Leveraging AI to Transform Manufacturing Operations
Manufacturing is among the industries that could benefit the most from the AI revolution. With the proper data structures and AI solutions in place, manufacturers can turn data from their machines, plants, people and facilities into actionable insights that inform business decisions across their organization.
Manufacturers that embrace AI early are gaining a competitive advantage and driving innovation within their organizations. When deployed effectively, AI solutions help lower costs, enhance throughput, reduce cycle time, and even pinpoint new product and service lines to meet demand.
Automate processes with AI
Some of our manufacturing clients are already using AI to automate repetitive tasks that are well-suited for machine execution. For example, computer vision and image recognition systems using AI can automate quality inspections on assembly lines. AI models can identify defects from camera feeds faster and more consistently than humans, significantly reducing inspection times.
AI planning tools can optimize production line processes like assembly and packaging, enhancing metrics such as units per hour and defect rate. Sensors provide real-time data to maximize equipment efficiency and throughput, closely monitoring parameters like machine uptime, cycle time and yield rate.
Warehouse logistics and material handling functions, including inventory management, order fulfillment and transportation routing, can utilize AI for predictive demand forecasting and optimized scheduling.
Customer service tasks like responding to FAQs can be automated through AI chatbots and virtual assistants to reduce costs while improving the customer experience.
Optimize equipment uptime with predictive maintenance
Optimizing servicing schedules, reducing cycle times and eliminating unexpected breakdowns directly benefits financial performance through improved operational efficiency and productivity. By analyzing vast amounts of sensor data using machine learning models, AI systems can detect anomalies and predict failures for production equipment. This helps avoid costly unplanned downtimes and improve overall equipment effectiveness.
As one example, John Deere implemented AI-powered predictive maintenance on critical machines, among other AI innovations, resulting in increased equipment uptime. We're also seeing manufacturers automate routine inspections and monitor with AI to spot defects early and schedule repairs proactively.
Prepare your organization for AI
AI solutions must be thoughtfully developed and implemented, and manufacturing organizations must be prepared to fully exploit AI capabilities. But what does that look like?
First, identify business problems that can be improved or solved with AI. Manufacturers should start by identifying repetitive, rule-based tasks for AI to automate, then scale implementations with cross-functional teams.
This requires bringing together the right teams, including IT, OT and data science teams, to serve as the center of excellence. A key role of this group is to identify, prioritize and ultimately execute these AI strategies safely. This can involve changing the organization's mindset to embrace AI and speaking with line-of-business leaders to understand safety considerations.
It is also critically important to ensure the organization's data is in order.
Why data governance matters
Data is the lifeblood of AI. Investing in strong data governance ensures your data is standardized, consistent and accessible for AI models and applications. This removes a major barrier that prevents many manufacturers from implementing successful AI strategies. With governance in place, any process or system across the organization can easily share its data for analysis without restrictions.
Establishing a centralized data platform also allows manufacturers to consolidate disparate data sources that have traditionally been siloed. This aggregation enables manufacturers to take full advantage of cutting-edge AI technologies that require large, diverse datasets for deep learning.
Do not wait to embrace AI
AI cannot replace human workers and human knowledge — especially institutional knowledge that is often undocumented. However, AI tools can supplement workers' expertise, create efficiencies in employees' day-to-day activities and reduce new hires' time to proficiency.
A well-governed, consolidated data environment is foundational for manufacturers seeking competitive advantages through advanced AI applications like predictive quality inspection, equipment monitoring, supply chain optimization and more. The cost of inaction is missing out on significant efficiency gains and new revenue opportunities that data-driven AI provides. Those who invest early in governance and technology will transform operations and lead their industries.
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This report is compiled from surveys WWT Research conducts with clients and internal experts; conversations and engagements with current and prospective clients, partners and original equipment manufacturers (OEMs); and knowledge acquired through lab work in the Advanced Technology Center and real-world client project experience. WWT provides this report "AS-IS" and disclaims all warranties as to the accuracy, completeness or adequacy of the information.