AI Based Demand Forecasting
AI-Based Demand Forecasting
Demand Forecasting refers to the process of predicting future demand for the firm’s product. In other words, demand forecasting consists of a series of steps that involves the anticipation of demand for a product in the future under both controllable and uncontrollable factors.
The goal of demand forecasting and demand planning is to predict customer demand as accurately as possible to minimize the loss of business and poor customer experience. Retail demand forecasting models are grouped into two categories: qualitative and quantitative. Quantitative methods rely on data, while qualitative methods rely on industry expert insights and feedback.
Critical Industry Challenges
Is your business struggling with revenue growth?
Does your Inventory run out of stock?
Do you end up with unhappy and unsatisfied customers?
Are you always struggling to forecast customer demands & preferences?
Benefits with AI-Based Demand Forecasting Engine
Accurate Demand Prediction to Increase Revenues
Enhanced Actionable Insights
Real-Time Data Analytics
Reduced time of Implementation by leveraging Transactional & Stored Data
How Synctactic AI supports Demand Forecasting?
SyntacticAI thrives in enforcing accurate demand forecasting by taking a data-centric and model-driven approach such as qualitative forecasting, Time series analysis, Causal model using their existing patent-pending data automation tools like Sync discover, Sync learn, Sync data, Sync analyze and bring the true power of AI in client’s systems using their internal and external data. SyntacticAI is a leader in providing a trusted AI-based demand forecasting implementation.
Internal & External Business Data-driven models
Syntactic.AI ensures Internal data including historical sales numbers, advertising expenditures vs performance, online/offline traffic, time spent by customers, etc is considered during the implementation of reliable demand forecasting engine. The various external data which SyntacticAI data scientist team considers for their client’s AI engine is based on business trends, industry and consumer trends, seasonal variations and changes, competitors data, etc.
Machine Learning based Self Improving Models
Synctactic.AI continuous machine learning models are designed to constantly align themselves to achieve predetermined business goals & KPI.
Profit & Revenue focussed Demand Predictions
Synctactic.AI goal-seeking models algorithmically vary the future values of the “inputs” to determine the values that achieve a certain goal (profit, revenue, or cost goal) based on the forecasts.
Seasonal Demand Forecasting
The AI engine further improves seasonal demand forecasting which leads to greater operational efficiency, reduced expenses and increased profits. Self-Improving AI models increase the reliability of AI-based demand forecasting engines which can persistently feed on the new data and processes which results in quarter to quarter revenue growth for Synctactic.AI clients and an increase in client’s market capitalization.