Measuring performance of healthcare supply chains in India: A comparative analysis of multi-criteria decision making methods

  • Sanjib Biswas Calcutta Business School, West Bengal, India
Keywords: Healthcare Supply Chain, Financial Metrics, PIPRECIA, MABAC, CoCoSo, MARCOS

Abstract

TThe supply chain forms the backbone of any organization. However, the effectiveness and efficiency of every activity get manifested in the financial outcome. Hence, measuring supply chain performance using financial metrics carries significance. The purpose of this paper is to carry out a comparative analysis of supply chain performances of leading healthcare organizations in India. In this regard, this paper presents an integrated multi-criteria decision making (MCDM) framework wherein we derive the weights of the criteria based on experts’ opinions using PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) method. We then apply three distinct frameworks such as Multi-Attributive Border Approximation area Comparison (MABAC), Combined Compromise Solution (CoCoSo) and Measurement of alternatives and ranking according to COmpromise solution (MARCOS) for ranking purpose. In this context, this paper presents a comparative analysis of the results obtained from these approaches. The results show that large cap firms do not necessarily perform well. Further, the results of three MCDM frameworks demonstrates consistency.

Downloads

Download data is not yet available.

References

Al-Hussaini, A. (2019). A study on sales growth and market value through supply chain. Uncertain Supply Chain Management, 7(3), 457-470.

Avelar-Sosa, L., García-Alcaraz, J. L., & Maldonado-Macías, A. A. (2019). Supply chain performance attributes and benefits in the manufacturing industry. In Evaluation of Supply Chain Performance (pp. 129-147). Springer, Cham.

Aouadni, S., Rebai, A., & Turskis, Z. (2017). The meaningful mixed data TOPSIS (TOPSIS-MMD) method and its application in supplier selection. Studies in Informatics and Control, 26(3), 353-363.

Askariazad, M., & Wanous, M. (2009). A proposed value model for prioritising supply chain performance measures. International Journal of Business Performance and Supply Chain Modelling, 1(2-3), 115-128.

Bagchi, B., Chakrabarti, J., & Basu Roy, P. (2012). Influence of working capital management on profitability: A study on Indian FMCG companies. International Journal of Business and Management, 7(22), 1-10.

Basak, I., & Saaty, T. (1993). Group decision making using the analytic hierarchy process. Mathematical and Computer Modelling, 17(4-5), 101-109.

Barzilai, J., & Lootsma, F. A. (1997). Power relations and group aggregation in the multiplicative AHP and SMART. Journal of Multi-Criteria Decision Analysis, 6(3), 155-165.

Bauer, D. (2007). Working capital management: Driving additional value within AP. Financial Executive, 23(8), 60- 63.

Bhagwat, R., & Sharma, M. K. (2007). Performance measurement of supply chain management using the analytical hierarchy process. Production Planning and Control, 18(8), 666-680.

Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P. K., Brady, M., Tiwari, M. K., & Nudurupati, S. S. (2014). Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach. Production Planning and Control, 25(8), 698-714.

Biswas, S., & Pamucar, D. (2020). Facility Location Selection for B-Schools in Indian Context: A Multi-Criteria Group Decision Based Analysis. Axioms, 9(3), 77. https://doi.org/10.3390/axioms9030077

Biswas, S., Bandyopadhyay, G., Guha, B., & Bhattacharjee, M. (2019). An ensemble approach for portfolio selection in a multi-criteria decision making framework. Decision Making: Applications in Management and Engineering, 2(2), 138-158.

Brewer, P. C., & Speh, T.W. (2000). Using the balanced scorecard to measure supply chain performance Peter C Brewer; Thomas WSpeh. Journal of Business logistics, 21(1), 75-93

Capkun, V., Hameri, A. P., & Weiss, L.A. (2009). On the relationship between inventory and financial performance in manufacturing companies. International Journal of Operations and Production Management, 29(8), 789-806.

Chen, H., Frank, M. Z., & Wu, O. Q. (2005). What actually happened to inventories of American companies between 1981 and 2000? Management Science, 51(7), 1015-1031.

Chithambaranathan, P., Subramanian, N., & Palaniappan, P. K. (2015). An innovative framework for performance analysis of members of supply chains. Benchmarking: An International Journal, 22(2), 309-334.

Christopher, M. (2005). Logistics and Supply Chain Management: Creating Value-Adding Networks, FT/Prentice-Hall, Harlow

Christopher, M. (1998). Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service, FT/Prentice-Hall, Harlow.

