Multifactor learning and forgetting models for productivity and performance analysis

Abstract

This article presents an approach to modeling multifactor learning curve models for productivity and performance analysis in manufacturing. The models account for alternate periods of learning and forgetting and the resulting performance of an operator. Multi‐factor learning curves facilitate the inclusion of more than one important factor in performance and productivity analysis. The inclusion of a component that accounts for the rate of forgetting establishes a realistic representation of the effects of learning and forgetting on performance. Analyses based on the proposed approach can facilitate more reliable management decisions in productivity analysis. Two‐factor models are used to illustrate cases where forgetting occurs continuously or intermittently over a given time period.

Depreciation and transfer of knowledge: an empirical exploration of a shipbuilding process

“…Recently, the reverse of learning-unlearning or knowledge depreciation-has been recognised and incorporated into learning models (Bailey 1989, Globerson et al 1989, Argote et al 1990, Shtub et al 1993, Badiru 1994, Darr et al 1995, Epple et al 1996, Arzi and Shtub 1997, Benkard 2000, Nembhard 2001, Seo 2003, Ash and Smith-Daniels 2004. Unlearning is a phenomenon that occurs when the amount of knowledge gained from past (Brainerd et al 1985).…”

Adjustment for cognitive interference enhances the predictability of the power learning curve

“…The models developed for psychomotor learning in this paper adopt both varying and constant effects of cognitive interference, which will be used to depict forgetting while a subject is learning (performing repetitions). J o u r n a l P r e -p r o o f Badiru (1994) suggests (which was later proven by Morehead and Smith, 2017), that if learning and forgetting start at a given performance point, the resultant performance function can be determined by finding the point-by-point average of the learning and forgetting functions, which he modelled both as power-based bi-variate functions of cumulative time and the number of repetitions. Tukel et al (2008) assumed forgetting was due to the dormant time between repetitions resulting in the loss of some of the accumulated learning (experience).…”

“…Neuroscientists found that motor adaptation is driven by the relative strength of interference (Morehead and Smith, 2017) and retention (Sing et al 2009). In the context of POM, Badiru (1994Badiru ( , 1995 termed this phenomenon "continuous forgetting" (Morehead and Smith (2017) later used the same term), or forgetting "throughout the learning process" and "due to some natural process" while not being specific in defining how it occurs. This phenomenon is different from what the LC literature has focused on, where it assumes that the knowledge (experience) acquired in a learning session corresponds to the number of repetitions, with forgetting occurring only when there is a production break between subsequent sessions (Jaber, 2006).…”

Learning in disrupted projects: on the nature of corporate and personal learning

“…Various authors have explored the impact of disruptions, such as a break in production, on learning through forgetting and re-learning. Examples of such work are Globerson et al (1989), Shtub et al (1993), Badiru (1994), Arzi andShtub (1997), Ash andSmith-Daniels (2004) and Norfleet (2004). These studies look at the overall cost to labour, but do not explore the impact in sufficient detail to enable decisions to be made on actions that need to be taken in response to this disruption.…”

“…If a project that anticipates gains from learning is disrupted, then it is likely that the expected learning will be impacted (Globerson et al 1989;Shtub et al 1993;Badiru 1994;Arzi and Shtub 1997;Ash and Smith-Daniels 2004;Norfleet 2004). For example, one of the most common disruptions to a project is the demand for a change by the customer.…”