Best Answers to Commonly Asked Risk Analysis Questions

Many managers don’t understand what exactly risk analysis is.  We put together some of the most common questions with responses for you.

What does the risk percentage mean?

The risk percentage approximates the on-time probability for an order with appropriate consideration of the number of replications or “experiments.”  It tells the user how confident they can be in meeting the due date given how many trials they have conducted. 

How does Simio calculate the on-time probability?

Simio adjusts from a base rate of 50% with each risk replication.  If an order is on time in an individual replication, Simio updates the probability, increasing it closer to 100%.  If the order is late, Simio decreases the probability closer to 0%.  Each replication is an experiment that provides new information about the likelihood of success or failure.  More experiments mean more confidence in the answer.

Why is the base rate 50%?

Before any plan is generated or any activity is simulated, there is no information about the order other than the possible outcomes.  Because there are only two outcomes that matter (on time or not), the base rate is set to 50%.

I have an overdue order in my system.  Why is it not always 0%?

Because the calculation is an adjustment of a base rate of 50%, Simio needs a lot of evidence before it will guarantee that an order will be late (or on time for that matter).  If the user runs 1000 replications, and the result is late in all of them, Simio will reflect a 0% on time probability. 

What formula does Simio use to calculate the probability?

For the statistics experts, Simio uses a binomial proportion confidence internal formula known as the Wilson Score.  We report the midpoint of the confidence interval as the risk measure.

Why not just report the outcome of the replications as the probability (e.g., if 9 of 10 are on time, report 90% on time probability)?

This was the original implementation.  However, it gives a false sense of confidence and can be misleading.  A single replication would always yield either 100% on time or 0% on time.  We wanted the answer to also give decision makers a sense of how confident they could be in the answer.  Using the Wilson Score, a single replication will yield a result of 60% at best and 40% at worst (using 95% confidence level).  This helps the decision maker identify that they have a very small sample of data and would encourage them to run additional replications. 

Can you give me an example of how this works?

Risk analysis can be demonstrated using any scheduling example.  It is best viewed in the Entity Gantt.  In the screenshots below, we’ve included 2 orders from the Candy Manufacturing Scheduling example.  One of the orders is overdue (will be late always), and the other has plenty of time (will be on time always).

The base rate is 50%.  After 1 replication, Simio updates the probabilities.  Order 1 now has a 60% on time probability.  Order 2 has a 40% on time probability.

After 2 replications, 67% and 33%:

After 5 replications, 78% and 22%:

After 100 replications, 98% and 2%:

Finally, after 1000 replications, 100% and 0%:

How many replications should I run?

By default, we suggest 10 replications (and 95% confidence level).  With these settings, a risk measure of 86% is a good sign, while 14% is a bad one.  Beyond the default settings, there are several additional factors which are dependent on the situation and use case.  One of these factors is slack time (the time between estimated completion and due date).  On the Gantt, slack time is the distance between the grey marker and the green marker.  If the slack time is large, a single replication may suffice.  If the slack time is small, additional replications will help identify if the order is in trouble or not. 

Now that I know my risk, what can I do about it?

Depending on your position in the organization (and therefore your decision rights), you can change either the design or operation of the system.  Example design changes include things like adding another assembly line or buying another forklift.  These changes are long term and may require approvals for capital expenditure (which the model facilitates by quantifying the impact of the expenditure).  Example operational changes include things like adding overtime, expediting a material, or changing order priorities, quantities, due dates etc.  Bridging the gap between design and operation are the dispatching rules, which relate to overall business objectives.  They are also flexible parameters which control how Simio chooses the next job from a queue (e.g., earliest due date, least setup, critical ratio, etc.).  All of these parameters influence risk and can be changed, provided that the user has the authority to change them.

Will Simio choose the best design and operation for me?

