Discrete Event Simulation: 5 Reasons Why Engineering and Business Students Should Know It.

The pursuit of a smart interconnected world is being made possible by the integration of simulation and Digital Twin concepts in traditional business processes. Discrete Event Simulation (DES) is one such concept that falls into this bracket as it can be used to model complex business systems and operations. With DES, developing digital simulations of industrial process and business operations to receive business insights or operational excellence is achievable. In fact, the manufacturing industry currently makes use of DES tools to improve complex systems and process such as supply chain management and production lifecycles.

Although discrete event simulation has secured a place of importance in manufacturing cycles, it is still being overlooked in engineering operations and the wider business community. According to research, the lack of integration of DES in these circles is due to an unhealthy dislike of statistics by engineering students and business students are not far behind. Also, students generally overlook the importance of statistical analysis and its importance in simplifying decision-making processes. These among other personal reasons have hampered the integration of DES in engineering and business cycles.

Despite these challenges, discrete event simulation still has a lot to offer engineering and business students who intend to ply their trade in an increasingly digitalized world. To serve as an encouragement as well as outline the importance of DES, the key reasons why discrete event simulation should be taught at engineering and business schools will be discussed.

At the end of this article, students will understand:

  • What discrete event simulation is about and its application in business and engineering industrial niches.
  • The importance of teaching and learning about discrete event simulation in schools.
  • How DES is being applied using case studies to highlight its application and benefits.

What is Discrete Event Simulation About?

Discrete event simulation is a method for the modelling of complex environments or systems where events occur in sequences. It also models the interactions between objects, and system operations within the system where these interactions are time-dependent. Discrete event simulations can also include the uncertainties, constraints, and interdependencies that occur among different events into the model.

A simplistic example of the application of DES is the modeling of a toll-gate system where vehicles must pay a fee to pass through the gate. In this scenario, the system entities in the model are the workstations with tellers and the vehicle queue. The system event is the vehicle arrival and vehicle departure that occurs after a payment. The system states which changes with each event are the number of vehicles in the queue and the workstation’s status which is either engaged with or has disengaged a vehicle. The random variables or uncertainties modeled into the system will be vehicle inter-arrival times and the workstation-service time.

DES tools or software applications capture the different processes that occur around the toll-gate in discrete events. This makes it easier to analyze even more complex systems and factor in hundreds of variables within a DES model and this is what manufacturers do. In manufacturing, a DES tool is used to create a Digital Twin – a digital model of physical entities – of the entire manufacturing system. The digital model makes analyzing the effects of additional processes such as increased supply or demand to the system. Thus creating a valuable digital environment for evaluating the effects of diverse factors to a manufacturing system before making business decisions.

Understanding the Importance of Teaching Discrete Event Simulation in Industrial Engineering Schools

In 2017, the Baker Dearing Educational Trust Fund released a report that stated the deficiencies in Science Technology Engineering and Mathematics (STEM) education. The report highlighted the fact that 45% of students who went through engineering schools believe the knowledge they acquired is difficult to apply in the real world. 61% of students that went through STEM institutes also believe that learning technical skills would have better prepared them for the real world.

In the engineering and industrial community, the real world is being dominated by emerging technologies and Industrie 4.0 where digitalization plays important roles. Thus, a more practical approach to preparing students for this world is needed. This is where the importance of teaching discrete event simulation techniques, analysis, and modeling is needed. The 5 reasons why DES should be taught at engineering schools include:

  • Prepping Students for an Industry 4.0 World – The world is moving to a smarter, interconnected community of things and this also applies to industrial endeavors. Today, engineering is moving from a world dominated by computers and CAD software to one dominated by cyber physical systems driven and the autonomous transfer of information which is what Industry 4.0 is about. While computer-aided design is still an important process in industrial manufacturing, Industry 4.0 focuses more on modeling complete production systems, facilities, and processes to automate every industrial operation.

In a world dominated by cyber physical systems, knowledge about discrete event simulation and the creation of Digital Twin environments is important. Integrating discrete event simulation and the tools used for DES modeling in industrial engineering curriculums is better preparation for a future defined by Industry 4.0.

  • Receiving Business InsightsStatistics from the US. Bureau of Labor shows that industrial engineers who choose to earn a Masters in Business Administration (MBA) earn more than their counterparts who do not. This is because in industrial circles, knowledge about machines or equipment is no longer enough to climb the career ladder. Enterprises now focus on engineers who can handle business analysis and make informed decisions that lead to higher ROIs or enhances efficiency levels.

Discrete event simulation models are renowned for their ability to handle simulations that highlight how events affect specific business operations. This event could be the purchase of new equipment or the introduction of a new material handling system. A DES model can simulate the effects of pending events to entire operations which makes for a more informed decision-making process.

Introducing DES concepts in engineering schools give students the knowledge needed to create models and execute simulations that provide real insight into business operations. This helps students make better decisions and function in diverse industrial niches.

