# Consensus Forecast Weighted Blend Calculator

> Source: https://netray.co/tools/forecast-consensus-weighted-blend-calculator
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: Free Tools

## Short answer

Weight four forecast sources into one consensus figure and see how far apart the sources are.

## Category

supply-chain

## Audience

- Demand planner
- S&OP lead
- Sales operations

## Tool

**Kind:** calculator

**Inputs:**

#### Statistical forecast

**Key:** stat

**Type:** number

**Default:** 5000

**Min:** 0

**Step:** 50

**Unit:** units

#### Sales forecast

**Key:** sales

**Type:** number

**Default:** 5600

**Min:** 0

**Step:** 50

**Unit:** units

#### Marketing forecast

**Key:** mkt

**Type:** number

**Default:** 5300

**Min:** 0

**Step:** 50

**Unit:** units

#### Management or finance target

**Key:** mgmt

**Type:** number

**Default:** 6000

**Min:** 0

**Step:** 50

**Unit:** units

#### Weight on statistical

**Key:** w_stat

**Type:** slider

**Default:** 50

**Min:** 0

**Max:** 100

**Step:** 5

**Unit:** %

#### Weight on sales

**Key:** w_sales

**Type:** slider

**Default:** 30

**Min:** 0

**Max:** 100

**Step:** 5

**Unit:** %

#### Weight on marketing

**Key:** w_mkt

**Type:** slider

**Default:** 15

**Min:** 0

**Max:** 100

**Step:** 5

**Unit:** %

#### Weight on management

**Key:** w_mgmt

**Type:** slider

**Default:** 5

**Min:** 0

**Max:** 100

**Step:** 5

**Unit:** %

**Outputs:**

#### Total weight

**Key:** wsum

**Expr:** w_stat+w_sales+w_mkt+w_mgmt

**Format:** percent

**Description:** Weights are normalised, so they need not add to 100.

#### Consensus forecast

**Key:** blend

**Expr:** if(gt(wsum,0),(stat*w_stat+sales*w_sales+mkt*w_mkt+mgmt*w_mgmt)/wsum,0)

**Format:** integer

**Primary:** true

#### Consensus vs statistical

**Key:** vs_stat

**Expr:** if(gt(stat,0),(blend-stat)/stat*100,0)

**Format:** percent

#### Spread between sources

**Key:** spread

**Expr:** max(max(stat,sales),max(mkt,mgmt))-min(min(stat,sales),min(mkt,mgmt))

**Format:** integer

**Description:** Highest source minus lowest source.

#### Spread as share of consensus

**Key:** spread_pct

**Expr:** if(gt(blend,0),spread/blend*100,0)

**Format:** percent

**Primary:** true

**Disclaimer:** Weights are a judgement. Calibrate them from the past accuracy of each source, for example with the forecast override log, rather than from seniority.

## Content

**Intro:** In many companies the statistical, sales, marketing and finance numbers differ, and the demand review has to settle on one. This calculator blends them using explicit weights and shows how far apart the sources are, so the disagreement is visible.

**Sections:**

#### How to use the calculator

**Body:** Enter the forecast from each source for the same item or family and period. Set the weight you place on each. Weights are normalised, so 50, 30, 15 and 5 work the same as 0.5, 0.3, 0.15 and 0.05. The result is the weighted average, how much it moves from the statistical forecast, and the spread between the highest and lowest source as a percentage of the consensus.

#### The formula

**Body:** The consensus equals the sum of each forecast times its weight, divided by the sum of the weights. Spread is the highest forecast minus the lowest. A wide spread means the sources disagree on the facts, not just the number, and that deserves discussion before averaging. Blending hides disagreement, so use the spread as a prompt.

**Bullets:**

- Consensus = sum(forecast x weight) / sum(weights)
- Spread = highest minus lowest
- Large spread = go and ask why

#### Choosing weights

**Body:** Base weights on measured accuracy. If the statistical forecast has the lowest error over the last six cycles, it deserves the highest weight. Sales may deserve a higher weight for short horizon, large customer items, and a lower one for the long horizon. Management targets are goals, not forecasts, so give them little or no weight in the unconstrained demand plan.

#### Common mistakes

**Body:** Averaging in a stretch target pulls the plan up and builds inventory. Another mistake is fixing the weights forever. Revisit them each quarter against actual error. Do not use a blend to avoid the discussion on assumptions: record why the sources differ in the demand review minutes.

**Cta:** Netray can compare each source's historical accuracy from your ERP and planning data and suggest evidence-based weights for the team to review.

**Faq:**

#### What is a consensus forecast?

**Answer:** A consensus forecast is a single demand number agreed between functions such as sales, marketing, finance and planning. It may be a weighted blend of their separate forecasts or a negotiated figure documented in the demand review.

#### Does combining forecasts improve accuracy?

**Answer:** Often, yes. Combining independent forecasts usually reduces error compared with a typical single source, provided the sources are not all biased in the same direction. Check the result against actuals.

#### How should I set the weights?

**Answer:** Use each source's historical accuracy at the relevant horizon. Give more weight to sources with lower error and little bias. Reassess every quarter.

#### Should targets be part of the blend?

**Answer:** Generally no. A target says what the company wants to sell, not what it expects to sell. Keep it visible as a gap to the demand plan, rather than mixing it into the unconstrained forecast.

## Search terms

consensus forecast calculator, weighted forecast blend, combine forecasts weighted average, sales and statistical forecast blend, consensus demand plan, forecast combination calculator, S&OP consensus forecast

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