The Need for Analysis
Prevent Production Upsets
Industry experts estimate that over 50% of all production upsets in large, complex industrial operations are avoidable. Predictive feed-forward process controls can correct deteriorating operating conditions before they halt production. Fewer production upsets means greater throughput and millions of dollars gained in both direct cost savings and top-line revenue.
Unfortunately, such great gains have been extremely hard to realize because real-world plant conditions do not always behave according to conventional expectations. These industries need predictive models built on real-world operational data – not theory – to truly understand and improve production behavior.
Implement Continuous Improvements
Companies spend millions of dollars each year on popular programs like ISO, TQM, Six Sigma and Lean Manufacturing in an effort to maximize returns on assets, implement best operating practices, reduce or eliminate costly problems, and extract additional production capacity from existing facilities. In return, these programs contribute millions of dollars of value in both direct cost savings and top line revenue gains. But ultimately, the impact these programs have on operational performance is limited by how well they can analyze real operational data.
The behavior of a complex system depends on many factors, often related in a subtle, interconnecting manner that is very difficult to detect. The typical approach to analysis is to build simplified models of discrete parts of the system, isolating individual problems to 2 or 3 measurable factors at the most. But while this approach can provide value, it inevitably misses or misrepresents more subtle, but still significant, interactions.
Designing and delivering true improvement requires a detailed and thorough real-world understanding of the entire production process. And that level of understanding requires a data analysis engine that takes into account all operational data.
