Industry 4.0 Revolution: Understanding the Digital Twin and Its Benefits

The world is moving toward an era of more efficient business operations driving by automation. This was one of the key messages of Mitsubishi Hitachi Power Systems CEO, Paul Browning, at the just concluded 2019 CERAWeek held in Houston. Paul Browning, who was the keynote speaker on the ‘digital transformation agenda’, spoke about Mitsubishi’s use of artificial intelligence (AI), machine learning, and digital Twin technologies to create the world’s first autonomous power plant.  He ended his speech by saying ‘Mitsubishi is building the world’s first autonomous power plant capable of self-healing.’

The use of digital transformation technology to eliminate downtime and reduce unplanned shutdowns are just a few of what can be accomplished with Digital Twin technologies. In fact, the ability to virtualize workspaces and complex systems have important roles to play in achieving the smart factory and Industry 4.0 revolution the most industries dream off. This is because no other emerging technology has the potential to bridge the gap between the physical world driven by machines and the virtual world like the Digital Twin. This is why the Digital Twin market is forecasted to be worth approximately $26billion by 2025.

While the numbers highlight the growing acceptance of Digital Twin solutions, many businesses are a bit skeptical about its implementation and benefits. This is why practical case studies are needed to highlight the application of the Digital Twins and how others have benefited from it.

The Most Important Benefits of Digital Twin Technology

Industry 4.0 business model relies on data to automate business processes. The Digital Twin, in turn, creates the perfect environment for collecting data from every aspect of the manufacturing process for analytics and simulation. When data is accurately collected and a Digital Twin is designed, system integrators, data analysts, and other stakeholders can use it to drive business policies and improve decision-making processes. The benefits Industry 4.0 and manufacturers stand to gain from Digital Twins include:

Enhanced Plant Performance – Having the capacity to access and quantify every information produced from a manufacturing process and the shop floor is key to automation. Digital Twin technologies allow manufacturers collect data from the sensors and embedded systems integrated onto a shop floor. The Digital Twin also takes things a step further by replicating physical manufacturing processes and creating a digital environment where these processes can be assessed.

With the necessary data from equipment, machines, material handling, and production cycles in place, manufacturers can develop policies and run simulations to determine how efficient they are. Once determined, the manufacturing policies and regulations can then be applied on the shop floor. This gives large enterprises a cheaper way to access the effects of decisions on productivity levels.

A DHL study on the importance of Digital Twin in enhancing plant performance highlights the use of Digital Twin by Iveco solutions to optimize welding capabilities. The Iveco manufacturing line struggled with constant breakdown of its welding components which delayed production. The cause of these breakdowns were pin-pointed to a lamellar pack which wore out constantly. To enhance performance and reduce downtime, Iveco designed a Digital Twin model of its manufacturing line/

The Digital Twin model helped Iveco understand the different welding concepts and requirements, as well as, their effect on the lamellar pack. Using simulation and machine learning, Iveco developed an optimal welding process that could forecast the probability of component failures in other to reduce them.

Driven Predictive Maintenance –  One of the benefits of designing a Digital Twin of manufacturing shop floors or plants is the opportunity to integrate predictive maintenance into business models. Predictive maintenance involves the prediction of a component or machine failure and the taken of preemptive action to forestall failure. Digital Twin technology has created an environment that drives predictive maintenance across various systems.

Once again, The Mitsubishi Hitachi Power System plant serves as an example where Digital Twin technology can drive the predictive maintenance policy in Industry 4.0. The Digital Twin model created by Mitsubishi gives power plants the ability to monitor sensors and other parameters that determine the plant’s performance levels. On application, the Digital Twin, alongside AI, and machine learning provided insight on the best time to schedule maintenance activities without disrupting production.

The benefits Mitsubishi reaped from its use of Digital Twins include a more efficient way to discover fault components and a maintenance culture that reduced downtime. The autonomous plant was also able to run self-diagnostics and repair stuck valves that affected power generation. Smart facilities can take advantage of the Digital Twin to drive a predictive maintenance culture which will eliminate resource waste and downtime caused by faulty equipment.

