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
LLC is delighted to announce once again that the opportunity to learn more
about the state of simulation and digital twin technologies is here. And yet
again, this promises to be one of the biggest simulation and digital twin of
the year. Simio Sync Digital Transformation Conference will focus on digital
transformation technologies and how enterprises can tap into Simio to unleash
the digital potential of business processes.
event will be taking place on the 4th and 5th of May 2020
in Pittsburgh with advanced training about using Simio from the 6th
to 8th of May. The first event will introduce you to Simio, the
recent updates in Simio 12, and its application across industries. Keynote
speeches and event programs will consist of Simio use cases and application
across industries of interest. But before delving into the opportunities of the
2020 conference, here is a recap of 2019.
2019 – A Recap
2019 Simio Sync conference was the third annual event on
simulation and digital transformation built around Simio technologies and
solutions. The event brought together an ensemble of experienced speakers to
inspire the crowd on the role of simulation and digital transformation in the
real-world. Speakers included; Chris Tonn from SPIRIT Aero systems, Ian
Shillinger from Mckinsey & Company, Antonio Rodriguez from the National
Institute of Health (NIH), and Dusan Sormaz from the University of OHIO among
speaker presented case studies highlighting the application of Simio within the
aviation industry, manufacturing, healthcare, education, hospitality, and
simulation engineering. These events proved inspiring to participants from
varying industries and opened up new possibilities about applying Simio within
their specific industries.
to Jarred Thome, from USPS, his first Simio Sync event was an eye-opener in
many ways. He said “This was my first year attending the conference and I was
blown away by the extent to which the folks at Simio went to ensure it was a
success. The content, presenters and networking opportunities were all top-notch
and the Simio staff was always accessible and willing to chat. I will
definitely be coming back.”
2019 event was one of a kind and next year’s event is expected to take things
up a notch. The Simio Sync Digital Transformation Conference will consist of
speakers from Fortune 500 companies willing to share their experiences with
digital transformation using Simio with you. The event will also serve as a
networking arena for stakeholders within the simulation and digital
transformation community and participants.
you need to know about the Simio Sync Digital Transformation Conference for
2020 will be highlighted here. In the meantime, you can sign up with Simio
to receive conference updates and to register as a participant today.
fourth annual Simio Sync conference gives you the choice to learn, no matter
the role you play in your company’s digitization efforts. At the end of the
days, you will work away with the knowledge to help you and your company refine
your digital transformation strategy to reap the rewards digitization brings.
networking is your thing, how about coming to listen and catch up with
individuals from fortune 500 companies among others. Through the years, Simio
conferences have been fertile grounds for communicating and building
relationships and this year’s event will be no different.
Sync Digital Transformation provides an excellent opportunity to learn about
simulation and its application in the real-world. Attending the event can help
kick start your company’s digital transformation or refine transformation
strategies to meet your defined goals. Thus, everyone is invited to attend,
network, get inspired, and create fun memories while learning about digital
Sync Digital Transformation conferences are safe spaces created for everyone
interested in the digital twin, simulation, scheduling, and digitization. The
event is open to everyone and the conference areas are safe, inviting, and
representatives are also available in every location to ensure your
participation is a memorable one. If you are in need of answers to
Simio-related questions or event-related questions, you can reach out to a
representative and your questions will be answered.
the increasing number of participants, the golden rule of mutual understanding
also applies. This will help you build better networks, and truly take
advantage of the different sessions, and labs that are parts of the event.
is now open. You can now take advantage of early bird tickets to become one of
the first individuals or organizations to register for the Simio Sync Digital
Transformation Conference for 2020. There are a plethora of excellent hotels
and living areas around the event holding in Pittsburgh and the earlier you
register, the quicker you will be about planning your travel and relaxation
register, visit the Simio Sync event page and go through the registration
process. The process is quite straightforward and intuitive to accomplish. You
will also have the choice of registering for the conference event and adding
advanced training options to your registration form.
