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.