Business Metric-Aware Forecasting for Inventory Management
Time-series forecasts play a critical role in business planning. However, forecasters typically optimize objectives that are agnostic to downstream business goals and thus can produce forecasts misaligned with business preferences. In this work, we demonstrate that optimization of conventional forecasting metrics can often lead to sub-optimal downstream business performance. Focusing on the inventory management setting, we derive an efficient procedure for computing and optimizing proxies of common downstream business metrics in an end-to-end differentiable manner. We explore a wide range of plausible cost trade-off scenarios, and empirically demonstrate that end-to-end optimization often outperforms optimization of standard business-agnostic forecasting metrics (by up to 45.7% for a simple scaling model, and up to 54.0% for an LSTM encoder-decoder model). Finally, we discuss how our findings could benefit other business contexts.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderManagementTime SeriesMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
An Efficient Intelligent Semi-Automated Warehouse Inventory Stocktaking System
In the context of evolving supply chain management, the significance of efficient inventory management has grown substantially for businesses. However, conventional manual and experience-based approaches often struggle t…
Decision MakingManagementBridging Forecast Accuracy and Inventory KPIs: A Simulation-Based Software Framework
Efficient management of spare parts inventory is crucial in the automotive aftermarket, where demand is highly intermittent and uncertainty drives substantial cost and service risks. Forecasting is therefore central, but…
Backorder Prediction in Inventory Management: Classification Techniques and Cost Considerations
This article introduces an advanced analytical approach for predicting backorders in inventory management. Backorder refers to an order that cannot be immediately fulfilled due to stock depletion. Multiple classification…
Decision MakingManagementStreamlined Framework for Agile Forecasting Model Development towards Efficient Inventory Management
This paper proposes a framework for developing forecasting models by streamlining the connections between core components of the developmental process. The proposed framework enables swift and robust integration of new d…
ManagementTime SeriesCombating the Bullwhip Effect in Rival Online Food Delivery Platforms Using Deep Learning
The wastage of perishable items has led to significant health and economic crises, increasing business uncertainty and fluctuating customer demand. This issue is worsened by online food delivery services, where frequent …
Demand Forecasting