Skip to main content
SpringerLink
Log in

SCM Enterprise Solution Using Soft Computing Techniques

  • Conference paper
  • First Online:
Soft Computing: Theories and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1053))

Abstract

SCM is a well-defined domain with business process working in predefined manner, but agility handling is not so well defined for SCM. In SCM, most of the agile affecting areas are distribution, network optimization, Shipment consolidation, Cross docking, Supplier management, and integration. Agile supply chain mainly focuses on the manufacturing and logistics strategies. Changes depend on organizational policy; hence it can be incomplete or uncertain. To manage this unpredictable environment, a Soft computing technique is used for constructing intelligent system. This paper helps to understand the supply chain domain using soft computing techniques. This paper shows the survey of experts from the SCM domain to predict different soft computing techniques that can be used in handling agile supply chain business processes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bonissone, P.P., Chen, Y.-T., Goebel, K., Khedkar, P.S.: Hybrid soft computing systems: industrial and commercial applications. Proc. IEEE, Spec. Issue Comput. Intell. 87(9), 1641–1667 (1999)

    Google Scholar 

  2. Oh, S.K., Pedrycz, W.: Genetically optimized hybrid fuzzy neural networks analysis and design of rule-based multi-layer perceptron architectures. Stud. Comput. Intell. (SCI) 82, 23–57, Springer Verlag, Berlin Heidelberg (2008). www.springerlink.com

  3. Agile Supply Chain Management (ASCM): The need for agility. Retrieved from http://conspecte.com/Supply-Chain-Management/agile-supply-chain-management.html

  4. Thipparat, T., Dadios, E.: Application of adaptive neuro fuzzy. Fuzzy Log. Algorithms, Techn. Implement. (2012). ISBN 978-953-51-0393-6

    Google Scholar 

  5. Victoria de la Fuente, M., Ros, L., Ortiz, A.: Enterprise modeling methodology for forward and reverse supply chain flows integration. Comput. Ind. 61, 702–710 (2010)

    Article  Google Scholar 

  6. Cordón, O., Herrera, Francisco, Villar, Pedro: Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base. IEEE Trans. Fuzzy Syst. 9(4), 667 (2001)

    Article  Google Scholar 

  7. Zur Muehlen, M., Ho, D.T.Y.: Risk management in the BPM lifecycle. Bussler, C., et al.: BPM 2005 Workshops. LNCS 3812, pp. 454–466, ©Springer-Verlag, Berlin, Heidelberg (2006)

    Google Scholar 

  8. Pan, S.L., Pan, G., Chen, A.J.W., Hsieh, M.H.: The dynamics of implementing and managing modularity of organizational routines during capability development: insights from a process model. IEEE Trans. Eng. Manag. 54(4), 800 (2007)

    Article  Google Scholar 

  9. Yang, M.: Supply chain management under E-commerce environment. Int. J. Innov. Manag. Technol. 3(3), 210 (2012)

    Google Scholar 

  10. Jaina, V., Benyoucef, L., Deshmukh, S.G.: A new approach for evaluating agility in supply chains using Fuzzy Association Rules Mining. Eng. Appl. Artif. Intell. (2008)

    Google Scholar 

  11. Bolia, N., Saxena, P., Bhandari, J.: Quantification of agility of a supply chain using fuzzy logic. IJMIE 2(3), 141 (2012). ISSN: 2249-0558

    Google Scholar 

  12. Vergidis, K., Turner, C., Tiwari, A.A.A.: An automated optimization framework for the development of re-configurable business processes: web services approach. Int. J. Comput. Integr. Manuf. (2013). https://doi.org/10.1080/0951192X.2013.814159

    Article  Google Scholar 

  13. Andersson, E.A.: Minimization of regret versus unequal multi-objective fuzzy decision process in a choice of optimal medicines. In: XI the International Conference IPMU (2006)

    Google Scholar 

  14. Lin, C.T., Chiu, H., Chu, P.Y.: Agility index in the supply chain. Int. J. Prod. Econ. pp. 285–299 (2006)

    Google Scholar 

  15. Ammar, Salwa: Analyzing customer satisfaction surveys using a fuzzy rule-based decision support system: enhancing customer relationship management. J. Database Market. Custom. Strat. Manag. 15, 91–105 (2008). https://doi.org/10.1057/dbm.2008.2

    Article  Google Scholar 

  16. Collin, J., Lorenzin, D.: Plan for supply chain agility at Nokia Lessons from the mobile infrastructure industry. Int. J. Phys. Distrib. Log. Manag. (Emerald Group) 36(6), 418–430 (2006)

    Google Scholar 

  17. Kelde, D., Nagde, D., Patel, R., Pawar, P.: Information forensic application using soft computing techniques. Int. J. Comput. Sci. Inf. Technol. 4, 69–72 (2013)

