Stuck in your smart factory initiative? Start small, iterate often, scale fast.
Design Thinking: A Catalyst for Accelerating IoT Projects
In 2010, the term “Industrie 4.0” was first published, focusing on cyber-physical systems that enable machine-to-machine communication. The concept has evolved over the last decade and aims to develop a smart factory with new applications driven by direct machine-to-machine interaction and the advanced processing of data gathered by connected sensors. (Cf. VDMA 2016)
Many manufacturers have learned in recent years that a successful digital transformation strategy requires an interconnected and scalable architecture, a strong focus on value-driven project selection, and the buy-in of all stakeholders involved, from IT developers over production managers to maintenance teams and machine operators. In a joint study conducted by Intel and Microsoft, industrial companies surveyed identified “incomplete IT-OT convergence” as one of the key challenges they face when scaling their initiatives.
Starting very fast and opportunistic with a first connected machine while applying a scalable and secure architecture at the same time seems like a contradiction in itself. In this blog post, I would like to present how CloudRail makes achieving smart manufacturing possible for industrial companies through an iterative project approach.
The iterative approach on IoT projects
As a tool for customer-focussed product design, Tim Brown introduces Design Thinking as “a discipline that uses the designer’s sensibility and methods to match customers’ needs with what is technologically feasible and what a viable business strategy can convert into customer value and market opportunity” (Brown 2008 p.86). Design thinking is defined as an iterative process that must pass through the following three stages: Inspiration, Ideation, and Implementation.
In the complex journey towards a smart manufacturing ecosystem, strategies built on incorrect assumptions can lead to high costs and dissatisfaction. Therefore, it is crucial to validate these assumptions as early as possible. Ideally, the first implementation should be achieved quickly in order to uncover any unexpected challenges and gather feedback from users to continuously improve the system.
In the following sections, we will explore the different stages of the design thinking process and how CloudRail’s technology can play a crucial role in facilitiating this process. By leveraging Cloudrail’s capabilities, workflows in IoT projects can be streamlined, cross-divisional collaboration is fostered, and important ideas can be realized more quickly and efficiently than ever before.
Iteration steps in the phases of the design thinking process
Phase 1 – Inspiration:
During the inspiration phase, the team must empathize with key users and define the underlying core problem. To do so, the requirements and incentives of potential customers are identified using interviews, observations, as well as techniques to create empathy for the user.
CloudRail projects usually start with the following core questions:
- How can value be created from machine data connected to the cloud?
- Does the implementation result in monetary or non-monetary benefits that justify the costs and efforts involved?
- What are the key requirements and scope of the project?
While empathizing with the user, first assumptions of the desired function of the solution are derived. In this first phase, CloudRail’s experience in OT/IT integration and the small investment required to connect the first machines proved to be a valuable approach for IoT projects
As Rimsha Tariq, Continuous Improvement and Digital Transformation Technician at NGF Europe Limited stated, “[she] really appreciated the seamless connectivity to the AWS services. It reduced set-up time and allowed us to run fast PoCs to identify promising projects.”
Phase 2 – Ideation:
During the ideation phase, numerous ideas for potential solutions are gathered. The selected ideas are then specified and advanced by creating narrative storylines that depict users interacting with the solution. Methods such as the Value Proposition Design (Cf. Osterwalder 2014) or the KANO model can assist in identifying the most promising ideas from a collection of product features and potential value propositions.
In IoT projects, the following core topics get addressed in the ideation phase:
- What are the core requirements of the different key users of the solution?
- What data points are required in the cloud services and how frequently should they be updated?
- Which architecture is recommended for the desired solution?
The seamless integration into AWS and Microsoft Azure IoT services provided by CloudRail enables 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 the operational technology (OT) with the information technology (IT) world, without requiring dedicated automation experts to participate in the project.
Phase 3 – Implementation:
The implementation phase focuses on building minimum viable products (MVPs) early in the project to facilitate direct interaction with the product. Gathering user feedback is crucial for gaining 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 using CloudRail, the implementation phase mainly includes:
- Physical installation and wiring
- Integration in OT and IT networks
- Gathering feedback from daily users of the system, such as production planners, operators, and maintenance teams
- Re-iteration of the technical product drafted in the ideation phase
- Validation / falsification of assumptions made in the inspiration phase
As the remote provisioning of the CloudRail solution requires no IT teams or automation experts on-site, 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 including all required security standards out-of-the-box, industrial manufacturers can swiftly create real-life MVPs.
„The combination of AWS and CloudRail enable our development teams to setup and validate new use-cases within hours instead of days and 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 topics.” Tobias Haungs, Managing Director of nexineer digital GmbH
The success of an IoT project primarily depends on the value it creates for the customer. Therefore, it is crucial to constantly monitor key assumptions about business benefits, user requirements, and technical design, and validate them at the earliest stage 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 castle in the sky.
The CloudRail solution offers a flexible edge-to-cloud layer that can easily adapt to upcoming requirements, even those that were not known at the beginning of the project. If a customer, for example, wants to transition from using AWS IoT Core to AWS IoT Sitewise, they only need to perform a few clicks in the CloudRail.DMC (Device Management Cloud). 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.
List of references:
Brown, Tim (2008): Design Thinking. In Harvard Business Review (June 2008), pp. 84–92. Available online at https://hbr.org/2008/06/design-thinking , checked on 08/09/2023.
Leadership Tribe (2023): Design Thinking Training. Available online at: https://leadershiptribe.co.uk/design-thinking-training , checked on 08/09/2023
Osterwalder, Alexander; Pigneur, Yves; Smith, Alan; Bernarda, Gregory (2014): Value Proposition Design. How to Create Products and Services Customers Want. 1st edition: John Wiley & Sons.
Ries, Eric (2011): The Lean Startup. 1st edition. New York: Random House USA Inc.
VDMA (2016): Guideline Industrie 4.0. Guiding principles for the implementation of Industrie 4.0 in small and medium sized businesses. With assistance of Technical University Darmstadt, Karlsruhe Institute of Technology (KIT). Frankfurt am Main: VDMA Verlag GmbH.