Assessing the influence of expert video aid on assembly learning curves

Andrea De Giorgio,Stefania Cacace,Antonio Maffei,Fabio Marco Monetti,Malvina Roci,Mauro Onori,Lihui Wang

doi:10.1016/j.jmsy.2021.11.019

Andrea De Giorgio, Stefania Cacace + Show 5 more

Open Access

https://doi.org/10.1016/j.jmsy.2021.11.019

Copy DOI

Abstract

Since the introduction of the concept of learning curves in manufacturing, many articles have been applying the model to study learning phenomena. In assembly, several studies present a learning curve when an operator is trained over a new assembly task; however, when comparisons are made between learning curves corresponding to different training methods, unaware researchers can show misleading results. Often, these studies neglect either or both the stochastic nature of the learning curves produced by several operators under experimental conditions, and the high correlation of the experimental samples collected from each operator that constitute one learning curve. Furthermore, recent studies are testing newer technologies, such as assembly animations or augmented reality, to provide assembly aid, but they fail to observe deeper implications on how these digital training methods truly influence the learning curves of the operators. This article proposes a novel statistical study of the influence of expert video aid on the learning curves in terms of assembly time by means of functional analysis of variance (FANOVA). This method is better suited to compare learning curves than common analysis of variance (ANOVA), due to correlated data, or graphical comparisons, due to the stochastic nature of the aggregated learning curves. The results show that two main effects of the expert video aid influence the learning curves: one in the transient and another in the steady state of the learning curve. The transient effect of the expert video aid, where the statistical tests suffer from a high variance in the data, appears to be a reduction in terms of assembly time for the first assemblies: the operators seem to benefit from the expert video aid. As soon as the steady state is reached, a slower and statistically significant effect appears to favor the learning processes of the operators who do not receive any training aid. Since the steady state of the learning curves represents the long term production efficiency of the operators, the latter effect might require more attention from industry and researchers.

Highlights

In 1885, Hermann Ebbinghaus proposed the concept of learning curve in the field of the psychology of learning, the name did not come into use until 1903 [1]
We propose a statistical method, functional analysis of variance (FANOVA), that performs the comparisons between two learning curves based on experimental data
This article provides an overview of the works in manufacturing using the learning curve as a mathematical model and finds a method ological gap in the analysis and comparison of assembly assistance technology, whether researchers use or not the learning curves to model its influence on the operators

Summary

Introduction

In 1885, Hermann Ebbinghaus proposed the concept of learning curve in the field of the psychology of learning, the name did not come into use until 1903 [1]. In 1936, Theodore Paul Wright described the effect of learning on production costs in the aircraft in dustry and proposed a mathematical model of the learning curve [2]. In the 1970s, the learning curve theory was commonly accepted in the airframe industry as a tool for cost estimating [3]. Klenow [13] acknowledged that learning by doing is largely specific to each production technology and used the learning curves to estimate when to update a certain technology in in dustry, in particular, he noticed that technology updates temporarily decrease productivity. On the same line is the work from Ngwenyama et al [14] that proposed to use the learning curves to determine when to execute software upgrades to maximize productivity in industry.

Objectives

Methods

Results

Conclusion