Eco Performance Award Finalist s2 data & algorithms Introduces Itself

The Eco Performance Award 2023 has concluded, and the winners and finalists have been announced and honored. In a series, Loginfo24 presents the winners and finalists. Today, concluding this series, we introduce s2 Data & algorithms from Graz, a finalist of the Eco Performance Award 2023 in the Start-ups category.

(Graz/Munich) The complexity of logistics processes creates untapped potential. Many trucks on the roads are traveling empty or only partially loaded. On average, the transport utilization is 67.4%. This is bad for companies and bad for the environment. S2 data & algorithms has made it their mission to work daily on helping companies better utilize or save on transports, thus contributing to a sustainable future.

Using self-developed algorithms, the software solution MasterScheduler plans transports in the supply chain holistically. This innovative approach allows for the proactive identification of optimization potentials in operational and tactical transport planning, completely revolutionizing known planning approaches. Moving away from manual static demand planning towards automated dynamic route and load space optimization across the entire supply network.

Simple Approach – Highly Complex Mathematics

An approach that sounds simple, yet is based on highly complex mathematics. It is necessary to combine a vast range of planning parameters so that as few fully loaded trucks as possible, while still meeting all safety requirements, are planned simultaneously. To achieve this, a completely new perspective on logistics planning had to be implemented. And here lies the innovation. Previous solutions on the market have primarily planned transports in a one-dimensional manner. This means that only the factor of loading meters or weight was used as a decisive criterion. MasterScheduler takes a holistic approach here.

From demands, orders, inventory, or production data, transports including full loading planning are generated simultaneously and planned with all stacking conditions. This allows the truck to be viewed in three dimensions, consolidating free loading space and loading it in sequence. This achieves a loading space utilization of over 90%. And this is the case for every transport. Additionally, further parameters are incorporated into the planning.

Effectiveness Through Diverse Consideration

In addition to the perfect tariff choice, inventory levels, ranges, current production, and much more are also taken into account. Through this diverse consideration, the algorithm creates an effectiveness that can never be achieved through manual planning or standard planning tools. As a result, companies save an average of every fifth transport. This significantly minimizes transport costs and CO2 emissions, while maximizing planning efficiency.

Moreover, the visibility of the entire supply chain is enhanced. The goods flows in the supply network are analyzed using TCO and visualized in 3D in terms of utilization. By using MasterScheduler, the customer gains access to a comprehensive data pool. This allows for a precise examination of all relevant factors. Customers even have the option to break down costs and CO2 emissions down to a material level. This enables effective forecasting and more accurate budgeting. Furthermore, this feature can be used to substantiate any sustainability reports with clear data.

Software Solution Easily Integrated into Workflow

MasterScheduler represents a revolutionary new software solution that can be easily integrated into the workflow. Since the SaaS solution docks as an add-on to the existing ERP system (e.g., SAP), the optimization software can be implemented in just a few days without significant IT resource expenditure. This enables a short project time and savings from the very first second. Thus, MasterScheduler is a win for companies and the environment.

Photos/Graphics: © S2 data & algorithms / Caption for Title Photo (l. to r.): Sabina Kiss, Software Development; Dr. Stefan Kremsner, CEO and Founder; Lisa Drekonja, Innovation Management (all S2 data & algorithms)