AI-Powered

AI-Driven Demand Forecasting
Makes Supply Chains More Efficient and Responsive

When you can predict what customers want, it’s easier to anticipate their needs, adjust inventory, fulfil deliveries, and above all save operational expenses. Relying on AI-driven demand forecasting offers a smarter, more adaptive approach to meet modern logistics demands.
AI-driven demand forecasting:

Reduces human errors by 20–50%

Reduces administration costs by 25–40%

Challenges of Manual Demand Forecasting Methods

Traditional demand forecasting models rely heavily on historical sales data and generic statistical models. While these methods provide a baseline, they fail to keep up with the scale and complexity of modern logistics.
A leading e-commerce enablement platform in the US struggled with problems meeting demand due to a flawed forecasting strategy. The company suffered costly customer churn, which made them rethink their demand forecasting approach.

Difficult to analyze massive volumes of data using spreadsheets or antiquated software

Inability to account for unprecedented shifts in consumer behavior, weather, economic factors, and supply chain dynamics

Inaccurate predictions can lead to embarrassing situations like stockouts, excess inventory, or delayed deliveries

Failure to incorporate real-time data and adapt to sudden changes can damage reputation and erode customer trust

Enhance your production planning with real-time forecasting.

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The Solution:
AI-Driven Demand Forecasting

To overcome the flaws of outdated forecasting, the e-commerce enablement platform implemented a Machine Learning prediction model that predicts demand accurately and optimizes prices for individual sellers. The AI-driven demand forecasting solution:

Analyzes massive volumes of information, including historical data, macroeconomic factors, and data from various external sources

Helps determine optimal prices for private sellers by performing demand and supply analysis

Allows optimizing resource usage and cutting operational costs, while meeting customer demands

Automatically forecasts monthly and weekly sales by individual products and companies

Offers a unified view of demand to adjust logistics and supply chain operations in real-time

Results of Using AI for Demand Forecasting

6–8 points
Improvement in forecast errors
$100–$130M
In potential benefits
Up to 50%
Reduction in inventory errors

Accuracy of AI-Driven Demand Forecasting,
Within Six Months of Implementation

reduction in excess inventory
0 %
decrease in stockouts
0 %
increase in sales
0 %

Benefits of AI-Driven Demand Forecasting

Anticipate demand and respond to market fluctuations effectively

Make informed inventory decisions with a unified view of demand

Reduce errors and delays in delivering precise forecasts

Identify potential supply chain disruptions to ensure smooth operations

Better scalability for businesses expanding into new markets

Optimize resource allocation, reduce waste, and cut operational costs

Optimize your inventory with AI-driven demand forecasting.

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How Can Fingent Help You With AI-Powered Demand Forecasting

With our extensive expertise and experience in custom AI solutions development, we help businesses across different industries to modernize their supply chain workflows, optimize resource management, and improve budget planning.

Custom AI solutions, including Applied AI and Gen AI-based applications, Chatbots, Smart AI Assistants, and more

Tailored AI solutions for businesses in logistics, warehousing, transportation and asset management, retail, CPG, etc.

Integration of various logistics management platforms, such as TMS, WMS, and ERP to drive automation and responsiveness

Scalable AI and ML model development for businesses handling large volumes of shipments or expanding into new markets

ML model development and training to analyze diverse data sources, including third-party databases

AI-driven forecasting model development to facilitate proactive inventory and resource planning across the supply chain

Key Takeaways

AI-driven demand forecasting in logistics and supply chain management:
Mitigates the risk of lost sales, overstock, and understock by ensuring products are available when customers need them
Raises your organization’s responsiveness to unforeseen circumstances by coordinating merchandising, procurement, and logistics operations
Higher accuracy in demand forecasting leads to significant improvements in productivity, speed to market, and revenue
Analyzing data points in real-time and making informed decisions allows you to plan better and stay ahead in the competitive landscape

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