Predictive Delivery Planning System for Labor Block Release
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Solution Overview
Problem
Current delivery planning systems lack accuracy in estimating labor needs due to not considering physical routes and relying on a single demand scenario, making it difficult to accurately identify total customer orders for same-day delivery.
Innovation Solution
A predictive delivery planning system that integrates route generation with labor planning, using a forecaster to predict simulated orders for multiple demand scenarios, selecting simulated orders based on volume and geographic distribution, and releasing labor blocks that satisfy a utilization threshold, allowing drivers to select their work schedule flexibly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current delivery planning systems use simple deliveries per hour estimation, then the system is easy to operate, but the measurement precision of labor needs is poor
Solution Approach 1:
The system performs preliminary route generation and labor planning before the delivery period begins. Multiple demand scenarios are forecasted in advance, and labor blocks are pre-identified and released to drivers before they are needed, improving estimation accuracy without proportionally increasing operational complexity
Solution Approach 2:
The delivery period is divided into discrete labor blocks that can be independently planned, released, and assigned to drivers. This segmentation allows the complex planning problem to be broken into manageable units, maintaining ease of operation while improving precision through detailed route-level analysis
2Reliability
If a single demand scenario is used for labor estimation, then the system is simple to operate, but the reliability of total customer orders identification is poor
Solution Approach 1:
The system dynamically generates multiple demand scenarios with varying order volumes and characteristics instead of relying on a single static scenario. This allows the labor planning to adapt to different possible demand conditions, improving reliability of order identification while the automated scenario generation keeps system complexity manageable
Solution Approach 2:
The system varies key parameters such as order volume, geographic distribution, and time windows across multiple demand scenarios. By analyzing labor needs across these different parameter sets, the system achieves more reliable total customer orders identification without requiring manual complexity
3Productivity
If labor blocks are released in advance, then productivity is improved through better scheduling, but the device complexity increases due to forecast modeling
Solution Approach 1:
Labor blocks are released to drivers in advance based on forecasted demand scenarios and generated routes. This preliminary action allows drivers to plan their schedules ahead, improving productivity through better resource utilization. The forecasting complexity is managed by using historical data and standardized scenario generation
Solution Approach 2:
The system creates simplified copies or representations of future delivery scenarios through demand forecasting models. These scenario copies allow labor planning to be performed in advance without requiring the full complexity of real-time decision-making, balancing productivity improvement with manageable system complexity
Data Source
AI summary
Embodiments herein describe a predictive delivery planning system that includes a forecaster that predicts simulated orders (e.g., forecasted orders) for multiple different demand scenarios. Once the simulated orders are selected, a route planner can generate routes for delivering the simulated and actual customer orders for each scenario. The planning system then converts these routes in labor plans indicating the amount of time a delivery driver would need to deliver the orders. The planning system identifies a set of labor blocks from the labor plans and determines whether these blocks satisfy a utilization threshold. Put differently, the planning system uses a releasing policy that releases labor blocks whose expected utilization is higher than a predetermined threshold. The released labor blocks are then displayed to delivery drivers who can then select how many of the labor blocks they would like to work.