Churchill, N. C., & Mullins, J. W. (2001). How fast can your company afford to grow? Harvard Business Review, 79(5), 135-142.

D’Avanzo, R. L., Starr, C. E., & Von Lewinski, H. (2004). Supply chain and the bottom line: A critical link. Outlook, 1(1), 38-45.

Debnath, A., Roy, J., Kar, S., Zavadskas, E.K., & Antucheviciene, J. (2017) A hybrid MCDM approach for strategic project portfolio selection of agro by-products. Sustainability, 9(8), 1302.

Deloof, M. (2003). Does working capital management affect profitability of Belgian firms? Journal of Business Finance and Accounting, 30(3/4), 573-587.

de Vries, J., & Huijsman, R. (2011). Supply chain management in health services: an overview. Supply Chain Management: An International Journal, 16(3), 159-165.

Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2016). Multi objective performance analysis: A novel multi-criteria decision making approach for a supply chain. Computers & Industrial Engineering, 94, 105-124.

Elgazzar, S. H., Tipi, N. S., Hubbard, N. J., and Leach, D. Z. (2012). Linking supply chain processes’ performance to a company’s financial strategic objectives. European Journal of Operational Research, 223(1), 276-289.

Ellram, L.M., & Liu, B. (2002). The financial impact of supply management. Supply Chain Management Review, 6(6), 30-37.

Ellram, L.M., Zsidisin, G.A., Siferd, S.P., & Stanly, M.J. (2002).The impact of purchasing and supply management activities on corporate success. Journal of Supply Chain Management, 38(1), 4-17.

Falope, O.I., & Ajilore, O.T. (2009). Working capital management and corporate profitability: evidence from panel data analysis of selected quoted companies in Nigeria. Research Journal of Business Management, 3, 73-84.

Farris, M.T., & Hutchison, P.D. (2002). Cash-to-cash: the new supply chain management metric. International Journal of Physical Distribution and Logistics Management, 32(4), 288-98.

Farris, M. T. II & Hutchison, P. D. (2003). Measuring cash-to-cash performance. The International Journal of Logistics Management, 14(2), 83-91.

Fawcett, S. E., Waller, M. A., & Fawcett, A. M. (2010). Elaborating a dynamic systems theory to understand collaborative inventory successes and failures. The International Journal of Logistics Management, 21(3), 510-537.

Fekpe, E., & Delaporte, Y. (2019). Sustainability integration and supply chain performance of manufacturing small and medium size enterprises. African Journal of Economic and Management Studies, 10 (2), 130-147. https://doi.org/10.1108/AJEMS-05-2018-0152

Fleming, P. J., & Wallace, J. J. (1986). How not to lie with statistics: the correct way to summarize benchmark results. Communications of the ACM, 29(3), 218-221.

Frohlich, M.T., & Westbrook, R. (2001). Arcs of integration: an international study of supply chain strategies. Journal of Operations Management, 19(2), 185-200.

Ganesan, S., George, M., Jap, S., Palmatier, R. W., & Weitz, B. (2009). Supply chain management and retailer performance: emerging trends, issues, and implications for research and practice. Journal of retailing, 85(1), 84-94.

García-Teruel, J. P., & Martinez-Solano, P. (2007). Effects of working capital management on SME profitability. International Journal of managerial finance, 3(2), 164-177.

Ghosh, I., & Biswas, S. (2016). A comparative analysis of multi-criteria decision models for ERP package selection for improving supply chain performance. Asia-Pacific Journal of Management Research and Innovation, 12(3-4), 250-270.

Govindan, K., Mangla, S. K., & Luthra, S. (2017). Prioritising indicators in improving supply chain performance using fuzzy AHP: insights from the case example of four Indian manufacturing companies. Production Planning & Control, 28(6-8), 552-573.

Grida, M., Mohamed, R., & Zaid, A. H. (2020). A Novel Plithogenic MCDM Framework for Evaluating the Performance of IoT Based Supply Chain. Neutrosophic Sets and Systems, 33(1), Article 21, 323-341.

Gunasekaran, A., Patel, C. & McGaughey, R. E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, 87(3), 333-347.

Huang, X., Kristal, M. M., & Schroeder, R. G. (2008). Linking learning and effective process implementation to mass customization capability. Journal of Operations Management, 26(6), 714- 729.

Janaki, D., Izadbakhsh, H., & Hatefi, S. (2018). The evaluation of supply chain performance in the Oil Products Distribution Company, using information technology indicators and fuzzy TOPSIS technique. Management Science Letters, 8(8), 835-848.

