People Analytics Toolbox

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Key statistics

The identity block — every fact is canon, none is generated.

6 of 8

Track

Management

Family rungs priced

M1 · M2 · M3 · M4 · M5 · M6

11-0000

manufacturing

Coordinates

3 / 4 spaces

pay@2 · 2026-07

What this role is

Senior executive who leads the enterprise demand and forecast planning function across multiple product lines or business units. At the Vice President (M6) level, the role owns a major function or division, enterprise priorities and external factors, and drives division-wide / organization-wide. Leads the leadership of a division.

What this level does

Sets the long-term demand and forecast planning strategy across multiple divisions or functions, with lasting effect on company success
Translates quantitative demand data into actionable insight for executives and negotiates critical supply-demand tradeoffs at the executive S&OP table
Leads through senior managers to embed predictive analytics, machine learning, and automation into the enterprise forecasting capability
Defines the policies and standards governing single-number forecasting, forecast accuracy, and bias accountability across the enterprise
Influences cross-functional and cross-divisional strategy where abstract demand-supply tradeoffs affect long-range capacity, capital, and revenue

Knowledge applied: Sets long-term enterprise demand-planning strategy and defines the standards governing single-number forecasting and bias accountability.
Complexity & problem solving: Addresses abstract, cross-functional demand-supply tradeoffs with long-range effect on capacity, capital, and revenue.
Collaboration & interaction: Negotiates with executives on critical supply-demand matters; influences policy across divisions and functions.
Typical experience: 12–15+ years of deep leadership across departments or divisions; leads through senior managers.

Skills at this level

Statistical and analytical forecasting: — Applies forecasting methodologies backed by strong mathematical and statistical knowledge to estimate future product demand and analyze inventory flow.
S&OP and consensus demand review leadership: — Leads the monthly demand review within the S&OP cadence, driving a single-number mindset by reconciling Commercial, Finance, and Supply Chain inputs into one consolidated demand plan.
Forecast accuracy and bias management: — Owns and improves forecast accuracy and bias for a business segment, setting up mechanisms to detect critical forecast changes and reporting net change against prior forecast.
Supply risk and contingency planning: — Evaluates risk to supply, develops contingency plans with the Supply Chain Manager, and communicates them across the wider planning team.
Predictive analytics and machine learning: — Leverages predictive analytics and machine learning as demand planning increasingly relies on advanced analytics to improve forecast models.
ERP and demand planning systems: — Uses ERP and demand planning software such as SAP APO and SAP IBP to integrate demand signals and capitalize on company technological resources for planning.
Cross-functional stakeholder coordination: — Coordinates demand planning across Sales, Marketing, Finance, Sourcing, and Manufacturing Operations to align on a shared demand plan.
Advanced Excel for forecasting: — Uses advanced Excel to build, maintain, and model forecasts and to produce monthly demand-planning metrics and net-change analyses.
Executive insight translation: — Places granular, quantitative demand insight within the wider business context and translates it into actionable recommendations for senior management and executives in S&OP meetings.
Problem-solving and business acumen: — Blends analytical prowess with business acumen to question inputs, accurately identify a demand need, and present an effective planning solution.

Model-authored from 21 retrieved sources, then adversarially reviewed by an independent judge panel and held to a minimum-evidence floor before publication. Distinct from corpus-extracted content, which is labelled as such.

Pay across the ladder

Demand / Forecast Planning — median with , by . Real canon data.

Pay figures are modeled estimates from our market equation — level-differentiated, with honest-wide bands. They refresh as new leveling evidence lands; treat per-role dollars as directional.

Demand — is the field growing, or just turning over?

SOC 11-0000 · national

Employment growth 2019–2024, BLS OEWSobserved

+12.7%Purchasing Managers · United States

72,100 → 81,240 jobs

6,400 openings / year, split into:

replacement / exits 6,141

Growth — new jobs (4%)Exits — backfill (96%)

4% of hiring here is genuine expansion; the rest backfills people who move on.

Projected growth 2024–34 expanding: +3.1%
Growth share of hiring: 4%
Exit / retention pressure high: 96%
Openings / year: 6,400

Two quantities, kept apart: observed = employment growth differenced from two BLS OEWS vintages (what already happened); projected = BLS Employment Projections 2024–34, openings decomposed into growth + exits (growth derived from projected employment change). Neither is a pay figure.

Where this role sits — growth vs. pay

level M6 held

Demand axis is projected growth (not raw openings — that just measures churn). A curated, de-duplicated set of recognizable occupations held at this level; this role is highlighted.

Where it pays — by metro

OEWS metro median

San Jose-Sunnyvale-Santa Clara, CA$415k

San Francisco-Oakland-Fremont, CA$373k

Washington-Arlington-Alexandria, DC-VA-MD-WV$345k

Boulder, CO$340k

Seattle-Tacoma-Bellevue, WA$340k

Boston-Cambridge-Newton, MA-NH$325k

Lexington Park, MD$317k

Trenton-Princeton, NJ$315k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $249k; the demand-cube initiative will add growth-by-metro.

