DesigningAgent-Based Modeling in Process Performance Measurement

Document Type : مقالات

Authors

• majid hasani ¹
• Adel Azar ¹
• Jamal Shahrabi ²
• Abbas Moghbel ¹

¹ Tarbiat Modares University

² Amir Kabir University

https://doi.org/10.22067/pmt.v9i18.57246

Abstract

1- INTRODUCTION
Business process improvement for promoting product quality has continuous importance in every industry. Every organization should measure, control and analyze its performance. Therefore, the assessment and management of performance is considered as an important strategic process which is affected by various environmental and organizational factors. Management and performance assessment have been considered as critical tools to manage an organization and in order to survive and develop in a competitive environment, organizations require their own assessment and management performance system. Various performance measurement models have been designed and elaborated from the past to the present and each one has specific features. Regarding environmental complexities and competitive environments, it is necessary to design a process performance measurement model with new smart technological features for organizational needs.

2- THEORETICAL FRAMEWORK
Different methodologies and models have been provided in the management literature for the management of performance processes. Performance management can focus on the performance of an organization, a department, process and employee. In this research, based on the literature review, the interaction effect of process performance measurement model, intelligence agents, and process model were investigated. In this article, theoretical basis are examined in three categories (i.e., performance measurement model, intelligence agents, and process model). In terms of performance measurement model, some models from past to present were considered. In this group, more than thirty different models were studied. Regarding intelligence agents, their features and functions were considered. Intelligence agent’s technology has been used in various fields such as manufacturing, process control, communications, transportation systems, SCM, knowledge management, etc. The feature and characteristics of an agent depend on application and its target. Accordingly, some of their common features are as follows: Autonomy, mobility, adaptability, collaborate with others, and reliability. Moreover, in the category of process model, the most important and relevant process model was considered. In this group, process reference model and frameworks as APQC, Value Chain, SCOR, 6Sigma and others were studied.

3- METHODOLOGY
In this research, based on the literature review, the interaction effect of process performance measurement and intelligence agent were studied, and agent-based modeling in process performance measurement was presented. In this article, data was gathered from literature review and some documents. Experts’ opinions have also been used to design and evaluate the model. In addition, to obtain the opinion of experts, the Delphi method was used in two panels. Interviews with experts were used in order to elicit general organization knowledge and to acquire tacit knowledge related to design of model. Then, in order to design the agent-based model, RUP and UML techniques were used.

4- RESULTS andDISCUSSION
In this paper, based on the literature review and experts opinions, the design of agent-based modeling in process performance measurement was presented. The model designed has three layers; the first layer has five steps (i.e., define, measurement, analysis, improvement and control); the second layer includes intelligence agents and the last layer consists of databases and a process model. Agent-based architecture has been considered in the presented model and some agents were defined in this model as definer, reporter, recommender, processer and observer. In this research RUP and UML techniques were used for architecture of agents. In addition, experts were asked to evaluate the model with Delphi methodology in two panels.
5- CONCLUSIONSandSUGGESTIONS
The research findings are in harmony with the aim of the research proposing an agent based conceptual model of process performance measurement with specified and categorized results. This model was designed in three layers, the first layer has five steps, and each step involves specified activities and is related to certain agents. The second layer includes some agents and the last layer consists of databases and reports. In this research, in order to design the optimal agent-based modeling, we tried to use the RUP method and language of UML modeling; hence, the results shown as user diagrams, activity charts, and class diagrams are all related to the agents. A future study would also aim to develop an agent-based modeling in performance measurement systems, and implement the agent-based model for pilot studies in organizations. It is hoped that the findings of this research suggest an adequate level of interest in both process performance and business intelligence in the field of agents, and encourage further investigation to address process performance using intelligence agent techniques, as discussed in this paper.

Keywords

• Performance measurement
• Process
• Model
• Agent
• Intelligence

References

References

Akbarpour, S. M., & Soroor, J. (2007). An intelligent agent-based architecture for strategic information system applications, Knowledge-Based Systems, 20(8), 726–735.

APQC. (2014). Process Classification Framework, Version, 6.1.1

Atkinson, M., & Maxwell. V. (2007). Driving performance in a multi-agency partnership using outcome measures: a case study, Measuring Business Excellence, 11(2), 12-22.

Azar, A., & Amirkhani, T. (2013). Performance Based Budgeting: Theory and Implemention Model, IMPS-press,Tehran. (in Persian)

Azar, A., & Khadivar, A. (2013). Performance Based Budgeting: Paradigm of Modeling, Islamic Parliament Research Center-press,Tehran. (in Persian)

Bititci, U. S., & Turner, T. (2000). Dynamics of performance measurement systems, International Journal of Operations & Production Management, 20(6), 692-704.

Blasini, J., & Leist, S. (2013). Success factors in process performance management, Business Process Management Journal, 19(3), 477-495.

Bourne, M.; Franco, M., & Wilkes, J. (2003). Corporate performance management. Measuring Business Excellence, 7(3), 15–21.

Chaib-diaa, M. (2006). Multiagent Based Supply Chain Management, Springer International Publishing.

Chamoni, P.; Gluchowski, P.; Dinter, B., & Bucher, T. (2006). “Business performance management”, Analytische Informations System, Springer, 23-50.

