What makes DecisionNext stand out? The answer becomes clear after a one-on-one session to identify your business’ analytics and forecasting needs, review relevant models and historical accuracy records, create scenarios, and analyze how they impact business decisions. We work directly with our customers to answer market questions and create tailored value cases.
Below is a snapshot of how it works.
At the core of what we do sits a powerful, machine-learning market forecasting platform. Tested, validated, and always learning, this tool gives our customers daily-updated market views in two ways – simultaneously. The platform is built to interact with data (proprietary internal, third-party, and publicly sourced) from across all commodities; feed grains, food grains, edible oils, animal proteins, finished goods, etc.
This is one example of how DecisionNext works within the grains and oilseeds space, offering two distinct forecasting models:
1. CBOT Forecast: Takes the forward curve (futures prices) for key traded commodities (wheat or soybeans, for example), as well as their components (soybean meal and soybean oil).
2. Machine Learning: A mathematical approach that analyzes seasonality, price history, trend, and the AI-selected data points for the specific commodity and geographical location in question, using millions of simulations to forecast its future price.
Of course, testing the historical accuracy of the two models for a specific item is important. The DecisionNext platform affords users the ability to backtest these models at any time, and decide which one works best for their specific needs and important decision-making time horizons. User interaction with the forecast models is a unique differentiator, ensuring that DecisionNext forecasting is never a black box.
Scenarios: Once users gain confidence with the forecast, DecisionNext allows them to run real-time “what if” scenarios about the impact of events on future pricing. For example, users can input scenarios, such as altering quantities of wheat exports, and model out the impact on the spot market in coming weeks.
Certainty: Forecasting a price is one thing, but understanding the certainty of the forecast is necessary for making the right decisions. DecisionNext gives users the ability to see how confident they should be in a forecast by showing the confidence intervals between the expected high and low price for an item.
Expertise: You have experts in your organization, and what they know matters to the business. DecisionNext and the modeling layer give them the platform to use that knowledge to make the best decision. No more spreadsheets. No more guesswork.
The DecisionNext Transaction module uses the forecast and associated risk profile to model expected cash flows for each buy and sell decision, all the way down to the gross margin level. Customizing each decision with variables such as freight; storage; toll; yield; formulations for finished goods; etc. makes the tool incredibly flexible. This module works for all types of buy/sell transactions and business types from cash sales and purchases to complex risk sharing arrangements and financial hedges.
Build a Library
Users format and store various commonly-used deal scenarios and customized timeframes for things such as buy/sell options; hedging strategies; and risk-sharing structures with suppliers/ customers which adds confidence that the deal is right for them. Each future decision is only a few keystrokes away.