Inventory Management Via Topsis Analytical Hierarchy Process (AHP) Method Embedded With Economic Order Quantity

Inventory management is the process of keeping track of all the material on the manufacturing industry has in its industry stock. Effective inventory management aligns all inventory types to the efficient creation of the production process to finished products and delivery to the customer's sat...

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Main Author:	Leow, Mei Mei
Format:	Thesis
Language:	English English
Published:	2020
Subjects:	T Technology (General) TS Manufactures
Online Access:	http://eprints.utem.edu.my/id/eprint/25418/1/Inventory%20Management%20Via%20Topsis%20Analytical%20Hierarchy%20Process%20%28AHP%29%20Method%20Embedded%20With%20Economic%20Order%20Quantity.pdf http://eprints.utem.edu.my/id/eprint/25418/2/Inventory%20Management%20Via%20Topsis%20Analytical%20Hierarchy%20Process%20%28AHP%29%20Method%20Embedded%20With%20Economic%20Order%20Quantity.pdf
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id	my-utem-ep.25418
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Abdullah, Lokman
topic	T Technology (General) TS Manufactures
spellingShingle	T Technology (General) TS Manufactures Leow, Mei Mei Inventory Management Via Topsis Analytical Hierarchy Process (AHP) Method Embedded With Economic Order Quantity
description	Inventory management is the process of keeping track of all the material on the manufacturing industry has in its industry stock. Effective inventory management aligns all inventory types to the efficient creation of the production process to finished products and delivery to the customer's satisfaction. Ineffective inventory management either beyond having too much inventory or too little inventory, poor inventory management causes inefficiencies of production activity. This will be having reordering inventory from suppliers at last minute or increase risk of mistakes non-fulfilled customer orders on time. The consequence of poor inventory management can cause customers to withdraw orders or industry pay compensation due to order delivery date no achieve as agreement. Due to the poor inventory management problem, this project purpose to use economic order quantity (EOQ) application in a rubber manufacturing company and optimization of the economic order quantity (EOQ) with Technique for Order Preference by Similarity to Ideal Solution Analytic Hierarchy Process (TOPSIS-AHP). Nowadays, the choice of suppliers and the supplier material performance assessment are very important. This become the major challenges in the manufacturing industry which mainly faced by supply chain managers or purchaser. The progress to evaluating each supplier and selecting the best supplier are become complicated tasks. In the decision-making process, there are different criteria andalternatives must take into consideration. The objective is to determine and select the best supplier of inventory by using the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The economic order quantity (EOQ) model was used to calculate the best quantity of the material. Therefore, at the end of this project concluded the best supplier is supplier A with its material. The result is consistent in all methodologies. The economic order quantity (EOQ) to order upon purchasing of new inventory is 2847 kg
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Leow, Mei Mei
author_facet	Leow, Mei Mei
author_sort	Leow, Mei Mei
title	Inventory Management Via Topsis Analytical Hierarchy Process (AHP) Method Embedded With Economic Order Quantity
title_short	Inventory Management Via Topsis Analytical Hierarchy Process (AHP) Method Embedded With Economic Order Quantity
title_full	Inventory Management Via Topsis Analytical Hierarchy Process (AHP) Method Embedded With Economic Order Quantity
title_fullStr	Inventory Management Via Topsis Analytical Hierarchy Process (AHP) Method Embedded With Economic Order Quantity
title_full_unstemmed	Inventory Management Via Topsis Analytical Hierarchy Process (AHP) Method Embedded With Economic Order Quantity
