DecisionNext FAQs: What Commodity Teams Need to Know Before They Act
Published: March 24, 2026
How is DecisionNext different from traditional market data and pricing tools?
Most of these tools provide access to historical prices, benchmarks, and even simple trend based market signals & commentary. What they lack is confidence in what to do next – especially when markets are volatile and decisions carry real financial risk.
DecisionNext closes the confidence gap by turning data into decision-ready intelligence, not just reference points.
What makes the approach different:
- Unique, dynamic market data Analyze, correlate, and visualize internal data alongside trusted public and market sources—so decisions are grounded in the full market context.
- Multiple backtested curated forecast models you can trust Forward-looking price and volume forecasts built on machine learning, fundamentals, and market signals—continuously updated and supported by robust forecast analytics.
- Decision alternatives with clear risk and uncertainty Apply forecasts within supply-and-demand frameworks to evaluate timing, pricing, and contract options before committing real volume.
- Improved, consistent, repeatable decisions Evaluate complex transactions using risk-aware forecasts and ready-built visualizations so teams align faster and decisions are explainable and defensible.
In short, traditional tools help explain what has happened or what the market is implying today. DecisionNext helps teams decide what to do next—with a shared, defensible view of risk and tradeoffs.
How accurate are DecisionNext forecasts?
Simply put, in head-to-head comparisons, DecisionNext has consistently outperformed the alternatives teams rely on today.
In most markets and planning horizons, DecisionNext models deliver lower forecast error than common approaches such as spreadsheets, internal baseline forecasts, or market-implied forward curves. Accuracy is measured using out-of-sample backtests, so performance reflects real-world predictive power—not curve fitting.
We can benchmark DecisionNext directly against your current forecasting process using your own historical data, so you can see exactly how the results compare over a defined time period.
What are the most common DecisionNext use cases?
DecisionNext is most commonly used to improve buying, pricing, and risk decisions by giving teams a clearer forecast, scenario range, and measurable tradeoffs.
Common use cases include:
- Processors: Manage forward positions, optimize cut programs, and reduce exposure to market swings
- Further processors / manufacturers: Model input-cost risk, support pricing, and plan private-label or program commitments
- Retailers: Plan meat ads and promotions (often 8–12 weeks out) and align merchandising with procurement timing
- Foodservice operators: Forecast protein costs for menu cycles, coordinate purchasing across locations, and limit surprise cost spikes
- Traders and export-focused teams: Benchmark performance versus market indices and evaluate directional risk and timing decisions
The specifics vary by business, but the pattern is consistent: better forecasts, better scenarios, and better-measured decisions.
Where does the data come from?
DecisionNext forecasts are built on a wide range of trusted public and market data sources, combined with optional customer-provided inputs. This ensures forecasts are grounded in real supply-and-demand drivers—not just historical price patterns.
We pull from the data streams commodity teams rely on most, including:
- USDA mandatory reporting (e.g., AMS/LMIC and other USDA sources)
- Futures markets and traded curves (where applicable)
- Supply chain indicators (production, inventories, throughput, logistics)
- Macroeconomic signals (interest rates, FX, inflation, broader demand indicators)
- Feed and key input costs (corn, soybean meal, energy, etc.)
- Retail and demand signals (where available)
- Weather and climate data that can influence production and pricing
- International market data such as exchange rates, trade flows (imports/exports), and global pricing benchmarks
When useful, DecisionNext can also incorporate proprietary customer data (e.g., internal purchasing history, formulas, contract structures, or demand plans) to improve relevance and enable side-by-side benchmarking against your existing process.
If helpful, we can walk through the exact sources used for your key series so your team understands what drives the forecast—and why.
How far out does DecisionNext forecast?
DecisionNext forecasts typically extend 26 weeks forward out of the box, giving teams a clear mid-term planning view for timing, contracting, and pricing decisions. For customers who need longer-range planning, DecisionNext can support forecasts that extend up to 2 years, depending on the commodity and available data.
Forecast horizon is not one-size-fits-all — it varies by market behavior, decision cadence, and how much confidence is required at each time frame. That’s why we align the forecast range to your real workflows, from near-term execution to longer-term strategy.
Common ways teams use different horizons include:
- Near-term: support spot buys, negotiations, and weekly execution
- Mid-term (26+ weeks): guide contracting decisions and pricing windows
- Long-term (12–24 months): support strategic planning and budgeting
If helpful, we can recommend the right horizon for your key series based on historical behavior and decision needs.
What is an example of how DecisionNext improved a real buying or pricing decision?
DecisionNext customers use forecasting and scenario modeling to quantify the impact of timing, contract structure, and sourcing decisions using real market history. Results vary by commodity, volume, and how much flexibility a team has to act, but here are two illustrative examples:
- Forecast improvement at scale: One manufacturer with roughly $350M in annual raw-material spend used DecisionNext to reduce forecast error from approximately 35% to ~20% over their planning horizon. Despite constraints (availability, storage capacity, etc.), applying DecisionNext forecasts to high-volume buying decisions translated to an estimated ~$21M in value in year one.
