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The Optimization of Total Laboratory Automation by Simulation of a Pull-Strategy

  • Systems-Level Quality Improvement
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Abstract

Laboratory results are essential for physicians to diagnose medical conditions. Because of the critical role of medical laboratories, an increasing number of hospitals use total laboratory automation (TLA) to improve laboratory performance. Although the benefits of TLA are well documented, systems occasionally become congested, particularly when hospitals face peak demand. This study optimizes TLA operations. Firstly, value stream mapping (VSM) is used to identify the non-value-added time. Subsequently, batch processing control and parallel scheduling rules are devised and a pull mechanism that comprises a constant work-in-process (CONWIP) is proposed. Simulation optimization is then used to optimize the design parameters and to ensure a small inventory and a shorter average cycle time (CT). For empirical illustration, this approach is applied to a real case. The proposed methodology significantly improves the efficiency of laboratory work and leads to a reduction in patient waiting times and increased service level.

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References

  1. Pribor, H. C., Expert systems in laboratory medicine: A practical consultative application. J. Med. Syst. 13(2):103–109, 1989.

    Article  Google Scholar 

  2. Graban, M., Lean hospitals: improving quality, patient safety, and employee satisfaction. CRC Press, New York, 2009.

    Google Scholar 

  3. Holland, L. L., Smith, L. L., and Blick, K. E., Total laboratory automation can help eliminate the laboratory as a factor in emergency department length of stay. Am. J. Clin. Pathol. 125(5):765–770, 2006.

    Article  Google Scholar 

  4. Emmerich, K. A., Quam, E. F., Bowers, K. L., and Eggert, A. A., The combination of specimen tracking with an advanced AutoLog in a laboratory information system. J. Med. Syst. 22(3):137–145, 1998.

    Article  Google Scholar 

  5. Hawkins, R. C., Laboratory turnaround time. Clin. Biochem. Rev. 28(4):179–194, 2007.

    Google Scholar 

  6. Lam, C. W., and Jacob, E., Implementing a laboratory automation system experience of a large clinical laboratory. J. Lab. Autom. 17(1):16–23, 2012.

    Article  Google Scholar 

  7. Seaberg, R. S., Stallone, R. O., and Statland, B. E., The role of total laboratory automation in a consolidated laboratory network. Clin. Chem. 46(5):751–756, 2000.

    Google Scholar 

  8. Eggert, A. A., Bowers, K. L., Smulka, G. J., Emmerich, K. A., Iwanski, A. L., and Quam, E. F., An overhead specimen handling system for variable workloads. J. Med. Syst. 23(1):1–11, 1999.

    Article  Google Scholar 

  9. Sasaki, M., Kageoka, T., Ogura, K., Kataoka, H., Ueta, T., and Sugihara, S., Total laboratory automation in Japan: Past, present and the future. Clin. Chim. Acta 278(2):217–227, 1998.

    Article  Google Scholar 

  10. Tatsumi, N., Okuda, K., and Tsuda, I., A new direction in automated laboratory testing in Japan: five years of experience with total laboratory automation system management. Clin. Chim. Acta 290(1):93–108, 1999.

    Article  Google Scholar 

  11. Sarkozi, L., Simson, E., and Ramanathan, L., The effects of total laboratory automation on the management of a clinical chemistry laboratory: Retrospective analysis of 36 years. Clin. Chim. Acta 329(1–2):89–94, 2003.

    Article  Google Scholar 

  12. Chien, S.-W., Hsu, Y.-N., Kou, C.-Y., Hunag, K.-H., and Chen, Y.-F., Implementation of a central laboratory with consolidated laboratory network for central Taiwan national hospital union. Proceedings of the 5th World Scientific and Engineering Academy and Society (WSEAS) International Conference on Circuits, Systems, Electronics, Control and Signal Processing, 181–185, Dallas, Texas, November 1–3, 2006.

  13. Nolan, R. L., Norton, D. P., and Bowen, W. E., Computers and hospital management. J. Med. Syst. 1(2):187–203, 1977.

    Article  Google Scholar 

  14. Melanson, S. E., Lindeman, N. I., and Jarolim, P., Selecting automation for the clinical chemistry laboratory. Arch. Pathol. Lab. Med. 131(7):1063–1069, 2007.

    Google Scholar 

  15. Lian, Y. H., and Van Landeghem, H., Analysing the effects of lean manufacturing using a value stream mapping-based simulation generator. Int. J. Prod. Res. 45(13):3037–3058, 2007.

    Article  Google Scholar 

  16. Womack, J. P., and Jones, D. T., Lean thinking: banish waste and create wealth in your corporation. Simon & Schuster, New York, 1996.

    Google Scholar 

  17. Rother, M., and Shook, J., Learning to see: value-stream mapping to create value and eliminate muda. Lean Enterprise Institute, Brookline, 1998.

    Google Scholar 

  18. Spearman, M. L., Woodruff, D. L., and Hopp, W. J., CONWIP—A pull alternative to kanban. Int. J. Prod. Res. 28(5):879–894, 1990.

