Simio Announces Keynote Speakers for Simio Sync: Digital Transformation Conference

At this year’s Simio Sync: Digital Transformation conference (May 4 – 5), Simio is planning a bigger event covering all things digital by increasing the number of programs and expert speakers compared to last year’s event. The goal is to cover the expanding ecosystem around digital transformation while providing a platform for attendees to experience practical examples of its application across every human endeavor.

To this end, we are enlisting great speakers with years of hands-on experience in digital transformation to lead expansive sessions on application, strategy, and charting a course using digital technologies. Today, Simio is excited to announce two great speakers who will be sharing their experiences with digitally transforming supply chains and the manufacturing industry.

We are happy to announce Martin Barkman, Senior Vice President and Global Head of Solutions Management for Digital Supply Chain at SAP. Martin leads the strategy and go-to-market for SAP’s Digital Supply Chain solution portfolio, which encompasses software for R&D, engineering, supply chain planning, manufacturing, logistics, and asset management.

He will be speaking on the role digital transformation plays in enhancing supply chain management and implementation strategies. His session will also provide practical examples for enterprises interested in driving their supply chain strategy using technology. These practical examples will leverage on the 12 years’ experience he gained providing on-premise and cloud-based solutions for optimizing supply chains at SmartOps. 

We’re also pleased to announce Indranil Sircar, CTO Manufacturing Industry at Microsoft. Indranil has considerable experience with logistics and supply chain management using disruptive technologies. He has helped big businesses develop intelligent supply chain strategies using the Internet of Things (IoT), artificial intelligence (AI), virtual reality, and the digital twin.

At Simio Sync, the veteran with over 30 years of experience at Hewlett-Packard and Microsoft, will be speaking about the use of digital transformation to set and drive manufacturing visions or strategies. He will also share practical examples about using high-tech and edge solutions to accelerate digital transformation, as well as, how they bring value to enterprises.

The distinguished speakers will also be available to take questions from members of the audience which provides you with the opportunity to present the specific challenges you have faced with digital transformation.

You do not want to miss this and the other events including the networking dinner with industry stakeholders from Lockheed Martin, Exxon Mobile, BAE Systems etc. and Simio Spouses Agenda that we have lined up for you. Get your early bird tickets today.

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.

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.

Simio Sync Digital Transformation Conference 2020

Get Inspired – Stay Forward Thinking!

Simio LLC is delighted to announce once again that the opportunity to learn more about the state of simulation and digital twin technologies is here. And yet again, this promises to be one of the biggest simulation and digital twin of the year. Simio Sync Digital Transformation Conference will focus on digital transformation technologies and how enterprises can tap into Simio to unleash the digital potential of business processes.

The event will be taking place on the 4th and 5th of May 2020 in Pittsburgh with advanced training about using Simio from the 6th to 8th of May. The first event will introduce you to Simio, the recent updates in Simio 12, and its application across industries. Keynote speeches and event programs will consist of Simio use cases and application across industries of interest. But before delving into the opportunities of the 2020 conference, here is a recap of 2019.

Simio Sync 2019 – A Recap

The 2019 Simio Sync conference was the third annual event on simulation and digital transformation built around Simio technologies and solutions. The event brought together an ensemble of experienced speakers to inspire the crowd on the role of simulation and digital transformation in the real-world. Speakers included; Chris Tonn from SPIRIT Aero systems, Ian Shillinger from Mckinsey & Company, Antonio Rodriguez from the National Institute of Health (NIH), and Dusan Sormaz from the University of OHIO among others.

Each speaker presented case studies highlighting the application of Simio within the aviation industry, manufacturing, healthcare, education, hospitality, and simulation engineering. These events proved inspiring to participants from varying industries and opened up new possibilities about applying Simio within their specific industries.

According to Jarred Thome, from USPS, his first Simio Sync event was an eye-opener in many ways. He said “This was my first year attending the conference and I was blown away by the extent to which the folks at Simio went to ensure it was a success. The content, presenters and networking opportunities were all top-notch and the Simio staff was always accessible and willing to chat. I will definitely be coming back.”

