Achieving Industrial Connectivity at Scale with Wimera and MongoDB

Dr. Humza Akhtar
December 1, 2022 | Updated: June 27, 2023
#Industry

Industry 4.0 (I4.0) represents the beginning of the Fourth Industrial Revolution. It includes the current trend of automation technologies in the manufacturing industry as well as disruptive technologies and concepts, such as cyber-physical systems (CPS), Industrial Internet of Things (IIoT), cloud computing, and immersive visualization.

Through Industry 4.0, embedded systems, semantic machine-to-machine communication, IIoT, and CPS technologies are integrating the virtual space with the physical world. These technologies are enabling a new generation of industrial systems, such as smart factories, to deal with the complexity of fast-paced and hyper-personalized production.

In this article, we’ll explore Wimera’s unique solutions to the challenges of I4.0 and IIoT, built with MongoDB.

Information and insights

With IIoT, existing industrial systems will be modernized to drive digital transformation and unlock tomorrow's smart enterprise. IIoT has been finding its way into products and sensors while revolutionizing existing manufacturing systems; thus, it is considered a key enabler for the next generation of advanced manufacturing.

Industry 4.0 generally comprises many complex components and has broad applications in all manufacturing sectors. The first challenge faced by manufacturing companies when embarking on the I4.0 journey is to sensorize and connect their manufacturing equipment in order to collect, store, and analyze data for information and insights.

Wimera Systems is solving this challenge as an I4.0 enablement company offering IIoT solutions using their unique hardware, software application, and AI/ML-based analytics engine. Wimera’s Smart Factory Suite has seen tremendous growth, with 2500+ global installations across 50+ customers.

MongoDB has been pivotal to that growth, acting as the core component of the IIoT suite and enabling the company to offer its services at scale without having to worry about managing the complexity of an IIoT database.

Bringing AI-powered IIoT to the manufacturing shop floor

Manufacturing companies are emerging from the pandemic with a renewed focus on digital transformation and smart factories investment. COVID-19 has heightened the need for IIoT technology and innovation, forcing manufacturers to compete in a digitalized business environment.

Many manufacturers still operate using legacy technologies and systems; on most shop floors, equipment and operator efficiency are manually calculated and tracked using spreadsheets. The machines are maintained using time-based rather than condition-based maintenance strategies. And, no real-time visibility exists on consumables and tools usage. All these practices result in increased maintenance costs, suboptimal production, and ultimately, customer dissatisfaction.

Wimera understands these challenges all too well, which is why they created the Smart Factory Suite supporting both on-premise and cloud deployments.

The Smart Factory Suite provides insights for managing the entire production landscape through interconnected devices and machines, operations, and facilities. It can predict and make real-time adjustments for increased production efficiency and less downtime. The suite is primarily utilized for empowering manufacturing operations, equipment maintenance, warehouse operations, and inventory management. With Smart Factory Suite, Wimera serves a wide range of manufacturing industry sectors including, but not limited to, automotive, electronics, chemical, and food processing companies.

Deploy and run anywhere with MongoDB

MongoDB, with its freedom to run anywhere, lets Wimera offer both on-premises and cloud deployment options for its customers. In both cases, the suite is directly connected with machine controllers using Wimera libraries for all popular Programmable Logic Controller (PLC) brands. The suite is also connected to legacy machines through external sensors installed by the Wimera team.

Data is extracted via the Wimera ReMON Data Acquisition (DAQ) device (Figure 1) that utilizes the MongoDB database as the persistent data storage. MongoDB’s flexible data model makes it easy to combine and enrich this data and enables live dashboards and instant alerts for factory personnel. The data collected and optimized by ReMON DAQ is further fed to ReMON AI, an advanced analytics engine. ReMON AI provides advanced analytics through AI/ML models and leverages MongoDB to deliver application-driven analytics in real time.

Figure 1: ReMON DAQ and ReMON AI (source: Wimera ReMON).

Whether through on-premises or cloud deployment (Figures 2 and 3), Wimera’s customers have benefited from MongoDB’s capabilities that are critical for IIoT applications, such as time series collections and the flexible, intuitive document data model.

Figure 2: Wimera IoT architecture on premises.

Figure 3: Wimera IoT architecture on cloud (using MongoDB on AWS).

