In the dynamic landscape of manufacturing, efficiency reigns supreme. The ability to seamlessly connect disparate production lines is not just a matter of convenience; it's a strategic advantage. MongoDB Atlas Edge Server offers a solution that simplifies the process of linking multiple production lines to a centralized Cloud-DataHub hosted on MongoDB Atlas. Let's explore how this setup facilitates smooth data flow and enhances operational agility.

Simplifying The Communication Architecture

Traditionally, connecting disparate production lines involved complex integrations, custom APIs, and significant overhead, resulting in a complex architecture, high maintenance costs and  a very complex update process. Networks are not always available, but the applications still have to run at all times. Reconnecting and synchronizing after an interruption without data loss or data duplication is essential. Many manufacturing companies also do not want to send all data directly to the cloud, but rather pre-process it in the factory and only send the really relevant information to a centralized hub. Additionally, each production plant has its own architecture and given techstack which needs to be brought together. Geographical distances can lead to considerable latency times.

What is needed is a solution that can process a wide variety of protocols very easily, enables pre-processing in the production plant, can cope with network failures and is secure against unauthorized access at all times.

A Scalable Data Backbone Implementation By Capgemini And MongoDB

Capgemini & MongoDB have built a demo for an hybrid and scalable data backbone using PLC Machine Data from a production Line located in Aix en Provence and a Robot based Quality Control Line in Cadiz, in which the following two concepts play a crucial role: 

Hybrid Architecture:

The hybrid architecture integrates on-premise and cloud solutions for storage and processing. Nowadays cloud appears as the clear preferred choice; in fact, on-premise (or edge) is considered as a new domain by the Cloud in a continuum of architecture, data and security. The hybrid architecture based on MongoDB 3-levels architecture with MongoDB EA, Atlas  Edge Server and MongoDB Atlas ensures vertical and horizontal data consistency and scalability.

Data Integration Layer:

This is a centralized hub which standardizes and contextualizes data from the disparate systems around the plant, allowing systems to exchange consistent data from a unique source of truth in MongoDB Atlas. This layer provides users a 360° view of the production line and a path to analytics and AI, to quickly find the information they need, build business intelligence tools, and generate insights.

Advantages Of Streamlined Information Flow And A Data Backbone

The data backbone with its joined-up data and modeling systems holds it all together, linking sensors and machinery, engineering design and operational management all together in a combination of vertical and horizontal integration. A well-designed and robust data backbone enables manufacturing to get up and running quickly, enables real time monitoring, automation and data-driven decision making and is the foundation of digital twins.

Seamless Integration:

Well-integrated connections across and within the digital architecture and systems are a key value driver. That means a product can be designed digitally then tested virtually in the exact production setup. Any problems can be identified, and then the design can simply be transferred into the manufacturing facility’s IT and OT systems.

Data-Driven Decision Making:

Providing the conditions for a paradigm shift towards data-driven operations by measuring everything that happens at every step of the way: The data backbone supports analyzing the collective data in order to detect subtle signals that indicate issues, which can either lead to real-time responses, or provide the insight to design solutions. One example to illustrate this is mitigating supply risks. The data backbone does not just manage operations, but joins up the end-to-end process. It meticulously tracks supply availability, quality and origin, matching procurement to demand and helping navigate supply fluctuations.

A Digital Thread:

Data-driven decision making can only exist when digital continuity or a thread exists. All the collected real-time data from design, machine and quality control can create a chain of causality that traces problems back to their cause, and simulate alternative approaches.

A Foundation For Digital Twins And GenAI:

All these data backbone features offer a blueprint that allows the implementation of new technology and efficiently transposes to other factories, streamlining future expansion.

Data Handling And Connectivity With MongoDB Edge Server

The data backbone with its joined-up data and modeling systems holds it all together, linking sensors and machinery, engineering design and operational management all together in a combination of vertical and horizontal integration. A well-designed and robust data backbone enables manufacturing to get up and running quickly, enables real time monitoring, automation and data-driven decision making and is the foundation of digital twins.

Seamless Integration:

Well-integrated connections across and within the digital architecture and systems are a key value driver. That means a product can be designed digitally then tested virtually in the exact production setup. Any problems can be identified, and then the design can simply be transferred into the manufacturing facility’s IT and OT systems.