Decision rights and business processes have far reaching consequences.  A floor manager can probably authorize overtime if the schedule looks risky.  He probably cannot buy a piece of equipment.  To change a priority or a due date, he probably needs to consult with the commercial team and/or account managers.  To expedite a material, he probably needs to communicate with the procurement team.  To make a capital expenditure (i.e., change system design), he probably needs executive/financial approval.  Our solution respects those boundaries.  We treat priorities, due dates, etc. as inputs rather than outputs.  Any of these parameters can be changed by the appropriate decision maker.  They should not be changed by the tool without consent.  Simio assists the decision maker (at any level in the organization) by exposing the true consequences.

With so many choices, how can I quickly explore the consequences across multiple scenarios?

The experiment runner is used to explore consequences (which we call Responses) across multiple scenarios where a user can influence the parameters mentioned above (which we call Controls).  If the solution space is very large (i.e., there are many controls with a wide range of acceptable values), we recommend using OptQuest to automate the search of the solution space based on single or multiple objectives (e.g., low cost and high service level).  OptQuest uses a Tabu search which learns how the control values influence the objectives as it explores the solution space.

How often should I run these type of experiments?

Experiments are most relevant to design choices.  Operational decisions have many hard constraints which cannot be easily influenced.  For example, though Simio will allow you to adjust material receipt dates of critical materials and show you the impact on the schedule, many of them are inflexible and out of control of planner or even the business.  If you ask OptQuest how much inventory you would like to have, it will tell you, but this information adds no value because it is not actionable in the short term.  The planners need to work with what they have and make the best of it.  In practical application, we recommend running large experiments to explore design decisions on a monthly or quarterly basis.

Integrating Digital Transformation to Enhance Overall Equipment and Facility Efficiency

The digital transformation of traditional business process and the assets that run them have become one of the raves of the moment. A Forbes-backed research highlights just how popular the topic of digital transformation and the tools needed to accomplish it has become. Statistics like the fact that 55% of business intended to adopt digitization strategies in 2018 which grew to 91% in 2019 highlights just how popular this transformation has become.

The reason for its increased adoption rate is the ease it brings to managing business operations, facilitating growth, and a healthy return on investments made on digital transformation. The numbers from the 2019 digital business survey prove these benefits outlined earlier to be true. 35% of organizations have experienced revenue growth while 67% believe it has helped them deliver better services to customers. But despite its popularity, the adoption of digital transformation brings up a multitude of question many enterprises still struggle to answer. This post will answer some of the more important questions with special emphasis on facility management and efficiency.

What is Digital Transformation?

Digital transformation refers to the integration of digital technologies into business operations to change how an enterprise operates and delivers value to its customers or clients. Digital technologies generally refer to devices and tools that enable access to the internet thus its use allows organizations to bring operational processes to cyberspace.

The above definition is a simpler version of what digital transformation is about but because digital transformation looks different for every company and industrial niche, other definitions exist. In terms of enhancing equipment and facility efficiency levels, the definition by the Agile Elephant better encapsulates its meaning. Here, digital transformation is defined as digital practices that ‘involve a change in leadership thinking, the encouragement of innovation and new business models, incorporating digitization of assets, and increased use of technology to improve an organizations entire operations.’

In facility management, assets refer to the equipment, tools, and operation stations within the facility while new business models and innovation refer to the integration of digital technology concepts. These concepts can be the digital twin, discrete event simulation or predictive analysis.

What is Overall Equipment and Facility Efficiency?

Productivity within manufacturing facilities and warehouses are generally measured using the overall equipment effectiveness (OEE) concept. This concept measures the maximum output machines can achieve and compares subsequent output to the optimized value. In cases where the machine or equipment falls short, the OEE falls from 100% and the production cycle may be termed unproductive.

The OEE is calculated using three separate components within facilities and these are:

  • Availability – This focuses on the percentage of scheduled time an operation is available to function.
  • Performance – This refers to the speed at which work centers compared to the actual speed it was designed to achieve
  • Quality – This refers to the number of goods produced and the quality levels compared to optimal production qualities.

