Leveraging Artificial Intelligence in Operations Management

Owners and leaders of small to midsize businesses frequently find it challenging to spend enough time working ON their businesses because they too often find themselves working IN the business. Until now, few tools were available to help managers find the time to strategize and pursue business improvements. Artificial Intelligence (AI) can give the gift of time to manufacturing teams everywhere and make running a business easier. Learn about four key AI applications in manufacturing below:

1. Machine Monitoring/Upkeep
   Overview: Many manufacturers have machines that can shut down manufacturing operations in the event of mechanical failure. The tens of thousands of dollars required to repair such machines, plus the loss of productivity during a breakdown, can hamper a company’s ability to meet customers’ expectations. Even if they have a standing agreement with another company to provide the process step as an outside service, the transportation and additional costs can affect the company’s bottom line.
   
   Impact: AI-powered predictive maintenance relies on sensors and machine learning algorithms to analyze data from machinery. Additionally, AI provides superior analytical capability to determine machine conditions and tirelessly examine data to recognize changing performance. This data-driven approach minimizes unplanned downtimes, significantly reduces maintenance costs, and improves overall operational efficiency. According to industry reports, implementing a real-time predictive maintenance program can reduce maintenance costs by up to 30% and cut downtime by 50%.
   
   Availability: With the increasing affordability of IoT devices and the growing availability of machine data, real-time machine monitoring is a “now” proposition. Its ability to integrate with existing systems and provide real-time insights makes it an essential tool for manufacturers seeking to stay competitive. The important thing for company leadership is to understand which machines are critical enough to require investment. Where redundancies and additional capacity exists the return on investment may depend solely on reducing repair costs or spare parts inventory dollars.
   
2. Quality Control and Inspection
   Overview: AI in quality control and inspection involves using machine vision and deep learning algorithms to detect defects, inconsistencies, or anomalies in products during the manufacturing process. But perhaps the greatest advantage is that, once trained, the AI-driven inspection is continuous— it never gets tired, distracted, or bored, making the 100% inspection much more than 80% effective.
   
   Impact: Traditionally, quality control has been a labor-intensive process prone to human error. AI automates this task with high precision, ensuring that only products meeting the highest standards reach the market. AI-driven quality control enhances product reliability and reduces waste and rework, contributing to cost savings and higher customer satisfaction.
   
   Availability: The rise of high-resolution cameras, advanced imaging technology, and more sophisticated AI algorithms is driving the rapid adoption of AI in quality inspection. Training of AI vision systems can be a painful and expensive experience, but as AI capability increases and cameras become less expensive, applications will increase. Eventually, AI will be used to create the defect samples to reduce the implementation time and cost. When it comes to quality control that ensures customer satisfaction, the technology is ready to apply and is becoming a great investment.
   

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