S&OP vs MPS vs ATP
How demand planning, production planning and order availability connect.
S&OP Decision Lab
Turn planning constraints into business decisions.
Free Excel-based decision models to help students, analysts and supply chain practitioners compare service, cost, capacity, stock and cash trade-offs across S&OP and MPS scenarios.
Most supply chain planning resources explain formulas separately. This site connects them into decision models.
Excel-based decision models to turn S&OP, MPS and ATP constraints into clear business decisions.
This is not a library of simple templates. S&OP Decision Lab builds decision models that combine demand, capacity, stock, planning zones, exception costs and financial trade-offs into one recommendation logic.
Flagship Tool
S&OP / MPS Decision Simulator
The first model of S&OP Decision Lab is a complete Excel-based decision simulator designed to help supply chain teams assess planning decisions under constraint.
The business problem
The goal is not only to calculate whether demand can be served. The goal is to compare business options: accept, delay, allocate, use overtime, subcontract, escalate or reject.
- Service risk when demand changes late
- Stock gaps hidden by aggregate availability
- Capacity overload and expensive recovery options
- Cash and working capital pressure from inventory decisions
Model structure
It connects S&OP demand, MPS planned receipts, ATP availability, projected available balance, capacity limits, frozen/slushy planning zones and financial trade-offs into one decision model.
Decisions covered
Accept, delay, allocate, overtime, subcontract, escalate or reject.
Screenshots and formulas
Show the model outputs, key formulas and embedded comments without overloading the page.
Download and feedback
Download the Excel simulator and share feedback to improve the next scenario releases.
Download ExcelModel Logic
Planning logic, explained without drowning the reader.
Short educational notes help users understand why the model combines planning, operational and financial views before making a recommendation.
Projected available balance
Why future stock position matters more than today's inventory snapshot.
Frozen vs slushy zones
How late changes create exception costs and escalation needs.
Capacity overload
How to compare overtime, subcontracting, allocation and delay.
Stock is not enough
Why availability can still be unattractive once capacity, cash and risk are included.
Scenario Library
The first model already covers several core planning scenarios.
Included in the current tool
Urgent order acceptance, capacity overload, overtime decision, subcontracting decision, demand delay, allocation under constraint, stock shortage / no-stock gap, replenishment trigger and frozen/slushy exception.
Future scenarios
Supplier delay, inventory reduction without service loss, service level recovery, working capital pressure and forecast bias correction.
About / Method
Built from operational experience, not spreadsheet theory alone.
Hugo Stephan
Supply Chain specialist and Lean Six Sigma Master Black Belt, with a track record of delivering EUR 1.9 million in savings through practical performance improvement, inventory governance and business-focused decision support.
EUR 1.9Msavings delivered
MBBLean Six Sigma
Supply ChainS&OP, MPS, ATP
A strong planning principle
In supply chain planning, the right decision is rarely based on one metric. A new order may look feasible from a stock perspective but become unattractive once capacity, planning zones, exception costs, service risk and working capital are considered.
The model connects operational constraints with financial impact.
It makes assumptions visible before decisions are escalated.
It translates analysis into a business recommendation.
What the model helps you decide?
It helps you get clear answers on transversal topics: order acceptance, ATP, PAB, capacity, exception costs, replenishment and the most balanced business scenario.
Can we accept a new urgent customer order?
The model compares stock availability, ATP, projected balance, capacity, planning zone and financial impact before recommending acceptance, delay, allocation or rejection.
Is the order inside the frozen, slushy or free planning zone?
The model highlights the planning zone and links it to exception cost, escalation need and operational feasibility.
Do we have enough ATP and projected available balance?
It distinguishes non-cumulative ATP, cumulative ATP and PAB so demand is not accepted on a misleading availability signal.
What happens if demand exceeds available capacity?
The simulator compares overtime, subcontracting, demand delay and allocation against service, cost, stock, cash and risk.
What exception cost should be considered?
Frozen and slushy changes can trigger overtime, expediting, subcontracting, quality, carbon, obsolescence and opportunity costs.
When should replenishment be triggered?
Reorder point, safety stock and future no-stock gaps help identify when replenishment is needed before the shortage becomes visible.
Which scenario is most balanced?
The recommendation logic weighs service, cost, stock, cash, working capital and operational risk to identify the best business option.