Use digital twin models to streamline your operation logistics process.
If you are an operations manager looking for new tools to optimize your manufacturing processes, then consider digital twin modeling and the Internet of Things.
Business owners in complex technical fields are witnessing first-hand the ways in which their businesses are being changed by contemporary technology. Data analytics and the Internet of Things have fundamentally disrupted how we approach complex processes, in most ways for the better. For example, IoT solutions in some industries have provided managers and operators with tools for advanced optimization and predictive maintenance that saves money on costs stemming from process failures or necessary part repairs.
One major technology emerging in operations management is digital twin technology. The impact of digital twin technology on operations management cannot be overstated. With digital twin technology, data visualization, and machine learning algorithms, operation managers can utilize real-time data to model their manufacturing process and optimize management.
How Does Digital Twin Manufacturing Work?
Digital twin technology is deceptively simple. This digital twin concept involves a mixture of IoT technologies and cloud computing to present operators with data to model a given manufacturing process as a virtual object.
For example: take a heavy industrial process creating a type of widget. The production of this widget involves a complex procedure of numerous machines with different operating conditions. By placing sensors outfitted with wireless communication capabilities throughout the machinery, operators can measure production speed, heat, humidity, and any other conditions that might affect that process. Then, operators can connect those sensors to a cloud computing solution that takes that data and feeds it into a data visualization program, which models the process as a virtual twin of the physical machinery.
This is not just way to display data: the software literally presents the process as a facsimile of all the technology and operating conditions mapped by the sensors. Digital twin IoT technology is a way to run virtual simulations of your industrial process to make predictions about future problems or opportunities to optimize the entire system.
How Will Digital Twin Technology Impact Operations Management?
As the cost of producing digital twin models decreases, implementing such technology increases the potential for managers to optimize their manufacturing processes. This is because digital twin technology leverages the predictive power of digital technology to automate aspects of operations management that might otherwise distract or go overlooked by a human manager.
The primary job of operations management is to increase efficiency in getting products or services to clients. This is where digital twin technology shines. Unlike traditional optimization or process mapping, digital twin solutions use IoT philosophies and hardware to measure the industrial process and its environment in real-time. That means changes to the process can also be managed in real-time. Further, digital twin software can leverage machine learning AI algorithms to make predictions about future opportunities for optimization or potential trouble spots.
With this kind of predictive power, operational managers can work with a pattern-reading AI to tweak conditions in a manufacturing process as they happen. Optimization happens in real-time, meaning that savings (in time and money) also happen in real time. Additionally, this means that the manager can manage the use of assets in such a way as to minimize costs related to raw material overhead.
Small changes can equal big savings in complicated manufacturing processes. Sometimes, small patterns can turn into huge problems if overlooked or ignored. And, as is often the case, many of these potential changes and patterns are not observable or predictable by human minds. Digital twin technologies provide a tool to uncover these patterns, and make operational decisions based on the information contained therein. This can be the difference between operational waste and a truly optimized system.