By Eamonn Ryan
As the cold chain becomes more data-driven, the next frontier is integration – combining product-level temperature data with equipment telemetry, energy usage, and external events to build a truly intelligent system. Johan Ferreira, co-founder of Cold Cubed, describes how structuring data correctly unlocks these possibilities and prepares the cold chain for an AI-powered future.
© Cold Link Africa
From the outset, Cold Cubed designed its system so that all readings are structured and time stamped in a consistent way. This seemingly technical decision has far-reaching consequences: structured data can be easily exported, shared via APIs, and overlaid with other datasets.
For example, one customer wanted to understand the impact of load shedding on their operations. By overlaying generator runtime data and diesel consumption with Cold Cubed’s product temperature readings, they could see precisely how often backup power was needed, how long systems ran on generators, and whether product temperatures remained within safe limits. This kind of multi-layered analysis would be nearly impossible with unstructured or manually logged data.
Ferreira also points to opportunities for tighter integration between equipment monitoring systems and product-level tracking. If a refrigeration unit sends a failure alert, product temperature data can immediately show whether the issue is already affecting the goods – and how urgently engineers need to respond. Conversely, anomalies in product temperature can serve as an early warning of equipment problems not yet flagged by traditional controls.
These integrations rely on breaking down silos between ‘product people’ and ‘equipment people’. Ferreira notes that currently, systems often operate independently, with limited data sharing. By using standard formats and cloud-based architectures with well-documented APIs, Cold Cubed enables partners and customers to build combined views that support:
Predictive maintenance: Identifying patterns where certain temperature signatures precede equipment failure.
Energy optimisation: Correlating door openings, dwell times, and setpoint strategies with actual product temperatures and energy use.
Incident investigation: Reconstructing detailed timelines when something goes wrong, using synchronised data from multiple systems.
AI and analytics: Feeding clean, structured historical data into machine learning models to identify risk patterns and recommended interventions.
Trust and data governance are also crucial. Retailers and suppliers are understandably sensitive about how their data is stored and shared. Ferreira emphasises the importance of data security, access control, and long-term archiving – Cold Cubed retains data for at least five years to comply with food legislation and support retrospective investigations.
Ultimately, an AI-ready cold chain is not just about having more data; it’s about having the right kind of data, in the right structure, with the right connections. By designing for integration from the start, Cold Cubed positions the cold chain to move beyond basic monitoring toward intelligent, automated decision support that reduces waste, improves safety, and strengthens supply chain resilience.