BrazilAI in the production process: Use cases and getting started
AI has the potential to add unprecedented value to the manufacturing landscape. Experts from Emixa share three use cases of AI in production and share a 3-step approach for getting started.
The era of artificial intelligence (AI) is disrupting many industries with the promise of dramatically increasing efficiency and boosting decision-making. In the manufacturing industry, AI can provide an edge in several part of the value chain – here are three examples that related to the production process:
Prevent Downtime with Predictive Maintenance
Downtime is not only inconvenient but also costly. Predictive maintenance can help improve the maintenance equation. By using smart sensors in machines, companies can continuously collect and analyse performance data. AI identifies patterns in machine data and predicts when specific components are likely to fail. Instead of waiting for breakdowns or following rigid maintenance schedules, companies can prevent issues before they occur.
Resolve Downtime Faster with AI-Driven Manuals
Can maintenance itself be made smarter with AI? Absolutely. By applying Generative AI to all technical documentation, operators or technicians can find a solution to a problem within seconds. Simply by chatting with – often – thousands of manuals, manufacturing companies can reduce costly downtime.
Smarter Production Planning
AI excels at analysing complex variables such as order volumes, machine availability, and workforce capacity. It enables smart – and even real-time – optimisation of production planning. The result? Manufacturers can continuously maintain productivity, even under constantly changing conditions.
Getting started
Harnessing the power of AI is easier said than done, with many AI implementation projects hitting bottlenecks or suffering from well known caveats such as misaligned expectations or low adoption. In our AI projects with clients, we guide them through three key high-level steps to ensure a path to success.
1) Get Your Data in Order
It all starts with data. Today’s AI models are incredibly smart, but they are not magical. AI needs reliable business data to make meaningful predictions and analyses. The first step is to setup a solid data infrastructure to extract, transform, and load all data efficiently.
2) Define Your Focus
AI is not an end goal in itself; it should add value to the business. Identify which business processes will benefit the most from AI. Where can downtime be reduced? Where are repetitive tasks that could be automated? Where are the opportunities to serve customers faster?
3) Start Experimenting
While AI has great potential, the recommendation is to start small. Work on ‘use cases’ with a clear goal and business case. Develop a proof of concept in a short sprint to quickly determine if an idea has real potential. Working with AI in a pilot or controlled environment will enable a company to learn and gradually build its AI footprint in a proven manner.
Conclusion
The value of AI in manufacturing is evident – a research report from Research & Markets even estimates that the industry will grow at a CAGR of over 50% between 2024 and 2030 to a total of $203 billion by 2030. Among the key drivers of growth in the production domain, AI-driven systems can help companies significantly improve productivity, cost savings, and operational agility.
