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.

Effective Factory Scheduling with a Simio Digital Twin

Introduction

In today’s world, companies compete not only on price and quality, but on their ability to reliably deliver product on time.   A good production schedule, therefore, influences a company’s throughput, sales and customer satisfaction.  Although companies have invested millions in information technology for Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES), the investment has fallen short on detailed production scheduling, causing most companies to fall back on manual methods involving Excel and planning boards.  Meanwhile, industry trends towards reduced inventory, shorter lead times, increased product customization, SKU proliferation, and flexible manufacturing are making the task more complicated.  Creating a feasible plan requires simultaneous consideration of materials, labor, equipment, and demand.  This bar is simply too high for any manual planning method.  The challenge of creating a reliable plan requires a digital transformation which can support automated and reliable scheduling.

Central to the idea of effective factory scheduling is the concept of an actionable schedule.  An actionable schedule is one that fully accounts for the detailed constraints and operating rules in the system and can therefore be executed in the factory by the production staff.   An issue with many scheduling solutions is that they ignore one or more detailed constraints, and therefore cannot be executed as specified on the factory floor.  A non-actionable schedule requires the operators to step in and override the planned schedule to accommodate the actual constraints of the system.   At this point the schedule is no longer being followed, and local decisions are being made that impact the system KPIs in ways that are not visible to the operators.

A second central idea of effective scheduling is properly accounting for variability and unplanned events in the factory and the corresponding detrimental impact on throughput and on-time delivery.  Most scheduling approaches completely ignore this critical element of the system, and therefore produce optimistic schedules that cannot be met in practice.   What starts off looking like a feasible schedule degrades overtime as machines break, workers call off sick, materials arrive late, rework is required, etc.  The optimistic promises that were made cannot be kept.

A third consideration is the effect of an infeasible schedule on the supply chain plan.  Factory scheduling is only the final step in the production planning process, which begins with supply chain planning based on actual and/or forecast demand.   The supply chain planning process generates production orders and typically establishes material requirements for each planning period across the entire production network.  The production orders that are generated for each factory in the network during this process are based on a rough-cut model of the production capacity.  The supply chain planning process has very limited visibility of the true constraints of the factory, and the resulting production requirements often overestimate the capacity of the factory.  Subsequently, the factory schedulers must develop a detailed plan to meet these production requirements given the actual constraints of the equipment, workforce, etc.  The factory adjustments to make the plan actionable will not be transparent to the supply chain planners.  This creates a disconnect in a core business planning function where enormous spending occurs. 

In this paper we will discuss the solution to these challenges, the Process Digital Twin, and the path to get there.  The Simio Digital Twin solution is built on the patented Simio Risk-based Planning and Scheduling (RPS) software.   We will begin by describing and comparing the three common approaches to factory scheduling.  We will then discuss in detail the advantages of a process Digital Twin for factory scheduling built on Simio RPS.  

Factory Scheduling Approaches

Let’s begin by discussion the three most common approaches to solving the scheduling problem in use today:  1) manual methods using planning boards or spreadsheets, 2) resource models, and 3) process Digital Twin.

Manual Methods

The most common method in use today for factory scheduling is the manual method, typically augmented with spreadsheets or planning boards.   The use of manual scheduling is typically not the companies first choice but is the result of failure to succeed with automated systems.

Manually generating a schedule for a complex factory is a very challenging task, requiring a detailed understanding of all the equipment, workforce, and operational constraints.  Five of the most frustrating drawbacks include:

  • It is difficult for a scheduler to consider all the critical constraints.   While schedulers can typically focus on primary constraints, they are often unaware – or must ignore – secondary constraints, and these omissions lead to a non-actionable schedule.
  • Manual scheduling typically takes hours to complete, and the moment any change occurs the schedule becomes non-actionable. 
  • The quality of the schedule is entirely dependent on the knowledge and skill of the scheduler.  If the scheduler retires is out for vacation or illness, the backup scheduler may be less skilled and the KPIs may degrade.
  • It is virtually impossible for the scheduler to account for the degrading effect of variation on the schedule and therefore provide confident completion times for orders. 
  • As critical jobs become late, manual schedulers resort to bumping other jobs to accommodate these “hot” jobs, disrupting the flow and creating more “hot” jobs.  The system becomes jerky and the system dissolves into firefighting.