Simio Sync session catalog is the ultimate guide you need to navigate through
the conference while bookmarking areas you are particularly interested in. The
session catalog is currently live and you can browse through it while
year, there are approximately eight unique sessions divided across networking
breaks to ensure you take advantage of your participation. The sessions include
diverse keynote speeches from leading digital transformation experts and Simio
engineers. To get a real-world feel of the application of Simio and digital
transformation processes, case study sessions and presentations are also part
of the event catalog.
exciting events of note which you are also welcome to participate in includes
the Simio Pittsburgh exploration event where you and your spouse can line up
with Simio Spouses to explore the ancient city of Pittsburgh. If you are a
running enthusiast, you can also choose to participate with Simio in the
Pittsburgh marathon before the conference begins. These are part of the fun activities
lined up for you!
Training and Hands-on Labs
advanced training event will take place on the 6th to the 8th
of May. This training focuses on the application of Simio in the real-world.
Thus, you will be introduced to the different features of Simio and how they
can be applied to drive digital transformations, simplify discrete event
scheduling, and build digital twins of physical processes.
advanced training program will be a boon for organizations currently using
Simio and others who are interested in using it to drive digital transformation
strategies. Individuals interested in digital transformation are also welcome.
The hands-on lab integrates the use of case studies and the Simio interface to
ensure you understand every aspect of the digital transformation process with
of industry-leading organizations and individuals have already reserved their
spots for the Simio Sync Digital Transformation conference. And come the 4th
of May 2020, you too can pick the brains of your favorite personalities within
the aviation, hospitality, education, healthcare, automotive, automation,
manufacturing, and pharmaceutical industries.
from Lockheed Martin, Air Canada, Boeing, BAE Systems, OHIO University, United
States Postal Services, Exxon Mobil, Roche, FedEx, Honeywell, American Airlines
etc. will be there. The networking dinner and entertainment sessions at 6pm
create an excellent opportunity to build interpersonal relationships for the
get the best out of the Simio Sync Digital Transformation Conference, we
recommend that you participate in at least one of the following programs:
in at least one hands-on training covering the use of Simio.
and participate in a keynote session highlighting the use of Simio for your
industry or related industry
comfortable shoes and cover grounds during the networking dinner and
entertainment opportunities within the conference.
world is moving toward an era of more efficient business operations driving by
automation. This was one of the key messages of Mitsubishi Hitachi Power
Systems CEO, Paul Browning, at the just concluded 2019
in Houston. Paul Browning, who was the keynote speaker on the ‘digital
transformation agenda’, spoke about Mitsubishi’s use of artificial intelligence
(AI), machine learning, and digital Twin technologies to create the world’s
first autonomous power plant. He ended
his speech by saying ‘Mitsubishi is building the world’s first autonomous power
plant capable of self-healing.’
use of digital transformation technology to eliminate downtime and reduce
unplanned shutdowns are just a few of what can be accomplished with Digital
Twin technologies. In fact, the ability to virtualize workspaces and complex
systems have important roles to play in achieving the smart factory and
Industry 4.0 revolution the most industries dream off. This is because no other
emerging technology has the potential to bridge the gap between the physical
world driven by machines and the virtual world like the Digital Twin. This is
why the Digital Twin market is forecasted to be worth approximately $26billion
the numbers highlight the growing acceptance of Digital Twin solutions, many
businesses are a bit skeptical about its implementation and benefits. This is
why practical case studies are needed to highlight the application of the
Digital Twins and how others have benefited from it.
The Most Important
Benefits of Digital Twin Technology
4.0 business model relies on data to automate business processes. The Digital
Twin, in turn, creates the perfect environment for collecting data from every
aspect of the manufacturing process for analytics and simulation. When data is
accurately collected and a Digital Twin is designed, system integrators, data
analysts, and other stakeholders can use it to drive business policies and
improve decision-making processes. The benefits Industry 4.0 and manufacturers
stand to gain from Digital Twins include:
Having the capacity to access and quantify every information produced from a
manufacturing process and the shop floor is key to automation. Digital Twin
technologies allow manufacturers collect data from the sensors and embedded
systems integrated onto a shop floor. The Digital Twin also takes things a step
further by replicating physical manufacturing processes and creating a digital
environment where these processes can be assessed.
