Benefits Of Machine Learning For Manufacturers

Introduction

Artificial Intelligence (AI) and its various subsets benefit tons of fields, but the manufacturing sector seems to be benefitting from AI the most. Major companies worldwide are investing heavily in Machine Learning (ML) solutions across their manufacturing units and witnessing excellent results.

The past few years have been hard on the manufacturing sector due to the pandemic and the war in Europe. Machine Learning (ML) could just be the answer to the woes of manufacturers.

From reducing labor costs and minimizing downtime to enhancing workforce productivity and overall production speed, AI – along with Industrial IoT (IIoT) is steering the era of intelligent manufacturing.

How does AI/ML Help Manufacturing Plants?

Process plants depend on AI to collect data, analyze it, and generate deep insights and predictions that help with better decision-making. ML downsizes massive datasets to detect patterns and trends. These insights then help build models that forecast what may happen in the future. ML enables plants to predict fluctuations in supply and demand. With AI and ML, manufacturing companies can:

• Identify new efficiencies and reduce waste and save money.
• Comprehend market changes and trends.
• Meet industry standards and regulations, enhance safety and minimize the environmental impact.
• Improve the quality of products.
• Detect and eliminate bottlenecks in the production process.
• Enhance visibility into the supply chain and distribution networks.
• Identify the earliest signs of anomalies or failures to cut downtime and repair more quickly.

Next-generation optimization for manufacturers with Machine Learning

The two main use cases of ML in manufacturing are Predictive Maintenance and Predictive Quality & Yield.

Predictive Quality and Yield

Predictive Quality and Yield, or Predictive Quality, is a more advanced use case of Industrial AI. This use case reveals the causes of the recurrent process-based production losses manufacturers face daily. Examples are quality, waste, yield, throughput, emissions, energy efficiency, and more. Essentially losses due to process inefficiencies.

Predictive Maintenance

Predictive maintenance is the more commonly known of the two, due to the huge cost of maintenance issues. This use case uses algorithms to forecast the subsequent failure of a machine/component/system. With this use case, organizations can inform their staff to perform maintenance procedures to prevent failure. However, the alert need not be sent too early to waste downtime unnecessarily.

Benefits of ML for Manufacturers

The following are the main benefits of ML for manufacturers:

Becoming the Best Smart Factory

A smart factory is a highly digital factory that combines AI, ML, cloud computing, and the IIoT with thousands of sensors. The result is real-time data analysis and gathering on an ongoing, super-fast basis.

Enhance your Efficiency

A smart factory means better efficiency. Better efficiency rates are precious for organizations that deal with complex manufacturing, such as those in the automotive, food production, heavy engineering, and plastics industries.

Better Data Management

Modern factories are notable reservoirs of data. Maintenance management is a great example of a function that can put smart data to good use. Industrial maintenance automation can offer automatic, precise insights into equipment failure frequencies and modes. It can also automate the necessary maintenance scheduling.

Improve your Data Security

A huge amount of data also increases the risk of data breaches within production facilities. Thus, there is a need for better data security. ML can significantly enhance data security, specifically within a Zero Trust Security (ZTS) framework. ZTS works on a ‘never trust, always verify, enforce the least privilege policy’. This rigid, uncompromising approach controls access from both inside and outside the network.

Bottom line

By adopting the many use cases for AI and ML, manufacturers can enhance product quality, forecast fluctuations in market demand, and decrease the number of serious incidents. Implementing ML and AI in manufacturing is a continuing process that endlessly delivers value and enhances revenue in the long term.