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Evaluating Replenishment Systems for Disposable Supplies at the Operating Theater: A Simulation Case Study

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Health Care Systems Engineering (ICHCSE 2019)

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Abstract

Ensuring cost containment while providing high quality patient care is of paramount concern to hospitals. The operating theater in particular is a major cost driver for any hospital, and is among the most critical resources in terms of both capacity and patient care. Effective inventory and distribution systems are a prerequisite for realizing efficiency improvements in the internal operating theater supply chain. In this work, discrete-event simulation is used to model part of the internal distribution process in the operating theater at a Belgian Hospital and to identify improvements by focusing on the replenishment process. A logistics performance measurement framework based on Analytic Network Process (ANP), as a popular Multi-Criteria Decision-Making (MCDM) technique, is adopted to assess three replenishment scenarios. The best performing scenario is selected using the Internal Logistics Efficiency Performance (ILEP) index as an evaluation basis. This research indicates that industrial engineering techniques, such as simulation and MCDM, which are successfully applied in industrial sectors, can also be adopted to realize efficiency opportunities in healthcare logistics.

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References

  1. Camp, M., Pfister, J., Reeves, D., Kneedler, J.: Effective Operating Room Inventory Management. Pfiedler Enterprises 26 (2014)

    Google Scholar 

  2. Baltussen, R., Niessen, L.: Priority setting of health interventions: the need for multi-criteria decision analysis. Cost Effectiveness Resour. Allocation 4, 14 (2006). https://doi.org/10.1186/1478-7547-4-14

    Article  Google Scholar 

  3. Bélanger, V., Beaulieu, M., Landry, S., Morales, P.: Where to locate medical supplies in nursing units: An exploratory study. Supply Chain Forum Int. J. (2018). doi: https://doi.org/10.1080/16258312.2018.1433438

  4. Moons, K., Waeyenbergh, G., Pintelon, L.: Measuring the logistics performance of internal hospital supply chains—a literature study. Omega (United Kingdom) (2018)

    Google Scholar 

  5. Volland, J., Fügener, A., Schoenfelder, J., Brunner, J.O.: Material logistics in hospitals: a literature review. Omega (United Kingdom) 69, 82–101 (2017). https://doi.org/10.1016/j.omega.2016.08.004

    Article  Google Scholar 

  6. Rohleder, T., Bailey, B., Crum, B., Faber, T., Johnson, B., Montgomery, L., Pringnitz, R.: Improving a patient appointment call center at Mayo Clinic. Int. J. Health Care Qual. Assur. 26, 714–728 (2013). https://doi.org/10.1108/IJHCQA-11-2011-0068

    Article  Google Scholar 

  7. Hicks, C., McGovern, T., Prior, G., Smith, I.: Applying lean principles to the design of healthcare facilities. Int. J. Prod. Econ. 170, 677–686 (2015). https://doi.org/10.1016/j.ijpe.2015.05.029

    Article  Google Scholar 

  8. Hu, Q., Boylan, J.E., Chen, H., Labib, A.: OR in spare parts management: a review. Eur. J. Oper. Res. (2018)

    Google Scholar 

  9. Lanckzweirt, J.: Een Analyse van de Materiaalstromen in het Operatiekwartier (2010)

    Google Scholar 

  10. Abukhousa, E., Al-jaroodi, J., Lazarova-molnar, S., Mohamed, N.: Simulation and modeling efforts to support decision making in healthcare supply chain management. Sci. World J. 2014, 16 (2014). https://doi.org/10.1155/2014/354246

    Article  Google Scholar 

  11. Bijvank, M., Vis, I.F.A.: Inventory control for point-of-use locations in hospitals. J. Oper. Res. Soc. 63, 497–510 (2012). https://doi.org/10.1057/jors.2011.52

    Article  Google Scholar 

  12. Marsh, K., Goetghebeur, M., Thokala, P., Baltussen, R.: Multi-criteria decision analysis to support healthcare decisions (2017)

    Google Scholar 

  13. Di Martinelly, C.: Proposition of a framework to reengineer and evaluate the hospital supply chain. Department of Management 139 (2008)

