http://www.diva-portal.org/smash/get/diva2:833085/FULLTEXT01

Towards innovation measurement in the software industry

Business process management systems traditionally focused on supporting the modeling and automation of business processes, with the objective of enabling fast and cost-effective process execution. As more and more processes become automated, customers become increasingly interested in managing process execution. This paper presents a set of concepts and a methodology toward business process intelligence (BPI) using dynamic process performance evaluation, including measurement models based on activity-based management (ABM) and a dynamic enterprise process performance evaluation methodology. The proposed measurement models support the analysis of six process flows within a manufacturing enterprise including activity flow, information flow, resource flow, cost flow, cash flow, and profit flow, which are crucial for enterprise managers to control the process execution quality and detect problems and areas for improvements. The proposed process performance evaluation methodology uses time, quality, service, cost, speed, efficiency, and importance as seven evaluation criteria. A prototype system supporting dynamic enterprise process modeling, analysis of six process flows, and process performance prediction has been implemented to validate the proposed methodology.

/pdf/a-business-process-intelligence-system-for-enterprise-1aq4m6o2sn.pdf

A Business Process Intelligence System for Enterprise Process Performance Management

Software organizations are in need of methods to understand, structure, and improve the data their are collecting. We have developed an approach for use when a large number of diverse metrics are already being collected by a software organization (M.G. Mendonca et al., 1998; M.G. Mendonca, 1997). The approach combines two methods. One looks at an organization's measurement framework in a top-down goal-oriented fashion and the other looks at it in a bottom-up data-driven fashion. The top-down method is based on a measurement paradigm called Goal-Question-Metric (GQM). The bottom-up method is based on a data mining technique called Attribute Focusing (AF). A case study was executed to validate this approach and to assess its usefulness in an industrial environment. The top-down and bottom-up methods were applied in the customer satisfaction measurement framework at the IBM Toronto Laboratory. The top-down method was applied to improve the customer satisfaction (CUSTSAT) measurement from the point of view of three data user groups. It identified several new metrics for the interviewed groups, and also contributed to better understanding of the data user needs. The bottom-up method was used to gain new insights into the existing CUSTSAT data. Unexpected associations between key variables prompted new business insights, and revealed problems with the process used to collect and analyze the CUSTSAT data. The paper uses the case study and its results to qualitatively compare our approach against current ad hoc practices used to improve existing measurement frameworks.

/pdf/validation-of-an-approach-for-improving-existing-measurement-441evi4q2v.pdf

Validation of an approach for improving existing measurement frameworks

In order to achieve competitive business goals, every enterprise needs to evaluate the current level of information systems performance and their utilization The evaluation measures the technical capacity and operational capability of enterprise information systems and diagnoses their effectiveness in business goals and efficiency in resources An integrated evaluation system is developed based on the continuous improvement model of information systems performance The system has been applied to performance measurement of information systems with a huge set of data from Korean industries, and proven reliable and practical

An integrated evaluation system based on the continuous improvement model of IS performance

Data mining in software metrics databases

Software organizations are in need of methods for understanding, structuring, and improving the data they are collecting. This paper discusses an approach for use when a large number of diverse metrics are already being collected by a software organization. The approach combines two methods. One looks at an organization's measurement framework in a top-down fashion and the other looks at it in a bottom-up fashion. The top-down method, based on the goal-question-metric (GQM) paradigm, is used to identify the measurement goals of data users. These goals are then mapped to the metrics being used by the organization, allowing us to: (1) identify which metrics are and are not useful to the organization, and (2) determine whether the goals of data user groups can be satisfied by the data that are being collected by the organization. The bottom-up method is based on a data mining technique called attribute focusing (AF). Our method uses this technique to identify useful information in the data that the data users were not aware of. We describe our experience in analyzing data from a software customer satisfaction survey at IBM to illustrate how the AF technique can be combined with the GQM paradigm to improve measurement and data use inside software organizations.

/pdf/an-approach-to-improving-existing-measurement-frameworks-1nppmopoz0.pdf

J. Dawson

Papers

An approach to improving existing measurement frameworks