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AI is being used in demand forecasting, inventory decisions and broader supply-chain planning—but a deployment is not proof that AI caused a business improvement. The documented cases below show both how organizations put forecasting models into planning workflows and why reported outcomes need careful attribution.
Where AI forecasting fits into planning
Forecasting models estimate future demand or other operational needs from historical and current data. Planning teams can use those estimates to inform inventory levels, supply decisions and sales and operations planning (S&OP). In practice, AI may sit alongside existing planning tools rather than replace them: people can review model outputs, compare forecasts and choose scenarios.
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Three useful evidence examples illustrate different parts of that picture: a company-described planning transformation, a vendor-reported retail result and a peer-reviewed field study of algorithm use and human adjustments.
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What the documented deployments show
| Example | Task and deployment | Human role | What the evidence establishes |
|---|---|---|---|
| Ericsson | Machine-learning forecasting incorporated into an integrated business planning environment covering demand, supply, inventory and S&OP. | Forecasters could compare internal and SAP forecast inputs; business users selected scenarios. | The Infosys Knowledge Institute interview describes the deployment, but does not report a quantified accuracy gain. |
| OTTO | Demand forecasting with the Time-series Dense Encoder (TiDE) model on Vertex AI and Google Kubernetes Engine. | The cited case page does not detail the forecasting review workflow. | Google Cloud reports accuracy improvement of up to 30%, alongside lower inventory costs and better product availability. The page does not establish the measurement period or an independent evaluation. |
| Retail field study | Analysis of algorithm forecasts, user adjustments and sales observations in demand planning. | The study analyzes adjustment behavior; it does not identify individual users directly and uses SKU-store groups as a proxy for individual forecaster responsibility. | It provides field evidence about use and adjustments, not a universal benchmark for forecast accuracy. |
Ericsson: machine learning within integrated business planning
Ericsson leaders describe integrating demand, supply, inventory and S&OP in an integrated business planning (IBP) transformation. According to the Infosys Knowledge Institute interview, Ericsson developed an in-house machine-learning forecasting solution and incorporated it into the planning environment alongside SAP IBP. Forecasters could compare internal and SAP forecast inputs, while business users selected scenarios.
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The interview does not give a measured accuracy improvement. Diego Moreno, Ericsson’s head of supply-chain planning, described the approach as still evolving: “My personal expectation is that we will take the lessons learned while we and how we did in our in-house development, but we are not there yet to use machine learning as to replace this one.” The source’s point is not that machine learning had replaced the existing planning approach; it describes a blended environment and continued human involvement.
OTTO: AI-based retail demand forecasting
Google Cloud’s case collection says German ecommerce retailer OTTO implemented its Time-series Dense Encoder (TiDE) model on Vertex AI and Google Kubernetes Engine for demand forecasting. Google reports forecasting-accuracy improvement of up to 30%, as well as lower inventory costs and better product availability. Those are vendor-reported outcomes: the cited page does not establish the measurement period or provide an independent evaluation. Treat the figure as a claim about this case, not a general expectation for AI forecasting.
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Field evidence: algorithms and forecaster adjustments
A 2026 Journal of Operations Management study analyzed approximately 575,000 algorithm forecasts, user adjustments and sales observations across 91 stock-keeping units (SKUs), three general merchandise categories and 485 store locations over 84 weeks. It examines how users interact with algorithmic demand-planning forecasts and make adjustments.
The authors note that they could not identify individual users directly; SKU-store groups served as a proxy for individual forecaster responsibility. That qualification matters when interpreting who adjusted forecasts and how people behaved. The study adds scale and context on algorithm-assisted planning, but its sample figures are not an accuracy result and should not be compared with Google Cloud’s OTTO claim.
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How to judge a claimed AI planning result
When evaluating a deployment, separate the fact that a model is in use from evidence that it improved an outcome. For each case, ask:
- What was forecast? Demand, inventory needs or a wider set of supply-chain decisions are different tasks.
- What was deployed? Identify the model or system, its data inputs where stated, and how it fits with existing planning software.
- Who reviews the output? A workflow that lets people compare forecasts or select scenarios is different from fully automated decision-making.
- What outcome was measured? Forecast accuracy, inventory cost and product availability are distinct measures, not interchangeable proof of success.
- Who reports the result, and over what period? A vendor’s case summary, an operator interview and an independent field study offer different kinds of evidence. Look for a defined measurement window and evaluation method before treating a number as established impact.
Across these examples, there is no common outcome metric or independent head-to-head comparison. Ericsson’s source documents an implementation without a quantified accuracy gain; Google Cloud supplies the OTTO accuracy claim; and the academic study examines forecast use and adjustment behavior. Keeping those evidence types separate is essential to a fair comparison.
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Is the “11 deployments” count verified?
No. The AI Weekly category index titled “AI in forecasting & planning: real deployments” showed six deployments when opened, rather than eleven. Because the index is a changing directory, six is only an observed snapshot, not a permanent portfolio count. The available evidence here does not substantiate eleven separately named deployments, so this article does not present eleven examples.
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Sources
- AI Weekly, “AI in forecasting & planning: real deployments” (changing category index).
- McKinley et al., “Is AI an Algorithm by Any Other Name? Behavioral Reactions to AI- and Model-Based Demand Planning Algorithms,” Journal of Operations Management, 2026.
- Infosys Knowledge Institute, “Ericsson’s AI-enabled IBP transformation: Building the intelligent supply chain of the future”.
- Google Cloud, “Real-world gen AI use cases from the world’s leading organizations”.
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