Machine Learning in Industry: Predict Demand and Plan Your Operation
The revolution promised by Industry 4.0 necessarily involves smarter use of data. In addition, it involves the benefits brought by machine learning and automation. Therefore, learn more in this article
Moreover, machine learning has already changed daily life in production environments. A study by Fortune Business Insights projects strong growth for the technology. In 2022, the global market was estimated at US$ 21.1 billion. The projection points to US$ 209.9 billion in 2029. Therefore, the pace of adoption remains high.
“Machine learning is a subgroup of artificial intelligence. It is the method that teaches computers to learn from algorithms or data, imitating the way humans do. The adoption of artificial intelligence and machine learning is increasing across several industry sectors. Examples include healthcare, automotive, and retail, among others,” the study says.
But what are the reasons behind this revolution? The Internet of Things has transformed agribusiness, as we explained in this article. Likewise, machine learning is industry’s bet to improve predictability and operational planning. In practice, this is the technology that takes manufacturers to the concept of Industry 4.0.
Indeed, the ability to learn and perform tasks automatically changed the game. The resulting applications take industry to the next level. For example, they affect costs and internal processes, as well as competitiveness within the segment. In addition, they change the relationship with customers, regardless of a B2B or B2C operation.
How can machine learning impact industry?
Therefore, the possibilities for using machine learning in industry are countless, depending on the segment. Another key point is the company’s relationship with technology. The more strategic it is, the greater the chances of delivering the expected results. Ultimately, the focus is on automation.
Likewise, applying this technology became a matter of survival for factories. After all, the volume of data is now impossible to manage by hand. Among the most common possibilities of machine learning in industry are:
1 – Demand forecasting
First of all, the focus of machine learning is to use the industry’s historical data. From there, it anticipates future business demands. In other words, performance in earlier periods feeds the projection of scenarios. Consequently, this opens the possibility of demand forecasting. The package includes inventory management and the prediction of future results.
Additionally, machine learning connects different data sources. As a result, controlling inventory levels and product availability becomes easier. In this way, you prevent items from running out on important seasonal dates. For example, that matters at Christmas, on Black Friday and on Mother’s Day.
2 – Route planning
In a country of continental dimensions like Brazil, logistics plays a fundamental role for a manufacturer. Becoming smarter in this area ensures spending optimization and keeps costs under control. In addition, it maintains competitiveness in the sector, with safe margins for the business.
In short, succeeding in route planning is often a challenge. This is especially true in large cities and in the so-called last mile. That is the final stage of delivery. Therefore, the better the planning, the better the cost optimization and consumer satisfaction.
After all, machine learning captures data from previous deliveries and designs routes aligned with business strategies. In addition, the technology helps plan the fleet. The goal is the best cost-benefit ratio.
3 – Predictive maintenance
Moreover, one of industry’s biggest fears is an unplanned stop caused by machine failure. For this reason, predictive maintenance is essential to keep the operation running in a structured way.
In practice, with machine learning in industry, sensors anticipate potential operational problems. As a result, managers gain peace of mind when scheduling interruptions. In addition, many plants now use digital twins to simulate scenarios before the stop. In many cases, a planned stop prevents a far more serious problem.
4 – Quality control
No manufacturing operation runs entirely without flaws. For this reason, machine learning in industry is widely applied in quality control. Through cameras, tests and other automated checks, an out-of-standard product is spotted quickly. As a result, the decision about the batch becomes faster and safer.
5 – Customer Service
Furthermore, many manufacturers sell directly to consumers in the B2C market. For many businesses, the need to manage service for multiple customers is a real challenge. After all, those customers are on many different channels. Therefore, technology allows more efficient and optimized service, while also reducing costs.
Similarly, given this reality, machine learning is also present in customer service. Generative AI assistants are one example. These tools resolve most consumer questions. Moreover, when they detect a more complex situation, they call in a human operator.
6 – Optimizing human work and decision-making
Naturally, one common line of thought about machine learning in industry is that it will take over jobs. However, studies point to a transformation of roles rather than plain elimination. A World Economic Forum report already signalled that direction.
Back in 2020, the Forum estimated that by 2025 some 85 million jobs would shift in the division of labor between humans and machines. At the same time, it projected 97 million new roles emerging from that adaptation, according to the research. Since then, generative AI has accelerated the movement.
Meanwhile, in this new kind of relationship, people focus on more strategic initiatives. Repetitive operations, in contrast, stay with the machines. In this way, teams take part in decisions that fall outside the routine.
Clearly, the potential of machine learning is evident, but the transformation is not as simple as it seems. The technology depends on good data organization. In addition, it requires equipment that enables machine-to-machine communication. Moreover, it demands ERP integration and an IT department prepared for these needs. Ultimately, the IT maturity of organizations will be decisive in this process.