Bullwhip Effect Identification, ARIMA/SARIMA Demand Forecasting, and EOQ-Based Inventory Control at a Chicken Egg Distributor:  A Case Study of Distributor X

Olga Monica; Ratih Dyah K

doi:10.59261/jbt.v7i3.695

Authors

Olga Monica Universitas Indonesia
Ratih Dyah K Universitas Indonesia

DOI:

https://doi.org/10.59261/jbt.v7i3.695

Keywords:

ARIMA/SARIMA Forecasting, Bullwhip Effect, Economic Order Quantity, Inventory Management, Supply Chain

Abstract

Background: Effective inventory management is crucial for maintaining supply stability and avoiding imbalances between demand and product availability. In practice, poorly managed demand fluctuations can trigger the bullwhip effect, leading to supply chain inefficiencies.

Objective: This study aims to analyze the bullwhip effect phenomenon in chicken egg inventory at Distributor X, identify the most accurate forecasting model, and formulate appropriate inventory policies.

Methods: This study employed a quantitative descriptive case-study design using three years (2023–2025) of historical operational data. The analyses included bullwhip effect measurement, ARIMA/SARIMA demand forecasting, and inventory optimization using EOQ, safety stock, reorder point (ROP), and total inventory cost (TIC). Supporting interviews were conducted to interpret the company’s operational practices.

Results: The bullwhip effect values of 1.4 for regular eggs and 1.3 for omega eggs exceed the threshold value of 1.12, confirming demand amplification along the supply chain. ARIMA (0,0,1) provided the best fit for regular eggs (MAPE: 4.2%), while SARIMA (1,0,1)(0,1,1)[12] provided the best fit for omega eggs (MAPE: 6.8%). EOQ analysis yielded optimal order quantities of 245 kg and 265 kg, respectively, with safety stocks of 17 kg and 52 kg, and reorder points of 543 kg and 669 kg. Total inventory costs are projected to decrease by approximately 5–7% during 2026–2028 compared to the 2023–2025 period.

Conclusion: The integration of ARIMA/SARIMA-based forecasting and EOQ inventory control demonstrates measurable improvements in demand prediction accuracy and inventory cost efficiency, reducing total inventory costs by 5–7% and providing a systematic framework for mitigating the bullwhip effect at Distributor X.

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