Performance Measurement on Inventory Management and Logistics Through Various Forecasting Techniques

doi:10.23940/ijpe.21.02.p6.216228

Abstract

Abstract: Currently, inventory and logistics performance play a significant role for the efficiency augmentation and competitiveness of the manufacturing industry. The objective of this research study focuses on inventory management and measurement of logistics performance metrics of on-time in full and vehicle capacity utilization. The problem of maintaining inventory due to the dynamic fluctuating schedule of the customer results in stock-outs due to fluctuating demand and poor performance of on-time in full. Primarily, annual sales data were collected and classified based on ABC-FSN analysis. Then, the outcome of the analysis was used to classify about 123 parts into three classes such as fast, slow, and non-moving categories. Demand forecasting was carried out using various forecasting techniques, followed by ordering policy by economic order quantity. Reorder point model and safety stock were also analyzed. Logistics performance metrics, on-time in full, and vehicle capacity utilization were studied and measured. From the final observed results, it was found that vehicle capacity utilization was poor and all vehicles had an average utilization of 52.61%. Further suggestions were made to demand and adopt the maintaining inventory safety stock and proper packaging of goods to improve the overall performance.

Key words: performance, inventory, ABC-FSN analysis, ordering policy, on-time-in full, logistics, VCU

S. Nallusamy. Performance Measurement on Inventory Management and Logistics Through Various Forecasting Techniques [J]. Int J Performability Eng, 2021, 17(2): 216-228.

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References

1. S. Mitra, S. Reddy,K. Prince, “Inventory Control using FSN Analysis - A Case Study on a Manufacturing Industry,”International Journal of Innovative Science,Engineering and Technology, Vol. 2, No. 4, pp. 322-325, 2015
2. S. A. Khan, “Implementation of Inventory Management System in a Furniture Company: A Real Case Study,”Journal of Engineering Technology, Vol. 2, No. 8, pp. 1457-1474, 2012
3. H. Shen, Q. Deng, R. Lao,S. Wu, “A Case Study of Inventory Management in a Manufacturing Company in China,”Nang Yan Business Journal, Vol. 5, No. 1, pp. 20-40, 2017
4. H. Ravinder and R. Misra, “ABC Analysis for Inventory Management: Bridging the Gap Between Research and Classroom,”American Journal of Business Education, Vol. 7, No. 3, pp. 51-58, 2014
5. G. Majumdar and S. Nallusamy, “ABC Analysis and Diminution of Inventory Level Through Forecasting Technique in a Medium Scale Manufacturing Industry,” inProceedings of the International Conference on Industrial Engineering and Operations Management, pp. 23-26, Pilsen, Czech Republic, 2019
6. R. Kampf, et al., “The Application of ABC Analysis to Inventories in the Automatic Industry Utilizing the Cost Saving Effect,”Journal of Marine Sciences, Vol. 63, No. 3, pp. 120-125, 2016
7. S. Ziukov, “A Literature Review on Models of Inventory Management under Uncertainty,”Business Systems and Economics, Vol. 5, No. 1, pp. 35-48, 2015
8. I. Ahmed and I. Sultana, “A Literature Review on Inventory Modeling with Reliability Considerations,”International Journal of Industrial Engineering Computations, Vol. 5, pp. 169-178, 2014
9. R.S. Angel, S. N. Gomathi,G. Chitra, “Inventory Management - A Case Study,”International Journal of Emerging Research in Management and Technology, Vol. 3, No. 3, pp. 36-42, 2014
10. M. Ketkar and O. S. Vaidya, “Developing Ordering Policy based on Multiple Inventory Classification Schemes,”Social and Behavioural Sciences, Vol. 133, pp. 180-188, 2014
11. S. Nallusamy, R. Balaji,S. Sundar, “Proposed Model for Inventory Review Policy through ABC Analysis in an Automotive Manufacturing Industry,”International Journal of Engineering Research in Africa, Vol. 29, pp. 165-174, 2017
12. Q. Iqbal, D. Malzahn,L. E. Whitman, “Selecting a Multi Criteria Inventory Classification Model to Improve Customer Order Fill Rate,”Advances in Decision Sciences, Vol. 2017, pp. 1-11, 2017
13. Q. Iqbal and D. Malzahn, “Evaluating Discriminating Power of Single Criteria and Multi Criteria Models Towards Inventory Classification,”Computers and Industrial Engineering, Vol. 104, pp. 219-223, 2017
14. M. B. Jeddou, “Multi-Criteria ABC Inventory Classification a Case of Vehicles Spare Parts Items,”Journal of Advanced Management Science, Vol. 2, No. 3, pp. 181-185, 2014
15. J. Park, H. Bae and J. Bae, “Cross Evaluation based Weighted Linear Optimization for Multi-Criteria ABC Inventory Classification, ”Computers and Industrial Engineering, Vol. 76, No. 1, pp. 40-48, 2014
16. C. Burja and V. Burja, “Analysis Model for Inventory Management,”Annals of the University of Petrosani, Vol. 10, No. 1, pp. 43-50, 2010
17. A. Bacchetti, F. Plebani, N. Saccani,A. A. Syntetos, “Empirically Driven Hierarchical Classification of Stock Keeping Units,”International Journal of Production Economics, Vol. 143, pp. 263-274, 2013
18. F. Petropoulos, X. Wang,S. M. Disney, “The Inventory Performance of Forecasting Methods: Evidence from the M3 Competition Data,”International Journal of Forecasting, Vol. 35, No. 1, pp. 251-265, 2019
29. M. D. Nardo, M. Clericuzio, T. Murino,C. Sepe, “An Economic Order Quantity Stochastic Dynamic Optimization Model in a Logistic 4.0 Environment,”Sustainability, Vol. 12, pp. 1-25, 2020
20. K. Balakannan, et al., “Performance Evaluation of Supply Chain and Logistics Management System using Balanced Score Card for Efficiency Enhancement in Indian Automotive Industries,”Indian Journal of Science and Technology, Vol. 9, No. 35, pp. 1-9, 2016
21. L. Goonatilak, “Inventory Management in the Manufacturing Sector in Developing Countries,”Engineering Costs and Production Economics, Vol. 19, pp. 19-24, 2008
22. J. H. Cai, et al., “Optimal Inventory Decisions under Vendor Managed Inventory: Substitution Effects and Replenishment Tactics,”Applied Mathematical Modelling, Vol. 43, pp. 611-629, 2017
23. X. Hong, W. Chunyuan, L. Xu,A. Diabat, “Multiple-Vendor, Multiple-Retailer based Vendor-Managed Inventory,”Annals of Operations Research, Vol.238, No. 1-2, pp. 277-297, 2016
24. J. Wettasinghe and H.T. Luong, “A Vendor Managed Inventory Policy with Emergency Orders,” https://www.tandfonline.com/toc/tjci21/currentVol. 37, No. 2-3, pp. 120-133, 2020

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