In the last decade the practice of asset management has evolved to leverage new digital technologies. Enterprises across various industries rely on Enterprise Asset Management (EAM) and Asset Performance Management (APM) software solutions to optimize maintenance strategies, improve operational efficiency, and maximize the lifespan of critical assets.
These powerful tools have undoubtedly brought significant benefits to asset intensive organizations. However, most of them still struggle to derive key insights from the high volume of operational data to represent, prioritize, guide and monitor the underlying business operational scenarios and improve their understanding on how to solve complex problems.
Fortunately, new technologies such as AI Simulation have emerged to offer a new dimension of capabilities that complement existing EAM and APM software by addressing challenges in ways that were previously impossible / that were previously unmet.
In this article we explore the gaps that persist within traditional EAM and APM approaches while highlighting how AI Simulation enhances these solutions’ benefits even further.
The primary limitation of traditional approaches is that they provide only a static view of the state of a system of assets and the inter-relationships across the entire ecosystem. The fundamental difference between data-driven and simulation-driven approaches being that the first ones rely on data-correlation while simulation relies on expert knowledge of causation. For this reason, they lack an overview model that indicates which data is relevant in order to constrain the massive number of combinations possible.
These solutions heavily rely on accurate and reliable data from various sources. However, ensuring data quality and integrating data from multiple sources can be challenging, costly, potentially impacting the effectiveness of the solution.
While APM solutions aim to predict asset failures through advanced analytics techniques like machine learning algorithms, their accuracy is not always 100%. The model sensitivity and reliance on data only, can introduce a considerable amount of risk (e.g. improper AI model training, as a result of using poor quality or insufficient data). These models also find it difficult to consider uncertainty or adapt to evolving strategic and operational targets.
Understanding and explaining the results and correlations made by most AI models is difficult, if not impossible. Then, as the complexity of the task or the algorithm grows, the model opacity can render human supervision ineffective and have repercussions for asset managers.
First, the difficulty in predicting how AI models will respond to major unexpected events, could lead to systemic crashes. Then, even in the absence of unexpected events, AI models can make the same errors, at the same time, introducing the risk of cascading effect. Consequently, explaining to stakeholders how and why the plans or the asset management strategy failed can be difficult, which can undermine their trust in the chosen path.
Managing this complexity is difficult and risky as it limits the visibility on the consequences that each decision will have on the whole organization. Each asset could represent an investment of several million euros and, in case of unexpected downtime, it can jeopardize the entire production planning and have a huge impact on the production lines in terms of repair, costs and production losses.
As assets become progressively more complex, and with increased demand to incorporate metrics based on robust ROI assessments, the usability and effectiveness of outdated methods dramatically declines as they are not designed to deal with complex physical assets and asset inter-dependencies.
AI-Simulation technology is the next step forward in the evolution of asset management tools. It provides the capabilities that fundamentally change the way asset managers as well as maintenance and operation managers assess the condition and performance of their asset fleets.
Unlike traditional solutions that may require extensive customization or struggle with scalability challenges, AI Simulation offers unmatched flexibility. By creating digital replicas of assets and their operational environments, simulation enables organizations to predict asset behavior with remarkable accuracy. You can simulate multiple scenarios without disrupting real-world operations or incurring unnecessary costs.
The Cosmo Tech AI-Simulation Platform provides three levels of understanding: the description of what exists and what is happening, the prediction of possible futures, and finally the prescription of optimization and action paths. By combining these levels, the Cosmo Tech Asset Simulation Twin helps asset-intensive organizations to move from reaction to anticipation, find the right balance between competing objectives and constraints, allocate resources efficiently, mitigate risks before they become costly problems, and ensure regulatory compliance.
Unlike traditional software that relies on historical data, simulation uses expert knowledge. That means that it is possible to make a statement of what causes what and why, leading to robust and informed predictions.
Simulation models can accurately predict the behavior and performance of assets under various scenarios, enabling organizations to simulate future asset conditions and assess the impact of different strategies before implementing them. Cosmo Tech Asset allows users to run unlimited risk mitigation scenarios that simulate asset behavior (up to 2M assets) for various replacement scenarios (up to 30M simulated interventions per scenario) and time horizons (from one year to 70 years for example) as well as the cumulative financial impact on CAPEX, OPEX, downtime cost.
