COMPLEXITY AND USABILITY MODELS FOR BUSINESS PROCESS ANALYSIS
Open Access
- Author:
- Cheng, Chen-Yang
- Graduate Program:
- Industrial Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- August 21, 2008
- Committee Members:
- Vittaldas V Prabhu, Committee Chair/Co-Chair
Richard Allen Wysk, Committee Member
Ling Rothrock, Committee Member
Meg Leavy Small, Committee Member - Keywords:
- complexity analysis
business process analysis
usability study - Abstract:
- Business processes combine human activities, information technology, business rules, and organizational activities. The emergence of radio frequency identification (RFID), web services, and mobile applications technology has led to innovations in business processes, but their impact on usability and process structural change is uncertain. New technology implementation may, for example, improve usability and information exchange, while increasing process complexity, or reduce complexity at the expense of usability or information exchange. This research identifies three independent metrics, structural complexity, technology usability and interaction complexity, to characterize the business process complexity. The study has two objectives: (1) to design business process metrics that may be used in evaluating process structure, the ease of technology use in business processes and the complexity of resource interactions; and (2) to find tradeoffs among three metrics with regard to improving business processes. Evaluation of complex structural processes involves determining whether they generally have a clear structure and are easy to analyze, understand, and explain. Structural complexity is studied with respect to different business process structures. The hypothesis for this research is that more distinct process structures will lead to complex processes. A developed structural complexity metric enables analysis of basic process structures and measures them using an information theoretical metric. Information theory characterizes the randomness, disorder or uncertainty of the occurrence of process structures. Furthermore, compared with other process measurement metrics, the structural complexity metric shows more robust result in sample processes from literatures. This structural complexity model is particularly useful for evaluating business processes’ static features. A usability complexity metric that combines human-computer interaction and cognitive science is utilized to evaluate the cognitive loading of a business process. The resulting model, Business Process Analysis in Goal, Operations Methodology, and Selections (BPA-GOMS), is composed of two attributes—the internal complexity of an activity and the interaction complexity. The internal complexity models user-level behaviors and how to perform the business process using cognitive theory. Interaction complexity is identified through a business process analysis that examines interactions with other resources, including employees, customers and computer programs. Usability and interaction complexity provide insights into node level and information exchange in business process analysis. A process with different routing probabilities could result in different complexity measurements due to variability in the occurrence of the business process flow. The operational behavior of the business process is studied here to determine the stable probability of process tasks. This stable probability reflects the long-term likelihood that a random flow will visit the routing. If the routing has a complex structure and difficult usability, the process receives a higher operational complexity score. Operational complexity measurements can be determined using the transition matrix to represent the probable occurrence of each process activity. The rank of activities also shows the importance components and provides an insight for Business Process Reengineering (BPR). Findings from this study offer several major contributions: (1)Information theoretic metrics measuring the business process structural, interaction, and usability complexity.(2) Develop a BPA-GOMS model, extended GOMS in business process analysis (3)Find the long-term probability of activities in analyzing business processes (4) Diversity of Case Studies, including Industrial procurement, university/industry collaboration, RFID in healthcare, Data collection in education systems four case studies of complexity evaluation in system implementation, patient identification, the cutting tool supply chain problem, and educational systems that illustrate the results of the proposed metrics.