The Effects of Covid-19 on Supply Chains and How Simulation can help

Coronavirus cells in microscopic view. Virus from Wuhan casusing pandemic around the world. 3D render

The outbreak of Covid -19 has been the most defining occurrence in 2020 and its impact is been felt across all industries including the manufacturing industry. Its effects on manufacturing and production facilities have been varied depending on the nature of the product being produced. For discrete manufacturers in affected regions, daily operations are slowly grinding to a halt while for restaurateurs, it has proved to be a boon.

Meituan, one of the biggest food delivery companies in China, claims it receives four times more request than it before Covid-19 but there were challenges and the challenge? Delivery requests have become more difficult to fulfill as the Coronavirus has reduced the efficiency of its supply chains. Meituan’s case highlights the challenges enterprises currently face with handling logistics and managing supply chains in a time like this.

Since the spotlight was placed on the novel Coronavirus early February, US companies had begun to note its effects on global supply chains and strategize on limiting its effects on trade. Now, with the announcement of a few cases in the US, its effect on supply chains may become local. The reported case of an employee at ‘Amazon Brazil’ offices in Washington and the corresponding quarantine period of that facility mean an enforced downtime will occur. Thus, affecting optimal deliveries or the supply chains associated with that particular office.

The Challenges Coronavirus Introduces to Supply Chains

The ‘Corona leg-shake’ which has gone viral provides the perfect background for exploring the challenges coronavirus brings to supply chains. First and foremost, direct human contact using bare hands is not recommended and this makes things difficult for a large percentage of couriers that support the local supply chain.

The fact that the virus survives for hours on surfaces also puts shop floor pickers and employees at risk. Thus contact of any kind with bare surfaces such as material handling equipment is being shunned by most people. Coronavirus is also a novelty and lack of information about how it spreads has made many consider putting on surgical masks to avoid contamination. These limitations have led to reports and deductions showing that Coronavirus may shut down supply chains by mid-March which will force thousands of enterprises to halt operations.

One example of the effect of these challenges is Fiat Chrysler Automobile temporarily halting operations in Serbia and other locations in February. Others such as Hyundai are also expected to temporarily stop production due to breaks in their supply chain which affected their ability to source parts from China.

Mitigating Supply Chain Challenges with Simulation

Simulation provides enterprises with an assessment and predictive tool to determine the impact of the Coronavirus to their supply chains. In this case, simulation software can be used to evaluate various strategies to keep logistics and supply chains running at some capacity.

In a worst-case scenario where economic activities are suspended, results from simulations will help manufacturers come up with effective strategies for managing available inventory. This will ensure production continues for as long as possible until a resupply line can be established.

The use of simulation alongside interrelated emerging technological solutions such as robotics, autonomous vehicles, and drones could help ease the movement of supplies across local communities. These solutions could also ease fears about having to make contact with couriers or delivery men when taking possession of an order.

Simulation software can ensure the impact of unmanned delivery systems can be assessed and new delivery routes planned before implementation. For example, with Simio, Coronavirus can be modeled as a system dynamics problem alongside supply chains. Modeling the propagation of the virus and its effect on supply chains will provide multiple results on how best to keep supply chains functional if the virus continues to spread.

Simio offers two system dynamics features which make modeling the propagation of the Coronavirus and its effects possible. The Infection Propagation Using Continuous Flow, models disease propagation and it can also be applied by scientists in the healthcare industry to understand available patterns about its spread.

In Summary, government agencies believe the Coronavirus could have a major effect on manufacturing on a global scale for months. This leaves the task of keeping supply chains working in the hands of manufacturing stakeholders and relying on simulation can ease the difficult decision-making process ahead of everyone involved with manufacturing. This article focuses on Coronavirus and its effects on supply chains and does not seek to provide health tips on dealing with the virus. Staying informed and following the directions provided by healthcare and agencies concerning working in public facilities is what we recommend.

