Quality is a valuable NPI contributor for many manufacturers, and Quality 4.0 opens new insights and capabilities that lead to product...
On Thursday, May 17, LNS Research hosted the webcast, “Keys to Successful New Product Introduction (NPI): Bridging the Digital Quality Divide.” The presentation explained why market dynamics force companies to rethink NPI and how leading companies drive innovation in today’s digital world. The discussion included an examination of why today’s NPI success rate is relatively low, and why market trends inhibit NPI success.
Q1: How is the rate of NPI success defined?
A1: The NPI research took a top-down approach, starting with the overall process and flowing down to steps where handoffs and multi-functional engagements are needed. The research explores the idea of NPI success by ascertaining the primary criteria necessary to drive NPI success. The median manufacturer had four success criteria, and the most common success criteria were product quality, product performance, time to market, and project cost. Interestingly, 16% of the market did not have a formal definition, or the definition varied too much to pin down common criteria.
Q2: How can companies improve market requirements gathering for new products?
A2: For the NPI research, we captured the sources of data used to determine market requirements. The top five responses were from direct customer input or feedback, product and marketing personnel, benchmarking competitive products, lessons learned from existing offerings, and customer and industry focus groups. While these are traditional responses, it was interesting that 15% of respondents used social media and internet data mining, and 3%-11% of respondents used Industrial Internet of Things (IIoT) data from operations or in-service products to determine market requirements.
The research also revealed that firms plan to lean more heavily on social media and internet data mining as well as IIoT data in the next 24 months.
Q3: Updating from failure mode and effect analysis (FMEAs) to the new AIAG-VDA format, how can companies enhance engagement between quality and product development?
A3: Process FMEAs are widely adopted at 69% of the market, followed by design FMEAs, risk-based control plans, critical parameters, test planning, diagnostics, and service plans, in the order mentioned. Only 9% adopt risk-based service plans like reliability centered maintenance analysis. Spreadsheets were by far the most broadly used application but use of software for FMEAs and risk in general in increasing. The most substantial challenges faced by the market are disharmonious processes, disconnected operational and NPI risk analysis, and the lack of a cross-functional effort to address risk.
Effectively, our guidance would be to harmonize the process, and adopt a tool that manages FMEAs and connects FMEA data to other enterprise and operational systems. FMEAs should be tied to planning activities like test, control, and service plans, so the risks identified influence actions. This increases value and improves cross-functional participation. Additionally, connect FMEAs to execution systems, whether its customer complaints, non-conformance, or operational systems.
Q4: How can a company use Digital Transformation to pinpoint which “levers” are the right ones for them specifically to get the results it wants? In this case, when the outcome desired is customer perception of better quality?
A4: “Digital Transformation of quality (Quality 4.0) can pinpoint the levers. As LNS has identified, Quality 4.0 builds on traditional quality. Therefore, it is critical to ensure high quality data (high data veracity) that comes from repeatable processes. It is also important to have the correct operational architecture strategy – the connection of data from sensors, controllers, and processes in operations. For those with older equipment, this may require an upgrade strategy. In an environment with acceptable data veracity and repeatably executed processes, Quality 4.0 data can identify the levers, which may be a combination of several process variables.
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