Although the OEE process is quite popular and has proved to be efficient, a critical analysis shows that it does not take into consideration some important metrics. OEE calculations do not include the state of the shop floor, material handling processes, and connections to both upstream and downstream performances. This is why its effectiveness as a measuring tool has been lampooned by a plethora of manufacturers with skin in the game.

Criticism of OEE as a performance measurement tool include its lack of ability to breakdown or access granular information in facilities and its lack of multi-dimensionality. The fact that it struggles with identifying real areas that require improvement within facilities is also a deterrent to its efficiency in analyzing factory performances. And this is where digital transformation comes into play.

Digital Transformation and its Ability to Enhance Facility Efficiency

The ability to digitize assets within manufacturing shop floors have created an environment where granular data can be collected from the deepest parts of today’s facilities. With the data collected due to digital transformation, a clearer picture of how a facility function can be gotten. But the digitization of traditional manufacturing processes and operations have also been a source of debate for diverse professionals due to certain difficulties. These difficulties include assessing data from legacy or dumb assets, managing communications across diverse supply chains, and bringing captured data together to make sense of complex facility operations.

To manage these challenges, diverse emerging technologies have been built around each of them. In terms of capturing data from legacy assets, the use of smart edge technologies that can be attached to assets is currently eliminating this challenge. While standards and communication protocols such as those from the OPC foundation is solving the issue of communication across both smart and dumb assets. Finally, to make sense from the captured data in order to enhance shop floor activities, digital twin technology provides a streamlined approach to monitoring and managing facilities using captured data.

With these emerging technologies, detailed insight at the granular level can be assessed about a particular facility. More importantly, these technologies attached to digital transformation can be used to enhance operational processes by delivering real-time scheduling, analyzing complex processes, and simulating applicable solutions to manufacturing shortcomings.

Discrete Event Simulation and Enhancing Facility Efficiency

Discrete event simulation (DES) tools such as Simio are some of the emerging technologies that play important roles in transforming traditional factory or facility processes. The introduction of DES can help with mapping out previous event schedules to create optimized scheduling templates that can speed up production processes.

DES tools or software can analyze both minor processes that are subsets of a large one, as well as, the entire complex system to produce schedules that optimize these processes. An example of this was the integration of Simio by Diamond-Head Associates, a steel tubing manufacturing company. The challenges the steel tubing manufacturer faced involved meeting production schedules due to a very complex production process with hundreds of production variables.

With the aid of Simio simulation software and the digital transformation it brings, Diamond-Head associates were able to utilize the large data sets produced by the varying production processes. With this simulation model, optimized schedules built for its manufacturing processes were created and this helped with making real-time businesses decisions. The steel tubing manufacturer successfully reduced the time it took to make a decision from an hour and a half to approximately 10 minutes.

This case study highlights how digital transformation can be used to enhance facility efficiency in diverse ways. These ways include optimizing scheduling procedures and drastically reducing the time needed to come up with accurate solutions to complex manufacturing-related scheduling processes.

Enhancing Facility Productivity with the Digital Twin

Another aspect of digital transformation is the use of digital twin technologies to develop digital representations of physical objects and processes. It is important to note that the digital twin does more than a 3D scanner which simply recreates physical objects into digital models. With the digital twin, complex systems can be represented in digital form including the capture of data produced by assets within the system.

The digital twin ecosystem can also be used to conduct simulations that drive machine and facility performance, real-time scheduling, and predictive analytical processes. Thus highlighting how digital transformation provides a basis for receiving business insights that change the leadership of an organization thinks and make decisions.

An example that highlights the application of digital twin technology to enhance productivity or facility efficiency is that of CKE Holdings Inc. CKE Holdings is the parent company of restaurants such as Hardee’s and Carl’s Jr. Earlier this year, the enterprise was interested in providing efficient shop floors or restaurant spaces for its employees to increase productivity levels, train new employees, and deliver better services to its customers. To achieve its aims, the organization turned to the digital twin and augmented reality to aid its business processes.

