Prioritizing Factors Influencing the Performance of a Supply Chain System using Hybrid Structural Interaction Matrix

Document Type : Research Paper

Authors

1 Faculty of Management Sciences, Tshwane University of Technology, Pretoria, South Africa

2 Department of Industrial and Systems Engineering, University of Pretoria, Pretoria, South Africa

Abstract

Ascertaining and prioritising the various factors that can influence the performance of a supply chain system is vital towards creating measures that are tailored towards controlling these factors, in order to ensure a sustainable supply chain. This research subject matter has been solved in the literature using multi-criteria decision making techniques, whose prioritization solutions are generated using experts opinions, which are subjective, thereby decision obtained could be prone to biases. In light of this, this study present an Hierarchical Structural Interaction Matrix (HSIM) approach, whose prioritisation computation is premised on the theory of subordination derived via systems thinking, to prioritize various factors influencing the performance of a supply chain system. In order to achieve this, firstly, all the factors that could influence the performance of a supply chain system were identified from the literature. Thereafter, a Binary Interaction Matrix, which unveil the arrays of subordinations that exist amidst a number of the identified factors was developed. The result of this exercise was thereafter numerically analysed using appropriate mathematical equations to determine the intensity rating score of each supply chain performance factor. Furthermore, Pareto analysis was conducted using the latter results, to unveil the vital few factors that could influence the performance of a supply chain system used in an organisation. The result of the study unveiled that supply chain performance of an organisation can be exponentially improved, if supply chain managers can focus and concentrate their management efforts more on 11 critical factors obtained from the prioritisation analyses.

