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

Why Daily Plans Fail

At 6:00 Monday morning I create a plan for my day starting at 7:00. That doesn’t seem to be such a difficult task. Why is it that by 7:30 my plan already shows signs of being hopeless?

I’ve done the obvious things. First I upgraded from a magnetic Gantt chart based on hand-written information to Advanced Planning and Scheduling (APS) software. That was much easier to use, but frankly the results didn’t dramatically improve. Feeding it with live data from my Manufacturing Execution System (MES) got me a good starting point, with a lot less effort than the paper approach, but my plan still didn’t hold up to the test of time.

I then realized that my software was based on standard lead times and it assumed infinite capacity — it was constantly overestimating my production capability. So I updated to Finite Capacity Scheduling (FCS) software. That helped a lot. But I still had a lot of problems because the FCS tool was based on a “standard” data model for my industry. I guess we do things a bit different than most people in our industry, but the schedule it generates doesn’t recognize those differences.

So I updated to a general purpose simulation product with the flexibility to model my system as it really is AND generate the Gantt charts and other reports I need for scheduling. So now I can account for that problem aisle where my lift trucks get so congested. And I can account for that machine cluster that shares access to a single crane. As a bonus I also got an animation that lets me “play out” the day and visually see what I can expect.

Now I have a much better plan that is realistic and accurate as long as everything goes well. But it is always optimistic. While I can put in preventative maintenance, there is no way to factor in that my Cobalt 120 machine is 30 years old and breaks down almost every day. Or that my supplier for Jenkins 257 material is often way behind their promised delivery. I can pad the schedule to allow extra time, but that just guarantees that I will waste valuable production time when things go well.

In my simulation tool I can run my model with all that variability accounted for (stochastic analysis) and it gives me good long-term capacity analysis. But since there is no way to predict a specific “random” problem, like an equipment failure, I can’t use that knowledge in generating my plan for today — I am limited to a deterministic schedule … or am I?

Actually there is a new technique available called Risk-based Planning and Scheduling (RPS) that first generates a deterministic plan, then applies a stochastic analysis to that plan. It actually tells me how likely it is that I will meet the plan. For example, orders that require the Cobalt 120 machine or Jenkins 257 material may show a high risk of not completing on time. Since I know this before the shift starts, I have more options on how to deal with it – like adjusting labor assignments, rerouting a process, or expediting a material. I can even evaluate the various alternatives to determine which one performs best, and then base my plan on the alternative that generates an acceptable risk at the lowest cost.

Now that’s a plan I can live with!

Happy Modeling!
Dave Sturrock, VP Operations, Simio LLC

Simulation – What a Bother!

I don’t know why so many people waste so much time using simulation. I just ask Joe. Joe has worked here for 38 years and he’s seen it all. Joe is always willing to take the time to tell me how that proposed system or new process will work, because he knows how well it worked or didn’t work the last time we tried something similar.

Yea, things may be different now, but the technology is about the same right? And our supply chain isn’t changing. And customer demands are the same as they were 20 years ago. And Joe’s not going anywhere … I’m sure that rumor about Joe retiring next year is just a rumor – he wouldn’t leave us hanging like that.

Well if Joe does leave I’ll just use spreadsheets. I’m pretty good at Excel. I even know how to link multiple worksheets together and get totals, averages and even charts. So I can certainly capture enough of the details to predict system performance. There isn’t that much interaction between different system components. I can figure out how to deal with seasonal variation in a spreadsheet. Our equipment, suppliers, and people are pretty reliable so there is no reason I should have to deal with things like equipment downtime, late materials, and no-shows – they probably don’t have much impact on my system.

If I need to, maybe I can figure out how to use analytical techniques like queueing theory or linear programming. After all, there is nothing complex about my system. And someone once told me that queueing theory is easy if your system is simple enough.

So I’ll let others use simulation to exactly model their complex systems and fully account for variability. Who cares about 3D animation that helps everyone understand the system and better communicate? Not me! I’m not going to bother with any of that simulation stuff.