    Article  Google Scholar 

  19. Spearman, M. L., Hopp, W. J., and Woodruff, D. L., A hierarchical control architecture for Constant work-in-process (CONWIP) production systems. J. Manuf. Oper. Manag. 2(3):147–171, 1989.

    Google Scholar 

  20. Huang, M., Wang, D., and Ip, W. H., Simulation study of CONWIP for a cold rolling plant. Int. J. Prod. Econ. 54(3):257–266, 1998.

    Article  Google Scholar 

  21. Gilland, W. G., A simulation study comparing performance of CONWIP and bottleneck-based release rules. Prod. Plan. Control 13(2):211–219, 2002.

    Article  Google Scholar 

  22. Yang, T., Fu, H.-P., and Yang, K.-Y., An evolutionary-simulation approach for the optimization of multi-constant work-in-process strategy—A case study. Int. J. Prod. Econ. 107(1):104–114, 2007.

    Article  Google Scholar 

  23. Abdulmalek, F. A., and Rajgopal, J., Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study. Int. J. Prod. Econ. 107(1):223–236, 2007.

    Article  Google Scholar 

  24. Yang, T., and Lu, J.-C., The use of a multiple attribute decision-making method and value stream mapping in solving the pacemaker location problem. Int. J. Prod. Res. 49(10):2793–2817, 2011.

    Article  Google Scholar 

  25. Lu, J.-C., Yang, T., and Su, C. T., Analysing optimum push/pull junction point location using multiple criteria decision‐making for multistage stochastic production system. Int. J. Prod. Res. 50(19):1–15, 2012.

    Google Scholar 

  26. UniCel DxC 600 In-Lab Training Manual, Beckman Coulter, Fullerton, CA, 2010. Available at https://www.beckmancoulter.com/ucm/idc/groups/public/documents/webasset/glb_bci_150024.pdf (Accessed 1 June 2007).

  27. Sung, C. S., Kim, Y. H., and Yoon, S. H., A problem reduction and decomposition approach for scheduling for a flowshop of batch processing machines. Eur. J. Oper. Res. 121(1):179–192, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  28. McKenzie, P., and Jayanthi, S., Ball aerospace explores operational and financial trade-offs in batch sizing in implementing JIT. Interfaces 37(2):108–119, 2007.

    Article  Google Scholar 

  29. Womack, J. P., and Jones, D. T., Lean solutions: how companies and customers can create value and wealth together. Free Press, New York, 2005.

    Google Scholar 

  30. Little, J. D., OR FORUM—Little’s Law as viewed on its 50th anniversary. Oper. Res. 59(3):536–549, 2011.

    Article  MATH  MathSciNet  Google Scholar 

  31. Detty, R. B., and Yingling, J. C., Quantifying benefits of conversion to lean manufacturing with discrete event simulation: A case study. Int. J. Prod. Res. 38(2):429–445, 2000.

    Article  MATH  Google Scholar 

  32. Isken, M. W., and Littig, S. J., Simulation analysis of pneumatic tube systems. J. Med. Syst. 26(1):9–19, 2002.

    Article  Google Scholar 

  33. Banks, J., Carson, J. S., Nelson, B. L., and Nicol, D. M., Discrete-event system simulation. Prentice Hall, New Jersey, 2000.

    Google Scholar 

  34. Kelton, W. D., Sadowski, R. P., and Swets, N. B., Simulation with arena, 5th edition. McGraw Hill Higher Education, New York, 2010.

    Google Scholar 

  35. Zar, J. H., Briostatistical analysis, 5th edition. Prentice-Hall, New Jersey, 2010.

    Google Scholar 

  36. Yang, T., Hsieh, C.-H., and Cheng, B.-Y., Lean-pull strategy in a re-entrant manufacturing environment: A pilot study for TFT-LCD array manufacturing. Int. J. Prod. Res. 49(6):1511–1529, 2011.

    Article  Google Scholar 

  37. Wang, T., Chan, F., and Yang, T., 2014. The integration of group technology and simulation optimization to solve the flow shop with highly variable cycle time process: a surgery scheduling case study. Math. Probl. Eng. 2014.

  38. Glover, F., Kelly, J.P., and Laguna, M., New advances and applications of combining simulation and optimization. Proceedings of the 28th Conference on Winter Simulation, 144–152, Coronado, CA, December 8–11, 1996.

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Acknowledgments

The authors thank the anonymous medical center for providing the case study. This work was partially supported by the National Science Council of Taiwan, Republic of China, under grants NSC-101-2221-E-006 -137 -MY3 and MOST-103-2622-E-006 -025 -CC3.

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Authors and Affiliations

  1. Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, Taiwan

    Taho Yang, Teng-Kuan Wang & Chia-Lo Su

  2. Department of Business Administration, National Chiayi University, Chiayi City, Taiwan

    Vincent C. Li

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  1. Taho Yang
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  2. Teng-Kuan Wang
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  4. Chia-Lo Su
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Correspondence to Taho Yang.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Yang, T., Wang, TK., Li, V.C. et al. The Optimization of Total Laboratory Automation by Simulation of a Pull-Strategy. J Med Syst 39, 162 (2015). https://doi.org/10.1007/s10916-014-0162-6

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  • DOI: https://doi.org/10.1007/s10916-014-0162-6

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