Johnson, M., & Templar, S. (2011). The relationships between supply chain and firm performance: the development and testing of a unified proxy. International Journal of Physical Distribution and Logistics Management, 41(2), 88-103.

Jose, M. L., Lancaster, C., & Stevens, J. L. (1996). Corporate returns and cash conversion cycles. Journal of Economics and finance, 20(1), 33-46.

Jothimani, D., & Sarmah, S. P. (2014). Supply chain performance measurement for third party logistics. Benchmarking: An International Journal, 21(6), 944-963.

Kancharla, S. P., & Hegde, V. G. (2016). Inferences about Supply Chain Practices using Financial Ratios. Journal of Supply Chain and Operations Management, 14(1), 144-161.

Keršulienė, V., & Turskis, Z. (2011). Integrated fuzzy multiple criteria decision making model for architect selection. Technological and Economic Development of Economy, 17(4), 645-666.

Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of business economics and management, 11(2), 243-258.

Ketzenberg, M. E., Van Der Laan, E., and Teunter, R. H. (2006). Value of information in closed loop supply chains. Production and Operations Management, 15(3), 393-406.

Kocaoğlu, B., Gülsün, B., and Tanyaş, M. (2013). A SCOR based approach for measuring a benchmarkable supply chain performance. Journal of Intelligent Manufacturing, 24(1), 113-132.

Koumanakos, D. P. (2008). The effect of inventory management on firm performance. International Journal of Performance Management, 57(5), 355-369.

Krause, D. R., Vachon, S., and Klassen, R. D. (2009). Special topic forum on sustainable supply chain management: introduction and reflections on the role of purchasing management. Journal of Supply Chain Management, 45(4), 18-25.

Kremers, L. (2010). The link between supply chain and finance. Supply Chain Asia, 22-25.

Kwon, I. W. G., Kim, S. H., and Martin, D. G. (2016). Healthcare supply chain management; strategic areas for quality and financial improvement. Technological Forecasting and Social Change, 113, 422-428.

Lalonde, B.J. (2000). Making finance take notice. Supply Chain Management Review, 4(5), 11-12.

Lambert, D.M., and Cooper, M.C. (2000). Issues in supply chain management. Industrial Marketing Management, 29(1), 65-83.

Lazaridis, I., and Tryfonidis, D. (2006). Relationship between working capital management and profitability of listed companies in the Athens stock exchange. Journal of Financial Management and Analysis, 19 (1), 26 – 35.

Lester, M. (1999). An interview with Bill L. Ramsey: One on one. Journal of Supply Chain Management, 35(4), 2-6.

Li, S., Ragu-Nathan, B., Ragu-Nathan, T. S., and Rao, S. S. (2006). The impact of supply chain management practices on competitive advantage and organizational performance. Omega, 34(2), 107-124.

Lockamy III, A., and McCormack, K. (2004). Linking SCOR planning practices to supply chain performance: An exploratory study. International journal of operations and production management, 24(12), 1192-1218.

Moharamkhani, A., Bozorgi-Amiri, A., and Mina, H. (2017). Supply chain performance measurement using SCOR model based on interval-valued fuzzy TOPSIS. International Journal of Logistics Systems and Management, 27(1), 115-132.

Moran, P. (2011). Competitive edge: How early payment discounts can help your business. Accountancy Ireland, 43(1), 41-43.

Mentzer, J. T., Flint, D. J., and Kent, J. L. (1999). Developing a logistics service quality scale. Journal of Business logistics, 20(1.1999).

Mukhametzyanov, I., & Pamucar, D. (2018). A sensitivity analysis in MCDM problems: A statistical approach. Decision making: applications in management and engineering, 1(2), 51-80.

Najmi, A., & Makui, A. (2010). Providing hierarchical approach for measuring supply chain performance using AHP and DEMATEL methodologies. International Journal of Industrial Engineering Computations, 1(2), 199-212.

Nelsen, R. B. (1992). On measures of association as measures of positive dependence. Statistics and Probability Letters, 14(4), 269-274.

Okumuş, H., Ghorbani, S., and Karatepe, S. (2019). A study on relationship between financial performance and supply chain in the accepted companies in Borsa Istanbul. Uncertain Supply Chain Management, 7(3), 417-426.

Özbayraka, M., and Akgün, M. (2006). The effects of manufacturing control strategies on the cash conversion cycle in manufacturing systems. International Journal of Production Economics, 103(2), 535-550.

Padachi, K. (2006). Trends in working capital management and its impact on firms’ performance: an analysis of Mauritian small manufacturing firms. International Review of Business Research Papers, 2 (2), 45 - 58.

