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.

# Integrating Digital Transformation to Enhance Overall Equipment and Facility Efficiency

The digital transformation of traditional business process and the assets that run them have become one of the raves of the moment. A Forbes-backed research highlights just how popular the topic of digital transformation and the tools needed to accomplish it has become. Statistics like the fact that 55% of business intended to adopt digitization strategies in 2018 which grew to 91% in 2019 highlights just how popular this transformation has become.

The reason for its increased adoption rate is the ease it brings to managing business operations, facilitating growth, and a healthy return on investments made on digital transformation. The numbers from the 2019 digital business survey prove these benefits outlined earlier to be true. 35% of organizations have experienced revenue growth while 67% believe it has helped them deliver better services to customers. But despite its popularity, the adoption of digital transformation brings up a multitude of question many enterprises still struggle to answer. This post will answer some of the more important questions with special emphasis on facility management and efficiency.

## What is Digital Transformation?

Digital transformation refers to the integration of digital technologies into business operations to change how an enterprise operates and delivers value to its customers or clients. Digital technologies generally refer to devices and tools that enable access to the internet thus its use allows organizations to bring operational processes to cyberspace.

The above definition is a simpler version of what digital transformation is about but because digital transformation looks different for every company and industrial niche, other definitions exist. In terms of enhancing equipment and facility efficiency levels, the definition by the Agile Elephant better encapsulates its meaning. Here, digital transformation is defined as digital practices that ‘involve a change in leadership thinking, the encouragement of innovation and new business models, incorporating digitization of assets, and increased use of technology to improve an organizations entire operations.’

In facility management, assets refer to the equipment, tools, and operation stations within the facility while new business models and innovation refer to the integration of digital technology concepts. These concepts can be the digital twin, discrete event simulation or predictive analysis.

## What is Overall Equipment and Facility Efficiency?

Productivity within manufacturing facilities and warehouses are generally measured using the overall equipment effectiveness (OEE) concept. This concept measures the maximum output machines can achieve and compares subsequent output to the optimized value. In cases where the machine or equipment falls short, the OEE falls from 100% and the production cycle may be termed unproductive.

The OEE is calculated using three separate components within facilities and these are:

• Availability – This focuses on the percentage of scheduled time an operation is available to function.
• Performance – This refers to the speed at which work centers compared to the actual speed it was designed to achieve
• Quality – This refers to the number of goods produced and the quality levels compared to optimal production qualities.

Although the OEE process is quite popular and has proved to be efficient, a critical analysis shows that it does not take into consideration some important metrics. OEE calculations do not include the state of the shop floor, material handling processes, and connections to both upstream and downstream performances. This is why its effectiveness as a measuring tool has been lampooned by a plethora of manufacturers with skin in the game.

Criticism of OEE as a performance measurement tool include its lack of ability to breakdown or access granular information in facilities and its lack of multi-dimensionality. The fact that it struggles with identifying real areas that require improvement within facilities is also a deterrent to its efficiency in analyzing factory performances. And this is where digital transformation comes into play.

## Digital Transformation and its Ability to Enhance Facility Efficiency

The ability to digitize assets within manufacturing shop floors have created an environment where granular data can be collected from the deepest parts of today’s facilities. With the data collected due to digital transformation, a clearer picture of how a facility function can be gotten. But the digitization of traditional manufacturing processes and operations have also been a source of debate for diverse professionals due to certain difficulties. These difficulties include assessing data from legacy or dumb assets, managing communications across diverse supply chains, and bringing captured data together to make sense of complex facility operations.

To manage these challenges, diverse emerging technologies have been built around each of them. In terms of capturing data from legacy assets, the use of smart edge technologies that can be attached to assets is currently eliminating this challenge. While standards and communication protocols such as those from the OPC foundation is solving the issue of communication across both smart and dumb assets. Finally, to make sense from the captured data in order to enhance shop floor activities, digital twin technology provides a streamlined approach to monitoring and managing facilities using captured data.

With these emerging technologies, detailed insight at the granular level can be assessed about a particular facility. More importantly, these technologies attached to digital transformation can be used to enhance operational processes by delivering real-time scheduling, analyzing complex processes, and simulating applicable solutions to manufacturing shortcomings.

## Discrete Event Simulation and Enhancing Facility Efficiency

Discrete event simulation (DES) tools such as Simio are some of the emerging technologies that play important roles in transforming traditional factory or facility processes. The introduction of DES can help with mapping out previous event schedules to create optimized scheduling templates that can speed up production processes.

