This is the year manufacturing operations should finally get the planning tools they deserve.
After navigating supply chain chaos, tariff disruptions, and unreliable suppliers, manufacturers now have proven solutions that transform how MPR systems perform in volatile conditions.
You don’t need to replace your core systems to capitalize on this. You don’t need to prepare for a multi-year implementation project, either. What you need is intelligence working in tandem with your MPR. With purpose-built solutions designed to solve the unique challenges SMBs face, AI-driven tools can help predict supplier delays, dynamically optimize safety stock, and prioritize production when reality turns upside down.
Manufacturers who modernize their planning approach in 2026 will pull ahead measurably: better service levels, lower inventory costs, faster response to disruptions, and protected margins. This is your roadmap to make that happen.
Quick insights for manufacturing leaders
- MPR and MRP II systems are great at execution, but lack the AI-driven intelligence needed to adapt to demand variability and supply chain volatility.
- Recent supply chain data shows manufacturers reporting some of the lowest service levels across industries.
- Common MRP gaps include rigid forecasting, static safety stock levels, limited supplier performance visibility, and reactive scheduling.
- AI-enhanced planning fills these gaps by providing predictive forecasting, behavior tracking, and dynamic prioritization recommendations.
- The manufacturers that surpass their competition in 2026 will change how they conduct planning by means of their tools and mindsets.
Why MRP struggles in today’s manufacturing environment
Manufacturing Resource Planning (MRP) evolved to coordinate production scheduling, capacity planning, and material procurement across the entire operation. MRP II expanded the practice beyond basic material calculations to manage broader manufacturing resources.
The concept is sound and works beautifully when your operating environment behaves predictably. When it doesn’t (and today’s environment rarely does), MRP keeps executing its logic while reality gets further and further from the plan. Demand spikes in unexpected places. A key supplier extends lead times by three weeks with minimal notice. Raw material costs jump 15% between purchase orders. Customer priorities shift after materials are already committed.
MRP records all these changes, but it can’t anticipate them or optimize around them. Planners bridge that gap manually, spending valuable time adjusting what the system generates rather than using data to anticipate and prevent problems.
How 2025’s supply chain trends set the stage for 2026 challenges
Last year revealed which manufacturers had planning systems capable of handling volatility and which didn’t.
Supply chain benchmark data showed manufacturers clustering in the “straggler” category. This category is defined as businesses where key inventory KPIs are in the 25th percentile or below.
When considering lead time, stock turn, overstocking, and understocking, the only category which manufacturers were in the green was in stock turn.
- Lead time: The majority of manufacturers faced delays stretching longer than 80 days, closely tied to tariff and supplier disruptions.
- Stock turn: For the most part, manufacturing businesses saw an average of ~6+ stock turns in 2025, marking them as “stars” compared to the “stragglers that sat at ~2 turns or fewer.
- Overstocking: In North America, 50% of manufacturers had an inventory surplus of 50% or more.
- Understocking: Manufacturers in North America under-purchased, exposing themselves to lost sales at a rate of 15.3% of inventory.
Lead time deviations, cited by 68% of SMBs as the top challenge, were one of the causes of these lagging performance metrics. Unreliable lead times created cascading issues as suppliers missed delivery windows, resulting in production delays and expedited shipments that ate into margins. Planners who relied solely on MRP’s lead time assumptions found themselves constantly reacting to shortages.
The manufacturers who maintained higher service levels shared a common approach. They had purpose-built systems like Netstock that learned from supplier behavior, adjusted safety stock based on actual variability, and helped prioritize production when materials arrived out of sequence. These weren’t necessarily bigger companies with larger budgets. They were simply businesses that were smarter about how they planned.
Many expect 2026 to bring new complications. Tariffs will likely continue reshaping cost structures. Suppliers will adjust their own operations in ways that ripple through planning. Customer expectations haven’t softened, and they are unlikely to.The manufacturing operations that adapt their planning approach will separate from those still trying to make a playbook of the past work in today’s reality.
The most common manufacturing resource planning challenges and their causes
Walk into most manufacturing planning meetings, and you’ll hear different versions of the same frustrations:
| Challenge | What’s Really Happening |
| Inaccurate demand forecasts | MRP averages historical data without accounting for trends, seasonality, or recent demand shifts. |
| Safety stock that’s always wrong | Static calculations set months ago don’t reflect current supplier performance or demand patterns. |
| Blind spots in supplier reliability | Systems show what you ordered and when it’s “due,” but not whether that supplier actually tends to deliver on time. |
| Too much of the wrong inventory | Production schedules based on old forecasts create excess works in progress (WIP) while stock-outs happen elsewhere in the lifecycle. |
| Constant schedule chaos | MRP generates ideal sequences that crumble the moment a supplier ships late or a customer changes priorities. |
| Margin erosion nobody saw coming | Material cost increases happen gradually across dozens of components until quarterly reviews reveal real damage. |
These are intelligence gaps that highlight where MRPs need advanced solutions layers on top to best perform in modern supply chain environments. MRP does the math based on what you tell it after all! And if you’re experiencing those gaps yourself, it is expected that they’ll also show up in your MPR.
