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Machine tool selection and quotation costing have a low level of automation in today's engineer-to-order environments. The decision-making process is based on imprecise human judgment even if all final product characteristics are known. To improve precision and save time, we propose to apply artificial neural networks that are trained on data of already produced products. In particular, we address the selection of several grinding wheels to produce a milling cutter, which is a multiple-criteria decision, using multi-label classification. The quotation costing is a single output regression problem. In both tasks, our results on real-world data show high accuracy.
This paper presents a model for end-user-based evaluation of the usability of mobile ERP systems. Recent studies show that the mobile use of ERP software is both, crucial for user satisfaction and still improvable for many ERP systems. Therefore, ERP-specific usability models are necessary to meet the requirements of ERP systems in comparison to e.g., apps for private use. The research objective is therefore to develop a model that enables software providers to measure and benchmark the usability of their software products. Therefore, we introduce after a literature research a usability model for the mobile application of ERP systems (mobile ERP). Our usability model is based on the widely used PACMAD model. We modify the PACMAD model for the context of ERP systems. This results in a new end-user-based model, that differs from existing models, because of its focus on end-users and the ERP context. Subsequently, the model will be tested in an initial study with 19 test persons. The results of the study indicate two main findings. Firstly, the model allows the measurement of the usability of mobile ERP systems. Secondly, some key factors substantially affect the usability of mobile ERP systems.
Although the increasing use of automation in industry, manual assembly stations are still common and, in some situations, even inevitable. Current practice in manual assembly lines is to balance them using the takt-time of each workstation and harmonize it. However, this approach mostly does not include ergonomic aspects and thus it may lead to workforce musculoskeletal disorders, extended leaves, and demotivation. This paper presents a holistic human-centric optimization method for line balancing using a novel indicator ̶ the ErgoTakt. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and its balance in time. The authors used a custom version of the ErgoSentinel Software and a Microsoft Kinect depth camera to perform online and real-time ergonomic assessment. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-value and the cycle time of each assembly workstation with respect to the worker's ability. The paper presents the concept, the system-setup and preliminary evaluation of an assembly scenario. The results demonstrate that the new approach is feasible and able to optimize an entire manual assembly process chain in terms of both, economic aspects of a well-balanced production line as well as the ergonomic issue of long term human healthy work.
In the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials. Recycling these materials is crucial, but the presence of hazardous substances, i.e., in older building materials, complicates this effort. To be able to legally introduce substances into a circular economy, reliable predictions within minimal possible time are necessary. This work introduces a machine learning approach for detecting trace quantities (≥0.06 wt%) of minerals, exemplified by siderite in calcium carbonate mixtures. The model, trained on 1680 X-ray powder diffraction datasets, provides dependable and fast predictions, eliminating the need for specialized expertise. While limitations exist in transferability to other mineral traces, the approach offers automation without expertise and a potential for real-world applications with minimal prediction time.
Dienstleistungsmarketing
(2007)
Wellnessmanagement
(2007)
Internationales Management
(2005)
Mobil-Marketing
(2000)
Multi-Kanal-Dialogmarketing
(2008)
Call Center Management
(2008)
Kundenkarten-Boom in 2001?
(2001)