# Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art Review
## 5 Discussion
's model_[29], which considers a decaying learning rate with each unit produced. The authors [29] were able to show that their improved model is relatively better than the traditional univariate models proposed by [PERSON] [10] and [PERSON] [12], making it a new possible model for future references such as the traditional models of Stanford-B [15], [PERSON]'s [16], and S-Curve [17]. Also, other authors have addressed the learning curve in new ways, such as including it in the _OEE calculation method_[30]. The main limitation to the approach that these authors took is that they only addressed the learning of human factors in the maintenance activity and not as part of the main production process, meaning the calculation is mainly affected by the \"availability\" component of the OEE, were, if considering the human factor for a manual operation instead of a semi-automated, the effects would be seen on the performance component. However, parametrizing these learning curves is of uttermost importance since the parameters on any model are what characterize the curve and allow it to be studied. To conduct this, the main breakthrough, although not in manufacturing is the combination of machine learning with learning curves to estimate their parameters [26], which helps predict the learning curve from the first few tries to improve training programs. The adaptation of this method to other environments, such as manufacturing, testing additional learning curves could be further developed. _RQ2. What Other Usages Are Manufacturing Enterprises Giving the Modern Learning Curve Prediction Models According to the Recent Scientific Literature?_  
The main point of this discussion is to understand how real-time data is being used for the learning curve calculations and is in this regard that most papers provide an advancement on the state-of-the-art on learning curves. First of all, [PERSON] et al. [26] propose a machine learning approach to predict traditional learning curves with the first few data points of a surgeon. This approach considers real-time data from training boxes and VR simulations, which are then processed and analysed by a _KNN imputation algorithm_[27]. The benefits presented by such an approach include identifying early training opportunities for surgeons to improve their learning curves. Though the application is not directly involved with manufacturing, the approach could be well applied and carried over to the industry. [PERSON] et al. [23] have a similar goal, to estimate traditional learning curves with as few points as possible, however, their approach leans towards iterative predictions using different methods, in the end resulting in the approximation of the real performance evolution through [PERSON]'s learning curve with Invariant approximation method. [PERSON] [35] goes a step further, by not only predicting the learning curve, but generating an algorithm capable of generating suggestions to improve the learning curve based on the Kernel imputation method, using multiple linear regressions to predict the effect of changing one or more predictor variables, in the application case, calorie intake and speeds, and presenting these to the final user. It is important to mention that this discussion is framed and limited to the scope of the learning curve models and data collection methods state-of-the-art since this research work objective is to address the latest advancements in both domains. _RQ3. 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?_  
As presented by [PERSON] [31] and [PERSON] et al. [34], a highly detailed digitalization of the human operator in the production line is helpful to gather specific and precise operations data, both to improve workstations taking into account the human anatomy and positions and to track and monitor the performance of movements and interactions. Both of these approaches collect massive data sets from single operations or operators.