the necessary data from equipment, machines, material handling, and production
cycles in place, manufacturers can develop policies and run simulations to
determine how efficient they are. Once determined, the manufacturing policies
and regulations can then be applied on the shop floor. This gives large
enterprises a cheaper way to access the effects of decisions on productivity
DHL study on the importance of Digital Twin in enhancing plant performance
highlights the use of Digital Twin by Iveco solutions to optimize welding
capabilities. The Iveco manufacturing line struggled with constant breakdown of
its welding components which delayed production. The cause of these breakdowns
were pin-pointed to a lamellar pack which wore out constantly. To enhance
performance and reduce downtime, Iveco designed a Digital Twin model of its
Digital Twin model helped Iveco understand the different welding concepts and
requirements, as well as, their effect on the lamellar pack. Using simulation
and machine learning, Iveco developed an optimal welding process that could
forecast the probability of component failures in other to reduce them.
– One of the benefits of designing a
Digital Twin of manufacturing shop floors or plants is the opportunity to
integrate predictive maintenance into business models. Predictive maintenance involves
the prediction of a component or machine failure and the taken of preemptive
action to forestall failure. Digital Twin technology has created an environment
that drives predictive maintenance across various systems.
again, The Mitsubishi Hitachi Power System plant serves as an example
where Digital Twin technology can drive the predictive maintenance policy in
Industry 4.0. The Digital Twin model created by Mitsubishi gives power plants
the ability to monitor sensors and other parameters that determine the plant’s
performance levels. On application, the Digital Twin, alongside AI, and machine
learning provided insight on the best time to schedule maintenance activities
without disrupting production.
benefits Mitsubishi reaped from its use of Digital Twins include a more efficient
way to discover fault components and a maintenance culture that reduced
downtime. The autonomous plant was also able to run self-diagnostics and repair
stuck valves that affected power generation. Smart facilities can take
advantage of the Digital Twin to drive a predictive maintenance culture which
will eliminate resource waste and downtime caused by faulty equipment.
of Complex Systems or Processes
– Digital Twin ecosystems provide an avenue to control complex systems and
processes in ways other traditional technologies can’t. This is because, AI,
machine learning, and simulations can be applied to the digital environment
thereby allowing enterprises to see farther. Digital Twin takes control process
which involves comparing system performances with set standards, discover
deviations, and design corrective actions to greater heights. This makes it a
great resource for research in Industry 4.0.
example of how Digital Twins makes advanced control of complex systems possible
can be seen from how the U.S. Department of Energy National Energy Technology
Laboratory (NETL) deployed Digital Twin solutions. The Digital Twin of
the (NETL) plant was used to carry out research on the use of carbon dioxide to
power plants as a replacement to the hazardous coal-powered plants currently in
use. The Digital Twin also mapped out the plant’s sensor network in other to
optimize its use.
Digital Twin created by the research team served as a virtual testbed for
analyzing operational relationships and their effects on power generation. The
benefits of Digital Twins, in this case, included a cheaper more effective way
to analyze control process phenomena and reduce downtime. Increasing plant
reliability and optimizing the use of resources were also singled out as
and Onboarding Process
– The future of Industry 4.0 is being driven by emerging technology solutions
such as the industrial internet of things (IIoT), IoT, automated vehicles and
equipment. This means to effectively take advantage of the benefits of Industry
4.0 older and new employees must be thought to function in a smart facility. Digital
Twins of plant systems and processes provide a virtual environment for
employees to learn about operational processes.
a case study conducted in an automotive facility, employees were taught the repair
and assembling process in a virtualized environment and through manuals. At the
end of the training employees preferred the option of learning through
virtualized environments and retained more information compared to learning
from physical manuals. This means the hands-on learning approach driven by
Digital Twin technology creates a better environment for learning complex
Take Advantage of
The Benefits of Digital Twins
combination of Digital Twin technology and cloud computing has made the design,
emulation, scheduling, analytics, and simulation services it offers even more
affordable to end-users. Small and medium scale businesses can now access
Digital Twin solutions to solve complex problems. This means Digital Twin as a
Service is slowly but surely becoming an option for enterprises to explore. You
can learn more about the Digital Twin opportunities for your business by
contacting the experienced engineers at Simio.
