Dr. Humza Akhtar

15 results

Building Gen AI-Powered Predictive Maintenance with MongoDB

In today’s fast-evolving industrial landscape, digital transformation has become a necessity. From manufacturing plants to connected vehicles, the push towards predictive maintenance excellence is driving organizations to embrace smarter, more efficient ways of managing operations. One of the most compelling advancements in this domain is predictive maintenance powered by generative AI , a cutting-edge approach that will revolutionize how industries maintain and optimize their equipment. For manufacturers seeking maintenance excellence, a unified data store and a developer data platform are key enablers. These tools provide the foundation for integrating AI applications that can analyze sensor data, predict failures, and optimize maintenance schedules. MongoDB Atlas is the only multi-cloud developer data platform available that is designed to streamline and speed up developers' data handling. With MongoDB Atlas, developers can enhance end-to-end value chain optimization through AI/ML, advanced analytics, and real-time data processing, supporting cutting-edge mobile, edge, and IoT applications. In this post, we’ll explore the basics of predictive maintenance and how MongoDB can be used for maintenance excellence. Understanding the need for predictive maintenance Predictive maintenance is about anticipating and addressing equipment failures before they occur, ensuring minimal disruption to operations. Traditional maintenance strategies, like time-based or usage-based maintenance, are less effective than predictive maintenance because they don’t account for the varying conditions and complexities of machinery. Unanticipated equipment breakdown can result in line stoppage and substantial throughput losses, potentially leading to millions of dollars in revenue loss. Since the pandemic, many organizations have begun significant digital transformations to improve efficiency and resilience. However, a concerning gap exists between tech adoption and return on investment. While 89% of organizations have begun digital and AI transformations, only 31% have seen the expected revenue lift, and only 25% have realized the expected cost savings. These numbers highlight the importance of implementing new technologies strategically. Manufacturers need to carefully consider how AI can address their specific challenges and then integrate them into existing processes effectively. Predictive maintenance boosts efficiency and saves money Predictive maintenance uses data analysis to identify problems in machines before they fail. This allows organizations to schedule maintenance at the optimal time, maximizing machine reliability and efficiency. Indeed, according to Deloitte , predictive maintenance can lead to a variety of benefits, including: 3-5% reduction in new equipment costs 5-20% increase in labor productivity 15-20% reduction in facility downtime 10-30% reduction in inventory levels 5-20% reduction in carrying costs Since the concept was introduced, predictive maintenance has constantly evolved. We've moved beyond basic threshold-based monitoring to advanced techniques like machine learning (ML) models. These models can not only predict failures but also diagnose the root cause, allowing for targeted repairs. The latest trend in predictive maintenance is automated strategy creation. This involves using AI to not only predict equipment breakdowns but also to generate repair plans, ensuring the right fixes are made at the right time. Generative AI in predictive maintenance To better understand how gen AI can be used to build robust predictive maintenance solutions, let's dig into the characteristics of organizations that have successfully implemented AI. They exhibit common traits across five key areas: Identifying high-impact value drivers and AI use cases: Efforts should be concentrated on domains where artificial intelligence yields maximal utility rather than employing it arbitrarily. Aligning AI strategy with data strategy: Organizations must establish a strong data foundation with a data strategy that directly supports their AI goals. Continuous data enrichment and accessibility: High-quality data, readily available and usable across the organization, is essential for the success of AI initiatives. Empowering talent and fostering development: By equipping their workforce with training and resources, organizations can empower them to leverage AI effectively. Enabling scalable AI adoption: Building a strong and scalable infrastructure is key to unlocking the full potential of AI by enabling its smooth and ongoing integration across the organization. Implementing predictive maintenance using MongoDB Atlas When combined with a robust data management platform like MongoDB Atlas, gen AI can predict failures with remarkable accuracy and suggest optimal maintenance schedules. MongoDB Atlas is the only multi-cloud developer data platform designed to accelerate and simplify how developers work with data. Developers can power end-to-end value chain optimization with AI/ML, advanced analytics, and real-time data processing for innovative mobile, edge, and IoT applications. MongoDB Atlas offers a suite of features perfectly suited for building a predictive maintenance system, as shown in Figure 1 below. Its ability to handle both structured and unstructured data allows for comprehensive condition monitoring and anomaly detection. Here’s how you can build a generative AI-powered predictive maintenance software using MongoDB Atlas: Machine prioritization: This stage prioritizes machines for the maintenance excellence program using a retrieval-augmented generation (RAG) system that takes in structured and unstructured data related to maintenance costs and past failures. Generative AI revolutionizes this process by reducing manual analysis time and minimizing investment risks. At the end of this stage, the organization knows exactly which equipment or assets are well-suited for sensorization. Utilizing MongoDB Atlas, which stores both structured and unstructured data, allows for semantic searches that provide accurate context to AI models. This results in precise machine prioritization and criticality analysis. Failure prediction: MongoDB Atlas provides the necessary tools to implement failure prediction, offering a unified view of operational data, real-time processing, integrated monitoring, and seamless machine learning integration. Sensors on machines, like milling machines, collect data (e.g., air temperature and torque) and process it through Atlas Stream Processing , allowing continuous, real-time data handling. This data is then analyzed by trained models in MongoDB, with results visualized using Atlas Charts and alerts pushed via Atlas Device Sync to mobile devices, establishing an end-to-end failure prediction system. Repair plan generation: To implement a comprehensive repair strategy, generating a detailed maintenance work order is crucial. This involves integrating structured data, such as repair instructions and spare parts, with unstructured data from machine manuals. MongoDB Atlas serves as the operational data layer, seamlessly combining these data types. By leveraging Atlas Vector Search and aggregation pipelines , the system extracts and vectorizes information from manuals and past work orders. This data feeds into a large language model (LLM), which generates the work order template, including inventory and resource details, resulting in an accurate and efficient repair plan. Maintenance guidance generation: Generative AI is used to integrate service notes and additional information with the repair plan, providing enhanced guidance for technicians. For example, if service notes in another language are found in the maintenance management system, we extract and translate the text to suit our application. This information is then combined with the repair plan using a large language model. The updated plan is pushed to the technician’s mobile app via Atlas Device Sync. The system generates step-by-step instructions by analyzing work orders and machine manuals, ensuring comprehensive guidance without manually sifting through extensive documents. Figure 1: Achieving end-to-end predictive maintenance with MongoDB Atlas Developer Data Platform In the quest for operational excellence, predictive maintenance powered by generative AI and MongoDB Atlas stands out as a game-changer. This innovative approach not only enhances the reliability and efficiency of industrial operations but also sets the stage for a future where AI-driven insights and actions become the norm. By leveraging the advanced capabilities of MongoDB Atlas, manufacturers can unlock new levels of performance and productivity, heralding a new era of smart manufacturing and connected systems. If you would like to learn more about generative AI-powered predictive maintenance, visit the following resources: [Video] How to Build a Generative AI-Powered Predictive Maintenance Software [Whitepaper] Generative AI in Predictive Maintenance Applications [Whitepaper] Critical AI Use Cases in Manufacturing and Motion: Realizing AI-powered innovation with MongoDB Atlas

