Supply Chain Management in Hospital Laboratories

Supply Chain Management in Hospital Laboratories

Rahim Anwar

Assistant Professor of Department of Pathology and Microbiology and Medicine, Aga Khan University Karachi, Pakistan

 Mahmod Hasan

Assistant Professor of Department of Pathology and Microbiology and Medicine, Aga Khan University Karachi, Pakistan

Abstract

The purpose of this paper is to investigate the efficiency levels of the decision‐making units within the hospital laboratories in using their supply chain towards meeting the satisfaction of doctors. The effective management of supply chain in hospital laboratories has been an emerging interest. This paper aims to investigate a Logistic Management Information System coupled with Radio frequency identification to improve data collection, analysis and decision making during each phase of laboratories supply chain. The proposed model promotes to reduce cost, decrease delivery time and standardize the total process.  Results reveal that one of the laboratories satisfies doctors efficiently using the present levels for each scenario while the other failed.

Keywords

Laboratory Supply chain, Inventory, Information Management System, Radio Frequency Identification.

To cite this article

Anwar R., & Hasan, M. (2017). Supply Chain Management in Hospital Laboratorie, International Journal of  Management, and Social Sciences Review (IJMSSR). Vol. 1, No. 1, pp.33-25. Doi:10.5281/zenodo.2648113

 

Copyright

Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

 