While the benefit of helping asset managers understand the future impact of their decisions is tremendous, prescriptive simulation goes even further, by prescribing and automating decisions.
Users can create multiple scenarios by adjusting variables such as maintenance schedules, resource allocation or operational parameters. Thousands of simulations are then run automatically and compared against key performance indicators to recommend the optimal course of action. This capability enables organizations to evaluate different strategies and identify the optimal balance between risk, financial and operational performance.
AI Simulation enables not only a new generation of advanced predictive and prescriptive simulation but also a holistic approach for generating value from assets.
This technology not only encompasses process flows, resources, constraints, financial models, and operational performance indicators but also demonstrates how they work and interact together to assist in guiding, prioritizing, planning, and scaling complex initiatives. That means that any time a decision is made about assets, the whole asset lifecycle is considered: past influencing variables but also visibility on how the future will be affected by each decision.
In contrast with pattern recognition, which relies on high volumes of data, simulation uses just data that is relevant and supplements it with human knowledge of cause and effect. With simulation, everything that is known about the missing data can be included in the models and unknown factors can be estimated.
When data is scarce, simulation also helps by providing synthetic data for Machine Learning. This is important especially when systems are not easy to describe mathematically or when historical data is not available.
The breadth and depth of AI Simulation technology allows simulations to support accurate and robust decision making with a very fine granularity for both the short and long term. It provides a high degree of flexibility in modeling complex systems, allowing organizations to tailor simulations to their specific assets, processes, objectives and business requirements.
By extracting asset data from asset management systems, the Cosmo Tech AI-Simulation platform allows organizations to build digital replicas of asset classes, individual assets and components, supporting and capturing their failure modes, the causes and effects, along with the associated financial and human resources and policies.
Instead of representing a complex system as a statistical algorithm or generating a “fixed” data set, AI Simulation captures the characteristics and relationships of the system, to provide a “dynamic” model.
From the Asset Simulation Twin, which includes all the assets interactions with the industrial context, users can then define and calibrate the asset aging model and configure different operational, capital and maintenance costs to provide an optimal cost/value analysis that balances cost savings with operational effectiveness and business value. This enables businesses to proactively address risks while minimizing costs associated with downtime or unexpected failures.
In a real system, unexpected events may occur due to the high complexity and uncertainty of the real-world. Simulation allows the analysis of a system under abnormal conditions, a rare event or a completely new design, which is not possible with a data-modeling approach. It empowers risk analysis and mitigation by simulating potential failures or disruptions in asset performance under different conditions. By identifying vulnerabilities beforehand, organizations can develop contingency plans to mitigate risks effectively.
This way, organizations can detect new ways to reach the goals set, optimize capital expenditures, improve operational efficiency, reduce total asset management costs, all in accordance with decision-making policies and processes that are sensitive to uncertain real-world conditions.
This approach to asset management structures the decision-making policies and processes to provide knowledge and information that is useful both vertically for a wide range of stakeholders, as well as horizontally across a variety of departments. Decisions are smarter and are made with greater confidence. This helps organizations justify asset management strategies, reduce human errors, and avoid bias in significant asset maintenance or capital investment decisions.
As we move into an era where digital transformation plays a pivotal role in business success, embracing the potential of AI-Simulation technology becomes imperative for organizations seeking to stay ahead of competition.
While AI Simulation gives organizations superpowers with unique predictive and prescriptive capabilities, risk mitigation strategies, flexibility and unparalleled insights, it’s important to note/acknowledge that it is not a replacement for EAM or APM software. Rather, AI-Simulation technology complements and enhances existing asset management practices. The synergy between AI Simulation with EAM and APM software represents a significant leap forward in addressing the gaps that exist in traditional approaches to asset management.
In summary, AI Simulation serves as a catalyst for transforming asset management – an opportunity not only for the financial benefit but also for making a significant impact on how the business operates globally. It empowers organizations to make informed decisions, enhance risk mitigation strategies, optimize resource allocation efforts, improve safety measures, reduce downtime risks – ultimately driving asset management excellence.
