Since conceptualization in 1777, digital twins have evolved to provide acute business insights and planning capabilities through dynamic simulations. Today, smart manufacturing or Industry 4.0 leverages a combination of technology-driven approaches including Industrial Internet of Things (IIoT), Artificial Intelligence (AI), Machine Learning, analytics, as well as Digital Twins to optimize production process and streamline diagnostics – all through complex and highly advanced, real-time digital representations of thousands of parts moving in concert. 
Digital twins enable manufacturers to observe, scrutinize, and simulate processes and assets to augment the efficiency of production, ranging from monitoring machinery health to tracking the movement of materials across a production facility. According to a Gartner report, 24% of organizations that implement IoT projects use digital twins, while 62% of organizations plan to adopt digital twins in future operations.  Infact, the advantages brought about by digital twins has resulted in the market exceedinga valuation of USD 5 billion in 2020. 
With the advent of Industry 4.0, machinery across production units are now being equipped with IoT sensors, which collect data and share it with their digital twin. This not only enables machines to run optimally but can also help create a twin prior to the manufacturing of the actual product, helping plan and modify processes in advance. For instance, using a digital twin, a paper mill can run itself with negligible or no human intervention – with insights into process configurations, pulp quality, and operational constraints, among others, readily available.  As data is shared in real-time, a dynamic production process can be maintained, one that can adapt to rapid changes in market demand. Digital twins can also reduce the risk of accidents, lower maintenance costs by predicting failure, and ensure product quality through real-time performance testing.
The health of machinery and equipment can be determined by comparing real-time data captured through the sensors against historical data, with anomalies detected by neural networks. Digital twin models’ predictive capabilities here can identify patterns, including irregular fuel consumption and changes in auditory data, that help forecast malfunctions. Following this, once alerts are raised on machine malfunctions, technicians can take appropriate measures to eliminate unscheduled downtime. Such technology also facilitates data-backed decision-making, by identifying the root cause of the problem, determining the criticality of the problem, and charting a plan of action to resolve the problem. For example, by using digital twins to monitor an oil pipeline or wind turbine, maintenance and associated expenses can be saved. As a result of these benefits, the use of digital twins for predictive maintenance has become widespread in the manufacturing industry, with the market estimated to grow at a compound annual growth rate of 40% from 2018 to 2024.
Digital twins can be leveraged to monitor production processes as well as the quality of the endproduct. Combined with in-memory computing-based networks, digital twins enable a lightweight, change-controlled model capability that helps manufacturers visualize and analyze data in real-time, to tabulate and contrast quality data across product portfolios. They can be used to simulate production processes from end to end, to check for the feasibility of production process and estimate potential hiccups before executing physical processes
Digital twins also enable the following benefits for smart manufacturing:
Designers and engineers can simulate the production of various permutations of the same product, using smart technology. As digital twins offer manufacturers access to real-time data, helping streamline the production of customized products, this can help save costs and time, reducing the number of attempts required to get optimal final products, as well as the steps involved in identifying the best versions of products. Further, digital twins can also help formulate data on the impact of a particular product in the market –for instance, charting the impact of a particular wheel design on sales, and reducing expenses through optimal design.
Easy access to operational data obtained from digital twins can simplify data sharing across functions, enable better collaboration and communication, and facilitate prompt decision-making. Sifting through past, present, and future data can provide better visibility into how a particular product will fare in the market and helps engineers decide on which products to design. By leveraging real-time feedback, product design can therefore be revised to meet rapidly changing demand. It can also aid decision-making regarding the production process – specifically in terms of staffing and maintenance requirements. For instance, creating traffic and environment simulations with digital twins of autonomous vehicles embedded in the same is being explored to speed up development, validation, and testing processes significantly. Such digital twin applications further provide requisite data to train and validate data sets, which contribute to AVs’ability to recognize, handle, and navigate through complex real-world mobility conditions.
Businesses can now maximize the benefits of creating virtual replicas of assets – improving product quality, reducing costs, and optimizing production. These simulations, created by an amalgamation of the physical and digital worlds, can help gain valuable insights into all the what-if questions possible in manufacturing scenarios. In order to streamline operations, businesses can now reduce resource utilizations and physical usages, including for machinery or production processes, through simulations.
The future holds various applications of digital twins in the manufacturing industry– in fact, it has been predicted that by 2026, around 91% of IoT platforms will implement some form of digital twinning to streamline the production processes  – paving the way for smart, streamlined, and next-gen manufacturing.