In a fluid supply chain, demand planners must level-up their processes, embrace modern AI-driven solutions, and lean into adaptability as a way to keep up.
If you’re already feeling behind in these areas, it may seem daunting to make changes as tariff announcements continue to impact your supply chain, lead times swing, and product lifecycles compress.
You’re not alone in feeling this pressure. Netstock’s 2025 Benchmark Report reveals a widening performance gap. The difference between organizations working with established forecasting methods and those who’ve adapted isn’t about small adjustments. It’s a fundamental shift in how demand planning operates today.
Quick insights for demand planners
The supply chain landscape is now a fluid thing. Here’s what matters now:
- While accuracy remains paramount, speed must be prioritized. Top performers adjust forecasts weekly, not monthly
- Cross-functional collaboration directly correlates with forecast accuracy
- AI handles pattern recognition at scale. This functionality frees up human planners so they can prioritize using their judgment to interpret market signals and exceptions.
- Scenario planning replaces single-point forecasts as the standard approach
- Technology adoption separates leaders from laggards more than any other factor
What changed and why your approach needs evolution
Consider this scenario: You’re a demand planner at a mid-market manufacturer. It’s Monday morning, and you’re reviewing last week’s forecast variance. Your accuracy dropped 12 points because a key supplier’s port experienced congestion you didn’t anticipate, a competitor launched an unexpected promotion, and your sales team closed a deal that wasn’t in the pipeline three days ago.
A decade ago, these would have been isolated incidents. Today, this is very much commonplace.
Between 2023 and 2026, the conditions that supported traditional demand forecasting have fundamentally shifted. Tariff policies change quickly. Supply disruptions moved from quarterly exceptions to monthly occurrences. Consumer demand shifts between categories at social media speed. Product lifecycles that once gave planners breathing room now demand real-time adjustments.
The established approach: collect data, build models, lock forecasts, and review quarterly. It was designed for a more stable environment than we operate in today. Leading demand planning processes have evolved to match current conditions.
The Benchmark Report data: What separates high-performing demand planning from the rest
Netstock’s 2025 Benchmark Report, the first large-scale study of inventory management practices across SMBs managing $26 billion in inventory, reveals clear performance patterns across demand planning organizations. The data collected from more than 2,400 SMBs around the world tells an instructive story.
Forecasts are highly accurate
Top-performing organizations (those in the 75th percentile) maintain forecast accuracy rates 23% higher than average performers. They update their demand forecasts 3.2 times more frequently. They’ve invested in demand planning technology at nearly double the rate, with 48% using AI-powered forecasting tools compared to just 23% across all survey respondents.
Planners partner with leadership
The differentiators run deeper than technology adoption rates. Leading demand planners position their function differently within their organizations. They report directly to executive leadership 67% of the time, compared to 41% for average performers. They participate in weekly cross-functional planning meetings rather than monthly siloed reviews. They run 4-6 demand scenarios in parallel rather than betting on a single forecast.
Top-performers grapple with different challenges
Perhaps most telling: when asked about their biggest forecasting challenge, top performers cite “incorporating real-time market signals” while average performers still struggle with “getting clean historical data.” The leaders have moved past foundational issues and now focus on staying ahead of rapidly shifting conditions.
The gap isn’t just about forecast accuracy percentages. It reflects organizational readiness for today’s supply chain environment: and the practical systems that support rapid decision-making.
If you’re looking at these numbers and measuring gaps in your own organization, you’re not alone. Most companies fall somewhere between emerging and established practices.
The encouraging news? The distance from average to top performer isn’t crossed overnight: it’s a systematic journey that starts with one capability improvement.
Best in class demand planning processes: Rules top-performers follow
The following rules are best practices to achieve heightened demand planning performance. While each is highly nuanced, general principals of agility, collaboration, data-driven decision-making, and technology partnerships, guide planners in many industries.