The 2019 event was one of a kind and next year’s event is expected to take things up a notch. The Simio Sync Digital Transformation Conference will consist of speakers from Fortune 500 companies willing to share their experiences with digital transformation using Simio with you. The event will also serve as a networking arena for stakeholders within the simulation and digital transformation community and participants.

All you need to know about the Simio Sync Digital Transformation Conference for 2020 will be highlighted here. In the meantime, you can sign up with Simio to receive conference updates and to register as a participant today.

Why Attend?

The fourth annual Simio Sync conference gives you the choice to learn, no matter the role you play in your company’s digitization efforts. At the end of the days, you will work away with the knowledge to help you and your company refine your digital transformation strategy to reap the rewards digitization brings.

If networking is your thing, how about coming to listen and catch up with individuals from fortune 500 companies among others. Through the years, Simio conferences have been fertile grounds for communicating and building relationships and this year’s event will be no different.

Simio Sync Digital Transformation provides an excellent opportunity to learn about simulation and its application in the real-world. Attending the event can help kick start your company’s digital transformation or refine transformation strategies to meet your defined goals. Thus, everyone is invited to attend, network, get inspired, and create fun memories while learning about digital transformation!

Code of Conduct

Simio Sync Digital Transformation conferences are safe spaces created for everyone interested in the digital twin, simulation, scheduling, and digitization. The event is open to everyone and the conference areas are safe, inviting, and supportive.

Simio representatives are also available in every location to ensure your participation is a memorable one. If you are in need of answers to Simio-related questions or event-related questions, you can reach out to a representative and your questions will be answered.

With the increasing number of participants, the golden rule of mutual understanding also applies. This will help you build better networks, and truly take advantage of the different sessions, and labs that are parts of the event.

Registration

Registration is now open. You can now take advantage of early bird tickets to become one of the first individuals or organizations to register for the Simio Sync Digital Transformation Conference for 2020. There are a plethora of excellent hotels and living areas around the event holding in Pittsburgh and the earlier you register, the quicker you will be about planning your travel and relaxation itinerary.

To register, visit the Simio Sync event page and go through the registration process. The process is quite straightforward and intuitive to accomplish. You will also have the choice of registering for the conference event and adding advanced training options to your registration form.

Session Catalog

The Simio Sync session catalog is the ultimate guide you need to navigate through the conference while bookmarking areas you are particularly interested in. The session catalog is currently live and you can browse through it while registering.

This year, there are approximately eight unique sessions divided across networking breaks to ensure you take advantage of your participation. The sessions include diverse keynote speeches from leading digital transformation experts and Simio engineers. To get a real-world feel of the application of Simio and digital transformation processes, case study sessions and presentations are also part of the event catalog.

Other exciting events of note which you are also welcome to participate in includes the Simio Pittsburgh exploration event where you and your spouse can line up with Simio Spouses to explore the ancient city of Pittsburgh. If you are a running enthusiast, you can also choose to participate with Simio in the Pittsburgh marathon before the conference begins. These are part of the fun activities lined up for you!

Advanced Training and Hands-on Labs

The advanced training event will take place on the 6th to the 8th of May. This training focuses on the application of Simio in the real-world. Thus, you will be introduced to the different features of Simio and how they can be applied to drive digital transformations, simplify discrete event scheduling, and build digital twins of physical processes.

The advanced training program will be a boon for organizations currently using Simio and others who are interested in using it to drive digital transformation strategies. Individuals interested in digital transformation are also welcome. The hands-on lab integrates the use of case studies and the Simio interface to ensure you understand every aspect of the digital transformation process with Simio.

Networking

Tens of industry-leading organizations and individuals have already reserved their spots for the Simio Sync Digital Transformation conference. And come the 4th of May 2020, you too can pick the brains of your favorite personalities within the aviation, hospitality, education, healthcare, automotive, automation, manufacturing, and pharmaceutical industries.

