.png){kind=icon}
.png){kind=icon}
Return
SIMULATION STUDIES ON THE IMPACT OF USING DEMAND FORECASTS BASED ON DEEP NEURAL NETWORKS FOR INVENTORY REPLENISHMENT EFFICIENCY
.png){kind=icon}
PRE
AI
S
C
B
G
A
DOI：10.17270/J.LOG.001326.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Background: Recent advancements in supply chain management, supported by information technology, have enabled reductions in inventory levels, among other operational improvements. Nevertheless, inventory-related challenges persist. Economic factors, particularly various forms of uncertainty, often necessitate holding inventory to ensure product availability. Demand variability, which is frequently unpredictable, remains a major challenge in numerous industries and requires the creation of safety buffers. Addressing this issue calls for increasingly sophisticated forecasting methods within replenishment models. Forecasts based solely on traditional time series methods offer limited improvements, whereas advanced approaches using machine learning and deep neural networks provide significantly greater potential. These models are capable of identifying factors that influence customer purchasing decisions, leading to more accurate demand forecasts and, consequently, a stronger foundation for improving replenishment processes. Objective: The primary aim of the research was to illustrate the extent to which advanced demand forecasting models can improve replenishment efficiency, particularly by reducing inventory levels. Methods: Simulation techniques were employed to replicate the replenishment process under various forecasting scenarios based on historical data. This dataset consisted of demand patterns for 1,000 Walmart stock-keeping units (SKUs), publicly released by the retailer for research purposes. The forecast methods examined included a benchmark arithmetic mean model, seven traditional time series-based models, and five advanced models employing machine learning and deep learning techniques. All simulations were conducted using the Reorder Cycle replenishment model with a uniform inventory review cycle across products. The second control parameter, the maximum inventory level (S), was fixed for the benchmark model and dynamically adjusted for the remaining twelve models according to their respective forecasts. A total of 13,000 replenishment simulation cycles were performed. Key performance indicators included average inventory and the service level (SL alpha), defined as the probability of fully satisfying demand within a replenishment cycle. The parameter S was calibrated to ensure a consistent service level across models. Consequently, the inventory level index was the primary measure of replenishment efficiency, enabling comparisons between the twelve forecasting models and the benchmark. Additionally, the relationship between this index and forecast quality improvement was analyzed using forecast error measurements, specifically the root mean square error (RMSE). Results: The findings confirm that inventory levels can be reduced by an average of 10% through the application of machine learning and deep neural network-based forecasting methods, without compromising service quality. The magnitude of the reduction varied depending on specific temporal demand patterns. Conclusions: The observed improvements can be attributed to two main factors: increased forecast accuracy and the dynamic adjustment of the maximum inventory level (S), based on current forecasts and their associated error estimates. Furthermore, replenishment efficiency may be enhanced further by selecting the most appropriate forecasting method for individual products during specific time periods.
Keywords:
forecasting
neural networks
inventory replenishment
deep learning
Journal
L
IF:
1
Papers:
23
Citations:
402
Organization
Cited Papers