On its own, the MRP system can’t just predict supplier behavior, detect cost creep, or optimize dynamically when conditions change. But advanced inventory optimization solutions can, which is one of the many reasons manufacturers are turning to AI-powered software to overcome these common challenges.
Why AI-enhanced planning is becoming essential for MRP success
AI-driven planning tools work alongside MRP, analyzing the same data, but using it differently.
- Predictive forecasting with SKU-specific recommendations goes beyond simple averages. These systems examine demand patterns across multiple dimensions (by product, family, customer segment, sales channel, and more). They then apply forecasting methods that match each item’s unique behavior and qualities.
- Automated safety stock optimization adjusts continuously based on what’s actually happening with suppliers and demand. When a supplier’s lead time increases, safety stock recommendations are updated accordingly.When demand variability changes seasonally, the system catches it quickly and raises red flags for planners.
- Supplier behavior intelligence features track delivery performance over time and across different materials. To machine learning algorithms, patterns previously invisible to human planners or MRP systems become visible.
- Cost creep detection is possible when AI-powered features work together. The systems monitor material costs across your entire BOM, flagging increases before they compound and impact your bottom line. This provides valuable time for planners to adjust vendor commitments and production timelines, thereby maintaining service levels.
- Dynamic work order prioritization helps production teams decide what to run when materials arrive late or demand shifts. Instead of following the original MRP schedule regardless of current constraints, AI insights help rank work orders by their impact. This enables a proactive response to volatility.
These capabilities don’t require massive IT investments or multi-year implementation timelines. Modern AI-enhanced planning platforms integrate with existing enterprise resource planning (ERP) and MRP systems, pulling data, generating insights, and feeding optimized recommendations back into workflows planners use with seamless ERP integration and pairing with many WMS platforms.
A real-world example: How manufacturers overcome MRP limitations with Netstock
One business that has fully embraced AI-driven inventory management is GRPI, a U.S.-based manufacturer of inks and coatings. By integrating Netstock’s Opportunity Engine, an AI decision-support feature, the purchasing team can stay ahead of demand. The solution delivers daily replenishment and purchasing opportunities that align with real-time supply chain and demand conditions.
“The system highlights opportunities that might otherwise go unnoticed,” said Tony Mapes, GRPI’s lead on the Netstock implementation. “It helps our team focus their attention on where it matters most, ensuring we have the right stock, in the right place, at the right time.”
The insights the Opportunity Engine provides drive smarter inventory decisions. The full case study on GRPI’s success states that, “with Netstock, GRPI uncovered hidden inefficiencies in their inventory that weren’t visible through spreadsheets.”
The results of this implementation are already visible, and more are on the horizon as GRPI prepares to expand Netstock use to additional manufacturing sites in the U.S.
Some of the results cited by GRPI include:
- Improved visibility into $1.5M in excess inventory
- Greater control over safety stock and raw material planning
- Fewer stock-outs and smoother production processes
The 2026 manufacturing playbook: What successful businesses will do differently
The gap between manufacturers who modernize planning in 2026 and those who don’t will be measurable: better service levels, lower inventory investment, faster response to disruptions, and higher margins because cost creep gets caught early.
The manufacturers who thrive this year will make specific changes to how they plan.
They’ll treat supplier data as strategic intelligence
How it’s done:
- Track actual delivery performance, not just promised lead times.
- Identify which suppliers need backup options before they cause production delays.
- Use historical delivery data to set realistic planning assumptions instead of hoping suppliers improve performance.
They’ll demand inventory accuracy everywhere
What this means:
- Real-time visibility into raw materials, WIP, and finished goods to prevent a disconnect between what the system reports you have and what’s actually available.
- Manufacturers who maintain tight inventory accuracy spend less time expediting and more time executing.
They’ll forecast at multiple levels and reconcile intelligently
Why this works:
- Top-down forecasts by the production family inform capacity decisions.
- Bottom-up forecasts by SKU drive material purchases.
- Customer-specific forecasts reveal where demand concentrates and fluctuates seasonally.
Most importantly, these perspectives need to work together, not compete.
They let AI handle complexity while MRP handles execution
The split:
- Safety stock optimization, supplier performance tracking, cost monitoring, and work order prioritization are perfect AI applications.
- MRP continues managing the transaction layer while AI provides the intelligence layer.
They’ll watch leading indicators, not lagging ones
When tracking indicators, remember:
- Service levels and inventory turns tell you what already happened.
- Forecast accuracy, supplier on-time delivery trends, and WIP aging tell you what’s coming.
Track both, but act on indicators that give you time to respond.
It’s time to make MRP work without replacing it
Your MRP system handles what it was designed to handle: Production scheduling, material requirements, and work order generation. That foundation matters.
What it can’t do is predict supplier delays, optimize safety stock dynamically, detect cost trends, or help you prioritize when reality diverges from the plan. That’s where AI-enhanced planning platforms like Netstock make the difference.
Netstock works with your existing systems, integrating with ERPs and pulling MRP data to generate insights and recommendations that improve planning without disrupting production. More than 15 years of manufacturing experience globally means the platform understands the challenges you face and delivers solutions that work in real manufacturing environments.
