Video: Toyota Material Handling and Industry 4.0 – Creating a Smart Factory By Moving from Monolith to Microservices, Microsoft Azure and MongoDB Atlas

Mat Keep
March 19, 2020 | Updated: March 24, 2020
#Customer Stories

Huge wheels of cheese trundle across a factory floor riding automated forklifts that know exactly where, when and how to move their cargo. Humans and their traditional forklifts support the work for some of the more tricky jobs but the real magic at the heart of this operation is how billions of data points get generated, managed, stored and analysed.

Industry 4.0 has arrived and Toyota Material Handling Europe is building both the machines and the data platforms, that will make this historic shift in manufacturing a success. Toyota is famous for the quality of its vehicles. Now that same standard is being applied to how its development teams create Internet of Things software that will enable the move to smart, autonomous and safer factories of the future.

"At Toyota, we aim to be number one in industry 4.0,” explained Filip Dadgar, Principal System Architect and IT-Manager at Toyota Material Handling Europe.

In a recent presentation in Stockholm, Filip described how Toyota Material Handling Europe have already connected over 100000 warehouse trucks with integrated telematics. The trucks are put to use in virtually every business from manufacturing and warehousing to transport.

Filips covers a number of interesting topics in his talk, which you can see in full below. He gives the background on Industry 4.0, the developer manifesto his team sign, and the core principles that guide their work. He also discusses the shift from a monolithic codebase towards a microservices approach, which was undertaken so the development teams could be more productive and the platform more user-friendly. Underpinning this change is MongoDB Atlas, MongoDB’s fully-managed, global cloud database service, which Toyota Material Handling runs on Microsoft Azure.

Filip explained what it was he liked about MongoDB: "The most beautiful part is the data model. Everything is a natural JSON document. So for the developers, it is easy, really easy for them to work with quickly. Spending time on building business value, rather than data modeling."

For the developers, it is easy, really easy for them to work with quickly. Spending time on building business value, rather than data modeling.
Filip Dadgar, Principal Solutions Architect and IT-Manager, Toyota Material Handling Europe

Toyota Material Handling

But it wasn’t just the data model. Toyota Material Handling had eight evaluation criteria that were used to decide on which database platform they would use to make the switch:

Performance – latency, scalable reads and writes to meet customer SLAs and accommodate significant growth in the future
Automatic scalability, operations, and maintenance that enable developers to focus on applications, rather than backend database administration
Security and compliance to safeguard highly sensitive business and personal data
Data locality – able to host in Northwest Europe or West Europe in Microsoft Azure, therefore, ensuring close geographic proximity to factories for latency-sensitive operations
Automatic backups and restore to provide fully-managed data protection and disaster recovery
Cloud agnostic, giving customers the freedom to run anywhere, on any platform
Developer friendly database with a flexible data model so that developers can continuously integrate new application functionality and stay ahead of market demands
Ecosystem for developer productivity, using proven best practices and widely available skills

Watch the full video for more insight into why MongoDB’s technology was the best fit for Toyota Material Handling across those key criteria. Filip also covers two other important elements of the decision: access to MongoDB University for fast and easy web-based developer training and quality relationships with the MongoDB team.

Filip said: “Have you met these people? They are always happy to help us. They always smile and they always give us the best support. The team could plan MongoDB University into their sprint and ensure everyone gained a high level of knowledge on the technology very quickly.”

Check out the Full Talk Here

Learn how MongoDB Atlas can help you move faster.

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The Converged AI and Application Datastore for Insurance

In the inherently information-driven insurance industry, companies ingest, analyze, and process massive amounts of data, requiring extensive decision-making. To manage this, they rely on a myriad of technologies and IT support staff to keep operations running smoothly but often lack effectiveness due to their outdated nature. Artificial intelligence (AI) holds great promise for insurers by streamlining processes, enhancing decision-making, and improving customer experiences with significantly less time, resources, and staff compared with traditional IT systems. The convergence of AI and innovative application datastores is transforming how insurers work with data. In this post, we’ll look at how these elements are reshaping the insurance industry and offering greater potential for AI-powered applications, with MongoDB at the heart of the converged AI and application datastore. 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Figure 1: Celent Technology Insight and Strategy Survey 2023 However, there's tension between maintaining existing enterprise systems and innovating with AI. Insurance companies must balance keeping the lights on with investing in AI to meet the expectations of boards and stakeholders. The solution lies in integrating AI in a way that enhances operational efficiency without overwhelming existing systems. However, data challenges need to be addressed to achieve this, specifically around access to data. According to a Workday Global Survey , only 4% of respondents said their data is fully accessible, and 59% say their enterprise data is somewhat or completely siloed. Without a solid data foundation, insurers will struggle to achieve the benefits they are looking for from AI. Data architectures and unstructured data When adopting advanced technologies like AI and ML, which require data as the foundation, organizations often grapple with the challenge of integrating these innovations into legacy systems due to their inflexibility and resistance to modification. A robust data architecture is essential for future-proofing and consolidating technology investments. Insurance companies often deal with a vast amount of unstructured data, such as claim images and videos, which can be challenging to manage. By leveraging AI, specifically through vector search and large language models, companies can efficiently process and analyze this data. MongoDB is ideal for managing unstructured data due to its flexible, JSON-like document model, which accommodates a wide variety of data types and structures without requiring a predefined schema. 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By making smart technology investments, leveraging AI to manage unstructured data, and building robust data architectures, insurance companies can future-proof their operations and embrace innovation. A versatile and flexible data platform provides the foundation for these advancements, enabling companies to make their data more accessible, actionable, and valuable. The MongoDB Atlas developer data platform puts powerful AI and analytics capabilities directly in the hands of developers and offers the capabilities to enrich applications by consolidating, ingesting, and acting on any data type instantly. Because MongoDB serves as the operational data store (ODS)—with its flexible document model—insurers can efficiently handle large volumes of data in real-time. By integrating MongoDB with AI/ML platforms, insurers can develop models trained on the most accurate and up-to-date data, thereby addressing the critical need for adaptability and agility in the face of evolving technologies. With built-in security controls across all data, whether managed in a customer environment or through MongoDB Atlas, a fully managed cloud service, MongoDB ensures robust security with features such as authentication (single sign-on and multi-factor authentication), role-based access controls, and comprehensive data encryption. These security measures act as a safeguard for sensitive data, mitigating the risk of unauthorized access from external parties and providing organizations with the confidence to embrace AI and ML technologies. If you would like to learn more about the convergence of AI and application datastores, visit the following resources: Video: The Converged AI and Application Datastore: How API's, AI & Data are Reshaping Insurance Paper: Innovation in Insurance with Artificial Intelligence The MongoDB Solutions Library is curated with tailored solutions to help developers kick-start their projects

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