Big data analytics for cold chain logistics optimization in refrigerated trucks.

Data analysis to predict the demand of goods, optimise the routes in real-time and provide visualizations and descriptions.

Odin Solutions (OdinS) is a SME founded in August 2014 and accredited as innovative ICT company (EIBT) by MINECO and ANCES. OdinS has a strong background in the R&D fields of Internet of Things, Security, and Data Analytics. The pilot ontributes to the development of the supply chain 4.0, specifically the cold chain, and it is aligned with the interest of OdinS to contribute to the emergence of smart environments.


A cold chain is a supply chain of perishable items which protects food from degradation, improper exposure to temperature, humidity, and other harms that can compromise their integrity. Deterioration of food during transportation causes adverse effects on human health, product prices, and food availability.

Thanks to in-vehicle IoT deployments, vehicles can be more efficient, connected and automatised. However, ‘cold chain optimisation’ in refrigerated trucks has not been properly studied so far. Therefore, there is a need to understand how continuous monitoring of truck conditions such as temperature, humidity, opennings, etc. can be used to support real-time assessment of quality and decision-making in cold chains.

At the same time, given that many companies have been capturing their trips’ data so far, we can also apply Big Data analytics on the long period data in order to discover their practices using pattern analysis of the data. This can lead to redesigning the transportation network to minimize quality loss and to avoid the adverse transportation conditions.

With BIGcoldTRUCKS we will optimise the cold chain through Big Data analytics and also study the characteristics of the trips and products in order to save petrol and reduce food waste. Our goal is twofold:  first, we will develop a system that analyses historical data in order to extract existent patterns in the transportation of perishable goods and identify malpractice. Second, we will analyse real-time data in order to support decision-making in relation to the routes and the grouping of food items for their transportation.


We will clean and pre-process the historical data monitored from trucks (we count with a big transport company working within Europe to provide the data). Such pre-processing includes the creation of several new variables that will provide more information such as:

  • Ranking of transported goods.
  • How goods are usually grouped.
  • Average trip temperatures visualisations
  • Time that doors remained open in each trip
  • Trip duration
  • Seasonal exports
  • Visualization of picking points and destinies on a map

Relate all the previous new variables to the quality of the goods in order to create classification models that help us find what factors influence the most to the goods integrity. Furthermore, we propose to use time series analysis to observe the evolution and find inflections point in the considered variables.

The previous analysis, ranking and clustering or grouping will lead to the creation of restrictions that are going to be used for an optimisation model that will help decision-tacking depending on the product that should be transported, its initial point and destination. This will imply a better cold chain conservation and therefore less waste of food.