title_sort	inventory management via topsis analytical hierarchy process (ahp) method embedded with economic order quantity
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Manufacturing Engineering
publishDate	2020
url	http://eprints.utem.edu.my/id/eprint/25418/1/Inventory%20Management%20Via%20Topsis%20Analytical%20Hierarchy%20Process%20%28AHP%29%20Method%20Embedded%20With%20Economic%20Order%20Quantity.pdf http://eprints.utem.edu.my/id/eprint/25418/2/Inventory%20Management%20Via%20Topsis%20Analytical%20Hierarchy%20Process%20%28AHP%29%20Method%20Embedded%20With%20Economic%20Order%20Quantity.pdf
_version_	1747834123664228352
spelling	my-utem-ep.254182021-12-07T15:34:27Z Inventory Management Via Topsis Analytical Hierarchy Process (AHP) Method Embedded With Economic Order Quantity 2020 Leow, Mei Mei T Technology (General) TS Manufactures Inventory management is the process of keeping track of all the material on the manufacturing industry has in its industry stock. Effective inventory management aligns all inventory types to the efficient creation of the production process to finished products and delivery to the customer's satisfaction. Ineffective inventory management either beyond having too much inventory or too little inventory, poor inventory management causes inefficiencies of production activity. This will be having reordering inventory from suppliers at last minute or increase risk of mistakes non-fulfilled customer orders on time. The consequence of poor inventory management can cause customers to withdraw orders or industry pay compensation due to order delivery date no achieve as agreement. Due to the poor inventory management problem, this project purpose to use economic order quantity (EOQ) application in a rubber manufacturing company and optimization of the economic order quantity (EOQ) with Technique for Order Preference by Similarity to Ideal Solution Analytic Hierarchy Process (TOPSIS-AHP). Nowadays, the choice of suppliers and the supplier material performance assessment are very important. This become the major challenges in the manufacturing industry which mainly faced by supply chain managers or purchaser. The progress to evaluating each supplier and selecting the best supplier are become complicated tasks. In the decision-making process, there are different criteria andalternatives must take into consideration. The objective is to determine and select the best supplier of inventory by using the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The economic order quantity (EOQ) model was used to calculate the best quantity of the material. Therefore, at the end of this project concluded the best supplier is supplier A with its material. The result is consistent in all methodologies. The economic order quantity (EOQ) to order upon purchasing of new inventory is 2847 kg 2020 Thesis http://eprints.utem.edu.my/id/eprint/25418/ http://eprints.utem.edu.my/id/eprint/25418/1/Inventory%20Management%20Via%20Topsis%20Analytical%20Hierarchy%20Process%20%28AHP%29%20Method%20Embedded%20With%20Economic%20Order%20Quantity.pdf text en public http://eprints.utem.edu.my/id/eprint/25418/2/Inventory%20Management%20Via%20Topsis%20Analytical%20Hierarchy%20Process%20%28AHP%29%20Method%20Embedded%20With%20Economic%20Order%20Quantity.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=119590 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Manufacturing Engineering Abdullah, Lokman 1. Behzadian, M., Otaghsara, S.K., Yazdani, M, Ignatius, J., 2018. A state-of-the-art survey of TOPSIS applications. Expert Syst Appl, 39(17), pp.13051–13069. 2. Bhushan, N. and Ria, K., 2004. Strategic Decision Making: Applying the Analytic Hierarchy Process. London: Springer-Verlag London Limited, pp.1567-1580 3. C. S. Yu., 2012. A GP-AHP method for solving group decision-making fuzzy AHP problems. Comput Oper Res, vol. 29, no. 14, pp. 1989-2011. 4. Chakraborty, S. and Yeh, C.H., 2019. A Simulation Comparison of Normalization Procedures for TOPSIS. IEEE, ISSN, pp. 978-1-4244-4136-5/09. 