What We’ll Unpack in This Article (TL;DR)
The modern supply chain is volatile and requires businesses to take a sophisticated approach to demand forecasting. However, many organizations use tools like ERPs and spreadsheets, which weren’t built to forecast accurately in this level of volatility. Instead, you can deploy one of the new forecasting tools on the market, which are designed for accuracy (even in the face of uncertainty).
Here’s what makes a modern forecasting tool built for volatility:
- They integrate real-time, cross-functional inputs.
- They use AI to detect patterns humans can’t (easily) see.
- They provide SKU-level insights.
- They update fast, often, and with minimal effort.
This article explores why demand forecasting today is particularly challenging, and how new tools can help you thrive in unpredictable environments.
The modern supply chain is anything but stable. Demand and costs swing overnight as global trade tariffs, lead times, and supplier constraints change with little warning. And while many organizations rely on ERP or spreadsheet-based forecasting, those tools weren’t built for this level of disruption and fluctuation. Instead, modern supply chain businesses need more advanced, nuanced forecasting tools. The best tools can significantly support in uncertain times.
What makes these tools different (and better suited) for today’s supply chain? They provide more accuracy and depth than other options, and they’re better able to operate in uncertain environments. For example, they can interpret erratic demand signals, isolate anomalies, and turn massive amounts of shifting data into clear, confident takeaways.
This article explores why forecasting today is particularly challenging, and how new platforms can help you thrive in the face of uncertainty – instead of just reacting to it.
Why is Forecasting Challenging in Today’s Supply Chain?
Accurate demand forecasting has always been challenging, but today’s environment has raised the difficulty to an entirely new level. The core issues? Nearly every factor that influences demand is more variable, faster-changing, and less predictable than it was even a few short years ago.
1. Volatile demand patterns
In an era marked by eCommerce, social media-inspired shopping trends, and price sensitivity, customer behavior is quick to shift. Research from the World Economic Forum also shows that supply chains are vulnerable to risks ranging from extreme weather events to inflation. Distributors and manufacturers are seeing abrupt shifts, making historical data alone insufficient for future planning.
2. Long (and uncertain) lead times
For many industries, lead times now stretch 90-150+ days, with studies showing that lead times are still longer than their pre-pandemic peaks. When it takes months to replenish inventory, even small forecast inaccuracies compound into major service issues or excess stock.
3. Global trade tariffs
Global trade tariffs have made headlines in 2025, as governments launched into rounds of trade wars and “reciprocal tariffs.” While they’re intended to do things like protect domestic industries or penalize trade partners, they have a ripple effect, and can lead to the increased costs of goods and raw materials. They also complicate demand forecasting: spikes or dips related to tariffs can easily disrupt a forecast if not identified and isolated.
4. Other tools aren’t built for modern forecasting
While supply chain tools like ERPs have broad functionality, they often have shallow abilities when it comes to demand forecasting. For example, they might offer limited statistical methods or minimal anomaly detection. This means they often fall short if they’re used as a primary (or sole) forecasting method.
Put simply: today’s supply chain requires forecasting tools that keep pace with real-world chaos.
What Makes a Modern Forecasting Tools Built for Volatility?
How can you tell if a supply chain forecasting tool can interpret chaotic signals, adapt instantly, and help teams make decisions with confidence? Here’s what sets modern, volatility-ready forecasting tools apart.
They integrate real-time, cross-functional inputs
Accurate demand forecasts call for multiple data inputs. Top forecasting tools pull together:
- Historical data, such as seasonality, trends, and patterns.
- Sales and marketing insights, such as promotions and customer events.
- Customer-level insights, such as POS data and large order alerts.
- Supplier and lead time behavior, such as late deliveries.
This connected view ensures that demand forecasts take all relevant data into account.
They use AI to detect patterns humans can’t (easily) see
Even the most talented forecasting experts can benefit from the power of artificial intelligence (AI). AI-driven forecasting tools help you cut through the noise by:
- Detecting hidden seasonality or micro-trends.