June 27, 2024

Unified Namespace Implementation with MongoDB and MaestroHub

In the complex world of modern manufacturing, a crucial challenge has long persisted: how to seamlessly connect the physical realm of industrial control systems with the digital landscape of enterprise operations. The International Society of Automation's ISA-95 standard, often visualized as the automation pyramid, has emerged as a guiding light. As shown below, this five-level hierarchical model empowers manufacturers to bridge the gap between these worlds, unlocking a path toward smarter, more integrated operations. Figure 1: In the automation pyramid, data moves up or down one layer at a time, using point-to-point connections. Manufacturing organizations face a number of challenges when implementing smart manufacturing applications due to the sheer volume and variety of data generated. An average factory produces terabytes of data daily, including time series data from machines stored in process historians and accessed by supervisory control and data acquisition (or SCADA) systems. Additionally, manufacturing execution systems (MES), enterprise resource planning (ERP) systems, and other operations software generate vast amounts of structured and unstructured data. Globally, the manufacturing industry generates an estimated 1.9 petabytes of data annually . Manufacturing leaders are eager to leverage their data for AI and generative AI projects, but a Workday Global Survey reveals that only 4% of the survey’s respondents believe their data is fully accessible for such applications. Data silos are a significant hurdle, with data workers spending an average of 48% of their time on data search and preparation. A popular approach to making data accessible is consolidating it in a cloud data warehouse and then adding context. However, this can be costly and inefficient, as dumping data without context makes it difficult for AI developers to understand its meaning and origin, especially for operational technology time series data. Figure 2: Pushing uncontextualized data to a data warehouse and then adding context is expensive and inefficient. All these issues underscore the need for a new approach—one that not only standardizes data across disparate shop floor systems, but also seamlessly weaves context into the fabric of this data. This is where the Unified Namespace (UNS) comes in. Figure 3: Unified Namespace provides the right data and context to all the applications connected to it. Unified Namespace is a centralized, real-time repository for all production data. It provides a single, comprehensive view of the business's current state. Using an event-driven architecture, applications publish real-time updates to a central message broker, which subscribers can consume asynchronously. This creates a flexible, decoupled ecosystem where applications can both produce and consume data as needed. Figure 4: UNS enables all the enterprise systems to have one centralized location to get the data they need for what they want to accomplish. MaestroHub and MongoDB: Solving the UNS challenge Initially introduced in 2011 at the Hannover Fair of Industrial Technologies, the core idea behind Industry 4.0 is to establish seamless connectivity and interoperability between disparate systems used in manufacturing. And UNS aims to solve this. Over the past five years, we have seen interest in UNS ramping up steadily, and now manufacturers are looking for practical ways to implement it. In particular, a question we’re frequently asked is where does UNS actually live. To answer that question, we need to look at popular architecture patterns, and the pros and cons of each. The most common pattern is implementing UNS in an MQTT broker. An MQTT broker will act as an intermediary entity that receives messages published by clients, filters the messages by topic, and distributes them to subscribers. The reason most manufacturers choose MQTT is it is an open architecture that is easy to implement. However, the challenge with just using the MQTT broker is that the clients don't get historical data access (which will be required to build the analytical and AI applications). Another approach can be to just dump all the data in a data warehouse and then add context to it. This can solve the problem of historical data access but it is an inefficient approach to standardize messages after they have been landed in the data warehouse in the cloud. A superior solution for comprehensive, real-time data access is combining a single source of truth (SSoT) Unified Namespace platform like MaestroHub with a flexible multi-cloud data platform like MongoDB Atlas. MaestroHub creates SSoT for industrial data, resulting in an up to 80% reduction in integration effort for brownfield facilities. Figure 5: MaestroHub SSoT creates a unified data integration layer, saving up to 50% of time in data contextualization (Source: MaestroHub). MaestroHub provides the connectivity layer to all data sources on the factory floor, along with contextualization and data orchestration. This makes it easy to connect the data needed for the UNS, enrich it with more context, and then publish it to consumers using the protocol that works best for them. Under the hood, MaestroHub stores metadata of connections, instances, and flows, and uses MongoDB as the database to store all this data. MongoDB’s flexible data modeling patterns reduce the complexity of mapping and transforming data when it's shared across different clients in the UNS. Additionally, scalable data indexing overcomes performance concerns as the UNS grows over time. Figure 6: MaestroHub and MongoDB together enable a real-time UNS plus long-term storage. MongoDB: The foundation for intelligent industrial UNS In the quest to build a unified namespace system (UNS) for the modern industrial landscape, the choice of database becomes paramount. So why turn to MongoDB? Scalability and high availability: It scales effortlessly, both vertically and horizontally (sharding), to handle the torrent of data from sensors, machines, and processes. Operational Technology (OT) systems generate vast amounts of data from these sources, and MongoDB ensures seamless management of that information. Document data model: Its adaptable document model accommodates diverse data structures, ensuring a harmonious flow of information. A Unified Namespace (UNS) must handle data from any factory source, accommodating structure variations. MongoDB's flexible schema design allows different data models to coexist in a single database, with schema extensibility at runtime. This flexibility facilitates the seamless integration of new data sources and types into the UNS. Real-time data processing: MongoDB Change Streams and Atlas Device Sync empower third-party applications to access real-time data updates. This is essential for monitoring, alerting, and real-time analysis within a UNS, enabling prompt responses to critical events. Gen AI application development with ease: Atlas Vector Search efficiently performs semantic searches on vector embeddings stored in MongoDB Atlas. This capability seamlessly integrates with large language models (LLMs) to provide relevant context in retrieval-augmented generation (RAG) systems. Given that the Universal Name Service (UNS) functions as a single source of truth for industrial applications, connecting gen AI apps to retrieve context from the UNS database ensures accurate and reliable information retrieval for these applications. With the foundational database established, let's explore MaestroHub, a platform designed to leverage the power of MongoDB in industrial settings. The MaestroHub platform MaestroHub is a provider of a SSoT for industrial data, specifically tailored for manufacturers. It achieves this through: Data connectors: MaestroHub connects to diverse data sources using 38 different industrial communication protocols, encompassing OT drivers, files, databases (SQL, NoSQL, time series), message brokers, web services, cloud systems, historians, and data warehouses. The bi-directional nature of 90% of these protocols ensures comprehensive data integration, leaving no data siloed. Data contextualization based on ISA-95: Leveraging ISA-95 Part 2, MaestroHub employs a semantic hierarchy and a clear naming convention for easy navigation and understanding of data topics. The contextualization of the payload is not just limited to the unit of measure AND definitional but also contains Enterprise/Site/Area/Line/Cell details, which are invaluable for analytics studies. Data contextualization is an important feature of a UNS platform. Logic flows/rule engine: Adhering to the UNS principle "Do not make any assumptions on how the data will be consumed," the data should flow flexibly from sources to brokers and from brokers to consumers in terms of rules, frequencies, and multiple endpoints. MaestroHub allows you to set rules such as Always, OnChange, OnTrue, and WhileTrue, where you can dynamically determine the conditions using events and inputs via JavaScript. Insights created by MaestroHub: MaestroHub provides real-time diagnostics of data health by leveraging Prometheus, Elasticsearch, Fluentd, and Kibana. Network problems, changed endpoints, and changed data types are automatically diagnosed and reported as insights. Additionally, MaestroHub uses NATS for queue management and stream analytics, buffering data in the event of a network outage. This allows IT and OT teams to monitor, debug, and audit logs with full data lineage. Conclusion The ISA-95 automation pyramid presents significant challenges for the manufacturing industry, including a lack of flexibility, limited scalability, and difficulty integrating new technologies. By adopting a Unified Namespace architecture with MaestroHub and MongoDB, manufacturers can overcome these challenges and achieve real-time visibility and control over their operations, leading to increased efficiency and improved business outcomes. Read more on how MongoDB enables Unified Namespace via its multi-cloud developer data platform. We are actively working with our clients on solving Unified Namespace challenges. Take a look at our Manufacturing and Industrial IoT page for more stories or contact us through the web form in the link.

June 18, 2024

Transforming Predictive Maintenance with AI: Real-Time Audio-Based Diagnostics with Atlas Vector Search

Wind turbines are a critical component in the shift away from fossil fuels toward more sustainable, green sources of energy. According to the International Energy Agency (IEA), the global capacity of wind energy has been growing rapidly, reaching over 743 gigawatts by 2023. Wind energy, in particular, has one of the greatest potentials to increase countries' renewable capacity growth. Solar PV and wind additions are forecast to more than double by 2028 compared with 2022, continuously breaking records over the forecast period. This growth highlights the increasing reliance on wind power and, consequently, the need for effective maintenance strategies. Keeping wind turbines operating at maximum capacity is essential to ensuring their continued contribution to the energy grid. Like any mechanical device, wind turbines must undergo periodic maintenance to keep them operating at optimal levels. In recent years, advancements in technology—particularly in AI and machine learning—have played a significant role by introducing predictive maintenance breakthroughs to industrial processes like periodic maintenance. By integrating AI into renewable energy systems, organizations of all sizes can reduce costs and gain efficiencies. In this post, we will dig into an AI application use case for real-time anomaly detection through sound input, showcasing the impact of AI and MongoDB Atlas Vector Search for predictive maintenance of wind turbines. Predictive Maintenance in Modern Industries Companies increasingly invest in predictive maintenance to optimize their operations and drive efficiency. Research from Deloitte indicates that predictive maintenance can reduce equipment downtime by 5–15 percent, increase labor productivity by 5–20 percent, and reduce overall new equipment costs by 3–5 percent. This helps organizations maximize their investment in equipment and infrastructure. By implementing predictive maintenance strategies, companies can anticipate equipment failures before they occur, ultimately resulting in longer equipment lifetimes, tighter budget control, and higher overall throughput. More concretely, businesses aim to reduce mean time to repair, optimal ordering of replacement parts, efficient people management, and reduced overall maintenance costs. Leveraging data interoperability, real-time analysis, modeling and simulation, and machine learning techniques, predictive maintenance enables companies to thrive in today's competitive landscape. However, despite its immense potential, predictive maintenance also presents significant challenges. One major hurdle is the consolidation of heterogeneous data, as predictive maintenance systems often need to integrate data from various formats and sources that can be difficult to integrate. Scalability also becomes a concern when dealing with the high volumes of IoT signals generated by numerous devices and sensors. And lastly, managing and analyzing this vast amount of data in real-time poses challenges that must be overcome to realize the full benefits of predictive maintenance initiatives. At its core, predictive maintenance begins with real-time diagnostics, enabling proactive identification and mitigation of potential equipment failures in real-time. Figure 1: Predictive Maintenance starts with real-time diagnostics However, while AI has been employed for real-time diagnostics for some time, the main challenge has been acquiring and utilizing the necessary data for training AI models. Traditional methods have struggled with incorporating unstructured data into these models effectively. Enter gen AI and vector search technologies, positioned to revolutionize this landscape. Flexible data platforms working together with AI algorithms can help generate insights from diverse data types, including images, video, audio, geospatial data, and more, paving the way for more robust and efficient maintenance strategies. In this context, MongoDB Atlas Vector Search stands out as a foundational element for effective and efficient gen AI-powered predictive maintenance models. Why MongoDB and Atlas Vector Search? For several reasons, MongoDB stands out as the preferred database solution for modern applications. Figure 2: MongoDB Atlas Developer Data Platform Document data model One of the reasons why the document model is well-suited to the needs of modern applications is its ability to store diverse data types in BSON (Binary JSON) format, ranging from structured to unstructured. This flexibility essentially eliminates the middle layer necessary to convert to a SQL-like format, resulting in easier-to-maintain applications, lower development times, and faster response to changes. Time series collections MongoDB excels in handling time-series data generated by edge devices, IoT sensors, PLCs, SCADA systems, and more. With dedicated time-series collections, MongoDB provides efficient storage and retrieval of time-stamped data, enabling real-time monitoring and analysis. Real-time data processing and aggregation MongoDB's adeptness in real-time data processing is crucial for immediate diagnostics and responses, ensuring timely interventions to prevent costly repairs and downtime. Its powerful aggregation capabilities facilitate the synthesis of data from multiple sources, providing comprehensive insights into fleet-wide performance trends. Developer data platform Beyond just storing data, MongoDB Atlas is a multi-cloud developer data platform, providing the flexibility required to build a diverse range of applications. Atlas includes features like transactional processing, text-based search, vector search, in-app analytics, and more through an elegant and integrated suite of data services. It offers developers a top-tier experience through a unified query interface, all while meeting the most demanding requirements for resilience, scalability, and cybersecurity. Atlas Vector Search Among the out-of-the-box features offered by MongoDB Atlas, Atlas Vector Search stands out, enabling the search of unstructured data effortlessly. You can generate vector embeddings with machine learning models like the ones found in OpenAI or Hugging Face, and store and index them in Atlas. This feature facilitates the indexing of vector representations of objects and retrieves those that are semantically most similar to your query. Explore the capabilities of Atlas Vector Search . This functionality is especially interesting for unstructured data that was previously hard to leverage, such as text, images, and audio, allowing searches that combine audio, video, metadata, production equipment data, or sensor measurements to provide an answer to a query. Let's delve into how simple it is to leverage AI to significantly enhance the sophistication of predictive maintenance models with MongoDB Atlas. Real-time audio-based diagnostics with Atlas Vector Search In our demonstration, we'll showcase real-time audio-based diagnostics applied to a wind turbine. It's important to note that while we focus on wind turbines here, the concept can be extrapolated to any machine, vehicle, or device emitting sound. To illustrate this concept, we'll utilize a handheld fan as our makeshift wind turbine. Wind turbines emit different sounds depending on their operational status. By continuously monitoring the turbine’s audio, our system can accurately specify the current operational status of the equipment and reduce the risk of unexpected breakdowns. Early detection of potential issues allows for enhanced operational efficiency, minimizing the time and resources required for manual inspections. Additionally, timely identification can prevent costly repairs and reduce overall turbine downtime, thus enhancing cost-effectiveness. Now, let’s have a look at how this demo works! Figure 3: Application Architecture Audio Preparation We begin by capturing the audio from the equipment in different situations (normal operation, high vs. low load, equipment obstructed, not operating, etc.). Once each sound is collected, we use an embedding model to process the audio data to convert it to a vector. This step is crucial because by generating embeddings for each audio track, which are high-dimensional vector representations, we are essentially capturing the unique characteristics of the sound. We then upload these vector embeddings to MongoDB Atlas. By adding just a few examples of sounds in our database, they are ready to be searched (and essentially compared) with the sound emitted by our equipment during its operation in real-time. Audio-based diagnosis Now, we put our equipment into normal operation and start capturing the sound it is making in real-time. In this demonstration, we capture one-second clips of audio. Then, with the same embedding model used before, we take our audio clips and convert them to vector embeddings in real-time. This process happens in milliseconds, allowing us to have real-time monitoring of the audio. The one-second audio clips, now converted to vector embeddings, are then sent to MongoDB Atlas Vector Search, which can search for and find the most similar vectors from the ones we previously recorded in our audio preparation phase. The result is given back with a percentage of similarity, enabling a very accurate prediction of the current status of the operation of the wind turbine. These steps are performed repeatedly every second, leveraging fast embedding of vectors and quick searches, allowing for real-time monitoring based on sound. Check out the video below to see it in action! Transforming Predictive Maintenance with AI and MongoDB Predictive maintenance offers substantial benefits but poses challenges like data integration and scalability. MongoDB stands out as a preferred database solution, offering scalability, flexibility, and real-time data processing. As technology advances, AI integration promises to further revolutionize the industry. Thank you to Ralph Johnson and Han Heloir for their valuable contributions to this demo! Ready to revolutionize your predictive maintenance strategy with AI and MongoDB Atlas Vector Search? Try it out yourself by following the simple steps outlined in our Github repo ! Explore how MongoDB empowers manufacturing operations by visiting these resources: Generative AI in Predictive Maintenance Applications Transforming Industries with MongoDB and AI: Manufacturing and Motion MongoDB for Automotive: Driving Innovation from Factory to Finish Line

