A Process-Oriented Model to Business Value – the Case of Real-Time IT Infrastructures

Which investments in real-time capabilities and decision-support IT-infrastructures are appropriate? In view of the recent in-memory systems this poses an urgent question to companies in many industries. Despite ample research on the causal relationship between IS investments and business value, especially the value quantification remains a difficult challenge. This paper contributes a business value measurement model that structures and assesses the internal organizational benefits of real-time IT infrastructures. A case study from the automotive industry aims to validate the model

Real-time Business Process Intelligence. Comparison of different architectural approaches using the example of the order-to-cash process.

Business operations are becoming more and more integrated with the real-time intelligence. Core business activities are being carried out through OLTP systems that provide limited monitoring capabilities of the running process instances. The article shows how to turn the gap between the classic transactional system and the process-centric approach into an organization that provides more accurate and faster decisions on the strategic and operational management levels. This study aims at determination of what kind of information can be retrieved during the process execution and it tries to identify the need for the real-time process intelligence on the example of the order-to-cash process. Furthermore, we compare two architectural approaches of the real-time process intelligence monitoring system. The proposed frameworks retrieve process data from the ERP system in order to record crucial performance indicators on a real-time basis with the use of the in-memory technology

Predicting Account Receivables Outcomes with Machine-Learning

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe Account Receivables (AR) of a company are considered an important determinant of a company’s Cash Flow – the backbone of a company’s financial performance or health. It has been proved that by efficiently managing the money owed by customers for goods and services (AR), a company can avoid financial difficulties and even stabilize results in moments of extreme volatility. The aim of this project is to use machine-learning and data visualization techniques to predict invoice outcomes and provide useful information and a solution using analytics to the collection management team. Specifically, this project demonstrates how supervised learning models can classify with high accuracy whether a newly created invoice will be paid earlier, on-time or later than the contracted due date. It is also studied how to predict the magnitude of the delayed payments by classifying them into interesting, delayed categories for the business: up to 1 month late, from 1 to 3 months late and delayed for more than 3 months. The developed models use real-life data from a multinational company in the manufacturing and automation industries and can predict payments with higher accuracy than the baseline achieved by the business

Editorial

It is tradition that the Electronic Journal of Information Systems Evaluation (EJISE) publish a special issue containing the full versions of the best papers that were presented in a preliminary version during the 8th European Conference on Information Management and Evaluation (ECIME 2014). The faculty of Economics and Business Administration of the Ghent University was host for this successful conference on 11-12th of September 2014. ECIME 2014 received a submission of 86 abstracts and after the double-blind peer review process, thirty one academic research papers, nine PhD research papers, one master research paper and four work-in-progress papers were accepted and selected for presentation. ECIME 2014 hosted academics from twenty-two nationalities, amongst them: Australia, Belgium, Bosnia and Herzegovina, Brazil, Finland, France, Greece, Ireland, Lebanon, Lithuania, Macedonia (FYROM), Norway, Portugal, Romania, Russia, South Africa, South Korea, Spain, Sweden, The Netherlands, Turkey and the UK. From the thirty-one academic papers presented during the conference nine papers were selected for inclusion in this special issue of EJISE. The selected papers represent empirical work as well as theoretical research on the broad topic of management and evaluation of information systems. The papers show a wide variety of perspectives to deal with the problem