Demand forecasting software we provide

Demand forecasting software that plans by SKU, location, and week

Planners still rebuild last year’s spreadsheet and adjust it by gut feeling. We build forecasting software where AI reads promotions, prices, weather, and lead times, then shows planners why each number changed.

Why a forecast that’s right on average still empties shelves

Most forecasts are judged at the total level. A category can hit its monthly number while half its SKUs run out and the other half pile up. The error hides in the mix, by item, location, and week.

Svitla builds demand forecasting software that works at the level where stock decisions happen. Each item and location gets a model suited to its demand pattern. Planners review exceptions, not every line. Approved forecasts flow straight into replenishment, production, and transport plans.

Who we serve

High-volume, steady sellers

Consumer goods, groceries, and staples where small accuracy gains free up large amounts of working capital.

Seasonal and promotion-driven ranges

Fashion, consumer electronics, and seasonal goods where demand spikes around events, launches, and price changes.

Intermittent and long-tail demand

Spare parts, industrial supplies, and slow movers where most weeks show zero sales and standard models fail.

Challenges we address

McKinsey research finds AI-driven forecasting can cut forecast errors by 20% to 50% and lost sales from stockouts by up to 65%. Most companies are far from that range because their forecasting process, not their math, is the bottleneck.

One model for every SKU

Symptom: Accuracy looks fine for top sellers and collapses for everything else.
Root cause: A single method can’t handle steady, seasonal, and intermittent items at once.
How Svitla solves it: The system tests several models per item and picks the best one by back-testing on your history.

Promotions planned in one tool, forecast in another

Symptom: Promo weeks sell out early or leave pallets of leftover stock.
Root cause: Marketing calendars and price changes never reach the forecast.
How Svitla solves it: Promotions, price changes, and events feed the model as inputs, with uplift estimated from past campaigns.

Overrides nobody tracks

Symptom: Planners adjust numbers every cycle, and nobody knows if it helps.
Root cause: Manual changes overwrite the statistical forecast without a record.
How Svitla solves it: Every override is logged with a reason, and the forecast value added shows whether it improved accuracy.

Forecasts that stop at the planning team

Symptom: Purchasing, production, and logistics build their own numbers.
Root cause: The forecast isn’t connected to the systems that act on it.
How Svitla solves it: Approved forecasts flow to the ERP, replenishment, and transport planning on a fixed schedule.

Our process at Svitla

STEP 1

We measure your current accuracy

We back-test your existing forecasts against actual sales by item and location. You see where errors cost the most.

STEP 2

We build models on your own history

We clean demand data, flag stockout periods, and test model types on each demand pattern. Planners review results before anything goes live.

STEP 3

We run the new forecast beside the old one

Both forecasts run in parallel for several cycles. You switch once the new one proves more accurate on your data.

Schedule a call

Demand history cleansing

Detects stockout periods and estimates lost demand.

Removes one-off orders and data errors.

Separates baseline demand from promotional uplift.

Automatic model selection

Tests statistical and machine learning models per item and location.

Back-tests each model on history it hasn't seen.

Re-selects models as demand patterns shift.

Promotion and event modeling

Imports promotion calendars and planned price changes.

Estimates uplift and cannibalization from past campaigns.

Models holidays, launches, and local events.

New product forecasting

Finds similar past products by attributes.

Uses their launch curves as a starting point.

Updates the forecast as real sales arrive.

Collaborative planning and overrides

Collects input from sales, marketing, and key accounts.

Logs each override with a reason and author.

Measures forecast value added by step and person.

Forecast accuracy tracking

Reports MAPE, bias, and weighted accuracy by item, location, and horizon.

Flags items whose accuracy drops below target.

Ranks exceptions by inventory or revenue impact.

Is custom demand forecasting software the best option for your needs?

Answer a few simple questions to find out whether you should choose a custom solution or a pre-built demand forecasting platform.

1 / 9

Do your planners build forecasts in spreadsheets?

Does your current tool use one forecasting method for most items?

Do promotions or price changes regularly cause stockouts or excess stock?

Do you sell many slow-moving or intermittent items?

Have you tried a planning tool that couldn't model your demand drivers?

Do purchasing and logistics use different numbers than the forecast?

Would weather, events, or point-of-sale data improve your forecast?

Do you plan to add channels, regions, or product lines in the next two years?

Important integrations for demand forecasting software

ERP

The ERP supplies order history, item master data, and lead times. Approved forecasts flow back as planning inputs for purchasing and production. Both sides use the same item and location codes.

Point-of-sale and e-commerce systems

Sell-through data shows real consumer demand, not just orders from distribution centers. Daily feeds drive short-term demand sensing. Forecast online and store channels separately or together.

Inventory and warehouse systems

On-hand stock and stockout history help the model separate low demand from lost sales. Forecasts then feed replenishment and labor planning in the warehouse.

Promotion, pricing, and CRM tools

Planned campaigns and price changes reach the model before they happen. Sales teams can add key account forecasts through the CRM.

External data providers

Weather, holiday calendars, and economic indicators enter the model where testing shows they help. Track each source so you can drop one that adds cost but not accuracy.