May 28, 2024

Transforming Industries with MongoDB and AI: Manufacturing and Motion

This is the first in a six-part series focusing on critical AI use cases across several industries . The series covers the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries. The integration of artificial intelligence (AI) within the manufacturing and automotive industries has transformed the conventional value chain, presenting a spectrum of opportunities. Leveraging Industrial IoT, companies now collect extensive data from assets, paving the way for analytical insights and unlocking novel AI use cases, including enhanced inventory management and predictive maintenance. Inventory management Efficient supply chains can control operational costs and ensure on-time delivery to their customers. Inventory optimization and management is a key component in achieving these goals. Managing and optimizing inventory levels, planning for fluctuations in demand, and of course, cutting costs are all imperative goals. However, efficient inventory management for manufacturers presents complex data challenges too, primarily in forecasting demand accurately and optimizing stock levels. This is where AI can help. Figure 1: Gen AI-enabled demand forecasting with MongoDB Atlas AI algorithms can be used to analyze complex datasets to predict future demand for products or parts. Improvement in demand forecasting accuracy is crucial for maintaining optimal inventory levels. AI-based time series forecasting can assist in adapting to rapid changes in customer demand. Once the demand is known, AI can play a pivotal role in stock optimization. By analyzing historical sales data and market trends, manufacturers can determine the most efficient stock levels and even reduce human error. On top of all this existing potential, generative AI can help with generating synthetic inventory data and seasonally adjusted demand patterns. It can also help with creating scenarios to simulate supply chain disruptions. MongoDB Atlas makes this process simple. At the warehouse, the inventory can be scanned using a mobile device. This data is persisted in Atlas Device SDK and synced with Atlas using Device Sync, which is used by MongoDB customers like Grainger . Atlas Device Sync provides an offline-first seamless mobile experience for inventory tracking, making sure that inventory data is always accurate in Atlas. Once data is in Atlas, it can serve as the central repository for all inventory-related data. This repository becomes the source of data for inventory management AI applications, eliminating data silos and improving visibility into overall inventory levels and movements. Using Atlas Vector Search and generative AI, manufacturers can easily categorize products based on their seasonal attributes, cluster products with similar seasonal demand patterns, and provide context to the foundation model to improve the accuracy of synthetic inventory data generation. Predictive maintenance The most basic approach to maintenance today is reactive — assets are deliberately allowed to operate until failures actually occur. The assets are maintained as needed, making it challenging to anticipate repairs. Preventive maintenance, however, allows systems or components to be replaced based on a conservative schedule to prevent commonly occurring failures — although predictive maintenance is expensive to implement due to frequent replacement of parts before end-of-life. Figure 2: Audio-based anomaly detection with MongoDB Atlas. Scan the QR code to try it out yourself. AI offers a chance to efficiently implement predictive maintenance using data collected from IoT sensors on machinery trained to detect anomalies. ML/AI algorithms like regression models or decision trees are trained on the preprocessed data, deployed on-site for inference, and continuously analyzed sensor data. When anomalies are detected, alerts are generated to notify maintenance personnel, enabling proactive planning and execution of maintenance actions to minimize downtime and optimize equipment reliability and performance. A retrieval-augmented generation (RAG) architecture can be deployed to generate or curate the data preprocessor removing the need for specialized data science knowledge. The domain expert can provide the right prompts for the large language model. Once the maintenance alert is generated by an AI model, generative AI can come in again to suggest a repair strategy, taking spare parts inventory data, maintenance budget, and personal availability into consideration. Finally, the repair manuals can be vectorized and used to power a chatbot application that guides the technician in performing the actual repair. MongoDB documents are inherently flexible while allowing data governance when required. Since machine health prediction models require not just sensor data but also maintenance history and inventory data, the document model is a perfect fit to model such disparate data sources. During the maintenance and support process of a physical product, information such as product information and replacement parts documentation must be available and easily accessible to support staff. Full-text search capabilities provided by Atlas Search can be integrated with the support portal and help staff retrieve information from Atlas clusters with ease. Atlas Vector Search is a foundational element for effective and efficiently powered predictive maintenance models. Manufacturers can use MongoDB Atlas to explore ways of simplifying machine diagnostics. Audio files can be recorded from machines, which can then be vectorized and searched to retrieve similar cases. Once the cause is identified, they can use RAG to implement a chatbot interface that the technician can interact with and get context-aware, step-by-step guidance on how to perform the repair. Autonomous driving With the rise of connected vehicles, automotive manufacturers have been compelled to transform their business models into software-first organizations. The data generated by connected vehicles is used to create better driver assistance systems, paving the way for autonomous driving applications. However, it is challenging to create fully autonomous vehicles that can drive safer than humans. Some experts estimate that the technology to achieve level 5 autonomy is about 80% developed — but the remaining 20% will be extremely hard to achieve and will take a lot of time to perfect. Figure 3: MongoDB Atlas’s Role in Autonomous Driving AI-based image and object recognition in automotive applications face uncertainties, but manufacturers must utilize data from radar, LiDAR, cameras, and vehicle telemetry to improve AI model training. Modern vehicles act as data powerhouses, constantly gathering and processing information from onboard sensors and cameras, generating significant Big Data. Robust storage and analysis capabilities are essential to manage this data, while real-time analysis is crucial for making instantaneous decisions to ensure safe navigation. MongoDB can play a significant role in addressing these challenges. The document model is an excellent way to accommodate diverse data types such as sensor readings, telematics, maps, and model results. New fields to the documents can be added at run time, enabling the developers to easily add context to the raw telemetry data. MongoDB’s ability to handle large volumes of unstructured data makes it suitable for the constant influx of vehicle-generated information. Atlas Search provides a performant search engine to allow data scientists to iterate their perception AI models. Finally, Atlas Device Sync can be used to send configuration updates to the vehicle's advanced driving assistance system Other notable use cases AI plays a critical role in fulfilling the promise of Industry 4.0. Numerous other use cases of AI can be enabled by MongoDB Atlas, some of which include: Logistics Optimization: AI can help optimize routes resulting in reduced delays and enhanced efficiency in day-to-day delivery operations. Quality Control and Defect Detection: Computer or machine vision can be used to identify irregularities in the products as they are manufactured. This ensures that product standards are met with precision. Production Optimization: By analyzing time series data from sensors installed on production lines, waste can be identified and reduced, thereby improving throughput and efficiency. Smart After Sales Support: Manufacturers can utilize AI-driven chatbots and predictive analytics to offer proactive maintenance, troubleshooting, and personalized assistance to customers. Personalized Product Recommendations: AI can be used to analyze user behavior and preferences to deliver personalized product recommendations via a mobile or web app, enhancing customer satisfaction and driving sales. The integration of AI in manufacturing and automotive industries has revolutionized traditional processes, offering a plethora of opportunities for efficiency and innovation. With industrial IoT and advanced analytics, companies can now harness vast amounts of data to enhance inventory management and predictive maintenance. AI-driven demand forecasting ensures optimal stock levels, while predictive maintenance techniques minimize downtime and optimize equipment performance. Moreover, as automotive manufacturers work toward autonomous driving, AI-powered image recognition and real-time data analysis become paramount. MongoDB Atlas emerges as a pivotal solution, providing flexible document modeling and robust storage capabilities to handle the complexities of Industry 4.0. Beyond the manufacturing and automotive sectors, the potential of AI-enabled by MongoDB Atlas extends to logistics optimization, quality control, production efficiency, smart after-sales support, and personalized customer experiences, shaping the future of Industry 4.0 and beyond. Learn more about AI use cases for top industries in our new white paper, “ How Leading Industries are Transforming with AI and MongoDB Atlas .”