DES tools or software can analyze both minor processes that are subsets of a large one, as well as, the entire complex system to produce schedules that optimize these processes. An example of this was the integration of Simio by Diamond-Head Associates, a steel tubing manufacturing company. The challenges the steel tubing manufacturer faced involved meeting production schedules due to a very complex production process with hundreds of production variables.

With the aid of Simio simulation software and the digital transformation it brings, Diamond-Head associates were able to utilize the large data sets produced by the varying production processes. With this simulation model, optimized schedules built for its manufacturing processes were created and this helped with making real-time businesses decisions. The steel tubing manufacturer successfully reduced the time it took to make a decision from an hour and a half to approximately 10 minutes.

This case study highlights how digital transformation can be used to enhance facility efficiency in diverse ways. These ways include optimizing scheduling procedures and drastically reducing the time needed to come up with accurate solutions to complex manufacturing-related scheduling processes.

## Enhancing Facility Productivity with the Digital Twin

Another aspect of digital transformation is the use of digital twin technologies to develop digital representations of physical objects and processes. It is important to note that the digital twin does more than a 3D scanner which simply recreates physical objects into digital models. With the digital twin, complex systems can be represented in digital form including the capture of data produced by assets within the system.

The digital twin ecosystem can also be used to conduct simulations that drive machine and facility performance, real-time scheduling, and predictive analytical processes. Thus highlighting how digital transformation provides a basis for receiving business insights that change the leadership of an organization thinks and make decisions.

An example that highlights the application of digital twin technology to enhance productivity or facility efficiency is that of CKE Holdings Inc. CKE Holdings is the parent company of restaurants such as Hardee’s and Carl’s Jr. Earlier this year, the enterprise was interested in providing efficient shop floors or restaurant spaces for its employees to increase productivity levels, train new employees, and deliver better services to its customers. To achieve its aims, the organization turned to the digital twin and augmented reality to aid its business processes.

Once again, it is worth noting that both the digital twin and virtual reality tools are digital transformation tools. And with these tools, CKE Holdings Inc. succeeded in developing optimized restaurants with shop floor plans that played to the strength of its employees. The digital twin was also used to test and implement new products at a much faster rate than the traditional processes previously employed by the enterprise.

The end result was a user-friendly kitchen layout that delivered innovation in how CE Holdings restaurants function. The use of augmented reality also added another dimension to the training of new employees. The use of technology ensured new employees learnt through live practical involvement without any of the consequences attached to failure. This also reduced the hours experienced workers spent getting new employees up to speed within the restaurants. Thus highlighting another aspect in which digital transformation can be applied to drive facility efficiency levels.

## The Benefits of Digital Transformation to Manufacturing and Production-Based Facilities

The examples outlined already spell out the benefits of digital transformation and its role in enhancing overall equipment and facility effectiveness levels. But, it is only right to compare and highlight what digital transformation brings to the table against the traditional OEE calculations still used within many shop floors.

• A Complete Picture – Unlike OEE calculations which rely solely on manufacturing data produced from equipment and tools, digital transformation technologies can capture every aspect of the production process. This includes capturing data from the diverse algorithms, scheduling details, assets, sub-systems, and events that occur within the shop floor. This makes the level of details provided by digital twin environments superior to analyzing and enhancing facility productivity.
• Improved Customer Strategy – Digital transformation enables the capture of data highlighting customer satisfaction with end products. This information can also be integrated into the manufacturing circle to ensure customers get nothing but the best service. This means with digital transformation the feedback of customers and employees can be used to enhance production facility processes.
• Improved Employee Retention Strategy – The manufacturing industry is notorious for its high employee turnover rate due to diverse factors that make it unattractive to the new generation of workers. The integration of digital transformation can enhance workplace layout, as well as, bring a more modern and captivating process to manufacturing. These enhancements can reduce the turnover rate and get the younger generation interested in manufacturing.
• Enabling Innovation – The increased adoption rate of industry 4.0 business concepts and models in manufacturing means businesses must adapt if they intend to retain their competitive edges. Digital transformation offers a pathway to innovating legacy business process and increasing an enterprise’s ability to stay competitive in a changing manufacturing industry.