    Google Scholar 

  18. Zhelyazkov, G. Agile Supply Chain: Zara's case study analysis. Pers. Website (2011)

    Google Scholar 

  19. Lor, R.F.E.: Neuro-fuzzy methods for modelling and fault diagnosis. Lisbon Budapest VACATION (2001)

    Google Scholar 

  20. Yusuf, Y.Y., Gunasekaran, A., Adeleye, E.O., Sivayoganathan, K.: Agile supply chain capabilities: determinants of competitive objectives. Eur. J. Oper. Res. 159, 379–392 (2004)

    Article  Google Scholar 

  21. Sterritt, R., Bustard, D.W.: Fusing hard and soft computing for fault management in telecommunications systems. IEEE Trans. Syst. Man, Cybern.—Part C: Appl. Rev. vol. 32 (2002)

    Google Scholar 

  22. Christopher, M.: The agile supply chain competing in volatile markets. Ind. Market. Manag. (Elsevier Science Inc) 29, 37–44 (2000)

    Google Scholar 

  23. Abraham, A.: Hybrid soft and hard computing based forex monitoring systems. (2005). https://www.doi.org/10.1007/11339366_5 

  24. Rudas, I.J., Fodor, J.: Intelligent systems. Int. J. Comput., Commun. Control. ISSN 1841-9836, E-ISSN 1841-9844 vol. III, Spl. issue: Proceedings of ICCCC 2008 (2008)

    Google Scholar 

  25. Smith, K.A., Gupta, J.N.D.: Neural networks in business: techniques and applications for the operations. Res. Comput. Oper. Res. 27, 1023–1044 (2000)

    Article  Google Scholar 

  26. Bower, A.: Soft computing tessell a support services. PLC Issue V1.R1.M0 (2003)

    Google Scholar 

  27. Doeksen, B., Abraham, A., Thomas, J., Paprzycki, M.: Real stock trading using soft computing models. In: International conference on information technology: coding and computing, ITCC, vol. 2, pp. 162–167. (2005). https://www.doi.org/10.1109/ITCC.2005.238 

  28. Cloete, I., van Zyl, J.: Fuzzy rule induction in a set covering framework. IEEE Trans. Fuzzy Syst. 14(1) (2006)

    Google Scholar 

  29. Karande, A.M., Kalbande, D.R.: Selection of optimal services working on SCM strategies using fuzzy decision making methods. In: Second international conference on computational intelligence and communication technology (2016)

    Google Scholar 

  30. Lim, A.H.L., Lee, C.-S., Raman, M.: Hybrid genetic algorithm and association rules for mining work flow, best practices. Expert Syst. Appl. 39, 10544–10551 (2012)

    Article  Google Scholar 

  31. Hanafy, T.O.S., Zaini, H., Shoush, K.A., Aly, A.A.: Recent trends in soft computing techniques for solving real time engineering problems. Int. J. Control, Autom. Syst. vol. 3 (2014). ISSN 2165-8277

    Google Scholar 

  32. Reeves, C.: Genetic Algorithms. (2010). https://www.doi.org/10.1007/978-1-4419-1665-5_5

  33. Mukhopadhyay, D.M., Balitanas, M.O., Farkhod, A.: A genetic algorithm: a tutorial review. Int. J. Grid Distrib. Comput. 2(3), 25 (2009)

    Google Scholar 

  34. Jassbi, J., Seyedhosseini, S.M., Pilevari, N.: An adaptive neuro fuzzy inference system for supply chain agility evaluation. Int. J. Ind. Eng. Prod. Res. 20(4), 187 (2010)

    Google Scholar 

  35. Shahrabi, B.: The agility assessment using fuzzy logic. World Appl. Sci. J. 13(5), 1112–1119, ©IDOSI Publications (2011). ISSN 1818-4952

    Google Scholar 

  36. Hsu, T.-H., Tsai, T.-N., Chiang, P.-L.: Selection of the optimum promotion mix by integrating a fuzzy linguistic decision model with genetic algorithms. Chiang Inf. Sci. 179, 41–52 (2009)

    Article  Google Scholar 

  37. Aliev, R.A., Fazlollahi, B.: Decomposition of complex systems into set of autonomous agents by fuzzy-genetic approach and its application in economic and business environments. IEEE (2002)

    Google Scholar 

  38. Ardil, E., Sandhu, P.S.: A soft computing approach for modeling of severity of faults in software systems. Int. J. Phys. Sci. 5, 074–085, Feb 2010, ©Academic Journals (2010). ISSN 1992-1950

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Sardar Patel Institute of Technology, Andheri, Mumbai, India

    Aarti M. Karande & Dhananjay R. Kalbande

Authors
  1. Aarti M. Karande
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dhananjay R. Kalbande
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aarti M. Karande .

Editor information

Editors and Affiliations

  1. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Millie Pant

  2. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, Rajasthan, India

    Tarun K. Sharma

  3. Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Om Prakash Verma

  4. Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Rajesh Singla

  5. Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Afzal Sikander

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Karande, A.M., Kalbande, D.R. (2020). SCM Enterprise Solution Using Soft Computing Techniques. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-15-0751-9_13

Download citation

Publish with us

Policies and ethics

Search

Navigation