March 19, 2024

Integrate OPC UA With MongoDB - A Feasibility Study With Codelitt

Open Platform Communications Unified Architecture (OPC UA) is a widely recognized and important communication standard for Industry 4.0 and industrial IoT. It enables interoperability across different machines and equipment, ensuring reliable and secure information sharing within the Operational Technology (OT) layer. By providing a standard framework for communication, OPC UA enhances data integrity, security, and accessibility of data enabling many use cases for Industry 4.0. OPC UA focuses on standard data transmission and information modeling. It uses multiple data encoding methods such as binary or JavaScript Object Notation (JSON) and leverages different levels of security encryption to address security concerns. For information modeling, it adopts an object-oriented approach to abstract and model specific industrial assets such as robots, machines, and processes. Rich data models and object types can be created for a description of machine attributes and composition. Using OPC UA, the traditional context-less time-series machine data is transformed into a semantic-based information model. MongoDB's document model offers a straightforward and compelling approach for storing OPC UA semantic information models due to its flexibility and compatibility with complex data structures. The document model is a superset of all other types of data models, which makes it very popular in the developer community. OPC UA information models contain detailed relationships and hierarchies, making the dynamic schema of MongoDB a natural fit. Fields in the document are extensible at run time making dynamic updates and efficient querying a breeze. For example, consider an OPC UA information model representing an industrial robot. This model will encompass information about the robot's status, current task, operational parameters, and maintenance history. Example OPC UA information model for an Industrial Robot Robot RobotName (Variable) Status (Variable) CurrentTask (Variable) OperationalParameters (Object) MaxSpeed (Variable) PayloadCapacity (Variable) Reach (Variable) MaintenanceHistory (Array of Objects) Timestamp (Variable) Description (Variable) With MongoDB, this model can be easily represented in a document with nested fields. { "_id": ObjectId("654321ab12345abcd6789"), "RobotName": "Robot1", "Status": "Running", "CurrentTask": "Assembling Component ABC", "OperationalParameters": { "MaxSpeed": 80, // in cm/s "PayloadCapacity": 150, // in kg "Reach": 2.65 // in m }, "MaintenanceHistory": [ { "Timestamp": "2023-08-25T10:00:00", "Description": "Routine checkup" }, { "Timestamp": "2023-06-25T14:30:00", "Description": "Replaced worn-out gripper" } ] } This MongoDB document easily captures the complexities of the OPC UA information model. Hierarchical attributes in the model are maintained as objects and arrays can represent historical data and log files. As the robot runs during the production shift, the document can be easily updated with real-time status information. Instead of worrying about creating a complicated Entity Relationship diagram with SQL databases, MongoDB offers a superior alternative to represent digital shadows of industrial equipment. Now that we have seen how easy it is to model OPC UA data in MongoDB, let's talk about how to connect an OPC UA server to MongoDB. One of our partners, Codelitt is developing a connector that can ingest time-series OPC UA data into MongoDB in real time. Codelitt is a custom software strategy, engineering, and design company. The architecture of the end-to-end solution is shown in Figure 1. Figure 1: High-level architecture and data flow In Figure 1: Industrial equipment and controllers will transmit data to local servers using the OPC UA protocol. OPC UA servers will listen to these devices and broadcast them to all subscribed clients. Clients will listen to specific events/variables and queue the event to be stored. The message broker will provide the queue system to digest a large amount of data and provide reliability between the event source and the data storage. MongoDB Atlas will provide the final destination of data, and the ability to do analytics using the aggregation framework and visualization using Atlas Charts. Technical details It is assumed that the user already has machines that have OPC UA server enabled. For the OPC UA client, depending on the client's preferences, the Codelitt solution can switch between a custom-built OPC UA client based on the Node-OPCUA open source project, AWS IoT SiteWise Edge , or a Confluent-based OPC UA source connector . In the case of a custom-built client, it will connect to the machine's OPC UA server using OPC TCP and extract the necessary data that is then transmitted to a broker. The message broker could be any cloud-provided solution (Azure Event Hub, Amazon Kinesis, etc.) or any form of Kafka implementation from Confluence for example. In the case of Kafka, MongoDB Kafka connector can be leveraged to push data to the database. Finally, leveraging the aggregation framework , the operating parameters of each device are queried for visualization via MongoDB Atlas Charts . In summary, the MongoDB document model elegantly mirrors OPC UA information and there are multiple options available to users who would like to push data from their OPC UA servers to MongoDB. To learn more about MongoDB’s role in the manufacturing sector, please visit our manufacturing webpage . To learn more about how Codelitt is digitally transforming industries, please visit their website .

January 16, 2024

How MongoDB and Alibaba Cloud are Powering the Era of Autonomous Driving

The emergence of autonomous driving technologies is transforming how automotive manufacturers operate, with data taking center stage in this transformation. Manufacturers are now not only creators of physical products but also stewards of vast amounts of product and customer data. As vehicles transform into connected vehicles, automotive manufacturers are compelled to transform their business models into software-first organizations. The data generated by connected vehicles is used to create better driver assistance systems and paves the way for autonomous driving applications. It has to be noted that the journey toward autonomous vehicles is not just about building reliable vehicles but harnessing the power of connected vehicle data to create a new era of mobility that seamlessly integrates cutting-edge software with vehicle hardware. The ultimate goal of autonomous vehicle makers is to produce cars that are safer than human-driven vehicles. Since 2010, investors have poured over 200 billion dollars into autonomous vehicle technology. Even with this large amount of investment, it is very challenging to create fully autonomous vehicles that can drive safer than humans. Some experts estimate that the technology to achieve level 5 autonomy is about 80% developed but the last 20% will be extremely hard to achieve and will take a lot of time to perfect. Unusual events such as extreme weather, wildlife crossings, and highway construction are still enigmas for many automotive companies to solve. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. The answer to these challenges is not straightforward. AI-based image and object recognition still has a long way to go to deal with uncertainties on the road. However, one thing is certain, automotive manufacturers need to make use of data captured by radar, LiDAR, camera systems, and the whole telemetry system in the vehicle in order to train their AI models better. A modern vehicle is a data powerhouse. It constantly gathers and processes information from onboard sensors and cameras. The Big Data generated as a result presents a formidable challenge, requiring robust storage and analysis capabilities. Additionally, this time series data needs to be analyzed in real-time and decisions have to be made instantaneously in order to guarantee safe navigation. Furthermore, ensuring data privacy and security is also a hurdle to cross since self-driving vehicles need to be shielded from cyber attacks as such an attack can cause life-threatening events. The development of high-definition (HD) maps to help the vehicle ‘see’ what is on the road also poses technical challenges. Such maps are developed using a combination of different data sources such as Global Navigation Satellite Systems (GNSS), radar, IMUs, cameras, and LiDAR. Any error in any one of these systems aggregates and ultimately impacts the accuracy of the navigation. It is required to have a data platform in the middle of the data source (vehicle systems) and the AI platform to accommodate and consolidate this diverse information while keeping this data secure. The data platform should be able to preprocess this data as well as add additional context to it before using it to train or run the AI modules such as object detection, semantic segmentation, and path planning. MongoDB can play a significant role in addressing above mentioned data-related challenges posed by autonomous driving. The document model is an excellent way to accommodate diverse data types such as sensor readings, telematics, maps, and model results. New fields to the documents can be added at run time, enabling the developers to easily add context to the raw telemetry data. MongoDB’s ability to handle large volumes of unstructured data makes it suitable for the constant influx of vehicle-generated information. MongoDB is not only an excellent choice for data storage but also provides comprehensive data pre-processing capabilities through its aggregation framework. Its support for time series window functions allows data scientists to produce calculations over a sorted set of documents. Time series collections also dramatically reduce storage costs. Column compression significantly improves practical compression, reduces the data's overall storage on disk, and improves read performance. MongoDB offers robust security features such as role-based access control, encryption at rest and in transit, comprehensive auditing, field-level redaction and encryption, and down to the level of client-side field-level encryption that can help shield sensitive data from potential cyber threats while ensuring compliance with data protection regulations. For challenges related to effectively storing and querying HD maps, MongoDB’s geospatial features can aid in querying location-based data and also combining the information from maps with telemetry data fulfilling the continuous updates and accuracy requirements for mapping. Furthermore, MongoDB's horizontal scaling or sharding allows for the seamless expansion of storage and processing capabilities as the volume of data grows. This scalability is essential for handling the data streams generated by fleets of self-driving vehicles. During the research and development of autonomous driving projects, scalable infrastructure is required to quickly and steadily collect and process massive data. In such projects, data is generated at the terabyte level every day. To meet these needs, Alibaba Cloud provides a solution that integrates data collection, transmission, storage, and computing. In this solution, the data collected daily by sensors can be simulated and collected using Alibaba Cloud Lightning Cube and sent to the Object Storage Service (OSS) . Context is added to this data using a translator and then this contextualized information can be pushed to MongoDB to train models. MongoDB and Alibaba Cloud recently announced a four-year extension to their strategic global partnership that has seen significant growth since being announced in 2019. Through this partnership, automotive manufacturers can easily set up and use MongoDB-as-a-service-ApsaraDB for MongoDB from Alibaba Cloud’s data centers globally. Figure 1: Data collection and model training data link with MongoDB on Alibaba Cloud. When the vehicle is on the road, the telemetry data is captured through an MQTT gateway, converted into Kafka, and then pushed into MongoDB for data storage and archiving. This data can be used for various applications such as real-time status updates for engine and battery, accident analysis, and regulatory reporting. Figure 2: Mass Production vehicles data link with MongoDB on Alibaba Cloud For a company that is looking to build autonomous driving assistance systems, Alibaba Cloud and ApsaraDB for MongoDB is an excellent technology partner to have. ApsaraDB for MongoDB can handle TBs of diverse sensor data from cars on a daily basis, which doesn't conform to a fixed format. MongoDB provides reliable and highly available data storage for this heterogenous data enabling companies to rapidly expand their system within minutes resulting in time savings when processing and integrating autonomous driving data. By leveraging Alibaba Cloud's ApsaraDB for MongoDB, the R&D team can focus on innovation rather than worrying about data storage and scalability, contributing to faster innovation in the field of autonomous driving. In summary, MongoDB's flexibility, versatility, scalability, real-time capabilities, and strong security framework make it well-suited to address the multifaceted data requirements and challenges that autonomous driving presents. By efficiently managing and analyzing the Big Data generated, MongoDB and Alibaba Cloud are paving the path toward reliable and safe self-driving technology. To learn more about MongoDB’s role in the automotive industry, please visit our manufacturing and automotive webpage .

