This paper presents results from an ongoing empirical study that investigates the effect of human-centered performance management with regard to operational performance and work motivation of shop ...floor workers. The approach is based on gamified information provisioning. To date, the concept of gamification has been applied in numerous fields, yet hardly any related work provides empirical findings for the production environment. Based on previous approach, we realized our approach prototypically as an MES application. Then, we integrated our application into a business game simulation. The study design builds on an application scenario in manual assembly. Two treatment groups were defined for investigation. Qualitative observations show that the provision of gamified metrics-based information proves to be a motivation driver.
•A state-of-the-art review of parameterization techniques for human-manufacturing operations learning curve models.•A data simplification approach for reducing the computational complexity of ...parameterizing human learning curves.•A Human Learning Curve Forecasting & Optimization (HLCF&O) framework for maintaining updated worker learning curve models.•The HLCF&O framework supports the forecasting of workforce performance through learning curve modeling.•The HLCF&O framework supports production line optimization by using forecasted learning curve capacity.
A state-of-the-art literature review was conducted to explore the latest advancements in parameterization techniques to facilitate the effective and efficient modelling of human learning curve models. Findings helped to select the best techniques for parameterizing workers’ learning curves. Understanding and analyzing the learning curve of human-based manufacturing operations is highly important for production managers aiming to optimize workforce performance and enhance the productivity of manual and semi-automated manufacturing systems. Effective forecasting of workers’ performance rates based on accurate learning curves is crucial for achieving optimal workforce capacity utilization as it evolves to efficiently meet production objectives. However, most existing learning curve evaluation methods rely on standard values for the learning rate of different operations, which may not accurately capture the actual improvement pace of workers. The learning rates in human-based manufacturing operations can vary based on factors such as previous worker experience, operation complexity, and working conditions. To address this challenge, this research paper introduces a Human Learning Curve Forecasting & Optimization (HLCF&O) framework that combines advanced parameterization techniques with data simplification methods to streamline the calculation and updating processes of a worker learning curve.
The analysis and evaluation of manual assembly processes is related to high efforts and expenditures. Traditionally, assessments use visual and empirical methods with a low level of digitalization. ...These are often not cost-covering for small production quantities. This paper presents an approach to recognize assembly steps from individually detected sensory events. The approach can be integrated in a system for automatic analysis of manual assembly processes and is applicable when little training data is available. It is based on a hidden Markov model and combined with a decision logic. The methodology is tested on an exemplary use case.
Due to the increasing competition in the global market and to fulfill the need for rapid changes of products variability, it is necessary to introduce reconfigurability and smart solutions within the ...entire process chain including manual assembly. The aim of the paper is to introduce a new manual assembly station equipped with smart technologies and tools. The manual workstation is self-configurable according to the constitution of the individual worker, the product variety and the product structure in order to quickly adapt the workplace to new products, to optimize assembly process, to minimize assembly times and to provide ergonomic working conditions through Digital Human Modeling. The effectiveness of the implemented technologies and tools will be demonstrated through a case study, which includes a comparison of assembly times and number of errors when assembly operations are performed by an unskilled worker at a traditional and smart assembly workstations.
Due to the low volume high variety strategies of manufacturing companies, manual assembly operators have a much larger cognitive load than before. The expertise of the operators must be kept up to ...date at any time. Since the high investment and low flexibility of a real setting to perform a manual assembly training, a virtual replica is introduced in many cases. The aim of this paper is to study the effect of an elementary virtual training for manual assembly tasks. In literature, different studies on the topic can be found; nevertheless, a comparison between the different studies is not possible due to diverse evaluation methods and descriptions. A benchmark for a uniform evaluation of virtual training systems is presented and applied to this experiment. Two groups were submitted to a number of manual assembly tasks. The test group got a virtual training period in advance. A significant learning transfer during that training period was observed. When the first assembly of the reference group is counted as a real training, no significant difference can be found between the virtual and real training. The outcomes of this experiment will be used in future work to compare different virtual training systems and influential factors such as the assembly complexity. Furthermore, the application of virtual training to manual assembly in a mixed-model environment and its industrial usability are topics that still need to be studied.
