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Business process management and IT supported processes are an actual topic. The procedure of finding a business process system that implements your processes the best way is not easy and takes a lot of time. In this article you will find a recommendation for an open source system. Four selected open source workflow management systems are tested and analyzed. Mean criteria for the evaluation are listed in a criteria catalogue and rated by experts by their importance. Finally, the systems are evaluated by the criteria and the best evaluated system can be recommended.
Product engineering and subsequent phases of product lifecycles are predominantly managed in isolation. Companies therefore do not fully exploit potentials through using data from smart factories and product usage. The novel intelligent and integrated Product Lifecycle Management (i²PLM) describes an approach that uses these data for product engineering. This paper describes the i²PLM, shows the cause-and-effect relationships in this context and presents in detail the validation of the approach. The i²PLM is applied and validated on a smart product in an industrial research environment. Here, the subsequent generation of a smart lunchbox is developed based on production and sensor data. The results of the validation give indications for further improvements of the i²PLM. This paper describes how to integrate the i²PLM into a learning factory.
Managerial accountants spend a large part of their working time on more operational activities in cost accounting, reporting, and operational planning and budgeting. In all these areas, there has been increasing discussion in recent years, both in theory and practice, about using more digital technologies. For reporting, this means not only an intensified discussion of technologies such as RPA and AI but also more intensive changes to existing reporting systems. In particular, management information systems (MIS), which are maintained by managerial accountants and used by managers for corporate management, should be mentioned here. Based on an empirical survey in a large German company, this article discusses the requirements and assessments of users when switching from a regular MIS to a cloud-based system.
Forecasting demand is challenging. Various products exhibit different demand patterns. While demand may be constant and regular for one product, it may be sporadic for another, as well as when demand occurs, it may fluctuate significantly. Forecasting errors are costly and result in obsolete inventory or unsatisfied demand. Methods from statistics, machine learning, and deep learning have been used to predict such demand patterns. Nevertheless, it is not clear for what demand pattern, which algorithm would achieve the best forecast. Therefore, even today a large number of models are used to forecast on a test period. The model with the best result on the test period is used for the actual forecast. This approach is computationally and time intensive and, in most cases, uneconomical. In our paper we show the possibility to use a machine learning classification algorithm, which predicts the best possible model based on the characteristics of a time series. The approach was developed and evaluated on a dataset from a B2B-technical-retailer. The machine learning classification algorithm achieves a mean ROC-AUC of 89%, which emphasizes the skill of the model.
Intermittent time series forecasting is a challenging task which still needs particular attention of researchers. The more unregularly events occur, the more difficult is it to predict them. With Croston’s approach in 1972 (1.Nr. 3:289–303), intermittence and demand of a time series were investigated the first time separately. He proposes an exponential smoothing in his attempt to generate a forecast which corresponds to the demand per period in average. Although this algorithm produces good results in the field of stock control, it does not capture the typical characteristics of intermittent time series within the final prediction. In this paper, we investigate a time series’ intermittence and demand individually, forecast the upcoming demand value and inter-demand interval length using recent machine learning algorithms, such as long-short-term-memories and light-gradient-boosting machines, and reassemble both information to generate a prediction which preserves the characteristics of an intermittent time series. We compare the results against Croston’s approach, as well as recent forecast procedures where no split is performed.
In this paper it is first identified the trade-off among costs, flexibility and performances of autonomous robotic solutions for material handling processes, where adding value with automation is not as trivial as in production processes: hence the requirement for automated solutions to be simple, lean and efficient becomes even stricter. Then a method for modelling and comparing differential performances and costs of manual and autonomous solutions is developed. As a result of the method, a smart man-machine collaborative interface is designed and its impact evaluated on a specific case of study. Results are then generalized and prove the strong conclusions that in unconstrained environments, where full standardization cannot be achieved, the risk of investing in autonomous solutions can only be mitigated by creating a fast and smart man-machine collaborative interface.
While there has been increased digitization of private homes, only little has been done to understand these specific home technologies, how they serve consumers, among other issues. “Smart home technology” (SHT) refer to a wide range of artifacts from cleaning aids to energy advisors. Given this breadth, clarity surrounding the key characteristics and the multi-faceted impact of SHT is needed to conduct more directed research on SHT. We propose a taxonomy to help outline the salient intended outcomes of SHT. Through a process involving five iterations, we analyzed and classified 79 technologies (gathered from literature and industry reports). This uncovered seven dimensions encompassing 20 salient characteristics. We believe these dimensions/characteristics will help researchers and organizations better design and study the impacts of these technologies. Our long-term agenda is to use the proposed taxonomy for an exploratory inquiry to understand tensions occurring when personal and sustainability-related outcomes compete.
The time has come : application of artificial intelligence in small- and medium-sized enterprises
(2022)
Artificial intelligence (AI) is not yet widely used in small- and medium-sized industrial enterprises (SME). The reasons for this are manifold and range from not understanding use cases, not enough trained employees, to too little data. This article presents a successful design-oriented case study at a medium-sized company, where the described reasons are present. In this study, future demand forecasts are generated based on historical demand data for products at a material number level using a gradient boosting machine (GBM). An improvement of 15% on the status quo (i.e. based on the root mean squared error) could be achieved with rather simple techniques. Hence, the motivation, the method, and the first results are presented. Concluding challenges, from which practical users should derive learning experiences and impulses for their own projects, are addressed.
The success of an autonomous robotic system is influenced by several interdependent factors not easily identifiable. This paper is set to lay the foundation of a new integrated approach in order to deeply examine all the parameters and understand their contribution to success. After introducing the problem, two cutting edge autonomous systems for the process of unloading of containers will be presented. Then the STIC analysis, a recently developed method for modelling and interpreting all the parameters, will be introduced. The preliminary results of applying such a methodology to a first study case, based on one of the two systems available to the authors, will be shortly presented. Future research is in the end recommended in order to prove that this methodology is the only way to efficiently and effectively mitigate the risk that stops potential users from investing in autonomous systems in the logistics sector.
Internet of things innovations and the industrial internet these days become more and more decisive factors of future success for companies. Especially manufacturing oriented SME will face the challenge to develop innovative technology driven business models alongside technology innovations in this field which will be essential for future competitiveness. Failing in developing these technology driven business models in an internationally highly competitive environment will have a serious impact both on companies and on the society. Hence, securing economic stability and success of these technology driven business models is an indispensable task. To identify challenges for innovative industrial internet business models first it is necessary to understand what the industrial internet means to the leading parties and applying companies and start-ups in the field. Second, challenges from general business model development will be outlined. In a third step risks and challenges in business model development will be discussed with regard to the special characteristics of technology driven business models in the context of the industrial internet and the important role of the technological key component of the business model. Especially the capability to deal with an integrated consideration of the indivisible linked dimensions of economic and technological aspects of these business models is questioned. In the fourth place the specific challenges for industrial internet business models are derived. On the basis of these results it is also discussed what might be done to handle these challenges successfully with the goal to turn them into chances. The need for future research on the integration of the risk management perspective into the development of these technology driven business models is derived. This will help established companies and start-ups to realize great technological innovations for the industrial internet in sound and successful innovative business models.