While many organizations are still exploring and piloting AI, early adopters in manufacturing are already seeing measurable gains. From smarter forecasting to fewer product defects and more optimized inventories, AI is reshaping how manufacturers operate.
In late 2024, APQC surveyed 2,500 organizations to understand how AI is impacting performance on key manufacturing measures. The results are clear: Organizations that use AI extensively in manufacturing report significant improvements in forecast accuracy, product quality, and inventory management. Whether you're just beginning the AI journey or scaling up across plants, here’s where AI is delivering the most value in manufacturing and how you can start capturing it.
Unpacking the benefits of AI in manufacturing
APQC
Figure 1 shows the benefits that AI helped respondents to achieve in manufacturing. The most dramatic shifts were in the areas of forecast accuracy, product quality, and inventory management.
AI’s edge in forecasting
Demand forecasting has long been a challenge for manufacturing and supply chains more broadly. Overestimating demand means excess inventory and higher carrying costs, while underestimating could mean lost revenue and dissatisfied customers. Either way, the impact to an organization’s bottom line can be substantial even with slight variations in accuracy.
Unlike conventional forecasting tools that rely heavily on historical averages and linear models, many AI systems can incorporate real-time data from across the supply chain, from demand signals to inventory levels, supplier performance, geopolitical developments, and more. Leveraging this real-time intelligence and other benefits of AI helped respondents increase their forecast accuracy by a median 30 percentage points.
How AI reduces defects and downtime
Organizations that use AI in manufacturing saw their product defects decrease by a median 25 percentage points. Technology available in the marketplace today uses machine learning and computer vision to identify patterns and abnormalities in real-time and can even detect microscopic defects that aren’t visible to humans or traditional systems. Faster and more accurate in-process inspections mean lower waste, lower rework costs, and fewer expensive recalls.
AI also enables predictive maintenance by analyzing sensor data to anticipate equipment failures before they happen. This helps to minimize unplanned downtime and keep production on track.
AI’s role in inventory optimization
Effective inventory management requires a balancing act. Organizations need enough inventory to meet demand, but too much will mean increased carrying costs and other risks associated with excess inventory. AI can help strike this balance in manufacturing by more accurately:
» Predicting demand and automating order fulfillment
» Analyzing and learning from internal and external sources
» Using live data, supplier and production lead times, and demand shifts to calculate reorder timing
» Predicting the shelf life of perishable products based on factors like temperature and humidity, helping to minimize waste
Features like these helped respondents reduce excess inventory by a median 20 percentage points.
Why broad AI adoption matters
APQC
Many respondents are using AI across a majority of their plants. For example, respondents use AI to optimize forecasting and manage quality across about two-thirds of their plants (Figure 2). These two areas also represent two of the Top 3 areas where companies have seen the most benefit from AI in manufacturing.
Only 55% of plants extensively use AI for inventory management. The 45% of organizations that don’t are missing an opportunity to reduce their excess inventory.
Take action
As the benefits of AI become more solidified, it’s likely that adoption will grow across more organizations and manufacturing plants. Don’t wait until then to start getting the benefits of AI in manufacturing.
