Industrial data is doubling, roughly every two years. As per a recent report by the World Economic Forum, in 2021, industries created, captured, copied and consumed 74 zettabytes of data – by 2023 that is estimated to rise to 130 zettabytes. But, according to Forrester, on average, between 60% and 73% of all data within an enterprise goes unused. This is problematic because data that cannot be utilized for gaining actionable insights is meaningless and can potentially become a liability for organizations who are generating large volumes of data.
For the industrial sector, this means that common operational challenges such as batch-on-batch variability, unplanned downtime, and waste remain unresolved because existing methodologies lacked the ability to analyze and deliver accurate predictions for complex processes where time impacted the process outcome. Most industrial processes are not liner with respect to time and in many instances time can have a multi-dimensional impact on a process or asset. This means that the following questions will go-unanswered:
When will a variable affect the outcome? (This can be referred to as a time-lag).
How will a variable affect the outcome?
How will the relationships of multiple variables affect the outcome?
Canvass’s patented solution empowers industrial workforces with accurate times-series forecasts that assist them in making real-time adjustments to their processes and assets. This is particularly applicable to industrial companies across the oil and gas, chemicals and metals and mining operations, where delays or time lags may affect process outcomes. Early event detection, production forecasting, optimization, sustainability, reliability, safety, product quality and troubleshooting are some of the most common use cases that can be solved with accurate predictions using time series-based data.
Venkatesh Muthusamy provides a deeper dive on the benefits of Canvass AI’s patent for predictive analysis of industrial processes.
The solution is integrated with our platform and allows users to solve complex problems which were largely unresolved using traditional methods. This technique is a key differentiator between the success and failure of an AI project, be it solving for predictive maintenance or process improvement. For example, one of Canvass’s customers wanted to optimize the material composition within the chemical process. They were struggling to analyze the 100s of parameters within the process and couldn’t apply the required adjustments when needed. This led to ~70% of quality measurements being out of specification. Using our patented technique, the customer is now using the time-series predictions to optimize the complex continuous process, which has resulted in an increase in production, creating value of more than $6M, year over year.
Canvass’s patented solution is a gamechanger for industrials, enabling them to unlock true value from data and accelerating their time to AI success. By pushing the boundaries of AI innovation, industrial engineers have the confidence to trust AI and make accurate, data-driven decisions to positively impact their day-to-day operations.