Manufacturing processes are becoming increasingly automated, production chains ever more complex and time-to-market schedules ever shorter. These developments are once again forcing quality issues in productions to the top of their agenda. Quality management methods that have worked in the past are now reaching their limits. A transformation towards a holistic Quality 4.0 concept is therefore required – we explain why and how.
Standardized procedures for quality assurance have long been an essential part of manufacturing operations. Tools such as sensor systems, and processes such as stage-gate reviews are used to keep quality defects to a minimum. The goal is to balance the costs of ensuring quality (cost of quality) and the costs of correcting quality defects (cost of non-quality) – based on the principle of as little as possible but as much as necessary.
In recent years, however, the manufacturing industry has seen such rapid development that current quality management concepts are being pushed to their limits. In a client study undertaken by AT Kearney, in which 50 managers and experts from various industrial sectors around the globe were surveyed on the topic of quality management, 50% of respondents observed an increase in quality issues over the past 10 years. 40% stated that traditional quality methods are losing effectiveness. AT Kearney estimates that associated quality costs will increase by 30% if manufacturing companies do not act. For the top 100 companies worldwide in the automotive, industrial goods and consumer products sectors, this would mean a decline in operating profit of US$ 215 billion.
Various industrial developments have led to a situation in which today’s methods are becoming less effective. As a result, quality control suffers:
Manual processes are increasingly being replaced by automated processes. These do not only help reduce manual time and effort, but also the risk of human error. However, the growing complexity that electronic systems and software applications bring with them is not to be underestimated. In fact, quality defects and their root causes become less transparent. Machine settings operators previously used to control themselves are today executed by a machine programme. As such, it has become more difficult for machine operators to understand the manufacturing processes, as well as to trace and report any defects that occur.
Supply chains have become ever larger as a result of globalization. Manufacturing companies are focusing on their core business and prefer to buy in non-core components, tools and other necessary resources from third parties. Requirements catalogues are used to ensure consistent quality among suppliers. However, protocols and information delivered with respective products can vary dramatically – from printed manufacturing reports, through information on USB sticks, to download links, each in a wide range of data formats (such as csv, pdf, xlsx, txt, etc.). If a defect occurs during production, it takes too long to work through the various information available. It is often difficult to determine whether the defect was already present during supplier delivery, or only occurred during further processing. A root cause analysis to avoid defects the next time often does not take place.
Manufacturing companies have to cope with ever tighter time-to-market targets. On one hand, this can be attributed to growing competition, for example in the space industry. On the other hand, there is a clear trend towards “customized mass production”, even in complex industries such as the automotive industry. This requires far greater flexibility in productions. Quality approaches, such as the popular Six Sigma methodology, are not geared to handle variations or faster throughput. Process controls with standards, rigorous documentation and planning fail when used in agile productions that aim at flexibility and fast market adaptations.
A future-oriented quality management requires agility. It requires rapid collection, more flexible evaluation and continuous availability of information.
The prerequisite for such future-oriented quality management is a uniform information basis that is available at all times and throughout the entire production chain.
Digitization offers a great opportunity in this regard. In areas such as production planning and machine monitoring, we are seeing first Industry 4.0 applications. Here, information from machines and sensor systems is collected to monitor production sequences, machine downtime or inventories. The ability to collect data automatically and evaluate it at any time stands up to the growing complexity and fast pace of development.
However, the same transformation must also take place in the field of quality management. So-called Quality 4.0 concepts are needed that collect quality information throughout the entire production chain, pre-process it for each product and make it available in real time, for example in the form of the “digital component twin” from nebumind. Only on this basis can new, innovative quality management methods emerge that are up to the challenges faced by productions, such as complexity, networking and flexibility. The following examples show some first steps in this direction.
In many productions with complex manufacturing processes, such as in the aviation sector or the automotive industry, tracing defects back to their origin is often too expensive and time-consuming. It is cheaper to repair the defects and hope that they do not recur. A Quality 4.0 approach, on the other hand, allows information on all production steps that a component has passed through to be made available in real time. Defects can be traced back immediately. This not only helps ongoing manufacturing avoid recurring rejects and waste, it also allows predevelopment to understand quality drivers faster and in turn to introduce new processes or machines more quickly.
To keep a close eye on the quality of components during production, sensor systems are being used more and more to monitor known influencing factors. The correlation of various influencing factors, which may only come into play with subsequent production steps, cannot be detected with standard sensor systems. Quality 4.0 solutions offer the possibility to provide and evaluate all necessary information for a single component at the same time and in turn to report cross-process defects, as well.
Machine drift is a natural phenomenon in production. Over time, machines deviate from their original settings and require readjustment. To avoid a decline in quality as a result of undetected machine drift, regular maintenance is performed on machines. Such maintenance is costly, as the machine, and thereby the production, comes to a standstill. Predictive maintenance approaches try to predict maintenance needs in advance and optimize respective intervals. Typically, these approaches are based on monitoring machine wear. Quality 4.0 solutions go one step further. Here, maintenance requirements are not determined on the basis of machine condition, but rather on the basis of component quality or defect patterns observed. After all, maintenance only becomes absolutely imperative when the quality of the product begins to suffer.
Quality 4.0 essentially builds on uniform information. Data and information must be available in a standardized and consistent form at all times, as well as being accessible to all departments – from Design and Purchasing, to Production and Aftersales Service. It is a task that affects the entire company and must be steered by top management – especially now, as quality costs are once again gaining in importance in productions.
Read more about nebumind’s Quality 4.0 approach and establish exactly this kind of information basis in your own production, while transforming your quality management.