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Driving Green Logistic with the Help of Synthetic Data


In our ever-evolving world, the buzzword “Sustainability” resonates in our ears across all sectors. Among varied facets of sustainability, “Sustainable Logistics” or “Green Logistics”, has become one of the most heard ones since our thoughts gravitate toward carbon emissions from vehicles by having transportation a cornerstone of our lives. Unfortunately, the simplicity of thinking about eco-friendly logistics does not bring the simplicity of embracing it. Real- world logistics data is quite complex and costly to obtain and maintain when considering transportation networks, vehicle fleets, supply chain operations, environmental and socio- economic factors. To build sustainable logistics solutions, we need data for all these factors, but do we truly have access to it? Will data owners willingly share it? Can we trust its quality and usability?

Considering 5V of big data, privacy concerns, and having lots of different data sources from different relevant pieces in the logistics end-to-end chain, synthetic data as artificially generated data that mimics real-world data is a concept that we need. Here is why it is a game changer:

  1. Generating Essential Complementary Data: Synthetic data fills the gaps when real- world data is sparse, restricted, or partially missing, ensuring we have complete information we need.
  2. Increasing the quality level of current data: Synthetic data lets us fine-tune data accuracy by calibrating it based on well-defined parameters and models, offering more precise alignment with our objectives and data In contrast to real-world data’s inconsistencies (sensor misreading, human-caused errors, etc.), synthetic data maintains a consistent quality.
  3. For privacy concerns: Synthetic data relies on statistical dependencies, eliminating privacy concerns without compromising data utility. It does not anonymize; it creates artificial
  4. Scenario creation for testing purposes: Synthetic data enables risk-free simulation of various scenarios including stress testing (extreme conditions, unexpected events, ), fostering innovation, adoption of sustainable practices and repeated iterative testing without incurring additional costs.
  5. Training ML/AI models: Synthetic data’s value extends to expanding data volume for model training, ensuring robustness, and capacity to handle real-world scenarios including stress testing.

GREEN-LOG project, where we harness the power of synthetic data… In GREEN-LOG pilots, we create artificial demand/supply data to evaluate fleet performances considering future fleet sizes. For Routing Optimisation, every dataset undergoes quality checks, and synthetic data steps in when required to enhance data quality or to increase data size. When we deal with sensitive information such as connecting a vehicle/driver to a tracking component, synthetic data generation ensures privacy while balancing utility. For testing purposes of models and functionalities, scenario creation takes the spotlight enabling risk-free experimentation, stress testing and rapid innovation with low costs within GREEN-LOG pilots.

In a world where sustainability is no longer a buzzword but a necessity, synthetic data emerges as a powerful tool in Green Logistics. With its abilities, it paves the way for a greener and more efficient future in logistics. In GREEN-LOG, we are turning these advantages into a reality, driving innovation with the help of synthetic data.

Picture source: luchschenF/


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