    Google Scholar 

  14. Landry, S., Beaulieu, M.: The challenges of hospital supply chain management, from central stores to nursing units. In: International Series in Operations Research and Management Science, pp 465–482 (2013)

    Google Scholar 

  15. Carrus, P.P., Marras, F., Pinna, R.: The performance measurement of changes in the logistics of health goods: a theoretical model. In: Proceedings of the 18th Toulon-Verona International Conference 85–100 (2015)

    Google Scholar 

  16. Lapierre, S.D., Ruiz, A.B.: Scheduling logistic activities to improve hospital supply systems. Comput. Oper. Res. 34, 624–641 (2007). https://doi.org/10.1016/j.cor.2005.03.017

    Article  MATH  Google Scholar 

  17. Moons, K., Waeyenbergh, G., Pintelon, L., Timmermans, P., De Ridder, D.: Performance indicator selection for operating room supply chains: an application of ANP. Oper. Res. Health Care Under Revi., 1–25 (2019)

    Google Scholar 

  18. Saaty, T.L.: A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15, 234–281 (1977). https://doi.org/10.1016/0022-2496(77)90033-5

    Article  MathSciNet  MATH  Google Scholar 

  19. Saaty, T.L.: How to make a decision: the analytic hierarchy process. Eur. J. Oper. Res. 48, 9–26 (1990). https://doi.org/10.1016/0377-2217(90)90057-I

    Article  MATH  Google Scholar 

  20. Saaty, T.L., Vargas, L.G.: Decision making with the analytic network process. Economic, political, social and technological applications with benefits, opportunities, costs and risks. Int. Ser. Oper. Res. Manag. Sci. (2006). https://doi.org/10.1007/978-1-4614-7279-7

  21. Hariharan, S., Dey, P.K., Moseley, H.S.L., Kumar, A.Y., Gora, J.: A new tool for measurement of process-based performance of multispecialty tertiary care hospitals. Int. J. Health Care Qual. Assur. 17, 302–312 (2004). https://doi.org/10.1108/09526860410557552

    Article  Google Scholar 

  22. Soriya Hoeur, Duangpun Kritchanchai (2015) Key Performance Indicator Framework for Measuring Healthcare Logistics in ASEAN. Toward Sustain. Oper. Supply Chain Logistics Syst. doi: https://doi.org/10.1007/978-3-319-19006-8

  23. Sargent, R.G.: Verification and validation of simulation models. J. Simul. 7, 12–24 (2013). https://doi.org/10.1057/jos.2012.20

    Article  Google Scholar 

  24. Dyer, R. F., Forman, H.: Group decision support with the analytic hierarchy process. Decis. Support Syst. 8 (2), 99–124 (1992)

    Google Scholar 

  25. Harvey, L.F.B., Smith, K.A., Curlin, H.: Physician Engagement in improving operative supply chain efficiency through review of surgeon preference cards. J. Minim. Invasive Gynecol. (2017)

    Google Scholar 

  26. Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F.: Supply chain performance measurement systems: a systematic review and research agenda. Int. J. Prod. Econ. (2017)

    Google Scholar 

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Appendices

Appendix 1

Quality

Quality specifies how well a specific activity has been performed, ensuring that patients receive care service in a safe manner and that problems such as medical errors are minimized

Distribution service level (DSL)

The availability of logistics services to support clinical care processes

 

Urgent delivery rate

\({\text{Daily stock}} - {\text{out rate}} = \frac{{\mathop \sum \nolimits_{{{\text{i}} = 1}}^{{{\text{i}} = 454}} ({\text{Stockout}}_{\text{i}} )}}{{730\,{\text{days}}}}\)

Additional items needed

\({\text{Average replenishment per item}} = {\text{AvgMaxStock}} - \frac{\text{Average daily replenishment}}{\text{Average replenishment items per day}}\)

Delivery accuracy (DA)

The ability to pick and deliver the correct items and quantities from storage to point-of-use location

 

Perfect order fulfilment

\({\text{Daily number of incomplete refills}} = \frac{{\mathop \sum \nolimits_{{{\text{i}} = 1}}^{{{\text{i}} = 454}} \left( {{\text{MaxStock}}_{\text{i}} - {\text{ItemsInStock}}_{\text{i}} } \right)}}{{730\,{\text{days}}}}\)

Centralization impact (CI)

The ability to locate items only at a central storage room, or also at decentral storages.