Pamučar, D. S., Božanić, D., & Ranđelović, A. (2016). Multi-criteria decision making: An example of sensitivity analysis. Serbian journal of management, 12(1), 1-27. DOI:10.5937/sjm12-9464

Pamučar, D., and Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42(6), 3016-3028.

Parasuraman, A., Berry, L. L., and Zeithaml, V. A. (1991). Refinement and reassessment of the SERVQUAL scale. Journal of retailing, 67(4), 420-451.

Parkan, C., and Wang, J. (2007). Gauging the performance of a supply chain. International Journal of Productivity and Quality Management, 2(2), 141-176.

Pramod, V. R., and Banwet, D. K. (2011). Performance measurement of SHER service supply chain: a balanced score card–ANP approach. International Journal of Business Excellence, 4(3), 321-345.

Raghavan, N. S. and Mishra, V. K. (2011). Short-term financing in a cash-constrained supply chain. International Journal of Production Economics, 134(2), 407-412.

Raheman, A., and Nasr, M. (2007). Working capital management and profitability–case of Pakistani firms. International review of business research papers, 3(1), 279-300.

Ray, G., Barney, J. B., and Muhanna, W. A. (2004). Capabilities, business processes, and competitive advantage: choosing the dependent variable in empirical tests of the resource‐based view. Strategic management journal, 25(1), 23-37.

Richards, V. D., and Laughlin, E. J. (1980). A cash conversion cycle approach to liquidity analysis. Financial Management, 9(1), 32-38.

Rouyendegh, B. D., Baç, U., and Erkan, T. E. (2014). Sector selection for ERP implementation to achieve most impact on supply chain performance by using AHP-TOPSIS hybrid method. Tehnicki Vjesnik-Technical Gazette, 21(5), 933-937.

Roy, J., Ranjan, A., Debnath, A., and Kar, S. (2016). An extended MABAC for multi-attribute decision making using trapezoidal interval type-2 fuzzy numbers. arXiv preprint arXiv:1607.01254.

Roy, J., Chatterjee, K., Bandyopadhyay, A., and Kar, S. (2018). Evaluation and selection of medical tourism sites: A rough analytic hierarchy process based multi‐attributive border approximation area comparison approach. Expert Systems, 35(1), e12232.

Schneller, E. S., and Smeltzer, L. R. (2006). Strategic management of the health care supply chain. Jossey-Bass.

Shafiee, M., Lotfi, F. H., and Saleh, H. (2014). Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach. Applied Mathematical Modelling, 38(21-22), 5092-5112.

Shah, R., and Shin, H. (2007). Relationships among information technology, inventory, and profitability: An investigation of level invariance using sector level data. Journal of Operations Management, 25(4), 768- 784.

Sharma, H. K., Roy, J., Kar, S., and Prentkovskis, O. (2018). Multi Criteria Evaluation Framework for Prioritizing Indian Railway Stations Using Modified Rough AHP-Mabac Method. Transport and Telecommunication Journal, 19(2), 113-127.

Simanaviciene, R., & Ustinovichius, L. (2010). Sensitivity analysis for multiple criteria decision making methods: TOPSIS and SAW. Procedia-Social and Behavioral Sciences, 2(6), 7743-7744.

Singhal, V. (2005). Excess inventory and long-term stock price performance. Working Paper. Georgia Institute of Technology.

Stanković, M., Stević, Ž., Das, D. K., Subotić, M., & Pamučar, D. (2020). A new fuzzy MARCOS method for road traffic risk analysis. Mathematics, 8(3), 457. https://doi.org/10.3390/math8030457

Stanujkic, D., Zavadskas, E. K., Karabasevic, D., Smarandache, F., and Turskis, Z. (2017). The Use of the Pivot Pairwise Relative Criteria Importance Assessment Method for Determining the Weights of Criteria. Romanian Journal of Economic Forecasting, 20(4), 116-133.

Stanujkic, D., Djordjevic, B., and Karabasevic, D. (2015). Selection of Candidates in the Process of Recruitment and Selection of Personnel Based on the SWARA and ARAS Methods. Quaestus Multidisciplinary Research Journal,(7) June, 55, 53-64.

Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231. https://doi.org/10.1016/j.cie.2019.106231

Stević, Ž., & Brković, N. (2020). A Novel Integrated FUCOM-MARCOS Model for Evaluation of Human Resources in a Transport Company. Logistics, 4(1), 4. https://doi.org/10.3390/logistics4010004

Stewart, G. (1995). Supply chain performance benchmarking study reveals keys to supply chain excellence. Logistics Information Management, 8(2), 38-44.