Resource Model

Companies that utilize an automated method for factory scheduling typically use an approach based on a resource model of the factory.   A resource model is comprised of a list of critical resources with time slots allocated to tasks that must be processed by the resource based on estimated task times.   The resource list includes machines, fixtures, workers, etc., that are required for production.   The following is a Gantt chart depicting simple resource model with four resources (A, B, C, D) and two jobs (blue, red).  The blue job has task sequence A, D, and B, and the red job has task sequence A and B.

The resources in a resource model are defined by a state that can be busy, idle, or off-shift.  When a resource is busy with one task or off-shift, other tasks must wait to be allocated to the resource (e.g. red waits for blue on resource A).  The scheduling tools that are based on a resource model all share this same representation of the factory capacity and differ only in how tasks are assigned to the resources.

The problem that all these tools share is an overly simplistic constraint model.   Although this model may work in some simple applications, there are many constraints in factories that can’t be represented by a simple busy, idle, off-shift state for a resource.  Consider the following examples:

  • A system has two cranes (A and B) on a runway that are used to move aircraft components to workstations.   Although crane A is currently idle, it is blocked by crane B and therefore cannot be assigned the task.
  • A workstation on production line 1 is currently idle and ready to begin a new task.   However, this workstation has only limited availability when a complex operation is underway on adjacent line 2.
  • An assembly operator is required for completing assembly.   There are assembly operators currently idle, but the same operator that was assigned to the previous task must also be used on this task, and that operator is currently busy.
  • A setup operator is required for this task.  The operator is idle but is in the adjacent building and must travel to this location before setup can start.
  • The tasks involve the flow of fluid through pipes, valves, and storage/mixing tanks, and the flow is limited by complex rules.
  • A job requires treatment in an oven, the oven is idle but not currently at the required temperature.

This is just a few examples of typical constraints for which a simple busy, idle, off-shift resource model is inadequate.  Every factory has its own set of such constraints that limit the capacity of the facility.  

The scheduling tools that utilize a simple resource model allocate tasks to the resources using one of three basic approaches; heuristics, optimization, and simulation.

One common heuristic is job-sequencing that begins with the highest priority job, and assigns all tasks for that job, and repeats this process for each job until all jobs are scheduled (in the previous example blue is sequenced, then red).  This simple approach to job sequencing can be done in either a forward direction starting with the release date, or a backward direction starting with the due date.   Note that backward sequencing (while useful in master planning) is typically problematic in detailed scheduling because the resulting schedule is fragile and any disruption in the flow of work will create a tardy job.  This simple one-job-at-a-time sequencing heuristic cannot accommodate complex operating rules such as minimizing changeovers or running production campaigns based on attributes such as size or color.  However, there have been many different heuristics developed over time to accommodate special application requirements.  Examples of scheduling tools that utilize heuristics include Preactor from Siemens and PP/DS from SAP.

The second approach to assigning tasks to resources in the resource model is optimization, in which the task assignment problem is formulated as a set of sequencing constraints that must be satisfied while meeting an objective such as minimizing tardiness or cost.   The mathematical formulation is then “solved” using a Constraint Programming (CP) solver.  The CP solver uses heuristic rules for searching for possible task assignments that meet the sequencing constraints and improve the objective.  Note that there is no algorithm that can optimize the mathematical formulation of the task assignment for the resource model in a reasonable time (this problem is technically classified as NP Hard), and hence the available CP solvers rely on heuristics to find a “practical” but not optimal solution.   In practice, the optimization approach has limited application because often long run times (hours) are required to get to a good solution.   Although PP/DS incorporates the CP solver from ILOG to assign tasks to resources, most installations of PP/DS rely on the available heuristics for task assignments.

