RCASE

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Root Cause Analysis Solver Engine (RCASE)
Class Information science
Data structure inaccurate, incomplete and erroneous data

Root Cause Analysis Solver Engine (informally RCASE) is a proprietary algorithm developed from research originally at the Warwick Manufacturing Group (WMG) at Warwick University.[1][2] RCASE development commenced in 2003 to provide an automated version of root cause analysis, the method of problem solving that tries to identify the root causes of faults or problems.[3] RCASE is now owned by the spin-out company Warwick Analytics where it is being applied to automated predictive analytics software.

Algorithm[edit]

The algorithm has been built from the ground up to be particularly suitable for the following situations:

  • 'dirty' data
  • incomplete data
  • big data
  • small datasets
  • complex problems for example multi-modal failure or with more than one solution

RCASE is considered to be an innovator in the field of Predictive analytics and falls within the category of classification algorithms. Because it was built to handle the data types above, it has been proven to have many advantages over other types of classification algorithms and machine learning algorithms such as decision trees, neural networks and regression techniques. It does not require hypotheses.[4][5]

It has since been commercialised and made available for operating systems such as SAP,[6] Teradata and Microsoft.[7] RCASE originated from manufacturing and is widely used in applications such as Six Sigma, quality control and engineering, product design and warranty issues. However it is also used in other industries such as e-commerce, financial services and utilities where root cause analysis is required.[8]

Notable applications[edit]

Motorola, the home of Six Sigma, used the research technology behind RCASE to support their quality processes. It was used to eliminate No Fault Found quality issues for a particular mobile phone model.[9]

Mechanism & architecture[edit]

RCASE is non-statistical and thus does not require any hypotheses.[10] If the key parameters causing the issue or fault in a process are not present in a dataset, it will still narrow the search space and advise where the root cause may lie. This is a different approach to statistical theories which try to find a best fit.[11]

RCASE is based on optimised combinatorial theory and runs on either a grid cluster or a high performance in-memory database. The software will interface with all MES and ERP systems.[12] The result is a security system monitoring and preventing defective products from being produced. The output from the analysis will be markers that identify either an exact root cause of failure or a parametric region pointing high probability of failure (i.e. data-driven guidance on where to look next to gather data and resolve the root cause exactly).[13]

The software can be installed on Linux or Microsoft operating systems and deployed as On-Premises or Software-as-a-Service (“SaaS” or “cloud”).[14]

See also[edit]

References[edit]

  1. ^ "Overview of Warwick Analytical Software Limited". Business Week. Retrieved 8 November 2014. 
  2. ^ "Manufacturing Global, Emerging Predictive Analytics for the Manufacturing Industries". Issuu. Retrieved 8 November 2014. 
  3. ^ "When Academia Meets The Real World, The Experience Can Be Life-altering: A First Person Perspective by Dan Sommers, Warwick Analytics". TechNet. Retrieved 8 November 2014. 
  4. ^ "Manufacturing 4.0 – From Industrialisation to Data-Driven Product Lifecycle". Citizen Tekk. Retrieved 8 November 2014. 
  5. ^ "Removing hypotheses for fault-finding in Six Sigma to revolutionise quality management". Supply Chain Digital. Retrieved 8 November 2014. 
  6. ^ "SAP to boost growth opportunities, deliver innovation with disruptive solutions from partner tie-ups". InformationWeek. Retrieved 8 November 2014. 
  7. ^ "SAP Spurs Innovation by Powering More Than 500 Startups Globally With SAP HANA". SAP SE. Retrieved 8 November 2014. 
  8. ^ "How German-based SAP is creating a startup ecosystem from Silicon Valley". Pando Daily. Retrieved 8 November 2014. 
  9. ^ "Advanced analytics solves 'No Fault Found' issues". Warwick Manufacturing Group. Retrieved 8 November 2014. 
  10. ^ "Analytical software could solve mass product recall problems". Engineering and Technology Magazine. Retrieved 8 November 2014. 
  11. ^ "Warwick Analytics pioneers manufacturing fault finder software". The Engineer. Retrieved 8 November 2014. 
  12. ^ "Press release: Midlands Company could solve mass vehicle recall problems". University of Warwick. 25 October 2010. 
  13. ^ "Using Big Data to Achieve Zero Defects". European Business Review. Retrieved 8 November 2014. 
  14. ^ "Warwick Analytics Revolutionises Manufacturing Processes at DEMO Fall 2013". Boston.com. Retrieved 8 November 2014. 

External links[edit]