Analyzing the Paradigm Shift from Production Scheduling to Simulation-Based Scheduling

Through the long centuries of man’s existence, man has always produced materials and products for specific uses. But at the turn of the 17th century, something interesting happened. Man had built industrial equipment for the first time which ushered in the age of industrialization. This age came with larger facilities dedicated to every aspect of the production lifecycle as we know it today. With these large facilities came the need to manage hundreds of workers, the transportation of materials, and the stages of production for a product. And as early as the 1800s, the need for production scheduling methodologies was apparent.

This need led to the development of scientific management processes by legendary figures such as Henry Gantt. In the 1800s, charts and manual data collection techniques were introduced to manage production scheduling challenges. Although these solutions worked perfectly with the industrial equipment and facilities of that age, advancements in production technology made them redundant by early 1900s.

Moving forward to the 80s, production scheduling was being defined as the process of planning to ensure the raw materials and production capacity within a facility are optimally allocated to meet demand. With time, this definition was updated to account for complex tradeoffs between competing priorities and the hundreds of varying relationships that occur on manufacturing shop floors.

To handle these complex trade-offs and production variables, advanced planning and production scheduling systems where developed. These systems or solutions were fondly called APS solutions and they accounted for the materials available for a production cycle, available labor and production capacity. APS systems successfully handled the scheduling of complex production processes by applying a constraint-based approach to scheduling. Thus, these tools created schedules for:

  • Capital-intensive production process where constraints such as equipment and plant capacity where constraints to deal with
  • Production processes where hundreds of components needed to be assembled when building the product.
  • Production processes with changing schedules which were not predicted at the beginning of the process.

The success of production scheduling systems also led to the creation of hundreds of enterprises offering APS solutions and services to ease complex scheduling activities. Other spin-off solutions such as customer relationship management applications and enterprise resource planning solution were also developed due to the success of production scheduling systems.

As with most great technological advancements, the traditional product scheduling solutions began to meet more complex situations than it could handle due to the changing manufacturing landscape. These changes are both technological and conceptual in nature. In terms of technology, the advent of Industrial Internet of Things, smart manufacturing equipment, and automation were changes traditional scheduling software could not deal with. While the conceptual changes include the need to account for all data produced on the shop floor, make predictive analysis, manage disruptions in real-time, and cybersecurity challenges among others. These changes limited the efficiency of production scheduling software in diverse ways which will be further explored.

The Limitations of Production Scheduling Solutions

The limitations of production scheduling tools are all due to the increased complexity of today’s manufacturing and industrial facilities, as well as, the demand for more insight by enterprises. These limitations include:

Flexibility Challenges

The ever-changing processes in modern manufacturing facilities and the introduction of new equipment and process to the shop floor must be integrated into a functional scheduling system. The ability of traditional production scheduling tools to adapt to these changes is limited which means the schedule they produced will be skewed.

Challenges Integrating Real-Time Occurrences

The effects of downtime in manufacturing and industrial facilities have been highlighted in hundreds of reports. Downtime can be caused by a variety of issues but for the topic of production scheduling, a machine going down in a shop floor is the perfect scenario. Production scheduling tools will struggle to predict this event or even take it into account to reschedule events in real-time.

Although production scheduling tools can create schedules that take into consideration defective equipment, they make use of approximated data. This means the schedule they produce are static in nature and would not take into consideration real-time data such as the location of the machine, output at its workstation etc.

Requires Numerous Adjustments

This constraint is a follow up to the challenges production scheduling tools have with integrating real-time occurrences. To prevent trashing the systems integrator must create multiple custom algorithms for different scenarios. This means the product scheduling tool takes these algorithms and try to apply them to a new problem within a facility. To accomplish it multiple adjustments must be made to the initial adjustment which defeats the ability to create reschedule in real-time. According to Oracle, this challenge means the traditional product scheduling tools will struggle with finding good solutions to scheduling problems even when they exist.

With these limitations, a new process to accurately manage production scheduling tasks was needed. This led to the paradigm shift from traditional production scheduling solutions to simulation-based scheduling. Simulation-based scheduling involves the imitation of the operation of a real-world process over time using a digital model. The process involves building a simulation model of the physical process and populating the model with the detailed events and processes that occur in the real-world. The simulation model can then be run to produce an optimized production schedule.

