Why More Manufacturers are betting on Simulation Software in 2020

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

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

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

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

Risk Analysis with Simulation Software

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

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

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

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

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

Forward Scheduling with Simulation Software

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

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

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

Planning before Implementation with Simulation

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

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

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

Conclusion

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

Predictive Modeling In Healthcare and The Role of Digitization

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

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

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

The Importance of Digital Transformation and Predictive Modeling In Healthcare

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

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

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

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

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

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

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

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

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

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

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

The Significance of Predictive Modeling In Pharmaceutical Facilities

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

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

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

The Risks of Relying on Predictive Modeling In Healthcare

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

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

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

Conclusion

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

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

Speaker Announcements for Simio Sync Digital Transformation

Simio Sync 2020 is a meeting of like-minded professionals within the digital transformation space and as the event date gets closer, periodic announcements introducing speakers at the event will be provided. Today, we are pleased to announce the addition of expert speakers to the event line-up. These speakers bring decades of experience in diverse industries including healthcare, business development, hospitality, and education to the podium.

The confirmed keynote speakers for Simio Sync are Martin Barkman of SAP and Indranil Sircar of Microsoft. Martin brings his experiences as the lead strategist for SAP’s digital supply chain and the years spent optimizing supply chains using digital technology at SmartOps to the event. His keynote speech will focus on digital transformation and what it means to established enterprises and startups.

Indranil is expected to speak on the emergence of disruptive technological solutions and how manufacturing enterprises can harness digital technology to solve age-old problems. He will beam his searchlight on the application of artificial intelligence, augmented reality, and the digital twin to ease logistics and supply chain management challenges. Practical examples from his time at Hewlett-Packard and Microsoft will serve as relatable case studies for attendees.

The keynote session starts on the 4th of May. You and your team can take advantage of the early bird tickets by signing up now to secure seating and hotel space. Get your early bird ticket today.

Introducing the Confirmed Speakers for Simio Sync

We are also excited to announce the additional speakers who will be speaking at the event and the topics attached to their sessions. Starting with our very own, Molly Arthur, the Senior Customer Care Manager, who’ll be speaking on the John Hopkins University Applied Physics Laboratory (JHUAPL) tests and the use of Simio software for laboratory simulations. She’ll be presenting her ideas using case studies covering Simio’s application in physics laboratories.

Molly, will draw from her approximately seven-year experience providing support to Simio partners. Her session will include facts and figures which attendees from STEM-related fields will find educative concerning the application of Simio in educational institutions. This session will also include details on how Simio supports its customers, building solutions in the educational niche.

Simio Sync will also feature an industrial and business analytics session, headed by Roman Buil Gine of Accenture Analytics. He is an experienced partner, who has applied Simio in real-world settings to solve complex industrial challenges.

Roman, a functional and Industry Analytics professional, will headline the Simio case study session. His session will explore Simio’s application as an industrial analysis tool across a variety of industries including the manufacturing industry.

Roman will rely on his experiences at Accenture and his use of Simio software as an analytical tool for diverse enterprises. His session will focus on specific case studies which showcases Simio as a digital transformation tool while making a business case for its application in the diverse industries of every attendee. Roman’s session provides insight into Simio’s support for its partners and how business organizations can receive actionable intelligence from the data they produce using Simio.

Introducing Simulation and Digital Transformation in Service Industries

Attendees will also have the opportunity to learn more about digitally transforming organizations within the service and production industry from experienced consultants.

Paul Glaser, Principal Consultant at Kitchen Simp LLC, will speak on the role of digital technology in optimizing services within the hospitality industry. His session will focus on CKE Virtual Restaurants and the role augmented reality, digital twin technology, and simulation played in enhancing real-time supply, delivery, and customer services.

Paul is expected to speak on his role in optimizing productivity at established service businesses such as the Hardee’s and Carl Jr’s chain of restaurants. His session will include practical tips on applying Simio as a digital transformation tool and the importance of augmented reality in training and validation.

Simio Sync is also delighted to announce Mohamed Eldakroury of Danfoss Power Solutions, as a speaker for the event. Mohamed will be speaking on the role of digital transforming technologies in optimizing industrial processes in the energy sector.

During his session titled, “Receiving Central Paint and Packaging Simulation at Danfoss Power Solutions”, Mohamed will share applicable insights on the role of simulation in managing and predicting control process behavior, and scheduling in power plants.

He is expected to draw on his experiences as a process engineer at Danfoss Power Solutions and the application of Simio which forms the basis of his speech. Attendees can expect to learn about the role of simulation in monitoring, managing, scheduling, and predicting patterns in meeting the energy consumption needs of consumers.

Introducing Speakers on Digital Transformation in Healthcare

Finally, Simio Sync is excited to announce speakers who will host sessions covering the digital transformation of the healthcare industry. We would like to introduce you to Daniel O’Neil, the Health Systems Innovation Lead at John Hopkins University Applied Physics Laboratory.

Daniel O’Neil is experienced with the application of advanced technologies and digital transformation in delivering optimized services to customers. His experiences include applying technology to drive innovation within the healthcare industry at the Mayo Clinic and John Hopkins University Applied Physics Laboratory.