In one customer example, while deploying IIoT at a multinational CNC machine shop, the customer preferred to use their existing production monitoring application enriched with IoT data coming from Wimera’s Smart Factory Suite. In this case, MongoDB enabled easy and seamless integration of the IoT application with the customer's application via a simple API. Additionally, high-speed data coming from a vibration sensor was handled effectively by MongoDB time series collections, resulting in real-time alerts sent to maintenance teams for instant corrective actions on the shop floor.

In another example, a multinational automotive manufacturer wanted a single platform that could collect and combine data coming from vendors in different formats and contexts. MongoDB's flexible document model helped manage the varied data types easily, allowing the customer to benefit from a single application capable of managing multiple vendors in parallel. This flexibility offered by MongoDB enables the customer to keep adding new vendors instantly without changing the underlying cloud infrastructure or tweaking schemas.

Interested readers can check out additional case studies on Wimera’s website.

Building better together

Wimera and MongoDB’s partnership gives customers confidence with validated architectures to ensure successful, optimized, and scalable deployments at their facilities. Wimera’s continued partnership with MongoDB also helps guide the company’s product roadmap as we expand in the IIoT, Smart Factory market together.

MongoDB is the only enterprise grade database chosen by the Wimera development team due to easy handling of the large volume of data generated from machines and sensors while maintaining a high performance… If we want to insert thousands of records in a second, then MongoDB is the best choice for that given our solutions are for Industrial IoT. Also, horizontal scaling (adding new columns) is not an easy process in any RDBMS system. But in the case of MongoDB, it is very easy

Nagarajan Narayanasamy, CEO, Wimera Systems Private Limited

A bright future ahead

Since 2019, Wimera has been an early adopter of MongoDB for their Industrial IoT application for discrete manufacturing industries and process industries on multiple domains. “Currently, Narayanasamy says, “Wimera’s Industrial IoT solutions are matured, and we are focused on scaling globally.” Wimera now targets expansion in India, APAC, EU, and USA for the discrete manufacturing and process industries and also for select OEMs and machine builders.

“As MongoDB continues to scale itself globally through its multi-cloud data distribution strategy, we see a good synergy partnering with MongoDB for the mutual benefit of both companies and the community as a whole. We also would like to work with MongoDB on the technology roadmap and solve some of the real-life challenges faced by manufacturing industries,” Narayanasamy says.

Wimera has recently started their MongoDB Atlas journey, and the adoption will grow as their customers demand more cloud solutions compared to current on-premises deployments. MongoDB will continue to help IoT companies like Wimera take their product offering to the next level and enable their customers to digitally transform their manufacturing operations.

Thank you to Karolina Ruiz Rogelj for her contributions to this post.

To learn more about MongoDB’s role in industrial connectivity and IIoT, please visit our Manufacturing and Industrial IoT page.

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Choosing the Right Tool for the Job: Understanding the Analytics Spectrum

