Dynamic and fractal approaches to measure business process performance

Process modelling is one of the foundational characteristics of business process management and became key activities in understanding business processes and in formulating competitive business process management practices. Many process modeling are available, however, some of them are too costly to construct due to lack of enough knowledge or the application does not really need such models complexity. In view of the existing gap in the business process performance measurement literature, this research attempts to fill in the gap and propose some new approaches to the design and construction of business process performance measurement framework. This research consists of closely related chapters covering the issues and design of new business process performance measurement frameworks. The first involves a static model developed by defining the decision variables (revenue, cost) and the objective function(net profit). Static system representation is capable to provide the majority of information needed for dynamic system model construction, it does not possess the mechanisms needed to enact the process behavior constraints defined in its representation. The second model is constructed by design from the static approach into its corresponding dynamic framework by entering time-related data. Dynamic process modelling by construction is designed for communicating end-toend business processes. It enables the changed process outcome to be evaluated in advanced to its implementation into the physical environment. As business processes contain organized patterns of business activities, therefore, processes relations can generate fractal pattern. Thus, for the third approach, fractal can be used to measure business process performance in particular to address the extent of business complexity and dynamic environment of business companies. It can help organizations to describe the complexity and irregularity of business processes such as financial processes. Final part of the research aims to define and formulate an evolving and dynamic fractal model for measuring business process performance. Irregular sets provide a much better representation of many natural phenomena than the figures of classical geometry do. The box-counting method is used to estimate fractal dimension of the business process. This fractal dimension value is the same as the Sierpinski Gasket, which indicates that the net profit business process displays a fractal pattern. Therefore, a fractal index can be constituted to measure the net profit process and discriminate its similarity and dissimilarity. Consequently, interpretative indices are developed; for both dynamic modeling and for fractal modeling, Use and application of both indices respectively for the dynamic and fractal models are illustrated using real data gathered from five companies in Bursa Malaysia. In general, the results indicate that the fractal index reveals fractal behavior of the datasets of the five companies and reveals the real changes in revenue and cost of each company. The range of fractal index is greater than dynamic index range showing more capability in measuring the disorder and stochastic changes which provides more opportunity to measure any irregular behavior of profit and assists predict in the long term. Fractal model recommended to implement a forecasting model to improve the financial management and decision-making abilities of any business, particularly if the forecasts are updated on a future developing component is added during each time