Industry 4.0 Glossary

What Is Digital Manufacturing?

Digital manufacturing involves the use of computer systems in manufacturing, and the objective is delivering manufacturing excellence.

It uses cutting-edge technologies like digital twins, simulation, and machine learning to enable integration with enterprise systems and IoT enabled automation. Together these pieces of technology help manufacturers achieve perfect production with maximum productivity, minimum waste, and optimal asset utilization.

Digital Transformation In Manufacturing

Electronics in controls and automation kick-started the digital transformation in manufacturing. The flow of information and controls through devices with computing power enabled scalable automation in manufacturing. But the real acceleration of digital transformation started with the integration of field-level data and enterprise systems through manufacturing operations management systems (MOM). Line data began influencing business decisions and vice-versa.

With the advent of IoT, cloud computing and edge computing, the ability to handle, process and generate manufacturing data became manifold. These applications not only allow monitoring of real-time processes but also simulate and predict future possibilities. Thus, the use of digital information to aid human decision making and machine-level autonomy has truly transformed the manufacturing enterprise through digital manufacturing.

Three Aspects Of Digital Manufacturing

For a digital manufacturing initiative to be comprehensive, it needs to cover all stages of the journey of a product from its development until it reaches the store shelf. This falls under three major aspects.

Product Life Cycle

Digital manufacturing starts with the conceptualization of the product. From the ideation within a product lifecycle management system (PLM) to developing a proof of concept, building a prototype, product realization, field failure support, lifecycle extensions, and up to product retirement are all a part of the digital initiative. Continuity in the flow of digital data across each stage of the lifecycle with documented accountability, value addition, and technical information forms a critical part of the digital wisdom. It enhances manufacturing digitalization and also creates valuable insights into the product lifecycle management itself.

Smart Factory

The term smart factory refers to a highly digitalized and closely connected environment where equipment and systems consistently improve process performances through automation. The self-optimization benefits of smart factories are limited to production and extend to functions like production planning, supply chain, and even product development. Smart factory employs the use of smart machines, sensors and tools to provide real-time feedback about the processes and manufacturing technology. It provides for greater visibility of factory processes, control, and optimization. Smart factory applications also constitute a significant amount of effort and investment in a digital manufacturing initiative.

Value Chain Management

The primary focus of value chain management lies in creating an optimal process with minimal inventories without compromising product quality and customer satisfaction. Digital manufacturing that incorporates quality and operational excellence initiatives like trusted platform module (TPM), lean, just in time (JIT) and others make it important for a common digital platform for all stakeholders internal and external to the manufacturing enterprise. Hence a digital manufacturing organization allows meaningful visibility, controls, and robust automation workflows across the entire value stream.

Examples Of Digital Manufacturing

Below are a few use cases of big data, which is an arm of digital manufacturing. These use cases identify the possibilities of digital manufacturing.

Predictive And Preventive Maintenance

The widespread adoption of sensor technology has provided an unprecedented impetus to technologies like IoT that lie at the heart of industry 4.0. Sensors help collect and transmit data for real-time processing. The availability of real-time data and time series data with distributed computing power across terminal computing devices (edge computing) and cloud (cloud computing) has helped derive insights from this data using machine learning models to predict failure. These actionable insights can be used to schedule maintenance to avoid such unplanned downtime. Data also helps identify patterns based on historical performance and prescribe scheduled maintenance that prevents costly outages and asset failures.

Decision Support Systems

Recommendation engines and decision support systems are tools that can harness the power of computing devices to run scenarios using complex models and increase the accuracy and effectiveness of human decision making in a manufacturing environment. They can be applied in scenarios like debugging, load planning, and maintenance.

Cost Of Quality

Big data can be effectively used to reduce in-process deviations to minimize wastage. By leveraging the data from sensors on turning shafts and tool jaws, deviations in specifications can be easily predicted. Predictive analytics can also model and alert potential future defects if current operating conditions are allowed to continue.

Benefits Of Digital Manufacturing

Below are a few use cases of big data, which is an arm of digital manufacturing. These use cases identify the possibilities of digital manufacturing.

Enables increased efficiency through digital workflows and automated exchange of data
Improves decision making based on analytics
Improves accuracy and helps avoid errors that can prove to be costly
Enables swift turnaround times across all levels of the value chain
Improves and enhances safety measures on the factory floor
Reduces the cost of production and maintenance
Helps in planning while avoiding expensive mistakes through the power of simulation
Enables the visualization of patterns and trends with real-time data dashboards

As industry 4.0 and its allied forces of disruption redefine manufacturing standards, the use of digital manufacturing continues its upward trajectory. Today, digital manufacturing finds its application across industries, as the use of data to drive production processes is becoming increasingly automated.