Advanced Control of Complex Systems or Processes – Digital Twin ecosystems provide an avenue to control complex systems and processes in ways other traditional technologies can’t. This is because, AI, machine learning, and simulations can be applied to the digital environment thereby allowing enterprises to see farther. Digital Twin takes control process which involves comparing system performances with set standards, discover deviations, and design corrective actions to greater heights. This makes it a great resource for research in Industry 4.0.

An example of how Digital Twins makes advanced control of complex systems possible can be seen from how the U.S. Department of Energy National Energy Technology Laboratory (NETL) deployed Digital Twin solutions. The Digital Twin of the (NETL) plant was used to carry out research on the use of carbon dioxide to power plants as a replacement to the hazardous coal-powered plants currently in use. The Digital Twin also mapped out the plant’s sensor network in other to optimize its use.

The Digital Twin created by the research team served as a virtual testbed for analyzing operational relationships and their effects on power generation. The benefits of Digital Twins, in this case, included a cheaper more effective way to analyze control process phenomena and reduce downtime. Increasing plant reliability and optimizing the use of resources were also singled out as benefits. 

Easing Training and Onboarding Process – The future of Industry 4.0 is being driven by emerging technology solutions such as the industrial internet of things (IIoT), IoT, automated vehicles and equipment. This means to effectively take advantage of the benefits of Industry 4.0 older and new employees must be thought to function in a smart facility. Digital Twins of plant systems and processes provide a virtual environment for employees to learn about operational processes.

In a case study conducted in an automotive facility, employees were taught the repair and assembling process in a virtualized environment and through manuals. At the end of the training employees preferred the option of learning through virtualized environments and retained more information compared to learning from physical manuals. This means the hands-on learning approach driven by Digital Twin technology creates a better environment for learning complex process safely.

Take Advantage of The Benefits of Digital Twins

The combination of Digital Twin technology and cloud computing has made the design, emulation, scheduling, analytics, and simulation services it offers even more affordable to end-users. Small and medium scale businesses can now access Digital Twin solutions to solve complex problems. This means Digital Twin as a Service is slowly but surely becoming an option for enterprises to explore. You can learn more about the Digital Twin opportunities for your business by contacting the experienced engineers at Simio.

Resources:

https://www.ice.org.uk/knowledge-and-resources/case-studies/digital-twins-for-building-flexibility-into-power

https://www.logistics.dhl/content/dam/dhl/global/core/documents/pdf/glo-core-digital-twins-in-logistics.pdf

https://www.bloomberg.com/news/articles/2018-04-09/forget-cars-mitsubishi-hitachi-sees-autonomous-power-plants

Digital Twin Technology: 5 Challenges Businesses Face By Overlooking It

A Disruptive technology is a product, concept or service that has the ability to redefine the traditional way of doing things.  Today, the digital twin concept is being hailed as a disruptive technology with the capacity to change how we design, solve complex problems and collaborate. In fact, a Gartner Report predicted that by 2021 approximately 50% of industrial companies will integrate the use of digital twin technologies to increase workforce performance and manufacturing efficiency. So, what is this disruptive technological concept?

The Digital Twin refers to a real-time replica of a physical entity. This entity could be a living thing, an inanimate physical object, as well as, assets, processes and systems that function in the physical world or environment. Although this concept is actually three decades old, the convergence of emerging technologies such as the internet of things (IoT), artificial intelligence (AI), machine learning has taken it to new heights. Digital twins juxtapose these emerging technologies to create digital models of physical entities with the ability to simulate real-time changes that occur to the physical model.

An example of how this concept work involve the development of the digital twin of an aircraft. With the digital twin, finite element analysis (FEA) can be applied to determine the fatigue limit of the aircraft’s structure. The results of this simulation can then be used to design or choose more suitable materials or design for a more durable aircraft. Outside manufacturing, digital twins can be employed in diverse industries including healthcare to simulate how the human body reacts to external forces. The benefits of integrating digital twins include increased design efficiency, enhancing predictive analysis, and collaboration.  This is why the market for it is expected to hit approximately $15billion by 2023. The benefits of digital twins are huge but the challenges business will face not embracing it is even bigger.

This article will discuss:

  • The challenges businesses face not integrating the digital twin in business operations.
  • The effects of not embracing the digital twin.
  • The disruptive capabilities of the digital twin.

