Optimizing Manufacturing Efficiency with AI-Powered Production Planning at Peak

UI visuals have been intentionally removed to avoid copyright issues. However, the process, challenges, and impact remain unchanged.

The Challenge

Manufacturing is a delicate balancing act.

Producing the right amount at the right time is the difference between maximized efficiency and operational waste. Overproduce, and you’re left with excess inventory, high storage costs, and capital tied up in unsold stock. Underproduce, and you risk stockouts, delayed shipments, and lost revenue.

For decades, production planning has relied on manual processes, static rules, and outdated forecasting models—systems that can’t keep up with today’s supply chain volatility. The challenges are well known:

• Demand unpredictability leads to stockouts or overproduction.
• Limited warehouse space makes excess stock costly to manage.
• Supply chain disruptions create production bottlenecks and inefficiencies.
• Siloed teams—procurement, logistics, and production—struggle to stay aligned.‍

For many manufacturers, planning remains reactive and labor-intensive, requiring constant human intervention to adjust for real-world fluctuations. Peak set out to change that.

The goal? Could AI create production plans that are dynamic, precise, and aligned with real-time demand?

‍

My Role

As a Product Designer at Peak, I was responsible for:

1. Leading the design strategy for AI-powered Production Planning.
2. Creating an intuitive experience that simplified AI-driven decision-making for supply chain managers.
3. Ensuring seamless integration with inventory management, procurement, and supply chain workflows.
4. Collaborating with engineers and data scientists to translate AI-powered insights into real-world action.

This wasn’t about designing another planning tool—it was about rethinking how manufacturing operates in an AI-driven world.

‍

Understanding the Problem

To design an AI-powered production planning system, I worked closely with supply chain managers, production planners, and logistics teams to understand their challenges.‍

‍

Key Insights from Research:

1. ‍Most production planning tools were too broad—they grouped products into categories instead of optimizing at the SKU level.‍
2. Manufacturers needed long-term visibility—without clear production schedules months in advance, procurement teams struggled to prepare.
3. ‍Planning was reactive, not proactive—teams constantly adjusted schedules after disruptions instead of anticipating them.‍
4. Cross-team collaboration was missing—sales, procurement, and production lacked a shared, real-time view of production needs.
   

These insights shaped our approach: an AI-driven system that connects production planning with real-time demand, ensuring businesses always manufacture the right products in the right quantities.

‍

Designing AI-Powered Production Planning

Traditional production planning tools often focus on rules-based decision-making—if inventory drops below a certain threshold, produce more. But this approach doesn’t consider real-world constraints like production capacity, lead times, or fluctuating demand.

Our AI-driven system had to be:‍

• ‍Precise – Making SKU-level recommendations instead of broad estimates.‍
• Flexible – Allowing manufacturers to configure plans based on their constraints.‍
• Collaborative – Giving all teams a single, real-time view of production needs.

‍

Key Features & Design Decisions

Intelligent SKU-Level Forecasting

Instead of broad estimates, AI predicts demand at the SKU level, dynamically adjusting forecasts based on real-time data.

Impact: Manufacturers produce exactly what’s needed—reducing waste and preventing stock shortages.

‍

Customizable Planning Windows

Businesses needed flexibility—some planned weekly, others months ahead. We designed an interface that allowed users to set planning horizons based on their production cycles.‍

Impact: Procurement and logistics teams could coordinate raw material sourcing and labor planning efficiently.‍

‍

Real-Time Adjustments & Demand Sensing

Market conditions change fast—our AI continuously monitors demand fluctuations, supply chain disruptions, and capacity constraints, adjusting recommendations in real time.

Impact: Businesses could respond instantly to unexpected changes instead of relying on static monthly plans.‍

‍

Unified Cross-Team Dashboard

Planning isn’t just a production issue—it involves sales, procurement, logistics, and finance. We designed a centralized dashboard where all teams could align around a single source of truth.

Impact: No more conflicting priorities—everyone works off the same AI-driven recommendations.‍

‍

Seamless ERP & Supply Chain Integration

AI-driven production plans integrate directly with ERP systems, inventory platforms, and procurement tools.

Impact: Businesses could execute AI-driven plans without disrupting existing workflows.

‍

Testing & Iteration

To refine the system, we ran usability tests with supply chain managers and production planners. Their feedback led to key design improvements:

• ‍Fine-Tuned Forecast Adjustments – Users wanted more control over AI recommendations. We introduced manual overrides and confidence scores to show prediction accuracy.‍
• Multi-Site Planning – Manufacturers with multiple locations needed site-specific production schedules. We redesigned the dashboard to support multi-site views.‍
• Scenario Modeling & What-If Analysis – Users requested the ability to simulate different production scenarios before committing. We built a simulation tool to test AI-generated recommendations.

These iterations ensured AI wasn’t just automating decisions—it was helping businesses make better ones.

‍

‍

The Impact

• ‍50% reduction in overproduction and inventory holding costs.
• 30% increase in production efficiency.
• 90% faster response time to supply chain disruptions.

‍

For manufacturers, this translated to:

1. ‍Higher service levels—ensuring customer demand is met ahead of competitors.‍
2. Lower operational costs—by reducing waste and optimizing inventory.‍
3. Greater agility—allowing businesses to adjust production instantly as market conditions changed.

‍

Lessons Learned

AI must be adaptable.

Every manufacturer operates differently. By making AI forecasts configurable, we ensured businesses could tailor recommendations to their specific needs.

‍

AI doesn’t replace humans—it enhances decision-making.

Users wanted control over AI suggestions, so we designed features that allowed manual adjustments and scenario testing.

‍

Seamless integration drives adoption.

AI planning had to work within existing supply chain systems, not replace them—ERP integration ensured easy implementation.

‍

‍Final Thoughts

Production Planning at Peak wasn’t just about automating forecasting—it was about helping manufacturers plan smarter, move faster, and make better decisions.‍

By eliminating inefficiencies, improving service levels, and enabling real-time adaptability, we designed a system that helps manufacturers future-proof their operations and gain a competitive edge.

‍

Smarter production. Greater efficiency. AI-powered manufacturing for the future.