Data-driven organizations share a common desire to get more value out of the data they're generating. To maximize that value, many of them are asking the same or similar questions: How long does it take to get analytics and insights from our application data? What would be the business impact if we could make that process faster? What new experiences could we create by having analytics integrated directly within our customer-facing apps? How do our developers access the tools and APIs they need to build sophisticated analytics queries directly into their application code? How do we make sense of voluminous streams of time-series data? We believe the answer to these questions in today's digital economy is application-driven analytics. What is Application-Driven Analytics? Traditionally, there's been a separation at organizations between applications that run the business and analytics that manage the business. They're built by different teams, they serve different audiences, and the data itself is replicated and stored in different systems. There are benefits to the traditional way of doing things and it's not going away. However, in today's digital economy, where the need to create competitive advantage and reduce costs and risk are paramount, organizations will continue to innovate upon the traditional model. Today, those needs manifest themselves in the demand for smarter applications that drive better customer experiences and surface insights to initiate intelligent actions automatically. This all happens within the flow of the application on live, operational data in real time. Alongside those applications, the business also wants faster insights so it can see what's happening, when it's happening. This is known as business visibility, and the goal of it is to increase efficiency by enabling faster decisions on fresher data. In-app analytics and real-time visibility are enabled by what we call application-driven analytics. You can find examples of application-driven analytics in multiple real-world industry use cases including: Hyper-personalization in retail Fraud prevention in financial services Preventative maintenance in manufacturing Single subscriber view in telecommunications Fitness tracking in healthcare A/B testing in gaming Where Application-Driven Analytics fits in the Analytics Ecosystem Application-driven analytics complements existing analytics processes where data is moved out of operational systems into centralized data warehouses and data lakes. In no way does it replace them. However, a broader spectrum of capabilities are now required to meet more demanding business requirements. Contrasting the two approaches, application-driven analytics is designed to continuously query data in your operational systems. The freshest data comes in from the application serving many concurrent users at very low latency. It involves working on much smaller subsets of data compared to centralized analytics systems. Application-driven analytics is typically working with hundreds to possibly a few thousand records at a time. And it's running less complex queries against that data. At the other end of the spectrum is centralized analytics. These systems are running much more complex queries across massive data sets — hundreds of thousands or maybe millions of records, and maybe at petabyte scale — that have been ingested from many different operational data sources across the organization. Table 1 below identifies the required capabilities across the spectrum of different classes of analytics. These are designed to help MongoDB’s customers match appropriate technologies and skill sets to each business use case they are building for. By mapping required capabilities to use cases, you can see how these different classes of analytics serve different purposes. If, for example, we're dealing with recommendations in an e-commerce platform, the centralized data warehouse or data lake will regularly analyze vast troves of first- and third-party customer data. This analysis is then blended with available inventory to create a set of potential customer offers. These offers are then loaded back into operational systems where application-driven analytics is used to decide which offers are most relevant to the customer based on a set of real-time criteria, such as actual stock availability and which items a shopper might already have in their basket. This real-time decision-making is important because you wouldn't want to serve an offer on a product that can no longer be fulfilled or on an item a customer has already decided to buy. This example demonstrates why it is essential to choose the right tool for the job. Specifically, in order to build a portfolio of potential offers, the centralized data warehouse or data lake is an ideal fit. Such technologies can process hundreds of TBs of customer records and order data in a single query. The same technologies, however, are completely inappropriate when it comes to serving those offers to customers in real time. Centralized analytics systems are not designed to serve thousands of concurrent user sessions. Nor can they access real-time inventory or basket data in order to make low latency decisions in milliseconds. Instead, for these scenarios, application-driven analytics served from an operational system is the right technology fit. As we can see, application-driven analytics is complementary to traditional centralized analytics, and in no way competitive to it. The benefits to organizations of using these complementary classes of analytics include: Maximizing competitive advantage through smarter and more intelligent applications Out-innovating and differentiating in the market Improving customer experience and loyalty Reducing cost by improving business visibility and efficiency Through its design, MongoDB Atlas unifies the essential data services needed to deliver on application-driven analytics. It gives developers the tools, tech, and skills they need to infuse analytics into their apps. At the same time, Atlas provides business analysts, data scientists, and data engineers direct access to live data using their regular tools without impacting the app. For more information about how to implement app-driven analytics and how the MongoDB developer data platform gives you the tools needed to succeed, download our white paper, Application-Driven Analytics: Defining the Next Wave of Modern Apps .

November 30, 2022
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Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. Cohere’s leading Embed 3 and Rerank 3 models can be used with MongoDB Atlas Vector Search to convert MongoDB data to vectors and build a state-of-the-art semantic search system. Search results also can be passed to Cohere’s Command R family of models for retrieval augmented generation (RAG) with citations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. A new approach to vector embeddings It is in the realm of embedding where Cohere has made a host of recent advances. Described as “AI for language understanding,” Embed is Cohere’s leading text representation language model. Cohere offers both English and multilingual embedding models, and gives users the ability to specify the type of data they are computing an embedding for (e.g., search document, search query). The result is embeddings that improve the accuracy of search results for traditional enterprise search or retrieval-augmented generation. One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. The unification and synchronization of source application data, metadata, and vector embeddings in a single platform, accessed by a single API, makes building gen AI apps faster, with lower cost and complexity. Those apps can be layered on top of the secure, resilient, and mature MongoDB Atlas developer data platform that is used today by over 45,000 customers spanning startups to enterprises and governments handling mission-critical workloads. What's next? To start your journey into gen AI and Atlas Vector Search, review our 10-minute Learning Byte . In the video, you’ll learn about use cases, benefits, and how to get started using Atlas Vector Search.

April 25, 2024