Participants from Lockheed Martin, Air Canada, Boeing, BAE Systems, OHIO University, United States Postal Services, Exxon Mobil, Roche, FedEx, Honeywell, American Airlines etc. will be there. The networking dinner and entertainment sessions at 6pm create an excellent opportunity to build interpersonal relationships for the future.

Conclusion

To get the best out of the Simio Sync Digital Transformation Conference, we recommend that you participate in at least one of the following programs:

  • Participate in at least one hands-on training covering the use of Simio.
  • Bookmark and participate in a keynote session highlighting the use of Simio for your industry or related industry
  • Wear comfortable shoes and cover grounds during the networking dinner and entertainment opportunities within the conference.
  • Talk with others and explore Pittsburgh!

Top Trends in Simulation and Digital Twins Technology for 2020

Digital Twins refers to the digital representations of people, processes, and things. It is used to analyze operations and receive insight into complex processes. As 2019 comes to an end, the need to define digital twin technology still exists and hopefully by this time next year, its growth and popularity will make this need obsolete…

In 2018, digital twins were included as a top technology trend by the big names covering the tech industry. According to Orbis research, the digital twin market is expected to grow by 35% within a 5-year time frame and 2020 is right in the middle of this period. But before highlighting the trends to expect in 2020, it is only right to do a recap of the year so far. This is to note if earlier predictions have come to pass before mapping the future.

In terms of popularity, coverage of the digital twins is definitely on the right track as continuous studies by Gartner and other publications show. Today, many professionals across the technical and non-technical divide understand the digital twin concept and how it can be used to drive business processes and concepts. This is why many industries are currently integrating digital twins to bolster business insight and understand data.

The biggest adopters of digital twin technology in the geographical sense remains North America. Enterprises within the US and Canada currently leads the way in terms of adopting digital twin technology. North America accounts for approximately 59% of the digital twin market and economy while Europe and the Asian pacific comes next.

The very nature of the digital twin and simulation, as well as, the solutions they provide makes them attractive business tools for the manufacturing industry and this fact is backed up by data. The manufacturing industry’s affinity to digital twins is powered by Industry 4.0 and the varied ongoing processes that occur within shop floors. The use of smart edge devices, equipment, robots, AI, and automation also fits nicely into the digital twin concept thus making it attractive to manufacturers.

In 2019, manufacturers account for approximately 36% of the digital twin market. Other industries such as the energy and power industry, Aerospace, Automobile and Oil & Gas complete the top five industries who make use of the digital twin to enhance operations. Analyzing this trend highlights the fact that digital twins are important to simplifying complex processes where hundreds or thousands of variables and relationships are needed to successfully accomplish set tasks.

Is the Digital Twin for Only Production-based Industries?

Although the Oil & Gas industry, as well as, the energy and power sector are not tagged as manufacturing industries, a case can be made for it. Therefore, many may assume or wonder if digital twin technology is only useful within production-based industries where discrete or process manufacturing takes place. And the answer is No.

The digital twin is also being used in other industry verticals such as the hospitality industry and in restaurants. One example is the use of Simio by CKE Holdings Inc. to ease workloads in its Carl’s Jr and Hardee’s restaurants. The Digital twin is also being used to support discrete event simulations in hotels, real-estate, and tolling facilities.

The use of interconnected devices and automation within service and hospitality businesses are the driving forces behind the adoption of digital twin within a variety of industries. And the coming year is expected to witness continuous growth as more industries and professionals understand what the digital twin brings to the table.

Top 5 Trends for the Digital Twins and Simulation Technology for 2020

Interrelated Technologies will Boost Adoption Rate – The growth and maturity of interrelated technologies such as 3D printing, metal printing, and mapping will play a part in accelerating the adoption rate of digital twins in 2020. This is because of the need to monitor and consistently improve these technologies and the systems that drive them.

