Smart factory: start small, iterate fast, scale with design thinking
When the idea of Industry 4.0 was first introduced, it centered around the concept of cyber-physical systems that enable machine-to-machine communication. Over the last several years, however, the term has come to represent much more: today, it includes smart factories not only run by direct machine-to-machine interactions but also advanced data processing made possible by connected sensors.
Successful Industry 4.0 and smart factory strategies require an interconnected and scalable architecture, a strong focus on value-driven project selection and the buy-in of all stakeholders involved, from IT developers and production managers to maintenance teams and machine operators. And industry leaders are well aware of the challenges involved: in a joint study conducted by Intel and Microsoft, industrial companies reported that “incomplete IT/OT convergence” was one of the key barriers standing in the way of scaling their Industry 4.0 initiatives.
Success comes from finding ways to move quickly toward a smart factory while keeping an eye on scalability and security. This often means relying on modular, adaptable solutions that can evolve as your application or environment grows.
Taking an iterative approach to your digital transformation projects can help you cultivate a smart factory without unnecessary risk or disruption.
Taking an iterative approach to smart factory and IoT projects: what it means
As the leading figure in design thinking, former IDEO CEO and Partner Tim Brown introduces the concept as: “a discipline that uses the designer’s sensibility and methods to match customer needs with what’s technologically feasible and what a viable business strategy can convert into customer value and market opportunity.”
The term is defined as an iterative process that must pass through three stages:
- Inspiration
- Ideation
- Implementation
Along the complex journey toward a smart factory, strategies built on incorrect assumptions can lead to high costs and dissatisfaction. Challenging these assumptions as early as possible is critical. Ideally, the first implementation of your Industry 4.0 application should be achieved quickly so you can uncover any unexpected challenges and gather feedback from users to continuously improve the system.
The 3 phases of the design thinking process
Technology from CloudRail, a Belden connected brand, can play a crucial role in facilitating the three phases of design thinking to drive your Industry 4.0 projects.
By leveraging the platform’s capabilities, IoT project workflows can be streamlined, cross-divisional collaboration can be fostered and important ideas can be realized quickly and efficiently.
Phase 1: inspiration
During this phase of design thinking, we work to define the underlying problem that needs to be resolved. To do this, we identify your requirements and challenges through interviews, observations and techniques that help us a deeper understanding of your environment.
For instance, CloudRail projects usually start with tackling this list of questions:
- How can value be created from machine data connected to the cloud?
- Will implementation result in financial and/or non-financial benefits that justify the costs and efforts involved?
- What are the project’s scope and key requirements?
As we gather this information, we begin to make initial determinations about possible solutions. By drawing on our experience with IT/OT integration, we can quickly connect your first machines with minimal investment, making it easier to validate ideas and adapt as new needs arise.
As Rimsha Tariq, continuous improvement and digital transformation technician at NGF Europe Limited, states, “[I] really appreciated the seamless connectivity to AWS services. It reduced setup time and allowed us to run fast PoCs to identify promising projects.”
Phase 2: ideation
During this phase, numerous ideas for potential smart factory solutions are gathered and assessed. After collecting and evaluating potential solutions, the next step is to make these ideas more concrete and detailed. Through real stories, you can learn how others have used or experienced the solution in real-world situations.
We help you think through concepts like:
- What do different key users across your organization need from the solution to succeed?
- What data needs to be stored in the cloud, and how often should it be updated?
- Which architecture is recommended for the desired solution?
In terms of integration, CloudRail supports seamless integration into AWS and Microsoft Azure IoT services for an adaptable architecture. Additional flexibility is provided through the flexible usage of data sources, which includes over 12,000 sensors, an OPC UA server and Modbus devices.
The automatic data transformation for IO-Link sensors makes it incredibly easy to connect operational technology (OT) to information technology (IT), without requiring dedicated automation experts to be part of the project.
In this phase, prioritization methods like value proposition design and the KANO model help you pick the best ideas from a list of product features and value propositions by showing you which ones are most likely to create value in your situation.
Phase 3: implementation
This phase of design thinking focuses on building minimum viable products (MVPs) early in the project to facilitate direct interaction. This means building simple, early versions of the product so users can try them out and give feedback right away. Gathering feedback is crucial to gain insights into real-life requirements. These insights can then be used to validate or dismiss assumptions and refine the prototype in an iterative loop.
In IoT projects driven by CloudRail, the implementation phase typically includes:
- Physical installation and wiring
- Integration in OT and IT networks
- Gathering feedback from daily system users, such as production planners, operators and maintenance teams
- Reiteration of the technical product drafted during the second (ideation) phase
- Validation of assumptions made in the first (inspiration) phase
CloudRail’s remote provisioning means IT teams and automation experts don’t have to be onsite; more users can be involved in the implementation process.
Furthermore, cloud-based device management enables quick implementation of changes. With CloudRail devices ready to use and already including required security standards out of the box, industrial manufacturers can swiftly create real-life MVPs.
As Tobias Haungs, managing director of nexineer digital GmbH, says, “The combination of AWS and CloudRail enables our development teams to set up and validate new use cases within hours instead of days or weeks. With the heavy lifting of data ingestion taken away, they can focus on building applications and processes that bring the business forward instead of fighting with infrastructure.”
The importance of iteration and flexibility in smart factory success
The success of an IoT project depends primarily on the value it creates for you. Therefore, it’s crucial to constantly monitor key assumptions about business benefits, user requirements and technical design, and to validate them as early as possible.
Unmanaged gateways and DIY solutions make iteration tasks manual and, as a result, cumbersome. Since findings gathered along the way cannot be easily incorporated into the system’s design, the project could increasingly become a series of disconnected fixes.
CloudRail offers a flexible edge-to-cloud layer that can easily adapt to upcoming requirements, even if they weren’t known at the beginning of the project. For example, if you want to transition using AWS IoT Core to AWS IoT Sitewise, the change requires just a few clicks in the CloudRail.DMC (Device Management Cloud) console.
Especially when building a preventive or predictive maintenance solution, making flexible changes in data acquisition points or connecting additional sensors can significantly improve the long-term accuracy of the model.
By embracing an iterative approach and leveraging adaptable solutions like CloudRail, you can respond quickly to new insights and evolving requirements to deliver sustained value over time. This approach not only minimizes risk and complexity but also positions your IoT initiatives for scalable, secure growth as your needs evolve.