Why More Manufacturers are betting on Simulation Software in 2020

In the past decade, the manufacturing industry struggled to increase its production rate even with the implementation of lean manufacturing concepts within shop floors. A major cause of the stagnant productivity levels manufacturers’ encountered was strategy planning and its implementation. In many cases, the inability to accurately assess how strategic changes affect production led to downtime, resource waste, and an increase in expenditure. This is where simulation software as an advanced planning tool helps.

Statistics from 2018 showed that approximately 60,000 manufacturing enterprises worldwide made use of simulation as a planning and scheduling tool. The reason for the low adoption rates was because most enterprises see simulation as a fad which may not work as expected when assessing business strategies. For others, the tried and tested traditional planning tools such as excel and intuition is more than enough when developing business strategies. This left the task of sensitizing the manufacturing industry in the hands of simulation software vendors.

By 2020, the number of manufacturing enterprises using simulation had increased to 110,000 which highlights the efforts vendors such as Simio have put into simplifying simulation for everyone. Progress in the development and features of simulation software is also playing a part in convincing manufacturing enterprises to consider simulation software as planning tools. An example is the addition of 3D modeling and animation into simulation platforms which provides more detailed visuals which explain KPI’s better.

The growing reliance on simulation as a planning and scheduling tool is due to the benefits it offers both discrete and continuous manufacturing facilities. These benefits include enhancing risk analysis procedures, forward scheduling, and effective monitoring of existing systems. Below is a holistic view of these advantages simulation offers.

Risk Analysis with Simulation Software

Risk analysis is the process of identifying and analyzing potential challenges that could negatively affect the manufacturing process and an enterprise’s ability to meet production timelines. Once the limiting factors or issues have been identified through risk analysis, decisions to mitigate them can then be taken.

Simulation software is an excellent tool for risk analysis within manufacturing facilities and operations. With simulation, a manufacturer can determine if available resources such as an inventory list, number of equipment, and workstations are enough to meet production schedules. An example is conducting a risk analysis to determine the probability of meeting an order with the resources available to the manufacturer.

In this instance, Simio is used to conduct a risk assessment which will provide the risk percentage attached to supplying the requested order. Risk percentage refers to on-time probability for an order with considerations for the number of replications run using the simulation software. We recommend running at least 10 replications to determine the risk percentage. This is because it provides a more accurate risk analysis for producing the order within the specified time frame. You can learn more about risk assessment with Simio here.

If the result for the ‘risk measures’ after running a risk analysis is within the 80 to 90% mark, this is a good sign that the order will be fulfilled on-time. A risk measure of less than 30% is a sign that more resources are needed to ensure an order is produced on-time. The manufacturer can then choose to make design or operational changes to ensure production is on-time. Here, a design change could mean adding an extra assembly line or purchasing more material handling equipment to speed up operations. Operational changes refer to expediting a material or working to extend the due date of the requested order.

Making these decisions reduce the challenges that affect manufacturing procedures such as downtime, resource waste, and overshooting production timelines. Eliminating these challenges or keeping them at bay comes with its benefits which include revenue growth and enhancing customer experience.

Forward Scheduling with Simulation Software

In advanced planning, forward scheduling refers to a feasibility analysis of manufacturing plans before implementation. This allows the schedule to take into consideration all known constraints and condition of a system that could affect production, unlike backward scheduling which makes assumptions without considering some important constraints.

The application of forward scheduling in manufacturing provides enterprises with a valuable tool to make plans with the resources currently available, as well as, plan for the future. An example is scheduling the production of an order of 100 items. Forward scheduling will consider the available inventory and its ability to support the order. If the available materials are not enough, this constraint is taking into consideration and the system sends out a purchase order for more supplies. In a situation where the start date has elapsed or the demand exceeds the manufacturer’s capacity, forward scheduling continues to create schedule past the due date.

This information gives stakeholders options. The decision to be made here may involve trying to push the due date forward or outsourcing a percentage of the order if the delivery date cannot be changed.

Planning before Implementation with Simulation

Simulation software is a virtualization tool built for planning factory layouts or settings to enhance productivity. Manufacturers intending to build new facilities or add new workstation or production lines to existing facilities can predict their impact before executing the implementation procedure.

