Many managers don’t understand what exactly risk analysis is.  We put together some of the most common questions with responses for you.

What does the risk percentage mean?

The risk percentage approximates the on-time probability for an order with appropriate consideration of the number of replications or “experiments.”  It tells the user how confident they can be in meeting the due date given how many trials they have conducted.

How does Simio calculate the on-time probability?

Simio adjusts from a base rate of 50% with each risk replication.  If an order is on time in an individual replication, Simio updates the probability, increasing it closer to 100%.  If the order is late, Simio decreases the probability closer to 0%.  Each replication is an experiment that provides new information about the likelihood of success or failure.  More experiments mean more confidence in the answer.

Why is the base rate 50%?

Before any plan is generated or any activity is simulated, there is no information about the order other than the possible outcomes.  Because there are only two outcomes that matter (on time or not), the base rate is set to 50%.

I have an overdue order in my system.  Why is it not always 0%?

Because the calculation is an adjustment of a base rate of 50%, Simio needs a lot of evidence before it will guarantee that an order will be late (or on time for that matter).  If the user runs 1000 replications, and the result is late in all of them, Simio will reflect a 0% on time probability.

What formula does Simio use to calculate the probability?

For the statistics experts, Simio uses a binomial proportion confidence internal formula known as the Wilson Score.  We report the midpoint of the confidence interval as the risk measure.

Why not just report the outcome of the replications as the probability (e.g., if 9 of 10 are on time, report 90% on time probability)?

This was the original implementation.  However, it gives a false sense of confidence and can be misleading.  A single replication would always yield either 100% on time or 0% on time.  We wanted the answer to also give decision makers a sense of how confident they could be in the answer.  Using the Wilson Score, a single replication will yield a result of 60% at best and 40% at worst (using 95% confidence level).  This helps the decision maker identify that they have a very small sample of data and would encourage them to run additional replications.

Can you give me an example of how this works?

Risk analysis can be demonstrated using any scheduling example.  It is best viewed in the Entity Gantt.  In the screenshots below, we’ve included 2 orders from the Candy Manufacturing Scheduling example.  One of the orders is overdue (will be late always), and the other has plenty of time (will be on time always).

The base rate is 50%.  After 1 replication, Simio updates the probabilities.  Order 1 now has a 60% on time probability.  Order 2 has a 40% on time probability.

After 2 replications, 67% and 33%:

After 5 replications, 78% and 22%:

After 100 replications, 98% and 2%:

Finally, after 1000 replications, 100% and 0%:

How many replications should I run?

By default, we suggest 10 replications (and 95% confidence level).  With these settings, a risk measure of 86% is a good sign, while 14% is a bad one.  Beyond the default settings, there are several additional factors which are dependent on the situation and use case.  One of these factors is slack time (the time between estimated completion and due date).  On the Gantt, slack time is the distance between the grey marker and the green marker.  If the slack time is large, a single replication may suffice.  If the slack time is small, additional replications will help identify if the order is in trouble or not.

Now that I know my risk, what can I do about it?

Depending on your position in the organization (and therefore your decision rights), you can change either the design or operation of the system.  Example design changes include things like adding another assembly line or buying another forklift.  These changes are long term and may require approvals for capital expenditure (which the model facilitates by quantifying the impact of the expenditure).  Example operational changes include things like adding overtime, expediting a material, or changing order priorities, quantities, due dates etc.  Bridging the gap between design and operation are the dispatching rules, which relate to overall business objectives.  They are also flexible parameters which control how Simio chooses the next job from a queue (e.g., earliest due date, least setup, critical ratio, etc.).  All of these parameters influence risk and can be changed, provided that the user has the authority to change them.

Will Simio choose the best design and operation for me?

Decision rights and business processes have far reaching consequences.  A floor manager can probably authorize overtime if the schedule looks risky.  He probably cannot buy a piece of equipment.  To change a priority or a due date, he probably needs to consult with the commercial team and/or account managers.  To expedite a material, he probably needs to communicate with the procurement team.  To make a capital expenditure (i.e., change system design), he probably needs executive/financial approval.  Our solution respects those boundaries.  We treat priorities, due dates, etc. as inputs rather than outputs.  Any of these parameters can be changed by the appropriate decision maker.  They should not be changed by the tool without consent.  Simio assists the decision maker (at any level in the organization) by exposing the true consequences.

With so many choices, how can I quickly explore the consequences across multiple scenarios?

The experiment runner is used to explore consequences (which we call Responses) across multiple scenarios where a user can influence the parameters mentioned above (which we call Controls).  If the solution space is very large (i.e., there are many controls with a wide range of acceptable values), we recommend using OptQuest to automate the search of the solution space based on single or multiple objectives (e.g., low cost and high service level).  OptQuest uses a Tabu search which learns how the control values influence the objectives as it explores the solution space.

How often should I run these type of experiments?