The Impact of Simulation-Based Scheduling

It is important to note that simulation-based scheduling can be handled in two ways. These are through a discrete event simulation and a continuous simulation process. The discrete event simulation models the operation of a manufacturing or industrial facility as a discrete sequence of events that occur with time. In this model, events occur at a particular instant in time and record the change of state in the facility.

On the other hand, continuous simulation models continuously track the events and the changes they produce in the facility. Both the discrete event simulation and continuous simulation model take production scheduling to heights traditional production scheduling tools cannot reach. This paradigm shift has made real-time production scheduling more accurate and flexible enough to deal with the changes that occur in modern facilities.

As stated earlier, the introduction of production scheduling tools led to the development of other complementary technology solutions and this is also the case for simulation-based scheduling. One such concept is simulation-based Digital Twin solutions. The Digital Twin involves the mirroring of physical objects to create a virtual model through simulation-based engineering tools.

The ability to create Digital Twins of every facility and industrial process also takes simulation-based scheduling to new heights. Creating virtual mirrors of real-time systems or facilities and simulate the complex process that occurs in these facilities to create a far more accurate schedule than traditional production scheduling tools.

In the case off dealing with downtime, simulation-based digital twin environments can collect data from real-world sensors and use the data to predict asset –manufacturing equipment—behavior. This allows for the scheduling process to account for defective equipment and quickly reschedule the production process around the defective equipment. Also, simulation-based scheduling tools can manage what-if scenarios better than the alternative. Making it possible for operations teams to simulate possible challenges and create optimized schedules that take these constraints into consideration.

An example of how simulation-based scheduling alongside digital twin technology has been used to develop more efficient schedules. Is in the case of CKE Restaurants. Here, a Digital Twin of the restaurant facilities made it possible to create implementation schedules, supply and delivery schedules in its kitchen facilities. The end result was a more efficient production and service process driven by simulation-based scheduling and Digital Twin solutions.

How Simulation-Based Scheduling Transverses through Diverse Industries

Traditional production scheduling tools were designed and developed primarily for use in manufacturing settings and this still remains its key area of application. Unlike production scheduling, simulation-based scheduling can be integrated into any industrial process to produce accurate schedules.

Once again, its affinity with Digital Twin technology makes this possible. This is because, with digital twin technology, every process and asset in an industrial setting can be modeled and brought into a digital environment. The integration of simulation-based software in this digital environment can then simulate the industrial process and create schedules for them. Simulation-based scheduling can be used in the healthcare industry, pharmaceutical facilities, dockyards, ports, and in every facility where a process can be modeled and mapped.

The rise of Industry 4.0 manufacturing facilities and processes where data is king provides another avenue for simulation-based scheduling to prosper. Smart factories are being run by machines and devices with sensors, embedded systems, and system on modules solutions. This makes it possible to assess data from every asset and process in a facility.

Simulation-based scheduling software can leverage the data collected in an Industry 4.0 – compliant facilities to create real-time schedules. Computing simulations of schedules can also be achieved in real-time with increased accuracy due to the widespread availability of data in facilities that integrate Industry 4.0.

Simulation-Based Scheduling and the Road Ahead

The paradigm shifts from production scheduling solutions to simulation-based scheduling is still very much an on-going journey. This is due to emerging technologies which complement and enhance the use of simulation-based scheduling software. Examples include the rise of cloud-computing and high-performing computers (HPCs). These technologies make it possible to create models of very complex systems such as facilities or processes with thousands of variables while producing accurate scheduled for them.

The combination of these technological process will enhance real-time scheduling and rescheduling as we know it. As simulation-based schedule software leverage on the cloud and HPCs, complex simulations can be done in micro-seconds thereby delivering accurate real-time results that enhance productivity in industries. Thus completing the paradigm shift from manual and constraint-based scheduling to a responsive real-time scheduling era.

