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Soft Computing Approach for Measuring Business Process Agility in an Agile Environment

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Aarti M. Karande, D. R. Kalbande
10.5120/ijca2017914534

Aarti M Karande and D R Kalbande. Soft Computing Approach for Measuring Business Process Agility in an Agile Environment. International Journal of Computer Applications 168(11):12-20, June 2017. BibTeX

@article{10.5120/ijca2017914534,
	author = {Aarti M. Karande and D. R. Kalbande},
	title = {Soft Computing Approach for Measuring Business Process Agility in an Agile Environment},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {168},
	number = {11},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {12-20},
	numpages = {9},
	url = {http://www.ijcaonline.org/archives/volume168/number11/27918-2017914534},
	doi = {10.5120/ijca2017914534},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Agile environment checks Organization’s capacity or flexibility to accept the changes. Working in agile development needs to check their process, effect in the collaboration with other processes present in the enterprise solution. But agile environment is unpredictable. Hence to measure the process agileness, soft computing approach is used. In this paper, Hybrid Neuro-fuzzy approach is proposed to measure agility of business process with respect to the architectural level. This approach uses different weighting algorithm, for fully connected neural network evaluated on the basis of pairwise comparison of process type and architecture type. This method can be used to take check importance or effect of change of process used in the software solution used in the industry. In case of changing environment, this method will give a selection path to expert for selection of changes in the enterprise solution.

