Author(s):  
Michael Genrich ◽  
Alex Kokkonen ◽  
Jürgen Moormann ◽  
Michael zur Muehlen ◽  
Roger Tregear ◽  
...  

2021 ◽  
Vol 69 (1) ◽  
pp. 1141-1157
Author(s):  
Abid Sohail ◽  
Dhanapal Durai Dominic ◽  
Mohammad Hijji ◽  
Muhammad Arif Butt

Author(s):  
Arta Moro Sundjaja

Higher demand from the top management in measuring business process performance causes the incremental implementation of BPM and BI in the enterprise. The problem faced by top managements is how to integrate their data from all system used to support the business and process the data become information that able to support the decision-making processes. Our literature review elaborates several implementations of BPI on companies in Australia and Germany, challenges faced by organizations in developing BPI solution in their organizations and some cost model to calculate the investment of BPI solutions. This paper shows the success in BPI application of banks and assurance companies in German and electricity work in Australia aims to give a vision about the importance of BPI application. Many challenges in BPI application of companies in German and Australia, BPI solution, and data warehouse design development have been discussed to add insight in future BPI development. And the last is an explanation about how to analyze cost associated with BPI solution investment.


2004 ◽  
Vol 53 (3) ◽  
pp. 321-343 ◽  
Author(s):  
Daniela Grigori ◽  
Fabio Casati ◽  
Malu Castellanos ◽  
Umeshwar Dayal ◽  
Mehmet Sayal ◽  
...  

2013 ◽  
Vol 19 (2) ◽  
pp. 237-256 ◽  
Author(s):  
Aleksander Pick ◽  
Olegas Vasilecas ◽  
Diana Kalibatienė ◽  
Rok Rupnik

Nowadays, organisations aim to automate their business processes to improve operational efficiency, reduce costs, improve the quality of customer service and reduce the probability of human error. Business process intelligence aims to apply data warehousing, data analysis and data mining techniques to process execution data, thus enabling the analysis, interpretation, and optimisation of business processes. Data mining approaches are especially effective in helping us to extract insights into customer behaviour, habits, potential needs and desires, credit associated risks, fraudulent transactions and etc. However, the integration of data mining into business processes still requires a lot of coordination and manual adjustment. This paper aims at reducing this effort by reusing successful data mining solutions. We propose an approach for implementation of data mining into a business process. The confirmation of the suggested approach is based on the results achieved in eight commercial companies, covering different industries, such as telecommunications, banking and retail.


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