To cope with the complexification of manual assembly, new assistance methods are developed continuously. However, those hardware-dependent methods are not deployed context-aware. Hence, workers are ...not supported situationally and new methods have to be implemented at great expense on a heterogeneous system landscape, evoking an inappropriate maintenance effort. As known from the plant engineering, standardized encapsulation of specific methods provides a solution to integrate heterogeneous applications into one generic system. Therefore, besides the propose of a novel Extensible Worker Assistance (EWA) framework, the underlying novel concepts of so-called Assistance Model Units (AMUs) is utilized as a standardized way to abstract from specific implementations and thus, enable the integration of various assistance methods into one generic system. Furthermore, the applicability of the EWA framework with its underlying core concepts is shown by a use case-specific implementation within the bus assembly. Hence, a first step towards the provision of an optimal worker assistance tailored to the individual needs is done, by the presentation of the EWA framework with the ability to integrate different assistance methods, devices and consider various contextual knowledge within the context-aware assistance selection. Future work has to be done to further develop and investigate the single components of the comprehensive framework.
A new heuristic algorithm is presented to manage the effect of internal disruptions in an assembly, or other multi-stage value adding systems, where, at least, a proportion of resources are ...multi-functional. The idea is that when some resources become unavailable for a time, other resources as identified by the algorithm are relocated from other parts of the system to the affected processes to minimise the loss of overall throughput. The context of the problem is first established by defining the system at an abstract level, and the various types of disruptions are identified. A formal definition of the problem is provided followed by the design of the heuristic algorithm based on the establishing the dominant variable. The performance of the algorithm is then assessed using a Monte Carlo parametrisation of simulations. As the baseline solution, the output of the heuristic is compared with that of a genetic optimisation algorithm running an agent-based simulation in a first-order Monte Carlo experiment to produce robust results. Several disruption scenarios are used to validate the heuristic across various values of parameters such as number of resources affected by the disruption, concentration of the disruption in the assembly system and the time of the distribution. The reduction in throughput is used as the measure of comparison in the experiments. The heuristic is found to be effective when the disruption time is more than four times as long as the time that it takes to relocate resources.
Due to socio-demographic and technological changes, companies face new challenges to achieve an efficient and competitive production. Increasing cost and time pressure combined with an aging ...workforce with declining physical and mental performance requirements, as well as the increasing shortage of skilled workers, lead to reduced productivity. Therefore, the reduction of human-caused errors is becoming more important. This paper analyzes the connection between human-caused work errors, mental and physical strain, and workplace ergonomics. Four variables are used to analyze the interaction between ergonomics and work errors within the framework of a study in manual assembly. The study sample consists of 21 employees from a manual truck assembly at six different workstations. Models with different combinations of variables are developed in a multiple linear regression framework. Regression model (RM) 1 predicts the variance of the criterion work error with the predictors NASA-RTLX, Borg, and EAWS. It has high predictive power (adjusted R² = 0.746) but is not significant.
•Proposes and develops novel AR functions that facilitate manual assembly involving regions visually hidden in work environment from human operator.•Validates the effectiveness of various assistive ...information in AR, including assembly interface, operator’s hand movement, and operator held components.•Analyses of the experimental data reveal whether or not and how effectively each information offers guidance in occluded condition.•Provides preliminary guidelines of AR interface design for manual assembly of occluded components.
Augmented Reality (AR) technology has increasingly been applied to facilitate manufacturing tasks such as training, maintenance, and safety management, in which manual assembly frequently occurs. Previously, studies confirmed the values of AR-based assembly systems, but few explored support of spatially restricted assembly where components are visually occluded from operator. In this regard, this research aims to develop new AR functions that assist manual assembly in such situations. The focus is on validating the effectiveness of various assistive information in AR, including assembly interface, operator’s hand movement, and operator held components. Subjective and objective measures are used to evaluate assembly experiments of different degrees of difficulty. Analyses of the experimental data reveal whether or not and how effectively each information offers guidance in occluded condition. In particular, a time reduction in more difficult assembly is realized by showing operator hand movement in AR. The hand model initially offers an operator a visual clue for quickly and roughly locating the assembly interface in a large unseen area, prior to precisely localizing in a smaller region guided by tactile sensing. However, the effectiveness of incorporating the held component is not evident, as positional deviation between real and virtual objects may reduce human’s hand-eye coordination. These findings not only provide preliminary design guidelines of AR assembly functions for occluded components but also demonstrates a novel yet practical application of AR technology in smart manufacturing.
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