Integrating industrial equipment with IoT solutions can be a game-changer for you, offering improved efficiency, cost savings, and enhanced decision-making capabilities. However, several challenges can arise during the integration process. Some of these challenges include:
If you are new to the Microsoft Azure cloud, you will struggle to adopt Azure into your own environment and operating model. Questions like how to purchase cloud services, what governance needs to be put in place, what operational processes are needed have to be answered.
Older industrial equipment may not have the built-in capabilities to connect with IoT devices or platforms. Every plant tends to be a snowflake, meaning none of them is an exact replica from another one. This means that the integration approach which works for one plant may not work for another one.
The interconnected nature of IoT networks raises concerns about data security and privacy. You need to ensure that your networks are secure, and data is protected from unauthorized access and breaches. Industrial OT networks tend to follow the air-tight principle: the network is completely isolated from external networks, therefore, the security inside the OT network tends to be more relaxed. Integrating with any system outside this air bubble will break this “air tight” assumption, thus exposing your OT network to cyber security attack vectors which were not present before at all.
As your IoT solution grows, you must ensure that your infrastructure is scalable to handle increasing amounts of data and accommodate additional devices. At the same time, you don’t want to start your system with an overengineered architecture which will be complex to build and expensive to maintain. Your architecture needs to be modular and horizontally scalable, so that it can start small (when your IoT footprint is small, thus keeping costs under control) but be able to scale out as your footprint expands.
With the vast amount of data generated by industrial IoT devices, you need to have effective data management and analytics solutions in place to make sense of this information and derive valuable insights. Traditional databases won’t cut it, as they are optimized for OLTP (transactions) and not for the analysis of time series data which, by its nature, does not change.
Integrating IoT solutions with your existing enterprise systems can be complex, requiring seamless data sharing and communication between disparate platforms. Telemetry from your equipment needs to be enriched with data from MES, ERP or even your CRM. The high speed of the IoT message flow makes a direct integration with these systems impractical. Other abstraction layers need to be defined instead.
Reliable and consistent connectivity is essential for IoT networks to function effectively. However, industrial equipment might be installed in locations with poor, unstable or intermittent connectivity. Your solution needs to have resiliency built-in, and be designed for transient network failures to happen.
The IoT ecosystem is diverse, and the lack of widely accepted standards can create challenges in integrating devices from different manufacturers or platforms. You need modern edge devices on-plant who can provide protocol translation and integrate with a plethora of on-plant protocols.
To solve these challenges, Option 4.0 can deliver a comprehensive industrial IoT architecture made for your scenario. To address the challenges enumerated above, this architecture will tackle the following topics:
If your organization is new to Azure, we will propose a governance model for you, which includes topics like:
Your IoT architecture should include a detailed assessment of your existing equipment and identify necessary upgrades, interface requirements, or protocol translation options which can be used to securely integrate with your brownfield on-plant equipment. Design a modular and flexible architecture that can accommodate various types of equipment, ensuring seamless integration and future-proofing the system.
We design with robust security measures such as encryption, authentication using X.509 client certificates or TPM attestation, as well as proposing a device onboarding process which is secure and trustworthy. Furthermore, we mitigate the risks created by removing the “airtight” isolation of your OT network through the usage of modern authentication and encryption protocols, as well as using security layers to reduce the blast radius of a potential network compromise.
We design the IoT architecture to be scalable from the outset. This ensures that your IoT solution is divided into different modules, each of them able to scale horizontally to accommodate the expansion of your IIoT footprint. This gives you certainty that your rollout plans won’t be jeopardized by a solution which suddenly finds itself unable to scale out.
The architecture includes data schemas and how to manage their versioning. This ensures that you have your data under control, and that your database does not end up becoming a data swamp. These data structures will accommodate your analytics and visualization needs, thus ensuring that the data is useful for visualizations to your end users, but also as raw material for your data scientists to explore with their specialized tools.
The modular architecture incorporates abstraction layers that are able to enrich your IIoT data with information from mission-critical systems. This is done in a way that does not overload such systems, and decouples the IIoT message flow from them. This ensures that your IIoT data can benefit from your enterprise data state, without overwhelming those systems.
The architecture assumes that networks are unreliable and follows the “at-least-once” message delivery semantics. It considers offline capabilities for your on-plant devices, message deduplication mechanisms in the cloud, aggressive message compression during transmission and storage to reduce bandwidth and cost, amongst other features which work well on those occasions when networks are down or slow.
The architecture will leverage widely-accepted IoT standards and protocols to ensure that the IoT architecture is interoperable with devices from different manufacturers and platforms. Open data standards are a guiding principle for the architecture. For you, this means that each step in the architecture is a programmable interface. You can expose and consume your data not only on cloud systems, but you can also do so inside your plant, thus being able to create close-loop systems if needed.
As part of this engagement, we will produce the following deliverables:
At the end of this engagement, we will deliver this IIoT architecture in your own wiki. The architecture will include the following scope:
👉 Azure governance model. If you are new to Azure, we will provide this guidance as well, which includes things like your Azure Active Directory tenant integration, how you purchase Azure, structuring your subscriptions and resources groups, environment configuration management, Azure resource naming convention and a tagging policy.
👉 Architecture Options. Your requirements might be complex or still partly unknown. If this is the case, we might deliver more than one architecture option, and provide a recommendation about which one we believe you should pursue deeper.
👉 Network Topology and Connectivity. Depending on whether you already have Azure landing zones or a networking integration, you might need to expand on this. This will include topics liks VNet Design, IP address space definition, subnets splitting, integration options to connect your Azure VNets to your on-prem systems, connectivity options for your IIoT devices, and DNS resolution.
👉 Logical Architecture. This is the core deliverable of the engagement, where we will provide an IIoT architecture which covers the Edge Architecture (your on-plant architecture and integration with equipment), message ingestion, processing and storage, and the API environment that will expose this data securely to your users.
👉 Data Architecture. The deliverable will include a definition for a Common Data Model for IoT telemetry data, design the hyperscale time series database for long term storage and analytics of telemetry messages, define data retention policies for this data, and define how to archive it for the future in a cost effective way (if necessary).
The investment in this solution ranges between 15’000 and 35’000 CHF.