September 11, 2023

Empowering Automotive Developers for the Road Ahead

MongoDB 7.0 is here, and companies across industries are benefiting from being early adopters of cutting-edge data platform technology. Let’s take a closer look at the automotive industry specifically, and how many of MongoDB’s new features and capabilities can revolutionize the way automotive developers build, iterate, and scale their applications. In the fast-changing automotive landscape, development teams face the challenge of delivering compelling user experiences faster and smarter than ever before. MongoDB's developer data platform becomes a vital tool for developers striving to innovate quickly and efficiently, supporting a wide range of application use cases while streamlining development and ensuring optimal performance. MongoDB Atlas Stream Processing MongoDB Atlas Stream Processing , coming soon in private preview, will be a game-changing advantage for the automotive industry, offering real-time data insights and rapid responses to critical events. As vehicles generate an ever-increasing stream of sensor data, this capability enables automotive developers to process, analyze, and act upon data in real-time. Manufacturers and fleet management companies can monitor vehicle health, track performance, and optimize maintenance schedules on the fly, while proactive safety measures and anomaly detection ensure utmost safety for drivers and passengers. Moreover, MongoDB Atlas Stream Processing enables developers to unlock the potential of connected car applications, making real-time data processing imperative for intelligent navigation, personalized infotainment services, and efficient route planning . MongoDB Atlas Vector Search MongoDB Atlas Vector Search , currently in public preview, holds immense potential for revolutionizing the automotive industry. By utilizing vector representations of unstructured data such as audio, images, and text, MongoDB Atlas enables developers to store, index, and query data based on similarities in high-dimensional vector spaces alongside operational data. For the automotive industry, this means unlocking a world of possibilities in data analysis, anomaly detection, and predictive maintenance. In fact, as mentioned in the MongoDB.local Chicago keynote , a top 10 auto manufacturer leveraged Vector Search to enable engine diagnostics based on engine audio. Watch the video below to learn more. Atlas Vector Search empowers automotive developers to create smarter, data-driven applications that deliver more relevant and accurate insights, ultimately enhancing the driving experience and safety for all. MongoDB Atlas Vector Search allows manufacturers to query and qualify possible equipment and product failure causes and get AI-generated recommendations on how to adjust operational parameters and extend the life of their equipment and products. The automotive industry thrives on innovation and efficiency, and Atlas Vector Search opens new avenues for optimizing vehicle performance, predicting maintenance needs, and enhancing overall user experiences on the road. MongoDB Relational Migrator In the ever-evolving automotive industry, legacy relational databases often pose challenges in scalability, flexibility, and performance. Relational databases are very prevalent in the manufacturing industry and hinder innovation due to rigid data models and limited scalability. MongoDB Relational Migrator addresses these pain points by assisting with several critical steps in the path to modernization for automotive developers. By migrating data from common relational databases to MongoDB, automotive companies can break free from the limitations of legacy systems and embrace the full potential of a NoSQL database . This migration process streamlines data transfer, offers valuable data modeling recommendations, and empowers developers to refactor applications quickly and efficiently. Embracing MongoDB's flexible document data model optimizes performance, scales applications effortlessly, and unlocks the potential for real-time analytics, enabling the industry to stay ahead in the race for innovation. MongoDB Relational Migrator becomes a catalyst for driving transformative change in the automotive sector, enabling faster and more efficient data processing for mission-critical applications and paving the way for sophisticated AI-driven solutions. As automotive companies embrace data-driven insights and strive to deliver unparalleled user experiences, MongoDB Relational Migrator empowers the industry to leverage the full potential of NoSQL databases, enabling automotive applications to zoom ahead in the fast lane of innovation. MongoDB 7.0 promises to be a game-changer for developers across industries , empowering them to build innovative, scalable, and secure applications that drive the future. With the power of MongoDB, developers can accelerate their journey toward automotive innovation and build the vehicles and experiences of tomorrow. Watch the full MongoDB.local lineup to learn more .

August 22, 2023

Real-Time Energy Monitoring for Smart Buildings with MongoDB and HiveMQ

The Internet of Things (IoT) has ushered in a new era of energy efficiency, enabling the deployment of energy-efficient sensors for energy conservation and resource utilization. With over 1.5 billion connected IoT devices already installed in commercial smart buildings in 2022 and a projected surge to 3.25 billion devices by 2028, the volume of data generated is staggering. To put it in perspective, an average home in 2020 would generate approximately 4.7 terabytes of data annually. However, managing and harnessing this immense amount of real-time streaming data poses a significant challenge for developers. In smart buildings, where a multitude of IoT sensors continuously gather event streaming data, developers often grapple with integrating disparate technologies and investing significant time into data streaming integration. In this blog, we present a simple yet powerful solution to this challenge. We will demonstrate how you can effortlessly move IoT data using standard protocols such as MQTT to MongoDB Atlas using the HiveMQ Enterprise Extension for MongoDB . By optimizing smart buildings with real-time energy monitoring through the seamless integration of MongoDB and HiveMQ, we unlock the potential for efficient energy management and a sustainable future. Let’s get started! Dream team: HiveMQ + MongoDB In a world where energy conservation and efficient resource utilization are essential, let’s go through Figure 1 and see how simple it is to use MongoDB, HiveMQ’s MQTT broker, and the Enterprise Extension for MongoDB to enable real-time energy monitoring for smart building. Figure 1: Combining HiveMQ and MongoDB process data in real-time Step 1: Data transmission Using MQTT-based IoT devices deployed throughout the building, electricity consumption, temperature, and occupancy data is collected and sent to the HiveMQ MQTT broker. The MQTT broker acts as a central hub, efficiently and securely handling the communication between devices and backend systems. The HiveMQ MQTT broker also ensures reliable message delivery. It also provides MQTT-specific features like quality of service, session management, and topic-based message routing. Step 2: Data ingestion The HiveMQ MongoDB extension seamlessly integrates with MongoDB, allowing for persistent storage of the MQTT data in a highly scalable and flexible manner. The fully customizable templating system allows MQTT data to be stored according to the building’s specific operational requirements. MongoDB's document-based model accommodates the varying data formats and structures generated by different IoT devices. Step 3: Data visualization and analytics Once the MQTT data is securely stored in MongoDB, using its powerful in-app analytics, building managers can gain deep insights into energy consumption patterns, identify anomalies, and optimize energy usage. By leveraging MongoDB's rich query support and aggregation framework, building managers can make data-driven decisions promptly, reducing costs and enhancing sustainability. In cases where data needs to be exported to an ML/AI engine, MongoDB Spark and Kafka connectors can be used. Users of MongoDB Atlas can leverage Atlas Device Sync and Realm to send real-time alerts and messages to mobile devices. Data can be visualized using MongoDB Atlas Charts or through a third-party Business Intelligence (BI) tool connected via MongoDB BI connector or Atlas SQL interface. Conclusion By seamlessly integrating HiveMQ's MQTT broker with MongoDB, developers can efficiently handle data transmission, ingestion, and storage. This integration enables building managers to gain valuable insights into energy consumption patterns, make data-driven decisions, and optimize energy usage. To learn more about MongoDB’s role in IoT, please visit our IoT webpage . You can also try the HiveMQ platform now with the Enterprise Extension for MongoDB for free . Thank you Ainhoa Múgica for her contributions to this blog.