References

  1. Asif Ali, Muhammad Yousif Elfadul (October 2009) Fuzzy Decision Making in Business Intelligence Application of fuzzy models in retrieval of optimal decision School of Engineering Blekinge Institute of Technology
  2. Aditi Barua, Lalitha Snigdha Mudunuri, and Olga Kosheleva (2014) Why Trapezoidal and Triangular Membership Functions Work So Well: Towards a Theoretical Explanation Journal of Uncertain Systems Vol.8
  3. Aarti M. Karande & Dr. D.R. Kalbande (2015) “Business Process Analyzed factors affecting Business Model Innovation” International Conference on Nascent Technologies in the Engineering Field (ICNTE-2015)
  4. Angela Bower (July 2003) Soft Computing Tes Sell A Support Services PLC Issue V1.R1.M0
  5. A. Maithili Dr. R. Vasantha Kumari Mr. S. Rajamanickam (2012) Neural Network Towards Business Forecasting IOSR Journal of Engineering Apr, Vol. 2(4) 831-836 ISSN: 2250-3021
  6. Anthony H. Dekker Agility in Networked Military Systems: A Simulation Experiment 11TH ICCRTS Coalition Command And Control In The Networked Era Topics: C2 Analysis, C2 Modeling and Simulation, Network-Centric Metrics
  7. Behzad Shahrabi, (2011) The Agility Assessment Using Fuzzy Logic World Applied Sciences Journal 13 (5): 1112-1119, 2011 ISSN 1818-4952 © IDOSI Publications
  8. Bernadette Bouchon-Meunier, Mariagrazia Dotoli, Bruno Maione (June 2016) On The Choice Of Membership Functions In A Mamdani-Type Fuzzy Controller Volume 6, Issue 6ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering
  9. Chapter I Ajith Abraham Hybrid Soft and Hard Computing Based Forex Monitoring Systems
  10. Ching-Torng Lin & Chen-Tung Chen (MAY 2004) New Product Go/No-Go Evaluation at the Front End: A Fuzzy Linguistic Approach IEEE Transactions On Engineering Management, Vol. 51, NO. 2, 197
  11. Dragan Z. Šaletic (2006) On Further Development of Soft Computing, Some Trends in Computational Intelligence SISY 4th Serbian-Hungarian Joint Symposium on Intelligent
  12. D R Kalbande, G T Thampi, N T Deotale (2011) E-Procurement for Increasing Business Process Agility International Conference and Workshop on Emerging Trends in Technology (ICWET 2011)–TCET, Mumbai, India
  13. David Chen & Guy Doumeingts & Francois Vernadat (September 2008) Architectures for enterprise integration and interoperability Computers in Industry 59(7):647-659 
  14. Evangelos Triantaphyllou and Chi-Tun Lin (1996) Development and Evaluation of Five Fuzzy Multi-attribute Decision -Making Methods International Journal of Approximate Reasoning 1996; 14:281-310 Elsevier Science
  15. Ebru Ardil and Parvinder S. Sandhu (2010) A soft computing approach for modeling of severity of faults in software systems International Journal of Physical Sciences Vol. 5, pp. 074-085, Feb 2010 ISSN 1992-1950© Academic Journals
  16. Ehsan Kamalloo and Mohammad Saniee Abadeh (2014) Credit Risk Prediction Using Fuzzy Immune Learning Hindawi Publishing Corporation Advances in Fuzzy Systems, Article ID 651324, 11 pages http://dx.doi.org/10.1155/2014/651324
  17. Ezhilarasi G and Dhavachelvan P (October, 2010) Effective Web Service Discovery Model Using Neural Network Approach International Journal of Computer Theory and Engineering, Vol. 2,
  18. Erich L. Kaltofen, Arne Storjohann The Complexity of Computational Problems in Exact Linear Algebra Encyclopedia of Applied and Computational Mathematics, Bjorn Enquist, Mathematics of Computer Science, Discrete Mathematics, Johan Hastad, field, Springer.
  19. Fu-Ren Lin & Yu-Huapai (May 2000) Using Multi-Agent Simulation And Learning To Design New Business Processes IEEE Transactions On Systems, Man And Cybernetics Part A Systems And Humans, Vol. 30, No. 3
  20. Geoff Coyle (2004) Practical Strategy. Open Access Material. AHP © Pearson Education Limited
  21. Hao Ying (Nov 1998) General SISO Takagi–Sugeno Fuzzy Systems with Linear Rule Consequent Are Universal Approximators IEEE Transactions On Fuzzy Systems, Vol. 6
  22. Imre J. Rudas, János Fodor (2008) Intelligent Systems Int. J. of Computers, Communications & Control, ISSN 1841-9836, E-ISSN 1841-9844 Vol. III, Spl. issue: Proceedings of ICCCC 2008
