Artificial intelligence (AI) and the Internet of Things (IoT) enable new approaches to industrial data capture, management, and decision-support.
At the same time, realizing value from an industrial AIoT solution goes beyond deploying sensors and a machine learning model: critical consideration must be given to operational workflows, technology integration, and proof of value.
For developers, engineers, and technology founders, this poses a set of challenging questions: how can a technically viable solution be designed around industrial requirements, and what steps must be taken to turn it into a viable business?
This note covers six critical areas of industrial AIoT solution development:
Define industrial problem-to-be-solved
Specify data capture requirements
Determine data processing needs
Apply artificial intelligence for industrial utility
Engineer for technology integration and deployment
Evaluate business opportunity
These areas form the basis for building end-to-end industrial AIoT solutions.
- Define Industrial Problem-to-be-solved
Industrial technology projects benefit from starting with a clearly defined problem.
Take, for example, an asset tracking application in a manufacturing environment: is the goal to gain insight into equipment utilization, reduce time spent searching for a particular tool, or better manage inventory?
Depending on the use case, requirements for tracking accuracy, data update frequency, and more will differ significantly.
Prior to designing the technical solution, therefore, it is advisable to understand and document:
• Information to be captured or inferred
• Required data update rates
• Environmental variables affecting data capture and processing
• Systems that must consume or act on the information
• Metrics for evaluating solution performance
These considerations can help guide solution design and prevent scope creep.
- Specify Data Capture Requirements
IoT sensors, RFID tags and readers, and other technologies can be leveraged to capture information about physical assets and industrial processes.
In general, a wide variety of technologies are available for different use cases: RFID can be used to identify objects or equipment, environmental sensors can be used to capture temperature or humidity, and more.
At the same time, additional factors must be considered, including device integration, data quality, reliability, and operating environment.
A logical starting point is therefore to define a set of required information about assets or processes, and select devices that can capture this information given environmental conditions.
An effective data capture architecture should also consider device connectivity and data transmission needs.
- Determine Data Processing Needs
Industrial AIoT solutions can leverage edge and cloud computing to process data captured from the field.
Edge processing can enable faster response times and reduce the amount of data that needs to be transmitted, while cloud infrastructure can offer additional computation, storage, and orchestration capabilities.
The choice of architecture, however, should be based on specific use case requirements.
A comprehensive edge-cloud architecture should therefore take into account processing needs, latency tolerance, security, and other factors in order to maximize solution effectiveness.
- Apply Artificial Intelligence for Industrial Utility
Artificial intelligence can provide valuable insights when applied to industrial data.
Depending on the use case, AI can be leveraged for pattern recognition, event detection, classification, and other purposes.
At the same time, it is important to recognize that an AI model is only as useful as the impact it delivers.
When applied to an industrial use case, therefore, AI should be evaluated according to its ability to inform decision-making and improve operations.
For example, an AI model that supports predictive maintenance can be used to identify equipment patterns that require human operator attention.
As such, the value of an AI model depends on the surrounding application: what information does it provide, and how is this information used?
Other critical considerations include data quality, false positives, model transparency, monitoring, and drift, as well as the cost and risk of model error.
- Engineer for Technology Interoperability and Deployment
Industrial applications typically have to interact with legacy systems, controllers, databases, and other technology.
A production-ready solution, therefore, should consider technology integration requirements and design features that enable interoperability, security, maintenance, and operator effectiveness.
At the same time, practical considerations must be made for solution deployment, which can raise additional questions:
• How will devices be deployed and maintained?
• How will devices behave in the presence of connectivity issues?
• How will data be managed, processed, and stored?
• How will the system scale across facilities?
• What support will be required from customers?
Addressing such questions can help eliminate surprises that may arise when moving from a proof-of-concept to a production-ready solution.
- Evaluate Business Opportunity
A technology concept may be viable, but it is important to determine whether it addresses a customer need.
Before committing to extensive product development, therefore, it is often necessary to analyze the opportunity and understand the potential market fit.
Such analysis should include a comprehensive understanding of the problem, alternatives available to potential customers, cost of implementation, willingness-to-pay, and other factors.
A pilot project can be an effective way to test key hypotheses, but it is advisable to have specific success criteria, which may include reducing search time for particular assets, improving inventory management, or realizing other benefits.
Ultimately, the goal is to ensure that a given solution addresses a particular need in a way that offers sustainable value to customers.
This is especially important in industrial technology development, where significant time and resources must be devoted to product development.
The connection between technical solution design, customer validation, and business development is what makes industrial technology venture building such an interesting endeavor, and it is what drives Aperture Venture Studio’s work in applied AI and IoT for industrial operations.
Overview
Developing industrial AIoT solutions requires a comprehensive perspective that takes into account sensing, data capture, data processing, AI utility, technology deployment, and business opportunity.
The discussion above highlights some of the key areas that must be considered when designing an industrial AIoT solution.
For developers, engineers, and technology founders, the key challenge is to design a reliable and effective solution that can serve as the foundation for a viable business.
Industrial applications present a compelling opportunity for founders willing to engage with the unique challenges of the domain.
Discussion: which of the challenges described in this note do you think is the most difficult to address in an industrial AIoT solution: device integration, data capture, edge/cloud processing, security, or proving value?
Top comments (0)