Why Consider a Cloud QMS for FRACAS Automation?

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Authors:
Sal DiIorio, Principal Consultant, Semi-Tech Group
Dave Vadas, VP of Special Projects, AssurX

 

Quality management software by design is built to automate processes for specific industries. It has been tested and adapted to ever-changing demands and stringent compliance requirements. However, a configurable QMS platform can be adapted to automate a myriad of processes, quality methods, and regulations. Manufacturing industries are looking for software that guides quality and compliance but is also flexible enough to create unique processes specific to their operations.

This article discusses how a configurable QMS can adapt to perform exceptionally for FRACAS automation.

FRACAS and QMS: Complementary Methodologies

A quality management system is designed to collect and analyze data on manufacturing issues and drive an efficient path to remediation.  Furthermore, a QMS is designed to ensure continuous improvement through the application of consistent use of data, risk analysis, and methodology and controls.

In manufacturing industries, a modern FRACAS (failure reporting, analysis, and corrective action system) is no different in terms of how issues are resolved and corrected. An automated FRACAS gives organizations a high-performing way to collect data on failures, analyze that data, and plan corrective reactions to remediate and prevent failures. It eliminates manual, siloed systems, and speeds time to resolution, therefore creating efficiencies and ultimately a higher quality product.

Although different industry-speak may be used, both QMS and FRACAS systems control and measure reliability, availability, and maintainability (RAM) of products and/or services. Both systems involve documenting and automating the processes that create a product. Both systems build a traceable pathway to resolve issues.  Both can provide detailed reporting, dashboards, alerts, and other built-in features inherent in process automation solutions.

FRACAS for Collaborative Reliability Based on a Medical Device QMS Model

Manufacturers in the semiconductor industry and other high-tech component manufacturers can utilize a configurable QMS based on the medical device QMS model to design a system that tracks and measures performance, drives corrective actions, and adds top-tier data privacy.

Root Cause Analysis: Finding and correcting the root cause of failure faster is necessary for industries where product lifespans are continually getting shorter. Automating FRACAS detects and trends pre-market failures and accelerates root cause analysis.  Furthermore, releasing a new product that has been tested harder and fixed better has a direct correlation to a reduced risk of in-market failures and increased customer satisfaction and retention.

Configurability: A configurable, cloud-based QMS system for powerful functionality plus the ability to custom configure workflows that are specific to its own operations. The system can gather pre-market failure data and equipment RAM (E-10) performance data from customers to help automate the identification and resolution of reliability improvement projects.

Security: To provide a secure repository for the data (internal and customer), the FRACAS system uses the same supplier/customer relationship used in a medical device quality software.  It is imperative to ensure customers that the privacy of their information is protected. Therefore, the “medical model” approach proves compelling.

A customer’s data is protected with safeguards, data security, and access controls inherent in leading FDA-regulated industry quality management software. Customer data is treated as securely as personal health information (PHI) and the strict rules that are in place for the doctor/patient relationship apply.

Data Analysis: By incorporating fleet-wide data from multiple sources, the manufacturer can better understand platform-based issues and drive reliability projects to address them.  Additionally, by comparing each individual customer to “the rest of the world,” the OEM can help address specific issues they may be having.

Corrective Actions: Furthermore, corrective and preventive actions affect changes to the design file, ultimately leading to better products entering the market with a significant reduction in the risk of failure. In-market failure analysis can be expedited and compared against the previous records which creates a cycle of continuous improvement.

CASE STUDY: Semiconductor Manufacturer Automates FRACAS

A manufacturer of an extensive array of microelectronics in the semiconductor industry recently implemented the AssurX QMS platform for FRACAS. The company is focused digital improvements and knew that to improve quality and time-to-market, it needed a virtual system for early data collection, failure reporting, analysis, and corrective actions (FRACAS) for fast and accurate root cause analysis.

The core AssurX platform was easily configured to meet SEMI E10 Metrics standards for the analysis of system performance.  The ability to control for signatory authority, information protection, and auditability are already part of the core software.

The value-add with a quality automation platform comes from the information protection customers need to assure that their downtime event information (as specified in SEMI E10) would be kept confidential and secure for collaborative reliability and resolution of field issues.

In the medical device model, information is protected by FDA/HIPAA privacy regulations regarding personal health information (PHI). These regulations are best practices for guarding any type of sensitive or personal information. With AssurX, mechanisms are already in place to:

  • Restrict unauthorized access to PHI/PII
  • Audit who, how and when PHI/PII is accessed
  • Ensure that PHI is not altered or destroyed inappropriately
  • Make sure people are who they say they are (ID authentication)
  • Prevent the unauthorized disclosure of PHI when it is being communicated

All of the company’s requirements for its FRACAS were met within a highly configurable, workflow-driven system designed to meet current good manufacturing processes as well as CFR 21 Part 11 compliance. The system has already enabled the manufacturer to fabricate more wafers in a single day than it used to move in a month.

Conclusion

While there are “FRACAS automation solutions” in the market, the key to selecting and implementing a new system should be based on workflow configurability for unique business needs, the ability to integrate additional processes, and customer concerns for data sharing for collaborative reliability.

References:

  1. SEMI E10-0814E © SEMI 1986, 2015

E01000 – SEMI E10 – Specification for Definition and Measurement of Equipment Reliability, Availability, and Maintainability (RAM) and Utilization

  1. FDA 21 CFR Part 11

https://www.fda.gov/regulatory-information/search-fda-guidance-documents/part-11-electronic-records-electronic-signatures-scope-and-application

  1. HIPPA Security Rule 45 CFR Part 160 and Subparts A and C of Part 164
  2. MIL-STD-2155, Military Standard: Failure Reporting, Analysis And Corrective Action System (FRACAS) (24 JUL 1985) [S/S BY MIL-HDBK-2155]
  3. Collaborative Reliability: A Concept Whose Time Has Come-Again. Sal Diiorio, Principal Consultant, Semi-Tech Group

Contributor: Sal DiIorio
Principal Consultant, Semi-Tech Group

During his career, Sal has been involved with nearly all aspects of the semiconductor industry, including R&D, Process and Equipment Engineering, IT and software deployment.  Sal has employed computerized data analysis to transform raw data into usable information to drive process and equipment reliability improvement programs.  He has been involved in SEMI Standards, specifically, the Metrics TC and the RAMP associated Standards (E10, E58, and E79) sub-committees and task forces.  He most recently headed a project to gather equipment RAM (E-10) performance data from customers to help identify equipment issues, in order to focus on reliability improvement projects. He can be contacted at [email protected].

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