What changes across the ladder

Level-distinction canon isn't available for the adjacent levels of this profile yet.

Level differentiators

What separates M6 (M6 — Vice President) from the levels around it — this level only. Every word is real canon for this profile.

Scope: A major function or division
Autonomy: Accountable for division strategy and outcomes
Complexity: Enterprise priorities and external factors
Impact: Division-wide / organization-wide
Decision rights: Owns strategy, budget, and org for a division
Leadership: Leads the leadership of a division

Full level-by-level matrix (side by side across the ladder) lives on the Ladder drill-down — not here.

Promotion

M6is the top of this family's ladder — progression from here is a lattice move (see adjacent roles below), not a rung.

Performance criteria for this profile render in the assessment kit →

People also watch

Nearest coordinate neighbors — same , same level. Each row's (confident / review / no match) comes from its — computed, not curated.

Profile	Key	Median	Δ structural
Principal Demand Planner	jf_demand_forecast_planning::P6	$194,302	confident0.06
Director of Demand Planning	jf_demand_forecast_planning::M5	$194,360	confident0.06
Chief Demand Planning Officer	jf_demand_forecast_planning::E5	$351,571	confident0.12
Staff Demand Planner	jf_demand_forecast_planning::P5	$142,824	confident0.12

Capability cluster

O*NET 11-3061

Core to this role. Click any element with an ⓘ for its O*NET definition. A job-specific capability model (SkillFrame) will sharpen this and add worked examples.

The Job Swatch

M6

jf_demand_forecast_planning::M6

Senior Director, Demand Planning

Demand / Forecast Planning

The swatch is JobFrame's core primitive: a hueset deterministically by the role's superfunction, the profileKey as its color code, the level ladder, and a content fingerprint. Two swatches that look alike are alike.

Because every role carries computable coordinates, similarity, leveling, and pricing become measurable instead of arguable— the whole point of "Pantone for jobs."

Structural

structural@1

Where it sits— position in the work taxonomy + level (weighted Gower, level > function > focus).

Track: M
Superfunction: manufacturing
Function: supply_chain
Family: demand_forecast_planning
Universal level: 6 / 8

Content

content@2

What it's about— a fingerprint over the role's tasks & capabilities.

Two roles can sit far apart structurally yet be close in content — that gap is itself a signal.

3-state matching

How any two swatches compare — a real distance in coordinate space, resolved to a calibrated verdict (never a fake "94% match").

Distribution Center / Warehouse · M6review0.17

Materials · M6review0.17

Purchasing / Procurement · M6review0.17

Transportation · M6review0.17

Editions — frozen & versioned

structural@1live

Released 2026-06-17

content@2live

Released 2026-07-28

pay@2live

Released 2026-07-02

semantic@1live

Released 2026-07-02

Coordinates belong to a frozen, versioned edition — a coordinate computed today still means the same thing next quarter. Re-fitting issues a new edition, so comparisons are never silently redefined.

How to read this profile

Every number here is traceable — to a named source, a stated method, and an as-of date — and each value is labeled by how we know it: observed from data, computed by a model, projected forward, or governed canon. Nothing is a black box, and nothing is invented to fill a gap.

Where every number comes from

What	Source & method	Edition	State
Median pay + range	JobFrame pay equation (function base × family multiplier × level × focus differential)	pay@2	computed
Pay across the ladder	Same equation applied at each universal level	pay@2	computed
Demand — growth & openings	BLS Employment Projections; openings decomposed into growth + exits	EP 2024–34	projected
Pay by metro	BLS OEWS metropolitan medians	OEWS 2024	observed
Level distinctions	JobFrame universal-level canon (scope · autonomy · complexity · impact · decision rights · leadership)	canon	canon
Capability cluster	O*NET Knowledge / Skills / Abilities	O*NET 11-3061	computed
Coordinates	JobFrame coordinate system — structural (weighted Gower) + content fingerprint	structural@1 · content@2 · pay@2 · semantic@1	computed
Related roles	Nearest neighbors in the structural coordinate space	structural@1	computed

The principles behind the numbers

Ranges, not false precision

Pay is P10 · median · P90, never a single misleading number.

Three honest states, never a fake %

Role matches resolve to confident / review / no-match — a calibrated verdict, not a made-up percentage.

Provenance on every value

Each number carries whether it's observed, computed, projected, or canon — so you always know how much to lean on it.

Gaps shown as gaps

When a figure isn't available at the grain you need, we say so — we don't fabricate a number to fill the cell.

Frozen, versioned editions

Pay and coordinate editions are pinned, so a comparison you make today still means the same thing next quarter.

Privacy by min-N

Any aggregate over people is suppressed below a minimum count, so no individual is identifiable.

Freshness

As of2026-07-30Editioncanon-v2@2.1.0-completeSourcecanon-v2 cell + salary-benchmark (model)