5. Chang H.C., 2004. An application of fuzzy sets theory to the EOQ model with imperfect quality items, Computers & Operations Research 31, pp. 2079–2092 6. Chen, T.Y. and Tsao, C.Y., 2018. The interval-valued fuzzy TOPSIS method and experimental analysis. Fuzzy Sets and Systems, Vol.159, pp. 1410 – 1428. 7. Choi, T.M., 2013. Handbook of EOQ Inventory Problems, Springer Dordrecht Heidelberg London. 8. Choo, E.U. and Wedley, W.C,. 2014. A common framework for deriving preference values from pairwise comparison matrices. Computers & Operations Research, Vol. 31, pp.893–908. 9. Chu, T.C., and Lin, Y.C., 2019. An interval arithmetic based fuzzy TOPSIS model. Expert Systems with Applications, Vol. 36, pp. 10870–10876. 10. Digiesi, S., G. Mossa, and G., Rubino, S., 2014. A sustainable EOQ model for repairable spare parts under uncertain demand. IMA Journal of Management Mathematics , Vol 26, Issue 2, pp. 185–203 11. Erlenkotter, D., 1989. An Early Classic Misplaced: Ford W. Harris's Economic Order Quantity Model of 1915. Management Science 35:7, pp. 898–900. 12. F. A. Mohammed, and M. A. E. Hagag., 2013. Integrating AHP and genetic algorithm model adopted for personal selection. Int J Eng Trend Technology, vol. 6, no. 5, pp. 247-256. 13. Federgruen, A., and Y. S. Zheng., 1992. An Efficient Algorithm for Computing an Optimal Policy in Continuous Review Stochastic Inventory Systems, Operations Research 40:4, pp. 808–813. 14. Gallego, G., 1998. New Bounds and Heuristics for Qtr. Policies. Management Science 44:2, pp. 219–233. 15. Goh, H. H. & Kok, B. C., 2010. Application of Analytic Hierarchy Process (AHP) in Load Shedding Scheme for Electrical Power System. 2010 9th International Conference on Environment and Electrical Engineering (EEEIC). pp. 365 - 368. 16. Govindan, K., Khodaverdi, R., and Jafarian, A., 2012. A fuzzy multi-criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. Journal of Cleaner Production, pp. 1-10. 17. Guo, C.G., Liu, Y.X., Hou, S.M. and Wang, W., 2016. Innovative Product Design Based on Customer Requirement Weight Calculation Model. International Journal of Automation and Computing, Vol.7, No.4, pp.578-583. 18. Guo, Z., and Zhang, Y., 2019. The third-party logistics performance evaluation based on the AHPPCA model. IEEE, ISSN. pp. 978-1-4244-7161-4/10. 19. Harris, F. M., 1913. How Many Parts to Make at Once Factory. The Magazine of Management.pp 10:2, 135–136, 152. Reprinted in Operations Research 38:6 (1990), 947–950. 20. Ilgın M. A., 2019. A spare parts criticality evaluation method based on fuzzy AHP and Taguchi loss functions. Maintenance, and Reliability Vol. 21, No. 1, 2019, pp. 145-152. 21. Kamaludin, KN., Abdullah, L., Maslan, MN., Zamri, R., Ali., and Syed Mohamad, MS.,2019. Utilization of Analytical Hierarchy Process (AHP) for selecting the best design concept of the conveyor system. Lecture Notes in Mechanical Engineering, pp. 265–279. 22. Khalilpourazari S., Pasandideh S.H.R., 2018. Modelling and optimization of multi-item multi-constrained EOQ model for growing items. Knowledge-Based Systems, Volume 164, pp. 150-162 23. Koumanakos, D.P., 2008. The effect of inventory management on firm performance. International Journal of Productivity and Performance Management. Vol. 57 No. 5, pp. 355-369. 24. L. Gao and A. Hailu., 2013. Identifying preferred management options: an integrated agent-based recreational fishing simulation model with an AHP-TOPSIS evaluation method. Ecological Modeling, vol. 249, pp. 75-83. 25. Li, C., Sun, Y., Jia, Y., and Li, H., 2008. An Improved Ranking Approach to AHP Alternatives Based on Variable Weights. 7th World Congress on Intelligent Control and Automation. Chongqing: China. 2008. pp.8255-8260. 26. Lowe, T. J., and L. B. Schwarz., 1983. Parameter Estimation for the EOQ Lot-Size Model: Minimax and Expected-Value Choices. Naval Research Logistics Quarterly, pp. 367–376. 27. M. Bhaskar, M. M. Das, T. Chithralekha, and S. Sivasatya., 2010. Genetic algorithm-based adaptive learning scheme generation for context-aware E-learning. Int J Comput Sci Eng, vol. 2, no. 4, pp. 1271-1279. 