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.

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

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

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.

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

See into the Future using Digital Twin

Simulation through digital twin allows you to identify problems before they occur, to plan for the future and even develop new opportunities.

Early examples of digital twin were created in NASA’s ground stations where they would model their spacecraft conditions during a mission to allow them to usefully assist those in space. This proved vital when Apollo 13 ran into trouble and had to be rescued in a very limited time using a plan devised by engineers on the ground through simulation, or twin, of the astronaut’s real situation.

Nowadays, we have many examples of how digital twin is driving innovation and performance:

In Transportation:

• Real time feeds from public transport for mapping of the location and condition of trains
• Live simulation of aircraft flight data for early detection of potential defects or faults
• Models of wind turbines that help predict potentially damaging weather conditions

In Healthcare:

• Tracking of important patient data relating to their health and lifestyle parameters
• Modeling the human heart for diagnosis and to test medications and side effects
• Hospital environment simulation to predict optimal climate control and ward layout

An operational twin brings together all the current available status data, to be at your fingertips when needed. Modeling your operations allows you to make better decisions, based on trusted, up to date and accessible data. Furthermore, historical data can be applied to models in order to project possible outcomes for comparison purposes.

• faster, easier decision making
• reduced risk of error and downtime
• improved productivity and performance.

Bridging the physical and digital worlds, digital twin allows you to uncover opportunities in the virtual environment that can then be applied in the real world to benefit your business.

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.

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

Production Planning Software and Industry 4.0

The latest era of industrial revolution – Industry 4.0 connects and revolutionizes various aspects of the industry including manufacturing processes as well as business processes such as supply chain. The increasing demand of customized product from the customer end is a major driving theme of this transformation in the industry. The traditional processes are highly efficient for batch production and low cost scaling in bulk manufacturing but are relatively time consuming inefficient for manufacturing customized products. Similar is the case for the business processes and models that being used around this manufacturing style. There is need of new production planning style which can simulate the costs, efficiency and resource requirements in real time for any product for mass customization.Industry 4.0 uses Cyber Physical Systems (CPS) and Internet of Things (IoT) to introduce technological and human improvements, which ultimately results in enhanced productivity, product quality with reduced manufacturing time and product price. Hence, the requirement of an advanced production planning and scheduling scheme becomes paramount. In this article,we will discuss how production planning can be implemented in Industry 4.0 and the ways in which it will help manufacturers of any and every product to adapt easily to customer demands and transition smoothly into the upcoming industrial economy.

Industry 4.0 brings along the requirement of new process and production planning where most of the working environment is automatized and the data recorded is processed using fog computing, on-premise clouds or cloud computing servers. Machine to Machine communication is expected to increase more than ever. These changes raise some critical questions and concerns regarding the manufacturing and planning processes:

• Is it possible to completely automatize production planning using CPS and IoT?
• Can human knowledge be translated into future products?

The role of Production Planning Software in Industry 4.0 will be to address these concerns effectively and ensure that the decision making processes involved in process selection, resource allocation, operation sequence and scheduling and sufficiently automatized with knowledge importer from previous processes. This should then result in the modeling of the future product including customer based customization demands as well.

Traditional process planning being used in many industries presently is completely based only on the knowledge and experience of the individual or team working on the system. The people working on the systems are technology experts from experience rather than knowledge. The existing demand for change to the new technology solutions can be a big transition for such individuals. This might slow down the progress of these industries, especially SMEs which are slower in the adaptation process. Hence, it is important for each industry to build their own strategy to implement Industry 4.0.

All the manufacturing resources in the industry are now connected to data and information exchange enabling better quality and process control. Scheduling of the product manufacturing and supply chain are being solved by using dynamic scheduling with the help of Structure Dynamics Control (SDC). Data and knowledge is transformed to software that makes a decision based on the technical specification of the order and available material combinations. This type of process planning has been adopted completely in very few industrial processes such as welding.It is still a challenge for many manufacturers to figure out what would be the optimal technique if an industry manufactures various products with different set of technologies. Also, the scaling of this single technology-single product scheme( e.g. welding) might not be easy on multiple types of products. Visualization of the process and predetermining the resource requirements will become more important. Simulation of the complete Production Planning using real time data can be an effective solution to this problem.Let us see how a product planning software can make the manufacturing process ’smarter’.

