One image shows a modern factory with a conveyor belt on which robotic arms are working on a cloud, symbolizing IoT Operations.

Building a smart factory platform using Azure IoT Operations

From a solution assessment to a productive Minimum Viable Product (MVP)

Modern manufacturing sites with oftentimes complex automated production processes produce terrabytes of data, sometimes daily. Just storing the raw data in a database or a datalake and trying to extract insights out of it is time consuming, causes a lot of effort and is a potential roadblock for fast realization of digital use cases.

The problem with raw process data is, that it is not normalized or standardized and often lacks context. Although a single process value can be used for simple monitoring, deeper insights can only be gained if process values can be correlated with other data and information like machine failures, environmental data or order information.

Normalization helps, when it comes to analyzing the data: Naming conventions, standardized units and timestamps allow data scientists to do their work more efficiently. They might not have a direct connection to the process. Therefore, they need additional context such as the units of measurement of the datapoints and a clear assignment to the data sources.

The following steps show, how connectivity solutions can help to improve data quality to enable data driven use cases:

Efficient data processing and networking

Connect & Get Data

Getting access to the raw data is the first step. Connectivity solutions support machine protocols to directly connect to the machines, e.g. via OPC-UA.

Transform & Normalize

In the transformation and normalization step, raw data can be transformed to common units of measurement and defined data types.

Standardize

The use of standard data models helps to abstract from the differences between machines from different generations or different manufacturers. This step is crucial to harmonize data from different sites and to allow for scalability of faster roll-out.

Publish to Unified Namespace (UNS)

By publishing normalized and standardized data to a UNS, this data gets additional context from the topic hierarchy. It is easily accessible for other interested clients and systems in a company and allows for faster problem solving and use case realization.

Machine connectivity

We leverage machine connectivity tools that can connect directly to machines and processes and provide data transformation and normalization as close to the data source as possible. The use of standardized data models to harmonize data for example across similar machines from different manufacturers is another feature.

Standardized data level

Azure IoT Operations is a unified data plane for the edge. It’s a collection of modular, scalable, and highly available data services. It enables data capture from various systems: It allows for example to connect to machines via OPC-UA, transform and normalize the data close the edge and publish it via industry standards like MQTT. Using 3rd Party solutions it can be extended to support additional industrial protocols beyond OPC UA.

UNS based architecture

Besides this “DataOps” functionality Azure IoT Operations also includes the highly available and scalable MQTT broker Azure IoT MQ which can be used to build a Unified Namespace based architecture. With these capabilities Azure IoT Operations makes it possible to publish normalized data from the edge to a Unified Namespace running inside Azure IoT MQ. This potentially makes data accessible to all other interested clients and systems in the company.

Key features of Azure IoT Operations

It is built from ground as a Kubernetes-native application.

It Includes an industrial-grade, highly available and scalable MQTT broker that powers event-driven architectures and can form the foundation for a Unified Namespace.

It is highly extensible, scalable, resilient, and secure.

It lets you manage edge services and resources from the cloud by using Azure Arc.

It can integrate customer workloads into the platform to create a unified solution.

It supports a GitOps- and IoC-based approach for deployment and configuration.

It natively integrates with Azure Event Hubs, Azure Event Grid’s MQTT broker, and Microsoft Fabric in the cloud.

Building a Smart Factory is more than connecting machines

When working together with our customers on their Smart Factories, a fit gap analysis is always part of our approach. We take a close look at the existing application landscape and examine if tools like Azure IoT Operations can fill the gaps or if additional components are necessary for example to connect to legacy devices, that do not provide their data via OPC-UA.

Even though quality data is the basis for actionable insights, building a Smart Factory is more than connecting machines, and providing access to data. Besides the data layer the infrastructure is key success factor for digital transformation and has significant influx on the operability, scalability and security of the Smart Factory.

When a company is starting to plan their digital infrastructure, several requirements and constraints influence what an overall Smart Factory platform based on tools like Azure IoT Operations should look like:

In the Solution Assessment we…

educatate on the success factory and core concepts for a Smart Factory 
create guide our customers to define an aligned product vision & roadmap  
define Core capabilities and constraints 
create a high level overview of potential use cases and specify an initial use case for the MVP pahse 
make a fit gap analysis and define the tools set, that are best suitable for our clients
define the Smart Factory architecture & tech stack 
define the MVP scope 
prepare the implementation phase

We build a Minimum Viable Product: 

Iteration & Progressive Handover

Why MaibornWolff?

As one of the most innovative IT service providers with a great passion for AI, we focus entirely on the project business and individual software development - without our own products. To stay at the forefront, we continuously invest in our team of digital technology engineers and develop digital solutions that are well thought-out, efficient and reduced to the essentials.

Our principle: simplicity instead of complexity. We only develop what is really needed - tailor-made, useful and reliable. Our results speak for themselves. With over 800 large-scale systems and more than 10,000 person-years of experience in high-end software engineering, we are one of the few who can reliably implement even the largest and most complex IT landscapes. Thanks to close partnerships with leading hyperscalers, our customers operate their solutions in today's most modern and powerful environments.

Less technology. Better Business.

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