Risk Assessment

Risk Rating: Scoring Challenges and Solutions

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Risk rating is a complex scoring system, which relies upon previous experiences and foresight to anticipate risks that might impact the smooth functioning of business. When risks are well managed, a business can maximize opportunities and control the impact of disruptive events. Alternatively, if a risk is underestimated—or not anticipated at all—a business can suffer significant economic or reputational damage as a result of this unpreparedness.

What is the Purpose of Risk Rating?

Businesses use risk rating to assess the risks involved in an activity. The goal is to anticipate and classify potential risks as low, medium or high based on the expected impact and the likelihood of such an event occurring in the near future. This allows businesses to adopt control measures that can help cure, mitigate or remove the anticipated impact of the risk.

Risk Rating

In situations where risk cannot be controlled or removed, a business may choose to accept the risk or cancel the activity. This decision depends on the likelihood of the risk event occurring and the expected severity of the impact.

Why is Risk Rating a Challenge?

Experience and historical data are useful references when rating the probability and impact of risks that have happened before and may happen again under the same set of circumstances. The bigger challenge is managing unknown risks. Obvious examples include Covid-19, the economic meltdown of 2007-2008, the Fukushima nuclear accident. Unknown risks typically form through complex interdependencies and dynamics that are not fully understood until the event happens.

Human imagination is limited in such cases. Further, humans have a tendency to underestimate the probability of an outlier event or downplay the expected impacts. Even as the economic events of 2007-2008 were unfolding, the US Fed’s risk model did not predict the scope or severity of the resulting recession. With Covid-19, scientists warned governments and healthcare to prepare for the possibility of a coronavirus pandemic, but most did not have sufficient plans in place to contain the health and economic impacts of the virus.

The unexpected impacts of climate change provide another example of how domino effects can catch entire industries and governments off guard. Extreme weather events are causing slowdowns across major supply chains. And rising temperatures are threatening the safety of water supplies. As the world becomes more globally connected and the pace of change intensifies, it will become even more difficult to anticipate the full range of risks that need to be managed.

How Can a Digital Twin Help Improve Risk Scoring?

A digital twin provides a software representation of a physical asset, system or process designed to detect, prevent, predict and optimize the system being studied through real time analytics. Digital twins created within X-ACT include time dependent behaviors, which is key if you want to accurately model the dynamics of assets that change over time—for instance aging or cyclical changes that may impact performance.

Using the digital twin to perform stress testing and sensitivity analysis allows users to quickly identify any conditions that might cause a physical asset to deviate from its expected behavior or find the root cause of problems. As risks are revealed, fixes or controls can be identified using the digital twin and implemented when needed. Users can also gain confidence in decisions and see how different choices playout under various conditions. This is particularly useful to predict the outcome of scenarios that would be difficult, costly or even impossible to test in the real world.

For example, a digital twin can be used to evaluate the benefit versus risk of deploying innovative technologies, like artificial intelligence, blockchain or Internet of Things. Or even help rate the risk of a new business activity, like funding loans for smart city projects, when there is no historical precedent to reference.

By covering every variable which may directly or indirectly influence risk, digital twin analysis can predictively cover known risks and expose unknown risks. This arms decision makers with the missing risk intelligence they need to successfully navigate the complex dynamics and uncertainty of modern business.

 

The Value of Digital Twin Technology

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Interest in digital twin technology is surging as use cases demonstrate its problem-solving value across diverse applications including operations, manufacturing, supply chains, utilities and enterprise management.

A digital twin is commonly defined as a software representation of a physical asset, system or process designed to detect, prevent, predict and optimize the system being studied through real time analytics. Using the algorithmic capabilities of X-ACT, we extend the definition of digital twin to cover the replication of process dynamics. This means the digital twin created by X-ACT covers time dependent behaviors for complex relationships.

Gain Confidence in Decisions and Control Risks

Digital twins let users understand the present and predict the future. Many industries are applying these capabilities to gain confidence in decisions, maintain better control of risk or find opportunities for improvement. A virtual model can span the full lifecycle of any asset or process, product or service and provides an easy way to build and validate new projects or planned changes.

Once it has been verified that the digital twin produces the same results as the target physical asset, the model can be used to determine what is happening or what could happen in the future. This information is gained within minutes by testing a full range of scenarios—including events that would be difficult, costly or even impossible to test in the real world.

Through stress testing and sensitivity analysis users can quickly identify any conditions that might cause a physical asset to deviate from its expected behavior or find the root cause of problems. As risks are revealed, fixes or controls can be identified using the digital twin and implemented when needed. Users can also gain confidence in decisions and see how different choices playout under various conditions.