## The Next Steps

The advantages digital transformation brings to enhancing facility efficiency comes with a butterfly effect that affects leadership, innovation, and problem-solving activities. Although the integration process involves technical knowledge of applying digital twin technologies and simulation software, these skills can be acquired with a little effort.

Simio Fundamentals Course offer businesses and other organizations with the opportunity to train staffs about digital transformation and its specific techniques. You can also choose to register employees to participate in the upcoming Simio Sync Digital Transformation 2020 Conference to learn more about digitally transforming your business processes and how to reap the rewards.

# 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.

# Evolution of Discrete Event Simulation Software

Today, discrete event simulation (DES) software and the benefits it provides are currently being used across a majority of industries to simplify business operations, make predictions, and gain insight into complex processes. But before modern simulation software such as Simio could be used to create shiny models and execute real-time simulations, there were earlier technologies that formed the foundation built upon by modern simulation software. As you can probably tell, there is definitely a story behind the evolution of simulation software and today, that story is being told.

To accurately tell this story, the evolution must be arranged in chronological order. The traditional order currently in use today is the order outlined by R.E. Nance in 1995. This chronological order will be used here but with slight edits to accommodate the earliest memories of simulation software and the current strides being made. This is because the most referenced order outlined in 1995, did not take into account the efforts of Jon von Neumann and Stanislaw Ulam who made use of simulation to analyze the behavior of neutrons in 1946.

RE. Nance’s chronology which was written in 1995 could not and did not account for the recent paradigm shifts in DES software. This understandable omission will also be highlighted and included in this post. Therefore, this paper on discrete event simulation should be seen as an update of the history and evolution of DES software.

## The Early Years (1930 – 1950)

Before discrete simulation came to prominence, early mathematicians made use of deterministic statistical sampling to estimate uncertainties and model complex processes. This process was time-consuming and error-prone which led to the early DES techniques known as Monte Carlo simulations. The earliest simulation was the Buffon needle technique which was used by Georges-Louis Leclerc, Compte de Buffon to estimate the value of Pi by dropping needles on a floor made of parallel equidistant strips. Although this method was successful, simulation software as we know it got its origin in 1946.

Sometime in the fall of 46’, two mathematicians were faced with the problem of understanding the behavioral pattern of neutrons. To understand how neutrons behaved, Jon von Neumann and Stanislaw Ulam, developed the Roulette wheel technique to handle discrete event simulations. The light bulb moment came to Ulam while playing a game of Solitaire. Ulam successfully simulated the number of times he could win at Solitaire by studying hundreds of successful plays.

After successfully estimating a few games, he realized it would take years to manually observe and pick successful games for every hand. This realization led to Ulam enlisting Jon von Neumann to build a program to simulate multiple hands of solitaire on the Electronic Numerical Integrator and Computer (ENIAC). And the first simulation software was written.

## The Period of Search (1955 – 1960)

The success of both mathematicians in simulating neutron behavioral patterns placed the spot light on simulation and encouraged government agencies to explore its uses in the military. As with all technological processes, the growth of discrete simulation software could only match the computing units available at that time. At that time, analog and barely digital computers were the springing board for development.

Around 1952, John McLeod and a couple of his buddies in the Naval Air Missile Test Center undertook the responsibility of defining simulation concepts and the development of algorithms and routines to facilitate the design of standardized simulation software. In the background, John Backus and his team were also developing a high-level language for computers. The efforts of the multiple teams working independently of one another led to the development of the first simulation language and software that would lead to the evolution of DES software.

It also highlights the general theme of how technological advancements and software evolutions occur which is through advancements in diverse interrelated fields.

## The Advent (1960 – 1965)

By 1961, John Backus and his team at IBM had successfully developed FORTRAN, the first high-level programming language for everyday use. The success of FORTRAN led to the creation of a general-purpose simulation language based on FORTRAN. This language was SIMSCRIPT which was successfully implemented in 1962 by Harry Markowitz.

Other general-purpose simulation software and systems also sprang up within this period as competing contractors continued to develop simulation languages and systems. At the tail end of 1965, programs and packages such as ALGOL, General Purpose Simulation System (GPSS), and General Activity Simulation Program (GASP) had been developed. IBM computers and the BUNCH crew consisting of Burroughs, UNIVAC, NCR, Control Data Corporation, and Honeywell were developing more powerful computers to handle complex simulations.

One of the highlights of this period was the successful design of the Gordon Simulator by IBM. The Gordon Simulator was used by the Federal Aviation Administration to distribute weather information to stakeholders in the aviation industry. Thus highlighting the first time simulation was used in the aviation industry.