How to Sell the Idea of Digital Twin to Your Manager

The business world as we know it is changing. Never have there been so many emerging technologies, models, and business concepts competing for the attention of the business community. Today, we have cloud computing services, the Internet of Things, Artificial Intelligence, Robotics, Automation, Blockchains, and the Digital Twin providing timely business insights for enterprises. This is why the internet and even physical business entities have hundreds of salesmen and women trooping in and out of your private space. Selling the ‘next best thing’ in technology like pharmaceuticals marketers do, to CTOs, CIOs, and other decision-makers.

 In this whirlwind of changing activities and millions of ads advertising the best technology solutions is Digital Twin technology. For those who know the benefits of the Digital Twin and its ability to enhance every aspect of an enterprise’s operation, the challenge of convincing management to take a chance with it remains. This leaves one with the question of what are the best techniques to sell the idea of integrating Digital Twin technology to the boss? As with most sales challenges, the traditional answer generally involves listing its value-added propositions and outlining the returns to be made investing in the technology.

Although the traditional answer to selling new ideas to management remains efficient, the increased competition among cutting edge tech services means more selling points are needed. Thus, to effectively answer the question ‘how do I sell the idea of Digital Twin technology to management’, here are some new and timely tips to consider.

5 Tips for Selling the Idea of Integrating Digital Twin Technology to Decision-makers

As a sales representative, business development, or system integrator staff/employee who is part of a team, the successful introduction of new technology solutions depends on your approach. This is because you will serve as the driving force behind ensuring the implementation of Digital Twin technology improves the company’s operational processes to deliver optimal services to customers. The tips for selling the idea of Digital Twin Technology include:

Making Your Case with Data – The task of convincing those who control the money and decide what investments are to be made is not for the faint-hearted. You must come prepared and one of the ways to prepare for every question that may come your way is having the required data in place that answers the most important questions. According to a Mckinsey report, integrating data analytics in the right place or in your sales pitch is one way to convince skeptics on the importance of Digital Twin technology. The data to be included must be relevant to the situation or scenario you intend to create when selling Digital Twin Technology. To simplify your search for adequate information, here are some of the data you will need:

  • To answer the question of the adoption rate and how the competition intends to use Digital Twin technology to enhance business operations, the IDC data on adoption can help. The IDC forecasts that 40% of IoT platform vendors and 70% of manufacturers will be making use of Digital Twin technology by 2022.
  • If the question of how digital twin technology can help increase the revenue of the business, data from the Juniper Research can help you answer the question. According to the research, the use of Digital Twin technology has helped enterprises increase their revenue by 25 to 35%. This is due to the ease in which business insights can be gotten from complex processes and the predictive analytical features of Digital Twin technologies.

Armed with this information, your sale pitch will highlight the importance of staying ahead of the competition by integrating Digital Twin technology to simplify complex processes and difficult business decisions. It can also be applied to drive development and predict future scenarios in a wide variety of industries including manufacturing, architecture, construction, technology, engineering, and healthcare industries.

Make Use of Case Study – With your data in place, the next step to convincing decision-makers in your organization is through the provision of confirmable case studies on how Digital Twin technology can help. This is where a little personal effort comes into play if interested in creating personalized case studies for stakeholders to scrutinize. You can find applicable case studies that highlight how Digital Twin technology has been applied and is still been applied by your competitors here:

  • You can find case studies on the application of Digital Twin technology in the aviation industry, automotive industry, manufacturing, healthcare, mining, and engineering at Simio’s resource center. The case studies here are practical examples that can be integrated into your presentation when making your sales pitch.
  • If you are certain a pitch with case studies may not be enough, then more effort is needed from your end. This effort involves the design of a Digital Twin of a phase of your facilities operations to showcase the benefits of a digital model of physical systems where events can be simulated. Many Digital Twin technology providers offer free trials which can be used to accomplish this task. You can make a request for a Simio Demo to quickly kick start the process of designing a Digital Twin.
  • Provide specific answers to your enterprise or the enterprise’s pain points. Once again, although case studies may be customized to show how Digital Twin technology alleviate business challenges, creating a functional model will do more to pass the message across.