His session titled “Building Capacity for Healthcare Modeling and Simulation” will expand on Molly Arthur’s speech on JHUAPL tests. He will provide use cases covering the application of Simio within the healthcare industry and how it enhances the care patients receive and the functional capabilities of healthcare providers.

Attendees interested in applying Simio as a digital transformation tool within healthcare facilities will find the wealth of information shared in his session useful to their organizations.

The digital transformation of the healthcare industry sessions will also see Nayaf Ahmad, of Vancouver Coastal Health, speak on capacity planning using digital technology. Nayaf will introduce attendees to the importance of simulation, scheduling, and advanced planning in delivering quality healthcare.

Nayaf will provide details on how digital technology and the data produced from healthcare facilities can be used to make intelligent business decisions. He will share case studies on the application of data analytics and how it helps simplify decision-making processes in healthcare facilities. His session titled “Capacity Planning for a Primary Healthcare Clinic’ will be a goldmine of information for attendees within the healthcare industry.

Networking at Simio Sync

We promised a great event for networking and the reception at the historic Heinz History Center in association with the Smithsonian Institute delivers on that promise.

The networking reception will provide attendees with the opportunity to speak with the highlighted speakers and other attendees at Simio Sync. The chosen location also ensures that everyone has ready ice-breakers to easily kick-start conversations. The Historical Center celebrates 250 years of Pittsburgh’s notable innovations and their impact on society through the years.

Attendees will learn about Pittsburgh’s role in redefining civil society, digital technology, and innovation. This makes the networking reception a must-attend event for every individual and working teams planning to be a part of Simio Sync. To participate in this thrilling reception, you can kick-start the process by registering for Simio Sync today.

Round-Up an Exciting Week by Participating in Training Classes

Simio Sync offers a festival celebrating all things digital transformation with the aim being to inspire you to start the digital transformation process now. While the conference provides use cases on digital transformation across diverse industries, the training classes will introduce you to the basics of simulation, scheduling, and digital analytics with Simio.

The training classes offer attendees a starting point to digital transformation through the Simio Fundamentals Course. This course is designed for individuals who have a basic understanding of simulation and would like to refresh their knowledge of it.

Simio Advanced Course takes things to the next level by providing advanced training to simulation professionals. This course is for professionals with applicable knowledge of simulation and it makes use of the Simio Professional Edition software to enhance your knowledge of data-driven models and advanced process management.

The final training option focuses on scheduling with Simio. The course titled Simio Advanced Course Plus Scheduling, provides advanced training to simulation professionals and data analysts interests in using the RPS edition. The course will teach you how to get started with building scheduling projects using detailed demonstrations and case studies.

The training courses start right after the conference. They will be held between May 6 – 8. While the “Refresher Course” and the “Advanced Course Plus Scheduling‘ will be available for the 3 days, the Simio Advanced Course will only be available from the 6th of May to the 7th.

It is important to note that the Simio Advanced Course prepares you for the Advanced Course Plus Scheduling. Thus, we recommend that experienced simulation professionals participate in the former before undertaking the advanced scheduling course. Tickets for the training classes are currently on sale and you can purchase yours today.

Click here to fill out the registration form which takes about 4 minutes. The registration form provides you with the choice of registering for multiple events and as an attendee or a presenter. It also provides you with details about the training classes and the opportunity to register.

What are the Differences Between Simulation Software: Discrete, Continuous, and Agent-Based?

Simulation has become an integral part of many industries due to its capacity to provide insight into complex operations and processes. This post deals with the different types of simulation software applications, their capabilities, and application. Here, discrete event, agent-based, and continuous simulation will be defined and the differences across all options highlighted to help enterprises make easy decisions when choosing a simulation software.

Definition

Discrete event simulation (DES) models the operation of a system as a sequence of discrete events that occur in different time intervals. The discrete events occur at specific points in time thus marking the ongoing changes of state within the modeled system.

Continuous simulation (CS) models the operations of a system to continuously track system responses through the duration of the simulation. This means results are produced at every point during the simulation and not in intervals. Continuous simulations also produce data in instances where no ongoing changes occur.

Agent-based models (ABM) simulate the actions and interactions of individual agents within a system. The agents can either be a piece of singular equipment or a group of assets working towards a similar goal. ABM simulations are run to determine the effects of these agents on the functions of the entire system an agent is a part of.

An example that highlights the functions of these different simulation techniques is that of a simple check out point in a supermarket. A DES model will view the arrival of a customer and the moment the customer departs as two separate events while the time spent will be represented as a time lapse between both events. The continuous simulation will continuously count the number of customers passing through the checkpoint and its general effect on the checkout system. The ABM simulation sees the customer and checkout point as autonomous agents and tracks their effect on the entire sales process.

With this explanation, it is easy to note that DES technique models physical phenomena or reality excellently as it is able to track occurring events. The agent-based and continuous options are excellent at determining the behavioral pattern of a system. In many cases, a combination of the different simulation techniques provides more-rounded results, especially when modeling complex processes with diverse variables and events.