Here again, the increase in processing speed and the prominent entry of a new term known as computer-aided design was to play a role in advancing the development of simulation software for use. At this stage, early simulation packages and languages were still being used predominantly by the government, as well as, a few corporations. Also, ease of use, intuitive, and responsive packages were slowly being integrated into simulation software such as the GPSS which had become popular in the 60s’.

The Formative Years (1966 – 1970)

The formative years were defined by the development of simulation software for commercial use. By this time, businesses had begun to understand simulation and the role it plays in simplifying business process and solving complex problems. The success of systems such as the Gordon Simulator also got industry actors interested in the diverse ways DES software could be employed.

Recognizing the need to apply simulation in industrial processes, the first organization solely dedicated to simulation was formed in 1967 and the first conference was held in New York at the Hotel Roosevelt. In the second conference, 78 papers on discrete event simulation and developing DES software were submitted. Surprisingly some of the questions asked in the 1968 conference still remain relevant to this day. These questions include:

• The difficulties in convincing top management about simulation software
• How simulation can be applied in manufacturing, transportation, human behavior, urban systems etc.

## The Expansion Period (1971 – 1978)

The expansion period was dedicated to the simplification of modeling process when using simulation software and introducing its use in classrooms. At this stage, diverse industries had begun to understand the use and benefits of simulation software to their respective industries. This, in turn, led to discussing the need to prepare students for a world that integrates simulation.

Also, advancement in technology such as the introduction and wide spread use of the personal computer made the case for developing simulation software for dedicated operating systems. This led to the development of the GPSS/H for IBM mainframes and personal computers. The GPSS/H also introduced interactive debugging to the simulation process and made the process approximately 20 times faster than previous simulation packages. In terms of technological evolution, the GASP IV also introduced the use of time events during simulations which highlights the growth in simulation software available to industries at that time.

By the fifth simulation conference tagged the ‘Winter Simulation Conference’ of 1971, diverse tutorials on using simulation packages such as the GASP2 and SIMSCRIPT had become available to the public. The growing popularity of simulation also led to increased commercial opportunities and by 1978, simulation software could be purchased for less than \$50,000.

## The Consolidation and Regeneration (1979 – 1986)

The consolidation age was defined by the rise of the desktop and personal computer which led to the widespread development of simulation software for the personal computer. Simulation software also witnessed upgrades through the development of simulation language for alternative modeling (SLAM). The SLAM concept made it possible to combine diverse modeling capabilities and obtain multiple modeling perspectives when handling complex processes.

These upgrades or development made simulation for production planning possible and the manufacturing industry began to take a keen interest in simulation software. The increase in computing and storage capacity also led to the creation of factory management systems such as the CAM – I. CAM – I effectively became the first simulation software used solely for closed-loop control of activities and process within shop floors.

By 1983, SLAM II had been developed and this was an industrial-grade simulation package ahead of its time. SLAM II provided three different modelling approaches which could also be combined at need. These approaches included discrete event modeling approach, network modeling, and the ability to integrate discrete modeling and network modeling in a particular simulation model. More importantly, SLAM II cost approximately \$900 which was relatively cheap at that time. This can be signified as the moment where discrete event simulation came into its own as commercial software options for discrete event simulation modeling became available to the general public

## The Growth and Animation Phase (1987 – 2000)

The 90s’ witnessed a consolidation of the strides made in the earlier years and many interrelated technologies and processes also came off age within this decade. This era focused on simplicity, the development of interactive user-interfaces, and making simulation available for everyone including non-technical individuals.

In the mid-nineties, simulation software was being used to solve even more complex issues such as simulating every event and process in large-scale facilities. The Universal Data System example was a first in those days. Universal Data System was stuck with converting its entire plant to a hybrid flow-shop which enhanced production. To achieve this, the company made use of GPSS and the end result was a successful flow that enhanced daily operations and the entire process was modeled and simulated within 30 days.

In 1998, vendors began to add data collection features to simulation software. These features included the automation of data collection processes, the use of 3D graphics and animation to make the simulation process more user-friendly and non-technical. Needless to say, the technological advancements in animation, modeling, graphics design, and UI building played roles in enhancing simulation software during this period.