Disruptive technology is a product, concept or service that has the ability to
redefine the traditional way of doing things.
Today, the digital twin concept is being hailed as a disruptive
technology with the capacity to change how we design, solve complex problems
and collaborate. In fact, a Gartner Report predicted that by 2021 approximately 50% of industrial companies will
integrate the use of digital twin technologies to increase workforce
performance and manufacturing efficiency. So, what is this disruptive
Digital Twin refers to a real-time replica of a physical entity. This entity
could be a living thing, an inanimate physical object, as well as, assets,
processes and systems that function in the physical world or environment. Although
this concept is actually three decades old, the convergence of emerging
technologies such as the internet of things (IoT), artificial intelligence
(AI), machine learning has taken it to new heights. Digital twins juxtapose
these emerging technologies to create digital models of physical entities with
the ability to simulate real-time changes that occur to the physical model.
example of how this concept work involve the development of the digital twin of
an aircraft. With the digital twin, finite element analysis (FEA) can be
applied to determine the fatigue limit of the aircraft’s structure. The results
of this simulation can then be used to design or choose more suitable materials
or design for a more durable aircraft. Outside manufacturing, digital twins can
be employed in diverse industries including healthcare to simulate how the
human body reacts to external forces. The benefits of integrating digital twins
include increased design efficiency, enhancing predictive analysis, and
collaboration. This is why the market
for it is expected to hit approximately $15billion by 2023. The benefits of digital twins
are huge but the challenges business will face not embracing it is even bigger.
article will discuss:
challenges businesses face not integrating the digital twin in business
effects of not embracing the digital twin.
disruptive capabilities of the digital twin.
The Five Challenges
Businesses Face Not Embracing Digital Twins
approximately 50% of industrial companies integrating the use of digital twins,
the 50% who don’t will definitely be losing their competitive edge. This is
because the digital twin will redefine real-time simulation applications in
ways the average 3D modelling software or Building Information Modelling platform
can’t aspire to. The challenges to expect include:
Solutions, Designs or Data –
As the generation of baby boomers continue to retire daily, the probability of
losing the knowledge that built legacy equipment and systems could be lost.
This includes the Mylar copies of traditional manufacturing equipment or the
designs of legacy military aircraft. Regardless of technological advancements,
the loss of legacy data destroys the foundations newer prototypes were built
the aid of the digital twin concept, businesses across every industry, can
create an accurate digital model of legacy equipment or solutions. The digital
model can then be stored for posterity sake or analyzed with the aim of
developing upgraded prototypes. Models can also be used as materials for
training the younger generation of workers through virtual reality
Lean Manufacturing Processes – Toyota’s integration of lean manufacturing
to speed up production while efficiently using resources has become folklore in
the automotive industry. The integration of lean manufacturing models – which
were disruptive at that time – helped Toyota dominate the industry for decades.
This is the leverage the digital twin concept offers. The ability to optimize
entire product value chains is something that can be achieved in real-time
through the digital twin.
study at the Bayreuth University, Germany focused on analyzing
the impact of digital twins in collecting real-time data and optimizing
production systems. The study compared the efficiency of digital twins and the
commonly used value stream mapping solutions. In the end, the results showed
that digital twins exceeded traditional solutions in data acquisition,
automated derivation of optimization measures, and the capturing of motion
data. These data which are crucial to optimizing production could also be
utilized in a digital twin environment to optimize diverse processes. Thus,
shunning digital twins will leave firms in the lurch while competitors who
leverage this concept can optimize production variables in real-time.