Once again, it is worth noting that both the digital twin and virtual reality tools are digital transformation tools. And with these tools, CKE Holdings Inc. succeeded in developing optimized restaurants with shop floor plans that played to the strength of its employees. The digital twin was also used to test and implement new products at a much faster rate than the traditional processes previously employed by the enterprise.

The end result was a user-friendly kitchen layout that delivered innovation in how CE Holdings restaurants function. The use of augmented reality also added another dimension to the training of new employees. The use of technology ensured new employees learnt through live practical involvement without any of the consequences attached to failure. This also reduced the hours experienced workers spent getting new employees up to speed within the restaurants. Thus highlighting another aspect in which digital transformation can be applied to drive facility efficiency levels.

The Benefits of Digital Transformation to Manufacturing and Production-Based Facilities

The examples outlined already spell out the benefits of digital transformation and its role in enhancing overall equipment and facility effectiveness levels. But, it is only right to compare and highlight what digital transformation brings to the table against the traditional OEE calculations still used within many shop floors.

  • A Complete Picture – Unlike OEE calculations which rely solely on manufacturing data produced from equipment and tools, digital transformation technologies can capture every aspect of the production process. This includes capturing data from the diverse algorithms, scheduling details, assets, sub-systems, and events that occur within the shop floor. This makes the level of details provided by digital twin environments superior to analyzing and enhancing facility productivity.
  • Improved Customer Strategy – Digital transformation enables the capture of data highlighting customer satisfaction with end products. This information can also be integrated into the manufacturing circle to ensure customers get nothing but the best service. This means with digital transformation the feedback of customers and employees can be used to enhance production facility processes.
  • Improved Employee Retention Strategy – The manufacturing industry is notorious for its high employee turnover rate due to diverse factors that make it unattractive to the new generation of workers. The integration of digital transformation can enhance workplace layout, as well as, bring a more modern and captivating process to manufacturing. These enhancements can reduce the turnover rate and get the younger generation interested in manufacturing.
  • Enabling Innovation – The increased adoption rate of industry 4.0 business concepts and models in manufacturing means businesses must adapt if they intend to retain their competitive edges. Digital transformation offers a pathway to innovating legacy business process and increasing an enterprise’s ability to stay competitive in a changing manufacturing industry.

The Next Steps

The advantages digital transformation brings to enhancing facility efficiency comes with a butterfly effect that affects leadership, innovation, and problem-solving activities. Although the integration process involves technical knowledge of applying digital twin technologies and simulation software, these skills can be acquired with a little effort.

Simio Fundamentals Course offer businesses and other organizations with the opportunity to train staffs about digital transformation and its specific techniques. You can also choose to register employees to participate in the upcoming Simio Sync Digital Transformation 2020 Conference to learn more about digitally transforming your business processes and how to reap the rewards.

Industry 4.0 Revolution: Understanding the Digital Twin and Its Benefits

The 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 CERAWeek held 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.’

The 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 by 2025.

While 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

Industry 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:

Enhanced Plant Performance – 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.

With 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 levels.

A 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 manufacturing line/

The 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.

Driven Predictive Maintenance –  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.

Once 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.

The 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.

Advanced Control 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.

An 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.

The 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 benefits. 

Easing Training 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.

In 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 process safely.

Take Advantage of The Benefits of Digital Twins

The 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.

Resources:

https://www.ice.org.uk/knowledge-and-resources/case-studies/digital-twins-for-building-flexibility-into-power

https://www.logistics.dhl/content/dam/dhl/global/core/documents/pdf/glo-core-digital-twins-in-logistics.pdf

https://www.bloomberg.com/news/articles/2018-04-09/forget-cars-mitsubishi-hitachi-sees-autonomous-power-plants