MongoDB for Manufacturing & Motion
Power end-to-end value chain optimization with AI/ML, advanced analytics, and real-time data processing for innovative mobile, edge, and IoT applications.
Driving end-to-end innovation across the value chain
How are databases used in manufacturing?
The manufacturing and automotive industries are under pressure for constant innovation — delivering better products as fast as possible and at the lowest cost for both producers and end buyers. Massive volumes of data trapped in legacy systems are holding these industries back from reaching their true potential.
Instead of depending on reactive data analysis, slowed by siloed and legacy systems, MongoDB’s developer data platform connects operational technology and IT data for improved overall equipment effectiveness (OEE), and enables the jump from manufacturer to a business able to accelerate customer satisfaction and monetize connected, smart products. Whether it’s adopting Industrial Internet of Things (IIoT) solutions or gaining a single view of your business from raw goods to shipped products, data underpins the entire operation.
With MongoDB’s developer data platform, manufacturers and automotive industry leaders can combine the enormous variety and volume of data their equipment and products produce into a single view and analyze it all in one place. This enables them to make real-time decisions that increase OEE, automate the factory, and serve customers long after their products have left the shop floor.
What is Industry 4.0?
Industry 4.0 (I4.0) symbolizes the beginning of the Fourth Industrial Revolution. It represents the current trend of automation technologies in the manufacturing industry and includes the enabling disruptive technologies and concepts such as Cyber-Physical Systems (CPS), Industrial Internet of Things (IIoT), cloud computing, and immersive visualization.
IIoT and CPS technologies are integrating the virtual space with the physical world. This is resulting in a new generation of industrial systems, such as smart factories, to deal with the complexity of fast-paced and hyper-personalized production in current macro environments.
What can be achieved through Industry 4.0?
Industry 4.0 technologies, such as IIoT and CPS, are integrating the virtual space with the physical world. This is resulting in a new generation of industrial systems, such as smart factories, to deal with the complexity of fast-paced and hyper-personalized production in current macro environments.
IIoT is expected to offer promising transformation of existing industrial systems enabling digital transformation and unlocking tomorrow’s smart enterprise. The technology has been finding its way into products and sensors all while revolutionizing the existing manufacturing systems; thus, it is considered to be a key enabler for the next generation of advanced manufacturing.
How are industrial intelligence applications driving digital transformation?
Industry 4.0 generally comprises many complex components, and has broad applications in all manufacturing sectors. One of the biggest challenges faced by manufacturing companies is to make use of data generated by connected equipment and products to drive insights.
The digital economy is demanding that manufacturing applications become smarter, drive better customer experiences, surface insights, and take intelligent action directly within the application on live operational data — in real-time. The objective is to always out-innovate the competitors. To meet those demands for working with fresh data, we can no longer rely only on moving data out of our operational systems into analytics stores — this adds too much latency and separates the application from the insight that is created. To overcome these challenges, analytics processing has to be “shifted left” to the source of the data — to the applications themselves. MongoDB calls this shift “Application-Driven Analytics.'' And it’s a shift that both developers and analytics teams need to be ready for because it impacts their roles and responsibilities, along with the tools and technologies they are using.
MongoDB serves application-driven analytics through a set of platform capabilities and features — from database through data lake, a federated query service and connectors.
What are some of the challenges and risks associated with Industry 4.0?
One of the biggest challenges for manufacturing companies is to modernize the legacy infrastructure with new technologies, which can be time consuming and difficult to maintain. Additionally, the increased use of IoT sensors and devices result in large amounts of data being generated that need to be stored effectively and analyzed upon. MongoDB helps manufacturers overcome these challenges by offering scalability, flexibility, advanced security features, real-time application-driven analytics, and the freedom to run anywhere.
How can manufacturers ensure data privacy and security in their Industry 4.0 applications?
Connected IoT devices publish tremendous amounts of data, which increase the risk of cyberattacks and data breaches. To counter this risk, manufacturers can implement security measures, such as encryption, access controls, and auditing and logging to protect their data. MongoDB Atlas is designed with security in mind, providing robust access control mechanisms, native encryption at rest and in transit to protect sensitive data in the database.
What MongoDB features are most relevant for Industry 4.0?
1. Data sources in a smart factory can be varied and complex. MongoDB's flexible document model allows manufacturers to store and manipulate data in a way that best fits their application.
2. IIoT devices generate huge amounts of data and MongoDB’s distributed architecture allows manufacturers to scale their infrastructure horizontally allowing for near-limitless scaling to handle big data and intense workloads.
3. With native time series support and a strong aggregation framework, MongoDB can easily ingest, query, and process time series data. This is particularly important for IoT applications where data tends to be time series.
4. Robust security features include encryption, authorization, authentication, and auditing.
5. Embedded database (Realm) for IoT gateways and reliable data sync ensuring data from connected gateways never gets lost even when the signal drops.