Companies and organizations must continuously adapt their decision-making processes to account for an ever-changing array of technologies, challenges, and opportunities. CAE workflows, digital engineering, simulation-based design and analysis, digital twins, internet of things are all ways of maximizing the information available to decision makers while reducing uncertainty. However, care must be taken to avoid introducing errors and inaccurate information with new methods and models as the most critical decisions are often those with the greatest uncertainty. This is why modern engineering requires verification, validation and uncertainty quantification to ensure the best decisions given the uncertainties and information on hand.
For manufacturing and engineering, uncertainties can appear throughout the entire product lifecycle such as variations in production quality, how well the simulation model in the design mimics reality, or wear and tear on the product once it is out in the field. Beyond the product lifecycle, uncertainty can be found across all departments including sales, marketing, human resources, and accounting and finance.
For retail, uncertainties can include demand for certain products due to a severe weather event or pandemic, a failure in the supply chain, a decline in consumer spending, selection of new store locations, and growth of the workforce.
For finance, uncertainties may come from new governmental regulations or initiatives such as tariffs having implications on the market, natural disasters, or the perceived strength of the stock market conditions.
For all companies and organizations, ignoring or omitting these uncertainties can mislead decision makers and result in painful and costly outcomes.
Now more than ever, executives, managers, engineers, and data scientists must ask themselves: How do I allow for variability or uncertainty in my decision making?
Though constantly faced with uncertainty, many companies and organizations cannot overcome the challenges of performing decision making under uncertainty. These challenges include:
SmartUQ’s predictive analytics and Uncertainty Quantification (UQ) tool set is essential for modeling risk and variation and ultimately, optimizing decisions for practitioners and decision makers alike. Many companies and organizations have put in place programs to account for the variation in their decision-making processes and thus saved millions of dollars and thousands of hours of work. In addition, major federal agencies, including the Department of Defense (DoD), the Food and Drug Administration (FDA), and the Federal Aviation Administration (FAA), have guidance documents encouraging the use of UQ in simulation analysis and other data sets.
Performing predictive analytics and UQ requires a change in thinking for decision makers. Instead of depending on the deterministic point estimate that could drastically miss the mark, decision makers must rely upon data-driven decision making that incorporates a range of possible outcomes and results in actionable insights. Here are some of the methodologies and advantages of utilizing SmartUQ’s predictive analytics and UQ tool set.
No matter where in decision-making process, uncertainty abounds, and SmartUQ’s predictive analytics and Uncertainty Quantification toolset is there to reduce the risk at each decision-making step.
Contact us or click here for our white paper “Decision Making Under Uncertainty” to learn more about how SmartUQ software can save you time and money by solving your most difficult decision-making and analytics problems.
