• AI Projects with practical Experience

Placing an order

Placing an order

Project Profile

Forecast total customer demand and orders for change based on operational data for several months for 5000 locations. Within the supply chain, all possible orders for customer requirements are predicted. The ordering process is reduced to reporting for the customer and allows the logistician dispositive freedom.

At a glance - essential project data

DurationFrom 8/1/2019 to 11/29/2019 with about 4 months of full engagement
Data and ToolsMarket - Logistics
 • Order database
 • Customer information
 • Transportation database (MS-SQL) of 8 years
 • Weather
 • GEO information
 • Holiday calendar
 • Bank holidays
Integration •Web API for metadata enrichment with model generated data
 • Filling the order store with the predictions
 • Quality Reporting: Power BI
 • ML-Ops: Coupling with the customer systems for continuous transfer to and from our models
AI Methods
 • Feature Engineering
 • Feature Importance
 • Deep Learning

Engagement Use-Case

Forecast customer demand/orders change based on operational data for several months for 5000 locations.

Kundenmotivation / Lösungsansätze

  • Increase customer loyalty

  • Degree of automation for OneStop-Shop

  • Increase integrated planning of orders

  • Forcasting clarity for customer meetings/budgeting sessions

  • Know-your-customer

AI Approach

AI key technology used in our solutionProcessing (capturing, cleansing, unbundling) of raw data and complex data structure. Very good correlation of cyclic components and delivery frequency.
Solution Approach3-layer Deep Learning Network with cyclic features Categorical Embeddings
Project ApproachSimply agile
Project TypeOperations/ML Ops
ML Integration and ML Operations • Operations-Integration API
 • VIsualizationPower BI and Webshop Customer One-Stop Shop
 • ML-Ops at Customer Infrastructure onPremise

Insights and Details

Accurate prediction from operational data with the Deep Learning approach!

Accuracy of the approach as a check of the ACTUAL order (if necessary outliers, vacations etc...)

The forecast covers a whole year. This makes budgeting for the coming year possible

Error evaluation is essential for a correct approach. Feedback to the customer for the ideal package size becomes possible.

Recognition of a vacation coverage by order is recognizable.