Limitations in the
Integration and Use of Data
– The Industry 4.0 revolution currently going on relies heavily on the
collection and use of data to receive important business insight and automate
processes. The tools or applications currently used today are enterprise
relationship management software, and industrial cloud solutions. Although
these solutions do excellently well in collecting data from smart or industrial
internet of things (IIoT) devices, they still struggle with collecting data
from legacy or dumb equipment. This limits the penetration of Industry 4.0 in
the deepest layers of manufacturing shop floors which is what the OPC
Foundation intends to solve.
twin concepts can help smart factories integrate dumb equipment from the
deepest levels of a shop floor into models of the manufacturing plant. This
makes it possible to capture the hundreds of
non-measured information in the shop floor into a digital environment thereby truly meeting
Industry 4.0 and OPC UA standards. If successfully done, the digital twin with
the captured data can be used to predict the facilities transient response to
external disturbances, equipment failure, and system malfunctions.
who overlook digital twin concepts will be stuck with using data from only
smart equipment and IoT devices to track real-time changes on the shop floor.
The limitations associated with not capturing non-measured data will lead to
approximations when automating operations in a smart factory. This could lead
to downtime, an inefficient workforce, and in extreme situations accidents to
Effectiveness of Predictive Analysis
– Another important challenge shunning the integration of digital twins into
industrial operations is the difficulties that come with making blind or
half-informed changes. Making blind changes when making important decisions
such as designing a new material handling system or reducing the number of
processes needed to develop a product will have terrible consequences. These
consequences will include wastage of resources, a subpar end product or
confusion on the shop floor.
to Gartner, downtime in the manufacturing industry could lead to huge losses.
In the automotive industry alone, downtime is responsible for a loss of $22,000 per minute. Although the numbers may be
less in other industries, the effects are still considerable. Digital twins can
help eliminate these challenges or losses by helping businesses simulate the
real-time effect of making certain changes. For example, a change of production
schedule while going through a transition period would have left the aviation
manufacturer Lockheed Martin unable to meet its delivery timelines. With the
aid of the Simio simulation software, the manufacturer was able to make
informed decisions that optimized the production process.
match to industry 4.0 and a more connected factory is one that must be planned
for if manufacturers intend to remain competitive for the long run. One way to achieve
this is by integrating a digital twin for simulating and receiving the insights
needed to automate industrial processes. If properly executed, you will be
turning the disruptive nature of the digital twin to your benefit.
We are pleased to announce that Simio has been awarded
the coveted U.S. General Service Administration (GSA) IT-70 contract for
Simio’s object-oriented Simulation and Production
Scheduling software is ideally suited for all aspects of state and local
government use. Now, government agencies can more easily purchase Simio
products and services for design, emulation and scheduling of their complex
“We’re proud and excited to offer a
straightforward solution for simulation needs,” says Anthony Innamorato, a
former Platoon Commander of the U.S. Navy and the current Vice President of
Customer Solutions at Simio LLC. “With Simio now being awarded a GSA
contract, government employees can more easily access and utilize the power of
Simio now proudly displays the GSA Starmark Logo on our website, along with our Contract Number: 47QTCA19D008W.
The GSA’s federal procurement approval process means
that Simio’s product offering has been screened and accepted as the best
possible simulation software, at a fair price, within our industry. This means
that state and local government buyers and their agencies can order and buy
with confidence. They can implement strategic purchases more easily to expedite
acquisitions. It also means that they obtain best prices, at the same time
ensuring Federal Acquisition Regulation (FAR) compliance.
Already major suppliers to the Government, Military and Department of Defense, Simio’s solutions encompass a broad set of issues related to production scheduling, supply chain and logistics, as well as resource staffing. Typical application areas include fleet sizing and design, refurbishment operations planning and overall process improvement using Lean concepts.
Simio simulation can assist in any scenario where
modeling is needed to determine solutions or to improve communication of ideas
and promote understanding. Our software can help make important decisions that
are critical for the reduction of risk.
Applications for Simio’s simulation in the federal
government environment include:
large or complex systems with a great degree of
critical situations where it is too expensive or risky
to do live testing, and
systems where data is missing or incomplete.
