Executive Briefings

Managing Knowledge to Achieve Zero Product Failures

Analyst Insight: Manufacturers are under greater pressure to improve product reliability and reduce cost-of-quality non-conformance, while increasing product functionality. This can be done through increased use of software, mechatronic systems, new materials and manufacturing technologies. Still, more pressures await, as manufacturers struggle to reduce a product’s time-to-market, all while keeping capital investment low. Doing more with less requires manufacturers to reduce the time from detecting a product issue to correcting it, known as detection to correction (D2C). - Kevin Reale, Senior Manager, Advisory, Ernst & Young LLP

Managing Knowledge to Achieve Zero Product Failures

To reduce D2C, manufacturers are establishing unified data environments and enabling advanced reliability engineering analytics to predict early failures and improve the traceability of product issues.

Unfortunately, manufacturers are finding that traditional approaches to improve reliability, quality and reduce warranty-related costs are falling short. Reliability and quality management in the 21st Century must extend far beyond assessing warranty claims. As many practitioners know, it can take up to 120 days from the time a claim is processed and staged to the time it gets analyzed. Plus, data accuracy and quality in the claim can be suspect.

Applying advanced reliability engineering analytics on the increasing amounts of on-board machine diagnostics data generated by today’s intelligent products - correlated with information developed during product development and production monitoring - can reduce D2C cycle times by more than 50 percent.

These are some key process and technology enablers that can reduce D2C:

Systems engineering-based product reliability: Leading practice manufacturers have tools and processes that systemically create and manage the relationships between a machine’s systems, electronic components, mechanical components, software, service parts and on-board diagnostics used to measure and detect a system fault. Several manufacturers have been able to detect an issue through diagnostic trouble codes weeks and months prior to the problem emerging as a detectable warranty claim.

Functional alignment on product reliability: Some manufacturers may use different data sources to establish product reliability-related metrics. One functional organization may use warranty data, while another may use machine diagnostics to establish metrics like mean time between failures of warranty claims per machine. The manufacturer uses these measures and others to detect, select and prioritize the failures that require corrective actions. Both approaches may be necessary, depending on the type of failure and the relationships established by the systems and quality engineers. The key for manufacturers is establishing a single product reliability metric across various functions, ensuring a holistic approach to reducing D2C. Some manufacturers use issues per 100 machines, an assessment of the combination of things gone wrong that cause a machine to not operate to its specifications.

Business-driven unified data environment: The starting point to determining data requirements should be establishing a list of measurable business questions:

What systems have the greatest negative impact on product reliability with the first 100 hours in service?

What sequence of events leads up to and/or follows a product failure?

Is there anything unique about the product’s configuration, manufacturing location, component supplier, build date and/or operating conditions that could have caused the failure?

Manufacturers should avoid integrating millions or billions of records prior to understanding the questions they need to answer.

The Outlook

As the Internet of Things continues to grow and more objects or “things” are embedded in industrial, automotive and aerospace products, the management and use of machine diagnostic data will be critical to improving the reliability of a manufacturer’s existing and future products while reducing cost of non-conformance.

To reduce D2C, manufacturers are establishing unified data environments and enabling advanced reliability engineering analytics to predict early failures and improve the traceability of product issues.

Unfortunately, manufacturers are finding that traditional approaches to improve reliability, quality and reduce warranty-related costs are falling short. Reliability and quality management in the 21st Century must extend far beyond assessing warranty claims. As many practitioners know, it can take up to 120 days from the time a claim is processed and staged to the time it gets analyzed. Plus, data accuracy and quality in the claim can be suspect.

Applying advanced reliability engineering analytics on the increasing amounts of on-board machine diagnostics data generated by today’s intelligent products - correlated with information developed during product development and production monitoring - can reduce D2C cycle times by more than 50 percent.

These are some key process and technology enablers that can reduce D2C:

Systems engineering-based product reliability: Leading practice manufacturers have tools and processes that systemically create and manage the relationships between a machine’s systems, electronic components, mechanical components, software, service parts and on-board diagnostics used to measure and detect a system fault. Several manufacturers have been able to detect an issue through diagnostic trouble codes weeks and months prior to the problem emerging as a detectable warranty claim.

Functional alignment on product reliability: Some manufacturers may use different data sources to establish product reliability-related metrics. One functional organization may use warranty data, while another may use machine diagnostics to establish metrics like mean time between failures of warranty claims per machine. The manufacturer uses these measures and others to detect, select and prioritize the failures that require corrective actions. Both approaches may be necessary, depending on the type of failure and the relationships established by the systems and quality engineers. The key for manufacturers is establishing a single product reliability metric across various functions, ensuring a holistic approach to reducing D2C. Some manufacturers use issues per 100 machines, an assessment of the combination of things gone wrong that cause a machine to not operate to its specifications.

Business-driven unified data environment: The starting point to determining data requirements should be establishing a list of measurable business questions:

What systems have the greatest negative impact on product reliability with the first 100 hours in service?

What sequence of events leads up to and/or follows a product failure?

Is there anything unique about the product’s configuration, manufacturing location, component supplier, build date and/or operating conditions that could have caused the failure?

Manufacturers should avoid integrating millions or billions of records prior to understanding the questions they need to answer.

The Outlook

As the Internet of Things continues to grow and more objects or “things” are embedded in industrial, automotive and aerospace products, the management and use of machine diagnostic data will be critical to improving the reliability of a manufacturer’s existing and future products while reducing cost of non-conformance.

Managing Knowledge to Achieve Zero Product Failures