Designing a model for healthcare services supply chain performance evaluation using neutrosophic multiple attribute decision-making technique

Document Type : Research Paper

Authors

1 Department of Industrial Management, Alborz Campus, University of Tehran, Tehran, Iran

2 Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran

10.22075/ijnaa.2023.31915.4732

Abstract

Healthcare services supply chain performance evaluation in hospitals that deal with society's well-being has significant importance on their performance improvement. The purpose of this research was to evaluate healthcare services supply chain performance using a neutrosophic multiple attribute decision-making technique in Tehran’s hospitals. Comprehensive performance evaluation was conducted by applying both objective attributes that focused on the outcome along subjective attributes that were based on the judgment of evaluators. In this regard, neutrosophic logic has been deployed to face uncertainties in the expert’s judgment for determining the priority of attributes over each other explained via linguistic variables in the form of trapezoidal neutrosophic numbers. Eigenvector-power as one of the multiple attribute decision-making techniques concerned with evaluating and choosing the best option among the available ones based on diverse and conflicting attributes was used to ascertain attributes’ importance in addition to guaranteeing obtainment of the largest eigenvalue of the characteristic polynomial, has led to reduction of calculations. Neutrosophic algebraic operations embedded in the eigenvector-power technique after efficiency confirmation of the technique was acquired. In order to gap analysis, a paired t-test was exploited to discover the existence of differences between the current and desired performance of attributes. Then, attributes’ weighted performance gaps were calculated by multiplying the weight of each attribute by its performance gap which smoothed attributes’ performance gap criticality definition by applying quartiles. ``Staff job satisfaction" and ``response to demands" attributes were categorized as very highly critical, ``treatment branding" and ``proportion of service with cost" attributes were classified as highly critical, ``technological comfort" and ``stakeholder’s interests" attributes were grouped as moderately critical and ``access" and ``treatment plan fulfilment" attributes were relegated as low critical, respectively according to the weighted performance gap.