- Clearly highlighting key metrics, such as cost of goods sold (COGS).
- Automatically flagging anomalies or unusual spikes.
- Continuously learning from new data to adjust the forecast.
This automation cuts through complexity and dramatically reduces the manual work that slows teams down.
They provide SKU-level insights
Not every SKU behaves the same. Some are predictable, some are erratic, and some are more valuable than others. Top-tier forecasting tools use SKU-specific insights, inventory segmentation (such as ABC analysis), helps you predict demand for SKUs with no historical data (like new items), and can help you prevent overbuying obsolete or end-of-life items.
They update fast, often, and with minimal effort
Modern forecasting tech is built for the speed of today’s supply chain. These tools can recalculate forecasts in minutes (or less), refresh inputs automatically, and deliver critical alerts – so planners can quickly see what has changed, and decide how to act.
Best Practices for Choosing the Right Forecasting Tool
There are plenty of options for forecasting tools in the multi-billion dollar demand planning solutions market. The right one can support your team, your processes, and your long-term growth – even in a volatile environment.
Here’s what to look for when evaluating potential solutions:
- Prioritize tools that reflect real supply chain complexity: A strong forecasting platform should support your full planning ecosystem. Look for tools that handle long and variable lead times, multi-location inventory needs, supplier performance monitoring, and customer-level forecasting.
- Seek AI and automation that reduce manual work: AI shouldn’t be a buzzword – it should materially improve accuracy and reduce effort. Validate that the tool can detect anomalies, improve forecast accuracy over your current benchmark, and reduce required safety stock levels.
- Ensure it supports a granular strategy: Volatility affects every SKU differently. The right tool should dynamically manage things like ABC/XYZ classification, lead-time variability, service level, and order policy recommendations.
- Look for clear, actionable dashboards (not just data dumps): Your tool of choice should support better decision-making by distilling raw data into clear takeaways. Planners need to be able to quickly see where forecasts changed, as well as exceptions, anomalies, and financial implications.
Today’s supply chains demand more accurate forecasting than ever before. With volatile demand patterns, shifting costs, and unpredictable supplier performance, organizations can’t rely on legacy systems alone. By choosing an advanced supply chain tool built for modern challenges, you can ensure accurate, actionable forecasts – even in volatile conditions.
StockIQ: A Forecasting Tool Built for Modern Supply Chain Chaos
In a supply chain environment defined by sudden demand swings, unpredictable (and long) lead times, and shifting consumer demand, your forecasting tools need to be built to predict the unpredictable. If you’re ready to launch a supply chain tool that supports your business in the face of uncertainty, StockIQ is here to help.
StockIQ is advanced, user-friendly supply chain management software that allows you to control inventory, simplify ordering, and improve forecasting. With StockIQ, you’ll boost operational efficiency, improve forecast accuracy, and simplify strategic planning.
Find out how StockIQ can support your organization’s supply chain network optimization by contacting us today or request a StockIQ demo.
Frequently Asked Questions About Forecasting Tools for Today’s Supply Chain Challenges
1. Why is accurate forecasting challenging in today’s supply chain?
Demand has become more volatile due to shifting customer behavior, long and inconsistent lead times, cost fluctuations, sourcing changes, and macroeconomic uncertainty. These factors make historical averages unreliable, requiring more advanced forecasting technology.
2. What makes modern forecasting tools built for volatility?
Demand planning tools are purpose-built for handling volatility when they integrate real-time data, use AI to detect patterns, provide SKU level insights, and update quickly (and with minimal-to-no effort).
3. What are best practices for choosing the right forecasting tool?
Start by prioritizing tools that reflect real-world supply chain complexities, such as long lead times and multi-location inventory needs. Next, seek AI and automation to reduce manual work and errors. Also, ensure it supports a SKU-level strategy, and look for clear dashboards (instead of raw data dumps).