The increased complexity of industry means supply chains have become substantially more difficult to manage. Uncertainty and variability of demand, supply volatility, long lead times, climate anomalies, and the COVID-19 pandemic are just some of the factors impacting the predictability of the supply chain. Taking a broad view of supply chain uncertainty, threats and risks are hard to measure because they are so much more than a single predictable event.
To understand how the supply chain will be impacted, we need to treat today’s complex and dynamic environment as random and uncertain. But developing a better understanding of both uncertainty and risk remains a challenge in the current competitive economy with so many new challenges continuing to emerge and unfold.
Despite the growth in advanced data-driven and simulation-driven technologies (including forecast and planning software solutions, AI, IoT, and digital twins), companies are struggling to make sense of their most pressing challenges in the face of unexpected events. Existing solutions are effective at forecasting in situations where data remains static and easily predictable (e.g., fixed prices, consistent production downtime, or stable demand).
However, the reality is that markets shift, and demand and prices constantly fluctuate.
Most decision-making processes are complicated because input parameters are uncertain and every evaluation of alternative scenarios involves shifting assumptions. New software tools and simulation capabilities are therefore needed to help decision-makers adapt to high volatility and variability, and to assume strong visibility on possible futures in a timely manner.
Cosmo Tech provides the deep holistic and dynamic simulation technology needed for industrial real-time predictive “what-if” and prescriptive “how-to” simulation solution driving sustainability, efficiency, and profitability across the entire value chain.
Unlike other solutions, the Cosmo Tech Simulation Platform helps business leaders and operational efficiency experts in all industries manage uncertainty while supporting strategic, tactical, and operational decision-making. Advanced features such as uncertainty and sensitivity analysis consider variables which present uncertainty due to volatility (such as demand) or are subject to externalities (such as price) and may lead to serious disruptions of the supply chain. What that means is that we work with probabilities of unfavorable events so we can manage the ability of supply chains to quickly respond to changing circumstances and unforeseen conditions.
In terms of solution design, this translates into a great quality assurance of model reliability and predictions accounting for various sources of uncertainty.
Uncertainty analysis helps decision makers understand the probability of different simulated results due to uncertainties and hazards in the input data. It offers a first level of understanding as to which configuration leads to a more fragile or a more robust system, and respond to key questions such as:
Sensitivity analysis allows users to see which input data or parameters have the greatest impact on the attainment of corporate KPIs. When the stakes of a decision are high, it helps them determine on which decisions or action levers they should focus on first. At an operational level, sensitivity analysis can determine the main bottlenecks impacting performance across the value chain (e.g., price fluctuations, machine availability, long lead times, or late delivery).
In short, uncertainty and sensitivity analyses allow users to identify uncertainties in the inputs and outputs of a system of simulation model and assess the robustness of simulation results despite systemic uncertainty.
By estimating the level of uncertainty associated with key KPIs and by identifying the most influential parameters, the Cosmo Tech 360° Simulation Digital Twin platform provides users with a robust analysis tool for evidence-based decision making, as well as an increased understanding of the available alternative scenarios. This is extremely useful for decisions where multiple alternatives present a similar outcome and there is no simple basis by which to choose between them.
Cosmo Tech’s reliability-based approach and stochastic model is a key feature for fostering a collaborative learning process and enabling decision-makers to examine the robustness of a plan when navigating a large and complex variety of uncertain parameters.