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
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
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.
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
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
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.
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
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.
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.
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
The Importance of Digital
Transformation and Predictive Modeling In Healthcare
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.
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.
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.
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.
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
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.
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.
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.
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
The Risks of Relying on Predictive
Modeling In Healthcare
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.
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.
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
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.
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.
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.
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
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
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.
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.
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.
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
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
will also have the opportunity to learn more about digitally transforming
organizations within the service and production industry from experienced
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.
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
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
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.
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
Introducing Speakers on Digital Transformation in
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.
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.
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.
interested in applying Simio as a digital transformation tool within healthcare
facilities will find the wealth of information shared in his session useful to
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.
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
promised a great event for networking and the reception at the historic Heinz
History Center in association with the Smithsonian Institute delivers on that
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.
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
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.
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.
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.
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
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.
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
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.
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.
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.
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
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.
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
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
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.
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.
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
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.
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.
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
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.
– The continuous nature of this simulation technique makes it a unique tool for
analyzing flowing processes or elements with non-linear relationships.
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.
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.
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.
– 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.
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.
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.
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
its facility and how they interact with customers to enhance its operations.
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.
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
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.
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.
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.
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
We’re also pleased to announce
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.
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
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
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
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
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.
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
Manual scheduling typically takes hours to
complete, and the moment any change occurs the schedule becomes
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.
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
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
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
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.
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
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.
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
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.
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.
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
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
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.
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
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.
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
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.
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
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
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
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
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
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
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
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
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.
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.
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.
You Should Know About Creating a Digital Twin
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:
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.
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
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
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.
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
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.
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
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.
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.
the Security Considerations
– According 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.
Digital Twin with 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.
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.