Finance
Carries the working capital and the write-offs
Improve demand forecasting accuracy to reduce inventory waste
Demand forecasting accuracy measures how closely predicted demand matches what customers order, by SKU, channel and time period. It determines how much safety stock you carry, how often you stock out and how much margin either mistake costs you.
Getting demand wrong costs your business in two directions. Forecast too high and excess safety stock ties up working capital and turns into markdowns. Forecast too low and stockouts cost sales and trigger emergency freight to recover. Most businesses pay on both sides of that ledger from the same forecasting process.
The reason usually comes down to method. Historical averages, static assumptions and manual planner adjustments describe what already happened. They lag behind current channel behavior and cannot react fast enough to prevent overstock and stockouts as conditions shift.
Infios AI reads demand and inventory data together, connecting the signal in Infios Order Management (OM) with the stock position in Infios Warehouse Management (WM). Forecasts stay current as conditions change, and teams can hold leaner inventory positions without taking on more service risk.
The challenge: inaccurate demand signals cost more than most businesses realize
Most forecasting processes are still built to describe the past, not predict what's next. That gap shows up as both excess stock and stockouts, often in the same SKU within the same quarter.
Forecasting process limitations:
Most forecasting processes still run on historical averages, which lag behind how channels are behaving right now. Seasonality and promotional uplifts get layered on as manual adjustments that depend on a planner's memory rather than any systematic analysis. Channel-level demand is often treated as one aggregate number, masking meaningful variation between business-to-business (B2B), direct-to-consumer (DTC) and marketplace performance. Supplier lead time variability rarely factors into replenishment timing either, so orders get placed against nominal lead times instead of how suppliers perform.
Underneath all of it, order management and warehouse inventory data usually live in separate systems with no connection between them. Demand signals and stock positions are never read together in the first place.
What inaccurate forecasting costs
Excess safety stock inflates working capital and warehouse occupancy. Stockouts cost sales, trigger emergency purchasing and force expedited inbound freight. Inventory carrying costs are commonly estimated at 20–30 percent of inventory value per year. Every unit of excess stock is capital and space spent on inventory that may still be marked down later.
The cost compounds across the business:
Procurement
Absorbs the price variance of reactive, off-cycle purchasing
Operations
Pays for expedited freight and emergency sourcing
Planning teams
Spend their time reconciling demand and inventory data instead of improving the models that would prevent the problem
Organizations that improve forecast accuracy recover margin at every one of those points at the same time.
The solution: how Infios AI improves demand forecasting accuracy
Infios AI models the drivers of demand together and keeps forecasts current as new order and inventory data arrives. Manual planning cannot sustain this information flow across a large stock keeping unit (SKU) base. It operates as a continuous capability within your connected Infios OM and Infios WM environment, not a periodic planning exercise.
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Demand rarely follows one trend line. Infios AI incorporates seasonality, promotional calendars, channel trends and external signals into a single model, producing forecasts that reflect how demand forms.
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Aggregate forecasts hide the differences between channels. Infios AI generates separate demand signals for B2B, DTC and marketplace demand within Infios OM, so channel mix no longer distorts the overall forecast and stock is positioned to how each channel behaves.
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Replenishment timing is only as good as its lead time assumptions. Infios AI draws on actual inbound performance data from Infios WM and factors supplier lead time variability into ordering decisions, narrowing the gap between when stock is expected and when it arrives.
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Forecasts age quickly. Infios AI updates models as new order and inventory data flows through Infios OM and Infios WM, reducing the lag between a change in the demand signal and the forecast catching up to it.
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Safety stock should reflect risk, not habit. Infios AI calculates buffers dynamically by SKU and channel, helping you reduce excess inventory where demand is stable while protecting service levels where variability is higher.
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Reviewing every SKU by hand is not practical at scale. Infios AI directs planners to the forecasts that carry the most risk or uncertainty, concentrating expertise on the exceptions where human judgment adds the most value.
