Why Allocation Management Is Hard to Manage?

Resource and allocation management sounds simple in theory — assign people to projects, add percentages or hours, and track capacity. But when teams try to map real-life allocation tracking into a no-code tool like Fibery, Notion, Airtable, etc., they quickly discover that the concept hides a surprising amount of complexity.

Let’s outline the core challenges teams face when designing allocation management systems from scratch, especially in no-code environments. And will see, whether you agree, or not 🙂

1. Granularity mismatch: Reality vs. Assumptions

Most allocation templates assume a fixed granularity — usually weekly or monthly. But real teams rarely plan work so neatly.

What most templates assume:

• Allocations are stable across full weeks or months
• Percentages convert uniformly into hours

What teams actually need:

• Daily or even hourly granularity
• Changing workloads mid-sprint, mid-week, or mid-month
• Frequent recalculation as client timelines shift

Why this becomes a problem

A task running from Oct 20 to Dec 20 spans three partial months. Monthly aggregation will misrepresent workload unless hours are split proportionally. Here’s the catch: this requires fractional calculations that most templates don’t support out of the box.

Example:

30 hours over 3 months starting mid-month rarely means 10h per month — it may need to be 7.5h / 15h / 7.5h depending on actual days available.

Result: Systems either oversimplify (and become inaccurate) or require complex formulas that break easily.

2. The blind spot of Monthly or Weekly aggregation

Even if monthly or weekly totals are correct, they fail to reveal where overload happens.

Two tasks may both be planned “5 hours/day,” but if they overlap on the same days, a weekly summary may hide the issue.

Example scenario:

• Project A: Mon–Thu, 5h/day → 20h/week
• Project B: Wed–Fri, 5h/day → 15h/week
• Weekly total: 35h (seems fine)
• Actual: Wed+Thu = 10h/day (often flagged as overload)

Most tools would warn the person is overallocated on Wed/Thu — yet in reality, the team may choose to redistribute or absorb this easily.

This illustrates a key granularity trade-off:

• Day-level tracking: precise but results in “spiky” overload warnings
• Week-level tracking: digestible but hides daily peaks

Teams need both — detail for planners, summaries for decision-makers — but designing a system that supports both is hard.

3. Predictive allocation: Fixed vs. Flexible constraints

Planning future work adds another layer of complexity. Forecasted allocations tend to be rough and uncertain, especially weeks or months ahead.

Example:

“Michael works 10% for the next two weeks” → ~8h/week (based on 40h/week)

A full-time week = 40 hours.

10% allocation of that means 10% × 40h = 4 hours per week

So if Michael is allocated 10% for two weeks, that’s roughly 8 hours total (4h × 2 weeks).

But if the real work turns out to require 20 hours:

Should the system assume that:

• Michael now works more hours ( >100% capacity)?
• The task extends for more weeks?
• Only part of the planned scope will be delivered?
• Something else gets deprioritized?

Different organizations treat these constraints differently — and a no-code system must pick rules, otherwise it cannot automate calculations.

4. People are not robots

No matter what the HR department might want to believe, people don’t usually work exactly 8hrs/day, 5 days a week. Also, they take time off (vacations and public holidays).

So how should an allocation system cope with this? There would be no point assuming that Michael will complete a 40hr task in the week that contains Christmas Day, if we know he’s going to be taking time off to spend with his family. Whereas maybe his colleague Anna is more likely to be celebrating Rosh Hashanah.

Also, the definition of a working week might depend upon where an employee lives - two colleagues in Malaysia might not even take off the same days as weekend as each other, let alone observe the same holidays.

Good luck with devising the correct allocation rules to accommodate these variations.

5. No-Code systems force choices that reality doesn’t have

In real life, teams think intuitively:

“We’ll adjust — it’ll work out.”

But no-code systems need explicit logic. For example, if % allocation drives an Hours field:

• You can’t manually edit hours — you must edit %
• If both % and Hours are editable and auto-sync via automations → high risk of infinite loops or conflicting updates

Some teams do this anyway (bidirectional automation), but it often leads to circular updates, inconsistent records, and broken logic.

No-code tools require formal rules, but resource planning often lives in fluid ambiguity.

This gap is one of the biggest sources of frustration.

6. A “Perfect” allocation model requires intentional decisions

Before building an allocation model, teams must define:

Topic	Questions / Options
a. Granularity	Should the source of truth be: - Day-level? - Week-level? - Month-level?
b. Work pattern assumptions	Are weekends “work days” for allocation math or should they be excluded?
c. Overcapacity thresholds	When should the system warn: - Over 100% on any day? - Over 100% on average across the week? - Over allocated on a project, but still acceptable overall?
d. How summaries vs. detail coexist	Teams typically need: - A daily view for accuracy - A weekly roll-up for planning - A monthly overview for forecasting

Designing a model that supports all three is possible — but not trivial.

The “Entity per Day” solution: the most accurate but most complex

The most robust approach in a no-code tool is to create a database of days and assign allocations to each day via automation.

This enables:

• Precise daily capacity checks
• Roll-ups to week, month, quarter
• True overlap detection

But it comes with cost:

• Requires extra database entities (hundreds per person per year)
• More formulas, more automations, more maintenance
• Harder onboarding for users

🧑

Building allocation management from scratch in a no-code tool is not just about formulas — it requires making explicit decisions about how work should be interpreted. The biggest challenges stem from:

• mismatched granularity between planning and reality,
• ambiguity in forecasting,
• rigidness of no-code systems compared to real-world flexibility, and
• the need to balance detailed accuracy with usable summaries.

A sustainable allocation model emerges only once the team consciously defines how capacity, time, percentages, and constraints are meant to behave.

Until then, any system — no matter how well-built — will feel “wrong” or “incomplete,” because the rules behind it were never aligned.