Experiments are most relevant to design choices.  Operational decisions have many hard constraints which cannot be easily influenced.  For example, though Simio will allow you to adjust material receipt dates of critical materials and show you the impact on the schedule, many of them are inflexible and out of control of planner or even the business.  If you ask OptQuest how much inventory you would like to have, it will tell you, but this information adds no value because it is not actionable in the short term.  The planners need to work with what they have and make the best of it.  In practical application, we recommend running large experiments to explore design decisions on a monthly or quarterly basis.

# Forward Scheduling: What is it and how does it differ from backwards scheduling?

Simio is a forward scheduling simulation engine.  We do not support backwards scheduling.  We have found the backwards scheduling approach fails to represent reality, thus generating an infeasible plan that is unhelpful to planners.  Many of our customers have learned this lesson the hard way.

The underlying principle of forward scheduling is feasibility first.  A schedule is built looking forwards considering all the constraints and conditions of the system (e.g., resource availability, inventory levels, work in progress, etc.).  The schedule is optimized in run time while only considering the set of feasible choices available at that time.  Decisions are made according to user specified dispatching rules (the same as backwards scheduling).  The output is a detailed schedule that reflects what is possible and tells the planner how to achieve it.  As in real life, a planner can only choose when to start an operation.  Completion date is an outcome, not a user specified input.

The most salient technical difference between the two approaches is material availability (both raw and intermediate manufactured materials).  A forward-looking schedule makes no assumptions.  If materials are available, a finished good can be produced.  Otherwise, it cannot.  If the materials must be ordered or manufactured, the system will order them or manufacture them before the finished good can start.  A backwards schedule plans the last operation first, assuming that materials will be available (*we have yet to find an environment where future inventory can be accurately forecast).  If the materials must be produced or purchased, it will try to schedule or order them prior, hoping that the start date isn’t yesterday.  If the clock is wound backwards from due date all the way to present time, the resulting schedule shows the planner what their current stockpile and on-order inventory would have to be to execute the idealized plan.  It does not tell the planner what they could do with their actual stockpile and on-order inventory.

Next consider a situation where demand exceeds plant capacity (this is reality for most of our customers).  The plant cannot produce everything that the planner wants.  The planner must choose amongst the alternatives and face the tradeoffs.  Forward scheduling deals with this situation by continuing to schedule into the future, past the due date, showing the planner which orders will be late.  By adjusting the dispatching rules, priorities, and the release dates, the planner can improve the schedule until they reach a satisfactory alternative.  Every alternative is a valid choice and feasible for execution.  Backwards scheduling deals with this situation by continuing to schedule into the past, showing the planner which orders should have been produced yesterday.  The planner must tweak and adjust dispatching rules and due dates until finding a feasible alternative.  In our experience, the planner can make the best decision by comparing multiple feasible plans, rather than searching for a single one.

Any complete scheduling solution must also be capable of rescheduling.  Rescheduling can be triggered by any number of random events that occur daily.  In rescheduling, the output must respect work in progress.  Forward scheduling loads WIP first, making the resource unavailable until the WIP is complete.  Backwards scheduling loads WIP last, if at all.  Imagine building a weekly schedule backwards in time, hoping that the “ending” point exactly equals current plant WIP.  The result is often infeasible.

In terms of feasibility, the advantages of forward scheduling are clear.  But we also get questions about optimization, particularly around JIT delivery.  A quick Google search on forward scheduling reveals literature and blog posts that describe forward scheduling “As early as possible” (meaning a forward schedule starts an operation as soon as a resource is available, regardless of when the order is due).  This is false.  Forward scheduling manages the inventory of finished goods the same way the plant does.  A planner specifies a release date as a function of due date (or in some cases specifies individual release dates for each order).  In forward scheduling, no order is started prior to release date.  The power of this approach is experimentation.  Changing lead time is as easy as typing in a different integer and rescheduling.  As above, the result is a different feasible alternative which makes the tradeoff transparent.  Shorter lead times minimize inventory of finished goods but increase late deliveries and vice versa.  We have found many customers focus on short lead times based on financial goals rather than operational goals.  Inventory ties up cash.  Typically, the decision to focus on cash is made without quantifying the tradeoff.  We provide decision makers with clear cut differences between operational strategies so that they can choose based on complete information.

Forward scheduling is reality.  It properly represents material flows and constraints, plant capacity, and work in progress.  It manages the plant the same way a planner does.  Accordingly, it generates sets of feasible alternatives that quantify tradeoffs for planners and executive decision makers alike.  It answers the question “What should the plant do next?” as opposed to “What should the plant have done before?”  We’ve found the feasibility first approach is the most helpful to a planner and therefore the most valuable to a business.

# How Do You Create a Digital Twin – 5 Things to Know

The Digital Twin is reminiscent of the early days of the personal computer in many ways. Initially, creating a digital twin required excessive computing power and multiple engineers working round the clock to develop digital representations of physical models. And just like the personal computer, technological advancement led to the creation of cloud-based digital twin solutions which made it possible for everyone to explore digital transformation and it is benefits.

Today, the digital twin market is expected to grow exponentially and this growth is being driven by the approximately 20 billion sensors and endpoints around the world. Advancements in IoT and IIoT have also played a role in increasing the adoption rate of digital twin technology which are some of the reasons why digital representations of almost any entity or process can be created today.