So, the second tip here is making use of case studies to address exactly how Digital Twin technology can be used to eliminate specific challenges an enterprise experiences. The efficient use of case studies is one of the quickest ways to get the ball rolling when trying to sell Digital Twin solutions to the decision-makers in your organization.  

Showcase the ROI – It is a well-known fact that one of the most publicized benefits of Digital Twin technology is the returns it offers enterprises who choose to invest in it. Also, your manager, as well as, stakeholders would definitely expect a breakdown of how much the investment will cost and the returns to expect. It is important to have this in mind because, finances are generally the deciding factor that determines if a positive decision will be made.

According to research by High Tech Software Cluster, the threshold for integrating Digital Twin technology for enterprises costs approximately €50, 000 ($55,000). The study goes on to show that to create digital twin solutions for more complex systems may cost approximately €150, 000 or $165,000. As stated earlier, the returns on this investment can be as much as 35% of the total cost needed to create a Digital Twin. In some cases, returns of approximately 50% have also been reported which highlights the financial leverage Digital Twin technology offers enterprises.

As you probably know, approximations are not enough to sway managers and other decision-makers. This means more exact figures that showcase the total cost of owning a Digital Twin of complex process is needed to successfully sell the idea to management. Calculating the total cost of ownership can be done using an estimate calculator. The estimate calculator is capable of calculating the cost of purchasing the necessary hardware, software packages, energy costs, and other costs associated with owning Digital Twin technologies.

It is also important to highlight any supporting technologies that will be needed for data collection if an accurate Digital Twin is to be developed. These technologies may include embedded systems in manufacturing equipment, IoT devices, cloud computing services to scale simulations, and augmented reality devices. These complementary technologies and services may also add to the bottom line of designing a functional Digital Twin environment of complex systems. Thus, using the estimate calculator to highlight the ROI of creating a Digital Twin is one of the major steps that must be taken to convince management about the need for a Digital Twin.

Ask the Right Questions – During strategy sessions, some push backs are expected and this will definitely be the case when selling the idea of a Digital Twin to enhance business operations. This push back should be expected even after using data to answer questions, creating case studies or applicable scenarios, and defining the return on the investment made. When the expected push back occurs, the best way to understand how management thinks and the challenges they foresee is by asking questions. Asking the right questions provides you with the foundation needed to provide the answers needed to convert non-believers into believers.

The questions to ask are varied and should be determined by the level of skepticism shown by particular decision-makers. Some of the questions you must ask to assess the mindset of your superiors include:

  • Do you need more information to make a decision and what information do you need? The purpose of asking this question is to have an idea about what your audience or manager is thinking. Remember that IT managers are notoriously skeptical about new technology therefore, having an understanding of the prejudices management has against Digital Twin technology is important.
  • I know you love the way things are going, but would you be interested in a 6-month trial? If the feedback you get from the manager and decision-makers is negative due to their satisfaction with how things are within the company, this question might help break the ice. Satisfaction with the present condition of things can dampen the enthusiasm for Digital Twin technology. But pushing for a trial could be the turning point that turns ‘no’ into a ‘yes’.
  • If it helps you surpass your personal targets will you try it? It may come as a surprise to you that managers think more about self-preservation than the success of a business. This is one reason why your manager may be resistant to change. Thus, making your questions a bit personal and putting the manager’s self-interest first may be the strategy that gets him/her to experiment with Digital Twin technology.

These questions should be asked without sounding too pushy to your manager and other decision-makers. This is because a pushy attitude could be interpreted as a desperate attempt to make some money for yourself on the side.

Associate the Integration of Digital Twin with Achieving Business Goals – Finally, the ace in your back pocket should be tying the integration of Digital Twin technology to the ideology of the enterprise. This includes highlighting how Digital Twin can be used to realize the business’s mission statement or meet certain key performance index (KPIs). With the answers to the questions asked above, accomplishing this task should be a bit easier than the first steps of selling the idea to management.