An example is assessing the impact of adding an assembly line to a discrete manufacturing facility. With Simio, a discrete event simulation assesses the speed of the new system and the scheduling probabilities it offers. This assessment provides a glimpse into the future manufacturing system and if the prognosis looks good for optimizing productivity, then real-time implementation comes next.

Assessment before implementation also applies to planning new shop floor layouts that optimize available manufacturing assets and workspace. An agent-based simulation highlights the effects or impact of individual assets to the entire facility. With this information, an enterprise can place assets in specific sections while modeling the entire facility to take advantage of the location of assets. On implementation, a properly planned shop floor layout reduces workplace traffic, speeds up production activities, and optimizes available resources.

Conclusion

The value-added proposition simulation brings to the table is why manufacturers are expected to spend approximately $2.5billion annually on simulation software. Simulation software is used by machinery and appliance facilities, the automobile industry, in aviation, healthcare and any other industry where goods are produced or services are rendered. You can learn more about how simulation software has been applied to simplify manufacturing processes within your industry by going through the list of Simio case studies.

Predictive Modeling In Healthcare and The Role of Digitization

The digital transformation of the healthcare industry is moving at an accelerated pace as healthcare facilities continue to reap its benefits in operational management, epidemiology, and personal medicine. And one subset of Digitization that is proving to be extremely useful is the integration of predictive modeling and analytics in healthcare.

Predictive modeling refers to the use of historical data or available data to make predictions of future events which helps with making better decisions. It is also used to troubleshoot or anticipate future behavioral patterns or outcomes using multivariate data sets or events.

In medicine, predictive models are being used to see into the future to define expected trends in operational management, and patient care both on an individual level and at a larger scale. While in pharmaceutical laboratories, predictive models are being used to predict future demand, enhance productivity, and in advanced planning and scheduling.

The Importance of Digital Transformation and Predictive Modeling In Healthcare

The digitization of healthcare data has provided the public with access to large repositories of health-related matters. Today, with a smartphone any individual can look up symptoms and seek medical advice from the comfort of their homes.

Digitization has also put patient data and educational resources at the fingertips of healthcare providers worldwide. Although these are excellent examples of the advantages of digitization, predictive maintenance takes things to the next level. With predictive modeling comes the option to enhance operational management in ways never experienced before.

One example is the use of predictive models to analyze patient no-shows, treatment schedules, and optimizing hospital resources. In 2018, The Elmont Teaching Health Center introduced the use of predictive modeling to track its patient no-shows which were costing the center money. To better anticipate no-shows and plan accordingly, the health center turned to predictive modeling.

With the hospital’s historical data, a predictive model for the patients most likely to not show up was developed. This model was then simulated against hospital resources with the aim of rerouting these resources to other patients. The result was a 14% reduction in its no-show rates which saved the healthcare center hundreds of thousands of dollars caused by patients.

Another important scenario which predictive models and simulations help with is in dealing with optimizing operations in emergency departments. First and foremost, it is important to establish the importance of timing and resource availability within emergency units to understand the importance of predictive modeling.

Medical errors in emergency rooms and inadequate facility allocation cause approximately 250,000 deaths annually in the United States and 1,500, 000 globally. Although solving the issues related to emergency care does not rely on operational management alone, the ability to predict the number of emergencies, allocate resources, and develop functional schedules can ease important challenges. These challenges include facility overcrowding and overworking emergency healthcare providers.

An example of how predictive models and Simulation aids emergency response can be seen from the example of Wake Forest Baptist Health Center. In this case study, predictive models of its patient inflows were developed and used to analyze the rate of patient inflow and how best to allocate hospital resources to cater for both emergency patients and others.

With the model, the hospital was able to manage the inflow of patients and emergency situations. The simulation results also provided actionable intelligence to hospital management which helped with crafting better policies for dealing with emergency and excessive patient visits.

Another example of the importance of predictive modeling in healthcare is its use in developing strategies for handling complex scenarios. One such scenario is the evacuation of patients with mobility challenges during disasters. The experiences of hospitals during Hurricane Harvey and Katrina led a team of researchers from John Hopkins University to apply simulation and scheduling to simplifying evacuation efforts.