Take advantage of our new alignment with the GSA’s
purchasing program to implement Simio’s leading edge software in your
The latest era of industrial revolution – Industry 4.0 connects and revolutionizes various aspects of the industry including manufacturing processes as well as business processes such as supply chain. The increasing demand of customized product from the customer end is a major driving theme of this transformation in the industry. The traditional processes are highly efficient for batch production and low cost scaling in bulk manufacturing but are relatively time consuming inefficient for manufacturing customized products. Similar is the case for the business processes and models that being used around this manufacturing style. There is need of new production planning style which can simulate the costs, efficiency and resource requirements in real time for any product for mass customization.Industry 4.0 uses Cyber Physical Systems (CPS) and Internet of Things (IoT) to introduce technological and human improvements, which ultimately results in enhanced productivity, product quality with reduced manufacturing time and product price. Hence, the requirement of an advanced production planning and scheduling scheme becomes paramount. In this article,we will discuss how production planning can be implemented in Industry 4.0 and the ways in which it will help manufacturers of any and every product to adapt easily to customer demands and transition smoothly into the upcoming industrial economy.
Industry 4.0 brings along the requirement of new process and production planning where most of the working environment is automatized and the data recorded is processed using fog computing, on-premise clouds or cloud computing servers. Machine to Machine communication is expected to increase more than ever. These changes raise some critical questions and concerns regarding the manufacturing and planning processes:
Is it possible to completely automatize production planning using CPS and IoT?
Can human knowledge be translated into future products?
The role of Production Planning Software in Industry 4.0 will be to address these concerns effectively and ensure that the decision making processes involved in process selection, resource allocation, operation sequence and scheduling and sufficiently automatized with knowledge importer from previous processes. This should then result in the modeling of the future product including customer based customization demands as well.
Traditional process planning being used in many industries presently is completely based only on the knowledge and experience of the individual or team working on the system. The people working on the systems are technology experts from experience rather than knowledge. The existing demand for change to the new technology solutions can be a big transition for such individuals. This might slow down the progress of these industries, especially SMEs which are slower in the adaptation process. Hence, it is important for each industry to build their own strategy to implement Industry 4.0.
All the manufacturing resources in the industry are now connected to data and information exchange enabling better quality and process control. Scheduling of the product manufacturing and supply chain are being solved by using dynamic scheduling with the help of Structure Dynamics Control (SDC). Data and knowledge is transformed to software that makes a decision based on the technical specification of the order and available material combinations. This type of process planning has been adopted completely in very few industrial processes such as welding.It is still a challenge for many manufacturers to figure out what would be the optimal technique if an industry manufactures various products with different set of technologies. Also, the scaling of this single technology-single product scheme( e.g. welding) might not be easy on multiple types of products. Visualization of the process and predetermining the resource requirements will become more important. Simulation of the complete Production Planning using real time data can be an effective solution to this problem.Let us see how a product planning software can make the manufacturing process ’smarter’.
”Smart products” enable an industry to include information about customization demands of the consumer,collect feedback which can then be used in knowledge databases used in the various phases product design, development and manufacturing process. These include process planning, operation sequencing and scheduling. The collaboration of various product parameters and consumer needs in each stage of product development cycle allows the manufacturer to continuously improve the product quality and optimize the manufacturing costs effectively in real time. This results in an overall better product from both consumer and manufacturer’s point of view. Product Planning Software enable this whole cycle managing various processes starting from material selection, shape, geometry, operation priority, time of operation, machine cost and avail- ability and many more. The Product Planning can also be linked to the ERP( Enterprise Resource Planning Software) in the cloud to include insights and data to other parts of product lifecycle resulting in a better product with every iteration.
A good production planning software that automatizes the various tasks of the product development cycle is a must for mass customization and improved efficiency in Industry 4.0. Thus, it can be easily concluded that a good Planning Production Software will form a critical building block of the industry in Industry 4.0.