References

  1. Blake, J.T., Dexter, F. & Donald, J. (2002). Operating room managers’ use of integer programming for assigning Block Time to surgical groups: a case study. Anesthesiology, 96: 718–24 [CrossRef][Google Scholar]
  1. Chaabane, S. (2004). Gestion Prédictive des Blocs Opératoires. PhD Thesis: Informatique et Systèmes Coopératifs pour l’Entreprise. Institut National des Sciences Appliquées de Lyon, 167p. [Google Scholar]
  1. Combes, C., Meskens, N., Levecq, P., Privat C. & Vandamme, J.P. (2005). Using KDD Process to predict the duration of surgery. In: IESM 2005, Marrakech, Morocco, May 16–19, 2005 [Google Scholar]
  1. Dexter, F., Macario, A. & Traub, R.D. (1999). Which algorithm for scheduling add-on elective cases maximizes opertaing room utilization? Use of Bin Packing and fuzzy constraints in operating room management. Anesthesiology, 91: 1491–1500. [CrossRef] [Google Scholar]
  1. Gordon, T., Lyles, A.P.S. & Fountain, J. (1998). Surgical unit time utilization review: Resource utilization and management implications. Journal of Medical Systems, 12: 169–179. [CrossRef] [Google Scholar]
  1. Guinet, A. & Chaabane, S. (2003). Operating theatre planning. International Journal of Production Economics, 85: 69–81. [CrossRef] [Google Scholar]
  1. Hammami, S., Hadj-Alouane, A.B., Ladet, P. & Kharraja, S. (2004). Une approche pour la constructionde plages flexibles. In: Conférence de MOdélisation et SIMulation, MOSIM’2004, Nantes, France.[Google Scholar]
  1. Judge, WQ and Elenkov, D. 1995. Organizational capacity for change and environmental performance: an empirical assessment of Bulgarian firms. Journal of Business Research, 58(7): 893–901. [Crossref] [Google Scholar]
  1. Kainuma, Y and Tawara, N. 2006. A multiple attribute utility theory approach to lean and green supply chain management. International Journal of Production Economics, 101: 99–108. [Crossref][Web of Science ®][Google Scholar]
  1. Koufteros, X, Vonderembse, M and Jayaram, J. 2005. Internal and external integration for product development: the contingency effects of uncertainty, equivocality, and platform strategy. Decision Sciences, 36(1): 97–133. [Crossref][Web of Science ®][Google Scholar]
  1. Lai, KH, Cheng, TCE and Tang, AKY. 2010a. Green retailing: factors for success. California Management Review, 52(2): 1–26. [Crossref][Google Scholar]
  1. Lai, KH, Cheng, TCE and Yeung, ACL. 2005. Relationship stability and supplier commitment to quality. International Journal of Production Economics, 96(3): 397–410. [Crossref] [Web of Science ®][Google Scholar]
  1. Lai, KH, Wong, CWY and Cheng, TCE. 2008. A coordination-theoretic investigation of the impact of electronic integration on logistics performance. Information & Management, 45(1): 10–20. [Crossref][Web of Science ®] [Google Scholar]
  1. Lee, SY and Klassen, KD. 2008. Drivers and enablers that foster environmental management capabilities in small and medium-sized suppliers in supply chains. Production and Operations Management, 17(6): 573–586. [Crossref][Web of Science ®] [Google Scholar]
  1. Malone, TW and Crowston, K. 1994. The interdisciplinary study of coordination. ACM Computing Surveys, 26(1): 87–120. [Crossref][Web of Science ®] [Google Scholar]
  1. Podsakoff, PM. 2003. Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5): 879–903. [Crossref][Web of Science ®][Google Scholar]
  1. Rao, P and Holt, D. 2005. Do green supply chains lead to competitiveness and economic performance?. International Journal of Operations & Production Management, 25(9): 898–916. [Crossref][Web of Science ®][Google Scholar]
  1. Russo, MV and Harrison, NS. 2005. Organizational design and environmental performance: clues from the electronics industry. Academy of Management Journal, 48(4): 582–593. [Crossref] [Web of Science ®][Google Scholar]
  1. Sarkis, J. 2001. Manufacturing’s role in corporate environmental sustainability: concerns for the new millennium. International Journal of Operations and Production Management, 21(5/6): 666–686. [Crossref][Web of Science ®][Google Scholar]
  1. Sarkis, J. 2003. A strategic decision making framework for green supply chain management. Journal of Cleaner Production, 11(4): 397–409. [Crossref][Web of Science ®][Google Scholar]
  1. Seuring, S and Muller, M. 2008. From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 16(15): 1699–1710. [Crossref][Web of Science ®] [Google Scholar]
  1. Shah, R. 2008. Explaining anomalous high performance in a health care supply chain. Decision Sciences, 39(4): 759–789. [Crossref][Google Scholar]
  1. Simpson, D, Power, DJ and Samson, D. 2007. Greening the automotive supply chain: a relationship perspective. International Journal of Operations & Production Management, 27(1): 28–48. [Crossref][Web of Science ®] [Google Scholar]
  1. Vachon, S and Klassen, RD. 2006. Green project partnership in the supply chain: the case of the package printing industry. Journal of Cleaner Production, 14: 661–671. [Crossref] [Web of Science ®][Google Scholar]
  1. Vachon, S and Mao, Z. 2008. Linking supply chain strength to sustainable development: a country-level analysis. Journal of Cleaner Production, 16: 1552–1560. [Crossref] [Web of Science ®][Google Scholar]
  1. Wong, CWY, Lai, KH and Cheng, TCE. 2009. Complementarities and alignment of information systems management and supply chain management. International Journal of Shipping and Transport Logistics, 1(2): 156–171. [Crossref][Google Scholar]
  1. Yang, CL. 2010. Mediated effect of environmental management on manufacturing competitiveness: an empirical study. International Journal of Production Economics, 123(1): 210–220. [Crossref][Web of Science ®] [Google Scholar]
  1. Yang, J. 2008. Relational stability and alliance performance in supply chain. Omega, 36(4): 600–608. [Crossref] [Web of Science ®][Google Scholar]
  1. Yang, J. 2009. The antecedents of dyadic quality performance and its effect on buyer–supplier relationship improvement. International Journal of Production Economics, 120(1): 243–251. [Crossref][Web of Science ®][Google Scholar]
  1. Yeung, ACL, Lai, KH and Yee, RWY. 2007. Organizational learning, innovativeness and organizational performance: a qualitative study. International Journal of Production Research, 45(11): 2459–2477. [Web of Science ®][Google Scholar]