## The Flexibility and Scalability Phase (2000 – 2019…)

Finally, we come to the last evolutionary phase of the DES software as we know it. Once again, advancement in interrelated technologies have made scaling simulation and speeding up its process possible. The evolution that came with the new millennium saw DES vendors leverage the use of cloud computing, AI, and high-performance computing to take simulation to greater heights.

Other changes that came within these decades was the evolution of production-based scheduling process to a simulation-based scheduling process. This shift allowed for real-time simulation scheduling, processing, and decision-making. This shift also comes with the fourth industrial revolutions were data collection, automation, and interconnectivity rule. Simulation software of this generation has evolved to become tools capable of digitization and the development of digital twins.

Discrete event simulation software such as Simio are examples of the comprehensive simulation technologies that are needed to drive Industry 4.0. This is because new age DES software must be able to collect and store its own data, model accurate 3-D graphics, animation, manage real-time scheduling, and digitization. They must also be equipped with features that market it possible to leverage cloud computing, integrate enterprise resource planning applications, and high-performance computing. These features all work together to ensure the most complex simulations are executed to deliver accurate answers or insights when applied in professional settings.

## Summary

The future of discrete event simulation is by no means set in stone as the experiences from previous eras have shown. This means with the advancement in interrelated technologies and simulation software, more industrial concepts and business models will be disrupted in the coming decade.

# Simio Training and Certification – Introducing Simio Fundamentals

Organizations across every industry need individuals with simulation, modelling, and digital transformation skills to help transform their business processes. Simio Fundamentals will help you learn, relearn, and validate your simulation and modelling abilities with this introductory course. Simio Fundamentals is an online course which consists of 14 modules. Each module was designed and created by simulation experts including Dr Jeffery Smith, a professional with decades of experience in teaching and solving simulation-related problems.

The 14 modules that make up the Simio Fundamentals training are all video instructions consisting of practical information that eases you into the technical aspects of simulation, animation, and modeling. Although the course is focused on Simio’s simulation software, the knowledge and skillset to be gained can be applied in other simulation ecosystems. This is why interested students, educators, employers, and employees should view this course as one that covers the fundamentals of simulation.

## Is the Simio Fundamentals Course for You?

The course was designed for Simio customers and students across the globe who currently use Simio simulation software and digital twin solutions for learning and simulating business processes. Individuals within this category can up their skills and accomplish more with Simio by taking advantage of the information and practical solutions in the fourteen modules.

Simulation, modeling, and digital twin solutions are currently being employed across diverse industries to monitor and manage complex processes, as well as, implement new business concepts. Thus, system integrators, project managers, data analyst, and engineers can also take advantage of the information in this course. Simo Fundamentals offer you, regardless of your experience with simulation, the opportunity to re-learn simulation from scratch and an entry point to mastering digital twin technologies.

Employees can also take advantage of the certification opportunity that comes with completing the Simio Fundamental course and the certification process that comes with it. A Simio Fundamental Certification will highlight your abilities in simulation and modeling tasks. The certification will also highlight your ability to apply simulation processes in solving complex business operational challenges and real-world problems. Employers can also take advantage of this opportunity to teach staff about the basics of simulation and train them on its application within a facility. This ensures everyone is on the same page and understand the integration of simulation technology into business processes.

## How Important Is Simio Fundamentals to Your Industry?

Simulation and its interrelated fields such as scheduling, digital twin, and process control are used across every industrial niche where business operations take place. This means regardless of your industry, some knowledge of simulation and its processes will be helpful to an individual’s career and business growth.

In the tech or IT industry, simulation is widely used to test and explore different business processes, implement new strategies, and analyze prototypes. The Simio Fundamentals course include modules that cover modeling and animation which are important for testing new ideas, hardware designs, and IoT devices to note how they will function in the real world. This is where knowledge in simulation and taking advantage of the Simio Fundamentals course comes into play.

In education, Simio simulation software is currently being used in 800 universities across the globe to teach students about STEM-related concepts. This includes modeling and animation which are staples of engineering and computer science. Educators and students can now learn the fundamentals of simulation and working with Simio by studying the modules in this course.

In banking and finance, simulation is being used to design check out points to deliver enhanced customer services to clients. Simulation and modelling can also be used to organize the layout of banking halls to optimize productivity within a workforce. Managers, stakeholders, and decision-makers can take advantage of this course to learn about simulation and its ability to gain business insights from the banking and finance industry.