Rule #1: Speed beats perfection
Leading demand planners shifted their focus from perfect forecasts to responsive forecasts. They realized something critical: a good forecast executed quickly beats an excellent forecast that arrives too late.
An “excellent” forecast is one that is highly accurate for the particular moment and demand cycle it is predicted. However, in a world where those moments are brief, planners that spend excess hours fine-tuning predictions and spreadsheets may miss it, thus invalidating the forecast.
To avoid this, top-performing organizations update their demand forecasts weekly or even daily for key SKUs. They’ve built workflows that allow rapid adjustments when new information emerges: a supplier delay, a competitor price change, an unexpected weather event. Their forecasting cycles measure in days, not weeks.
This doesn’t mean accepting lower quality. It means recognizing that forecast responsiveness often matters more than precision. A forecast that’s 80% accurate but adjusts to new information immediately can outperform one that’s 90% accurate but locked in for the quarter.
The technology supporting this shift makes the difference. Automated data pipelines eliminate manual data collection. Real-time dashboards surface exceptions needing human attention. AI-powered forecasting engines recalculate recommendations continuously, flagging significant changes for planner review. The infrastructure exists to move quickly: high performers built their processes to use it effectively.
Rule #2: Collaboration is non-negotiable
Walk into a top-performing demand planning meeting, and you’ll notice who’s in the room, including the demand planner, yes, but also sales leadership, finance, operations, and often marketing. The forecast isn’t owned by one person: it’s built by a team.
High-performing organizations broke down the silos that historically separated planning from the rest of the business. They understand that sales knows about pipeline deals before they hit the CRM. Finance sees budget constraints that will impact promotional spending. Operations understands capacity limitations affecting what you can fulfill. Marketing has campaign timing that drives demand spikes.
When these groups work together, forecast accuracy improves. Netstock’s Benchmark Report shows organizations with weekly cross-functional planning meetings achieve forecast accuracy 18% higher than those with monthly departmental reviews.
The collaboration extends beyond internal teams. Leading demand planners maintain direct communication channels with key suppliers and major customers. They share relevant forecast information (within appropriate boundaries) because better visibility across the supply chain benefits everyone. When a supplier knows about your demand spike three months out instead of three weeks out, they can actually prepare for it.
Rule #3: AI amplifies expertise (it doesn’t replace it)
The organizations getting real value from AI in demand forecasting leverage its strengths alongside human expertise.
AI excels at pattern recognition. It processes millions of data points, identifies seasonal trends, accounts for promotional lift, and adjusts for dozens of variables simultaneously. It handles the computational heavy lifting that would take human planners days or weeks. Leading demand planners use AI to handle exactly these tasks, freeing them to focus on what algorithms miss.
Advanced demand planning processes layer human judgment on top of AI-generated forecasts. The AI provides the baseline forecast and flags significant deviations. Planners review those flags, apply business context, and adjust accordingly. This combination (computational power plus human expertise) consistently outperforms either approach alone.
Technology like Netstock’s AI Pack make this collaborative approach possible. The system analyzes complex inventory data, identifies optimization opportunities, and surfaces recommendations. It presents those recommendations to planners who understand their business, their customers, and their supply chain constraints. The AI amplifies their expertise rather than attempting to replace it.
Want to see how AI-powered inventory solutions work in practice? Explore Netstock’s AI Pack to understand how this technology can enhance demand planning processes.
Rule #4: Scenario planning is standard operating procedure
Ask an average demand planner for their forecast, and you’ll get a number. Ask a top performer, and you’ll get three scenarios plus an explanation of which conditions would trigger each one.
Leading organizations moved away from single-point forecasting that happened on a monthly basis. They recognize that uncertainty is now inherently part of the environment they operate in. Rather than predicting the future with false precision, they continually prepare for multiple possible futures.
A leading demand planning process includes baseline, optimistic, and conservative scenarios as standard output. The baseline reflects the most likely outcome given current information. The optimistic scenario plans for stronger-than-expected demand or favorable supply conditions. The conservative scenario prepares for demand softness or supply disruptions.
