Learning Curves of Temporary Workers in Manual Order Picking Activities

Person-to-stock order picking is highly flexible and requires minimal investment costs in comparison to automated picking solutions. For these reasons, tradi-tional picking is widespread in distribution and production logistics. Due to its typically large proportion of manual activities, picking causes the highest operative personnel costs of all intralogistics process. The required personnel capacity in picking varies short- and mid-term due to capacity requirement fluctuations. These dynamics are often balanced by employing minimal permanent staff and using seasonal help when needed. The resulting high personnel fluctuation necessitates the frequent training of new pickers, which, in combination with in-creasingly complex work contents, highlights the im-portance of learning processes in picking. In industrial settings, learning is often quantified based on diminishing processing time and cost requirements with increasing experience. The best-known industrial learning curve models include those from Wright, de Jong, Baloff and Crossman, which are typically applied to the learning effects of an entire work crew rather than of individuals. These models have been validated in largely static work environments with homogeneous work contents. Little is known of learning effects in picking systems. Here, work contents are heterogeneous and individual work strategies vary among employees. A mix of temporary and steady employees with varying degrees of experience necessitates the observation of individual learning curves. In this paper, the individual picking performance development of temporary employees is analyzed and compared to that of steady employees in the same working environment.

A Fast Platform for Interactive E-Learning Systems

Modern e-learning systems represent a special type of web information systems. By definition, information systems are special computerized systems used to perform data operations by multiple users simultaneously. Each active user consumes an amount of hardware resources. A shortage of hardware resources can be caused by growing number of simultaneous users. Such situation can result in overall malfunctioning or slowed-down system. In order to avoid this problem, the underlying hardware system gets usually continuously upgraded. These upgrades, typically accompanied with various software updates, usually result in a temporarily increased amount of available resources. This work deals with the problem in a different way by proposing an implementation of a web e-learning system with a modified software architecture reducing resource usage of the server part to the bare minimum. In order to implement a full-scale e-learning system that could be used as a substitute to a conventional web e-learning system, a Rich Internet Application framework was used as basis. The technology allowed implementation of advanced interactivity features and provided an easy transfer of a substantial part of the application logic from server to clients. In combination with a special server application, the server part of the new system is able to run with a reasonable performance on a hardware with very limited computing resources.