In this case study, agent-based modeling was used to model the complex individual assets and varying conditions of patients into an evacuation simulation. The simulation also integrated micro-scale models of agents and mesoscale models of population densities to understand the relationship and behavioral patterns of the diverse agents within an evacuating system.

The result of the study showed the extent to which macroscopic and mesosscopic models produce system-level behaviors in agent-based models.

The Significance of Predictive Modeling In Pharmaceutical Facilities

As with every manufacturing-based industry, the pharmaceutical industry relies on managing logistics chains, optimizing shop floor operations, and manufacturing workstations to meet customer demand. Thus, the integration of predictive modeling and simulation has significant roles to play in delivering actionable intelligence and insights into optimizing production.

With a simulation or digital twin software, comprehensive models of a pharmaceutical manufacturing facility can be developed. This predictive model can be used to introduce external phenomena such as increased demand and scheduling delays to understand their impact on the manufacturing process.

In this model of a digital twin developed using Simio Simulation Software, the activities and capacity of a manufacturing facility can be seen as well as the discrete events happening within the shop floor. Within a digital twin, predictive simulations can be run to optimize the facilities supply chain with the aim of optimizing productivity. And according to RevCycle, healthcare facilities including big pharma can save approximately $9 million yearly in supply chain management costs. These benefits also apply to health-related manufacturing niches including biomedical devices, dental, and orthodontics manufacturers.

The Risks of Relying on Predictive Modeling In Healthcare

The healthcare industry is built around catering to humans and human relationships which means data analytics alone will not cut it. According to Deloitte, the integration of digital technologies in healthcare comes with risks such as moral hazards, privacy issues, and a lack of regulation.

Here, moral hazards refer to the machine-like application of healthcare in complex situations. With predictive analysis, patients in critical condition may be overlooked in order to ensure healthcare professionals have more time for other patients. A lack of regulation can also see harmful policies sneak into healthcare with the aim of managing resources and making the most profit.

Privacy concerns are also significant challenges that digitization brings up. One example is the loss of patient data by the National Health Service (NHS) due to a ransomware attack. In this instance, over 300,000 patients across the United Kingdom were affected by the security breach. Thus, cybersecurity must be taken into consideration when integrating predictive modeling and simulation in healthcare.

Conclusion

The importance of predictive modeling and simulation in healthcare, as well as, its risks will be discussed in more details at the Simio Sync Digital Transformation Event. Dean O’Neil of John Hopkins University will speak on ‘Building Capacity for Healthcare Modeling and Simulation’.

His session will provide in-depth information concerning the application of simulation at his time at the John Hopkins University Applied Physics Laboratory. Attendees will go away with about applicable modeling and simulation strategies which can be used to optimize their healthcare facilities. Simio Sync Conference will take place on the 4th to 5th of May in Pittsburgh. You can register for an early bird ticket here.

Best Answers to Commonly Asked Risk Analysis Questions

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

Learn from Simulation Experts, Advance Your Skills and Knowledge.

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.

It is also important to note that this course is licensed for and per individual use. Thus, educators who intend to use it in their classrooms can contact us to learn more about how we can help. Subscribing to the course gives you access to the videos and workshops in every module. This means you can pace the learning process to fit your schedule. If you would like continuous access to the course, you can choose the licensing option that makes this possible.

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.google.com/url?sa=t&source=web&rct=j&url=https://marriott.gcs-web.com/static-files/b82978a6-9d28-4e38-9855-fc4ae2cebe11&ved=2ahUKEwjH8YGxjKPlAhWNTsAKHesCC3EQFjAOegQIBxAB&usg=AOvVaw1wOGkSQxcJ8O7VZBmYm1xF

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/

https://www.google.com/url?sa=t&source=web&rct=j&url=https://inbuildingtech.com/uncategorized/digital-twins-proptech/&ved=2ahUKEwi6ieSMjaPlAhWMXsAKHYvxC94QFjACegQIAhAB&usg=AOvVaw2JkDmpLHU5Vs0BI4inPD4n