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What is smart manufacturing?

The Smart Manufacturing Leadership Consortium (SMLC) defines smart manufacturing as “the ability to solve existing and future problems via an open infrastructure that allows solutions to be implemented at the speed of business while creating advantaged value.”

“Smart manufacturing is becoming the focus of manufacturing globally,” according to an Infiniti Research expert. “As it permeates the manufacturing process in the form of smart factories and the adoption of Industry 4.0, it is transforming traditional manufacturing for the better.”

The rapid evolution of technology is leading this new industrial revolution. According to MIT Professional Education “This revolution, based on cyberphysical production systems, challenges the traditional methods of completing operations in the manufacturing sector, making it increasingly dynamic.”

“Smart manufacturing consists of the convergence between techniques used in modern data science and artificial intelligence to create processes that can be used in the factory of the future. But why is it necessary today?”

“Smart manufacturing technology increases efficiency and eliminates points of weakness in the system. It’s characterized by a highly connected, knowledge-enabled industrial enterprise where all organizations and operating systems are linked, leading to enhanced productivity, sustainability, and economic performance.”

Smart manufacturing also makes it possible for manufacturers to use cloud technology to store and use important amounts of data. This data becomes available to be further used in manufacturing applications within a factory or across an entire supply chain.

In the past, this type of data was very hard to get access to or analyze effectively. Today, it allows manufacturers to see the whole picture, make better, informed decisions, and act accordingly.

Gain competitive advantages with smart manufacturing

Smart manufacturing (SM) uses connectivity and real-time access to data to improve manufacturing processes.

Advantages of a smart manufacturing approach

Increased quality: The digitization of processes reduces the chance of human error and failure. It allows you to monitor process and performance to help you increase yield and use resources more effectively.

Lower operational costs with predictive maintenance: Smart factories can predict and resolve maintenance issues better and faster, which can help reduce expensive equipment repairs and avoid disruptions in production.

Higher customer satisfaction: Smart manufacturing provides managers with access to more-precise data, giving them the ability to measure key performance indicators more efficiently and to serve customers better, aligning to their needs in real time.

Significant cost reductions: Better access to supply chain and production data and analytics increases forecast accuracy and reduces waste, helping to reduce costs through proper demand management.

Enhanced productivity: Autonomous machines communicate with each other, generating a lot of data and making new analytics scenarios possible. This data provides real-time insight into production processes, which helps managers adjust efficiency planning and enhance productivity.

Higher employee satisfaction: Access to the most modern technology can attract and retain new talent. Modern technology also reduces human error, which can mean employees have to deal with fewer issues related to dissatisfied customers.

Energy efficiency: All manufacturers can reduce their carbon footprint by reducing waste. However, energy-intensive industries have the most to gain in terms of energy savings, which will not only reduce energy waste but also help make products more affordable as a result.

Adapting manufacturing for the digital age

Future factories are becoming a competitive imperative as the adoption of advanced manufacturing 4.0 technologies continues to drive efficiency, flexibility, and product innovation.

Manufacturers of all sizes must adopt smart manufacturing initiatives to remain competitive. But to do this, their organization’s leadership must first adopt a new mindset.

Investing in equipment with a view toward integrating smart manufacturing applications is a good first step. Over time, these investments will help improve processes, save money, and increase sales.

Growing digitization and significant technological advancements have already propelled the innovation and growth of smart manufacturing. According to Dataplace, “Smart industry is a popular trend within manufacturing companies. Data integration makes it possible for production systems to work together and react to live changes in the company, at the customer, or in the supply chain.”

Smart manufacturing–related technologies and solutions

Combining and implementing the right mix of smart manufacturing solutions into the traditional manufacturing process can help you accurately predict requirements, identify errors, and make innovation and the manufacturing process more manageable.

A number of technologies are particularly important when implementing a smart manufacturing approach, including data lakehouse solutions, Internet of Things integrations, AI/machine learning–based analytics, digital twins, and augmented reality and robotics.

Data lakehouses

A data lakehouse is a modern, open architecture that enables a manufacturer to store, understand, and analyze all types of data. It combines the power and richness of data warehouses with the breadth and flexibility of the most popular open source data technologies manufacturers use today.

A data lakehouse can easily bring together, analyze, and find new insights from various data sources, including invoices and forms, and data formats, including text, audio, and video, enabling the use of the latest AI frameworks and prebuilt services.

Having access to powerful solutions for collecting and aggregating operational data in real time, gleaning insights from the data, communicating rapidly, and making holistic and collaborative decisions are all critical components of an efficient decision-making process.

A representative use case is helping manufacturers achieve supply chain resilience by supporting their ability to source from a variety of suppliers. A data lakehouse does this by enabling them to blend data from the ERP system that handles order management down through the inventory, warehouse management, and transportation systems used to transport and deliver materials required for production.


Industrial Internet of Things

The Industrial Internet of Things (IIoT) plays a crucial role in successfully implementing smart manufacturing and efficiently achieving business goals.

An example of how IIoT can be deployed is in a connected factory, enabling it to gather real-time data from equipment sensors, cameras, production robots, and other intelligent devices, all connected through a 5G local network. The data is pushed into an AI/machine learning (ML) solution that’s able to provide real-time suggestions to inform decisions related to predictive maintenance, remote monitoring of production assets, asset utilization, or the automation of various processes and tasks.


AI/ML

Artificial intelligence and machine learning are two types of intelligent software solutions that are impacting how past, current, and future technology is designed to mimic more-humanlike qualities.

At the core, artificial intelligence is a technology solution, system, or machine that is meant to mimic human intelligence to perform tasks while iteratively improving itself based on the information it collects.

Machine learning is a subset of AI that focuses on building a software system that can learn or improve performance based on the data it consumes. This means that every machine learning solution is an AI solution but not all AI solutions are machine learning solutions.

Manufacturers leverage machine learning to identify hidden root causes for quality, yield, and other operational issues. Their experts can use deep insights to make faster decisions and eliminate bottlenecks in production.

Smart manufacturing solutions use artificial intelligence and machine learning to contextualize information and provide actionable insights, enabling you to predict machine failures to get ahead of maintenance, adjust production schedules, and avoid costly downtimes.

Manufacturers can therefore automate various internal processes, such as inventory counting, document processing, or analyzing productivity and efficiency, to be able to instantly respond to trends and improve quality across the board.


Anomaly detection

Anomaly detection solutions can be used for predictive maintenance for manufacturing equipment. Anomaly detection uses prebuilt algorithms to detect various anomalies in time series data to automate manufacturing processes, tasks, and decisions, such as servicing hardware equipment, ordering replacements or supplies, and taking predictive steps to avoid disruptions and to increase efficiency.

Monitor factory efficiency to detect any unusual production behavior using predictive analytics and multiple sources of data. Use a machine-monitoring platform to detect and predict unusual equipment behaviors and recommend and automate the next best action to fix anticipated failures.

Implement quality monitoring all along the production cycle to detect quality deviations and generate predictive alerts. This will enable you to run an immediate root cause analysis to identify the sources of a quality issue and set up best practices training using real data from past quality issues.

Get started with smart manufacturing

Smart manufacturing can help manufacturing companies become more resilient with the help of new approaches and smart technologies.

If so, take a look at Oracle’s smart manufacturing solutions to understand how to engage artificial intelligence and machine learning to contextualize information, provide actionable insights, and gain competitive advantages in an increasingly dynamic sector.

Does your company aim to achieve the following?

  • Improve visibility into costs, efficiency, and quality across factories
  • Monitor production performance and predict maintenance failure
  • Make better, faster decisions with AI
  • Monitor quality and apply best practices training