For example, a business that wants to move critical applications to the cloud can use a digital twin to see what would happen if transaction volumes unexpectedly double. Or a retailer might use a digital twin to analyze the fallout of a multi-day shutdown of a supplier’s manufacturing plant. A car manufacturer could use a digital twin to help answer, “What will be the realistic cost benefits and performance improvements gained by automating or digitalizing our processes?”

No matter the problem, the information gained from digital twins allow organizations to learn and make decisions faster with more certainty in the outcome. The speed and agility enabled by digital twin technologies is expected to become increasingly important as the complexity and pace of business continues to accelerate.

 

Using X-ACT Libraries for Intelligent Design and Prescriptive Management of Risk

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When business and IT stakeholders want to build best in class systems or ensure the optimal performance of exiting business ecosystems, mathematical emulation is an effective way to validate plans, predictively expose risks, and identify which corrective actions will achieve the desired results for any given situation.

But to gain dependable intelligence, emulations must mathematically encapsulate all the characteristics, dynamic behaviors and dependencies among each component of the ecosystem being analyzed. When the emulation adheres to all the applicable rules that govern system behaviors—whether these patterns of behaviors are known from the outset—it becomes possible to reliably predict the future behaviors of the system under any set of conditions and dynamics that may evolve. This includes conditions that have never occurred before or are believed to be highly improbable.

With this information, stakeholders can make decisions with confidence because they are able to realistically quantify the financial and operational impact of any decision and prepare for future changes that may occur for any reason—including changes outside their control, like a sharp increase in unemployment, a trade war or the introduction of a complete revolutionary technology.

X-ACT® Libraries Speed Delivery and Control Processes

A global business is an amalgamation of millions of dynamic parts and interdependencies. Manually building a mathematical model that sufficiently represents the dynamics of each component would be a monumental task. To make mathematical emulation practical for business use, X-ACT automates the steps necessary to accurately represent system dynamics and provide reliable predictive intelligence.

X-ACT libraries are a critical asset that shortens the time to value of an X-ACT deployment from months to weeks and allow businesses to benchmark their systems against best in class implementations, gain a forward-looking view of the health of interconnected systems and know when a system transformation or disruption is needed.

Every business system, whether it be a global supply chain, payment settlement system or manufacturing production line, has some characteristics that make the system unique, but in large part the system is built using common building blocks. X-ACT libraries contain many of these common building blocks with over 10,000 certified dynamic patterns that can be used in the same way pre-built models are used in Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) to speed the delivery of designs and control processes.

In X-ACT, a business system emulation starts at the organizational level, which is then served through the generation of processes and sub-processes, implemented though a certain logic or layout (architecture, urbanism or design), on a physical layer (factory, data center, sorting/transport configuration or economic instrumentation).

X-ACT Libraries Support End-to-End Modeling of Business and Technology Infrastructure

To build a representative model, users of X-ACT simply select dynamic patterns from the libraries that span everything from common business operating structures and technologies to specific platforms and databases.

These dynamic patterns are created from mathematical emulation of business and technology ecosystems through algorithmic models that encapsulate behaviors, dependencies, and surrounding rules for ecosystem behaviors so they can perform predictive analysis. Each model stores remedial options to support prescriptive actions for risk avoidance. They become, in effect, dynamic Legos modeling entire business ecosystems with technology and business infrastructure interdependencies.

When the modeling is complete, these “dynamic Legos” make up the X-ACT emulator. The deployed process allows users to control and manage the targeted environment, predict the eventual crisis or singularity points, and augment X-ACT libraries with their own newly discovered patterns to gradually build and support more intelligent automation.

Conclusion

Using libraries populated by the mathematical predictive platform represents a real breakthrough that alleviates many of the pains created by the traditional management, which starts with problem-analysis-diagnosis and ends with an eventual cure well beyond the point of optimal action.

As the libraries are continuously enriched by the dynamic characteristics that continuously evolve during a system’s lifetime, the knowledge contained within the libraries becomes more advanced. By continuously recording within the libraries foundational or circumstantial system changes, the predictive platform will identify any new risk, determine the diagnosis and define the remedial actions, and finally enhance the libraries with this new knowledge. Any decisions can be justified with accurate knowledge of the benefits, costs and constraints of any proposed change.

In this way, algorithms help businesses create a defendable advantage—by revealing the factors that may impact performance or limit agility and automatically retuning systems to maintain the highest level of performance and flexibility to respond to changing business dynamics.

Learn more about the benefits of X-Act libraries

The Using X-ACT Libraries for Intelligent Design and Prescriptive Management of Risk paper explains how businesses use X-ACT libraries in the same way pre-built models are used in Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) to speed the delivery of designs and control processes.

