A Comparative Study of Lot-Sizing Techniques under Fluctuating Demand
Article Sidebar
Main Article Content
Abstract
This research aims to compare the efficiency of different lot-sizing techniques under fluctuating demand patterns. Demand was simulated using a normal distribution and categorized into two levels based on the variability coefficient (VC): low variability (VC < 0.25) and high variability (VC ≥ 0.25), across two planning horizons: 12 periods (monthly) and 52 periods (weekly). The study evaluates both the exact optimization approach using the Wagner–Whitin algorithm (WW) and various heuristic methods, including Lot-for-Lot (LFL), Economic Order Quantity (EOQ), Period Order Quantity (POQ), and Part-Period Balancing (PPB). Each scenario was tested across two levels of ordering cost and five levels of holding cost. The results indicate that the WW algorithm outperformed all other techniques across all simulated scenarios by achieving the lowest total inventory management cost. Among the heuristic methods, LFL and POQ emerged as viable alternatives; their efficiency improved significantly as holding costs increased because they align order quantities closely with actual demand, thereby reducing average inventory levels and carrying costs.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Chopra S, Meindl P. Supply chain management: strategy, planning, and operation. 7th ed. New York: Pearson; 2019.
Nahmias S, Olsen T. Production and Operations Analysis: Strategy, Quality, Analytics, Application. 7th ed. Long Grove (IL): Waveland Press; 2015.
Harris FW. How many parts should we make at once?. Factory, The Magazine of Management. 1913;10(2):135-6.
Wahab MIM, Mamun SMH, Ongkunaruk P. EOQ models for a coordinated two-level international supply chain considering imperfect items and environmental impact. International Journal of Production Economics. 2011;134(1):151-8.
Silver EA, Peterson R. Decision Systems for Inventory Management and Production Planning. 2nd ed. John Wiley & Sons; 1985.
Sipper D, Bulfin R. Production: Planning, Control, and Integration. New York: McGraw-Hill College; 1997.
Silver EA, Pyke DF, Peterson R. Inventory management and production planning and scheduling. 3rd ed. New York: John Wiley & Sons; 1998.
Silver EA, Pyke DF, Thomas DJ. Inventory and Production Management in Supply Chains. 4th ed. Boca Raton: CRC Press; 2017.
Gáti KV, Bányai T. Impact of dynamic lot sizing techniques on costs of material requirement planning. Advanced Logistic Systems - Theory and Practice. 2023;17(1):71-8.
Ferdian R, Halim AH. Dynamic lot sizing model for retailers with multi suppliers, quantity discounts, and capacity constraints that consider advance demand informations. Int J Prod Manag Eng. 2024;12(2):158-68.
Martinez-Luna GE, Téllez-Ballesteros SC. Comparative Evaluation of Wagner-Whitin and Silver-Meal Lot Sizing Methods for Inventory Optimization in a Paper-Based Manufacturing Case Study. Procedia Comput. Sci. 2026;277:3827-3834.
Suwondo E, Yuliando H. DYNAMIC LOT- SIZING PROBLEMS: A Review on Model and Efficient Algorithm. Agroindustrial Journal. 2012;1(1):36-49.
Toy AO, Berk E. Dynamic lot sizing for a warm/cold process: Heuristics and insights. Int J Production Economics. 2013;145:53-66.
Soni B. Design and development of inventory system for optimal order size at Multimetals Limited, Kota. In: Proceedings of International Conference on Recent Innovations in Engineering and Technology; 2017 Feb 18-19; Jaipur, India.
Mahachandra M, Astri A. From planning to performance: MRP-driven material optimization for the bored pile construction of the MUI and Safa-Marwa towers in Indonesia. World Journal of Advanced Research and Reviews. 2025;26(3):1073-83.
Silfiana AH. Procurement of Production Materials for Cabinets using the Material Requirement Planning (MRP) Approach in the Furniture Industry. International Research Journal of Economics and Management Studies. 2026;5(2):104-8.
Dajani JS, Sincoff MZ, Talley WK. Stability and agreement criteria for the termination of Delphi studies. Tech Forecasting Soc Change. 1979;13(1):83-90.
Wagner HM, Whitin TM. Dynamic version of the economic lot size model. Management Science. 1958;5(1):89-96.
Orlicky J. Material requirements planning: the new way of life in production and inventory management. New York: McGraw-Hill; 1975.
Heizer J, Render B, Munson C. Operations management: sustainability and supply chain management. 13th ed. New York: Pearson; 2020.
Chase RB, Jacobs FR. Operations and supply chain management. 15th ed. New York: McGraw-Hill; 2018.
Arnold JRT, Chapman SN, Clive LM. Introduction to materials management. 7th ed. Boston: Pearson; 2012.
DeMatteis JJ. An economic lot-sizing technique I: The part-period algorithm. IBM Syst J. 1968;7(1):30-8.
Mendoza AG. An economic lot-sizing technique II: Mathematical analysis of the part-period algorithm. IBM Syst J. 1968;7(1):39-46.
Thuannadee S, Lekdee T, Sarajan M, Kaensanthia W, Phracharoen W. Comparing Lot Sizing Techniques for Unstable Demand Inventory of a Hardware Retailer. The Journal of KMUTNB. 2019;29(3):388-98.
Bumrungsri P. Reducing of Cost of Inventory using Heuristics Lot Sizing Methods: A Case of a Built-in Furniture Company [master's thesis]. [Bangkok]: Dhurakij Pundit University; 2023.
Uansamer J, Kittithreerapronchai O. Chemical Inventory Management in Tire Cord Fabric Industry Using Lot Sizing. The Journal of KMUTNB. 2014;24(2):308-17.