28. M. Ince, A. H. Isik, and T. Yigit., 2016. Multi-Criteria approach to learning object selection through fuzzy AHP. Journal of Multiple-Valued Logic & Soft Computing, vol. 27, no. 1, pp. 1-1, 2016. 29. M. Tyagi, P. Kumar, and D. Kumar,. 2014. A hybrid approach using AHP-TOPSIS for analyzing e-SCM performance. Procedia Engineering, vol. 97, pp. 2195-2203. 30. Panda, B.N, Biswal, B.B, Deepak. B., 2014. Integrated AHP and fuzzy TOPSIS Approach for the Selection of a Rapid Prototyping Process under Multi-Criteria Perspective. Operations Research, pp. 947–950. 31. Panigrahi, Mr. Rashmiranjan and Tanty, Dr & Jena., 2019. Advance Inventory Management Practices and Its Impact on Production Performance of Manufacturing Industry. International Journal of Recent Technology and Engineering. 8. pp.763-834. 32. S. L. Adeyemi, and A. O. Salami., 2010. Inventory Management: A Tool of Optimizing Resources in a Manufacturing Industry A Case Study of Coca-Cola Bottling Company, Ilorin Plant. Journal of Social Sciences, 23:2, pp. 135-142 33. Saaty, T.L., Vargas, L.G., 2001. Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. International Series in Operations Research and Management Science,pp. 2738-2784. 34. Saaty, T.L., and Vargas, L.G., 2006. Decision Making with the Analytic Network Process: Economic, Political, Social and Technological Applications with Benefits, Opportunities, Costs and Risks, New York: Springer. pp.34-35. 35. Sanni S., O.Neill, B., 2019. Inventory optimization in a three-parameter Weibull model under a prepayment system, Computers & Industrial Engineering 128, pp. 298–304. 36. Schmoldt, D., Kangas, J., Mendoza, G., and Pesonen, M., 2011. The Analytic Hierarchy Process in Natural Resource and Environmental Decision Making, Vol. 3 in the series Managing Forest Ecosystems, Dordrecht, Boston, London: Kluwer Academic Publishers. 37. Schwarz, L.B.,2008. The Economic Order-Quantity (EOQ) Model. Building Intuition. International Series in Operations Research & Management Science, vol 115. Springer, Boston, MA. 38. Schwarz, L. B., 1972. Economic Order Quantities for Products with Finite Demand Horizons. AIIE Transactions 4:3, pp. 234–237. 39. Scott C., Lundgren H., Thompson P., 2018, Guide to Supply Chain Management, Springer International Publishing. 40. Singh T., Mallick C. Singh R. K., 2018. Formulating an Economic order quantity model for items with Variable Rate of Deterioration and Two-Component Demand. Soft Computing for Problem Solving, pp. 273-282. 41. T. Chellatamilan, and R. M. Suresh., 2018. Automatic classification of learning objects through dimensionality reduction and feature subset selections in an e-learning system in Proc. IEEE International Conference on Technology Enhanced Education, pp. 1-6. 42. T. Yigit, A. H. Isik, and M. Ince., 2017. Web-based learning object selection software using analytical hierarchy process. IET Softw, vol. 8, no. 4, pp. 174-183. 43. Wyatt L, Caufield B, Pol D.,2019. Principles for an ETL Benchmark. Lecture Notes in Computer Science. pp 183–198. 44. Yavuz, M., Oztaysi, B., Cevik Onar, S., & Kahraman, C., 2015. Multi-criteria evaluation of alternative-fuel vehicles via a hierarchical hesitant fuzzy linguistic model. Expert Systems with Applications, 42(5), pp. 2835–2848. 45. Yigit, T., Isik, A.H., Ince. M., 2018. Web-based learning object selection software using the analytical hierarchy process. Expert Systems with Applications, pp. 1254-1354. 46. Zaki, A., Noor, M., Jafar, F. A., and Zainudin, S. F., 2017. Fusion of Fuzzy AHP In Selecting Material for Drinking Water Bottle Based on Customer Needs, Expert Systems with Applications, 12(14), pp.4243-4249. 47. Zheng, Y. S., 1992. On Properties of Stochastic Inventory Systems. Management Science 38:1, pp.87–103. 48. Zipkin, P., 2000. Foundations of Inventory Management, McGraw-Hill Higher Education, New York, New York Publisher.

Inventory Management Via Topsis Analytical Hierarchy Process (AHP) Method Embedded With Economic Order Quantity

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