Driving Quality – Part 1: The Automotive Supply Chain
How can automotive component parts suppliers comply with, and gain real business advantage from, quality management frameworks? What are the best strategies for managing the key tasks in a post-COVID-19 world?
Your average modern car is one of the most complex pieces of machinery you will ever own. With around 30,000 individual components, managing the quality of component parts through the supply chain is a vital task.
It’s no surprise then that the auto industry has had a structured approach to quality methodologies for some time. Lean manufacturing principles emerged from the Toyota Production System (TPS) in the 1950s. Ford, Chrysler, and GM began developing the APQP process (using SPC and Six Sigma techniques among others) in the 1980s. Toyota started sharing TPS with its parts suppliers in the 1990s. Some of the main automotive-producing countries built their own quality management system frameworks in the 1990s—VDA (Germany), AIAG (US), AVSQ (Italy), FIEV (France), and SMMT (UK).
By the late 90’s the industry realised this wasn’t a viable approach. Tier 1 suppliers, those supplying vehicle manufacturers, or “OEMs” (original equipment manufacturers) in industry parlance, were often supplying manufacturers in different jurisdictions. Tier 2 suppliers—those supplying the Tier 1s—were in an even more complex situation.
The first attempt at rationalising the situation was the ISO technical specification ISO/TS 16949:1999, revised over the years to ISO/TS 16949:2009 and later reworked as IATF 16949:2016. These standards were built on the more general ISO 9001 Quality Management System framework, with extensions. Some of the most demanding requirements are in measuring and monitoring the production process itself—Chapter 8 in the 2009 standard (“Measurement, Analysis & Improvement”) and Chapter 9 in the 2016 version (“Performance Evaluation”).
Both versions of the standard include a common set of requirements for managing the quality of production processes:
Measuring and recording each variable for each process in a reliable, repeatable way and assessing the variation and capability of the processes by means of a control plan
Use of statistical tools as part of the control plan (statistical process control, or SPC)
Having corrective action plans to return processes to stability that are triggered on key conditions
Recording when those events are triggered, the causes of those events and the corrective actions taken
Having the ability to perform long-term analysis of the relative prevalence of assignable causes and corrective actions to aid process improvement
Clearly, that didn’t work out so well. Suppliers found that complex manual processes were taking more and more time away from the business of actual production. Forms need to be maintained, distributed, version-controlled, transcribed, archived, and stored. Intangible costs are still, after all, costs.
The smarter suppliers quickly realised that there was an alternative. Instead of treating the standard as a compliance issue, could the principles in the standard actually produce a real business benefit? The closer they looked, the more they realised that was the case.
Through careful monitoring of process variation and capability, suppliers realised they could dramatically reduce their scrap and rework costs, as well as customer complaints. But to do this effectively, they’d need to collect and analyse their metrics in real-time. Also, by analyzing trends in key conditions and the application of reaction plans and corrective action plans, they could see “hot spots” in their business and bring appropriate resources to bear.
But they wouldn’t be able to achieve this with simple forms and manual processes. These challenges and opportunities are driving increased adoption of digital transformation across the automotive sector. Under the Industry 4.0 framework, physical manufacturing processes are being increasingly integrated with advanced information technologies and services—from data collection and real-time monitoring to advanced analytics capabilities.
This union of the physical and digital realms is being leveraged to achieve levels of manufacturing optimisation that are becoming a critical operational imperative in an increasingly dynamic and competitive industry sector. Enact®, the Quality Intelligence platform from InfinityQS, is designed to achieve exactly this.
How Enact Helps
Enact helps manufacturers collect all their quality-related metrics into a single version of the truth, in real time, no matter how or where they are generated. Whether from manual tests by operators, from batch operations via CMM, or from unattended in-process devices or sensors, Enact can collect all data in real-time into a single database.
Enact also allows you to define the events on which you want to trigger an action plan. These can be compliance events (something did or didn’t happen), limits (something was out of specification), or statistical events (some significant pattern occurred in the data). You can define mandatory actions and optional actions, and track who these are completed by, and when.
Then, Enact enables different audiences to view different analyses of this data—in real-time, without waiting for reports. Each audience can see the analysis that matters to them, based on physical scope (location, or groups of parts, or processes) and time (short-, medium-, or long-term). What’s more, each user can then ask questions of that analysis and get the answers—instantly.
That analysis is presented in a way that allows you to “roll up” data—so you can easily find out, for example, which shift has the biggest process variation? Or which line or machine? Or which plant? Or even which country? And you can then “drill down” to find out why.
Take advantage of the technology at your fingertips today: contact one of our account managers (firstname.lastname@example.org or via our website) for more information.
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