  23. Ian Cloete, (February 2006), Jacobus van Zyl Fuzzy Rule Induction in a Set Covering Framework IEEE Transactions On Fuzzy Systems, Vol. 14, No. 1, 93
  24. Jerry M. Mendel and Robert I. Bob John (May 2002) Type-2 Fuzzy Sets Made Simple in IEEE Transactions on Fuzzy Systems
  25. Joseph Bih (2006) Paradigm shift an introduction to fuzzy logic IEEE Potentials
  26. J. Jassbi, S.M. Seyedhosseini & N. Pilevari (March 2010) An Adaptive Neuro Fuzzy Inference System for Supply chain Agility Evaluation International Journal of Industrial Engineering & Production Research, Vol.20, No.4
  27. Kailan Shang, & Zakir Hossen,(November 2013) Applying Fuzzy Logic to Risk Assessment and Decision-Making Sponsored by CAS/CIA/SOA Joint Risk Management Section
  28. Kurhe A.B., Satonkar S.S., Khanale P.B. and Shinde Ashok (2011) Soft Computing and its Applications BIOINFO Soft Computing Volume 1, Issue 1, pp-05-07
  29. Lujuan Chen, E.V. Krishnamurthy, Iain Macleod (1994) Generalised matrix inversion and rank computation by successive matrix powering parallel Computing 20-297-311
  30. Lala Septem Riza, Christoph Bergmeir, Francisco Herrera, and Jose Manuel Benitez (May 2015) Fuzzy Rule-Based Systems for Classification and Regression in R Journal of Statistical Software, Volume 65, Issue 6. http://www.jstatsoft.org/frbs:
  31. Malú Castellanos (2008) Challenges in Business Process Optimization Mexican International Conference on Computer Science
  32. M.L. Caliusco and G. Stegmayer Semantic Web Technologies and A`rtificial Neural Networks for Intelligent Web Knowledge Source Discovery Y. Badr et al. (eds.) Emergent Web Intelligence: Advanced Semantic Technologies, Advanced Information and Knowledge Processing, DOI 10.1007/978-1-84996-077-92
  33. Meysam Shaverdi, Mahsa Akbari, Sajad Emamipour (2012) Using Fuzzy Multi Criteria Decision Making Approach For Ranking The Web Browsers International Journal of Economics and Management Sciences Vol. 1, No. 8,pp. 72-86
  34. Marco Miladinovic, Sladjana, predrag (2011) Modified SMS method for computing outer inverses of Toeplitz matrices applied Mathematics and computation.
  35. Muhammad Siddique (2009) Fuzzy Decision Making Using Max-Min Method and Minimization Of Regret Method(MMR) Thesis for the degree Master of Science
  36. Nour mohammad Yaghoubi, Mahboobeh Rahat Dahmardeh (2010) Analytical approach to effective factors on organizational agility J. Basic. Appl. Sci. Res., 1(1)76-87
  37. Nilesh N. Karnik, Jerry M. Mendel (December 1999) Type-2 Fuzzy Logic Systems IEEE Transactions On Fuzzy Systems, Vol. 7, No. 6,
  38. Omar Adil M. Ali, Aous Y. Ali, Balasem Salem Sumait (March 2015) Comparison between the Effects of Different Types of Membership Functions on Fuzzy Logic Controller Performance International Journal of Emerging Engineering Research and Technology, ISSN 2349-4395
  39. Piero P. Bonissone, Yu-To Chen, Kai Goebel, and Pratap S. Khedkar (1999) Hybrid Soft Computing Systems: Industrial and Commercial Applications Proceedings of the IEEE, Special Issue on Computational Intelligence, Vol. 87, No. 9, pp. 1641-1667,
  40. Peter Buba Zirra, Timothy Umar Maigari and Wallace Ebinum Ossai Zirra et al, (June- 2016) A Fuzzy Based System for Determining the Severity Level of Osteomyelitis International Journal of Advanced Research in Computer Science and Software Engineering
  41. Qing Zhou, Yuxiang Wu, Christine W. Chan, & Paitoon Tontiwachwuthikul (2011) “GHGT-10 From neural network to neuro-fuzzy modeling: applications to the carbon dioxide capture process” Energy Procedia 4- 2066–2073
  42. Rajni Mohana, Deepak Dahiya (September 2012) An Optimized Business Service Directory for the ESB Platform in SOA International Journal of Computer Networks & Communications (IJCNC) Vol.4, No.5, DOI : 10.5121/ijcnc.2012.4511 165
  43. Rohit Srivastava, Jwalant Baria (July Aug 2012 ) Realization of Autonomous Soft Computing System Using Computational Intelligence Methods international Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Vol. 1, Issue 2, ISSN 2278-6856
  44. Robert Fuller Eotvos Lor (2001) Neuro-Fuzzy Methods for Modelling & Fault Diagnosis Lisbon Budapest VACATION SCHOOL, August 31 and September 1
