Dependency Concentration Heatmap¶
Dashboard tool — instantiates Dependency Concentration Control
Lays weighted dependency exposure onto a coloured grid — provider against function, geography, platform, and criticality band — so the overweight cells announce themselves at a glance.
A concentration you can't see is a concentration nobody rebalances. Dependency Concentration Heatmap assembles the weighted dependency inventory into a two-dimensional grid and colours each cell by how much reliance sits there — provider along one axis, and function, region, platform, or criticality band along the other — so a dark row (one provider carrying half of everything) or a dark column (one region hosting every critical service) is visible without reading a spreadsheet. Its defining move is spatial legibility across several cut dimensions at once: it doesn't reduce concentration to a single number, and it doesn't just list providers — it arranges exposure so the shape of the over-reliance is obvious to anyone who glances at it, and weights the colour by criticality so a dark cell on a life-or-death function shouts louder than a dark cell on a trivial one.
Example¶
A national retailer's platform team maintains a Dependency Concentration Heatmap of its software and service vendors. Rows are vendors; columns are business functions (checkout, inventory, identity, email, analytics); cell colour is weighted spend-plus-traffic reliance, scaled up for functions tagged business-critical. At a glance the board sees what a vendor list would have buried: the "identity" and "checkout" columns are both dark red under a single authentication provider, and the entire "critical" band leans on one cloud region.
Nobody had to compute an index to see it — the picture did the arguing. The heatmap turns "we use lots of vendors" into "two dark cells sit on the functions that stop the tills," and hands the rebalancing conversation a ranked worklist of exactly which cells to lighten first. What the picture is really surfacing is a cluster of single points of failure wearing an enterprise's worth of vendor names.[n1]
How it works¶
- Assemble the weighted inventory. Pull every dependency with its exposure weight into one table — reliance, not mere presence, is what each cell encodes.
- Choose the cut axes. Cross provider against the dimension that matters for this view (function, geography, platform, criticality), so concentration that hides on one axis shows up on another.
- Weight the colour by criticality. Scale each cell's intensity by the criticality of the function it serves, so the eye is drawn to dangerous concentration rather than merely large concentration.
- Rank the hot cells. Order the dark cells into a worklist so the display drives action instead of decoration.
Its distinguishing trait is that it makes concentration pre-attentive — the viewer sees the problem before reading a label — which is exactly what a scalar index or a flat vendor list cannot do.
Tuning parameters¶
- Exposure weighting — what fills each cell: spend, traffic, request volume, or a blend. The choice decides which concentrations look alarming; a spend view and a traffic view can disagree sharply about who the dominant provider is.
- Cut dimension — the second axis (function / region / platform / criticality). Each axis reveals a different concentration; a portfolio that looks diversified by vendor can be starkly concentrated by region.
- Criticality scaling — how steeply colour intensifies with function criticality. Steep scaling foregrounds the dangerous cells but can wash out moderate risks worth watching.
- Colour thresholds — where the palette flips from calm to hot. Set them loose and everything looks fine; set them tight and the board tunes the alarm out.
When it helps, and when it misleads¶
Its strength is communication: it lets a non-specialist see concentration in seconds and lets a room agree on which cell to fix first, across several dimensions a single index would have collapsed. It is the shared picture the rest of the archetype argues over.
Its central failure mode is that colour choices are the message — loose thresholds or a flattering weighting can paint a genuinely fragile portfolio a reassuring green, and the classic misuse is tuning the palette until the dashboard looks calm for a review rather than honest for a decision. More subtly, a heatmap shows marginal exposure cell by cell; it cannot see a common-mode root shared behind several cells, so three light-green vendor cells that all route through one hidden upstream will read as safe. The discipline that guards against this is to fix thresholds before looking at the data, and to feed the grid the effective-provider and common-mode findings so the colours reflect real independence, not just apparent spread.
How it implements the components¶
Dependency Concentration Heatmap fills the display side of the archetype — it renders exposure legible, it does not reduce, detect, or govern it:
weighted_dependency_inventory— it assembles and holds the table of dependencies with their exposure weights, which the grid then renders.criticality_weighting_rule— it scales each cell's intensity by function criticality, encoding the rule that dangerous concentration should outshout merely large concentration.
It does not collapse the grid to a single index (that's Effective Independent Provider Count and Top-K Exposure Share), detect the shared roots hiding behind seemingly-cool cells (that's Common-Mode Dependency Audit), or set the caps the dark cells breach (Concentration Cap Policy); it makes visible what those mechanisms quantify, unmask, and bound.
Related¶
- Instantiates: Dependency Concentration Control — the heatmap is the shared picture the appraisal reads its concentration off.
- Consumes: Weighted Dependency Graph supplies the dependency population and exposure units the grid renders; Common-Mode Dependency Audit supplies the independence corrections that keep cool cells honest.
- Sibling mechanisms: Effective Independent Provider Count · Common-Mode Dependency Audit · Top-K Exposure Share · Weighted Dependency Graph · Concentration Cap Policy
Editorial Notes¶
Form Classification¶
Form family: Interface, Display & Cue
Rationale: Dependency Concentration Heatmap operates as a user-facing prompt, display, template, or perceptual cue that shapes attention and action at the point of use because it lays weighted dependency exposure onto a coloured grid — provider against function, geography, platform, and criticality band — so the overweight cells announce themselves at a glance.
Independent corroboration: The frozen evidence defines Dependency Concentration Heatmap as 'Lays weighted dependency exposure onto a coloured grid — provider against function, geography, platform, and criticality band — so the overweight cells announce themselves at a glance', so its operative form is Interface, Display & Cue.
Review outcome: Independent reviewer agreement; high confidence.
Origin Attribution¶
Primary origin: Logistics & Supply Chain Management
Origin pattern: Cross-disciplinary synthesis
Present-day reach: Multi-domain
Rationale: Supply-chain risk management cohered mapping weighted reliance by provider, function, geography, and criticality to expose concentration.
Related originating lineages:
- Data Science & Analytics — Analytical visualization supplied the weighted heatmap form that makes concentration patterns immediately legible.
- Economics & Finance — Concentration-risk analysis supplied weighting by exposure and counterparty importance.
- Engineering & Design — Reliability engineering supplied critical-function and single-point-of-failure interpretation.
Review resolution: Supply-chain risk management cohered mapping weighted reliance by provider, function, geography, and criticality to expose concentration. Supply-chain, financial, and reliability concentration inventories co-form the risk method, while data visualization supplies the heatmap artifact.
Attribution caveat: The heatmap is a cross-domain visualization of established concentration-risk inventories.
Encyclopedia synthesis: The exact catalogued form synthesizes established practice rather than reproducing a single standard historical label.
Review outcome: Reconciled after independent review; medium confidence.
Notes¶
[n1] A single point of failure is a component whose loss alone halts the whole system. A concentration heatmap is one of the plainest ways to spot candidates — a dark cell on a critical function is one — but the grid sees only the exposure it is fed, so a single point hidden in an upstream shared by several apparently-separate cells stays invisible until an audit traces it. ↩