Scenario planning goes beyond having three numbers in a spreadsheet; top performers build their inventory strategies, capacity plans, and supplier agreements to flex between scenarios. They identify trigger points: specific market signals or business metrics that indicate which scenario is materializing. When those triggers fire, they adjust operations quickly because they’ve already planned the response.
This approach requires sophisticated demand planning tools. Systems need to handle multiple forecast versions simultaneously, show the inventory and financial implications of each scenario, and make switching between scenarios operationally feasible. Leading organizations invested in platforms that support this level of planning sophistication.
Rule #5: When balanced correctly, excess stock can be an intentional buffer that protects service levels
Inventory scale inflation in Q2 of 2025 reflects a new approach to handling excess stock. Once considered something to generally avoid, SMBs appear to be moving in the direction of strategic stockpiling. While it is unclear whether this is reactive or intentional in the face of tariff spikes and demand shifts, the message is the same: Demand planning is moving toward a hybrid philosophy. Some buffer inventory is held intentionally as “insurance.”
When done correctly, this can be a smart strategic move. However, there are some risks (i.e. more working capital tied up in slow-moving inventory). To mitigate this, SMBs must utilize demand signaling and risk modeling. Without these predictive intelligence tools, the “just-in-case” stockpile is simply poorly informed excess that leads to dead stock and cash-flow drains.
What best-in-class demand planning looks like in action
Wondering how these principles translate to daily operations? Here’s a hypothetical morning at a top-performing business, utilizing these practices:
- Monday morning: Team reviews weekend sales performance and adjusts current-week forecasts for key SKUs showing significant variance
- Tuesday: Cross-functional planning meeting discusses pipeline deals, upcoming promotions, and supplier updates that might impact next month’s forecast
- Wednesday: Demand planners review AI-generated forecast recommendations, applying business judgment to adjust outliers
- Thursday: Finance reviews demand scenarios and their P&L implications; inventory strategy adapts to expected demand patterns
- Friday: Updated forecasts flow to purchasing and production planning; week’s forecast accuracy metrics are calculated and reviewed
Notice the rhythm: continuous adjustment, regular collaboration, technology supporting (not replacing) human decisions, and tight integration between forecasting and execution.
For a food and beverage manufacturer managing 800+ SKUs with varying shelf lives, this weekly cadence means forecasting adjustments happen before products expire in the warehouse. For an automotive parts distributor supporting 50+ service centers, it means identifying transfer opportunities between locations daily rather than quarterly. The specific applications vary by industry, but the underlying principle remains: responsive planning delivers better outcomes than perfect plans that arrive too late.
Building your path to best in class
If you’re measuring your practices against these benchmarks and seeing gaps, you’re not alone. Most organizations are on the journey toward these capabilities. The question isn’t whether you measure up perfectly today: it’s about identifying your next step forward.
- Start with technology. If you’re still managing demand forecasts in spreadsheets, that’s your first move. Modern demand planning platforms like Netstock integrate with your ERP, automate data processing, and provide the forecasting capabilities that make everything else possible. You can’t run weekly forecast cycles or multiple scenarios efficiently without the right tools.
- Next, address your processes. How often do you update forecasts? Who’s involved in forecast reviews? How quickly can you adjust when new information arrives? Advanced demand planning processes prioritize speed and collaboration: look for friction points that slow you down or keep critical stakeholders out of the loop.
- Finally, invest in skills. Demand planners who excel in today’s environment combine statistical expertise with business acumen. They understand forecasting models and supply chain flexibles. They interpret data and read market signals. They work effectively across functions. If your team has development opportunities in any of these areas, that’s where to focus.
The gap between average and top performance isn’t permanent. Organizations move from one category to the other by systematically addressing technology, process, and skills: usually in that order. Progress happens incrementally, not overnight.
