IoT R&D — RFID Powered Visibility for Logistics and Pharma

Talkin’ Things is an EU-based IoT company helping brands connect physical products with digital services through RFID and NFC.

In the air, everything must function flawlessly; on the ground, however, this is often not the case.

Problem

Aircraft maintenance depends on specialised tools that can sit in warehouses for years. They get misplaced, degrade over time, and are hard to track.

We needed to design a system that made tool usage and availability visible and actionable, not just nicely catalogued.

What we did

We designed a system where:

• every tool is tagged with RFID,
• antennas track movement inside the facility,
• workstations check whether the correct tools are present before a job starts,
• SAP acts as the single source of truth for inventory and usage.

Outcome

By digitising tool flows and integrating them with SAP, Airbus can now:



See where tools actually are, instead of where they should be on paper.

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Make better decisions about when to service, replace or retire equipment.

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Reduce waste from degraded or underused tools.



Support more predictable maintenance planning.

Reduced time to locate critical maintenance tools by roughly 30–40%, which cut delays caused by missing equipment and improved overall maintenance predictability.

Vaccines must be kept at cold temperatures to ensure their effectiveness.

Problem

Sanofi needed better control over the cold chain for sensitive products, such as vaccines.

The system had to make it very clear when a batch was safe, at risk, or should be stopped.

What we did

We designed a system where:

• each vial had an RFID tag with a temperature sensor and a power source
  
• industrial scanners registered batch movement when entering and leaving warehouses
• temperature and shock data were tied to specific units or batches of product
• all of this is integrated with SAP, which stores the whole history and status of each batch

Outcome

Thanks to the pilot implementation in its main EU supply chain, Sanofi can now:



Identify at risk batches much faster.



Reduce waste by precisely isolating only the vials that had actually been exposed.



Keep a complete route and environmental history for each batch in SAP.



Better document compliance with regulatory requirements.

Reduced avoidable waste in pilot cold chain lanes by an estimated 15–20% by allowing precise batch-level decisions instead of discarding entire shipments.

You become extraordinary when you nourish your spirit.

Problem

Mars wanted to understand what really happens between the shelf and at the checkout.

The gap was in understanding real behaviour around the shelf, not just transactions.

What we did

We designed:

• custom shelves with antennas,
• RFID tagged candy bar packaging,
• a data model that tracks the path of each item – picked up, held, put back, purchased,
• a dashboard that surfaces patterns instead of just raw events.

Outcome

By implementing the system and connecting it to the analytical system used by Mars, the company gained:



Visibility into what happens to the product before it is bought.



The ability to test different shelf setups and campaigns based on real behaviour, not only sales.



Better decisions about product placement, POS materials and promotions.



More grounded conversations between sales, marketing and retail execution teams.

Increased speed of in-store experiment analysis by around 2 to 3 times by replacing manual POS and survey reports with behaviour-level RFID data.

Final thoughts and impact

This interim role at Talkin’ Things was a dense crash course in treating hardware, data, and UX as one continuous system. Sensors and tags were only the starting point.

The real value came from mapping workflows, identifying which states and alerts truly mattered to people on the ground, and designing views that let them make decisions in seconds rather than after lengthy investigations.

It is the same mindset I use today when working on AI-assisted workflows, agents, and operational tooling: data alone is not enough without the right product logic and experience around it.