Three Ways Industrial AI Can Unleash Instant Value

Derick JoseFounder – Flutura.

Today, industrial AI is the holy grail for leaders and decision makers. Did you know OEMs are now creating new business models powered by industrial AI to generate lucrative revenue streams? Not only that, but even a 1% improvement in industrial operations can free up untapped cash flow through increased efficiency. Additionally, industrial AI can help reduce greenhouse gas (GHG) emissions by up to 7%.

Unleashing the instant value of AI in an industrial environment is not only possible, but beneficial to your bottom line. As field practitioners in the field of industrial AI, we have uncovered three key monetary constructs that will help show the value of industrial AI.

1. AI-powered business models that generate lucrative new revenue streams

Companies are now realizing that in addition to reducing costs, industrial AI can also help generate new revenue streams.

For example, companies that provide technology solutions to oil companies can offer their customers an end-to-end industrial AI solution to integrate surface technologies, thereby extracting unprecedented value from a whole new lucrative revenue stream. .

Problem

When producing oil and gas upstream, field operators often face several problems. For example, it is not uncommon for operators to travel several hours a day to collect and analyze field data. This leads to losses due to late decision-making and production inefficiencies that can easily reach $100,000/day. Missed information also leads to higher production downtime, safety risks and inefficiency issues in the event of irregularities in key parameters such as Reid Vapor Pressure (RVP).

The solution

Industrial AI can equip operators with early anomaly detection. By providing customers with near real-time reporting and last-mile visibility into operations, the technology provider can generate a whole new source of profitable revenue.

Impact

Some of the major areas of impact for this use case are reducing installation footprint, reducing commissioning time, decreasing production downtime, and reducing expenditure on capital.

2. Optimization of AI-based equipment and processes for cost savings through increased efficiency

Problem

Industrial AI can also help weed out quality-defective batches. For example, manufacturers catering to highly quality-conscious customers, such as those in the industrial adhesives industry, face the pressing challenge of inconsistent product quality in their in-spec batches. Due to the associated recall costs, they must discard all quality-defective batches, resulting in uncontrollable losses.

The solution

Manufacturers looking to achieve the gold standard of product quality for all batch can do this using industrial AI. A tailor-made industrial AI solution can help them achieve three major objectives:

1. Actualization of economic potential: Industrial AI solutions can accurately measure former factory output quality (by batch or line level) and then issue automatic alerts when KPI (key performance indicator) scores fall below of the acceptable threshold value.

2. Smart diagnostics: By assigning scores to various parameters, hundreds of quality indicators can be compared. In addition, one-click root cause analysis (RCA) is possible thanks to industrial AI.

3. Real-time predictions: Industrial AI can accurately predict output quality during production, provide instant recommendations for improvement, and consider suggestions from experienced end users for future use.

Impact

A substantial reduction in customer complaints and out-of-specification products could generate annual savings.

3. Accelerate net-zero profitably and sustainably with AI

For conglomerates working in energy and infrastructure, combining profitability and net zero goals is one of the defining use cases for the future of industrial AI.

Problem

For example, if a company experiences frequent fouling (heat buildup) in its heat exchangers, this will lead to a significant increase in energy consumption and process inefficiency. As a result, the extra energy consumed would not only reduce the company’s profitability, but would also jeopardize the company’s net zero goals due to increased carbon emissions.

The solution

First, a digital twin or digital replica of the system for real-time equipment monitoring and live diagnostics must be implemented. Second, an ML-based process simulator should be implemented to uncover the relevant underlying patterns.

This way, field engineers can be alerted quickly (to heat exchanger fouling in this case) so they can quickly take the necessary corrective action.

Impact

By using industrial AI, the conglomerate can achieve better output stream quality, higher efficiency, lower energy consumption, and a dramatic decrease in the unit’s carbon footprint.

From AI to ROI: How (and where) to start the journey

1. Accurately identify and budget key operational issues

A great way to keep ROI at the center of everything you do is to break down your operational challenges into simple, measurable, and budgeted goals.

Here are some examples :

Reduce unseen loss time in upstream drilling operations by 8%.

Chemically reduce batch defects in an industrial process by 5%.

2. Map the data landscape for industrial AI

Being AI-ready (and ROI-ready) requires you to have a comprehensive data map so you can make the most of all the available data at your disposal to create smart, custom solutions to critical issues.

Whether it’s sensor data in SCADA or Historian systems or downtime data from maintenance ticketing systems, we recommend bringing it all together in a data map that can be used for maintenance solutions. industrial AI in order to obtain the maximum return on investment.

3. Rank use cases based on their potential impact on the bottom line

Choosing the right industrial AI use case for your business is critical to achieving a positive ROI. By placing greater emphasis on use cases that optimize the cost side of the balance sheet and/or generate new revenue pools, you can consciously start with use cases that have greater ROI potential.

For companies looking to capitalize on industrial AI, the three constructs discussed here will be a great starting point. By working closely with industry leaders, we hope to continue our efforts to show that profitability and sustainability are possible with industrial AI.


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