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Getting the money in time - Logistics

Getting the money in time - Logistics

Project Profile

At a glance - essential project data

DurationFrom 11/1/2019 to 2/29/2020 with about 4 months of full engagement
Data and ToolsMarket - Logistics
 • Routing information
 • Transport database (MS-SQL) of 8 years
 • GEO information
Integration • Web API for metadata enrichment with model generated data
 • Quality Reporting: Power BI
 • ML-Ops: Coupling with customer systems for continuous transfer to and from our models
 • Integration on the store floor as a benchmark for daily workload
AI Methods
 • Deep - Learning
 • Timeseries Models
 • Feature Engineering
 • Feature Importance
 • Unsupervised Training

Engagement Use-Case

Packaging unit pickup orders with simultaneous forecasting of content quantity based on operational data for two weeks in advance for >8000 pickups predictable.

Client motivation / Solution aims

  • Optimization of approach frequencies and routes

  • Optimization of resources for counting routes

  • Efficiency for ShopFloor planning

AI Approach

AI key technology used in our solutionGreat effort in obtaining domain knowledge (shift operation, closing/stop date, approach schedules, etc). Corona constrained optimization with 2-model strategy.
Solution ApproachDecision Tree based ML Timeseries models (market/day based)
Project ApproachSimply agile
Project TypeOperations/ML Ops
ML Integration and ML Operations • Operations Integration API
 • VIsualizationPowerBI and Shop-Floor Display
 • Capacity Management
 • ML Ops at Customer Infrastructure onPremise

Insights and Details

Models can only be as good as the ACTUAL situation itself generates errors (false runs)

During COVID it was possible to deduce from the returns which locations had closed or will close. Here Analytics is a right choice for evaluation of the ACTUAL situation.

The prediction landed on the store floor. So the resource usage can be optimized by a prediction!