Effects of operator learning on production output: a Markov chain approach

We develop a Markov chain approach to forecast the production output of a human‐machine system, while encompassing the effects of operator learning. This approach captures two possible effects of learning: increased production rate and reduced downtime due to human error. In the proposed Markov chain, three scenarios are possible for the machine at each time interval: survival, failure, and repair. To calculate the state transition probabilities, we use a proportional hazards model to calculate the hazard rate, in terms of operator‐related factors and machine working age. Given the operator learning curves and their effect on reducing human error over time, the proposed approach is considered to be a non‐homogeneous Markov chain. Its result is the expected machine uptime. This quantity, along with production forecasting at various operator skill levels, provides us with the expected production output.