Transition from spreadsheets to a working asset system in Jira
Industry
Fashion retail
Technologies
Jira Service Management
How the client replaced spreadsheets with a working asset system in Jira in just 10 days.
Summary
How the client replaced spreadsheets with a working asset system in Jira in just 10 days.
About the client
The client designs and sells shoes, accessories, and handbags. The company was founded in Canada and now operates globally.
The North American division supports stores in Canada and the US. Their Service-Delivery team handles all IT issues for stores and office staff, from broken POS terminals to employee onboarding.
What happened and why it matters
The client’s service-delivery team supports both head-office staff and stores across North America. They manage laptops, POS terminals, and other hardware used every day.
All the data they had lived in Excel sheets. Some were outdated. Some didn’t match. No one could say with confidence who had what, or where it was.
That created problems:
- Assigning a laptop to a new hire could take 2–3 days
- Techs lost hours figuring out where broken devices were
- Inventory reports were incomplete or wrong
- Devices went missing
When asset tracking lives in Excel, everything takes longer
At the start of the project, hardware tracking at the client was fully manual. Each department kept its own version of inventory. Some files were up to date, some were not.
Every time someone joined or left the company, or when a device failed, technicians had to check ticket comments, warehouse shelves, and spreadsheets and hope they matched.
Everyday problems
- Fragmented data: Dozens of spreadsheets with inconsistent information
- Delayed onboarding: Devices assigned 2–3 days after the employee start date
- No audit trail: Unclear who had what, or where it was
- Lost devices: Laptops disappeared with no record of where they went
Even when Jira Service Management was used for tickets, there was no link between requests and inventory.
What the client needed: asset visibility
The team did not want another training or new software. They already had Jira. They just wanted it to reflect what was happening with their hardware.
They wanted to:
- Bring all hardware data into one place
- Make sure devices were actually tracked from assignment to return
- Tie assets directly to tickets, so nothing lived outside the system
- Build internal skills while building the setup
Even with a working system, the client also needed the skills to manage it without depending on external support.
“We didn’t just want theory. We wanted to get our hands dirty, break things, fix them, and learn how to run the system ourselves.”
That’s why the work was not based on a fixed plan. Instead, the project moved step by step: set up one part of the system, test it, hit a blocker, fix it together, then move on.
“The show-try-fix cycle beats any classroom cram-session.”
The process had to fit into a 10-day consulting package, and it did. Even with the 6-hour time gap between Montreal and Europe, the team adjusted meeting hours and stayed focused on the next step.
The solution
With growing pressure to improve service metrics and reduce downtime, the client needed a system that made asset tracking part of the workflow, not an extra task outside it.
That’s when they brought in Deviniti to help implement Jira Asset Management with the Discovery module. The goal: turn spreadsheets into a working system they could trust.
After eight weeks, they saw the difference:
- Ticket resolution time was cut by more than half
- Laptop lead-time dropped from approximately 2.3 days to approximately 7 hours
- Inventory accuracy rose from 62% to 97%
- Lost devices per quarter fell from 18 to just 1
“We finally have one source of truth and can spot a missing laptop in seconds instead of emails-and-guesswork.”
What changed: five steps from spreadsheet to working system
| Step | Focus | Result |
|---|---|---|
| Kick-off | Mapped hardware types, key attributes, and current gaps | Clear structure and naming rules |
| Discovery setup | Tuned the auto-scanning engine | More than 70% of devices correctly categorized |
| Data modeling | Defined laptops, POS, servers, and lifecycle states | Scalable structure ready for rollout |
| Ticket integration | Linked Assets to Jira Service Management request types | Devices could be assigned directly from tickets |
| Coaching and playbook | Walkthroughs, solo tests, and an internal guide | The client team became able to manage the next rollouts |
Why it worked and kept working
Focus on the right numbers
The team tracked the metrics that mattered most: lead times, lost assets, and ticket resolution.
Build while learning
The team did not watch a training session. They built the system themselves with support. That hands-on approach meant they understood how it worked and how to fix it later.
Discovery plus a simple model
A lightweight structure, combined with automated scanning, gave them more than 90% visibility within weeks, without extra tools or manual input.
What changed once Jira became the source of truth
Before the project, finding a single device could take hours. Now, the team sees everything in one place directly from the ticket.
They no longer need to cross-check Excel files, ask the warehouse, or guess. Devices are tracked, assigned, and updated inside Jira. It became part of the process, not an extra task.
“We finally have one source of truth and can spot a missing laptop in seconds instead of emails-and-guesswork.”
Results after eight weeks
| Metric | Before | After |
|---|---|---|
| Hire-to-laptop-ready lead-time | Approximately 2.3 days | Approximately 7 hours |
| Inventory accuracy (complete attributes) | 62% | 97% |
| Devices lost in spreadsheets per quarter | 18 | 1€ |
| Average hardware-ticket closure time | 3.6 days | 1.4 days |
What used to require Excel and warehouse checks is now handled inside Jira, with tickets, asset lists, and ownership updates in one place.
Now that the system works, it’s time to scale it
The team chose to begin with laptops, knowing this would be the most complex group of assets. Scanning, mapping, and lifecycle fields had to be set up correctly.
Now that the structure is in place, rollout to other device groups will move faster.he company can scale support volume without scaling headcount; keeping the delivery promise as parcels, partners and customer expectations keep growing.
| What’s next | Goal |
|---|---|
| Extend to POS devices | Reuse the asset model and simplify categorization |
| Feed the store uptime dashboard | Use asset status for MTTR metrics |
| Add expiry tracking | Alert on approaching warranty end dates |
The team is also planning internal adoption campaigns to improve usage across departments now that they have a system that works.

