Ever since the first industrial revolution, automation has played an important role in industrial growth. From assembly lines to robotic arms, industries have seen automation in various forms and types. Although leaving everything to the machine in a manufacturing process is still not a realized dream, machine learning does hold the promise of seeing that someday. Probably soon.

Industrial Automation means using intelligent machines to carry out individual tasks without any or minimal human intervention. This decreases the time it takes for completing a task while reducing the risks from human error and reducing manual labor that significantly reduces the cost of production while increasing the production itself.

This transformation results in a significant increase in the revenue directly impacting the growth of a company and the entire industry. The mechanical automation of the late 20th century bore results that were neither expected nor imagined.

However, mechanical automation came to a halt after a peak in its development and although some improvements were being made, nothing of great value seemed to emerge. This was during the beginning of the Internet Age and there was a boom in computing. No one would have expected the kind of impact that computing would have on industrial growth shortly.

The Rise of Machine Learning

Machine learning is a field of computer science that engages statistical techniques to enable computer systems with the ability to learn by themselves. Today’s Machine Learning algorithms are so advanced that they can overcome static program instructions to make data-based predictions that empower companies with decisions without human intervention.

In a survey conducted by O’Reilly Media for MemSQL, 61% of the respondents said that Machine Learning is their companies’ most important data initiative for the year and 74% of them said that Machine Learning would have a significant impact on the industry.

The global spending on AI systems is expected to maintain a strong trajectory as businesses continue to invest in AI-led projects through various software and platforms. According to the IDC Worldwide Artificial Intelligence Systems Spending Guide, the spending of AI systems will reach $97.9 Billion in 2013 which is significantly higher than the spending in 2019 ($37.5 Billion). The forecast for the compound annual growth rate for the 2018-2023 period is going to be 28.4%.

How Machine Learning is impacting Industrial Production

With Machine Learning, industrial production can be made faster and more reliable at reduced costs. This is done by feeding into the system enormous amounts of historical and statistical data that is then collated, analyzed, and used to develop knowledge about the improvement in the production process. It is not a plug-and-play system but rather a detailed and tedious process that takes time but proves worth every second.

The increased speed to market and reduced costs helps companies remain competitive in the market and keep delivering what customers expect and more. Machine Learning can also be used to study and understand customer behavior to understand the needs and wants of the customers and make changes in the production accordingly.

Process-Based Machine Learning

In a process-based machine learning system, the data on how a production line works are sent to the system to be analyzed. This helps the system understand what to expect from the production process. The system then uses this data to teach its algorithm on what to expect from the machines they are monitoring to obtain training data, relying on pattern recognition and inference for developing the ability to make decisions and prediction without programming the machine to do the task.

Some of the ways Machine Learning is transforming the industrial production are:

• Predictive Maintenance

The ability to predict disruption in production in time to take necessary measures helps in reducing risks and improving on the production for maximum results. It allows scheduling of downtime to eliminate the loss of profit, customer base, and data.

• Network and Security Convergence

If the network is not reliable and comes to a halt due to some disturbance, the production stops. With Machine Learning, network and security are handled simultaneously by monitoring both departments at close quarters.

• Smart Manufacturing Digital Design

Artificial intelligence and Machine Learning enables the development of a digital twin to the production floor that helps design and monitor the production more efficiently. Using the systems algorithm and data collected from various sources scenarios are created to train crisis handling.

Six Ways Machine Learning is impacting the Industrial Growth

1. Data Analytics

Through the technologies based on Artificial Intelligence, Cognitive Computing, and Machine Learning, Data Analytics have given new life to actionable insights. The recent push to data-driven tasks and automation has helped move towards an effective product but the major issue is still BigData storage and silos. The computing abilities and data management in the manufacturing and production sectors have taken prime importance.

2. Cloud Computing

The ability to access and store data on the cloud rather than a hard drive helps companies save enormous amounts of storage space and equipment costs. Cloud Computing helps data exchange over the internet rather than physical servers or storage systems that brings ease and convenience to storage for an effective industrial process.

3. Smart Transport Systems

Smart transport systems have disrupted the automobile industry across the world and their impact on Industrial Growth is quite significant. The industry is forecasted to have a greater market share in the coming years as predictions have placed its value at $149.21 billion by 2023 at a CAGR of 14.7% for 2018-2023.

4. Smart Processes Application

Applications that use computer intelligence to draw important information about business processes are known as Smart Process Applications. Across the industry, such apps are either up and functioning or are in an experimental stage. In both these cases, the results are extremely promising. These smart process applications can integrate machine learning techniques with process workflows to make decisions for improving production. These apps help speed up customer responses and quicker end-to-end processes.

5. Smart Manufacturing

Monitoring systems that enhance the controlling and managing capabilities of a production system are part of cloud-based machine learning techniques for smart manufacturing. This system also employs the Internet of Things to collect data through sensors embedded across the production process to check operational status and performance for raising red flags on time and taking necessary actions promptly.

6. Smart Supply Chain

A smart supply chain management system considers several aspects of end-to-end logistics management through automation and other emerging technologies like cyber-physical systems, IoT, Data Analytics, and Cloud. This automation across the supply chain helps is identifying key differentiators and making data-driven decisions to improve the efficiency and effectiveness of the supply chain.

As industries in all sectors move towards digital transformation and there’s an earnestness in companies to digitize their production and manufacturing, there’s a huge demand for effective and time-friendly digital transformation strategies that focus on the key factors of digitization to bring about a faster, more efficient digital transformation.

In addition to the core technologies, strong leadership, engaging work culture, and value chain contribute equally to for the most beneficial digitization, leading the industries towards a higher than expected curve on the growth graph.

Author Bio :- Lily James is a technological enthusiast from Cincinnati who writes for Narwal Inc. She is passionate about Digital Transformation for businesses and keeps a watchful eye on industry trends.