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Cloud-Based Services Transforming Business

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To board members and business divisions, cloud-based services present an ideal solution for the delivery of adaptable technology services, which include services, applications, software licenses and infrastructure.  Analogous to how electricity is delivered today, cloud-based services allow businesses to provide technology services as a utility, where supply mirrors business demands and changing volumes.  

There are many business benefits that make cloud-based services attractive to executive leaders. First, the business’ capital expenditure decrease because the delivery of cloud-based services are charged as an operating cost. Additionally, development becomes agile and production lead times are reduced as business is componentized into a core set of services, which can be delivered as demand requires. This improves the time to market for new products and allows faster reactivity to changing business dynamics.  Finally, engineering the company and business processes around a standardized set of cloud-based business solutions provides better predictability as each service can be utilized as needed to meet varying business demands.

To a new business, using cloud-based services is often clearly the ideal choice. For established businesses, the choice can be more difficult, as transforming legacy operations and technology into a cloud-based services model presents a host of challenges. Transformation, which requires moving operations and technology, which is a sunk capital cost, to cloud-based, componentized services can be a complex, time consuming and expensive endeavor.  

Taking a CAE Approach to Cloud Migration 

To mitigate transformation risks, companies use the predictive capabilities of  X-ACT® to first prove the business case, then determine the best migration strategy and finally manage the execution of the change program. In today’s interconnected world, providing the promised service level to customers is key. Project failures have a direct impact on brand reputation and pose a continued operational risk to new and established businesses alike.

In order to fully exploit cloud-based services, businesses must take a top-down, business led approach that considers the entire operational model as well as the underlying technology services. X-ACT supports a Computer-Aided Engineering (CAE)  approach to cloud migration that allows users to predict how business and technology service components can be optimally used by the business for development and operations/production. 

To quickly build representative models, X-ACT libraries provide pre-built and fully configurable elements that cover a wide range of business and technology service components. Examples of X-ACT library’s 10,000 certified components include pre-built models of a payment service, customer database, reservation booking, settlement service and call handling service.

X-ACT’s built-in and configurable libraries of service components and predictive capabilities are used to develop and transform the business operating environment. Through this process, business volumes and costs, based on the utilization of underlying technologies, are optimized and adjusted in line with demand. In all cases, X-ACT ensures the customer’s service levels and experience is maintained and improved in alignment with the requirements of the business.

How Businesses Use X-ACT to Support Cloud Transformation Goals

Companies commonly use X-ACT in the following capacities to support their cloud transformation program goals:

  • Determine how a proposed or current business and the associated technology services can be re-architected, componentized to exploit cloud-based technology
    • How current business operations and technology be re-engineered to utilize cloud-based services 
    • Defines the optimal target operating model for the business  
  • Prove migration and test scenarios to minimize risk, address limits before migration commence, and minimize service disruptions
  • Assure delivery of the migration at minimal risk and optimal cost
  • Manage operational risk and cost through the migration
  • Ensure the target operating models meet the service level demands of Enterprise or SME customers
  • Increase customer’s confidence in delivery
  • Define the optimal configuration of the current or new development environment within the cloud
    • Predict how new developments can perform as volumes increase
    • Optimally manage workloads across public, private and hybrid cloud providers
    • Benchmark cloud service providers using the library of components and determine the optimal configuration, based on the business’ critical success factors (for example the service and response time)
    • Determine the optimal business case and operating cost model

Conclusion

X-ACT supports the level of due diligence and groundwork necessary to ensure a smooth transition to the cloud. By delivering clear and actionable intelligence, X-ACT helps answers the business’ questions so that stakeholders can identify and agree upon the best-fit solution before committing any time or resources to a cloud migration program. During the execution phase, X-ACT provides the foresights necessary to ensure an optimal outcome that weighs both short and long-term cost benefits in alignment with performance and scalability goals so that the business can minimize risks and consistently gain all the benefits the cloud has to offers.

 

Learn how businesses use X-ACT to successfully execute cloud transformation programs

The Implementing an Effective Cloud Migration Strategy white paper explains how businesses use X-ACT to build an integrated strategy with clear intelligence of the optimal service commitments for quality, quantity and continuity balanced against cost for each critical business and IT workload.

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Solving the Unsolvable Performance Issues

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Thomas Rose‘s latest blog post, Solving the Unsolvable Performance Issues in Complex Cloud and Bare Metal Environments, explains how IBM Cloud Load Simulation (CLS) Platform is using X-ACT® in IBM client engagements to diagnose dynamic complexity related problems that were previously believed to be unsolvable.

In this case, traditional monitoring and remedial tools were unable to identify the problem or solution when a cloud application began providing unstable service that ended by an operational collapse. Using the emulation and predictive capabilities of X-ACT, IBM was able to quickly pinpoint the problem and identified the changes necessary to keep the service running.

