Solutions · Demand Planning · Powered by Planamind

Demand Planning Software

Twelve forecasting models, from Prophet and neural networks to proven statistical methods, compete on every item. Add sales opportunities, plan promotions with an AI assessment, and factor in weather and the economy, all in one demand plan.

Why forecasts miss

Most demand plans are out of date before the S&OP meeting starts.

Demand planning breaks down in the same places everywhere: one model for every product, no view of what's moving the market, and promotions and new products planned on gut feel.

01

One model for every SKU

Fast movers, slow movers and seasonal lines all get the same method, so bias creeps in and nobody can see where accuracy is being lost.

02

Blind to the market

Weather, the economy, competitor moves and the sales pipeline live in email and memory, not in the forecast. Changes in demand show up only after sales are lost.

03

Promotions without evidence

Promotions are planned on hope and rarely measured afterwards, so the same mistakes are repeated next season.

What makes Planamind different

Six things your current demand planning tool doesn't do.

Demand planning in Planamind sits in the same workspace as supply and finance, so the forecast you agree on flows straight into inventory, procurement and the P&L.

01 · Forecasting models

New-age machine learning, alongside proven statistical models

Twelve models compete for every item and location: Prophet, Theta and MLP neural networks alongside Holt-Winters, ARIMA, Croston's method for intermittent demand, regression with seasonality and more, plus an ensemble that blends the top three. The best fit is selected automatically.

AI · Accuracy diagnosticsWhen a forecast misses, Ana explains in plain English why, and the root-cause analysis points to a better-suited model, such as Croston's for lumpy demand.
Models competing12
Machine learningProphet · Theta · MLP
StatisticalHolt-Winters · ARIMA · Croston
Model selectionAutomatic
+
02 · Opportunity addition

Add the demand your history can't see

New customers, tenders and large deals don't show up in past sales. Add them as opportunities with a pipeline stage and win probability, phase them as a one-off or a ramp, and add them on top of the baseline or net them against it.

Flows downstreamOpportunities become part of the final forecast, so supply and finance plan for them automatically. View the plan as weighted, best case or committed.
Pipeline stagesLead → Won
ViewsWeighted · Best case · Committed
PhasingOne-off or ramp
03 · Forecast accuracy recommendations

Know exactly where accuracy can improve

Planamind hides the last six months, forecasts them blind and checks which model would really have been right. Where a high-value item is missing by more than 15% and a better model is proven, you see the current error, the projected error and the value of closing the gap. Nothing changes until a planner clicks Apply.

AI · Ana's analysisThe forecast accuracy view comes with Ana's analysis and a prioritised list of actions, so planners know where to spend their time first.
Blind backtest6 months
FocusA-class items
Each tip showsError now vs after · value
%
04 · Promotions

Plan promotions with evidence, then measure what they really delivered

Plan a promotion at any product, location or customer level. Planamind estimates price elasticity from your history to suggest the discount, and shows margin, stock, stockout risk, weather and nearby events alongside expected ROI. After the promotion, it measures net incremental volume after forward-buying and cannibalisation.

AI · Promotion assessmentAna scores every promotion out of 10 on lift realism, supply, ROI and timing, flags the key risks, and writes a post-mortem once it closes. Lift is split between the promotion and unusual weather or economic conditions.
Ana assessmentScore out of 10
Suggested discountFrom price elasticity
Net liftAfter forward-buy & cannibalisation
Recommended promotionsOverstock · expiry · AOP gap · weather · events
05 · New products

A real baseline for new products from day one

Map the history of a predecessor or similar item to a new product, with a configurable share and overlap period, so replacements and range extensions start with a grounded forecast instead of a guess.

Suggested predecessorsPlanamind suggests likely predecessor items for a new product, so planners don't have to search the catalogue by hand.
History fromPredecessor or similar item
Share & overlapConfigurable
PredecessorsSuggested
Σ
06 · External factors

Weather, the economy and market trends, built into the plan

Planamind brings in historical and seasonal weather, macroeconomic and market indicators from publicly verifiable sources, search and trade trends, holidays and events. Apply their impact to the forecast as a scenario.

AI · Market intelligenceAna reads market and competitor news and turns it into planning-impact actions, and a market position commentary names your biggest opportunity and biggest risk.
WeatherHistory + seasonal outlook
MacroeconomicPublic indicators
TrendsSearch · trade · events
NewsAI-summarised
Also built in
  • Review and override at any level of the product, location and channel hierarchy
  • Every override records who changed it, when and why
  • Comments, @mentions and tasks on any part of the plan
Ana in action

Changes in demand, flagged before they cost you sales.

Ana watches the plan on every screen and recommends what to do next.

Market news

Competitor out of stock

Brand X is out of stock in 3 regions. Ana recommends lifting the forecast on 5 SKUs by 15–20% for 6 weeks.

Budget gap

Revenue 8% below AOP in the North

Division North is behind its AOP through Q3. Ana sets out best- and worst-case scenarios and the actions most likely to close the gap.

Illustrative examples

What planning teams gain
5–20%Forecast accuracy improvement
10–20%Inventory reduction
50–80%Order-loss prevention
48hFrom your data to first live plan
AI insights & self-service reporting

The story behind the numbers, written for you. Any other view, built by you.

Planamind turns planning data into decisions: AI-written reviews that explain what changed and where to act, and reporting tools that let planners, finance and leadership answer their own questions without waiting for IT.

A
AI · Monthly Review

A written review of the month, ready before the meeting

Ana writes the commentary: demand changes, explained and unexplained anomalies, promotions and mix shifts, stock position, and the impact of weather, the economy and competitors. It ends with where to intervene by SKU and location, plus the opportunities and risks to year end. Download it as a branded PDF.

AI · Ana's Analysis

Prioritised actions on revenue, stock and accuracy

Revenue gap, stock health and forecast accuracy each come with Ana's analysis and a prioritised action list. The revenue gap view adds best- and worst-case scenarios for closing the gap to AOP, and accuracy diagnostics explain in plain English why the forecast missed.

Self-service · Custom analysis

Build your own view, no analyst needed

Drag and drop demand and supply data into a pivot: choose rows, columns and filters, switch to charts, add your own formula columns, and save views for yourself or the whole project. Export any view to Excel, or pick from around 18 ready-made reports.

?
AI · Ask Ana

Ask a question in plain English

On every screen, ask Ana about demand, accuracy, supply, freight, promotions, finance or a single SKU. She answers from your live data and only shows each user the products and locations they are allowed to see.

Client stories

Trusted by demand planners at Asian Paints, Johnson & Johnson and more.

“Asian Paints International Business was conducting demand forecasting centrally for all the units across different countries. There was a need to decentralize the demand planning exercise and enable each unit. The interface is user friendly and intuitive which was a plus point considering personnel with different backgrounds are using it in different countries.”

Asian Paints · Planning System Implementation
Case study

Achieving 82%+ forecast accuracy in food & beverage

How a major coffee roaster used AI-driven forecasting to reduce inventory by 8% across a national distribution network.

Case study

Paints company achieves demand planning agility

Better efficiency and leaner inventory for a global paints enterprise.

Read the case studies →
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