Quality loss. A perishable supply chain challenge. Lots of tropical fruits is produced for European consumption and transported to the Netherlands by road, rail or by sea via the Port of Rotterdam. Product loss during transport, warehousing and distribution is estimated at 32 percent globally (FAO). Losses are estimated at 20-40% in developing countries and 10-15% in developed countries, depending on the crop. Just in the EU an estimated 4 billion EUR is lost due to postharvest losses and reduced quality of fruit. Apart from using temperature-controlled transport with fixed settings, there is no monitoring of the quality loss during transport.

Can we overcome this challenge in practice?

In three consecutive steps, we can intervene and avoid waste and quality loss.

• First, we need to monitor and predict the product quality loss. Temperature and humidity variations are important and low-cost sensor solutions already exist. But mangoes and many other climacteric fruits produce ethylene during ripening and this gas accelerates the ripening process. Today, we lack for instance small, affordable and reliable ethylene sensors for transport monitoring, but sensor technology developments are promising.
• Second, there is the challenge of how to get the right sensor data in real-time available for quality loss modelling, also when stuffed in a container and sailing on the open sea. Here, novel cloud communication technologies such as NB-IoT and LoRa offer promising perspectives for low-cost solutions.
• Third, if we have good real-time predictions of the quality loss, can we intervene in the transport chain, and in what way? Can we adjust the conditions in the truck or container, for instance lower the temperature or add ozone? Or can we speed up the transit by deploying a second driver? Can we better reroute the truck?
  This dynamic intervention is a completely new way of working and requires the right decision support models for executing the optimal intervention.

In this innovative project, we integrate these three process steps: sensing, real-time quality loss modelling and real-time logistics intervention, supported by a novel cloud data sharing infrastructure solution. And we developed a business case model that shows in what situation and supply chain this concept of integrated quality controlled logistics of perishables is commercially sound, and what the business model looks like.