Lower inventory at the same service level

Working capital is released from slow movers.

Fewer markdowns on overstocked items.

Lower storage and handling costs.

Fewer lost sales

Fewer stockouts on fast movers.

Better availability during promotions.

Higher fill rates for key accounts.

Planner time spent on exceptions

Most items need no manual review.

Planners work high-value, high-risk items first.

Planning cycles get shorter.

Steadier downstream plans

Purchasing orders closer to real need.

Production schedules change less often.

Transport and warehouse labor plans stay stable.

Demand data audit

We collect 2 to 3 years of sales, orders, stock, and promotion history, where available. We find stockout periods, missing locations, and changes to item codes. We also measure your current forecast accuracy by item, location, and horizon. That baseline sets the target and the ROI model.
Demand segmentation and model design

We group items by volume, variability, and seasonality. Each group gets candidate models, from exponential smoothing to gradient boosting. We define the forecast hierarchy, such as SKU, store, region, and channel, and how its levels reconcile.
Back-testing and planner review

We test every model on history it hasn’t seen. Planners review results on items they know well. A model that wins on paper but fails planner review gets investigated before launch.
Integration and workflow build

We connect ERP, point-of-sale, and inventory data on a fixed schedule. We build the planner workspace, override logging, and exception queues. Approved forecasts flow back to the systems that use them.
Parallel run and model monitoring

The new forecast runs beside the current process for several cycles. We compare accuracy and bias on live data. After switchover, we monitor drift and retrain models on a set schedule.

How much does demand forecasting software cost?

A focused forecasting tool for one business unit, with statistical models and ERP integration, typically starts around $40,000. An AI-driven system covering thousands of SKUs across channels and regions, with demand sensing and external data, can reach $250,000 or more. Data quality, integration count, and forecast granularity drive most of the difference.

Want to understand the cost of your demand forecasting software?

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Check our client success cases

Rapid Application Development Delivered Against Tight Deadlines

Project Type AU Solution, Back-end, Web Development

Industry Logistics & Transportation, Manufacturing

Svitla Systems partnered with Mondo to rapidly build the first iteration of a water-monitoring application, working from just a screenshot and wireframes. Integrating IoT usage data captured every 15 minutes, it tracks household usage, targets, and averages, and was delivered into users' hands for testing in a little over five weeks.

Exceptional Customer Service Hits A Tight Deadline

Project Type AU Solution, Back-end, Web Development

Industry Logistics & Transportation

Svitla Systems worked with MER Solutions to create MER Vet, a web-based vessel-vetting application that verifies bulk carriers' physical condition, operation, and safety performance to help clients identify non-compliant vessels. Delivered as a working demo against a tight conference deadline, it secured early buy-in from major port authorities.

Facility Emulation Platform

Project Type Digital Transformation, Quality Assurance

Industry E-commerce & Retail, Logistics & Transportation

Discover how we upgraded a Facility Emulation Platform, enhancing UI/UX, integrating new features, and optimizing performance through comprehensive QA strategies.

Consulting

We assess your forecasting process, data, and accuracy. You get a build plan, a data readiness report, and an ROI model based on your own inventory and stockout costs.

Engineering

We design, build, and test your forecasting solution. Our teams cover data pipelines, machine learning models, planner workspaces, and ERP integration.

Modernization

We audit your spreadsheets or legacy planning tool and plan the migration. We add what the old process lacked, such as model selection per item or demand sensing. Cutover follows a parallel run, then validation of accuracy on live data.

AI model retraining and support

Demand patterns shift with new products, channels, and markets. We monitor forecast accuracy and drift, then retrain models on your latest data.

FAQ

How is custom forecasting different from the forecasting module in our ERP?

ERP modules usually apply a few statistical methods across the whole range. They rarely handle promotions, external signals, or intermittent demand well. A custom solution picks the best model per item and connects to your real demand drivers. Your ERP still receives the final numbers.

How do planners learn to trust a machine learning forecast?

Each forecast shows the drivers behind it, such as promotions or price changes. Planners can override any number and record why. Forecast value-added reports show whether the model or the override was more accurate. Trust follows the evidence.

What happens when a disruption makes history useless?

The system flags periods when actual demand departs sharply from the forecast. Planners can exclude those periods from training or mark them as one-off events. Demand sensing then weights recent data more heavily until patterns settle.

Can the software forecast items that sell only a few times a year?

Yes. Intermittent items get models built for sparse demand, such as Croston-type methods. The goal is the right stock level, not a weekly number that is almost always wrong.

How often are forecasts updated?

Most clients run a weekly or monthly planning cycle with daily short-term updates. The schedule follows your purchasing and production rhythm. Models retrain on a separate, slower cycle.

Can the solution grow from one region to the whole business?

Yes. We design the forecast hierarchy for several regions, channels, and business units from the start. New units add data and models without changes to the core.

Forecast every SKU the way it actually sells

Share a sample of your sales and forecast history. We’ll show you where a custom forecasting solution could improve accuracy first.