No matter how efficient an industrial company’s production operations are right now, there’s a good chance of a further significant improvement in the future – all thanks to big data.
Mechanical engineering companies that are committed to lean production, the continuous improvement process and digital design are already on the right track when it comes to optimising their production efficiency. As an example from Europe shows, however, the possibilities of big data are set to unlock an unexpectedly large amount of further potential. According to the management consulting firm McKinsey & Company, a chemical business that has consistently exceeded industry standards for the average yield since the 1960s thanks to ongoing process improvements saw further significant optimisations after introducing big data. Energy costs are reportedly 15 percent lower and raw material wastage has been cut by as much as 20 percent. These impressive figures hint at why big data is also relevant for mechanical engineering. But what exactly is big data?
Digital strategy in mechanical engineering
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What is big data and how does it work?
Big data refers to volumes of data that are impossible to interpret using conventional data processing methods. This may be because they are too large or too complex, change too frequently or are not sufficiently well structured. However, the term also covers the analysis and utilisation of such data volumes. Big data thus describes both data volumes that cannot be evaluated in the conventional way and the use of advanced methods for automated analysis.
Big data refers to huge volumes of data and the advanced methods required to interpret them.
Further details of the extensive improvements from the above-mentioned example can be provided on this basis. Making use of a form of artificial intelligence, the chemical business in question analysed the data volumes generated during production to determine the impact of various factors on production yields, including the pressure, temperature and quantity of the coolant and the flow of carbon dioxide. The company achieved the above-mentioned savings in energy costs and raw material wastage by adapting its production parameters based on this evaluation.
The role of big data in mechanical engineering
Evaluations based on the big data principle are therefore potentially worthwhile whenever huge data volumes are generated during the value creation process or there are so many variables involved that their impact on efficiency cannot be determined using conventional methods. In mechanical engineering, big data can thus be applied, for example, to actual machine production to identify untapped potential and make production processes more efficient. Every silver lining has a cloud, though. When collecting large volumes of data, it’s vital to ensure processing and storage comply with data protection regulations – especially in the case of customer-related data.
The human factor should not be underestimated, either. After all, a number of specialists with appropriate skills are required to integrate and implement big data, and staff must be willing to work in a highly data-driven environment. Although some employees may find it hard relying less on intuition and experience and making decisions based mainly on facts, it’s well worth mechanical engineering companies tackling the challenges of big data head on. Successfully meeting these challenges will, in all likelihood, bring big rewards.
In the future, however, it will also be more important for mechanical engineering companies to ensure the machinery they manufacture is compatible with customers’ data analysis systems. Although only around 8 percent of German SMEs currently use big data analysis, approximately 46 percent already regard big data as being highly relevant, as indicated by the study (in German) entitled “Der Rohstoff des 21. Jahrhunderts [= the raw material of the 21st century]: Big Data, Smart Data – Lost Data?”. What’s more, a further 35 percent of the companies surveyed are convinced that big data will play a major role in the future. So it’s only a question of time before big data concepts such as advanced analytical methods and machine learning gain a firm foothold and thus become an integral part of day-to-day mechanical engineering operations
Big data is a must for mechanical engineering in the future
Big data technologies are also being used for predictive maintenance, which is part and parcel of Industry 4.0. The tiniest of anomalies flagged up during the automated evaluation of data from sensors indicate components and equipment requiring maintenance. These items can then be replaced or repaired as appropriate before the error rate increases or, worse still, a total failure occurs. Above all in the long term, introducing big data analyses has the potential to avoid huge costs and production stoppages.
Big data gives companies a glimpse of the future.
The potential applications of big data in mechanical engineering aren’t confined to production, though. An example from the automotive industry shows that aftermarket companies can also benefit. Car manufacturers can use appropriately evaluated workshop reports and communication with customers to identify sources of error – which otherwise only become apparent once vehicles are in everyday use – before they become widespread and cause negative publicity. When used correctly, big data thus provides a glimpse of the future – a capability that needn’t cost the earth but can be invaluable.
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