The need for smarter recalls.
The prevalence of recalls in recent years has contributed to a significant, yet unnecessary expense and burden for manufacturers. While 2015 led to big recalls becoming the new normal in the automotive industry, it pales in comparison to some of the biggest recalls of the past, such as the high-profile 2000–2001 Ford/Firestone tire and 2009–2010 Toyota acceleration recalls, as well as the General Motors ignition switch recalls that have dominated newspaper headlines in 2014. Depending on the size and severity of a problem, and on the number of products or consumers affected, product recalls can mean billions of dollars in recall costs for manufacturers, not to mention legal, government fines as well as millions of dollars in lost revenue due to damage to brand reputation. It is remarkable that manufactures normally recall a whole batch or series of a product and not individual products. One of the reasons is missing traceability under which surrounding conditions an individual product or its components has been manufactured. Most manufacturers still rely on antiquated quality controls with product samples off the production line. It is a common practice to bring the samples to a lab for performing various tests on the samples before being judged to be consumer-ready. Usually, the tests are executed with proprietary software tools and test results are kept in separate quality data systems or even in non-digital formats – disconnected from the production process itself and its characteristics during production time. Imagine the advantages of knowing the associated machine conditions of each SKU along the manufacturing steps. As soon as first quality problems are obvious, manufacturers could trigger a retrospective root cause analysis on the bad parts in order to understand which machine conditions caused a specific problem, focusing recalls not on the whole batch but only on those products with a higher defect probability.
Condition monitoring meets quality control.
The condition of a production machine directly impacts the quality of a work piece. Traceability based on an integrated approach towards process data and quality data is a prerequisite for both real-time in-line non-contact quality control and automated retrospective root-cause analysis. But traceability of machine data imposes a lot of technical requirements on data processing and data storage – for instance, relational database technologies are not able to process high-frequency data streams generated from machine sensors. Big Data is the key technology to make these data streams accessible and make them actionable. Pairing of machine condition data (originating from sensors to measure vibrations, pressures, speed changes, temperatures, torques etc.) and quality data of SKUs (provided by RFIDs) delivers new insights for quality control in an efficient and affordable way no one could have imagine before.
Quality fingerprinting – how it works.
Datatroniq delivers prebuilt functionalities to establish a data driven quality fingerprint for each manufacturing step of a work piece. By tracking machine health conditions for each work piece that is being processed we are creating a unique data quality universe for real-time quality control and retrospective root cause analysis. Along the production line, each work piece inherits a quality index derived from the health condition of the underlying machine, takes it to the next manufacturing step, synchronizes the quality index, takes it to the next manufacturing step etc., and keeps it until the end-of-line inspection. Having Datatroniq’s real-time traceability established, work pieces can be dropped from the production line if quality index falls below a work piece-specific threshold. As a consequence, poor quality of work pieces can be automatically identified and alerted before they are examined in end-of-line inspections. For root-cause analysis, Datatroniq takes on a new dimension of accelerated insights and actionable decisions. Imagine a set of SKUs originating from rejects of end-of-line inspections or consumer rejects. Given a set of SKUs Datatroniq automatically triggers a root-cause analysis based on the quality fingerprint history of the corresponding SKU. Resulting outputs are rule sets describing the failure reasons and continuous insights for sustainable quality improvements for future manufacturing processes.
Benefits and conclusions.
With Datatroniq manufacturers can protect themselves by detecting the first signs that problems may be on the horizon before they impose damage on their businesses and reputation. The traceability of process data and quality data reduces quality risks during the manufacturing process and leads to a reduced number of rejects. With quality checks automatically carried out 24/7 on each produced work piece (and no longer on samples) Datatroniq provides a comprehensive data driven non-contact in-line quality control solution, that is not only affordable and easy to deploy but one that also pays off after a very short time period. In case of looming recalls, manufacturers can now quickly respond with automated root-cause analysis based on quality fingerprinting to better understand and identify the affected products and components, in order to significantly limit the number and corresponding costs of recalls.