Keywords

[1] M. Abdel-Basset, G. Manogaran, A. Gamal, and F. Smarandache, A hybrid approach of neutrosophic sets and DEMATEL method for developing supplier selection criteria, Design Automat. Embed. Syst.22 (2018), 257–278.
[2] L. Abdullah, Z. Ong, and S.M. Mahali, Single-valued neutrosophic DEMATEL for segregating types of criteria: A case of subcontractors’ selection, J. Math. 4 (2021), 1–12.
[3] P. Adinolfi and E. Borgonovi, The Myths of Healthcare Towards New Models of Leadership and Management in the Healthcare Sector, Springer International Publishing, 2018.
[4] M. Al-Baali, L. Grandinetti, and A. Purnama, Numerical analysis and optimization, Springer International Publishing, 2017.
[5] E. Asgharizadeh, M. Taghaizadeh, and F. Andam, Developing a model of healthcare services supply chain performance evaluation in Tehran’s governmental, private and social security hospitals using grounded theory, J. Health Promotion Manag. 13 (2023), no. 1, 1–20.
[6] S. Bag, L.C. Wood, L. Xu, P. Dhamija, and Y. Kayikci, Big data analytics as an operational excellence approach to enhance sustainable supply chain performance, Resources Conserv. Recycl. 153 (2020), 104559.
[7] A. Barari, Healthcare supply chain evaluation using multi-criteria decision making, Industrial Engineering Master of Science Thesis, University of Science and Culture, 2018.
[8] B.P. Bergeron, Performance Management in Healthcare From Key Performance Indicators to Balanced Scorecard, CRC Press, 2017.
[9] S. Bordoloi, J. Fitzsimmons, and M. Fitzsimmons, Service Management: Operations, Strategy, Information Technology, McGraw-Hill Education, 2022.
[10] V. Duque-Uribe, W. Sarache, and E.V. Gutierrez, Sustainable supply chain management practices and sustainable performance in hospitals: A systematic review and integrative framework, Sustainability 11 (2019), no. 21, 5949.
[11] M. Ghahremanloo, A hybrid performance assessment model based on the data envelopment analysis and multicriteria decision making (Case study: Hospitals), Industrial Management Master of Science Thesis, Shahrood University of Technology, 2018.
[12] A. Golec and G. Karadeniz, Performance analysis of healthcare supply chain management with competency-based operation evaluation, Comput. Ind. Engin. 146 (2020), 106546.
[13] D. Ivanov and A. Dolgui, A digital supply chain twin for managing the disruption risks and resilience in the era of industry 4.0, Prod. Plann. Control 32 (2021), no. 9, 775–788.
[14] C. Kahraman and I. Otay, Fuzzy multi-criteria decision-making using neutrosophic sets, Springer, 2019.
[15] S.S. Kamble, A. Gunasekaran, A. Ghadge, and R. Raut, A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs-a review and empirical investigation, Int. J. Prod. Econ. 229 (2020), 107853.
[16] J.R. Langabeer, Performance Improvement in Hospitals and Health Systems Managing Analytics and Quality in Healthcare, CRC Press, 2018.
[17] E.B. Leksono, S. Suparno, and I. Vanany, Integration of a balanced scorecard, DEMATEL, and ANP for measuring the performance of a sustainable healthcare supply chain, Sustainability 11 (2019), no. 13, 3626.
[18] C.R. McConnell, Hospitals and Health Systems What They are and How They Work, Jones & Bartlett Learning, 2020.
[19] M. Mohammadian, M. Yaghoubi, M.A. Jarrahi, M. Babaei, M. Bahadori, and E. Teymourzadeh, Evaluating the performance of medical equipment supply chain management in military hospitals: A case study, J. Military Med. 23 (2021), no. 1, 75–89.
[20] K. Moons, G. Waeyenbergh, L. Pintelon, P. Timmermans, and D. De Ridder, Performance indicator selection for operating room supply chains: An application of ANP, Oper. Res. Health Care 23 (2019), 100229.
[21] M.A. Pfannstiel and C. Rasche, Service Business Model Innovation in Healthcare and Hospital Management Models, Strategies, Tools, Springer International Publishing, 2017.
[22] M.A. Pfannstiel and C. Rasche, Service Design and Service Thinking in Healthcare and Hospital Management Theory, Concepts, Practice, Springer International Publishing, 2019.
[23] K. Sari and M. Suslu, A modeling approach for evaluating green performance of a hotel supply chain, Technol. Forecast. Soc. Change 137 (2018), 1–8.
[24] H. Singh, Essentials of Management for Healthcare Professionals, CRC Press, 2021.
[25] G.L. Stewart and G.B. Kenneth, Human Resource Management: Linking Strategy to Practice, Wiley, 2010.
[26] T. Supeekit, T. Somboonwiwat, and D. Kritchanchai, DEMATEL-modified ANP to evaluate internal hospital supply chain performance, Comput. Ind. Engin.102 (2016), 318–330.
[27] K. Tas, A. Tas, and F.B. Isin, I-Valued neutrosophic AHP: an application to assess airline service quality after covid-19 pandemy, Neutrosophic Sets Syst. 49 (2022), no. 1, 424-437.
[28] N.W. Tierney, Value Management in Healthcare How to Establish a Value Management Office to Support Value-Based Outcomes in Healthcare, CRC Press, 2018.
[29] M.L. Tseng, M.K. Lim, W.P. Wong, Y.C. Chen, and Y. Zhan, A framework for evaluating the performance of sustainable service supply chain management under uncertainty, Int. J. Prod. Econ. 195 (2018), 359–372.
[30] P. Turner, Leadership in Healthcare Delivering Organisational Transformation and Operational Excellence, Palgrave Macmillan, 2019.
[31] G. Tzeng and J. Huang, Multiple Attribute Decision Making Methods and Applications, CRC Press, 2011.
[32] A. Vafadarnikjoo, M. Tavana, T. Botelho, and K. Chalvatzis, A neutrosophic enhanced best-worst method for considering decision-makers confidence in the best and worst criteria, Ann. Oper. Res. 289 (2020), no. 2, 391–418.
[33] X. Wu and H. Liao, Geometric linguistic scale and its application in multi-attribute decision-making for green agricultural product supplier selection, Fuzzy Sets Syst. 458 (2023), 182–200.
[34] R.M. Zulqarnain, X.L. Xin, M. Saqlain, F. Smarandache, and M.I. Ahamad, An integrated model of neutrosophic TOPSIS with application in multi-criteria decision-making problem, Neutrosophic Sets Syst. 40 (2021), 117–133.
Volume 15, Issue 9
September 2024
Pages 307-318
  • Receive Date: 14 July 2023
  • Revise Date: 26 October 2023
  • Accept Date: 28 October 2023