November 9, 2022
Machine learning and artificial intelligence are now the main drivers of manufacturing digital transformation. These technologies are poised to transform the majority of manufacturing processes including equipment maintenance, inventory management, supply chain, product design, and quality control.
In this article, we discuss ten use cases, the technology’s impact on the industry, and how it can help organizations become more efficient. Our machine learning consultants outline a step-by-step ML implementation roadmap and identify the common adoption pitfalls.
While many modern manufacturing firms employ various data analytics methods, the majority of them are rule-based, meaning that these tools make decisions based on a predefined set of rules and thresholds. When key indicator values differ from the expected ones, such systems automatically report deviations to operators and engineers, who then use their expertise to figure out the causes and come up with solutions. In the meantime, machine learning systems can detect issues, find their causes, and suggest solutions automatically without human involvement.
Take a look at the most telling statistics of machine learning in the manufacturing industry:
growth rate of AI in manufacturing from 2019 to 2027
Fortune Business Insights
KPIs for companies which actively adopt machine learning in manufacturing
McKinsey & Company
of companies expect to increase their efficiency with digital technologies
Chart 1: Average improvement through machine intelligence, by KPI
Data source: mckinsey.com-Toward smart production: Machine intelligence in business operations, 2022
Chart 2: Nine out of ten companies are investing in digital factories
Data source: pwc.de-Digital Factories 2020: Shaping the future of manufacturing
Computer vision is a field of artificial intelligence that allows computers to draw insights from visual information. Computer vision in manufacturing is used to automatically conduct a quality inspection of products at every stage of the production process, guide robots during product assembly, and ensure staff safety by proactively monitoring production facilities.
While many machine learning use cases are similar from one manufacturing sector to another (predictive maintenance, product quality control, inventory management), some of them have sector-specific use cases, which we outline below:
Use case definition
Data cleaning and formatting
Model validation and deployment
Continuous model retraining
Given an avalanche of potential use cases and benefits that machine learning and artificial intelligence can bring to manufacturing organizations, one may wonder why this technology is not adopted by more companies. In their 2021 report AI Adoption in the Enterprise, O’Reilly Media found out what aspects of the technology’s implementation proved particularly challenging.
In the manufacturing context, seemingly every use case is the right one for ML implementation. Every manufacturer would love to know the exact reasons for machinery failure and offload routine tasks like inventory management to the algorithms. Successful use case definition boils down to addressing real business needs. In other words, machine learning use cases have to bring tangible financial impact. Then, when you think that you've selected the right use case, it's always a good idea to start small. Deploy that machine learning tool on a single factory floor in a single production line before expanding to other facilities to see if the juice is worth the squeeze.
Get machine learning consulting services from certified machine learning experts and data scientists to reveal hidden insights and automate business processes.
Predictive analytics consulting will help you foresee customer behavior and market demand, predict fraud and customer churn, forecast sales and financial risks.
Explore the top use cases of machine learning in retail and find out what benefits this technology can bring to your business.
Discover how wealth management companies use AI to generate more leads, automate back-office tasks, improve customer relationships, and improve bottom lines
Learn how financial institutions can apply predictive analytics in finance to minimize risks, decrease operational costs, and improve their bottom lines.