The benefits of the digital twin include the ability to make real-time decisions, receive insight from complex processes or systems, and plan better for the future. You can explore how digital twin can help your enterprise or individual pursuits by reading relevant case studies here. Now, to reap these benefits, a digital twin of a chosen process, object, facility, or system must be created which is what this post is all about. Thus, if you have ever wondered what it takes to develop a digital twin then bookmarking this is recommended.

## 5 Things You Should Know About Creating a Digital Twin

The task of creating a digital twin may sound daunting but like most activities diving in headfirst without overthinking simplifies the process. Once you have the required tools needed to create a digital twin, the supporting technologies such as Simio provides you with prompts and interactive information needed to complete the process. To successfully create a digital twin, here is what you need to know and the resources you need to have:

1. Defining the System – The first step to creating a digital twin is defining the system, process or object to be digitized. To do this, an understanding of the entity is required and this can only be achieved through data capture. Thus, data defines the system to be digitized and introduced into the digital space.

The data capture process is generally fluid in nature and depends on the entity or system being considered. For manufacturing facilities, the data that defines a system or process can be gotten from assets within a facility these assets include original equipment, shop floor layouts, workstations, and IoT devices. Data from these assets are captured using smart edge devices, RFIDs, human-machine interfaces and other technologies that drive data collection.

With physical objects such as vehicles, data capture is done through sensors, actuators, controllers, and other smart edge devices within the system. 3D scanners can also be used to extract point clouds when digitizing small to medium-sized objects. The successful capture of the data a system or object produces defines the system and is the first step to creating a digital twin.

• The Identity of Things – One of the benefits of a digital twin is the ability to automate processes and develop simulations that analyze how a system will operate under diverse constraints. This means the system or facility to be digitized must have its own unique identity which ensures its actions are autonomous when it is introduced into a system.

To achieve this, many digital twin platforms make use of decentralized identifiers which verify the digital identity of a self-sovereign object or facility. For example, when developing a digital twin of a facility, the entire system will have its own unique identity and assets within the facility are verified with unique identities to ensure their actions are autonomous when executing simulations within the digital twin environment.

• An Intuitive Digital Twin Interface – Another important element or choice to make when creating a digital twin is selecting a technology or software that can help you achieve your goals. You must be clear about how the technology can help you achieve your goals of a digital twin. Some things you need to consider when choosing a digital twin software or platform include:
• How the software handles the flow of data from the IoT devices or facility and other enterprise systems needed to understand and digitize the chosen process.
• You need to understand how the software recreates physical objects or assets into its digital ecosystem. Some technologies support the use of 3D models and animations when recreating entities while others do not deliver that level of clarity.
• When digitizing complex systems with hundreds of variables that produce large data sets, the computing resources needed to create and manage a digital twin is increased. This makes computing power and resources a key consideration when choosing a digital twin platform or solution. The best options are scalable digital twin technologies that leverage the cloud to deliver its services.
• An intuitive digital twin solution also simplifies the process of creating digital representations of physical assets. The technology should also be able to understand the data produced across the life-cycle of an asset or at least integrate the tools that can manage the identity of assets within the digital twin.
• Another key consideration is the functions you expect the digital twin to perform. If it is to serve as a monitoring tool for facilities or for predictive maintenance, a limited digital twin software can be used while for simulations and scheduling a more advanced technology will be required.
• Start Small with Implementation – When taking on the implementation of digital twin technology, it is recommended you start small. This means monitoring the performance of simple components or a single IoT device within a system and expand with time. This hands-on approach is the best way to understand how the digital twin functions and how it can be used to manage larger systems according to your requirements.

With this knowledge, you can then choose to explore the more sophisticated aspects or functions the digital twin offers such as running complex discrete event simulations and scheduling tasks. A step by step approach to implementing or creating a digital twin provides more learning opportunities than initiating a rip and replace approach when developing one.

• Understand the Security ConsiderationsAccording to Gartner, there will be 50 billion connected devices and 215 trillion stable IoT connections in 2020. As stated earlier the increased adoption rate of digital twin technology and the connected systems around the world bring up security challenges. These security considerations also affect the digital twin due to the constant transfer of data from the physical asset or process to the digital twin ecosystems.

When creating a digital twin, a plan must be in place to handle secure communication channels across networks and other vulnerabilities. To effectively do this, an understanding of the different communication protocols used within a system is required. This is why when choosing a digital twin technology, security challenges and how the platform mitigates risk must also be considered.

## Creating a Digital Twin with Simio

Simio digital twin technology provides an extensive framework for creating digital twins of physical processes and facilities. The key considerations such as 3D representation, animation, scaling up functions, and simulation can be achieved within Simio’s environment.

If properly created, the digital twin can be used to drive data analytics initiatives, predictive maintenance, design layouts, and simulate diverse working scenarios. Thus, anyone or an enterprise can explore the benefits of the digital twin using Simio to create digital representations of complex systems or simpler ones. You can learn more about using Simio to create digital twin representations by registering for the Simio Fundamentals Course.

# 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.

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.

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.

## 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.

# Scheduling in the Industry 4.0

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.