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Manufacturing Technology Insights | Thursday, November 07, 2024
Implementing AI and ML necessitates specialized knowledge, and manufacturing organizations must invest in data scientists, analysts, and other algorithm and automation professionals. However, given the rapid rise of AI across industries, it can be challenging to locate people with the necessary knowledge to fill these roles.
Fremont, CA: AI can help firms enhance their current manufacturing processes, but supply chain management must also be aware of the possible problems associated with adopting AI in manufacturing.
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Algorithms, automation, and machine learning (ML) have the potential to help firms cut operational costs, boost efficiency, and improve product quality. However, integrating AI with other systems and recruiting personnel with the necessary AI skills may be problematic.
However, the use of AI in production can potentially cause issues. Supply chain executives should be aware of these risks so that they can take necessary actions.
Poor Data Quality
AI and ML require access to vast amounts of high-quality data. Therefore, their results will be unreliable if the company's data contains low-quality information.
Employee Job Security Concerns
Company executives should comprehend employees' anxieties about being replaced. Employees may be unwilling to use the company's AI technology, which could cause delays. Supply chain management should collaborate with other company leaders to prepare for these concerns by being open and honest about AI's possible impact on the organization and providing reskilling and training options for affected employees.
Limited Access to Talent
Implementing AI and ML necessitates specialized knowledge, and manufacturing organizations must invest in data scientists, analysts, and other algorithm and automation professionals. However, given the rapid rise of AI across industries, it can be challenging to locate people with the necessary knowledge to fill these roles.
A similar issue might affect an organization's current manufacturing workforce, as employees may lack the necessary knowledge and abilities.
Lack of System Integration
AI manufacturing systems must work with other technologies to improve industrial operations. Legacy systems are popular in industrial organizations for various reasons, including unknown ROI for upgrades and the cost of introducing newer technology; however, AI may be unable to interact with older systems.
Trying to Do Too Much Too Quickly
Many manufacturers are keen to incorporate AI swiftly to capitalize on possible benefits and strengthen their competitive advantage. Unfortunately, doing too much too quickly might lead to poor implementation and less-than-ideal results.
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