8) The KPI trap -Why Your P50 Mine Plan Breaks by Wednesday
- hlourens6
- 2 days ago
- 5 min read
Fix the physics with Flow (TOC), then let stochastic planning keep it honest
Walk into almost any mine and you’ll hear some version of the same story:
The monthly plan looks solid.
By Wednesday of Week 1, it’s already broken.
Planning says, “Operations won’t follow the plan.”
Operations say, “Planning builds fantasy schedules.”
Behind the finger-pointing is a shared frustration: plans have almost no shelf life.
We believe the core problem is not discipline, or even modelling skill. The real issue is more fundamental:
Your P50 plan is living in a P02 reality – because the departmental resourcing and measurement on the mine leads to a brittle system, unable to absorb the typical impacts to the performance of the system (Flow).
Stochastic planning is a big step up from traditional methods. But advanced maths cannot fix a physically brittle system. (See articles 1-3 in the KPI trap series)To get plans that last more than a few shifts, you have to change the physics of the mine (put in shock absorbers and sprint capacity) with Flow (TOC) first – then use stochastic tools to tune, not rescue, the plan.
Why Traditional (Deterministic) Planning Keeps Failing
Traditional mine planning is deterministic:
Tasks get single-point durations.
Fleets get single productivity numbers.
The plan assumes “average” conditions, line after line.
In reality, mines live in a world dominated by:
Variability – breakdowns, congestion, support delays, weather, geotech surprises, re-handle, shift-change inefficiencies.
Interdependency – each step relies on all the upstream and downstream steps behaving as expected.
The result: the system is rarely running at “average” anywhere, let alone everywhere. A supposedly “balanced” chain of 10s often delivers something closer to 6 or 7 – and sometimes less.
This is why deterministic plans:
Look believable at approval,
Then unravel at the first disturbance, due to variability and interdependency .
Every re-plan erodes trust. By the third or fourth cycle, workers start asking: “What’s the point of a plan that only survives a few days?”
Why Stochastic Planning Is a Big Upgrade
Stochastic (probabilistic) planning accepts reality: performance is not a single number; it’s a range.
Instead of one perfect schedule, it runs hundreds or thousands of simulations to produce a distribution of outcomes. That unlocks four big advantages:
It makes slippage visible.
You see how minor delays aggregate as they cascade through interdependent tasks, and why deterministic finish dates are statistically fantasy.
It highlights high-risk areas.
The model shows which tasks, interfaces or time windows drive most of the schedule risk.
It focuses attention on the true critical path.
Activities that most often appear on the simulated critical path become obvious targets for improvement – lifting average output and reducing downside.
It de-risks investment decisions.
You can test, probabilistically, how much upside you really get from extra equipment, additional headings, or a new process route.
In short, stochastic planning tells the truth about risk and uncertainty. It reveals that many of our deterministic plans were never realistic in the first place.
But here’s the problem:
Better maths cannot overcome bad physics.
When Good Maths Meets a Brittle Mine
Many mines are designed and managed around the idea of balanced capacity – making each major step in the chain roughly “the same size” in terms of average capacity.
It sounds efficient. But in a variable, interdependent system, a perfectly balanced chain is the most fragile thing you can build:
Any disruption anywhere starves the next step and blocks the previous one.
There is little or no protective capacity (insuarance)
There are inadequate buffers to absorb normal variability.
Feed this into a stochastic engine and you get an uncomfortable result:
You input what you think are P50 assumptions for duration and rate.
The model tells you that your “P50” completion date actually behaves more like P02 in reality.
The tool hasn’t failed. It has calmly informed you that the system you’re planning is incapable of delivering the promise with any reasonable level of confidence.
This is why some executives feel burned by advanced planning tools:
They invested in better modelling,
But they never changed the underlying Flow design of the mine.
So the tools became highly accurate messengers of bad news.
You can’t spreadsheet your way out of a brittle system.
Flow (TOC): Fixing the Physics So Plans Have Shelf Life
To get a plan that survives more than a few days, you need the mine itself to be robust – able to absorb normal shocks without losing Flow.
That’s where Flow, based on Theory of Constraints (TOC), and Stratflow’s approach come in.
Instead of chasing balanced capacity and local utilisation, you:
Find the real constraint (the Drum).
Longwall, hoist, CHPP, shaft, key plant section – whichever resource truly governs throughput.
Unbalance the chain around it.
Non-constraints are no longer expected to run flat-out. They are given protective capacity so that when things go wrong, the constraint is not starved or blocked.
Design buffers deliberately.
ROM pads, ore passes, WIP stocks and time buffers are sized and managed to absorb normal variability around the constraint.
Align control to Flow.
A Flow Room and constraint-aligned KPIs focus everyone on one question:
“Is the constraint running at the right rate, with the right mix, right now?” “Will it be able to run at this rate for the next 48 hours?”
The result is Superflow:
Higher average tonnes,
Much smaller day-to-day swings,
Far fewer emergency meetings and “heroic recoveries”.
Managers often describe it as “a strange sense of calm” replacing chronic firefighting – even as production goes up.
How Superflow Transforms Stochastic Planning
Now put stochastic planning on top of a Flow-designed system:
The constraint is protected, not constantly exposed.
Non-constraints have the protective capacity to absorb shocks.
Buffers are sized based on reality, not guesswork.
In that environment:
A P50 plan behaves like a P50 plan much more often.
The difference between modelled P50/P80 and actual outcomes shrinks.
The plan doesn’t fall apart by Wednesday – it remains a valid steering tool over the week or month.
Stochastic planning changes role:
From “predicting how badly the brittle system will miss its promises”,
To “fine-tuning buffers, fleets and sequences inside a robust Flow envelope”.
You’re no longer choosing between better planning and better physics. You’re combining them.
What This Means for Mine Executives and Planners
If you’re a GM, COO, Technical Services Manager or Planning Lead, a few practical implications:
Stop asking planning tools to fix system design problems.
If you are still driving for balanced capacity and local utilisation, your stochastic engine will keep flagging nasty risks – because the system really is that brittle.
Redesign for Flow first.
Name the constraint.
Create protective capacity and buffers around it.
Re-tune meetings and KPIs so that protecting Flow at the constraint becomes the game everyone is playing.
Then use stochastic planning as your risk radar.
Use it to:
Quantify residual risk.
Test alternative strategies and project options.
Justify investment in ways boards and finance teams trust.
Judge success by calmness plus tonnes.
The real win is a mine that is steadier, calmer and more predictable, even as it produces more.
Your P50 plan doesn’t have to be a P02 reality.
When you combine stochastic planning with Flow (TOC) as practised by Stratflow, you move from brittle, short-lived plans to robust, repeatable performance – and finally give both planners and operators a plan worth fighting for.
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