Staying ahead of the curve is challenging for any business — especially as economic uncertainty persists, and customer standards rise. But from what we’re seeing in the market and with clients, it’s primetime for manufacturing when it comes to leveraging AI and analytics to get ahead.
Let’s break down the biggest hurdles and strategic entry points into AI and analytics in the manufacturing space.
Top challenges for manufacturers: What’s driving AI in manufacturing?
- Ensuring safe operations: Safety is a serious concern for any manufacturer (especially in high-risk environments). But monitoring and detecting hazards, alerting staff, and preventing accidents and injuries can be a huge undertaking.
- Operational efficiency: Speed and efficiency are key drivers in the manufacturing space. With many processes and tasks still largely clunky and manual, leaders are looking for new ways to operate the business.
- Customer experience: Customer satisfaction and loyalty are essential for any business, especially in a competitive market like manufacturing. To meet customers where they are, speed and quality have become top priorities for leaders.
- Competition and economic pressures: Manufacturers face constant pressure from competitors, regulators and market fluctuations. Outdated processes prevent manufacturers from scaling and being agile.
- Visibility and monitoring: Manufacturers need to have a clear view of their operations, assets and performance. Smarter ways of collecting and analyzing data, and generating insights, are starting to separate stragglers from front frontrunners.
- Crisis response time: Manufacturers need to be able to respond quickly and effectively to any crisis, whether it’s a natural disaster, a cyberattack or a pandemic.
- Balancing IT/OT: Manufacturers need to balance their information technology (IT) and operational technology (OT) systems — which often have different goals, standards and cultures.
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Top opportunities for AI in manufacturing in 2024
Some of the opportunities that AI can create for manufacturers in 2024 include:
- Agile response and decision-making: With AI at the edge, manufacturers can leverage the power of actionable insights from data and even set up automated decision-making processes. This can help them respond faster and more accurately to changing situations and customer needs.
- Proactive monitoring: Enhanced monitoring through AI solutions can mean safer operations and quicker response during a crisis. Additionally, having access to better monitoring means disruptions are minimized and business can keep flowing.
- Maintenance and processes: With the help of AI and automation, repeatable processes can be handled automatically. Furthermore, maintenance monitoring and management can be streamlined and simplified. This can help reduce costs, improve quality and offload staff intervention.
- Converging IT/OT: Rather than viewing these as two separate aspects of manufacturing business, organizations can strategically align them to empower growth. With the help of data, automation, and AI, they can work in harmony and their combined value can be realized.
What to look for in AI solutions for manufacturing: 6 questions to ask
If your manufacturing organization is considering AI for any of the above challenges or use cases, brute forcing change without considering your options and AI strategy can lead to backpedaling, higher costs and even greater complexity. So if you’re looking into AI solutions, here are some key questions to ask yourself:
- How holistic and deep does my provider’s expertise go? AI deployments involve many moving parts, and a siloed approach typically leaves gaps in your strategy that can slow the business down later. End-to-end expertise in manufacturing-specific technology solution means extensive knowledge and skills around the hardware, software and services components of AI.
- Does the solution bring together trusted technology brands? Providers that have strong relationships with leading technology vendors are more likely to bring effective, robust and reliable solutions to the table.
- How much are we able to customize our solution? Your business is unique — and one-size-fits-all approaches won’t give you the ROI you’re looking for.
- Can we integrate the solution seamlessly? Technology environments are complex and layered. Avoiding a chaotic integration and implementation process requires skillsets across solutions integration, and proven methodologies and frameworks.
- Does our solution provider have a proven track record? You want a provider that has a history of successful AI deployments across various industries and use cases, and that can demonstrate their ability to deliver results and value. Asking potential providers for client stories can help your decision-making process.
- Does the solution provider use a data-driven approach? The most effective solutions providers will leverage data and analytics to inform the decisions and actions around your solution. You’ll know your provider is effective when you see them use data to help you optimize everything from your security posture to your shrinkage reduction strategy.
