CO2 emissions

Balance cost and CO2 emissions with statistical and AI modelling

Johan Öhlin
Head of Advanced Analytics

Business leaders often have to balance strategic goals for cost reduction, sustainability and customer satisfaction, while at the same time maintain an operationally healthy and efficient product distribution network. The lack of tools that help create a digital overview of the operational network further complicates this. It often results in silo thinking as well as static and ad-hoc route configurations and decisions.

Imagine that you are a manufacturing company that needs to distribute your products to end users or distributors, both nationally and internationally. So how should the item travel through the network? How should the policies regarding the use of 3rd party transport suppliers be put together? How is this carried out in the most sustainable and cost effective way?

Why is Artificial Intelligence / Statistical Modelling a Good Idea?
We see that using Artificial Intelligence/statistical modelling can create real business results. In the cases we have worked with so far, we have shown a potential of 4% in annual transport costs by using a share of the recommended routes. 23% of the new routes simulated had not historically been used before. This resulted in an estimated 1% reduction in annual CO2 emissions (when CO2 was weighted by 30% of the score). At the same time, this enabled a 360-degree full visualization of the entire network, which was the data basis for the analysis and the models.

The journey
Our network & route evaluation solution (or NRE for short) uses a combination of statistical and machine learning models to identify and recommend routes. They are maximized up against weighted costs, CO2 emissions and different types of customer scores. Via Optilon´’s web interface, the company’s employees who work with the optimization, are given several route alternatives to choose from and recommended actions.

A combination of statistical modeling and machine learning algorithms analyzes large combinations of existing “legs” in either existing or new routes and compares with historically used routes.

NRE not only helps with recommendations for routes, but it also provides a digital overview of the overall network. It can be at customer or order data level, transport waybill, data about the “leg” and last-mile data image.

The solution
Access to NRE can be provided via the Optilon web interface. Here, the company employees can easily evaluate, benchmark and approve/reject route recommendations.

Approved routes can be used via data integration in operational routing applications, while at the same time being monitored at a tactical and strategic level via interactive aggregated reports, either via the Internet or third-party BI apps.


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