Supply Chain Demand Forecasting AI

More Robust Data + AI Modelling = Better Forecasting + Lower Error Rates

Our cloud platform allows us to deploy in days, not months, while machine learning and the use of contextual data to build models remove the manual guesswork. Utilize retail and CPG demand planning to optimize product availability and decrease out-of-stocks, waste, and spoilage, all with a more detailed understanding of sales patterns and anomalies.

95%

Improvement in forecast accuracy

10X

Reduction in manual intervention

65%

Reduction in food waste headed for the landfill

Better predictions remove the guessing game

Accurate demand forecasting across all categories — including increasingly important fresh food — is key to delivering sales and profit growth. Our AI-powered models and analytic platform use shopper demand and robust causal factors to completely capture the complexity and reach of today’s retail supply chain.

Machine learning for cleansing, learning, and adapting

With supervised machine learning, our solutions can first understand the historical patterns and then be introduced to a broader data set over time, cleansing and allowing it to find patterns that humans can’t detect as accurately or efficiently.

We specialize in high-volume retail, so we know exactly how to train and optimize our models for the nuances and specifics of retail and CPG demand planning, rather than taking a generalized approach. And with external variables and contextual data such as events calendars, seasonality, weather, or even extreme outliers, we deliver the most accurate forecasts.

No need for an army of demand planners scientists

As data availability increases, detecting customer purchase patterns becomes more complex. And forecasts are only as accurate as the data, models, and resources you have to interpret them. So, what do you do?

With our low-touch Demand Forecasting models, you can automate the processes and improve the data sources — all without adding more human resources. With client-specific algorithms, our solution delivers significant improvements in forecasting speed, accuracy, and efficiency.

Ultra-fresh. Hyper-local. Customer-aware.

Fresh – from perimeter categories to prepared meals and kits – are a major area of differentiation; they’re also the hardest to accurately forecast. – are a major area of differentiation; they’re also the hardest to accurately forecast. Use the power of AI in your supply chain demand forecasting to make more accurate predictions, differentiate your offering, and meet consumer demand.

With Symphony’s supply chain Demand ForecastingAl, you can manage fresh item forecasting, as well as produce daily and intra-day forecasts to support in-store food production services, giving you an unprecedented level of accuracy and streamlining subsequent replenishment planning and inventory allocation efforts.

A more strategic view. A lower error rate

Demand ForecastingAl bridges the retailer ecosystem from end-to-end, maximizing return on investment. From assortment optimization and space planning down to supply chain execution, our AI-powered demand forecasting autonomously supports all activities on one data platform.

Thanks in part to a single, cloud-native platform – with a unified view of inventory visibility and traceability – these more intelligent, connected supply chains allow experienced associates to spend less time crunching data and more time responding to industry challenges.

CPG Demand Forecasting and Planning Software

The end goal is to have one version of the truth for our data to make better decisions and to have predictive capability in our demand forecasting and more accuracy in our inventory. That leads to the right assortment in front of their customers with the right pricing – all the while being able to provide a more personalized experience.

Alicia Samuel Director IT Business Solutions

Better predictions remove the guessing game

Machine learning for cleansing, learning, and adapting

No need for an army of demand planners scientists

Ultra-fresh. Hyper-local. Customer-aware.

A more strategic view. A lower error rate

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