Data Insight • October 4, 2024

23% of Supply Chain Professionals Say AI Can Be Used to Achieve Greater Product Quality. Here’s How.

The potential of AI seems almost limitless as companies see gains as varied as waste reduction and improved efficiency. Amongst the positive outcomes and an area of promise for the near future, at least according to supply chain practitioners? Improved product quality. We share how Colgate, Google Devices, and AB InBev are using AI to achieve exactly this.

Jalen Thibou Avatar
Jalen Thibou
Manufacturing
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The Data

  1. 1

    According to our most recent AI survey, 23% of supply chain professionals believe AI will have the biggest impact on product quality in the next three years.

  2. 2

    9% of implementations in The Zero100 AI Hub – our library of AI implementations in supply chain – are directly related to improving product quality.

  3. 3

    Of this 9%, the most popular implementations enable quality management (20%), process optimization (17%), and improved product development (13%).

Improving Product Quality with AI 

Performance improvements and cost optimization are always top of mind for businesses and amongst cost pressures and inflation, results generally come from a combination of improving product quality and increasing yields. In fact, in a recent Zero100 survey on AI, 23% of supply chain practitioners chose product quality as the area in which AI will have the biggest impact in the next three years.  

We wanted to dive deeper and understand how AI is already being used today to improve quality within operations. Analyzing Zero100’s AI Hub, which currently contains 700+ live AI implementations across The Loop, we found that 9% of those implementations directly relate to improving the quality of products.  

Chart showing breakdown of AI implementations in The AI Hub, further breaking down specific uses within Product Quality. Source: Zero100 AI Hub

The primary AI implementations that increase quality fall into the categories of product development, process optimization, and quality management. 

Below, we share some of these examples in detail. 

How AI Is Increasing Quality 

Product Development: 

AB InBev has incorporated services from both Google Cloud and Microsoft Azure to expand its predictive modeling and analytics capabilities. Currently, Smart Barely, a platform that helps farmers reduce water and optimize fertilizer usage based on historical data, helps to increase yields. Furthermore, the use of AI in the filtration process has given AB InBev a competitive edge on both cost and quality over recent years. 

Process Optimization: 

Hyundai has built a micro-factory for EVs in Singapore that is manned by autonomous robot dogs and robotic arms. It is also monitored by AI. Specialized cells (not production lines) enable over 50% of the manufacturing to be done by robots. This flexible automation means that only 100 people are required to reach the plant's capacity of 30,000  cars per year – workers here can each produce 2-3x more cars than in a traditional factory.  

Quality Management: 

Google Devices supplier FIH Mobile incorporated computer vision into the manufacturing process of printed circuit boards to decrease its escape rate from 40% to 10%. AutoML from Vertex AI (a Google AI model) built custom models to classify images and perform actions based on the data. It also helped FIH Mobile standardize inspection criteria and quality across inspection stations. 

The Takeaway

As you think about your priorities for 2025, evolving your AI strategy requires understanding how it can be used to reduce inputs while optimizing outputs – and the view that there is ample potential for this in terms of product quality is evident. To see this gain, start by auditing your operations and evaluating where the most waste occurs. Then, consider how an AI solution might offer support, looking to our AI Hub for inspiration and doing so in the context of your in-house tech capabilities and/or current or potential partnerships.  

Reach out to us at hello@zero100.com to learn more about our AI frameworks and the Zero100 AI Blueprint, which is how we help companies mold their data-centric supply chain strategies and prioritize AI investments. 

Methodology

Zero100’s proprietary data and analytics are a combined effort between our data scientists and research analysts. We provide data-first insights matched with our own research-backed points of view and bring this analysis to life via real-world case examples being led by supply chain practitioners today.   

For this study, we analyzed 542 supply chain organizations and 714 unique references of brands scaling AI implementations from The Zero100 AI Hub. We also surveyed 312 supply chain professionals, including CSCOs and COOs. Respondents include supply chain professionals from companies with an average company revenue of over $1 billion. Seniority of position ranges from Senior Director to C-suite. Respondents work across all functions and all regions, with the majority based in the USA. 

Further Reading