Demonstration case 02 - scarce stock allocation
Synthetic distribution scenarioHow should scarce stock be allocated?
A distributor has 600 units available while eight customers demand 820. Proportional allocation feels fair, but it does not distinguish contractual minimums, profitability or business priorities.
Synthetic demonstration based on a realistic business problem. It does not represent a project for a specific client.
See the analysisBaseline - proportional allocation
Every customer receives roughly the same share of its order.
With 600 units available against demand of 820, the simplest approach is to scale every order to roughly 73.2%. The integer proportional allocation uses all 600 units and produces expected gross-margin contribution of EUR 8,999.
- profitability is not differentiated
- strategic importance is not part of the decision
- business rules are reduced to the available quantity
Available / demand600 / 820
73,2%of aggregate demand can be served
Expected gross marginEUR 8,999
What does the model consider?
The objective is not simply maximum margin. The decision must respect business rules.
In this demonstration the model maximizes expected gross-margin contribution, but first respects the total available quantity, customer demand limits and defined customer minimums.
Objective
Maximize expected gross-margin contribution.
Constraints
At most 600 units, never above customer demand, and every minimum must be met.
Variables
How many units to allocate to each customer.
Business rules
Minimums and service scenarios are company choices, not universal mathematical truths.
View inputs and allocations
The first 420 units are committed through defined customer minimums. The remaining 180 units are then allocated within demand limits according to the selected business objective.
Business inputs
| Customer | Demand | Margin/unit | Priority | Minimum |
|---|---|---|---|---|
| A | 140 | 18 EUR | 5 | 90 |
| B | 120 | 14 EUR | 5 | 80 |
| C | 110 | 20 EUR | 4 | 60 |
| D | 100 | 9 EUR | 3 | 40 |
| E | 95 | 16 EUR | 4 | 50 |
| F | 90 | 11 EUR | 2 | 30 |
| G | 85 | 22 EUR | 5 | 50 |
| H | 80 | 8 EUR | 1 | 20 |
Allocation comparison
| Customer | Demand | Baseline | Spacelytics |
|---|---|---|---|
| A | 140 | 102 | 140 |
| B | 120 | 88 | 80 |
| C | 110 | 80 | 110 |
| D | 100 | 73 | 40 |
| E | 95 | 69 | 95 |
| F | 90 | 66 | 30 |
| G | 85 | 62 | 85 |
| H | 80 | 60 | 20 |
| Total | 820 | 600 | 600 |
Before and after
The same stock. A different decision.
After satisfying the defined minimums, the remaining 180 units are directed toward customers where an additional unit creates greater margin contribution, until their ordered quantity is reached.
Decision delta
The same stock. A different decision.
The model does not increase available stock. It changes how the 600 units are allocated according to rules defined in advance.
This is not a universally best allocation. If the company changes objectives, minimums or the importance of service equality, the recommended allocation also changes.All customers receive roughly 73% of requested quantity.
Minimums are satisfied and the remaining quantity is allocated according to the business objective.
Without increasing the available stock of 600 units.
What-if / scenario analysis
Changing the business priority changes the allocation.
Decision support does not claim that one allocation is best in every situation. The result depends on the rules the company chooses to protect.
Scenario A - margin as the main objective
Minimums are mandatory and remaining stock follows higher margin. Allocation: A140, B80, C110, D40, E95, F30, G85, H20.
Scenario B - more even service
The proportional baseline produces almost the same service rate for all customers, around 73% of demand.
Scenario C - priority-5 customers at least 80%
A, B and G must receive at least 80% service. One optimal solution under this rule is A140, B96, C110, D40, E79, F30, G85, H20.
XAIOR - explaining the result
Why is the allocation different?
The model respects the rules first, then calculates their consequences.
The proportional approach allocates stock almost equally relative to demand, but it does not distinguish the economic value of individual deliveries.
The model first guarantees the defined minimum quantities. Remaining stock goes where an additional unit creates greater contribution, until the customer reaches its ordered quantity.
That is why A, C, E and G receive their full requested quantities in the base optimization scenario, while other customers remain at the defined minimum.
If equal service or key-account service receives more weight, the recommendation changes. The model does not define company values; it calculates their consequences.
From demonstration to a real problem
What if these were your data?
In a real project, demand, margins, minimums and priorities can come from ERP, Excel, orders, price lists and contractual rules. The model would use your real business constraints.
How do you allocate stock when there is not enough for every order?
Do all customers, products and markets really have the same priority?
What is the cost of deciding only by proportional allocation or experience?