Industry 4.0 Revolution: Understanding the Digital Twin and Its Benefits

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

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

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

The Most Important Benefits of Digital Twin Technology

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

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

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

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

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

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

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

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

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

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

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

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

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

Take Advantage of The Benefits of Digital Twins

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

Resources:

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

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

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

Digital Twin Technology: 5 Challenges Businesses Face By Overlooking It

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

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

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

This article will discuss:

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

The Five Challenges Businesses Face Not Embracing Digital Twins

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

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

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

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

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

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

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

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

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

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

Next Steps

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

Resources:

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

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

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

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

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

Simio now has the GSA Advantage!®

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

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

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

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

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

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

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

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

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

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

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

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.

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

General Simulation Project Approach

People often wonder “When is the best time to incorporate simulation into a project?” The answer, without a doubt, is at the earliest possible moment — when an idea for a significant system change or major investment is first being discussed. While it is true that at this early point in a project there are many unknowns and often very little data, simulation can still provide significant value with often a very low level of effort. While the specific issues obviously vary based on the exact systems, at these early stages you are often looking for gross measures of capacity planning and throughput analysis, impact on other facilities, and early identification of potential problem areas.

With modern tools, you can often create high-level simulation models to study such issues in not much more time than it might take to develop a comparable spreadsheet. But instead of using a spreadsheet that is limited to often misleading static analysis and fairly simple relationships, simulation can take full account of the variation and complexity present in most real systems. And as the project concepts mature, the simulation can expand and mature along with it and continually provide value at each step of the project.

For example a project might go through phases with typical questions like these:

1. Early concept validation – How will this new system work? What is the estimated capacity and throughput? What impact will this have on existing facilities? How can I communicate potential issues to stakeholders?

2. High-level system design – What components should be included? What are realistic design objectives? Evaluation of trade-offs of various investments and level of capability provided. High-level bottleneck analysis. Identify “surprises” while they are still easy to deal with.

3. Detailed system design – What specific equipment should be used (e.g., degree and type of automation)? What procedures should be implemented? What reliability can be expected and how will that impact performance and costs?

4. Implementation –Does the system perform as expected and if not, why not and how can it be “fixed”? What is the optimal staffing? When is a “change order” worthwhile?

5. Start-up – What is the impact of learning curves? What are realistic expectations during transition to full capacity? How long will that transition require? What special procedures should be put in place during that transition, what is their cost, and how soon can they be phased out?

6. Operation – How to plan and schedule the intermediate and short-term facility operation? How to effectively deal with the variability present in all systems (e.g., equipment and personnel problems, demand variation, shifting priorities, …)? How well is the system performing on the actual demand as opposed to the originally anticipated or “optimal” demand?

7. System improvement/re-design – As the system reaches stable operation, new ideas, procedures, and technologies will occur. What would be the impact of incorporating changes? Which changes have the best ROI? How do the changes relate to each other?

Until next time … Happy Modeling!