  •  Generate Quantitative and Qualitative Data to Drive Operations – Discrete event simulations provide exact models that allow the data an enterprise generates work for it. This makes it possible to integrate a data-driven approach to optimizing research and development activities and industrial systems. In the real world, managers always want to know why certain phenomena occur in a real system and an in-depth understanding can be achieved through DES.

DES can be used to model entire industrial systems or process as sequences of operations being performed on passive entities. This provides an environment where discrete events can be analyzed and the data they generate used to determine how that particular event can be optimized. This leads to the generation of approximated data which can be used to enhance productivity and workforce performance. Thus, an engineering student can generate the data and sequence of optimized events that can enhance productivity in industrial settings.

Understanding the Importance of Teaching Discrete Event Simulation in Business Schools

The traditional teaching methods in business schools involves the use of traditional classroom teachings and case studies. These have been the backbone of imparting knowledge in MBA programs and these tools have limited value when it comes to practical testing what has been learnt in the classroom. This is where discrete even simulation can help in bringing active learning to MBA classrooms through the simulation of physical systems. Some of the reasons why it is important to introduce DES to the classroom include:

  • Enhancing Business Strategy Sessions –  The ability to create digital models that respond to constraints, and interactive relationships creates a platform for making difficult decisions without having to manage the consequences. This is what the integration of discrete event simulation can achieve in MBA programs. With DES managing supply chains, analyzing the effects of new business concepts moves from simple case studies to an actual digital environment where active learning is possible.

Recently, MBA programs in schools such as Harvard have introduced simulation through gamified scenarios to teach students about communication and project management. Although these games are useful in fostering teamwork, they lack the detailed structure DES offers. For example, a DES system can be used to model the exact business operations that occur in an industrial system. The model will contain a digital representation of equipment, the shop floor layout, supply chain, inventory, and even production and relationship variables. Constraints and additional variables can also be added in real-time and their impact analyzed.

With this knowledge business students or professionals will be better prepared to handle the uncertainties and real-time occurrences that can affect business operations in the real world.

  • Enhance Predictive Analytics – The ability to accurately predict how future events will affect complex systems provides businesses with the competitive edge needed to carve a market share in a competitive industrial space. Discrete event simulations create models that can be used to drive predictive analysis in complex systems. This is like the use of CAD simulations to conduct stress analytics on prototypes but at the systematic level. What this means is DES models can be used to create Digital Twins of very complex operations and accurately include every parameter or constraint that defines the complex system.

An accurate DES model is a powerful tool for business development professionals and project managers. The professional gets a digital representation of sequenced events and the ability to introduce even more events to determine their impact on the overall system. Thus, business insights can be received on the best facility layout that guarantees just-in-time delivery, the percentage increase in supply chain speed to meet set deadlines can also be calculated among other things. To take advantage of the predictive analytical powers of DES, it must first be understood. MBA programs can kick start the process of elevating students understanding of predictive analytics and its implementation by utilizing discrete event simulation tools.

  • Testing and Integrating New Business Policies – The responsibility of developing new policies that can lead to a high-performing system lies squarely on the shoulders of the business developer or analyst. In many MBA programs, the creation and experimentation phase attached to developing business policies is done through case studies and understanding older standards. This means new business policies or concepts are generally created on paper without having any means of determining their impact on existing systems.

Introducing discrete event simulation in MBA programs can change the rather limited methods of teaching policy developments using simplified business simulation tools. With a DES tool, facilities and business operational systems can be modeled for the introduction of policies in real-time. An example, can be the management of the move from a traditional shop floor to an automated factory using limited resources. In this scenario, a DES tool can be used to model the existing system operations and analyze the effects of introducing automated equipment or maintenance procedures to the system. The end result will be the development of an Industry 4.0 business model that highlights the business operations to automate for the best returns.

  • Preparing Students for a Changing Business Landscape – The business developer or administrator is expected to be able to function in every industry where business operations take place. This could be in the automotive industry or the healthcare industry. The purpose of business school is training students to apply business strategies, management, statistics, and economics to increase ROI. Therefore, the student must acquire the required knowledge needed to use available tools to grow business operations.

Discrete event simulation tools are examples of some of the tools that can be applied in any business operation regardless of its industrial niche. This is because DES can be used to model both simple and complex systems for simulation purposes. Thus introducing it to MBA programs is an excellent way to prepare MBA students for the career changes that will occur throughout their professional lives.

Conclusion

Discrete event simulation is set to play a starring role in the digital transformation taking place in every industry. The reasons stated above highlight the importance of integrating it in STEM and business schools. According to academic research, students learn best through active learning using technological tools. Thus, integrating DES tools in classrooms is a great option for analyzing complex systems through digitalization and 3D visualizations.

Resources:

https://link.springer.com/article/10.1186/s41039-015-0014-0

https://www.businessstudent.com/careers/salary-outlook-mba-in-engineering/

https://gineersnow.com/engineering/engineering-students-need-take-statistics-subjects-seriously

https://sciencecouncil.org/schools-are-ineffective-at-preparing-students-for-technical-careers/

https://www.businessstudent.com/careers/salary-outlook-mba-in-engineering/

https://journals.tdl.org/absel/index.php/absel/article/view/62

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/