Artificial intelligence (AI) and machine learning have significantly transformed various industries, including manufacturing.  These technologies excel in spotting trends or abnormalities within vast quantities of data. That capability encourages many leaders to explore addressing potential or known bottlenecks with AI-driven analyses. What are the primary advantages of this approach, and how have companies benefited?

Finding Production Bottlenecks With AI and Machine Learning Analyses

Success in the manufacturing industry requires efficient processes. Numerous hidden or obvious issues can interfere with that goal, but machine learning and AI can help by pinpointing areas for improvement.

Revealing Sustainable Growth Possibilities

Most manufacturing leaders realize growth is essential for continued competitiveness in a challenging marketplace. Bottlenecks can occur if they try to expand operations too quickly without first addressing the main inefficiencies.

According to a May 2025 study, 58% of manufacturing leaders revealed their primary reason for using AI was to boost operational efficiencies. However, 65% believe AI will primarily serve as a growth driver by 2027. Those takeaways demonstrate how the technology can help users overcome bottlenecks initially, leading to increased productivity that facilitates expansion later.

Preventing Unplanned Downtime

Due to the role of specialty equipment in many manufacturing operations, unexpected outages can cause gigantic bottlenecks that may last for days or weeks and prove extremely costly.  Sensors equipped with machine learning target issues by detecting unusual characteristics that technicians may overlook before downtime occurs. They alert people to potential problems such as elevated temperature, allowing them to react in time.

One snack food company fed more than 300 million hours of machine sound data into algorithms. Decision-makers intended to use them to flag abnormal wear, new vibrations and other matters requiring immediate attention. This application also collects overall machine condition insights, enabling plant managers to identify when assets may fail and schedule repairs or replacements before they do.

Optimizing Production

Most manufacturing facilities comprise numerous components that must work together to achieve results. Bottlenecks can occur at various levels, resulting in consequences that are not necessarily within the control of those affected. Strategically applied instances of AI and machine learning enable better oversight and faster responses, preventing unwanted outcomes.

AI platforms can reveal the primary reasons for delays on specific assembly lines, for example, helping decision-makers identify the most effective ways to accelerate processes without compromising quality.

One study found that nearly half of manufacturing leaders intend to bring AI and machine learning into their operations by 2026. They can begin by identifying the most time-consuming or error-prone processes and having the technologies analyze those to find the most meaningful ways to improve. That approach enables data-driven conclusions rather than guesswork.

Boosting Productivity

Facilities that use lean manufacturing methods identify sources of waste, which can include process-related delays. A machine-related limitation may require someone to wait several seconds before proceeding with a specific task. The stoppages accumulate, resulting in substantial amounts of wasted time in a typical workday. In other cases, inadequate training hinders productivity, either because employees lack the necessary skills or confidence to perform their jobs effectively.

AI and machine learning algorithms can identify the primary reasons for below-average productivity and suggest how extensively employers can address the issue. Actions such as hiring more team members or instituting regular training sessions are among the widely used options.

Manufacturers Enjoy Gains With AI and Machine Learning

Decision-makers must devote time and effort to their selected technology integration plans and recognize that it may take years to realize the full benefits. Most individuals achieve the best results when they set key performance indicators and track those metrics throughout the process. That approach helps them see when things are going well and if they need to develop new strategies to overcome temporary obstacles.

Which enhancements have brands achieved so far?

BMW Tightens Quality Control

At one BMW plant, a completed vehicle leaves the assembly line every 57 seconds, with each one built to customer or market-related specifications. This production pace results in approximately 1,400 vehicles made every day.

AI supports quality control by generating an individual inspection catalog for each automobile that contains details about equipment variants, models and production data. It then sends the information to a smartphone app with voice-recording capabilities, enabling employees to conduct thorough and accurate inspections and easily record relevant findings.

Leaders deployed this solution while attempting to make quality control more efficient, faster and increasingly reliable. The AI app supports greater digital transformation within the brand.

HelloFresh Targets a Menu Card Bottleneck

Meal kit delivery company HelloFresh operates eight brands across multiple countries, resulting in the production of hundreds of millions of meals every quarter. Updated menus and easy-to-follow recipe cards are hallmarks of a brand built on the promise that people can cook restaurant-quality meals at home, even with limited skills.

However, brand representatives reported that creating the instructions often took days or months, limiting the company's ability to respond to changing audience preferences or other variables.

It now uses generative AI to shorten that process to hours. Developers trained the system on 15,000 recipes accumulated over 14 years, resulting in it using wholly proprietary data. The new process automates many steps, including formatting, layout and design, while keeping humans in the loop to develop recipes and check the accompanying images.

Executives believe this revamped approach will allow the company to offer a broader range of meals and incorporate market-specific considerations.

Tackling Bottlenecks to Enhance Operations

Manufacturing slowdowns come from many sources. That diversity makes it challenging for executives to identify the top causes without help. Well-trained AI and machine learning algorithms can analyze the data and recommend suggestions for improvement. Decision makers can achieve the best results by clearly defining their primary goals and creating a detailed plan to meet them.

Lou Farrell is the Senior Editor at Revolutionized, and has several years of experience covering cutting-edge topics in the fields of Robotics, AI, and Manufacturing. He enjoys writing more than almost anything else, and has an intense passion for sharing his knowledge with anyone he can.