CASE STUDY –
SODEXO HJÄLPMEDELSERVICE
SODEXO HJÄLPMEDELSERVICE
How Sodexo went from fragmented asset data to faster refurbishment.
Sodexo Hjälpmedelsservice provides and adapts technical aids for mobility, cognition and communication, helping thousands of people i Sweden live more independently.
Behind the service is a large operational flow of equipment, spare parts, suppliers and information — all of which needs to work together every day.
CLIENT: Sodexo Hjälpmedelservice
INDUSTRY:
Healthcare & Assistive Equipment Services
SOLUTION SCOPE:
Asset Intelligence| Integration |Automation |
|3T-methodology |
OUTCOME IN SHORT:
-
30 % shorter refurbishment lead times
-
25 % higher productivity per employee
THE CHALLENGE:
The parts were there. The visibility wasn’t.
Sodexo had the data. The problem was that it lived across different systems, with different structures and different versions of what was available.
That made everyday decisions harder than they needed to be. Stock information could be outdated, supplier data was difficult to use consistently, and refurbishment and repair flows depended on information that was not always easy to find or trust.
In 2021, Sodexo set out to improve how returns, refurbishments and repairs were handled — and make the information behind those processes genuinely useful.
- Fragmented data across multiple systems
- Outdated stock balances and limited decision support
- Slow and costly refurbishment and service processes
- Growing expectations for faster digital access to information
- New MDR requirements increasing the need for accurate supplier and product data
As one project leader put it:
“We had the data, but not the language to make it work together. That stopped us from acting data-driven.”
THE SOLUTION
Connect what exists. Make the data useful.
Rather than replacing the systems already supporting the operation, Sodexo connected them.
Together with Ghost Labs and integration partner RelyITS, Sodexo used Ghost Nodes to create a common integration layer across systems, supplier data and operational processes.
Information that had previously been fragmented could now be brought together and used directly in refurbishment, repair and service workflows — while automation removed much of the manual work previously needed between systems.
This is Asset Intelligence in practice: making information about physical assets useful across the systems and processes that depend on it.
The principle applies far beyond technical aids. When assets, stock and product information live across different systems, the real challenge is often not whether the item exists — but whether the people who need it can see the full picture around it.
Alongside Ghost Nodes, Ghost Labs applied its 3T methodology — Team, Theme and Technology — bringing together operational knowledge, business needs and technology so the new flows were shaped around how the work actually gets done.
The goal was not another system. It was to make the systems they already had work better together.
As one operations manager summed it up:
“Ghost Nodes has given us control over our data – for real. We can now follow every product’s journey, make faster decisions, and work smarter.”
THE RESULT
When better visibility shows up in the numbers.
Once the information became easier to use across the operation, the impact became measurable — both in the refurbishment process and in the day-to-day work around it.
- 30 % shorter refurbishment lead times
- 25 % higher productivity per employee
- Faster onboarding of new customers — from months to weeks
- Fewer deviations and higher delivery precision
- Fewer deviations and higher delivery precision
- More reuse of aids, creating both environmental and financial gains
Turns out, data-driven works rather better when the data can actually drive something.
WHAT COMES NEXT
From better data to smarter automation
Once asset information is connected, structured and usable across the operation, it becomes possible to do more with it.
Automation can take over more of the repetitive work, while AI assistants and agents can support administration, decision-making and increasingly complex workflows.
The important part is the order. First make the information useful. Then make it intelligent.
That creates a foundation that can keep evolving as new processes, technologies and requirements are added.