July 11, 2023

Modernize Your Factory Operations: Build a Virtual Factory with MongoDB Atlas in 5 Simple Steps

Virtual factories are revolutionizing the manufacturing landscape. Coined as the "Revolution in factory planning" by BMW Group at NVIDIA, this cutting-edge approach is transforming the way companies like BMW and Hyundai operate, thanks to groundbreaking partnerships with technology companies such as NVIDIA and Unity. At the heart of this revolution lies the concept of virtual factories , computer-based replicas of real-world manufacturing facilities. These virtual factories accurately mimic the characteristics and intricacies of physical factories, making them a powerful tool for manufacturers to optimize their operations. By leveraging AI, they unlock a whole new world of possibilities, revolutionizing the manufacturing landscape, paving the way for improved productivity, cost savings, and innovation. In this blog we will explore the benefits of virtual factories and guide you through the process of building your own virtual factory using MongoDB Atlas. Let’s dive in! Unlocking digital transformation The digitalization of the manufacturing industry has given rise to the development of smart factories. These advanced factories incorporate IoT sensors into their machinery and equipment, allowing workers to gather data-driven insights on their manufacturing processes. However, the evolution does not stop at smart factories automating and optimizing physical production. The emergence of virtual factories introduces simulation capabilities and remote monitoring, leading to the creation of factory digital twins, as depicted in Figure 1. By bridging the concepts of smart and virtual factories, manufacturers can unlock greater levels of efficiency, productivity, flexibility, and innovation. Figure 1:  From smart factory to virtual factory Leveraging virtual factories in manufacturing organizations provides many benefits including: Optimization of production processes and identification of inefficiencies. This can lead to increased efficiency, reduced waste, and improved quality. Aiding quality control by contextualizing sensor data with the manufacturing process. This allows analysis of quality issues and implementation of necessary control measures while dealing with complex production processes. Simulating manufacturing processes and testing new products or ideas without the need for physical prototypes or real-world production facilities. This significantly reduces costs associated with research and development and minimizes the risk of product failure. However, setting up a virtual factory for complex manufacturing is difficult. Challenges include managing system overload, handling vast amounts of data from physical factories, and creating accurate visualizations. The virtual factory must also adapt to changes in the physical factory over time. Given these challenges, having a data platform that can contextualize all the data coming in from the physical factory and then feed that to the virtual factory and vice versa is crucial. And that is where MongoDB Atlas , our developer data platform, comes in, providing synchronization capabilities between physical and virtual worlds, enabling flexible data modeling and providing access to the data via a unified query interface as seen in Figure 2. Figure 2:  MongoDB Atlas as the Data Platform between physical and virtual Factories Now that we’ve discussed the benefits and the challenges of building virtual factories, let’s unpack how simple it is to build a virtual factory with MongoDB Atlas. How to build a virtual factory MongoDB Atlas 1. Define the business requirements The first step of the process is to define the business requirements for the virtual factory. Our team at MongoDB uses a smart factory model from Fischertechnik to demonstrate how easily MongoDB can be integrated to solve the digital transformation challenges of IIoT in manufacturing. This testbed serves as our foundational physical factory and the starting point of this project. Figure 3:  The smart factory testbed We defined our set of business requirements as the following: Implement a virtual run of the physical factory to identify layout and process optimizations. Provide real-time visibility of the physical factory conditions such as inventory for process improvements. This last requirement is critical; while standalone simulation models of factories can be useful, they typically do not take into account the real-time data from the physical factory. By connecting the physical and virtual factories, a digital twin can be created that takes into account the actual performance of the physical factory in real-time. This enables more accurate predictions of the factory's performance, which improves decision-making, process optimization, and enables remote monitoring and control, reducing downtime and improving response times. 2. Create a 3D model Based on the previous business requirements, we created a 3D-model of the factory in a widely used game engine, Unity . This virtual model can be visualized using a computer, tablet or any virtual reality headset. Figure 4:  3D-model of the smart factory in Unity Additionally, we also added four different buttons (red, white, blue, and “stop”) which enables users to submit production orders to the physical factory or stop the process altogether. 3. Connect the physical and virtual factories Once we created the 3D model, we connected the physical and virtual factories via MongoDB Atlas. Let’s start with our virtual factory software application. Regardless of where you deploy it, be it a headset or a tablet, you can use Realm by MongoDB to present data locally inside Unity and then synchronize it with MongoDB Atlas as the central data layer. Allowing us to have embedded databases where there's resource constrainment and MongoDB Atlas as a powerful and scalable cloud backend technology. And lastly, to ensure data synchronization and communication between these two components, we leveraged MongoDB Atlas Device Sync , providing bi-directional synchronization mechanism and network handling. Now that we have our virtual factory set-up, let’s have a look at our physical one. In a real manufacturing environment, many of the shopfloor connectivity systems can connect to MongoDB Atlas and for those who don't natively, it is very straightforward to build a connector. At the shopfloor layer you can have MongoDB set up so that you can analyze and visualize your data locally and set up materialized views. On the cloud layer, you can push data directly to MongoDB Atlas or use our Cluster-to-Cluster Sync functionality. A single IoT device, by itself, does not generate much data. But as the amount of devices grows, so does the volume of machine-generated data and therefore the complexity of the data storage architecture required to support it. The data storage layer is often one of the primary causes of performance problems as an application scales. A well-designed data storage architecture is a crucial component in any IoT platform. In our project, we have integrated AWS IoT Core to subscribe to MQTT messages from the physical factory. Once these messages are received and filtered, they are transmitted to MongoDB Atlas via an HTTP endpoint. The HTTP endpoint then triggers a function which stores the messages in the corresponding collection based on their source (e.g., messages from the camera are stored in the camera collection). With MongoDB Atlas, as your data grows you can archive it using our Atlas Online Archive functionality. Figure 5:  Virtual and physical factories data flow In figure 5, we can see everything we’ve put together so far, on the left hand side we have our virtual factory where users can place an order. The order information is stored inside Realm, synced with MongoDB Atlas using Atlas Device Sync and sent to the physical factory using Atlas Triggers . On the other hand, the physical factory sends out sensor data and event information about the physical movement of items within the factory. MongoDB Atlas provides the full data platform experience for connecting both physical and virtual worlds! 4. Data modeling Now that the connectivity has been established, let's look at modeling this data that is coming in. As you may know, any piece of data that can be represented in JSON can be natively stored in and easily retrieved from MongoDB. The MongoDB drivers take care of converting the data to BSON (binary JSON) and back when querying the database. Furthermore, you can use documents to model data in any way you need, whether it is key value pairs, time series data or event data. On the topic of time series data, MongoDB Time Series allows you to automatically store time series data in a highly optimized and compressed format, reducing customer storage footprint, as well as achieving greater query performance at scale. Figure 5:  Virtual and physical factories sample data It really is as simple as it looks, and the best part is that we are doing all of this inside MongoDB Atlas making a direct impact on developer productivity. 5. Enable computer vision for real-time inventory Once we have the data modeled and connectivity established, our last step is to run event-driven analytics on top of our developer data platform. We used computer vision and AI to analyze the inventory status in the physical factory and then pushed notifications to the virtual one. If the user tries to order a piece in the virtual factory that is not in stock, they will immediately get a notification from the physical factory. All this is made possible using MongoDB Atlas and its connectors to various AI platforms If you want to learn more, stay tuned for part 2 of this blog series where we’ll dive deep into the technical considerations of this last step. Conclusion By investing in a virtual factory, companies can optimize production processes, strengthen quality control, and perform cost-effective testing, ultimately improving efficiency and innovation in manufacturing operations. MongoDB, with its comprehensive features and functionality that cover the entire lifecycle of manufacturing data, is well-positioned to implement virtual factory capabilities for the manufacturing industry. These capabilities allow MongoDB to be in a unique position to fast-track the digital transformation journey of manufacturers. Learn more: MongoDB & IIoT: A 4-Step Data Integration Manufacturing at Scale: MongoDB & IIoT Manufacturing with MongoDB Thank you to Karolina Ruiz Rojelj for her contributions to this post.