The third approach to assigning tasks in the simple resource model is a simulation approach.   In this case we simulate the flow of jobs through the resource model of the factory and assign tasks to available resources using dispatching rules such as smallest changeover or earliest completion.   This approach has several advantages over the optimization approach.   First, it executes much faster, producing a schedule in minutes instead of hours.  Another key advantage is that it can support custom decision logic for allocating tasks to resources.  An example of tool that utilizes this approach is Preactor 400 from Siemens. 

Regardless which approach is used to assign tasks to resources, the resulting schedule assumes away all random events and variation in the system.  Hence the resulting schedules are optimistic and lead to overpromising of delivery times to customers.  These tools provide no mechanism for assessing the related risk with the schedule.

Digital Twin

The third and latest approach to factory scheduling is a process Digital Twin of the factory.  A Digital Twin is a digital replica of the processes, equipment, people, and devices that make up the factory and can be used for both system design and operation.  The resources in the system not only have a busy, idle, and off-shift state, but they are objects that have behaviors and can move around the system and interact with the other objects in the model to replicate the behavior and detailed constraints of the real factory. The Digital Twin brings a new level of fidelity to scheduling that is not available in the existing resource-based modeling tools.

Simio Digital Twin

The Simio Digital Twin is an object-based, data driven, 3D animated model of the factory that is connected to real time data from the ERP, MES, and related data sources.   We will now summarize the key advantages of the Simio Digital Twin as a factory scheduling solution.

Dual Use: System Design and Operation

Although the focus here is on enhancing throughput and on-time delivery by better scheduling using the existing factory design, unlike traditional scheduling tools, the Simio Digital Twin can also be used to optimize the factory deign.  The same Simio model that is used for factory scheduling can be used to test our changes to the facility such as adding new equipment, changing staffing levels, consolidating production steps, adding buffer inventory, etc.                 

Actionable Schedules

A basic requirement of any scheduling solution is that it provide actionable schedules that can implemented in the real factory.   If a non-actionable production schedule is sent to the factory floor, the production staff have no choice to be ignore the schedule and make their own decisions based on local information.

For a schedule to be actionable, it must capture all the detailed constraints of the system.  Since the foundation of the Simio Digital Twin is an object-based modeling tool, the factory model can capture all these constraints in as much detail as necessary.  This includes complex constraints such as material handling devices, complex equipment, workers with different skill sets, and complex sequencing requirements,          

In many systems there are operating rules that have been developed over time to control the production processes.  These operating rules are just as important to capture as the key system constraints; any schedule that ignores these operating rules is non-actionable.  The Simio modeling framework has flexible rule-based decision logic for implementing these operating rules.  The result is an actionable schedule that respects both the physical constraints of the system as well as the standard operating rules.    

Fast Execution

In most organizations, the useful life of a schedule is short because unplanned events and variation occur that make the current schedule invalid.   When this occurs, a new schedule must be regenerated and distributed as immediately as possible, to keep the production running smoothly.  A manual or optimization-based approach to schedule regeneration that takes hours to complete is not practical; in this case the shop floor operators will take over and implement their own local scheduling decisions that may not aligned with the system-wide KPIs.  When random events occur, the Simio Digital Twin can quickly respond and generate and distribute a new actionable schedule.  Schedule regeneration can either be manually triggered by the scheduler, or automatically triggered by events in the system.

3D Animated Model and Schedule

In other scheduling systems the only graphical view of the model and schedule is the resource Gantt chart.  In contrast, the Simio Digital Twin provides a powerful communication and visualization of both the model structure and resulting schedule.  Ideally, anyone in the organization – from the shop floor to the top floor – should be able to view and understand the model well enough to validate its structure.  A good solution improves not only the ability to generate an actionable schedule, but to visualize it and explain it across all levels of the organization. 