Cocca, P., & Alberti, M. (2010). “A framework to assess performance measurement systems in SMEs”, International Journal of Productivity and Performance Management, 59 (2), 186-200.

Edrogan, A., & Canatan, H. (2015). Literature search consisting of the areas of six sigma usage, social and behavioral science, 195 (3), 697-704.

Franco-Santos, M.; Lucianetti, L., & Bourne, M. (2012. “Contemporary performance measurement systems: a review of their consequences and a framework for research”, Management Accounting Research, 23(2), 79-119.

George, A. (2011). Intelligent agent based architecture for patient monitoring in bio sensor networks. In V. V. Das & N. Thankachan (Eds.). Communications in computer and information science, 250, 180–186.

Jevgeni, S.; Eduard, S., & Roman, Z. (2015). Framework for contionous improvement of production process and product throughput, Procedia Engineering, 100, 511-519.

Karadgi, S. (2014). A Reference Architecture for Real-Time Performance Measurement,Springer International Publishing Switzerland.

Kohlbacher, M., & Gruenwald, S. (2011). Process 45.orientation: conceptualization and measurement, Performance measurement system design, 36, International Journal of Operations and Production Management, 25 (2), 267-283.

Krause, O. (2005). Performance Management–Eine Stakeholder-Nutzen-orientierte und Gescha ftsprozess-basierte Methode, Technische Universitat Berlin

Kueng, P.; Meier, A., & Wettstein, T. (2001). Performance 50.measurement systems must be engineered, Communications of the Association for Information Systems, 7(3), 1-27.

Kwangyeol, R.; Youngjun, S., & Mooyoung, J. (2003). Modeling and specifications of dynamic agents in fractal manufacturing systems, Computers in Industry 52(2), 161–182.

London, M., & Beatty, R. W. (2006). 360-degree feedback as competitive advantage. Human Resource Management, 32(2–3), 353–372.

Lönnqvist, A. (2004). Measurement of Intangible Success Factors: Case Studies on the Design, Implementation and Use of Measures, Tampere University of Technology, Publication 475, Tampere.

Marques, G.; Gourc, D., & Lauras, M. (2010). Multi-criteria performance analysis for decision making in project management, International Journal of Project Management 29, 1057–1069.

Marx, F.; Wortmann, F. & Mayer, J.H. (2012), A maturity model for management control systems, Business & Information Systems Engineering, 4(4), 193-207.

Merrill, W.; Vijayan, S., & Sainsbury, R. (2012). The role of intelligent agents and data mining in electronic partnership management, Expert Systems with Applications, 39(18), 13277–13288.

Milanović, G. L. (2011). Understanding Process Performance Measurement Systems, Business System Research, 2(2), 1-56.

Moradi, M.; Aghaie, A., & Hoseini, M. (2013). Implementation of intelligent multi-agent system in decision-making with knowledge management approach, Journal of Information Management, 5(4), 219-244. (in Persian)

Moshabaki, A.; Hasani, M., & Bidgoli, D. (2014). Mining association rules base of identified of relationship between of challenge factors in knowledge management, Journal of Improve of Management, 8(3), 33-44. (in Persian)

O’Donnell, O., & Duffy, A. H. B. (2002). Modeling design development performance, International Journal of Operation & Production Management, 22(11), 1198-1221.

Porter, M. E. (1985). Competitive Advantage, New York, the Free Press.

Robson, I. (2004). From process measurement to performance improvement, Business Process Management Journal, 10(5), 510-521.

Rouhani, S.; Ghazanfari, M., & Jafari, M. (2012). Evaluation model of business intelligence for enterprise systems using fuzzy TOPSIS, Expert Systems with Applications, 39, 3764–3771.

Russell, S., & Norving, P. (2003). Artificial Intelligence- A modern approach, Englewod cliffs, prentice-Hall.

Sharabi, J. (2011). Data Mining, Tehran, Amir Kabir University Press. (in Persian)

Sharp, A., & McDermott, P. (2009). Workflow Modeling Tools for Process Improvement and Applications Development, 2nd Edition, ARTECH HOUSE, INC.

Shen, W.; Tan, W., & Zhao, J. (2007). A methodology for Dynamic Enterprise ProcessPerformance Evaluation. Computers in Industry, 58(5), 474-485.

Sidrova, A., & Isik, O. (2010). Business process research: A cross disciplinary review, Business Process Management Journal, 16(4), 566-597.

Sinclair, D., & Zairi, M. (1995). Effective process management through performance Measurement, Business Process Re-engineering & Management Journal, 1(1), 75-88.

Starbuck, W. H. (2013). James Gardner March: Founder of organization theory, decision theorist, and advocate of sensible foolishness. European Management Journal, 31, 88– 92.

Tuomela, T. S. (2000). Customer Focus and Strategic Control. Aconstructive Case Study of Developing a Strategic Performance Measurement System at FinABB, FINLAND, Publications of the TurkuSchool of Economics and Business Administration.

Won C, D.; Lee, Y. H.; Hwa, A. S., & Min, K. H. (2012). A framework for measuring the performance of service supply chain management, Computers & Industrial Engineering, 62(3), 801–818.

Yaghobi, N. M.; Shukuhy, J.; Raiisi, H., & Sayydi, F. (2016). Influences of ladership styles on organizational performance with mediating role of organizational learning and innovation, Transformation Management Journal, 2(14). 32-56. (in Persian)