June 20, 2023

Building an Industrial Unified Namespace Architecture with MongoDB and Arcstone

The fourth industrial revolution, also known as Industry 4.0 is rapidly transforming the manufacturing industry. Leveraging I4.0 reference architectures and Industrial IoT technologies, factories generate more data than ever. Market analyst reports tell us that the global number of Industrial IoT connections will increase to 36.8 billion in 2025. As factories become more connected and data-driven, it is essential to have a unified and standardized approach for manufacturing data management. In this article, we explain how MongoDB helps create a Industrial Unified Namespace (IUN) architecture that can act as a contextualized repository for data and information for all manufacturing assets. Manufacturing companies have been leveraging the International Society of Automation’s standard 95 (ISA-95) to develop automated interfaces between industrial control systems and enterprise systems. ISA-95 provides a hierarchical model for interfacing and integration also known as the automation pyramid. Figure 1 shows the five levels of the automation pyramid. Figure 1: ISA-95 Automation Pyramid. ISA-95 was introduced in 2000 to improve communication and data exchange between different levels of the manufacturing industry. With the advent of Industrial IoT (IIoT), the limitations of the ISA-95 model have become increasingly apparent. Lack of Interoperability: The model was developed for a more traditional, hierarchical approach to manufacturing, where there is a clear separation between operational technology (OT) and information technology (IT). In contrast, IIoT tries to blur the lines between OT and IT, with a greater emphasis on data interoperability and real-time analytics. Limited Flexibility and Agility: The rigid and hierarchical structure imposed by the automation pyramid goes against Industry 4.0 concepts of flexibility and agility. The data captured by sensors must go through the SCADA and MES layers to reach the top level. This makes it difficult for manufacturers to adapt to changing production requirements and integrate IIoT technology into their existing systems. Limited Scalability: The ISA-95 model was designed for a traditional manufacturing environment with a limited number of production lines and machines. However, with the growth of Industry 4.0, the number of connected devices and the amount of data generated has increased dramatically. The automated pyramid does not easily scale to handle this increased volume of data and devices, leading to potential bottlenecks and inefficiencies in the manufacturing process. For example, if a new machine is added to the production line, ISA-95 requires significant changes to the factory IT and OT architecture, which can be time-consuming and costly. Industrial unified namespace (IUN) architecture with MongoDB In order to overcome these challenges, we propose that manufacturers adopt an Industrial Unified Namespace (IUN) architecture leveraging MongoDB technology. Such an architecture will provide a single view of all manufacturing processes and equipment and will enable data interoperability between different layers of the ISA-95 automation pyramid. Figure 2 shows a conceptual diagram of the IUN architecture. Figure 2: Event driven industrial unified namespace IUN follows an event-driven architecture topology where different manufacturing applications publish events in real-time (publishers) to the central MongoDB Atlas database. Application services subscribe asynchronously to the event types or topics of interest and consume them at their own speed (consumers). This results in a decoupled ecosystem allowing applications and services to act interchangeably to provide and consume data when and where needed in real-time. It is understood that many applications and services may produce and consume data at the same time. MongoDB Atlas database plays a central role in the IUN architecture. The events can flow in through MongoDB Kafka Connector or Atlas Device Sync and MongoDB Atlas can aggregate, persist and serve them to consuming manufacturing applications. The core MongoDB Atlas database in this scenario provides a central repository for multiple independent event streams and the developer data platform helps to drive operational and analytical apps providing a complete end-to-end view of the production process. Data modeling for industrial unified namespace The document model is the most natural way to work with data stored in the database. It is simple for any developer to learn how to code against MongoDB, and as a result, industry surveys show it is wildly popular amongst developers. MongoDB provides flexible data modeling options to create a central repository for all factory production data. Asset-centric data model: Focusses on the assets, for example machines, equipment, tools in the manufacturing process. This data model is useful for tracking the performance, maintenance, and utilization of assets. Process-centric data model: Focuses on the day to day production processes. Such a data model is useful in optimizing the process flow and reducing bottlenecks. Product-centric data model: Focuses on the products produced in the manufacturing process. This data model is useful for tracking the production and quality of individual products. It is possible for a factory to have all three models at the same time. In fact, it is common for factories to use multiple data models and integrate them as needed to gain a complete view of their operations. For example, a factory may use an asset-centric model to track its equipment, and a product-centric model to track its finished goods, while also using a process-centric model to optimize its manufacturing processes. Let us take an example of a bicycle factory and look at example asset, process and product-centric data models. At a minimum, the following collections (Figure 3) will need to be created in the database. Figure 3: MongoDB collections for different IUN data models Each collection will have data coming from different sources such as Manufacturing Execution System (MES), IIoT Platform, and Enterprise Resource Planning (ERP) systems. An example document from the production equipment collection is shown in Figure 4. As it can be seen, the data comes from various sources and the MongoDB document model makes it very easy to combine this data together in one document generating a digital twin prototype of the machine. Figure: A sample document from the Production Equipment collection Architecture for industrial unified namespace Let us take our bicycle factory and create a solution architecture for the Industrial Unified Namespace. First, let us list down all the event producers and consumers. All these systems both consume and publish events: IoT Gateways / Edge Server Supervisory Control and Data Acquisition (SCADA) / Shop Floor Connectivity Platform (SCP) Manufacturing Execution System (MES) Enterprise Resource Planning (ERP) Arcstone toolsets for smart manufacturing Arcstone is a Singapore/US-based Industry 4.0 solutions company providing modular-based, next-generation MES alongside hardware integration and process orchestration toolsets. Arcstone delivers success to companies from diverse industries, including Global Fortune 500 companies, manufacturing companies, emerging facility management firms, and SMEs, globally. Arcstone arc.ops MES contains 15+ modules for full operational management that can be custom tailored to specific requirements, and is built to be end-user configurable for easy intuitive use. Arcstone understands that extracting data from legacy equipment is a challenging task. Therefore, they have created a low-code solution named arc.quire to handle the collection of raw data and streaming into a database for storage. arc.quire is used in tandem with a process orchestration tool called arc.flow to establish connectivity between arc.quire and the database, for example, MongoDB EA. Depending on the connectivity interface exposed by the production equipment, SCADA or SCP software can connect to the equipment and push the raw events and alerts to the arc.quire running in the edge server. MongoDB’s Enterprise Operator for Kubernetes , gives the flexibility to run MongoDB as a container in resource-constrained environments such as our IoT edge server. Figure 5 shows how the edge server can be connected with the SCADA and IoT gateways on the production shop floor. Figure 5: Edge Server with MongoDB and Arcstone toolsets The edge server performs the following functions: Aggregation of IIoT events and alerts via arc.quire Real-time analytics such as machine fault detection, process optimization, and process control via the MongoDB aggregation framework Transmitting control instructions back to the equipment via arc.quire Raw data and analytical results storage in MongoDB Edge servers act as one of the event producers for IUN. Using the MongoDB Kafka connector, events can be transmitted from the edge server to a centralized data repository in MongoDB Atlas. Figure 6: MongoDB can serve as both a Sink and a Source for Apache Kafka Bringing it all together Figure 7 shows the complete technical architecture of the Industrial Unified Namespace with MongoDB Atlas Developer Data Platform and Arcstone. Figure 7: In this architecture, arc.ops MES, ERP, and edge server publish data to the message stream in Apache Kafka where the event queue makes the data available for MongoDB Atlas to consume via Kafka connectors [1 and 2]. Depending on the factory requirements around batch processing and scalability, Kafka can be replaced by a MQTT broker. There are multiple community backed and commercial libraries to push MQTT data into MongoDB. The centralized database aggregates and persists events, enriches event streams with data from all sources, including historical data, and provides a central repository for multiple event streams. This enables applications and users to benefit from all data across all microservices and provides a unified view of the state across the factory. Atlas also leverages Atlas Charts for events visualization as well as Atlas Search for full-text search of events [3 and 4]. MongoDB’s Atlas Triggers provide a serverless way of consuming change stream events [5]. With Triggers, the manufacturer doesn't have to set up their own application server to run your change data capture process. Change streams flow change data to Atlas Triggers to create responsive, event-driven pipelines. Finally, Atlas Device Sync and Realm SDK can be leveraged to push real-time notifications and alerts to shop floor applications for users to consume. Use cases Predictive maintenance IUN can be deployed as the foundation for predictive maintenance applications. Edge server streams time-series event data from the production equipment into MongoDB to drive machine-learning models that will detect equipment health and performance degradation trends. The data is enriched using data streams about production jobs from MES. The factory can either repair equipment or swap it out for replacement parts before shutting down production lines. Atlas Device Sync can alert engineers on the shop floor to potential equipment failures, and help the company optimize the equipment maintenance strategy. Operational data layer The IUN architecture can be used to create a manufacturing Operational Data Layer (ODL). An ODL strives to centrally integrate and organize all siloed manufacturing IT/OT data and makes it accessible to stakeholders across the factory floor. This ODL will combine data from both OT and IT sources into a single MongoDB Atlas database where Atlas Search and Charts can be used to analyze this data and drive actions on the shop floor. IUN captures any changes in source systems and streams them into MongoDB to keep the ODL fresh, and helps to update the source systems in real-time. Conclusion In conclusion, the ISA95 Automation Pyramid presents significant challenges for the manufacturing industry, including a lack of flexibility, limited scalability, and difficulty integrating new technologies. By adopting an Industrial Unified Namespace architecture with Arcstone and MongoDB, manufacturers can overcome these challenges and achieve real-time visibility and control over their operations, leading to increased efficiency and improved business outcomes. Thank you to Karolina Ruiz Rogelj for her contributions to this post. To learn more about MongoDB’s role in the manufacturing industry, please visit our Manufacturing and Industrial IoT page.