  45. Salwa Ammar, David Moore & Ronald Wright (2008) Analyzing customer satisfaction surveys using a fuzzy rule-based decision support system: Enhancing customer relationship management Journal of Database Marketing & Customer Strategy Management (2008) 15, 91 – 105. doi: 10.1057/dbm.2008.2;
  46. Shan L. Pan, Gary Pan, Adela J. W. Chen, & Ming H. Hsieh (Nov. 2007) The Dynamics of Implementing and Managing Modularity of Organizational Routines During Capability Development: Insights From a Process Model IEEE Transactions On Engineering Management, Vol. 54, No. 4
  47. Sumeet Kaur Sehra Yadwinder Singh Brar Navdeep Kaur (January 2012) Multi Criteria Decision Making Approach for Selecting Effort Estimation Model International Journal of Computer Applications (0975 – 8887) Volume 39– No.1
  48. Santosh Kumar Das, Abhishek Kumar, Bappaditya Das and A.P.Burnwal (2013) On Soft Computing Techniques In Various Areas Rupak Bhattacharyya et al. (Eds) : ACER 2013, pp. 59–68,CS & IT-CSCP DOI : 10.5121/csit.2013.3206
  49. Selva Stauba , Emin Karamanb, Seyit Kayaa, Hatem KarapÕnara , Elçin Güvena ( 2015 ) Artificial Neural Network and Agility 1477 – 1485 doi: 10.1016/j.sbspro.2015.06.448 World Conference on Technology, Innovation and Entrepreneurship
  50. Soumyadip Ghosh Aliza R. Heching, & Mark S. Squillante,(2013) “A Two-Phase Approach For Stochastic Optimization Of Complex Business Processes” Proceedings of the 2013 Winter Simulation Conference
  51. S Xiaoji Liu, & Yonghui Qin (2012) Successive Matrix Squaring Algorithm for Computing the Generalized Inverse A2 T Journal of Applied Mathematics, Article ID 262034, 12 pages doi:10.1155/2012/262034
  52. S.Sanyal, S.Iyeng (1993) Defuzzif'ication Method For A Faster And More Accurate Control IEEE TENCON / Bcijn
  53. Saqib Ali, Ben Soh, & Torabtorabi (August 2006) A Novel Approach Toward Integration Of Rules Into Business Processes Using An Agent-Oriented Framework IEEE Transactions On Industrial Informatics, Vol. 2, No. 3
  54. Saeed Rouhani, Mehdi Ghazanfari, Mostafa Jafari (2012) Evaluation model of business intelligence for enterprise systems using fuzzy TOPSIS Expert Systems with Applications 39 3764–3771
  55. Tharwat O. S. Hanafy, H. Zaini, Kamel A. Shoush and Ayman A. Aly (January 2014) Recent Trends in Soft Computing Techniques for Solving Real Time Engineering Problems International Journal Of Control, Automation And Systems Vol.3 No.1 Issn 2165-8277 (Print) Issn 2165-8285
  56. Thoedtida Thipparat Prof. Elmer Dadios (Ed.) (2012) Application of Adaptive Neuro Fuzzy, Fuzzy Logic Algorithms, Techniques and Implementations, ISBN: 978-953-51-0393-6
  57. Thomas L. Saaty (2008) Decision making with the analytic hierarchy process Int. J. Services Sciences, Vol. 1, No. 1, 83 Copyright © 2008 Inderscience Enterprises Ltd
  58. Tzung-Pei Hong, Chai-Ying Leeb (1996) Induction of fuzzy rules and membership functions from training examples Fuzzy Sets and Systems 84 (1- 33 -47)
  59. Wil M.P. van der Aalst (Sep. 2004) "Workflow Mining: Discovering Process Models from Event Logs" IEEE Transactions on Knowledge and Data Engineering, Vol. 16
  60. Xiaohui Zhao & Chengfei Liu (July 2010) Steering Dynamic Collaborations Between Business Processes IEEE Transactions On Systems, Man, And Cybernetics Part A: Systems And Humans, Vol. 40, No. 4
  61. Yi-Hong Tseng, Ching-Torng Lin (2011) Enhancing enterprise agility by deploying agile drivers, capabilities and providers Information Sciences 3693–3708
  62. Ying Bai and Dali Wang Fundamentals of Fuzzy Logic Control – Fuzzy Sets, Fuzzy Rules and De-fuzzifications
  63. Zixiong Peng, Xiaowei Chen Uncertain Systems are Universal Approximators
  64. Zohreh Hamedi, Shahram Jafari (2011) Using Fuzzy Decision-Making in E-tourism Industry: A Case Study of Shiraz city E-tourism IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 3, No. 1, ISSN (Online): 1694-0814
  65. E. Czogala & J. Leski, (2000) “Neuro-Fuzzy Intelligent Systems, Studies in Fuzziness and Soft Computing”, Springer Verlag

Keywords

Business Process Agility, Neural Network, Agile development