- Scenario-based decision support: In another case, a customer evaluated the impact of a potential plant outage and used DecisionNext to stress-test outcomes. The scenario analysis supported a buying decision worth approximately $30,000 on a 200,000 lb purchase.
If helpful, we can run the same type of analysis on one market and a handful of your recent decisions, so you can see the value using your own data and constraints.
What does DecisionNext do that an experienced procurement or pricing team can’t do with internal models?
DecisionNext delivers capabilities that don’t scale in spreadsheets or one-off internal models: automated backtesting, multiple forecast models across hundreds of series, continuously updated data, and a single source of truth accessible across teams.
By automating the heavy lifting behind forecasting and market monitoring, DecisionNext frees your team to focus on the decisions that move margin:
- Comparing options (buy now vs later, spot vs contract, supplier A vs B)
- Stress-testing scenarios under different market assumptions
- Aligning stakeholders on a shared, defensible view of risk and tradeoffs
In short: DecisionNext doesn’t replace your team’s expertise — it scales it with a repeatable, decision-ready workflow.
What makes DecisionNext forecasts unique?
DecisionNext forecasts are next level. They combine multiple modeling approaches, continuous data updates, and transparent performance tracking—so teams can trust the signal and act on it with confidence. Instead of relying on a single “black box” output, DecisionNext provides an explainable, decision-ready forecast foundation that scales across markets and teams.
What makes the approach different is that forecasting isn’t treated as a one-time model build—it’s treated as a living system that is measured, monitored, and improved over time. Every forecast is backed by automated backtesting, giving your team clear visibility into accuracy by model, series, and horizon.
Key differentiators include:
- Automated backtesting at scale: transparent performance tracking across hundreds of series
- Multi-model forecasting: different model views for different market regimes and time horizons
- Always current: forecasts refreshed daily or weekly as new data arrives
- Explainable output: clear drivers and visibility into uncertainty—not a black box
- Single source of truth: consistent forecast signal accessible across teams and stakeholders
The result is a dynamic forecasting system that supports faster alignment and more defensible buying, pricing, and contracting decisions.
Why should I trust DecisionNext enough to move real volume or make pricing decisions?
DecisionNext is decision support, not autopilot — your team stays in control of risk, timing, and volume. Our job is to make tradeoffs measurable and transparent so you can act with more confidence.
We earn trust by showing the evidence:
- Historical performance: how the models have performed in out-of-sample backtests
- Uncertainty ranges: not just a single number, but the range of likely outcomes
- Decision comparisons: what different choices (buy now vs later, spot vs formula, Product A vs B) would have looked like historically if you had been using DecisionNext
Most customers start with a scoped proof of concept in one market and a small set of real decisions. That way, you can validate performance in your own data and constraints before expanding usage to larger volumes or higher-stakes decisions.
How long does it take to implement DecisionNext?
DecisionNext is a browser-based platform with no installation required. Most teams can start getting value on day one by running it alongside their existing process.
Implementation is designed to be light on your team. You’ll have two dedicated resources: an industry expert who understands your business and a customer success lead who manages setup and enablement. We handle the heavy lifting — including data mapping, model setup, and dashboard configuration — and your team contributes a few focused working sessions to align on how you buy, price, and plan today.
You don’t even need to replace existing tools. Most customers start with a parallel rollout and deepen integration only where it clearly pays off.
Where does DecisionNext drive value in the P&L?
DecisionNext primarily drives value in gross margin by improving decision quality and reducing avoidable downside risk. The impact typically shows up through a few clear levers:
- Better timing and contracting on large volumes, where small market moves can translate into meaningful dollars
- Smarter pricing and promotion planning that avoids last-minute cost surprises
- Fewer “off-market” buys and positions, supported by clearer benchmarks and forward visibility
The result is more stable margins, fewer negative surprises, and better outcomes on high-stakes decisions.
How does DecisionNext perform when markets get volatile, break historical patterns, or experience Black Swan events?
No model is perfect when the market shifts unexpectedly, and we’re transparent about that. DecisionNext shows up to four model views per series — when they agree, confidence is high; when they diverge, it’s a visible red flag that something unusual is happening.
Our forecasts are built on stable, predictable drivers. Thus, true “black swan” shocks can’t be predicted in advance. But as the new reality shows up in the data, our models adapt and update the near-term baseline week to week and month to month.
On top of that baseline, users can stress-test assumptions with scenarios (for example, reducing supply to model the impact of tariffs) to get a statistically grounded “if this, then what” range of outcomes — so teams react faster and make more defensible decisions should those “black swan” events come to pass.