The Simio Gantt chart has direct link to the 3D animated facility; right click on a resource along the time scale in the Gantt view and you instantly jump to an animated view of that portion of facility – showing the machines, workers, and work in process at that point in time in the schedule.  From that point you can simulate forward in time and watch the schedule unfold as it will in the real the system.  The benefits of the Simio Digital Twin begin with its accurate and fast generation of an actionable schedule.  But the benefits culminate in the Digital Twins ability to communicate its structure, its model logic, and its resulting schedules to anyone that needs to know.

Risk Analysis

One of the key shortcomings of scheduling tools is their inability to deal with unplanned events and variation.   In contrast, the Simio Digital Twin can accurately model these unplanned events and variations to not only provide a detailed schedule, but also analyze the risk associated with the schedule.

When generating a schedule, the random events/variations are automatically disabled to generate a deterministic schedule.  Like other deterministic schedules it is optimistic in terms of on time completions.  However, once this schedule is generated, the same model is executed multiple times with the events/variation enabled, to generate a random sampling of multiple schedules based on the uncertainty in the system.   The set of randomly generated schedules is then used to derive risk measures – such as the likelihood that each order will ship on time.  These risk measures are directly displayed on the Gantt Gannt chart and in related reports.   This let’s the scheduler know in advance which orders are risky and take action to make sure important orders have a high likelihood of shipping on time.

Constraint Analysis

It’s not uncommon that the supply chain planning process which is based on a rough-cut capacity model of the factory sends more work to a production facility than can be easily produced given the true capacity and operational constraints of the facility.   When this occurs, the resulting detailed schedule will have one or more late jobs and/or jobs with high risk of being late.   The question then arises as to what actions can be taken by the scheduler to ensure that the important jobs all delivered on schedule.

Although other scheduling approaches generate a schedule, the Simio Digital Twin goes one step further by also providing a constraint analysis detailing all the non-value added (NVA) time that is spent by each job in the system.  This includes time waiting for a machine, an operator, material, a material handling device, or any other constraint that is impeding the production of the item.   Hence if the schedule shows that an item is going to be late, the constraint analysis shows what actions might be taken to reduce the NVA time and ship the product on time.  For example, if the item spends a significant time waiting for a setup operation, scheduling overtime for that operator may be warranted. 

Multi-Industry

Although scheduling within the four walls of a discrete production facility is an important application area, there are many scheduling applications beyond discrete manufacturing.   Many manufacturing applications involve fluid flows with storage/mixing tanks, batch processing, as well as discrete part production.  In contrast to other scheduling tools that are limited in scope to discrete manufacturing, the Simio Digital Twin has been applied across many different application areas including mixed-mode manufacturing, and areas outside of manufacturing such as logistics and healthcare.  These applications are made possible by the flexible modeling framework of Simio RPS.

Flexible Integration

A process Digital Twin is a detailed simulation model that is directly connected to real time system data. Traditional simulation modeling tools have limited ability to connect to real time data from ERP, MES, and other data sources.  In contrast, Simio RPS is designed from the ground up with data integration as a primary requirement.

Simio RPS supports a Digital Twin implementation by providing a flexible relational in-memory data set that can directly map to both model components and to external data sources.  This approach allows for direct integration with a wide range of data sources while enabling fast execution of the Simio RPS model.    

Data Generated Models

In global applications there are typically multiple production facilities located around the world that produce the same products.  Although each facility has its own unique layout there is typically significant overlap in terms of resources (equipment, workers, etc.) and processes.   In this case Simio RPS provides special features to allow the Digital Twin for each facility to be automatically generated from data tables that map to modeling components that describe the resources and processes.   This greatly simplifies the development of multiple Digital Twins across the enterprise and also supports the reconfiguring of each Digital Twin via data table edits to accommodate ongoing changes in resources and/or processes.

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.