Data and its exploitation take on special importance in Industry 4.0, a result of transformation of various industrial sectors such as energy, retail or logistics through incorporation of technologies and the digitalisation of production processes in order to improve productivity and efficiency. Industry 4.0 has generated a series of new situations.
Integration of players in the supply chain, both vertically and horizontally has considerably improved industry results and efficiency, although standardisation of communication protocols is also indispensable. Improved connectivity from the design and planning stages through to production, delivery or analysis leads to increased efficiency and performance monitoring. Sensor technology and IoT optimise processes and shorten response times to production incidents or problems. The end goal is to interconnect all steps and devices involved so that the value chain is monitored and available for analysis.
Dependency on physical environments is substantially reduced, creating virtual environments throughout the supply chain. The application of artificial intelligence technologies gives machines ‘decision autonomy’ in the process without the need for direct action by the user, providing an additional supervisory function.
“If you can’t measure, you can’t improve” is a truth affecting many if not all sectors and professional environments. In the era of data, if you don’t measure, the capacity for improvement reduces considerably. Big data and cloud computing are key technologies in this transformation to Industry 4.0, allowing flow of data and enabling pattern identification, prediction of events from the event log via machine learning and all with an unprecedented speed, flexibility and reach.
The ability to cut response times to almost real-time or real-time adds intelligence as the business is able to take decisions in a matter of seconds after the event has occurred. The industry must be aware of what is happening at any given moment and also to predict what might happen and this is one of the advantages of digitalisation and more specifically, cloud computing technologies.
The new Industry 4.0 means thinking about the future in a modular and scalable manner. Concepts like automation or reuse have resulted in a less rigid industry that is more flexible with a greater ability to react to change. In this sense, this change is cultural as well as technological and requires the human components to be ready to collaborate and evolve with technology.
The client has become the centre of the 4.0 strategy on whom all production chain improvements will impact. Clients are also 4.0 and require their needs to be met immediately. To achieve a true digital transformation and thus an evolution towards this new industrial paradigm, all internal clients (employees) as well as external (private or industrial consumers) must be united, with the latter receiving greater flexibility, personalisation, adaptation to their needs, quality, reduction of time and cost.
Use Cases of Industry 4.0
Consumption control, product personalisation, sustainability and efficiency are challenges facing the industry and to achieve these, it is not enough to simply accumulate data; it must also be possible to analyse and present information from which conclusions may be drawn. Some use cases of Industry 4.0 supported by Big Data and Machine Learning technologies are as follows:
With machine learning it is possible to optimise resource sequences to reach maximum performance compared to operation and costs. This can increase system efficiency, since the process can learn over time how different performance subsystems work and therefore adapt automatically.
By analysis of history logs and registry systems containing information about weather forecast data, observation data, error prediction and data of actual errors, it is possible to predict important errors using Naive Bayes, logistic regression and other deep learning classifiers.
OUTAGE PREDICTION IN REAL TIME
This model takes the intelligent network concept to a new level, making it possible to predict incidents, vulnerabilities and the economic impact of potential damage. By analysis of the impact of potential vulnerability and the impacts of system outages, investigators can predict where and when these may occur.
Price optimisation can improve revenue by between 5 and 10%. Granular value mapping of device lifecycles, tariffs and channels; calculation of price elasticity in the device intersection, channels and price plan; and the subsequent combination of this information may be used in simulations to evaluate how price and promotions affect volume and future revenue. This strategy may be applied to provide key information about activities, such as price setting and promotions.
Artificial intelligence can make operations more efficient, thanks to a combination of robotics and process optimisation that improves productivity and reduces labour costs. In store, for example, automatic learning can help optimise volume of products and improve supply efficiency by 50%.
Image: unsplash | Viktor Kiryanov