Taking a look at manufacturing, simulation plays an important role in streamlining manufacturing process including production, material handling, and the varying relationships that go on in today’s shop floors. The rise of industry 4.0 has also created an avenue were simulation thrives. With knowledge of simulation, manufacturers can implement new strategies and industry 4.0 business concepts in facilities. Simulation also provides the opportunity to explore concepts of generative design for complex systems and products.

Like the manufacturing industry, production-based industries such as in Oil & Gas, mining, and the pharmaceutical industry, simulation also has an important role to play. Knowledge of simulation can be applied to enhancing material handling processes, digitizing shop floors, and determining time dependencies and other related modeling tasks. The facility management and hospitality industry can also take advantage of simulation to implement new processes and monitor the diverse ongoing systems within a facility. The Simio Fundamentals course provide the foundation needed to apply simulation and modelling techniques in these industries.

Gaining an understanding of Simio through the Simio Fundamentals Course gives you the knowledge needed to apply simulation in your industry however you choose. This includes solving real-world problems, educating students, and implementing new business concepts.

## Introducing Simio Fundamentals

Simio Fundamentals is a course offered by Simio University and it covers the fundamental of simulation and Simio. The course is made up of 14 modules which include the following subject matters:

1. An Introduction to Simulation – This introduces simulation and defines its application and impact.
2. Introduction to Simio & Success Tips – This provides an overview of Simio, its interface, and simulation tools.
3. Introduction to Animation – This introduces basic animation concepts and the use of animations in Simio.
4. Simio Modelling Framework – This introduces Simio’ modelling framework, interfaces, and commands.
5. Simio Standard Library Fixed Objects – This module includes workshops that introduce the resources available to you when using Simio.
6. Balking and Reneging – A workshop that focuses on balking and reneging.
7. Task Sequences – This introduces the basics of task sequencing and an introduction to materials.
8. Controlling Movement
9. Material Handling – This introduces the use of Simio to simulate material handling and the basics of manual and automated material handling.
10. Working with Model Data – This introduces the management of data tables and scheduling with Simio.
11. Process Logic – This module is an introduction to processes and its related concepts.
12. Debugging Tools and Techniques – This introduces the debugging techniques available with Simio.
13. Optimizing with OptQuest
14. Building Custom Object Definitions – This introduces you to Simio’s object libraries and how to make use of them.

Each module was designed by simulation experts and Simio professionals who have acquired real-world experience with applying simulation in diverse situations for decades. The modules are in video form and each module runs for 35 to 90 minutes depending on the topic. Simio Fundamentals modules are designed in such a way that you can complete the entire course within two weeks. The modules also consist of 23 workshops that provide you with the opportunity to get hands-on with simulation with Simio. Educators can also make use of these workshops as teaching tools for students.

## Next Steps…

The benefits of having an understanding of simulation and its application in the real world are varied for students and individuals. These benefits include:

• Providing prospective employees with an entry point into industries that deal with simulation.
• Helping students to learn about simulation with Simio and prepare them for the challenges in today’s workspaces.
• Receiving business insights from simulated models of real-world processes.
• Acquiring a Simio certificate that proves you understand simulation and its applications.

Simio Training and Certification – Introducing Simio FundamentalsThese benefits are why over 800 universities and hundreds of enterprises make use of simulation and Simio simulation solutions to solve complex challenges. Get started with Simio and simulation today by registering for the Simio Fundamentals course.

# Integrating Simulation and Digital Twin Technology in the Hospitality Industry

The numbers are in and they do look good for the hospitality industry which consists of hotels, restaurants, and other hospitality-related services. According to Forbes, profitability in the hospitality industry is finally on the increase after the slump of previous years. The report further stated that the net profit margins for full-service restaurants grew by approximately 6% which is 3.8% more than the previous year. The National Restaurant Association expects this growth to continue but early wins must also be consolidated if this is to be achieved. And this is where Digital Twin Technology comes into play.

With the expected growth figures also comes challenges and in the hospitality industry, these challenges generally include fending off the competition and enhancing operations to reap increased rewards. In terms of competition in the hospitality industry, the following statistics paint a clearer picture. In 2018, approximately 60,000 new restaurants and lounges were opened in the United States while 50,000 either filed in for chapter 11 or were closed down for other reasons. Although at the end of the year, the industry grew with the addition of 10,000 restaurants, this mass closure still highlights the competitive nature of the industry.

