Reimagine retail: Predict and plan demand, improve supply chain efficiency and drive profitability with AI solutions

Automate daily operational planning from exception alerting to root cause analysis, what-if scenario analysis and next-best-action recommendations.

The value of this solution:

• Faster innovation.
• Trustworthy, explainable results.
• Better outcomes.

AI techniques used in this solution:

• AI agent utilizing sophisticated prompt engineering (Chain-of-Thought architecture) and a natural language processing (NLP) interface trained on subject-specific large language models (LLMs) to support identification and response to user voice prompts and queries.

How AI helps:

• Faster innovation: Chatbot capabilities provide faster access to analytics, identifying and responding to voice prompts with context-aware, on-demand insights. This results in better planning, decisions and outcomes without requiring deep supply chain expertise.

The AI models provide:

• On-demand analytics that reduces user-based query workloads.
• Automated business rules that enable improved, timely decision making.
• Easy interaction with analytics using natural written language.

Enable warehouse specialists to utilize voice prompts to understand and optimize warehouse capacity. SAS analytics monitors inbound/outbound shipments and targets warehouse utilization and logistics operations to recommend inventory movement between main warehouses and overflow space to optimize space and minimize intermodal transfers.

The value of this solution:

• Maximized operational efficiency.
• Better outcomes from supply chain management decisions.
• Cost savings.

AI techniques used in this solution:

• AI agent-based interface to SAS Visual Analytics provides complex prompt engineering (Chain-of-Thought architecture) to seamlessly integrate multiple data sources to mathematically solve warehouse space optimization problems.
• Eliminate hallucinations for trustworthy decisions you can be confident in.

How AI helps:

• Faster Innovation: Chatbot capabilities respond to voice prompts, providing faster access to context-aware analytics and on-demand insights. This eliminates manual data manipulation, leading to quicker supply chain decisions and better cost control of materials and logistics.

The AI models provide:

• Improved decision making: By integrating generative AI models into decisioning workflows and business processes, inventory diversion and repatriation recommendations are accelerated and explainable based on the model's understanding of the impact of change in forecasted demand or set target warehouse utilization.

Use AI to analyze situational factors, including market trends, customer behavior and product life cycles, to gain optimal pricing and promotion strategies for retail products across channels.

The value of this solution:

• Increased revenue.
• Better customer experience.

AI techniques used in this solution:

• The SAS assistant is powered by a GenAI agent trained on subject-specific LLMs to recommend pricing strategies.

How AI helps:

• Provides suggestions on pricing actions to achieve the target margins based on the product costs, shelf-life/seasonality, promotional lift and other constraints.
• Informs procurement, allocation and replenishment decisions at multiple levels of the product hierarchy.

The AI models provide:

• Enables easy interaction with analytics and improved pricing and supply chain decision making for faster decisions that lead to greater revenue and margins.
• Pricing process optimization for greater efficiency and productivity.

Empower marketers with generated synthetic data to complete data that reflects the diversity of their customer base. Synthetic data is based on the same features and characteristics as real data, enabling you to protect sensitive information and avoid security issues while enhancing data quality and producing more effective models.

The value of this solution:

• Increased revenue and market share.
• Accelerated innovation.
• Greater productivity.
• Maximized operational efficiency.

AI techniques used in this solution:

• Synthetic data cost-effectively fills in data gaps, including for specific customer subsegments or irregular issues/events in the network and protects sensitive private or proprietary data.

How AI helps:

• Save money on the high costs of data collection.
• Improve precision in predictions, reducing errors in risk assessment while retaining explainability for decisions.
• Streamline analytics, accelerating data processing and decision making.
• Create more effective models with improved data quality.
• Optimize production processes for greater efficiency and productivity.
• Increase data monetization safely, avoiding legal risk.

The AI models provide:

• Trustworthy, simplified data augmentation and generation.
• More complete data that can improve the usefulness of the data set.
• Better protection of sensitive data.

Put the power of personalized advertising in your brand’s hands. This ad delivery solution works in your ecosystem and enables effective ad personalization based on your marketing and advertising data.

The value of this solution:

• Better customer experience.
• Increased revenue.
• Scalability.
• Regulatory compliance.

AI techniques used in this solution:

• Machine learning enables you to manage customers, business rules and analytics in a converged solution.

How AI helps:

• Make ad content decisions based on real-time testing and live results.
• Create a positive feedback loop that encourages iteration and improvement through personalized advertising.
• Improve advertising efficiencies by streamlining the ad-serving process, reducing the need for manual data movement.
• Capitalize on the zero-, first- and second-party data and audience information you need without the cost and risk associated with data movement and duplication.
• Increase ad personalization and targeted ad precision with information on customer behaviors, interests and demographics that resides outside of traditional advertising solutions.
• Create more robust audiences using data from multiple platforms, resulting in better performance and ROI.
• Maintain control over your advertising delivery engine so you can understand which data and analytical models you're using to deliver personalized advertising on your owned and operated channels.

The AI models provide:

• Automation of delivery, user data flows and campaign optimization.
• A deep understanding of viewer preferences for highly personalized campaigns.
• Highly-targeted, real-time ad decisioning on an immense scale.

Manage your call center’s schedule, more accurately forecast incoming calls, streamline correspondence and complaints processes and automate conversation transcript analysis. Use insights from the transcripts and even social media to improve communication and prevent customer churn.

The value of this solution:

• Cost savings.
• Better customer experience and lower churn.
• Greater productivity.
• Maximized operational efficiency.

AI techniques used in this solution:

• Machine learning, optimization and NLP enable you to create highly accurate forecasts, analyze communication, make operational improvements and improve customer service.
• Use LLMs to understand and update model metadata.
• Analytical capabilities like forecasting, machine learning and deep learning models, and other tools may be easily combined with LLMs and GenAI in business processes to provide actual value in automation and ease of use.

How AI helps:

• Create a more consistent customer experience with increased personalization.
• Upskill employees faster.
• Reduce complaint handling time and associated costs.
• Increase call center productivity and efficiency.
• Lower overhead costs.
• Prepare schedules faster, accommodate ad-hoc changes and generate a new schedule quickly and accurately.
• Enable better cooperation with employees and adapt quickly to employees' flexible scheduling needs.
• Ensure the best possible KPIs.
• Improve sales by including Next Best Offer/Next Best Action (NBO/NBA), along with anti-churn tasks.

The AI models provide:

• Improved resources and skill sets based on the number of call-center queues, opening hours, available shifts and service line agreements (SLAs) to fulfill.
• Automation and greater efficiencies for improved productivity.
• Accelerated schedule preparation, plus quick and accurate rescheduling.
• Improved KPIs.