Optimizing IT infrastructure with GLPI’s resource allocation tools

"How many people do we need on the helpdesk next quarter?" This is a planning question, not a guessing game — and GLPI has the data to answer it. Ticket volume trends, resolution times, workload per agent, seasonal patterns. It’s all in the database, waiting to be turned into a staffing forecast.

What GLPI data tells you about capacity

Ticket volume over time

Pull monthly ticket counts for the past 12 months. Look for trends: is volume growing? Flat? Seasonal? Many organizations see spikes after major rollouts, at the start of the school/fiscal year, or during audit periods. If volume is growing 10% per quarter and your team size is flat, you’re heading toward SLA breaches.

Resolution time by priority

Average resolution time is a lagging indicator of capacity. When agents are overloaded, P3 and P4 tickets start taking longer — not because they’re harder, but because nobody gets to them. If P4 resolution time has doubled in six months while P1/P2 is stable, your team is triaging by urgency and deferring everything else.

Tickets per agent

Divide monthly ticket volume by the number of agents. A rough benchmark for L1 helpdesk: 40-60 tickets per agent per month is comfortable, 80-100 is stretched, above 100 is unsustainable. These numbers vary by complexity — a team handling SAP issues works differently than one doing password resets.

Building the forecast

A simple capacity model from GLPI data:

1. Extract 12 months of ticket volume — grouped by month and category
2. Calculate the trend — linear regression or just average month-over-month growth
3. Project 3-6 months forward — if growth is 8% per quarter, next quarter’s volume is this quarter × 1.08
4. Divide by your sustainable rate — if 60 tickets/agent/month is your target, projected volume ÷ 60 = agents needed
5. Factor in planned changes — a Windows migration will spike tickets. A new self-service portal will reduce them. Adjust the projection.

This won’t be perfect, but it replaces "I think we need two more people" with "Based on ticket trends, we’ll need two more people by Q3 to maintain our SLA."

Where to get the data

GLPI’s Statistics module gives you ticket counts over time. For more detailed analysis, query the database directly or use an external BI tool (Metabase, Power BI). The key tables:

• glpi_tickets — creation date, close date, category, assigned group, entity
• glpi_tickets_users — who was assigned (for per-agent analysis)
• glpi_slas — SLA targets (to compare actual vs. target)

Beyond headcount

Capacity planning isn’t only about people. The same data can inform:

• Skill development — if network tickets are growing but you have one network specialist, you need either another hire or cross-training
• Automation priorities — categories with the highest volume and lowest complexity are the best automation candidates
• Service catalog changes — if a self-service option could deflect 30% of a high-volume category, that’s equivalent to hiring a part-time agent

The data is already in GLPI. The question is whether you’re using it to plan or just to report.