Data-Driven Decision Making:

Providing the conditions for a paradigm shift towards data-driven operations by measuring everything that happens at every step of the way: The data backbone supports analyzing the collective data in order to detect subtle signals that indicate issues, which can either lead to real-time responses, or provide the insight to design solutions. One example to illustrate this is mitigating supply risks. The data backbone does not just manage operations, but joins up the end-to-end process. It meticulously tracks supply availability, quality and origin, matching procurement to demand and helping navigate supply fluctuations.

A Digital Thread:

Data-driven decision making can only exist when digital continuity or a thread exists. All the collected real-time data from design, machine and quality control can create a chain of causality that traces problems back to their cause, and simulate alternative approaches.

A Foundation For Digital Twins And GenAI:

Efficiently recording and sharing data provides the basis for digital twins, both of the product and process, which can simulate the entire product lifecycle in the future, and so enable incredibly sophisticated design and performance optimization at lower costs. It also facilitates GenAI applications, such as digital assistants which can reduce the number of technical experts needed onsite, and speed up response times.

Replicable And Scalable Setup:

All these data backbone features offer a blueprint that allows the implementation of new technology and efficiently transposes to other factories, streamlining future expansion.

Data Handling And Connectivity With MongoDB Edge Server

The Document Model as the core of MongoDB’s developer data platform, connected with Edge Server and Device sync, simplifies the process by providing a unified platform for data synchronization and access control. All signals to be processed on a production line can be sent locally via MQTT broker to a MongoDB EA. The local OLTP database processes the data and synchronizes via Kafka Change Stream to an Atlas Edge server. All production-line relevant data can already be displayed at plant level to provide the plant management valuable insights of the status of the production lines.

By leveraging Device Sync, all changes happening at the edge get seamlessly propagated to Atlas as the central cloud data hub, ensuring data consistency and security across the entire network. Device Sync provides data synchronization out of the box, as it is built for offline-first. Its seamless integration with MongoDB’s document database, the fully embedded conflict resolution, user authentication and serverless computing capabilities as well as a flexible architecture allows developers to create real-time applications that can scale effortlessly. MongoDB’s latest product, Atlas Edge Server is a MongoDB instance with a sync server that can be deployed on local or remote infrastructure, enabling real-time sync, conflict resolution, and disconnection tolerance. This helps ensure that mission-critical applications and devices function seamlessly, even with intermittent connectivity. It extends the capabilities of MongoDB Device Sync to the edge of the network, enabling data processing and storage closer to the source. This is particularly advantageous in scenarios where low latency and offline capabilities are crucial, such as manufacturing environments.

Atlas, MongoDB's fully managed cloud database service, serves as the centralized DataHub where all production line data converges. With built-in security features, robust scalability, and high availability, Atlas ensures that critical data is stored safely and is easily accessible for analysis and decision-making. With Atlas Charts, all possible graphics can be displayed very quickly and easily in a dashboard.

Enhancing Operational Efficiency By Real Time Insights

With MongoDB’s real-time capabilities, manufacturers gain instant visibility into production metrics, quality control parameters, and equipment performance. This empowers decision-makers to respond swiftly to anomalies, optimize processes, and minimize downtime. By deploying an Edge Server at production facilities, manufacturers can process data locally, reducing reliance on the cloud and mitigating latency issues. This enables critical functions to operate even in offline environments, ensuring uninterrupted production workflows. MongoDB cloud-native architecture allows production lines to scale effortlessly as demand fluctuates. Whether adding new lines or expanding existing ones, the system adapts seamlessly, providing the agility needed to stay ahead in a competitive market.

The convergence of MongoDB EA, Device Sync, Edge Server, and Atlas heralds a new era in manufacturing connectivity. By seamlessly integrating disparate production lines into a centralized DataHub, manufacturers can unlock operational efficiencies, gain real-time insights, and ensure data security and compliance. As industries continue to embrace digital transformation, leveraging modern technologies like MongoDB Edge Server becomes not just a choice but a necessity for staying competitive in today's fast-paced world.

Capgemini And MongoDB Partnership

Capgemini is recognized as one of the top global strategic System Integrator partners for MongoDB. Our relationship with Capgemini  is based on the principle of helping customers achieve their strategic initiatives around reducing IT debt, moving monoliths to microservices, and helping deliver key programs ahead of schedule. As a result of this robust partnership, Capgemini was consecutively awarded the Partner of the Year in 2022 and 2023.

Capgemini currently has a team of more than 5,000 professionally skilled MongoDB developers, with over 2,500 certified Associates. Together Capgemini and MongoDB are committed to helping our customers throughout their data journey and delivering the most valuable solutions to reach their strategic corporate objectives.