Using 3D printing as an example, many manufacturing outfits are currently making use of 3D printing clusters to speed up their production requirements. 3D printing clusters or farms refers to facilities where hundreds of 3D printers function simultaneously to manufacture physical items. Although these 3D printing clusters have dedicated software for managing the printing process, material delivery, scheduling, and managing the entire supply chain within these facilities are handled manually.

Digital twin solutions can eliminate the manual management and handling process in 3D printing farms to great effect. If properly executed, a digital twin of a large scale 3D printing cluster will provide a data-driven approach to optimize supply, scheduling and the manufacturing process. This will reduce expenditure including the energy expended in 3D printing cluster facilities.

Industry 4.0 will Continue to Drive Adoption – The growth in Industry 4.0 and the devices, as well as, communication channels driving the smart factory is expected to increase the adoption of digital twin solutions. In 2019, Industry 4.0 witnessed the creation of new standards from the OPC Foundation that supports the collection of data from the deepest corners of brownfield facilities. These data were collected from dumb equipment with legacy technologies using smart edge and embedded devices.

The success of this approach, means that digital twin technology can now integrate the data collected from dumb or legacy equipment when developing digital representations. This increases the accuracy levels of the representations thereby enhancing simulation results and scheduling plans. Thus, increasingly accurate digital twin ecosystems and results will create more use cases that will drive the adoption of digital twins in 2020.

IoT and IIoT to Drive Digital Twin Adoption Rate – The move to more interconnected environments across both manufacturing and service-based industries also have roles to play in 2020. As stated earlier, Industry 4.0 will enhance the adoption of digital twin technologies and this also true for the industrial internet of things (IIoT). The widespread adoption of IoT and IIoT devices or equipment have created a race to develop the best management solution to monitor interconnected activities.

This creates an avenue which digital twin service providers are currently taking advantage of and will continue to do so in 2020. The ability of the digital twin to create digital representations of IIoT devices and also integrate the data they produce creates multiple use cases enterprises will explore in the coming years. These use cases include running simulations in complex interconnected facilities to produce accurate results or to access processes that involve the use of IIoT technologies.

Digital Twin for Cybersecurity Challenges – With every passing decade, the cybersecurity challenges enterprises face keeps changing. The millennium brought Trojan horses and other viruses which were effectively stopped with anti-virus software apps and by 2010, attackers pivoted to using phishing attacks and malware. Today, ransomware, spyware, DDoS, and business email compromise attacks have become the new challenges enterprises face. Thus highlighting the ever-changing landscape of cyber threats.

To cater to these threats and attacks, digital twin solutions will be enlisted by enterprises in 2020. In this scenario, the digital twin will be used as a penetration testing tool to simulate the effects of successful data breaches or ransomware to an organizations business processes. Within the digital twin environment, attacks to core equipment can be simulated and the result will be a response pattern that ensures the crippled equipment does not lead to extended downtime.

2020 will also be expected to witness an increase in the cybersecurity threats facing cloud-based digital twin solutions. Thus, more secure communication protocols and standards regulating data use will be developed to protect enterprises making use of digital twin technology. This means developers and service providers will have an increased role to play in securing digital twin environments.

Simulation-based Scheduling – The drive to deliver real-time scheduling is expected to continue in 2020 as enterprises seek more accuracy with managing business process. The need for real-time scheduling is also driven by how enterprises intend to apply simulation and digital twin tools. An example includes the need to make business decisions in real-time, handle unforeseen occurrences such as machine downtime, and reschedule operations.

These challenges fall into the category of issues discrete event simulation (DES) software can handle. Once the required data is accessible, DES and digital twin applications can conduct simulations in real-time and provide accurate solutions to dealing with changing scenarios also in real-time. This will drastically reduce downtime and enhance performance within facilities and warehouses.

Although some DES software offers real-time simulation scheduling, many are still process-based scheduling applications and this is set to change in 2020.

Quantum Computing – If real-time simulation, scheduling, and process management is to be achieved, then digital twin solutions must take advantage of the speed, scalability, and high-performance quantum computing offers. Today, digital twin solutions currently leverage the cloud to provide stable and scalable services to enterprises and only a few integrate the use of high-performing computers to enhance or manage really large workloads.

