Food Delivery Supply Chain Demand Data at Weekly Center-Meal Level

A case study dataset representing a food-delivery supply chain where multiple centers serve multiple meals over a weekly planning horizon. Demand differs across center-meal combinations and changes over time due to local customer behavior, promotions, and price variation. The dataset was authored by Amani Omran and is hosted on the Harvard Dataverse platform.

Use Cases

• Forecasting weekly demand for specific meals at individual delivery centers based on historical order patterns.
• Analyzing the impact of local promotions and price variation on demand at a granular level.
• Optimizing raw-material procurement decisions based on per-meal, per-center demand forecasts.
• Modeling supply chain dynamics where procurement decisions are made locally on a weekly basis.

Strengths

• Focuses on a granular forecasting level (weekly center-meal) required for operational decisions.
• Describes a realistic scenario with demand drivers like promotions and local customer behavior.
• Authored by a named individual and hosted on the Harvard Dataverse platform.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Harvard Dataverse

Collection Method

Likely collected from a food-delivery supply chain operation, but the specific method is not detailed.

Freshness

Last updated 2026-06-23 17:38:35; freshness should be verified.