April 27, 2023

Connected Devices - How GE HealthCare Uses MongoDB to Manage IoT Device Lifecycle

GE HealthCare, a global leader in medical technology, has turned to MongoDB to manage the lifecycle of its IoT devices, from deployment (Beginning of Life or BoL) to retirement (End of Life or EoL). At GE HealthCare, MongoDB Atlas is used to persist device and customer data. These related data layers are utilized by the organization to develop customer experience strategies by providing greater efficiency, improving patient outcomes, and increasing access to care. The MongoDB document model easily combines data from diverse source systems while preserving its full fidelity. This flexibility allows seamless onboarding of new customers and related data sources without requiring time consuming schema modifications. According to Emir Biser, Senior Data Architect at GE HealthCare, MongoDB Atlas is very appealing to the team because of its effective management, built-in monitoring and backup, global vertical and horizontal scalability, built-in security, and multi-cloud support. MongoDB Atlas is a gamechanger. This technology stack is helping us streamline commercialization and bring market-ready solutions to deliver advanced healthcare. Some of the recent tests resulted in an *83% decrease in retrieval time for critical data elements. When all these features are put together, the tech stack is designed to help healthcare providers enhance productivity by reducing the complexity and time required to manage databases, enabling faster deployment of IoT devices. Enhancing the IoT life cycle with MongoDB GE HealthCare’s tech-stack is designed to accelerate the integration of healthcare applications by connecting IoT devices together with additional data sources into an aggregated clinical data layer. As the IoT device connections are established, multiple services are applied on the platform to support analytic and clinical applications. Beginning of life - Device provisioning and configuration As the device is being manufactured, the device parameters such as MAC and serial number are stored in MongoDB as a device digital representation. When the device is turned on, the GEHC team gets information about the device usage and the customer information. This information is used to validate the device. MongoDB is playing a crucial role in device provisioning by persisting the configuration information and making sure that the device is set up with the right configuration parameters. MongoDB change streams are used at this stage to make sure that the device gets the right parameters at the BoL stage. Middle of life - Device usage and maintenance Once the device comes online, it transmits both clinical and non-clinical information. The team at GE HealthCare uses MongoDB Atlas to help ensure clear separation between clinical and non-clinical as permissions, sensitivity, and access differs. Additionally, to understand how the device is being used compared to its standard configuration parameters. MongoDB’s real-time analytics capabilities help track key device performance metrics, such as battery life and identify trends and patterns in device usage. This enables the team to proactively address device issues, improve overall device performance and reliability for customers. GEHC is able to share these insights with customers to help optimize use of devices within their enterprise. MongoDB Atlas Search is used to retrieve information about status of connected devices and usage patterns. Search Compound Geo JSON queries are used to look at products in a certain geographic region. Horizontal scalability with automatic sharding across clusters ensures Edison applications can continue to be cost effective while delivering real-time results. MongoDB’s security features, including authorization, authentication and encryption, work with GEHC processes to enable teams working to protect device data from unauthorized access. End of life - Device decommissioning and archiving When the IoT device reaches the end of its lifecycle, GE HealthCare needs to decommission it and ensure that any data associated with the device is securely archived. By using MongoDB’s TTL (time-to-live) collections feature, the team automates the process of data deletion, reducing the data footprint. In addition, Atlas Online Archive helps to ensure that the data is always backed up and securely archived, reducing the risk of data loss and corruption. The authentication and authorization mechanisms help to ensure that decommissioned devices data can only be accessed by authorized personnel. The future of GE HealthCare According to Emir, the teams using MongoDB Atlas are excited about the benefits it brings, and they are looking forward to exciting new developments in Atlas platform. We are helping teams achieve business goals across Imaging, Ultrasound Digital Solutions, and Patient Care Solutions. Our current strategy focuses on building solid pipelines to further help our medical device engineering teams deliver interoperability resulting in better care for our customers. More on managing massive IoT devices Internet of Things (IoT) is transforming the healthcare industry by providing real-time, actionable insights that improve patient outcomes and drive operational efficiencies. According to market analyst reports , the global IoT healthcare market is projected to reach around USD 446.52 billion by 2028 while exhibiting a CAGR of 25.9% between 2021 and 2028. In hospitals, IoT-enabled medical devices help improve patient safety and clinical experience by transmitting real-time monitoring and alerts in the event of device malfunctions or irregularities. The life of an IoT device can be divided into three main stages: Beginning of Life (BoL), Middle of Life (MoL) and End of Life (EoL). During the BoL stage, the key activities are deployment design and provisioning. In this stage the device may be pre loaded with default credentials and configuration files. Once the device is installed and comes online, the focus in the MoL is to maintain its basic functional purpose as well as regularly updating firmware for reliability and security purposes. Over time, as new versions of devices are manufactured, the deployed devices need to be decommissioned by revoking the device certificate, archiving device data and disabling the model of device in the cloud as part of the EoL stage. Figure 1: Three stages of IoT device lifecycle management In each of the stages, the device has to be maintained to stay reliable, efficient, persistent and secure. Setting up telemetry from device to cloud/back end is just the tip of the iceberg. As the number of IoT devices deployed in healthcare continues to grow, so does the challenge of managing them efficiently. The large amount of data generated creates scalability challenges for IoT device management systems, which need to be able to handle large amounts of data and support the increased traffic. Different communication protocols make it challenging to integrate these devices into a unified system. Maintaining standard communication protocols and interoperability is critical to ensure seamless communication between devices and cloud backend. Finally, with the increasing number of cyber-attacks targeting IoT devices, it is critical to have robust security measures in place to protect against threats. To learn more about GEHC digital offerings please visit https://apps.gehealthcare.com/ Test performed internally by GE HealthCare on company datasets and may not be replicable. To learn more about MongoDB’s role in the healthcare and manufacturing industry, please visit our Manufacturing and Industrial IoT and Healthcare pages.

April 25, 2023

How MongoDB, A*STAR, and Industry Partners are Collaborating on Singapore’s Supply Chain 4.0 Initiative

Greater uncertainty in global trade flows and black swan events, such as COVID-19, have challenged the linear supply chain business model. Digital technologies are being recognized as a key enabler for resilient and responsive supply chains. Supply Chain 4.0 is the reorganization of supply chain – plan, source, make, deliver, return and enable from a linear business model to an integrated one using concepts of Industry 4.0 (I4.0). In this article, we’ll explore how MongoDB, together with our industry partners in Singapore, help businesses integrate technological innovations into their operations to deal with diverse challenges posed by growing supply chains. Supply chain trends and challenges In today’s world of uncertainties and disruptions, manufacturing supply chains are becoming increasingly complicated and opaque. This is happening alongside organic supply chain evolution involving digitalisation, unified ecommerce and sustainability awareness. Disruptions are costly to deal with, often requiring manufacturers to expend large amounts of (and sometimes evitable) resources correcting them. Many companies were unprepared for the shockwaves from COVID-19 global crisis and realized that they should not take the supply chain for granted and they must invest in digitizing their supply chain operations. In recent years, the investment in digital technologies for supply chain planning and execution has increased considerably. The emergence of Cloud Computing and the Industrial Internet of Things (IIoT) has promoted new opportunities for the supply chain and logistics domain. For example, real-time, cloud-based logistics and transport management systems have made logistics services more responsive and efficient, especially for small and medium-sized companies. However, supply chain management is much more complex than just logistics tracking. A reliable Supply Chain 4.0 platform should have some of these capabilities: End to End Visibility: Aggregates data from various systems supporting supply chain planning and execution processes (e.g ERP, MES, WMS, TMS) and provides a single view for monitoring supply chain performance in real-time. Decision Making Support: Contains tools and algorithms to support decision making for operations such as production scheduling, inventory assignment and order fulfillment optimization etc. Disruption Prediction and Management: To be able to predict anomalies and respond in time upon major disruptions events by orchestrating tools for network simulation, production re-planning etc. There are certain challenges associated with building a Supply Chain 4.0 platform that can enable above mentioned capabilities. Data Collection and Privacy Challenges: Brand owners need the ability to track products, raw materials and goods across the supply chain to get a clear picture of inventory and supply chain overall health. They can use this data to predict and manage supply chain disruptions and risks. Building this ability is a daunting task as it requires sharing data between supply chain tiers while navigating the data privacy and security risks to get real-time global visibility across all supply chain nodes. A federated infrastructure might be the answer where the private raw data is kept locally and transformed data is synced with the cloud. Data Modelling and Compatibility Challenges: A supply chain 4.0 platform must cater for the integration of the huge number of devices and services across the entire supply chain. These services and devices will transmit varied data in large volumes. This poses a data modeling challenge where a heterogenous data store is required to store this large amount of structured and unstructured data together. Real-time Analytics Requirements: Supply chain real time use cases such as delivery dispatching, production scheduling, inventory management and logistics tracking requires tools and APIs that help companies build more sophisticated queries against live data of any shape and structure in addition to mechanisms to separate operational from analytical processing so the application doesn’t slow down, along with the ability to land insights close to users. With these challenges in mind, the A* STAR Advanced Remanufacturing and Technology Centre (ARTC) initiated a Supply Chain 4.0 Program with other partners, to develop digital and automation solutions to meet businesses’ demands for technologies to make supply chains more agile, resilient, and secure. A*STAR also opened a Supply Chain Control Tower, to testbed these solutions with partners. Supported by research partners, the initiative has attracted over 50 companies from across five sectors (aerospace, fast-moving consumer goods (FMCG), pharmaceuticals, precision engineering, and semiconductors), including multinational companies and local small and medium enterprises (SMEs). Together with other Supply Chain 4.0 partners, MongoDB is supporting ARTC in developing an easy-to-use database platform, ORCA, that can enable data sharing and processing across and within enterprises. There are two main components in ORCA: ORCA hub and ORCA edge. ORCA hub takes care of cross-enterprise information sharing and is built on federated database architecture in which a collection of independent database systems are united into a loosely coupled federation in order to share and exchange information. The approach would be a Hybrid between Cloud and Local resources. The cloud will only keep the metadata, models, references while keeping actual data locally to the organization eliminating massive data migration and data privacy concerns between member organizations. ORCA edge, on the other hand, takes care of enterprise information aggregation and enables integrating legacy SCM systems (ERP, TMS, WMS) via a novel data exchange middleware. It provides a seamless and synchronized communication environment for different simulation platforms and risk management. MongoDB Atlas has been leveraged among other technologies to develop this easy-to-use data platform. The document model makes it possible for the developers to model heterogeneous data coming in from multiple sources in the supply chain. Realm database acts as the persistence layer in ORCA edge and filtered collections are synced with ORCA hub database via Atlas Device Sync. Figure 1: ORCA data fabric - Overall architecture The ORCA platform can collate data from multiple sources in the supply chain, as well as enable quick information sync and search. The technology developed by the Supply Chain 4.0 Program could help companies mitigate supply chain disruptions. Visit our Manufacturing hub to learn more about innovation in the manufacturing space.

April 4, 2023