The X-ACT diagnosis revealed many database related problems of channels, locks and different speeds that had not been discovered during traditional testing programs. Remedial analysis found that immediate issues could be fixed by adding monitoring capabilities to avoid duplicates and possible service degradation, but in the longer term, a redesign would be the only way to contain the systemic risks caused by dynamic complexity.

Moving forward, the redesign project will be regulated by a testing platform that includes efficient pre-production certification, using X-ACT emulation to facilitate the stress and sensitivity analysis.

This case provides an excellent example of how dynamic complexity can monopolize resources that are supposed to produce more business, but instead become lost in conflicts due to dependencies and internal influences.

Through partnerships, like this one with IBM, URM GROUP is working to arm business and technology leaders worldwide with the foresights they need to confidently respond to changing system dynamics and clearly understand which (and when) preventive and opportunistic actions should be taken to ensure the continuous efficiency and cost effectiveness of operations.

X-ACT supports IBM Cloud Innovation

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In his latest blog post, The Right Way to Ensure Operational Stability while moving to the Cloud, Thomas Rose, IBM Cloud Innovation Advisory Leader Germany, Austria and Switzerland, explained how IBM is using X-ACT® to optimize and improve the operational stability of their cloud services.

X-ACT is the innovation behind IBM’s Cloud Load Simulation solution. The emulation and analytics capabilities of X-ACT allows IBM to identify when and if dynamic complexity will lead to system limits within cloud environments, diagnose the root cause of the limit and determine the best remedial actions by weighing the benefits, complexity and cost of proposed solutions.

Many cloud initiatives fail to meet client expectations due to inadequate risk identification and mitigation early in the outsourcing project’s lifecycle. Due to the complexity of modern business, it is no longer possible to intuitively assess both the benefits and risks posed by moving services to the cloud.

Now with X-ACT, users can quickly model existing systems and evaluate how proposed changes will impact the cost efficiency, scalability and performance of business operations. By effectively identifying any potential risks that could impact the quality of service and verifying that all applications will behave correctly under any operational condition, decisions can be made with confidence in the outcome.

This is a win-win for cloud vendors and their clients, as it provides an insurance mechanism to verify that the cloud envrionment will continuously meet the promises made during the early stages of project definition.

Next we will be working to combine IBM Watson and X-ACT technology to implement cloud production control environments, which dynamically react to market events, and control system loads and resources to operate at an optimum cost level.

Using X-ACT Metrics to Guide Decisions

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Learn how to make operational risk decisions with confidence

The X-ACT: Using Metrics to Guide Decisions | How to Guide shows how companies use the advanced analytics and emulation capabilities supported by X-ACT to identify how dynamic complexity leads to system limits, diagnose the root cause of limits and determine the best remedial actions by weighing the benefits, complexity and cost of proposed solutions.

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The analytics and emulation capabilities supported by X-ACT® arm business and technology leaders worldwide with the foresights they need to confidently respond to changing system dynamics and clearly understand which (and when) preventive and opportunistic actions should be taken to ensure the continuous efficiency and cost effectiveness of operations.

Using accurate, representative and reproducible models of business processes, applications and infrastructure, X-ACT delivers an end-to-end emulation of a service that accurately represents the behavior of system dynamics. The emulation replaces structures, characteristics and behaviors by perturbations exerted on dynamic equations through multiple order perturbations on dynamic coordinates such as volume, service quality and cost. This is very complex math, but it is handled entirely by X-ACT.

Once a system is transformed into an emulation, it allows users to quickly test and economically explore an unlimited number of scenarios that would otherwise be complex, expensive or even impossible to test on a real system. In comparison to other practices, such as simulation, emulation is superior in its ability to accurately replicate a system, but its biggest advantage is that it allows for the discovery of previously unknown patterns, which cannot be determined using any other method.

Now users can emulate risk because X-ACT can mathematically reproduce unknowns that may happen under certain conditions. Once the emulation process is complete, X-ACT users can change variables—such as volume, architecture and infrastructure or perform sensitivity predictions on changing process dynamics—to observe the outcomes (even when we have no historical record of these events ever happening).

Discovering the cause and effects of dynamic complexity is foundational to our universal risk management approach. Since conventional methods ignore the unknowns, risk often appears as a surprise that may potentially impact operational performance. To predict risk and anticipate the appropriate course of treatment, we must discover these unknowns and determine their current and future influence on system behavior.

The X-ACT: Using Metrics to Guide Decisions | How to Guide shows how companies use the advanced analytics and emulation capabilities supported by X-ACT to identify risk and take remedial actions by weighing the benefits, complexity and cost of available solutions.