The modern industry has seen great advances since its earliest iteration at the beginning of the industrial revolution in the 18th century. For centuries, most of the goods including weapons, tools, food, clothing and housing, were manufactured by hand or by using work animals. This changed in the end of the 18th century with the introduction of manufacturing processes. The progress from Industry 1.0 was then rapid uphill climb leading up to to the upcoming industrial era – Industry 4.0. Here we discuss the overview of this evolution.
Industry 1.0 The late 18th century introduced mechanical production facilities to the world. Water and steam powered machines were developed to help workers in the mass production of goods. The first weaving loom was introduced in 1784. With the increase in production efficiency and scale, small businesses grew from serving a limited number of customers to large organizations with owners, manager and employees serving a larger number. Industry 1.0 can also be deemed as the beginning of the industry culture which focused equally on quality, efficiency and scale.
Industry 2.0 The beginning of 20th century marked the start of the second industrial revolution – Industry 2.0. The main contributor to this revolution was the development of machines running on electrical energy. Electrical energy was already being used as a primary source of power. Electrical ma- chines were more efficient to operate and maintain, both in terms of cost and effort unlike the water and steam based machines which were comparatively inefficient and resource hungry. The first assembly line was also built during this era, further streamlining the process of mass production. Mass production of goods using assembly line became a standard practice.
This era also saw the evolution of the industry culture introduced in Industry 1.0 into management program to enhance the efficiency of manufacturing facilities. Various production management techniques such as division of labor, just-in-time manufacturing and lean manufacturing principles refined the underlying processes leading to improved quality and output. American mechanical engineer Fredrick Taylor introduced the study of approached to optimize worker, workplace techniques and optimal allocation of resources.
Industry 3.0 The next industrial revolution resulting in Industry 3.0 was brought about and spurred by the advances in the electronics industry in the last few decades of the 20th century. The invention and manufacturing of a variety electronic devices including transistor and integrated circuits auto- mated the machines substantially which resulted in reduced effort ,increased speed, greater accuracy and even complete replacement of the human agent in some cases. Programmable Logic Controller (PLC), which was first built in 1960s was one of the landmark invention that signified automation using electronics. The integration of electronics hardware into the manufacturing systems also created a requirement of software systems to enable these electronic devices, consequentially fueling the software development market as well. Apart from controlling the hardware, the software systems also enabled many management processes such as enterprise resource planning, inventory management, shipping logistics, product flow scheduling and tracking throughout the factory. The entire industry was further automated using electronics and IT. The automation processes and software systems have continuously evolved with the advances in the electronics and IT industry since then. The pressure to further reduce costs forced many manufacturers to move to low-cost countries. The dispersion of geographical location of manufacturing led to the formation of the concept of Supply Chain Management.
Industry 4.0 The boom in the Internet and telecommunication industry in the 1990’s revolutionized the way we connected and exchanged information. It also resulted in paradigm changes in the manufacturing industry and traditional production operations merging the boundaries of the physical and the virtual world. Cyber Physical Systems (CPSs) have further blurred this boundary resulting in numerous rapid technological disruptions in the industry. CPSs allow the machines to communicate more intelligently with each other with almost no physical or geographical barriers.
The Industry 4.0 using Cyber Physical Systems to share, analyze and guide intelligent actions for various processes in the industry to make the machines smarter. These smart machines can continuously monitor,detect and predict faults to suggest preventive measures and remedial action. This allows better preparedness and lower downtime for industries. The same dynamic approach can be translated to other aspects in the industry such as logistics, production scheduling, optimization of throughput times, quality control, capacity utilization and efficiency boosting. CPPs also allow an industry to be completely virtually visualized, monitored and managed from a remote location and thus adding a new dimension to the manufacturing process. It puts machines,people, processes and infrastructure into a single networked loop making the overall management highly efficient.
As the technology-cost curve becomes steeper everyday, more and more rapid technology disruptions will emerge at even lower costs and revolutionize the industrial ecosystem. Industry 4.0 is still at a nascent stage and the industries are still in the transition state of adoption of the new systems.Industries must adopt the new systems as fast as possible to stay relevant and profitable. Industry 4.0 is here and it is here to stay, at least for the next decade.