Sufiyan, M., Haleem, A., Khan, S., & Khan, M. I. (2019). Evaluating food supply chain performance using hybrid fuzzy MCDM technique. Sustainable Production and Consumption, 20, 40-57.

Swamidass, P. M. (2007). The effect of TPS on US manufacturing during 1991-1998: Inventory increased or decreased as a function of plant performance. International Journal of Production Research, 45(16), 3763- 3778.

Tan, K. C., Kannan, V. R., Handfield, R. B., and Ghosh, S. (1999). Supply chain management: an empirical study of its impact on performance. International journal of operations and production Management, 19(10), 1034-1052.

Tangsucheeva, R., and Prabhu, V. (2013). Modeling and analysis of cash-flow bullwhip in supply chain. International Journal of Production Economics, 145(1), 431-447.

Timme, S. G., and Wanberg, E. (2011). How supply chain finance can drive cash flow. Supply Chain Management Review, (January/February), 18-24.

Tripathi, S., Rangarajan, K., & Talukder, B. (2019). Segmental differences in pharmaceutical industry and its impact on supply chain performance. International Journal of Pharmaceutical and Healthcare Marketing, 13(4), 516-540. https://doi.org/10.1108/IJPHM-12-2018-0063

Tyagi, M., Kumar, P., and Kumar, D. (2014). A hybrid approach using AHP-TOPSIS for analyzing e-SCM performance. Procedia Engineering, 97, 2195-2203.

Tseng, M. L., Lin, Y. H., Tan, K., Chen, R. H., and Chen, Y. H. (2014). Using TODIM to evaluate green supply chain practices under uncertainty. Applied Mathematical Modelling, 38(11-12), 2983-2995.

Uyar, A. (2009). The relationship of cash conversion cycle with firm size and profitability: an empirical investigation in Turkey. International Research Journal of Finance and Economics, 24(2), 186-193.

Uygun, Ö., & Dede, A. (2016). Performance evaluation of green supply chain management using integrated fuzzy multi-criteria decision making techniques. Computers & Industrial Engineering, 102, 502-511.

Varma, S., Wadhwa, S., and Deshmukh, S. G. (2008). Evaluating petroleum supply chain performance: application of analytical hierarchy process to balanced scorecard. Asia Pacific Journal of Marketing and Logistics, 20(3), 343-356.

Vesković, S., Stević, Ž., Stojić, G., Vasiljević, M., and Milinković, S. (2018). Evaluation of the railway management model by using a new integrated model DELPHI-SWARA-MABAC. Decision Making: Applications in Management and Engineering, 1(2), 34-50.

Wagner, S. M., Grosse-Ruyken, P. T., and Erhun, F. (2012). The link between supply chain fit and financial performance of the firm. Journal of Operations Management, 30(4), 340-353.

Wisner, P. (2011). Linking supply chain performance to a firm’s financial performance. Supply Chain Management Review.

Wong, W. P., & Wong, K. Y. (2007). Supply chain performance measurement system using DEA modeling. Industrial Management & Data Systems, 107(3), 361-381.

Xue, Y.X., You, J.X., Lai, X.D., and Liu, H.C. (2016). An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information. Applied Soft Computing, 38, 703–713.

Yang, J. (2009). Integrative performance evaluation for supply chain system based on logarithm triangular fuzzy number-AHP method. Kybernetes, 38(10), 1760-1770.

Yazdani, M., Zarate, P., Zavadskas, E.K., and Turskis, Z. (2018). A Combined Compromise Solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision. https://doi.org/10.1108/MD-05-2017-0458 (Accessed on Jan 10, 2019)

Yu, S. M., Wang, J., and Wang, J. Q. (2017). An interval type-2 fuzzy likelihood-based MABAC approach and its application in selecting hotels on a tourism website. International Journal of Fuzzy Systems, 19(1), 47-61.

Zeleny, M. (1973). Compromise programming, in Cocchrane, J.L. and Zeleny, M. (Eds), Multiple Criteria Decision Making, University of South Carolina Press, Columbia, SC, 262-301.

Zhu, Q., and Sarkis, J. (2004). Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises. Journal of operations management, 22(3), 265-289.

Published
2020-10-11
How to Cite
Biswas, S. (2020). Measuring performance of healthcare supply chains in India: A comparative analysis of multi-criteria decision making methods. Decision Making: Applications in Management and Engineering, 3(2), 162-189. Retrieved from https://dmame.rabek.org/index.php/dmame/article/view/133