In 2020, further strides will be made to speed up simulations within digital twin environments using high-performing computers. The success of this initiative will speed up real-time scheduling and complex process management for the foreseeable future.

Planning for 2020…

The benefits of the digital twin have played an important role in ensuring its adoption across diverse industries and the expected trends of 2020 will continue the increased adoption rate that came with 2019. Although digital twin solutions have become more interactive and intuitive to use, enterprises still require the assistance of experienced professionals to get the best out of their digital twin environment and this is where Simio can help.

IT managers, cybersecurity experts, and project managers can take advantage of the Simio Fundamentals Course to learn more about simulation and Digital Twin technology including its application in real-life scenarios.

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 Showcases the Digital Twin at INFORMS Annual Meeting 2019 – A Recap

The INFORMS Annual Meeting for 2019 has come and gone with multiple keynotes shared, workshop activities, and hundreds of excellent companies sharing their experiences and solutions from brightly colored booths. Once again, Simio was at the thick of things evangelizing the benefits of simulation and Digital Twin technology to the world. As with all annual meetings, the focus was on the strides been made in operational research and analytics. And the meeting provided the chance to explore emerging technologies and its applications across all ‘works of life’.

The term ‘all works of life’ wasn’t used lightly as sessions covering social media analytics and e-learning to applying analytics to human trafficking were explored. As for Simio, our role was somewhere in the middle and as stated earlier, our participation was centered around the digital twin. But before going into details of how the event panned out and Simio’s roles, here is an outline of what the INFORMS Annual Meeting is about for interested individuals.

INFORMS Annual Meeting

INFORMS which stands for the Institute for Operations Research and the Management Sciences is an umbrella organization for professionals plying their trade in operations research and analytics. INFORMS currently boasts of approximately 12,500 members across the globe which highlights its global or international reach.

The organization also sets standards and guidelines to ensure research and analytics within its field are ethically done. To bring its thousands of members together under one roof, the INFORMS Annual Meeting event was created and it holds once a year. The event features keynote sessions, workshops, publication presentations, and an exhibition area for member and enterprises to showcase their wares.

The INFORMS Annual Meeting also coincides with its community service drive to assist non-profit organizations with meeting their obligations. This is done through the INFORMS Pro Bono Analytics section of the organization. If you are wondering why the information about Pro Bono Analytics is included here, then I ask for patience as it will all make sense in the end.

Now to the annual meeting of 2019!

The 2019 INFORMS Annual Meeting was a 3-day event which ran from the 20th of October to the 23rd. This year’s event was definitely a success as more than 5,000 people breezed through the different sessions, exhibition areas, workshops, and lunch areas throughout the 3-day event. The convention center buzzed with activities through these days and we are proud to say Simio capitalized on these activities in different ways. Our participation included a dedicated Simio booth highlighting the use of Simio digital twin technology, a session handled by Jason, and workshop presentations from Renee.

Keynote Sessions and Workshops of Note at the INFORMS Annual Meeting

Tens of session covering operations research and analytics were covered throughout the event which makes mentioning and discussing every one of them impossible. So, the focus will be on simulation, digital transformation, cloud computing, and digital twin sessions.

One of the exciting sessions within the above category was the session about the computational infrastructure for operations research, COIN-OR, initiative. The IBM project focuses on providing open-source technologies solely for computational operations research. The end goal is to provide an open-source library of tools which will ensure researches do not have to start from scratch when handling complex research. This creates a foundation that will be built on and maintained by researchers over the years.

The session ‘Robust Optimization and Learning Under Uncertainty’ was also interesting as it discussed the challenges stakeholders face with decision-making and policy creation. Han Yu, a PhD student at the University of California spoke about how important data collection, and an understanding of history should drive real-world decision-making. The session also discussed how modeling and robust optimization techniques can enhance the decision-making process.