”Smart products” enable an industry to include information about customization demands of the consumer,collect feedback which can then be used in knowledge databases used in the various phases product design, development and manufacturing process. These include process planning, operation sequencing and scheduling. The collaboration of various product parameters and consumer needs in each stage of product development cycle allows the manufacturer to continuously improve the product quality and optimize the manufacturing costs effectively in real time. This results in an overall better product from both consumer and manufacturer’s point of view. Product Planning Software enable this whole cycle managing various processes starting from material selection, shape, geometry, operation priority, time of operation, machine cost and avail- ability and many more. The Product Planning can also be linked to the ERP( Enterprise Resource Planning Software) in the cloud to include insights and data to other parts of product lifecycle resulting in a better product with every iteration.

A good production planning software that automatizes the various tasks of the product development cycle is a must for mass customization and improved efficiency in Industry 4.0. Thus, it can be easily concluded that a good Planning Production Software will form a critical building block of the industry in Industry 4.0.

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.

Not Just Another Industrial Revolution

As we experience this exciting time in history, the 4th Industrial Revolution is happening all around us, without most people even knowing about it. Massive leaps forward are being made possible by the digital platform that the whole world is adopting.

But how did we get so far in the relatively short time of 200 years – in the span of a few generations?

It was only in the final years of the 18th century that the 1st Industrial Revolution began when steam power changed how things were made and transported. The invention and refinement of the steam engine and the use of hydraulic power enabled the economy to develop, and allowed people to move forward and experience growth and travel.

100 years later, in the 2nd Industrial Revolution, electricity facilitated assembly lines and mass production, sparking the consumer age and creating further opportunity for innovation and discovery.

The momentum continued when, at the beginning of the 1960s and throughout the 3rd Industrial Revolution, computing allowed machines and networks to spread to homes, schools, universities and workplaces, developing the potential for study and the exchange of ideas, promoting further advancement.

Each subsequent decade brought significant progress; semiconductors and mainframe computers in the 1960s, personal computers in the 1970s and 1980s and the internet in the 1990s.

The 4th Industrial Revolution, or Industry 4.0, is upon us in this 21st century as we build even further on that foundation, with the potential to achieve exponential growth in what we are able to attain. Reaching across disciplines, we are now transferring technology between the physical, digital and biological domains.

Change is happening faster and new developments are spreading more quickly than ever. Cyber-physical systems are melding the physical and virtual worlds, using simulation and virtual reality and even creating Digital Twins.

All of this allows us to study and understand the world we are creating, speed up the design process and predict behavior, in order to boost productivity and prevent disaster.

In this way, simulation and scheduling software is an important part of our latest Industrial revolution, sitting comfortably alongside the other enablers:

 Additive Manufacturing Augmented Reality Autonomous Robots Big Data Cloud Computing Cyber Security System Itegration The Internet of Things (IoT)

The perfect tool for the Smart Factory, Simio Simulation Software helps capitalize on the Industrial Revolution that is happening around us; improving agility, increasing productivity and mitigating risk as the next stages in the process of disruptive change unfold in our exciting new digital age.

Contact Simio today to speak with our Engineers about the Simio advantage:

1-412-528-1576  inquiries@simio.com

Take advantage of our fantastic Simio Training offer.

For a limited time only, we are offering a \$2,000 training voucher with every Simio software license purchased.*

\$2,000 Credit towards Simio Standard Training

You can also use your voucher towards one of our Simio online Paid Certification Programs or for books or publications available to help you learn Simio faster.

The Benefits of Simio Training

Learning from our experienced trainers, Simio Simulation and Scheduling Software allows you to:

• explore the capabilities and advantages of Simio software
• update your skills to the latest in simulation technology
• learn using examples relevant to your own application

Simio’s professional trainers customize the course for you, in order to make the most of the learning opportunity. All printed course material is included, as well as the necessary access to Simio software.

There is Simio training can be with Simio, any participating partner or on-site:

4-Day Training

Taking it further, this training develops competence by using specific, relevant examples for the model data and scenarios and focusing on the interpretation of results.  It also includes many tips on how to fast track your simulation projects and ensure their success.

Call Us Now to claim your \$2,000 Training Voucher and find the right Simio training for your needs: 1-877-297-4646(Toll Free) or Direct 1-412-528-1576

* Terms & Conditions of offer:

Purchase must be a perpetual license of the Design Edition, or higher.

Payment or PO must be received before December 31st, 2017.

Voucher value is \$2,000 US Dollars and expires October 31st, 2018.

No other discounts apply to the license purchase which must be for commercial use.

Offer ends 31 December, 2017.