The Differences in Agent-based, Discrete Event, and Continuous Simulation Features

To highlight these differences a few criteria will be used. These criteria include the following features:

  • What they simulate – This refers to the models they are best at simulating
  • Time step – This refers to how the techniques view the passage of time and time intervals
  • Queuing – This refers to how queue flows are managed
  • Statistical details – This refers to how they define or evaluate events within a system

What they Simulate

Starting with DES, as stated earlier, DES software applications are used to simulate discrete events, needs, and requirements. Continuous simulations are generally applied to flowing continuous processes while ABM is applied to autonomous agents and systems.

Time Step

For DES software, the time step changes according to the occurrence of individual events while for continuous simulation, time steps basically remain unchanged. In AGM software apps, time steps change according to the changing interactions of the autonomous agent.

Queuing

 DES software applies diverse techniques or systems to manage queues. This includes the use of a first-in-first-out (FIFO) approach or the last-in-first-out (LIFO) approach to managing queues. Continuous simulation software makes use of only the first in and first out system to manage queues. As for ABM, the management of queues is a bit different as it describes a system from the perspective of the agent. But a FIFO or LIFO system can be used to manage queues in ABM simulations.

The Differences in Application

Use cases provide realistic examples of defining or highlighting the differences encountered using these different simulation techniques. Starting with discrete event simulation, the discrete nature of this technique makes it an excellent choice for industrial simulations where events occur.

This includes the manufacturing industry, pharmaceutical production enterprises, plants, and industries with functional logistics systems. Here, the ability to simulate the arrival and departure of entities or queuing problems provide a level of insight into industrial operations in ways other methods cannot. An example is the use of Simio’s DES software to optimize activities within the Nebraska Medical Center. In this example, DES modeling was used to optimize hospital operations by reducing the travel time of surgeons and patients, as well as, the use of operating rooms across the medical facility.

Discrete event simulations are also powerful tools in capital intensive industries due to their ability to perform what-if analysis before pursuing further implementation initiatives. The experimentation capacity it brings to the table can save these enterprises from financial loses on specified business operations. The ability to also speed up or slow down specific phenomena to analyze expansive shifts or systems makes it a powerful tool for business applications.

Other application advantages DES software apps bring to the table include; its use as a training and validation tool in Industry 4.0, and its ability to kick start the digital transformation initiatives of enterprises.

Continuous Simulation Software – The continuous nature of this simulation technique makes it a unique tool for analyzing flowing processes or elements with non-linear relationships.

Continuous simulations are generally used within advanced engineering fields where simulator engines are designed. This includes the aviation industry for designing flight simulators, and autopilot programs. It is also used in designing gaming engines for video games such as the Nintendo Wii.

In industrial settings, discrete event simulation software applications are favored but continuous simulation is being used for generative design tasks and managing control systems in the pharmaceutical industry. It is also used in predicting or estimating the probability of natural phenomena such as the occurrence of flooding and hurricanes. These application examples mean continuous simulation is predominantly applied in STEM-related fields.

The advantages continuous simulation brings to the table include; the ability to describe systems with varying activities occurring within the same time interval. Continuous simulations are also used in enhancing artificial intelligence systems due to their theoretical analytical capabilities.

Agent-based Simulation Software – ABM models are generally used in the social sciences. It is extensively used to study interdependencies between different human activities, social and economic systems, and in facilities where the interactions between diverse systems define operations.

The three concepts that define the application of ABM are its flexibility, its ability to capture emergent phenomena, and its ability to define systems. With these abilities comes certain advantages such as the ability to integrate ABM simulations into DES or continuous simulation environments.

Its ability to simulate interactions between autonomous agents also makes it an excellent tool for understanding shop floor behavior. For example, can be used to analyze the cause of shop floor traffic across a facility where both humans and autonomous machines interact. Here, it’s individualistic approach to simulation provides different perspectives from active agents explaining the cause of phenomena such as an unexpected traffic jam within a system.

ABM is actively used in to monitor flowing process such as traffic and customer flow management within physical shops, parks, and recreational centers. An example is its use in a Macy’s store. In this example, ABM was used to estimate the distribution of sales people within its facility and how they interact with customers to enhance its operations.

It is also used to analyze stock market phenomena and operational risk within organizations in diverse industrial niches. Thus, highlighting the versatility and flexibility ABM simulations bring to diverse interactive processes.

In Summary

Simulation provides insight into human relationships, industrial processes, urban and regional planning, and complex systems across every niche. Thus cementing its status as a major data analytics and digital transformation tool designed for every organization.

Although DES, CS, and ABM simulations apply different approaches to simulation, the results they produce optimize human and industrial endeavors in different ways. These ways include planning and implementation, enhancing customer relationships, training staff, developing strategies, and design. The Simio modeling and Simulation software provides an intuitive platform for modeling, running, managing, and sharing DES, CS, and ABM simulations to optimize your organization’s operational processes. You can learn more about specific use cases by browsing through our catalog of case studies.

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.