Business outcomes: forecasting that improves without losing control
Infios AI operationalizes planner judgment at scale. It produces forecasts, models safety stock dynamically and directs attention to the exceptions that need review. For decisions such as committing to a large replenishment or changing safety stock policy, human review stays part of the process. Planners and finance keep control of the tradeoffs.
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For supply chain planners
Forecasting shifts from a periodic exercise to a continuous capability, with attention directed to the SKUs and channels that carry the most risk.
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For inventory managers
Safety stock reflects actual demand variability by SKU and channel instead of fixed rules applied across very different products.
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For operations directors
Leaner inventory positions without added service risk, and fewer emergency interventions to manage around a lagging plan.
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For finance leaders
Reduced working capital tied up in excess safety stock, with write-offs and markdowns concentrated where risk is unavoidable rather than spread across the catalog.
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For procurement teams
Replenishment timing built on actual supplier lead time performance, reducing the price premium of reactive, off-cycle purchasing.
Measuring demand forecasting improvement
Measure your forecast improvement through operational outcomes, not model accuracy in isolation:
Forecast accuracy by SKU and channel: Track prediction accuracy at the level where decisions get made.
Mean absolute percentage error (MAPE) trend: Watch whether forecast error is improving over time, not just in a single period.
Safety stock value as a percentage of total inventory value: Understand how much capital is tied up in buffer stock.
Stockout frequency and estimated lost sales: Measure the cost of under-forecasting alongside the cost of over-forecasting.
Write-off and markdown rate by category: Track the downstream cost of excess stock in seasonal and short-lifecycle categories.
Emergency purchase frequency and cost premium: Measure how often reactive buying is replacing planned replenishment.
Supply chain demand forecasting accuracy is ultimately a margin question. Every percentage point of improvement compounds across safety stock reduction, fewer write-offs and less emergency sourcing. Sustained gains matter more to the bottom line than any single planning decision.
Closing the gap between forecast and action
Forecast accuracy is the number underneath your working capital, your service levels and how much of your team's week goes to firefighting instead of improving the model. Closing that gap starts with reading demand and inventory together.
See how Infios AI can improve demand forecasting accuracy across your Infios OM and Infios WM environment.
FAQs
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Demand forecasting predicts future customer demand so inventory, purchasing and fulfillment can be planned against it. Errors are costly in both directions: over-forecasting ties up working capital and leads to write-offs. Under-forecasting causes stockouts, lost sales and emergency sourcing. Those costs compound across finance, procurement and operations. Modest improvements in accuracy can have an outsized effect on margin.
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Historical average models describe past demand as a single trend and rely on planners to layer on manual adjustments for seasonality and promotions. Infios AI models many demand drivers together, including seasonality, promotional calendars, channel behavior and external signals, and updates as new data arrives. Forecasting shifts from a lagging, memory-based process to one that reflects how demand is forming in real time.
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Safety stock is the buffer inventory held to absorb demand and supply variability. Fixed rules apply the same logic across very different SKUs, which usually means carrying too much in some places and too little in others. Safety stock optimization uses demand variability to set buffers dynamically by SKU and channel, reducing excess stock without increasing stockout risk.
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Demand signals live in order management and stock positions live in warehouse management. When those systems are separate, planners spend time reconciling data before they can act, and the reconciled view is often already out of date. Reading Infios OM and Infios WM data together, forecasting works from one current picture.
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No. Infios AI is designed to support planners, not replace them. It handles continuous, data-intensive modeling that is impractical to do manually and directs attention to the forecasts that carry the most risk. Decisions with real financial consequences, such as large replenishment commitments or changes to safety stock policy, keep human review in the loop.
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Organizations that start with a specific objective, such as improving accuracy in one seasonal category or separating channel-level demand, typically see impact as soon as models are adopted and forecasts are acted on. The broader benefit builds as continuous refinement keeps forecasts current and accuracy gains compound across safety stock, write-offs and emergency sourcing over time.
Additional Resources
- Blog
7 min
Inside a consumer goods leader's inventory strategy
How a leading consumer brand manages inevitable demand pe...
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Infios Advances Intelligent Supply Chain Execution with AI-Powered Warehouse Innovations
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How to protect inventory using AI
4 min