Other notable sessions highlighted or raised questions about the role digital twin and simulation could play in enhancing agriculture and the healthcare industry. According to Greg from Syngenta, AI, computer vision, and bioINFORMSatics modelling currently assist Syngenta with making data-driven seed selections and breeding. This raises the question of the role of the digital twin in agriculture which may be explored in other blog posts.

In the ‘Healthcare Modeling and Optimization’ session, Dr. Zlatana Nenova spoke about the role modeling and data analytics play in improving healthcare. Her speech also touched on the use of digital technology to analyze medical care policies for both off and on-site healthcare delivery. In terms of on-site healthcare, there are certainly diverse ways the healthcare industry can benefit from digital twin technology. Although this was not covered in this year’s event, it highlights the possibilities of applying digital twin to the healthcare industry.

Simio’s Events at the INFORMS Annual Meeting

Now, to Simio and our role at the INFORMS conference. In last year’s event, Ms. Renee Thiesing, the VP of Strategic Alliances,  spoke excellently on the role Simio plays in driven discrete event simulation and the digital transformation of brownfield systems as the move to Industry 4.0 continues. She also highlighted the importance of real-time event scheduling and how Simio can help enterprises solve real-time scheduling challenges using Simio.

In this year’s event, Ms. Renee built on her earlier foundation by focusing on the digital twin capabilities of Simio and its application in diverse industries. Her session titled ‘New Innovations: Cloud Computing, Real-Time Scheduling, Industry 4.0 and more’ discussed how Simio leverages cloud computing to deliver high-performing scheduling and simulations.

Through the session, she discussed how Simio leverages the computing power of Microsoft Azure to support complex applications. Simio’s compatibility with Schneider Electric’s Wonderware was also discussed in detail. This includes the leveraging of Wonderware to achieve detailed production scheduling in real-time, as well as, manage real-time risk analysis. The new Simio features such as Simio’s cloud portal and OptQuest were also covered during her workshop. She highlighted OptQuest abilities to optimize scheduling and simulation with the aim of delivering optimal solutions to complex business problems.

Jason Ceresoli also spoke on the benefits of using Simio’s 3D modelling capabilities to solve real—world problems. His presentation covered Simio’s features for system design and operation. Practical examples of how Simio’s rapid 3D modeling, planning and scheduling, and optimization capabilities can be used by enterprises were also discussed by Jason. Finally, his session highlighted the difference between Simio and other simulation tools with a focus on how professional researchers and analysts can use these features. 

Our participation at the INFORMS Annual Meeting will not be complete without recounting our experiences at booth 28 in the exhibition area. The targeted message used in the Simio booth drew its own audience of professionals, entrepreneurs and business representatives interested in the digital twin. This gave us the opportunity to showcase Simio’s features and real-world applications to interested individuals. We can categorically say our booth played a role in the sales leads and opportunities we got from the event.

Simio and the Pro Bono Analytics Event

I remember introducing you to the INFORMS Pro Bono Analytics and now here we are! This year, Pro Bono Analytics partnered with a Seattle-based non-profit organization, FareStart, to assist individuals interested in building careers in food service and culinary arts. At this year’s INFORMS Annual Meeting, Simio alongside other participants made donations to the FareStart initiative.

The event was a success and according to Elise Tinseth, Community Engagement Manager with FareStart, shared, “The INFORMS Pro Bono micro-volunteer opportunity of creating hygiene kits is impactful to eliminating barriers our students who are experience poverty and homelessness have to getting jobs in the food service industry.’ She also thanked everyone who made out time and donated resources to help FareStart meet its goals.

INFORMS Annual Meeting Awards and the Future

And lastly, the INFORMS Annual Meeting Awards. Although Simio did not bag any of the awards, the pomp and pageantry, as well as, the strides made by researchers are worthy of a mention in this post. Hopefully, the prize for teaching operations research and management science may be ours. That being said, the 2019 event was a success and Simio will continue to be a part of the INFORMS Annual meeting for the foreseeable future.