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Automate Back Office Activity Monitoring to Drive Operational Excellence

  • Miao He
  • Tao Qin
  • Sai Zeng
  • Changrui Ren
  • Lei Yuan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7636)

Abstract

Business process outsourcing (BPO) is growing rapidly with intensive competition. BPO providers aim to deliver high quality services with low cost. One of the key drivers is to optimize human resource utilization. It is critical to monitor and measure the activities of the practitioners in order to identify inefficient workers, unnecessary waste in operations, and non-standardized operations. Today’s practices to monitor and measure the human activities are manual and error-prone. Motivated by increasing the accuracy and eliminating manual efforts for monitoring and measuring human activities, in this paper we present our research work to automatically classify and time the daily activity of a practitioner. Even though human behavior variations and noises brings substantial deviations and randomness, the developed activity classifier and timer handles the variations and reduces the noise to a satisfactory extent. The pilot results demonstrate 98.18% accuracy to classify transactions into the activity taxonomy, and 91.54% accuracy to find out the transaction cycle time therefore to aggregate to the time spent on each activity. The results are highly valued by our business partners, and the tool is considered as a revolutionary solution for human activity monitoring and measurement.

Keywords

Activity Type Sequential Pattern Business Partner Work Unit Sequential Pattern Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
  2. 2.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the 11th International Conference on Data Engineering, pp. 3–14 (1995)Google Scholar
  3. 3.
    Bharadwaj, S.S., Saxena, K.B.C., Halemane, M.D.: Building a successful relationship in business process outsourcing: An exploratory study. European Journal of Information Systems 19(2), 168–180 (2010)CrossRefGoogle Scholar
  4. 4.
    Bozorgi, M., Saul, L.K., Savage, S., Voelker, G.M.: Beyond heuristics: Learning to classifify vulnerabilities and predict exploits. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 105–113 (2010)Google Scholar
  5. 5.
    Brown, R.H.: Business process outsourcing vendor consolidations: Is your contracts at risk? Gartner (2009)Google Scholar
  6. 6.
    Du, Z., Liao, X.: Well-defined processes and their effects on business process outsourcing vendor’s success: An integrated framework. In: The 2010 International Conference on E-Business Intelligence, pp. 27–36 (2010)Google Scholar
  7. 7.
    Forman, G., Rajaram, S.: Scaling up text classification for large file systems. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 239–246 (2008)Google Scholar
  8. 8.
    Han, J., Pei, J., Mortazavi-AsI, B., Chen, Q., Dayal, U., Hsu, M.: FreeSpan: Frequent pattern-projected sequential pattern mining. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 355–359 (2000)Google Scholar
  9. 9.
    Karu, K., Jain, A.K.: Fingerprint classification. Pattern Recognition 29(3), 389–404 (1996)CrossRefGoogle Scholar
  10. 10.
    Lacity, M.C., Willcocks, L.P., Rottman, J.W.: Global outsourcing of back office services: lessons, trends, and enduring challenges. Strategic Outsourcing 1(1), 13–34 (2008)CrossRefGoogle Scholar
  11. 11.
    Nenkova, A., Bagga, A.: Email classification for contact centers. In: Proceedings of the 2003 ACM Symposium on Applied Computing, pp. 789–792 (2003)Google Scholar
  12. 12.
    Pei, J., Han, J., Mortazavi-AsI, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: PrefixSpan: Mining sequential patterns efficiently by prefixed-projected pattern growth. In: Proceedings of the 17th International Conference on Data Engineering, pp. 215–224 (2001)Google Scholar
  13. 13.
    Qin, T., He, M., Zeng, S., Ren, C., Dong, J.: An effective pattern mining algorithm to support automatic process classification in contact center back office. In: Proceedings of the 2012 IEEE SOLI (2012)Google Scholar
  14. 14.
    Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: Proceedings of the 5th International Conference on Extending Database Technology, pp. 3–17 (1996)Google Scholar
  15. 15.
    Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. China Machine Press (2010)Google Scholar
  16. 16.
    Tang, M., Pellom, B., Hacioglu, K.: Call-type classification and unsupervised training for the call center domain. In: Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 204–208 (2003)Google Scholar
  17. 17.
    Tapper, D.: Worldwide and U.S. IT outsourcing services 2004-2008 forecast: A potential perfect storm. IDC # 31089 (2004)Google Scholar
  18. 18.
    Tapper, D.: U.S. customers select IBM, HP-EDS, Unisys, Accenture, Infosys, ADP, and Fedelity as top 5 ranked BPO vendors for transformation, integration, innovation and cost optimization - excerpt from IDC # 216191. IDC # 216191 (2009)Google Scholar
  19. 19.
    Zaki, M.J.: SPADE: An efficient algorithm for mining frequent sequences. Machine Learning 42(1-2), 31–60 (2001)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Miao He
    • 1
  • Tao Qin
    • 1
  • Sai Zeng
    • 2
  • Changrui Ren
    • 1
  • Lei Yuan
    • 3
  1. 1.IBM ResearchChina
  2. 2.IBM T.J. Watson Research CenterUSA
  3. 3.IBM China Development LabChina

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