Analyzing One-Step and Multi-Step Forecasting to Mitigate the Bullwhip Effect and Improve Supply Chain Performance
This study presents the results of an investigation into effectiveness of one-step and multi-step (h-step-ahead) forecasting methods in mitigating the Bullwhip Effect and improving supply chain performance within an order-up-to-level inventory control system. the Bullwhip Effect, a phenomenon in whi...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10772207/ |
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| author | Alper Saricioglu Mujde Erol Genevois Michele Cedolin |
| author_facet | Alper Saricioglu Mujde Erol Genevois Michele Cedolin |
| author_sort | Alper Saricioglu |
| collection | DOAJ |
| description | This study presents the results of an investigation into effectiveness of one-step and multi-step (h-step-ahead) forecasting methods in mitigating the Bullwhip Effect and improving supply chain performance within an order-up-to-level inventory control system. the Bullwhip Effect, a phenomenon in which small variations in consumer demand cause increasingly larger fluctuations upstream in the supply chain, presents significant challenges for inventory management and cost control. Traditional forecasting methods, such as Moving Average and Exponential Smoothing, have been extensively studied for their impact on supply chain performance. This study is among the first to introduce machine learning forecasting models, specifically Long Short-Term Memory and LightGBM, within this research domain, comparing their performance under various demand conditions, including autoregressive processes with and without seasonality, as well as the well-known M5 forecasting competition dataset. The results reveal that the multi-step-ahead forecasting capability of Long Short-Term Memory and LightGBM significantly outperforms one-step-ahead forecasting in reducing demand amplification, leading to improvements in key supply chain metrics such as order fulfillment rate, variance of inventory, and average end inventory across all autoregressive and M5 series. The findings demonstrate the superior accuracy and stability of machine-learning methods, particularly in scenarios with high demand autocorrelation, seasonality, and variability. These results provide new insights into the potential of advanced forecasting techniques to better manage supply chain variability and reduce the Bullwhip Effect, thereby offering valuable guidance for optimizing inventory control strategies. |
| format | Article |
| id | doaj-art-91a190f5347040aebb4abd2a40bcd23a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-91a190f5347040aebb4abd2a40bcd23a2024-12-14T00:02:01ZengIEEEIEEE Access2169-35362024-01-011218016118017410.1109/ACCESS.2024.351017510772207Analyzing One-Step and Multi-Step Forecasting to Mitigate the Bullwhip Effect and Improve Supply Chain PerformanceAlper Saricioglu0https://orcid.org/0000-0002-4015-7200Mujde Erol Genevois1https://orcid.org/0000-0001-5324-0612Michele Cedolin2https://orcid.org/0000-0003-2397-0010Graduate School of Science and Engineering, Galatasaray University, Istanbul, TürkiyeFaculty of Engineering and Technology, Galatasaray University, Istanbul, TürkiyeCollege of Engineering and Technology, American University of the Middle East, Egaila, KuwaitThis study presents the results of an investigation into effectiveness of one-step and multi-step (h-step-ahead) forecasting methods in mitigating the Bullwhip Effect and improving supply chain performance within an order-up-to-level inventory control system. the Bullwhip Effect, a phenomenon in which small variations in consumer demand cause increasingly larger fluctuations upstream in the supply chain, presents significant challenges for inventory management and cost control. Traditional forecasting methods, such as Moving Average and Exponential Smoothing, have been extensively studied for their impact on supply chain performance. This study is among the first to introduce machine learning forecasting models, specifically Long Short-Term Memory and LightGBM, within this research domain, comparing their performance under various demand conditions, including autoregressive processes with and without seasonality, as well as the well-known M5 forecasting competition dataset. The results reveal that the multi-step-ahead forecasting capability of Long Short-Term Memory and LightGBM significantly outperforms one-step-ahead forecasting in reducing demand amplification, leading to improvements in key supply chain metrics such as order fulfillment rate, variance of inventory, and average end inventory across all autoregressive and M5 series. The findings demonstrate the superior accuracy and stability of machine-learning methods, particularly in scenarios with high demand autocorrelation, seasonality, and variability. These results provide new insights into the potential of advanced forecasting techniques to better manage supply chain variability and reduce the Bullwhip Effect, thereby offering valuable guidance for optimizing inventory control strategies.https://ieeexplore.ieee.org/document/10772207/Bullwhip effect (BWE)supply chain performancemachine learning forecastinglong short-term memory (LSTM)LightGBMmulti-step forecasting |
| spellingShingle | Alper Saricioglu Mujde Erol Genevois Michele Cedolin Analyzing One-Step and Multi-Step Forecasting to Mitigate the Bullwhip Effect and Improve Supply Chain Performance IEEE Access Bullwhip effect (BWE) supply chain performance machine learning forecasting long short-term memory (LSTM) LightGBM multi-step forecasting |
| title | Analyzing One-Step and Multi-Step Forecasting to Mitigate the Bullwhip Effect and Improve Supply Chain Performance |
| title_full | Analyzing One-Step and Multi-Step Forecasting to Mitigate the Bullwhip Effect and Improve Supply Chain Performance |
| title_fullStr | Analyzing One-Step and Multi-Step Forecasting to Mitigate the Bullwhip Effect and Improve Supply Chain Performance |
| title_full_unstemmed | Analyzing One-Step and Multi-Step Forecasting to Mitigate the Bullwhip Effect and Improve Supply Chain Performance |
| title_short | Analyzing One-Step and Multi-Step Forecasting to Mitigate the Bullwhip Effect and Improve Supply Chain Performance |
| title_sort | analyzing one step and multi step forecasting to mitigate the bullwhip effect and improve supply chain performance |
| topic | Bullwhip effect (BWE) supply chain performance machine learning forecasting long short-term memory (LSTM) LightGBM multi-step forecasting |
| url | https://ieeexplore.ieee.org/document/10772207/ |
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