Keywords

References

Abbas, H., Alawi, A. A. and Maktoumi, K. A. (2020). Prioritising service supply-chain performance measures using multi-criteria decision-making methodologies. The Journal of Asian Finance, Economics, and Business, Vol. 7(11), pp. 843-851.
Aramyan, L. H., Lansink, A. G. O., Van Der Vorst, J. G. and Van Kooten, O. (2007). Performance measurement in agri‐food supply chains: a case study. Supply Chain Management: An International Journal, Vol. 12(4), pp. 304-315.
Arzu Akyuz, G. and Erman Erkan, T. (2010). Supply chain performance measurement: a literature review. International Journal of Production Research, Vol. 48(17), pp. 5137-5155.
Ayomoh, M. K. O. and Oke, S. A. (2006). A framework for measuring safety level for production environments. Safety Science, Vol. 44(3), pp. 221-239.
Ayomoh, M. K. O., Oke, S. A., Adedeji, W. O. and Charles-Owaba, O. E. (2008). An approach to tackling the environmental and health impacts of municipal solid waste disposal in developing countries. Journal of Environmental Management, Vol. 88(1), pp. 108-114.
Barber, E. (2008). How to measure the “value” in value chains. International Journal of Physical Distribution & Logistics Management, Vol. 38(9), pp. 685-698.
Bhagwat R. and Sharma M.K. (2007). Performance measurement of supply chain management: A balanced Scorecard approach. Computers & Industrial Engineering, Vol. 53(1), pp. 43-62.
Bhatnagar, R. and Sohal, A. S. (2005). Supply chain competitiveness: measuring the impact of location factors, uncertainty and manufacturing practices. Technovation, Vol. 25(5), pp. 443-456.
Bigliardi, B. and Bottani, E. (2014). Supply chain performance measurement: a literature review and pilot study among Italian manufacturing companies. International Journal of Engineering, Science and Technology, Vol. 6(3), pp. 1-16.
Cagnazzo, L., Taticchi, P. and Brun, A. (2010). The role of performance measurement systems to support quality improvement initiatives at supply chain level. International Journal of Productivity and Performance Management, Vol. 59(2), pp. 163-185.
Chae, B. K. (2009). Developing key performance indicators for supply chain: an industry perspective. Supply Chain Management: An International Journal, Vol.14 (6), pp.  422-428.
De Leeuw S. and Beekman L. (2008). Supply chain-oriented performance measurement for automotive spare parts. International Journal of Automotive Technology and Management, Vol. 8(1), pp. 56-70.
El-Baz, M. A. (2011). Fuzzy performance measurement of a supply chain in manufacturing companies. Expert Systems with Applications, Vol. 38(6), pp. 6681-6688.
George, J. and Pillai, V. M. (2019). A study of factors affecting supply chain performance. In: Journal of Physics: Conference Series, Vol. 15(1), pp. 1-9.
Goli, A. and Mohammadi, H. (2021). Developing a sustainable operational management system using hybrid Shapley value and Multimoora method: case study petrochemical supply chain. Environment, Development and Sustainability, Vol. 1(1), pp. 1-30.
Gopal, P. R. C. and Thakkar, J. (2012). A review on supply chain performance measures and metrics: 2000‐2011. International Journal of Productivity and Performance Management, Vol. 61(5), pp. 518-547.
Govindan, K., Mangla, S. K. and 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, Vol. 28(6-8), pp. 552-573.
Gunasekaran, A., Patel, C. and McGaughey, R. E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, Vol. 87(3), pp. 333-347.
Kleijnen, J. P. and Smits, M. T. (2003). Performance metrics in supply chain management. Journal of the operational research society, Vol. 54(5), pp. 507-514.
Kocaoğlu, B., Gülsün, B. and Tanyaş, M. (2013). A SCOR-based approach for measuring a benchmarkable supply chain performance. Journal of Intelligent Manufacturing, Vol. 24(1), pp. 113-132.
Kurien, G. P. and Qureshi, M. N. (2011). Study of performance measurement practices in supply chain management. International Journal of Business, Management and Social Sciences, Vol. 2(4), pp. 19-34.
Mabrouk, N., Omri, A. and Jarraya, B. (2020). Factors influencing the performance of supply chain management in Saudi SMEs. Uncertain Supply Chain Management, Vol. 8(3), pp. 569-578.
Makinde, O.A., Mowandi, T., Munyai, T.T. and Ayomoh, M.K. (2019). Performance assessment of the supply chain system of a food industry using a Questionnaire-based approach. Procedia Manufacturing, Vol. 42, pp. 1- 9.
Neely, A., Adams, C. and Crowe, P. (2001). The performance prism in practice. Measuring business excellence.
Ouzrout, Y., Savino, M., Bouras, A. and Di Domenico, C. (2018). Supply chain management analysis: a simulation approach of the Value Chain Operations Reference model (VCOR). arXiv preprint arXiv:1811.01683.
Öztayşi, B. and Sürer, Ö. (2014). Supply chain performance measurement using a SCOR based fuzzy VIKOR approach. In Supply chain management under fuzziness, Springer, Berlin, Heidelberg, pp. 199-224.
Quesada, H., Gazo, R. and Sanchez, S. (2012). Critical factors affecting supply chain management: A case study in the US pallet industry. Pathways to Supply Chain Excellence, pp. 33-56.
Ramírez-Granados, M., Hernández, J.E. and Lyons, A.C. (2014). A discrete-event simulation model for supporting the first-tier supplier decision-making in a UK's Automotive Industry. Journal of Applied Research and Technology, Vol. 12(5), pp. 860-870.
Ren, C., Dong, J., Ding, H. and Wang, W. (2006). A SCOR-based framework for supply chain performance management. In 2006 IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 1130-1135.
Rungklin, D. and Srimai, S. (2019). Supply Chain Performance Measurement in the Manufacturing Industry. WMS Journal of Management, Vol. 8(1), pp. 98-109.
Sezen, B. (2008). Relative effects of design, integration and information sharing on supply chain performance. Supply Chain Management: An International Journal, Vol. 13(3), pp. 233-240.
Singh, R.K. (2013). Prioritising the factors for coordinated supply chain using Analytic Hierarchy Process (AHP). Measuring Business Excellence, Vol. 17(1), pp. 80-97.
Soni, G. and Kodali, R. (2010). Internal benchmarking for assessment of supply chain performance. Benchmarking: An International Journal, Vol. 17(1), pp. 44-76.
Tarasewicz, R. (2016). Integrated approach to supply chain performance measurement–results of the study on Polish market. Transportation Research Procedia, Vol. 14, pp. 1433-1442.
Vernadat, F., Shah, L., Etienne, A. and Siadat, A. (2013). VR-PMS: a new approach for performance measurement and management of industrial systems, International Journal of Production Research, Vol. 51(23), pp. 7420-7438.
Wong, W.P. and Wong, K.Y. (2008). A review on benchmarking of supply chain performance measures. Benchmarking: An International Journal, Vol. 15(1), pp. 25-51.
International Journal of Supply and Operations Management
Volume 10, Issue 1
February 2023
Pages 23-37

Files

Share

How to cite

Statistics