The competitiveness in the hospitality industry is turning many small and large scale stakeholders to turn to emerging technologies to ease operational deficiencies. This is why today, the hospitality industry has become one of the major drivers of innovation in robotics, artificial intelligence, digital visualization, and the internet of things (IoT). The aim is to collect data from every aspect of a hotel or restaurants operational chain and use that data to receive the business insights needed to stay ahead of the competition.

Today, most hotels make use of interconnected devices to simply customer requests and analyze their peculiarities in order to deliver bespoke services. Examples of this include the use of concierge robots by the Hilton group and the design of smart hotels by Marriot and other stakeholders.

And to what benefits?

Integrating digital technology in the hospitality industry has led to a 40% increase in revenue for online travel agencies (OTAs) who streamline and personalize their services for customers. In brick and mortar hospitality facilities like the Marriot hotels, its financial report of 2018, highlighted a 38 percent increase in revenue with emerging technologies playing a starring role in simplifying operations. This led Arne Sorenson, CEO of Marriot, to state that ‘digital transformation is not only speeding up every aspect of our business, but it is also broadening operations’. And this transformation, as well as, the benefits they bring can be broadened much further with the integration of Digital Twin technology.

## What is A Digital Twin?

A digital twin refers to virtual representations of physical products, systems, facilities and the processes that occur in them. The technology can be used to create digital replicas of actual physical assets and processes and also integrate potential assets onto the created virtual environment. This means every asset that functions in a shop floor including devices or equipment and all business operation or process can be recreated in a digital environment.

Digital twin environments also create an enabling environment for testing new business policies, operations, and assets to access their performance levels before any physical implementation is undertaken. When put beside the recent adoption of smart technology in the hospitality industry, it is easy to see why digital twin technology is the solution every stakeholder has been waiting for to broaden business operations.

One of the major features of the digital twin is its ability to virtualize every asset and process that occurs in an environment. In the hospitality industry, these assets may include; the smart devices used in rooms, check-in and check-out points, robots, the equipment used for logistics and supply chain management, inventories, and every process that produces data. This means when correctly deployed, a digital twin can recreate assets and processes from the deepest parts of a hospitality system in a digital ecosystem.

## The Digital Twin and Enhancing the Hospitality Industry

The easiest way to understand how digital twin technologies can be leveraged to gain an edge over the stiff competition in the hospitality industry is through case studies and CKE Restaurants Holdings, Inc. provides an example.

CKE Restaurants Holdings, Inc. digital twin Story is one that showcases how harnessing digital twin technology and virtual reality can be used to test and implement new operational policies within the hospitality industry. In its case, CKE recreated hundreds of assets and kitchen configurations using the digital twin with the aim of deciding the best configuration that will increase productivity in its Carl’s Jr and Hardee’s restaurants. With the aid of Simio’s digital twin solutions, restaurant floors and kitchens were digitized which provided the perfect environment for reorganizing shop floor assets to reduce employee traffic and create an enabling environment for customers.

To achieve the level of detail needed to accomplish this task, CKE had mapped out every production aspect that occurs within a restaurant down to the plate cleaning process. With this data, accurate simulations could be executed which yielded highly-accurate results. Thus, integrating new equipment and testing how they function with other variables and assets within the restaurant was made possible. This meant receiving accurate business insight into new policies and the effects of introducing new assets before effecting a physical implementation.

According to Forbes, the integration of Simio’s digital twin helped CKE Restaurants, Holding, Inc. manage hundreds of simulations that consisted of the introduction of diverse assets and processes into the digital model. This allowed the restaurant to predict the effects of introducing approximately ten new equipment to the shop floor, as well as, test the efficiency levels of five layouts for the kitchen. The use of a digital twin also helped analyze new designs that would assist CKE with easing the workload on employees which would lead to higher employee retention in an industry notorious for low retention rates.

The example of CKE Holdings, Inc. still leaves the question of if the digital twin can enhance operations in larger more complex facilities. The short answer to this is, definitely yes!

Digital twin technologies have been made use of in large industrial settings such as Nestlé’s and Boeing facilities to implement new ideas and enhance production. Although these examples highlight the importance of digital twin technology, the focus is on the hospitality industry which leads to the longer answer.

In the large hotels with 300 rooms and above, more operational processes occur that dwarf the example highlighted in the CKE case study. These processes include; logistics and supply chain management, tracking the orchestration of hundreds of customers, power consumption, and correctly assigning workplace assets to meet demand. Other smaller systems within a large hotel’s immediate environment are the valet and parking system, concierge system, and manual workflows.

