Study on Effective Inventory Management by Determining the Appropriate Safety Stock in an Automobile Manufacturing Industry

International Journal of Mechanical Engineering
© 2020 by SSRG - IJME Journal
Volume 7 Issue 7
Year of Publication : 2020
Authors : D. Sobya, Parthasarathi Chakraborty
How to Cite?

D. Sobya, Parthasarathi Chakraborty, "Study on Effective Inventory Management by Determining the Appropriate Safety Stock in an Automobile Manufacturing Industry," SSRG International Journal of Mechanical Engineering, vol. 7,  no. 7, pp. 10-17, 2020. Crossref,


Inventory management mainly focuses on planning and controlling the inventory level of production industries and plays an important role in the production. The revenue and the reputation of the organization depend largely on the service level at which the customers are served. The service level can be improved by reducing the mismatch between the supply and the demand. The methods used to reduce the supply and demand mismatch are forecasting, determining the safety stock required, and determination of the service level required. The optimum forecasting method for the organization is selected based on the Mean Absolute Percentage Error (MAPE) method. Based on the forecast and the error in the forecast on the historical demand, the tracking signal or bias is calculated. Goods are segmented on the volume of the demand, and different service levels for different segments of products are used to calculate the safety stock. The total value of the stock is calculated based on the unit cost of the product and is compared with the actual value of the stock. The results obtained show that an overall savings of about Rs.75000 can be obtained.


Inventory, Forecasting, MAPE, Tracking Signal, Service Level, Safety Stock


[1] Arthur V. Hill et al, Forecasting the forecastability quotient for inventory Management, International Journal of Forecasting. 25 (2015) 651-663.
[2] S. Nallusamy, R. Balaji and S. Sundar, Proposed model for inventory review policy through ABC analysis in the automotive manufacturing industry, International Journal of Engineering Research in Africa. 29 (2017) 165-174.
[3] Qamar Iqbal, Don Malzahn, and Lawrence E. , Whitman Selecting a multi-criteria inventory classification model to improve customer order fill rate, Advances in Decision Sciences (2017) 01-11.
[4] Philipp Moser, Inventory dynamics in process industries: An empirical investigation, International Journal of Production Economics. 191 (2017) 253–266.
[5] M.B. Jeddou, Multi-criteria ABC inventory classification, a case of vehicles spare parts items, Journal of Advanced Management Science. 2(3) (2014) 181-185.
[6] J. Park, Bae and J. Bae, Cross evaluation based weighted linear optimization for multi-criteria ABC inventory classification, Computers and Industrial Engineering, 76(1) (2014) 40-48.
[7] Camelia Burja and Vasile Burja, Analysis model for inventory management, Annals of the University of Petrosani, 10(1) (2010) 43-50.
[8] A. Bacchetti, F. Plebani, Saccani and Syntetos, Empirically driven hierarchical classification of stock-keeping units, International Journal of Production Economics. 143 (2013) 263-274.
[9] Petropoulos, Wang and Disney, The inventory performance of forecasting methods: Evidence from the M3 competition data, International Journal of Forecasting. 35(1) (2019) 251-265.
[10] Mario Di Nardo, Mariano Clericuzio, Teresa Murino, and Chiara Sepe, An economic order quantity stochastic dynamic optimization model in a logistic 4.0 environment Sustainability, 12 (2020) 01-25.
[11] K. Balakannan et al, Performance evaluation of supply chain and logistics management system using balanced scorecard for efficiency enhancement in Indian automotive industries, Indian Journal of Science and Technology.9(35) (2016) 1-9.
[12] Lalith Goonatilak, Inventory management in the manufacturing sector in developing countries, Engineering Costs and Production Economics.19 (2008) 19-24.
[13] Jianhu Cai et al., Optimal inventory decisions under vendor managed inventory: Substitution effects and replenishment tactics, Applied Mathematical Modelling, 43 (2017) 611-629.
[14] X. Hong, W. Chunyuan, L. Xu and A. Diabat, Multiple-vendor, multiple-retailer-based vendor-managed inventory, Annals of Operations Research, 238(1-2) (2016) 277-297.
[15] Jayalal Wettasinghe and Huynh Trung Luong, A vendor managed inventory policy with emergency orders, Journal of Industrial and Production Engineering, 37(2-3) (2020) 120-133.