But hey, if things go wrong and my company goes under, and you happen to see me in the unemployment line, do you think you might bother to offer me a job?

Happy Modeling!
Dave Sturrock
VP Operations, Simio LLC

Professional Development

The annual Winter Simulation Conference (WSC) starts two weeks from today. Initially as a practitioner and then later as a vendor I have attended over 20 of these conferences in addition to dozens of other similar events. WSC is just one of many events that you could choose to attend. But why should you attend any of them?

All such events are not identical, but here are a few attributes of WSC that are often found in other events as well:

Basic tutorials – If you are new to simulation, this is a good place to learn the basics from experienced people.

Advanced tutorials – If you already have some experience, these sessions can extend your skills into new areas.

Practitioner papers – There is no better way to find out how simulation can be applied to your applications than to explore a case study in your industry and talk to someone who may have already faced the problems you might face.

Research – Catch up on state-of-the-art research through presentations by faculty and graduate students on what they have recently accomplished.

Networking – The chance to meet with your peers and make contacts is invaluable.

Software exhibits and tutorials – If you have not yet selected a product or you want to explore new options, it is extremely convenient to have many major vendors in one place, many of whom also provide scheduled product tutorials.

Supplemental sessions – Some half and full day sessions are offered before and after the conference to enhance your skill set in a particular area.

Proceedings – A quick way to preview a session, or explore a session that you could not attend. This serves as valuable reference material that you may find yourself reaching for throughout the year.

I think every professional involved in simulation should attend WSC or an equivalent conference at least once early in your career, and then periodically every 2-3 years, perhaps rotating between other similar conferences. If you want to be successful you have to keep your skills and knowledge up to date. And in today’s economy, a strong personal network can be valuable when you least expect it.

I hope to see you at WSC in Miami!

Dave Sturrock
VP Products – Simio LLC

Keep Simulation Projects Simple Too

We all have stories about company decisions that make us shake our head. If you have ever worked for a large organization, it may have seemed that some of their decisions were, shall we say, sub-optimal.

For example, one particular organization was using a “home grown” time reporting system that was simple, efficient, and worked well. However upper management felt the need to buy a more sophisticated “name brand” system. Unfortunately it was poorly designed and overly complicated. Rollout required extensive training and retraining to learn the simplest tasks. It was so difficult to use that many employees simply stopped using it in favor of informal arrangements with their managers (who also found it difficult to use). As a result, the company spent a lot of money and wasted a lot of employee time, and in the end they had a system that produced inferior results.

If this was an isolated case, it could be easily forgiven. But I expect most people working for large organizations could cite similar situations. Large organizations often tend to replace simplicity with complexity.

Last week in Keep Simulation Simple I talked about KIS; the Keep It Simple concept of doing just enough to do it well and no more!

I discussed how KIS could be applied to model building, but you can also extend the KIS principle to many other aspects of simulation, especially the tools you routinely use.

For time tracking, you can buy expensive highly integrated software systems like the organization above, or for desk- bound employees you can buy software that will sense or periodically ask and record what you are working on. But the cheaper, simpler and more effective solution is simply using a spreadsheet or paper form and having the employee take two minutes at the end of each day and record time against their tasks for that day. Sure there can sometimes be reason for other methods, but for the majority of us the spread sheet solution is superior.

For project management, choose the simplest tool that will meet your needs. Some projects are complex enough that they need project management software like Microsoft Project or something even better. But in many cases, such software results in a waste of time when a simple spreadsheet could meet your needs. In my experience project management software is often overkill for the types of projects we usually encounter.

In simulation software there is some inclination to buy the most comprehensive software that you can afford. But it is often better just to buy the simplest software that is likely to meet your short to intermediate-term needs. An important caution here – make sure that your software has an adequate upgrade path so that as your needs evolve you can migrate into more feature-rich software without losing your initial investment in software, training, and models.

Stay vigilant for time wasters – they often come disguised as “cool technology” and “time savers”.
Keep It Simple.

Dave Sturrock
VP Products – Simio LLC