As stated earlier, digital twin solutions are capable of recreating diverse assets, processes and system in a virtual environment when correctly applied. This actually makes the digital twin a solution custom-built for large hotels where the need to keep track of multiple processes within a system while implementing new ideas is a regular occurrence. With the aid of the digital twin, every data produced in large facilities can be collected and analyzed against the different assets within the system. This gives the system integrator or manager a contextual insight into every aspect of running a hotel facility in real-time.

Furthermore, the digital twin of large hotel facilities can be used to run both discrete and continuous event simulations to better understand the events occurring in different systems. A discrete event simulation can be used to test how the implementation of building of an additional check-out point at the parking lot will ease driving and foot traffic before a physical implementation is considered. Also, a simulation of the power consumption that occurs within the facilities can provide insight into which assets or processes can be periodically shutdown to reduce consumption.

## The benefits of Adopting Digital Twin in the Hospitality Industry

Although the earlier case studies provide an insight into the benefits of the digital twin to the hospitality industry, more information is sometimes needed when making decisions. In this case, the decision to be made is choosing to enhance operations using digital twin solutions.

One of the important benefits of integrating a digital twin is the clarity of purpose it provides to facility managers and hotel owners. The use of a digital twin means decisions no longer has to be made in the dark. An accurate digital twin built with every asset, process, and data coming from a hotel or restaurant is the perfect environment for testing out anything before implementation. The test can be as extensive as analyzing the effects of a new equipment transportation system or how automating a business process will turn out. The test can also be as little as analyzing how changes in shelf heights will increase employee productivity.

Another important challenge hospitality businesses face involves the reduction of operational expenses without having to reduce the quality of services offered. Here again, the insight a digital twin provides can be helpful with reducing waste. An example of this is the use of the digital twin by KONE, an elevator company. KONE makes use of digital twin technology to understand how people move through buildings and the decisions they take when riding an elevator. The knowledge gotten from the use of a digital twin helped the company cut out three to four minutes from the average elevator commute. This, in turn, reduced maintenance cost and increased productivity for building owners.

KONE’s case study highlights the fact that hotel owners can make use of the digital twin and scheduling software to analyze commutes, reception traffic, kitchen and dry cleaning process with the aim of increasing workforce productivity. The model can also be used to enhance customer experience by reducing commute from the reception floor to hotel rooms. As for restaurants, this can be taken further to simplify the drive-through process and increase worker efficiency thereby eliminating waste.

The journey to a smarter hospitality industry also provides the perfect environment for enhancing productivity and providing seamless experiences for customers. Embedded devices and IoT solutions can be used to map out customer attractions and the areas that witness more customer traffic. With this information, simulations run through the digital twin can create optimized schedules for visitation periods. This will ensure that customers do not wait in long queues before being able to access areas of attraction within a facility.

## Carving a Niche in the Competitive Hospitality Industry

Staying afloat in the hospitality industry in order to reap a part of its staggering \$550billion revenue requires some effort. These efforts consist of creating an efficient system that takes care of every need of the customer. With advancements in technology, the task of creating that system has become more streamlined and visible to business owners. The digital twin offers visibility and the ability to access real-time information before designing or recreating efficient systems.

CKE Restaurant Holdings, Inc.’s use of Simio’s digital twin solutions provides an excellent case study that highlights how important digital twin is to the transformation of the hospitality industry. With these solutions business owners can better access both small and large scale operational process and enhance these process to the benefit of your customers. You can learn more about the competitive edge the digital twin offers your hospitality and restaurant facilities by speaking to a Simio representative today.

Resources:

https://www.nextguestdigital.com/blog/hospitality-digital-tech/

https://www.simio.com/applications/industry-40/Digital-Twin.php

https://www-forbes-com.cdn.ampproject.org/c/s/www.forbes.com/sites/lanabandoim/2019/09/25/how-cke-restaurants-is-using-virtual-reality-to-innovate/amp/

# Digital Twin Technology: 5 Challenges Businesses Face By Overlooking It

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

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

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

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

## The Five Challenges Businesses Face Not Embracing Digital Twins

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

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

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

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

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

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

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

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

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

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

## Next Steps

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

Resources:

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

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

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

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

# Simio now has the GSA Advantage!®

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

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

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

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

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

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

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

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

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

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

# 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.