Driving Efficiency with AI: How SkuCaster Helped a Canadian Auto Parts Maker Optimize Supply Chain, Improve Inventory Forecasting, and Boost Margins by 20%

In the world of automotive manufacturing, supply chain efficiency can make or break profitability. Between fluctuating customer demand, unpredictable economic cycles, and the complexity of managing thousands of part numbers, many auto parts makers struggle to maintain the right balance between stock availability and capital efficiency. Too much inventory clogs warehouses and erodes margins; too little leads to lost sales and disappointed customers.

One Canadian auto parts manufacturer recently faced this very challenge. With a portfolio of tens of thousands of SKUs across multiple product categories, they were caught between costly overstocking and frequent stockouts. Their supply chain leaders were asking a critical question:

“How can we accurately forecast demand at the SKU level, reduce waste, meet KPIs, and still improve profitability?”

Enter SkuCaster — an AI-powered forecasting platform that promised to deliver SKU-level predictions with 94% accuracy. Within a year of adoption, SkuCaster transformed the company’s inventory management, reduced waste, helped them hit every key performance indicator (KPI), and ultimately increased their profit margins by 20%.

Here’s how they did it.

The Starting Point: A Supply Chain Under Pressure

Like many auto parts manufacturers, this Canadian firm had grown rapidly over the years, expanding its product portfolio to meet customer needs. But with growth came complexity:

• Tens of thousands of SKUs spanning fast-moving and slow-moving parts.
  
  

• Highly variable demand driven by seasonal patterns, OEM partnerships, and after-market fluctuations.
  
  

• Inefficient forecasting methods relying on spreadsheets, historical averages, and manual judgment.
  
  

• Excess inventory, tying up millions of dollars in capital and leading to waste when parts became obsolete.
  
  

• Stockouts in critical SKUs, leading to missed sales opportunities and strained customer relationships.
  
  

Despite a talented supply chain team, the tools simply weren’t sophisticated enough to deal with the data volume and variability. Forecast accuracy hovered around 70–75%, leaving plenty of room for errors that translated into millions in hidden costs.

Management set ambitious goals for the coming fiscal year:

• Improve forecast accuracy to 90%+.
  
  

• Reduce waste and obsolete stock by at least 15%.
  
  

• Improve inventory turns and optimize safety stock.
  
  

• Achieve all internal KPIs, including service level agreements (SLAs) and on-time delivery.
  
  

• Increase overall margins by at least 10%.
  
  

The targets were aggressive — but with SkuCaster, they became achievable.

The SkuCaster Approach: AI Meets Supply Chain Complexity

SkuCaster’s value proposition was clear: use AI-driven machine learning models to predict SKU-level demand with up to 94% accuracy. Unlike traditional methods, SkuCaster doesn’t rely on static averages or simplistic regression. Instead, it ingests and learns from a variety of data sources:

• Historical sales and shipment records.
  
  

• Dealer and distributor demand signals.
  
  

• External data such as vehicle registrations, macroeconomic indicators, and seasonal cycles.
  
  

• Inventory and production system data from ERP and MRP systems.
  
  

By leveraging these inputs, SkuCaster builds demand forecasts at the most granular level — the individual SKU.

Key Features that Made the Difference

1. 94% Forecasting Accuracy
   Machine learning models continuously retrain and improve, capturing subtle demand signals that humans or traditional tools miss.
   
   

2. Scenario Planning and Alerts
   SkuCaster integrated with Microsoft Teams and Slack to send real-time alerts when forecast deviations occurred, allowing supply chain managers to adjust production and procurement quickly.
   
   

3. Seamless Integration
   The system connected directly to the manufacturer’s ERP, automating data ingestion and eliminating manual uploads.
   
   

4. Actionable Dashboards
   Supply chain teams could see SKU-level forecasts, safety stock recommendations, and KPIs in real time, empowering them to make data-driven decisions.

5. Open-Source Extensibility
   For transparency and flexibility, SkuCaster provided open-source monitoring tools, allowing IT teams to validate and adapt forecasting models.
   