 

Permanent double stock

Number of copy carts, containing duplicate of items in decentral storages

Impact of centralization

Adjust max stock in decentral locations

Time

Time involves the time to complete the logistics operations to ensure that the right items are at the right place and time

Replenishment lead time (RLT)

The total amount of time that elapses from the moment an item is ordered until the item is back on the shelf

 

Transport time

Time to move items to the right place

Replenishing time

Time to replenish decentral stock

Scanning time

Time to scan items in decentral stock

Preparing time

Time to pick requested items from central stock

Other activities time

Time spent on other activities than replenishing due to interruptions

Replenishment lead time

\(= {\text{Transport}} + {\text{replenishing}} + {\text{scanning}} + {\text{preparing}} + {\text{other}}\,{\text{activities}}\)

Response time (RT)

The ability to deliver items on time, preventing delays in surgical procedures

 

On-time delivery

Average delivery time = finish time of replenishing decentral stock

Clinical staff involvement (CSI)

The amount of time clinical staff is busy with logistics tasks, rather than their core activities

 

Logistics employees involvement

Time spent by logistics employees (=RLT—other activities)

Financial

Financial indicators identify supply chain cost drivers, such as expenses incurred by departments for providing services, including direct and overhead costs for inventory and internal distribution

Distribution cost (DCo)

Total cost of handling and transporting to move supplies from storage rooms to point-of-care locations

 

Replenishing cost

Related to RLT

Personnel cost (PCo)

The cost related to the time personnel is involved with logistics activities

 

Personnel cost

Related to CSI

Inventory cost

The annual cost of holding inventory at a specific storage room

 

Holding cost

\({\text{Average holding cost}} = [\mathop \sum \nolimits_{{{\text{i}} = 1}}^{{{\text{i}} = 454}} \left( {\frac{{{\text{ItemsInStock}}_{\text{i}} }}{\text{InventoryCount}}} \right)]*{\text{UnitCost}}_{\text{i}} *0.25\)

Productivity/organization

Productivity/organization involves operational control metrics for logistics departments used for streamlining processes, reducing costs, facilitating information flow and enhancing provided care services

Case cart efficiency (CCE)

The availability and utilization of case carts to provide surgeons with the required supplies

 

Not applicable for replenishment process

Delivery frequency (DF)

The number of visits to decentral storage locations to deliver or replenish items in these locations

 

Percentage of items replenished

\({\text{Daily percentage of item replenishment}} = {\text{Total}}\,{\text{items}} - \frac{\text{Daily number of items replenished}}{\text{Total items included}}\)

Scanning frequency

Use of scanner (0/1)

Visits to decentral locations

Number of opening relay cabins

Standardization (S)

The ability to simplify workflows between operating rooms and improve working conditions

 

Percentage of scannable items for replenishment

\(= {\text{Total items}} - \frac{\text{Number of scannable items}}{\text{Total items}}\)

Personnel management (PM)

A measure of how to obtain, use and maintain a satisfied workforce

 

Personnel utilization

\(= \frac{{{\text{Time busy replenishing }}\left( {\text{RLT}} \right)}}{{480\,{ \hbox{min} }}}\)

Ergonomics friendliness

Use of double carts (0/1)

Workload distribution

Timeline of logistics employees interrupted by other activities

Appendix 2

figure a

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Moons, K., Waeyenbergh, G., Timmermans, P., De Ridder, D., Pintelon, L. (2020). Evaluating Replenishment Systems for Disposable Supplies at the Operating Theater: A Simulation Case Study. In: Bélanger, V., Lahrichi, N., Lanzarone, E., Yalçındağ, S. (eds) Health Care Systems Engineering. ICHCSE 2019. Springer Proceedings in Mathematics & Statistics, vol 316. Springer, Cham. https://doi.org/10.1007/978-3-030-39694-7_12

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