# Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art Review
## 1 Introduction  
Over the last decade, increasingly more manufacturing enterprises have adopted a _data-driven operations management approach_[1] based on the Industry 4.0 or Smart Manufacturing paradigm [2] to enhance their operations on the shop floor by combining machine learning and optimization techniques [3] for improved decision-making. This is particularly true when it comes to \"automated\" manufacturing processes where data is already available and can be used to empower shop floor managers to make effective _data-driven decisions_ based on facts, trends, and statistical numbers. However, things are much different regarding still \"manual\" manufacturing processes on the shop floor, such as human-based assembly operations, where data is limited or simply unavailable for supporting decision-makers.  
One operations management aspect that has roughly changed in 80 years [4], but is currently and rapidly changing, are the methods used to _monitor_ the execution of human-based _assembly operations in the production line to_ understand and calculate their long-term behaviour (e.g., accuracy, speed, repeatability) for effective production performance management [5]. In the past, monitoring human-based _assembly operations took in many cases_ additional steps to be integrated into an assembly sequence increasing its cycle time, while at present, new smart sensors based on computer vision systems and machine learningtechniques could offer non-invasive approaches making possible such monitoring without affecting the assembly operation performance [6].  
A relevant measurement involved in the performance evaluation of a human-based assembly operation is the _learning curve_ of an assembly operator, which represents his/her (assembly) skill increase over time [7]. Such measurement is of extreme relevance for objectively adjusting individual assembly operations cycle times and the overall assembly line takt time to improve its production performance (e.g., throughput) without compromising product quality. Other synonyms for the _learning curve_ concept are \"experience curve\", \"progress curve\", or \"improvement curve\" [8].  
In this state-of-the-art review, a study of _workforce learning curves_ is conducted to explore their latest measurement methods as well as the associated smart technologies used for collecting the data needed for their creation and continuous updating to always represent the present skill level (performance) of an assembly operator.  
The research questions this review aims to answer are:  
* What calculation methods for estimating the learning curve of a worker exist in the recent scientific literature?
* What other usages are manufacturing enterprises giving to the modern learning curve prediction models according to the recent scientific literature?
* What data collection and monitoring technologies exist to automatically acquire the data needed to create and continuously update the learning curve of an assembly operator?  
This article has been organised as follows: Section 2 provides the reader with a theoretical background on the most important _learning curve concepts and models_ based on their classic literature. Section 3 introduces the PRISMA methodology, which was used to conduct this state-of-the-art review in a scientific way. Section 4 presents the search strategy results, discussing in detail the review findings, which were divided into two domains: (D1) workforce learning curve models and measurement methods, and (D2) data acquisition and monitoring technologies for human-based assembly operations. In Section 5, the formulated research questions are answered with help of the information gathered from the reviewed journal articles and conference papers. Finally, Section 6 offers insights into possible future research directions and areas of opportunity in the current state-of-the-art of workforce learning curve measurement and monitoring approaches.