What is the limitation of aggregate data in demand forecasting?
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Aggregate data in demand forecasting has limitations due to the potential errors introduced by demand aggregation. While aggregation is necessary for handling large and stochastic spatiotemporal demand datasets, it can lead to inaccuracies in location optimization studies. The errors induced by aggregation methods can impact the outcomes of forecasting models, especially in scenarios like search and rescue operations where demand data is highly variable. Different aggregation approaches, whether deterministic or stochastic, come with trade-offs in terms of distance-based and volume-based aggregation errors. Practitioners must carefully consider these errors and choose the aggregation method that minimizes the most relevant errors for their specific study, as there is no one-size-fits-all solution.
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| Papers (5) | Insight |
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Open access•Posted Content | Aggregating demand data for location optimization introduces errors impacting results; higher fidelity reduces distance-based errors but increases volume errors, affecting demand forecasting accuracy. |
10 May 2021 1 Citations | Aggregated data in demand forecasting introduces errors in distance and volume metrics, impacting accuracy due to the nature of aggregation methods used. |
1 Citations | Aggregate data in demand forecasting lacks granularity, hindering accurate predictions for individual items. The proposed DAC method balances aggregation and flexibility, enhancing prediction accuracy across diverse items. |
| Aggregate data in demand forecasting may overlook the impact of finite demand history length, leading to potential inaccuracies in forecasting accuracy for auto-correlated demands. | |
Open access•Posted Content | Aggregate data in demand forecasting may overlook the impact of finite autocorrelated demand series lengths, affecting forecast accuracy due to insufficient attention in the literature. |
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