Abstract. Recent bureaucracy cost surveys identify the issue of high financial burdens on government authorities and businesses. These burdens are often caused by a large number of regulations. Therefore, the purpose of process bundling is to redesign Business-to-government processes in a way that eliminates redundant contacts, but still fulfils all reporting duties. The application of our bundling approach shows benefits by replacing similar reporting duties. One possibility to do so is to reuse available data on the business side for numerous duties. This research illustrates an approach for identifying opportunities for data reuse and thus to reduction of bureaucracy costs. The case study applies this approach for environmental reporting duties. After surveying reporting duties in Germany, similar reporting duties with overlaps concerning their content and process were selected. Finally, opportunities for data reuse were derived and implemented.
Businesses are aching under the burdens entailed by public reporting duties while public administrations are faced with rising cost pressures. Fostered by the diffusion and maturation of information technology, businesses put forth growing demands in regard to the quality, integration and usability of public services. An effective means of meeting these challenges is through the identification and bundling of processes caused by reporting duties. Incorporating corresponding concepts from the business sector, to the authors develop an approach for process bundling tailored to public organizations. The authors demonstrate the approach on waste management reporting duties. This paper provides guidance for practitioners striving to optimize information flows and reduce redundancies within B2G contacts. As a result, both public administrations and businesses benefit from a more straightforward and cost-efficient provision of public services.
This paper describes an implementation of a Knowledge Discovery in Databases (KDD) process for extracting the causes of iterations in Engineering Change Orders (ECOs). A data set of approximately 53,000 historical Engineering Change Orders (ECOs) was used for this purpose. Initially, the impact of iterations in ECO lead time and uncertainty is assessed. Subsequently, a semi-automatic text-mining process is employed to classify the causes of iterations. As a result, cost and technical categories of causes were identified as the main reasons for the occurrence of iterations. The study concludes that applying KDD in historic ECO data can help in identifying the causes of iterations of ECO which subsequently can provide a framework for companies to reduce these iterations. In addition, the case represents an example of benefits that can be achieved with the application of KDD in engineering change management.
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