Impact of Ordering Decisions on Performance of a Supply Chain – An Experimental and Simulation Study

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Authors

  • Associate Professor, Department of Mechanical Engineering, SITAMS, Chittoor – 517127, Andhra Pradesh ,IN
  • Professor, Department of Mechanical Engineering, NIT Calicut, Calicut – 673601, Kerala ,IN

DOI:

https://doi.org/10.18311/jmmf/2022/32000

Keywords:

Supply chain, inventory policies, supply chain performance

Abstract

A supply chain consists of a network of organizations. Each organization in it acts as either customer or supplier to others. Ordering and replenishment decisions of each organization or stage contribute to supply chain performance. A simulation study and experimentation were conducted in this study to determine the impact of ordering decisions on supply chain performance. The ordering decisions at each stage are taken by intuition for experimentation and are taken by inventory policies for simulation. Under customer demand distribution information sharing, the performance of a supply chain is evaluated and compared between those two conditions. A supply chain role play game software package is used to evaluate supply chain performance by intuition, and simulation is used to evaluate different inventory policies. Fixed order policy, Order Up-to Level (OUL), Modified OUL (MOUL), (r, Q) and (r, S) inventory policies are considered in this study. The performance measures used are the total supply chain inventory per period, bullwhip effect and supply chain fill rate. Grey Relational Analysis (GRA) is used to identify the best method to take decisions in supply chain. Results show that supply chain performance is best under MOUL policy. Based on the results of this study, supply chain members are encouraged to identify the best inventory policy and use that rather than making decisions based on intuition.

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Published

2022-12-08

How to Cite

Pamulety, T. C., & Pillai, V. M. (2022). Impact of Ordering Decisions on Performance of a Supply Chain – An Experimental and Simulation Study. Journal of Mines, Metals and Fuels, 70(8A), 370–375. https://doi.org/10.18311/jmmf/2022/32000

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References

Cachon, G.P., Randall, T. and Schmidt, G.M. In search of the bullwhip effect. Manufacturing & Service Operations Management. 2007; 9(4);457-479. DOI: https://doi.org/10.1287/msom.1060.0149

Croson, R. and Donohue, K. Impact of POS data sharing on supply chain management: an experiment. Production and Operations Management, 2003; 12(1);1-11. DOI: https://doi.org/10.1111/j.1937-5956.2003.tb00194.x

Croson, R. and Donohue, K. Behavioural causes of the bullwhip effect and the observed value of inventory information. Management Science. 2006; 52 (3): 323-336. DOI: https://doi.org/10.1287/mnsc.1050.0436

Int. J. Business and Data Analytics, Vol. 1, No. 3, 2020

Kumar, R., Johnson, R., Mohandas, R., Pramod, P., Dony, S.K. and Pillai, V.M. Determination of Optimal ordering policy using genetic algorithm for a multi-stage serial supply chain. Advanced manufacturing systems and innovative product design, Lecture Notes in Mechanical Engineering. 2021; 507-514. DOI: https://doi.org/10.1007/978-981-15-9853-1_42

Steckel, J.H., Gupta, S. and Banerji, A . Supply chain decision making: will shorter cycle times and shared point-of-sale information necessarily help? Management Science. 2004; 50 (4): 458-464. DOI: https://doi.org/10.1287/mnsc.1030.0169

Mathew, M. and Rajendrakumar, P.K. Optimization of process parameters of borocarburized low carbon steel for tensile strength by Taguchi method with grey relational analysis. Materials and Design. 2011; 32(6);3637- 3644. DOI: https://doi.org/10.1016/j.matdes.2011.02.007

Pamulety, T.C. and Pillai, V.M. Effect of customer demand information sharing on a four-stage serial supply chain performance: an experimental study, Uncertain Supply chain management. 2016; 4; 1-16. DOI: https://doi.org/10.5267/j.uscm.2015.10.001

Pamulety, T.C., Joby, G. and Pillai, V.M. an inventory position-based inventory policy for better supply chain performance, Journal of Industrial and Production Engineering. 2017; 34(3) ; 180-198. DOI: https://doi.org/10.1080/21681015.2016.1243166

Shi, C. and Bian, D. On impact of information sharing in the supply chain bullwhip effect, IEEE transactions. 2010; 329-333.

Sterman, J.D. Modelling managerial behaviour: Misperceptions of feedback in a dynamic decision making experiment. Management Science. 1989; 35(3); 321-339. DOI: https://doi.org/10.1287/mnsc.35.3.321

Sunny, J., Pillai, V.M., Hiran, V.N, Kenil, S., Prajwal, P.D., Manu, J.P., and Malhar, S., Blockchain-enabled beer game: a software tool for familiarizing the application of blockchain in supply chain management. Industrial Management & Data Systems. 2022; 122(4); 1025-1055. DOI: https://doi.org/10.1108/IMDS-10-2021-0609

Wadhwa, S., Bibhushan and Chan, F. T. S.. Inventory performance of some supply chain inventory policies under impulse demand. International Journal of Production Research. 2009; 47 (12); 3307-3332. DOI: https://doi.org/10.1080/00207540701689750

Zhang, C. and Zhang, C. Design and simulation of demand information sharing in a supply chain. Simulation Modelling Practice and Theory. 2007; 15; 32-46. DOI: https://doi.org/10.1016/j.simpat.2006.09.011