AI-based predictive solutions provide businesses with data-driven intelligence and actionable insights to help prevent unplanned downtime due to unexpected failures, avoid lost profits related to unnecessary maintenance, and improve product quality to reduce costs associated with scrap, rework, quality inspection, etc.

Selecting the predictive software that is right for your company’s unique operations and needs is an essential step at the start of your digital transformation journey.

There are multiple things to consider when choosing the right predictive software product for your business:

Suitability for Industrial Applications

There is an ever-growing number of ML/AI solutions on the market. Some claim to be designed specifically for the industrial sector at large or for a particular asset or industry. Others merely mention this sector as one of the many application areas in which their solution can be deployed. We would recommend some due diligence, ensuring your solution provider has achieved past successes within your specific industry.

Also, be sure to look into the background and experience of the leaders and key members of your solution provider’s team. The solution provider should have personnel with vast industrial domain knowledge within the industrial sector, as there are many specific challenges faced when developing data-driven ML/AI solutions that require much needed expertise.

Tools vs. Platforms vs. Solutions

When considering investment in predictive capabilities, a prospective customer should first determine if they are seeking a solution, a tool, or a platform. Each of these options comes with its own strengths and limitations.

Some customers may believe they are looking for a tool with which they can construct their own solutions. Such tools are enticing, as they present the customer with the potential to construct, configure, deploy, and maintain their own solutions for a wide range of assets without the necessity of outside service providers. However, the reality is that these tools come with extensive requirements, in terms of expertise and experience, in order to exploit them to their full potential.

These ML/AI tools require a coding background, more specifically one that includes an understanding of how to configure solutions for target applications. Even solutions that claim to be “no-code” often still require advanced knowledge of data science and coding skills to fully make use of them. These tools also do not eliminate the need for connectivity, instrumentation, data acquisition, feature engineering, and other capabilities, which are key steps in your digital transformation journey.

Industrial IoT platforms have been evolving in recent years to include more software tools and solutions that leverage ML/AI to address certain challenges in various industrial applications.

The draw of these platforms is their ability to provide a wide range of applications from multiple vendors all in one location without needing to engage each of these providers individually or maintain their offerings separately. These platforms also offer a connection layer between assets and applications, the computing resources to process data and feed it to these applications, and more.

However, the promise of these platforms has yet to be fully realized. These platforms still face critical limitations, such as the low number of solutions provided, the diversity of issues addressed by those solutions, the constraints on processing power, storage, and flexibility. A yet-to-be-proven business model for solution providers and platform developers, along with the cost of platform adoption, can make investing in these platforms prohibitive for medium to small industrial customers.

Customers can also find solutions which address a specific problem more directly, providing the desired results without additional efforts. However, these solutions can sometimes be so focused that the amount of effort needed to expand that solution to other applications can be quite high.

Acquiring a solution that directly addresses your most critical issues with the added flexibility to be applied to other assets and applications is key.

Scalability

Customers with a large number of assets and multiple facilities will want to ensure the solution they select is not only flexible in terms of applicability, but also scalability. For these customers, being able to monitor all of their critical assets within one solution would be ideal. The process for adding new assets should be as streamlined and economical as possible and the solution should include diverse machine types, makes, and models, which is often not possible in solutions provided by machine makers.

Usability

Some solutions can be overcomplicated and confusing or require particular, and sometimes prohibitive, levels of expertise.

The solution chosen should be useable by both your knowledgeable personnel working to develop the solution, as well as the key stakeholders that will make use of the provided information. These data insights should be digestible and actionable for the user, as they will inform crucial operation and business decisions.

Predictronics offers a scalable and flexible platform with a template-based approach to predictive solutions for common assets in a wide range of industrial applications, allowing for quicker and less costly deployments. Our team of experienced data analysts and software engineers helps customers with the development and deployment of their solution, as well as any necessary customization for their particular assets and applications. This, along with our intuitive software interface and easy-to-use dashboard, ensures our clients have the actionable data-driven intelligence they need to improve their operations and drive real-world impact.

No two companies are alike. Every business must take many considerations into account and make numerous decisions when selecting the correct predictive software for their assets and applications at the start of their digital transformation journey. Researching and exploring all your options and choosing the right solution for your unique predictive strategy at the start will ensure a successful deployment and effective, valuable insights later.

Check out part one of our blog miniseries where we share our six-step strategy for businesses preparing to adopt AI-driven predictive solutions.

Understand the steps to adopting a predictive strategy and learn how to ensure ROI by following our proven, and customer-validated, implementation process here.