AI-powered Forecasting Solutions in the UK
Achieve competitive edge in forecasting and planning using predictive intelligence.
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80%
Reduce operational costs
30%
Reduce time-to-market
50%
Increase operational efficiency
85%
Reduced job execution time
Overview
Forecasting is a key input for business decisions, both at the level of strategy and operations. Robust forecasting methods can provide a competitive edge to organisations and make them resilient against uncertain market dynamics. More and more organisations are increasing their adoption of AI techniques for forecasting and planning, which allows them to stay ahead of competition, maintain their market share and stay relevant.
AI-driven forecasting allows businesses to set measurable goals, anticipate future market changes, increase visibility by detecting seasonality, learn from external variables and drivers of demand, and predict hierarchical mix of business.
A Next-Gen Forecasting Solution
Visionet's AI Forecasting Platform is a complete solution for diverse industries that supports business decisioning as they compete, grow, and strive to meet customer expectations. Our solution delivers high-precision intelligent forecasting which provides automated adjustments with agility. It leverages our pre-configured integrations with external data sources which serve as signals to inform our AI about the future outlook, thereby aiding in developing precise forecasting models.
Inventory & Demand Planning
Our solution uses the power of AI/ML to optimise and automate the inventory management process. By using historical data and real-time analysis, it helps businesses predict demand, set optimal stock levels, identify slow- moving items and avoid stockouts - Improving inventory turnover, reducing costs, and increasing customer satisfaction.
Merchandising & Category
Planning
Visionet’s AI-powered merchandising and category planning capabilities optimise retail management of product offerings, pricing, and displays. This helps in improving sales, increasing efficiency, and enhancing the customer experience by using data analysis, demand forecasting and better assortment of product categories.
Sales Planning
Through AI-based sales planning techniques you can predict future sales and help build a sales strategy. It utilises machine learning and statistical models to analyse data such as past sales, customer demographics, and market trends.
Ad Spend Optimisation
Ad spend optimisation with AI uses data and technology to make smart decisions about allocating advertising budget for better results and ROI. Machine learning models analyse large amounts of historical data to predict the future performance of different ad channels and campaigns.
Discount Planning & Pricing Optimisation
Our intelligent discount planning and pricing optimisation capabilities optimise the pricing and discounts for products and services to increase sales and revenue - helping make real-time pricing and discount decisions to increase sales and revenue.
Manufacturing, Production & Raw Material Planning
Our solution optimises the manufacturing, production, and material planning process to improve efficiency, reduce costs, and increase output. It uses AI to schedule production, predict equipment failure, forecast demand, optimise inventory and plan logistics.
Key Features
Utilises Cloud-powered
scalability and native services
Handles millions of
transactions and years of
historical data
Automates forecasting with
business acumen input
Helps in memory calculation
for exceptional user experience
Delivers end-to-end
automated process and
integrations
Enables swift pattern
recognition
Case Studies
Fragmented customer data leads to inaccurate insights and missed opportunities. This case study shows how a hybrid AI and rule-based approach helped unify customer records and improve data accuracy in mortgage and real estate systems.
Disconnected systems and fragmented data slow down mortgage operations. This case study shows how a leading organization used middleware integration to automate data flows, improve accuracy, and enable real-time decision-making.
Manual data migration slows down operations and increases errors, especially in mortgage and lending environments. This case study shows how a leading organization automated data ingestion, transformation, and document processing to improve efficiency and scalability.
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