SAS AI Agents Driving Supply Chain Operational Efficiency

Data and analytics firm SAS has announced a suite of tools and models that could address operational challenges across multiple sectors.

The company is investing US$1bn in purpose-built solutions throughout 2026.

The offerings include agents for supply chain management, digital twin simulations for factory operations and fraud detection systems for financial institutions.

The launch represents a shift towards industry-specific applications that aim to deliver measurable operational improvements rather than experimental AI frameworks.

Real-time supply chain planning

SAS Supply Chain Agent aims to streamline sales and operations planning processes that retailers and manufacturers use to manage inventory. Traditional S&OP requires multiple days of work using spreadsheets to forecast inventory requirements for the coming year.

According to SAS, most organisations run this planning process once per month because of the time and resources involved. This monthly cadence can leave businesses vulnerable to sudden market shifts, demand fluctuations or supply disruptions that occur between planning cycles. The new agent continuously balances demand and operations, allowing users to optimise supply chains in near real-time.

Business users can interact with the agent through a chat interface to explore multiple scenarios. The tool can model outcomes such as a 15% drop in demand or supply constraints from specific suppliers, enabling procurement and logistics teams to test contingency plans before implementing them.

The Supply Chain Agent provides explanations on how it reached specific decisions.

Manisha Khanna, Global Market Strategy Lead at SAS, says: "When organisations are left stitching together ad-hoc AI frameworks and experiments, they often fail to achieve the competitive edge they're looking for when they invest in AI."

Kathy Lange, Research Director at IDC's AI, Data and Automation Software practice, says: "Current pre-packaged agents tend to tackle basic processes; with Supply Chain Agent, SAS is compressing a very complex process, which could deliver significant value. This offering positions SAS to bring its longstanding supply chain knowledge to a new generation of agentic AI solutions."

Digital replicas for operations

SAS uses digital twins and synthetic data to improve operational efficiency in factory environments. The company creates virtual replicas of factory floors using Unreal Engine, allowing teams to simulate scenarios without risking physical assets or disrupting production schedules.

First demonstrated at SAS Innovate 2025, the digital twins create a testing environment to explore potential outcomes. A medical device sterilisation provider is using a SAS digital twin to identify bottlenecks that slow down services.

SAS Worker Safety also uses synthetic data and digital twins to train computer vision models on rare but plausible workplace accidents. According to data from 2024-25, 124 workers were fatally injured in the UK, with falls and machinery accidents making up a number of these injuries.

The offering creates footage for training computer vision models on workplace safety scenarios. Organisations can model forklift collisions or equipment failures without involving real employees.

Using synthetic data means that no personally identifiable information is exposed during the training process.

Fraud detection for payments

SAS is launching Fraud Decisioning for Payments to help deliver real-time fraud detection across financial transactions. According to a study by SAS and the Association of Certified Fraud Professionals, 75% of anti-fraud professionals report a surge in financial fraud and scams targeting consumers.

Additionally, 55% of these professionals anticipate an increase in deepfake social engineering and gen AI document fraud over the next two years. Global banks, insurers and financial services organisations are using SAS for fraud detection models.

The Fraud Decisioning for Payments models are trained on patterns from a dataset contributed via consortium by global financial institutions. The dataset spans credit card, debit card and ATM fraud.

It also covers digital wallet fraud and emerging vectors like money mule detection. By deploying these models on the SAS platform, institutions do not have to start from zero.

By providing tools designed for specific functions that integrate with existing workflows, SAS aims to bridge the gap between experimentation and practical deployment. The company's investment in industry solutions is part of its strategy to meet demand throughout 2026.