The Five Challenges Businesses Face Not Embracing Digital Twins

With approximately 50% of industrial companies integrating the use of digital twins, the 50% who don’t will definitely be losing their competitive edge. This is because the digital twin will redefine real-time simulation applications in ways the average 3D modelling software or Building Information Modelling platform can’t aspire to. The challenges to expect include:

Keeping Legacy Solutions, Designs or Data – As the generation of baby boomers continue to retire daily, the probability of losing the knowledge that built legacy equipment and systems could be lost. This includes the Mylar copies of traditional manufacturing equipment or the designs of legacy military aircraft. Regardless of technological advancements, the loss of legacy data destroys the foundations newer prototypes were built on.

With the aid of the digital twin concept, businesses across every industry, can create an accurate digital model of legacy equipment or solutions. The digital model can then be stored for posterity sake or analyzed with the aim of developing upgraded prototypes. Models can also be used as materials for training the younger generation of workers through virtual reality environments.

 Enhancing Lean Manufacturing Processes – Toyota’s integration of lean manufacturing to speed up production while efficiently using resources has become folklore in the automotive industry. The integration of lean manufacturing models – which were disruptive at that time – helped Toyota dominate the industry for decades. This is the leverage the digital twin concept offers. The ability to optimize entire product value chains is something that can be achieved in real-time through the digital twin.

A study at the Bayreuth University, Germany focused on analyzing the impact of digital twins in collecting real-time data and optimizing production systems. The study compared the efficiency of digital twins and the commonly used value stream mapping solutions. In the end, the results showed that digital twins exceeded traditional solutions in data acquisition, automated derivation of optimization measures, and the capturing of motion data. These data which are crucial to optimizing production could also be utilized in a digital twin environment to optimize diverse processes. Thus, shunning digital twins will leave firms in the lurch while competitors who leverage this concept can optimize production variables in real-time.

Limitations in the Integration and Use of Data – The Industry 4.0 revolution currently going on relies heavily on the collection and use of data to receive important business insight and automate processes. The tools or applications currently used today are enterprise relationship management software, and industrial cloud solutions. Although these solutions do excellently well in collecting data from smart or industrial internet of things (IIoT) devices, they still struggle with collecting data from legacy or dumb equipment. This limits the penetration of Industry 4.0 in the deepest layers of manufacturing shop floors which is what the OPC Foundation intends to solve.

Digital twin concepts can help smart factories integrate dumb equipment from the deepest levels of a shop floor into models of the manufacturing plant. This makes it possible to capture the hundreds of non-measured information in the shop floor into a digital environment thereby truly meeting Industry 4.0 and OPC UA standards. If successfully done, the digital twin with the captured data can be used to predict the facilities transient response to external disturbances, equipment failure, and system malfunctions.

Manufacturers who overlook digital twin concepts will be stuck with using data from only smart equipment and IoT devices to track real-time changes on the shop floor. The limitations associated with not capturing non-measured data will lead to approximations when automating operations in a smart factory. This could lead to downtime, an inefficient workforce, and in extreme situations accidents to workers.

Limiting the Effectiveness of Predictive Analysis – Another important challenge shunning the integration of digital twins into industrial operations is the difficulties that come with making blind or half-informed changes. Making blind changes when making important decisions such as designing a new material handling system or reducing the number of processes needed to develop a product will have terrible consequences. These consequences will include wastage of resources, a subpar end product or confusion on the shop floor.

According to Gartner, downtime in the manufacturing industry could lead to huge losses. In the automotive industry alone, downtime is responsible for a loss of $22,000 per minute. Although the numbers may be less in other industries, the effects are still considerable. Digital twins can help eliminate these challenges or losses by helping businesses simulate the real-time effect of making certain changes. For example, a change of production schedule while going through a transition period would have left the aviation manufacturer Lockheed Martin unable to meet its delivery timelines. With the aid of the Simio simulation software, the manufacturer was able to make informed decisions that optimized the production process.

Next Steps

The match to industry 4.0 and a more connected factory is one that must be planned for if manufacturers intend to remain competitive for the long run. One way to achieve this is by integrating a digital twin for simulating and receiving the insights needed to automate industrial processes. If properly executed, you will be turning the disruptive nature of the digital twin to your benefit.

