, which can occur due to interruption in the production process. The Performance Evaluation Algorithm (PEA) was developed for two cases: fixed shift duration and fixed production output. The aim was to build a tool that better describes the capabilities of workers to perform repetitive tasks by binding together hierarchical competences modeled as a weighted digraph together with a learning and forgetting curve model (LFCM) to express individual learning rates.