   

The Transformation: From Guesswork to Precision

The results of the SkuCaster deployment were dramatic:

1. Forecast Accuracy Jumped to 94%

Within the first quarter, forecast accuracy surged from 72% to over 90%, and by the end of the year stabilized at 94%. This was a game-changer. Reliable forecasts meant production schedules, procurement plans, and logistics operations were aligned with actual demand, reducing surprises.

2. Inventory Levels Optimized

SkuCaster’s SKU-level visibility allowed the company to:

• Cut excess safety stock by 18% without risking stockouts.

• Improve inventory turnover ratio by 22%, freeing up working capital.

• Reduce obsolete parts inventory by 15%, saving millions annually.

Warehouses became leaner, carrying only what was necessary while still meeting demand.

3. Waste Reduction

Parts previously sitting idle and eventually scrapped were now ordered and produced in sync with actual market needs. Waste from obsolete parts was reduced significantly, contributing directly to sustainability goals.

4. KPI Achievement Across the Board

With SkuCaster’s accurate forecasting, the company hit or exceeded all KPIs:

• Service levels improved to 98%.

• On-time delivery rates reached 97%, up from 89%.

• Order fill rates exceeded targets for the first time in three years.

5. Margins Increased by 20%

Perhaps the most powerful outcome: by reducing waste, optimizing inventory, and aligning supply with demand, the company saw a 20% increase in profit margins. This exceeded initial goals and gave leadership renewed confidence in scaling operations.

Why This Matters for the Auto Parts Industry

The automotive sector faces unique challenges: thousands of part numbers, fluctuating aftermarket demand, OEM dependencies, and supply chain disruptions from global events. In such a landscape, traditional forecasting methods simply don’t cut it anymore.

SkuCaster demonstrated that AI-powered forecasting can move the needle on every major business outcome:

• Lower costs from leaner inventory.

• Improved cash flow by freeing up working capital.

• Higher customer satisfaction from better service levels.

• Greater sustainability through reduced waste.

• Tangible profitability improvements — in this case, 20% higher margins.

Lessons Learned from the Case Study

For other manufacturers considering AI-driven forecasting, here are key lessons from this Canadian auto parts maker’s success with SkuCaster:

1. Granularity Matters
   SKU-level forecasting is far superior to aggregate-level predictions. Precision reduces both overstock and understock.

2. Data Integration is Key
   Pulling in multiple data sources (internal and external) creates a holistic view that improves forecast reliability.

3. Real-Time Alerts Drive Agility
   It’s not enough to have forecasts — you need live alerts to respond quickly when demand shifts unexpectedly.

4. Forecasting Accuracy Impacts Profitability
   Every percentage point improvement in forecast accuracy translates directly into reduced costs and increased margins.

5. Partnership Accelerates Success
   By working with an experienced partner, the auto parts maker implemented SkuCaster quickly, avoiding long delays and ensuring measurable ROI in the first year.
   
   

The Bigger Picture: AI as the Future of Supply Chain

This success story underscores a larger trend: the future of supply chain management lies in AI-driven, cloud-based forecasting tools. Companies that adopt platforms like SkuCaster gain not only a competitive advantage but also the resilience to withstand market volatility.

For the Canadian auto parts maker, what started as an experiment in improving forecast accuracy became a catalyst for enterprise-wide transformation. Today, the supply chain team operates with confidence, knowing that inventory levels align with market demand, KPIs are consistently met, and profitability is stronger than ever.

Final Thoughts

SkuCaster’s work with this Canadian auto parts manufacturer illustrates a powerful truth: accurate forecasting is the cornerstone of supply chain optimization. By delivering SKU-level predictions with 94% accuracy, SkuCaster helped the company:

• Optimize inventory levels.

• Reduce waste and obsolete stock.

• Improve service levels and KPIs.

• Boost margins by an impressive 20%.

In an industry where efficiency and agility are paramount, these results demonstrate the immense value of AI-driven forecasting. For manufacturers looking to modernize their supply chains and gain a competitive edge, SkuCaster is not just a tool — it’s a strategic partner in achieving operational excellence.