Resources:

https://www.gartner.com/smarterwithgartner/prepare-for-the-impact-of-digital-twins/

https://news.thomasnet.com/companystory/downtime-costs-auto-industry-22k-minute-survey-481017

https://www.isw.uni-stuttgart.de/en/institute/highlights/digital-twin/

https://blogs.opentext.com/addressing-the-data-challenges-in-the-digital-twin/

https://www.gartner.com/smarterwithgartner/prepare-for-the-impact-of-digital-twins/

Simio now has the GSA Advantage!®

We are pleased to announce that Simio has been awarded the coveted U.S. General Service Administration (GSA) IT-70 contract for Government Services.

Simio’s object-oriented Simulation and Production Scheduling software is ideally suited for all aspects of state and local government use. Now, government agencies can more easily purchase Simio products and services for design, emulation and scheduling of their complex systems.

“We’re proud and excited to offer a straightforward solution for simulation needs,” says Anthony Innamorato, a former Platoon Commander of the U.S. Navy and the current Vice President of Customer Solutions at Simio LLC. “With Simio now being awarded a GSA contract, government employees can more easily access and utilize the power of Simio.”

Simio now proudly displays the GSA Starmark Logo on our website, along with our Contract Number: 47QTCA19D008W.

The GSA’s federal procurement approval process means that Simio’s product offering has been screened and accepted as the best possible simulation software, at a fair price, within our industry. This means that state and local government buyers and their agencies can order and buy with confidence. They can implement strategic purchases more easily to expedite acquisitions. It also means that they obtain best prices, at the same time ensuring Federal Acquisition Regulation (FAR) compliance.

Already major suppliers to the Government, Military and Department of Defense, Simio’s solutions encompass a broad set of issues related to production scheduling, supply chain and logistics, as well as resource staffing. Typical application areas include fleet sizing and design, refurbishment operations planning and overall process improvement using Lean concepts.

Simio simulation can assist in any scenario where modeling is needed to determine solutions or to improve communication of ideas and promote understanding. Our software can help make important decisions that are critical for the reduction of risk.

Applications for Simio’s simulation in the federal government environment include:

  • large or complex systems with a great degree of process variability,
  • critical situations where it is too expensive or risky to do live testing, and
  • systems where data is missing or incomplete.

Take advantage of our new alignment with the GSA’s purchasing program to implement Simio’s leading edge software in your organization.

Find out more today about how you can order Simio via the GSA Advantage!®

The Evolution of the Industrial Ages: Industry 1.0 to 4.0

The modern industry has seen great advances since its earliest iteration at the beginning of the industrial revolution in the 18th century. For centuries, most of the goods including weapons, tools, food, clothing and housing, were manufactured by hand or by using work animals. This changed in the end of the 18th century with the introduction of manufacturing processes. The progress from Industry 1.0 was then rapid uphill climb leading up to to the upcoming industrial era – Industry 4.0. Here we discuss the overview of this evolution.

Industry 1.0 The late 18th century introduced mechanical production facilities to the world. Water and steam powered machines were developed to help workers in the mass production of goods. The first weaving loom was introduced in 1784. With the increase in production efficiency and scale, small businesses grew from serving a limited number of customers to large organizations with owners, manager and employees serving a larger number. Industry 1.0 can also be deemed as the beginning of the industry culture which focused equally on quality, efficiency and scale.

Industry 2.0 The beginning of 20th century marked the start of the second industrial revolution – Industry 2.0. The main contributor to this revolution was the development of machines running on electrical energy. Electrical energy was already being used as a primary source of power. Electrical ma- chines were more efficient to operate and maintain, both in terms of cost and effort unlike the water and steam based machines which were comparatively inefficient and resource hungry. The first assembly line was also built during this era, further streamlining the process of mass production. Mass production of goods using assembly line became a standard practice.

This era also saw the evolution of the industry culture introduced in Industry 1.0 into management program to enhance the efficiency of manufacturing facilities. Various production management techniques such as division of labor, just-in-time manufacturing and lean manufacturing principles refined the underlying processes leading to improved quality and output. American mechanical engineer Fredrick Taylor introduced the study of approached to optimize worker, workplace techniques and optimal allocation of resources.

