Inventory executive dashboard
The executive dashboard summarises portfolio inventory health in six high-level indicators, computed from the most recent saved optimization run. It is designed for a managerial audience: at a glance it surfaces stockout risk, idle capital, and the projected cost of the current policy.
All indicators are derived from the most recent saved optimization run. If no run has been saved for the tenant, the dashboard will appear empty.
The six indicators
1. Projected total cost
| Field | Value |
|---|---|
| Unit | Currency ($ configured for the tenant) |
| Formula | Σ C_total (across all SKUs) |
Sum of the expected total cost (holding + shortage) across all SKUs in the portfolio. Represents the projected operating cost under the current inventory policy.
Trend: the indicator shows a green arrow if cost decreased vs. the previous run, and red if it increased.
Example: SKU 1: $4.2 M + SKU 2: $1.8 M + SKU 3: $0.9 M = $6.9 M (vs. $7.5 M previous run = −8 %).
2. Immobilised capital and % releasable
| Field | Value |
|---|---|
| Immobilised capital | Σ (net flow_SKU × price_SKU) (across all SKUs) |
| % Releasable | (Current capital − Optimal capital) / Current capital × 100 |
| Unit | Currency / % |
Immobilised capital is the monetary value of the inventory committed under the current policy. The % releasable indicates what fraction of that capital could be freed if the portfolio migrated to the optimal (lowest-cost) policy.
If a SKU is missing its configured unit price, it does not contribute to the immobilised capital or % releasable calculation. Check the master SKU configuration if the value appears understated.
Example: Current capital = $50 M. Optimal capital = $38 M. % Releasable = (50 − 38) / 50 × 100 = 24 %.
3. Portfolio expected fill rate
| Field | Value |
|---|---|
| Unit | % |
| Formula | Simple average of the fill rate of each SKU |
Averages the expected fill rate across all SKUs. A low fill rate on a few high-volume SKUs can be masked in the simple average; for more granular analysis, consult the per-SKU inventory metrics.
Example: SKU A: 99 %, SKU B: 97 %, SKU C: 100 %, SKU D: 96 %. Portfolio fill rate = (99 + 97 + 100 + 96) / 4 = 98 %.
4. SKUs in stockout
| Field | Value |
|---|---|
| Unit | Number of SKUs |
| Composition | SKUs in "No stock" state + SKUs in "Depleted" state |
Counts SKUs whose projected inventory level falls into critical stockout states. A high number signals that the current policy does not adequately protect the service level.
The "No stock" and "Depleted" states are defined on the Buffer states — traffic light page.
5. Critical over-stock
| Field | Value |
|---|---|
| Unit | Number of SKUs (and % of portfolio) |
| Criterion | SKUs in "Too much" state |
Counts SKUs whose projected inventory far exceeds the target stock, classified as "Too much". It indicates potential over-investment in inventory.
The portfolio percentage is computed as (SKUs in "Too much") / (total active SKUs) × 100.
The criteria for the "Too much" state are detailed on the Buffer states — traffic light page.
6. Value to order (week)
| Field | Value |
|---|---|
| Unit | Currency |
| Formula | Σ Q_adjusted × price_SKU (across SKUs with Q > 0) |
Sums the monetary value of all suggested replenishments in the immediate horizon (current week). It indicates the projected purchasing outlay if all suggested orders are executed.
Example: 150 units suggested (across several SKUs) × respective prices = $300,000.
Recommended executive reading
| Warning signal | Suggested action |
|---|---|
| Total cost rises > 5 % vs. previous run | Review changes in cost parameters (rates, penalties) or in the projected demand |
| % Releasable > 20 % | Evaluate reducing k for low-stockout-risk SKUs |
| Portfolio fill rate < 90 % | Identify low-fill-rate SKUs and increase their k or safety stock |
| SKUs in stockout > 0 | Prioritise immediate replenishment orders for those SKUs |
| Critical over-stock > 15 % of portfolio | Check whether demand parameters or lead times are outdated |
| Value to order very high vs. budget | Prioritise critical SKUs and defer those with lower fill-rate impact |
Review the executive dashboard at the start of each planning week or after every optimization run. The comparison against the previous run (shown by trend arrows) is especially useful for detecting policy drift.