Your ability to showcase the benefits of Digital Twin technology and how it meets your company’s culture or KPIs is determined by your knowledge about the transformative powers of the technology. To accomplish this, a lot of research is needed to know more about how this emerging technology can be applied to your business case. Once again, you can turn to the information highlighted in these tips to refine your pitch to resell the idea of integrating Digital Twin technology to your manager. For more information, you can also choose to attend conferences centered around the adoption of Digital Twin technology in your industry.

The Benefits of Digital Twin Technologies is Worth the Extra Effort

Digital Twin technologies create value in diverse ways that can ease the effort expended doing your job. Some of the more important benefits include:

  • Descriptive Values – The ability to visualize the status of an asset in real-time via its Digital Twin is valuable when those assets or facilities are either remote, complex, or dangerous. In plants and other facilities, a Digital Twin makes information easily accessible for interpretation and to make business decisions.
  • Diagnostic and Standardization Value – In facilities where hundreds of variables are involved with production, Digital Twin technology can be used to stabilize these variables, pinpoint the root cause of problems, and leverage analytics to standardize complex systems.
  • Predictive Value – Industry-leading enterprises like General Electric have used Digital Twin technologies to improve efficiency and the output of plants. This was accomplished using a Digital Twin to propose solutions that can lead to customer satisfaction and profitability.

You can learn more about getting started with Digital Twin technology in your facilities, plants, and business by speaking to a Simio engineer today.

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/

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.

Optimizing the Smart Factory

In the same way that a product development involves prototyping, the production process for manufacturing that product should also be optimized for maximum efficiency and productivity.Discrete Event Simulation (DES) software approximates the manufacturing process into individual events, so can be used to model each step in manufacturing process for overall performance optimization.

The IT innovations of Industry 4.0 allow data collected from its digitalized component systems in the Smart factory to be used to simulate the whole production line using Discrete Event Simulation software.

Real time information on inventory levels, component histories, transport, logistics and much more can be fed into the model, developing different plans and schedules through simulation. In this way, alternative sources of supply or production deviations can be evaluated against each other while minimizing potential loss and disruption.

When change happens, be it a simple stock out or equipment breakdown or an unexpected natural disaster on a huge scale, Discrete Event Simulation software can produce models showing how downstream services will be affected and the impact on production. Revised courses of action can then be assessed and a solution implemented.

The benefits of using Discrete Event Simulation software to schedule and reduce risk in an Industry 4.0 environment include assuring consistent production where costs are controlled and quality is maintained under any set of circumstances.

Scheduling in the Industry 4.0

Today started badly.

As soon as I hopped into my car, the GPS system was flashing red to show queues of stationary traffic on my regular route to the office. Thankfully, the alternative offered allowed me to arrive on time and keep my scheduled appointments.

In the same way as a GPS combines live traffic data with an accurate map of the city, Simio Software connects real time data sources with a modeled production situation. Just like a GPS, Simio can also impose rules, make decisions, schedule and reschedule.

The major difference is in the scale.

Simio Simulation and Scheduling Software can model entire factories, holding huge quantities of detailed data about each resource, component and material. It leverages big data analysis to run thousands of permutations of scenarios, finding the optimum outcomes for specific circumstances. Lightning fast, it can detect and respond to changes with suggestions that will keep everything flowing in the best possible way.

Thank goodness for Simio, because Industry 4.0 is here.

Smart Factories employ fully integrated and connected equipment and people, each providing real time feedback about their state. Data is constantly collected on each product component, for process monitoring and control. Every aspect of the entire operation is managed through its associated specifications and status data. This large, constant stream of information coming from a known factory configuration can be received, stored, processed and reported upon by the powerful Simio software.

With Industry 4.0, nothing is left to chance. Everything is monitored and optimized, and performance is predicted, measured, improved and adapted on an ongoing basis. Management of so many interconnected components requires a scheduling system that is specifically designed to operate in this dynamic data environment. Simio Production Scheduling Software can be relied upon to provide the integrated solution for enabling technology in the Smart Factories of the future.

We are already seeing a rise in robotics and the increasing digitalization of the manufacturing industry under the effects of Industry 4.0. Soon all components of the factory model will be interconnected, just like my future driverless car that will communicate directly with my GPS to take the best route using current traffic information.

All I will have to do is sit back and enjoy the ride.