Industry 3.0 The next industrial revolution resulting in Industry 3.0 was brought about and spurred by the advances in the electronics industry in the last few decades of the 20th century. The invention and manufacturing of a variety electronic devices including transistor and integrated circuits auto- mated the machines substantially which resulted in reduced effort ,increased speed, greater accuracy and even complete replacement of the human agent in some cases. Programmable Logic Controller (PLC), which was first built in 1960s was one of the landmark invention that signified automation using electronics. The integration of electronics hardware into the manufacturing systems also created a requirement of software systems to enable these electronic devices, consequentially fueling the software development market as well. Apart from controlling the hardware, the software systems also enabled many management processes such as enterprise resource planning, inventory management, shipping logistics, product flow scheduling and tracking throughout the factory. The entire industry was further automated using electronics and IT. The automation processes and software systems have continuously evolved with the advances in the electronics and IT industry since then. The pressure to further reduce costs forced many manufacturers to move to low-cost countries. The dispersion of geographical location of manufacturing led to the formation of the concept of Supply Chain Management.

Industry 4.0 The boom in the Internet and telecommunication industry in the 1990’s revolutionized the way we connected and exchanged information. It also resulted in paradigm changes in the manufacturing industry and traditional production operations merging the boundaries of the physical and the virtual world. Cyber Physical Systems (CPSs) have further blurred this boundary resulting in numerous rapid technological disruptions in the industry. CPSs allow the machines to communicate more intelligently with each other with almost no physical or geographical barriers.

The Industry 4.0 using Cyber Physical Systems to share, analyze and guide intelligent actions for various processes in the industry to make the machines smarter. These smart machines can continuously monitor,detect and predict faults to suggest preventive measures and remedial action. This allows better preparedness and lower downtime for industries. The same dynamic approach can be translated to other aspects in the industry such as logistics, production scheduling, optimization of throughput times, quality control, capacity utilization and efficiency boosting. CPPs also allow an industry to be completely virtually visualized, monitored and managed from a remote location and thus adding a new dimension to the manufacturing process. It puts machines,people, processes and infrastructure into a single networked loop making the overall management highly efficient.

As the technology-cost curve becomes steeper everyday, more and more rapid technology disruptions will emerge at even lower costs and revolutionize the industrial ecosystem. Industry 4.0 is still at a nascent stage and the industries are still in the transition state of adoption of the new systems.Industries must adopt the new systems as fast as possible to stay relevant and profitable. Industry 4.0 is here and it is here to stay, at least for the next decade.

Simio Partner Finalist in Franz Edelman Award

The prestigious Franz Edelman Award for Achievement in Operations Research and the Management Sciences was presented at the Edelman Gala on April 16th, 2018 in Baltimore, Maryland. The Franz Edelman competition honors distinction in the practice of Operations Research and Analytics, by both individuals and companies, with emphasis on the beneficial impact of their achievements.

To reach the finals, companies are required to demonstrate how their use of technology is transforming the approach to some of the world’s most complex problems.

Simio is proud to be the provider of the simulation that facilitated one of this year’s finalists, Europcar, through our partner, ACT Operations Research (ACTOR). Our combined technologies have been used to develop Opticar which provides forecasting, simulation and optimization of the processes relating to vehicle rental for the leading European car rental company.

Simulation for Decision Making

The vehicle rental industry is a huge, complex, constantly changing market, with cultural variations across countries. In order to meet dynamic demand, decisions are continuously made regarding fleet assets, their locations, usage and pricing. The combination of AI, statistical modeling and simulation allows all eventualities to be considered and evaluated in order to establish optimum processes and make informed decisions.

Simulation can be used to model the possibilities with respect to both capacity and revenue, helping managers of car rental companies to reduce their risks in terms of planning for optimal fleet saturation. By making quality decisions, they can constantly maximize business opportunity for the company and ensure consistent financial and service performances.

At Simio, we are constantly solving business problems of this kind through simulation. When complex system schedules and decisions are required, we deliver leading edge solutions across many industries, from manufacturing to transportation and logistics.

Simio is proud to congratulate our partner ACTOR, with Europcar, on their outstanding achievement of becoming a Franz Edelman Award finalist.