Derek JoseFounder – Flutura.
Today, artificial AI is the holy grail of leaders and decision makers. Did you know that OEMs are now creating new business models powered by artificial intelligence to generate profitable revenue streams? Not only that, but a mere 1% improvement in industrial operations can unleash untapped cash flow through increased efficiency. Moreover, AI can help reduce greenhouse gas (GHG) emissions by up to 7%.
Unleashing instant value from AI in an industrial setting is not only possible, but also beneficial to your bottom line. As AI field practitioners, we have explored three key critical combinations that will help demonstrate the value of AI.
1. AI-powered business models that generate new and profitable revenue streams
Companies are now realizing that apart from reducing costs, AI can also help generate new revenue streams.
For example, organizations that provide technology solutions to oil companies can offer their customers a comprehensive AI solution to integrate surface technologies, thereby gaining unprecedented value from an entirely new profitable revenue stream.
During primary oil and gas production, field operators often encounter many problems. For example, it is not uncommon for operators to travel several hours per day to collect and analyze field data. This causes losses due to delayed decision making and production inefficiencies that can easily reach $100,000 per day. Missed insights also lead to increased production downtime, safety risks and inefficiency issues if there are irregularities in key parameters such as Reed Vapor Pressure (RVP).
Artificial AI can provide operators with early detection of anomalies. By providing near-real-time reports and the ability to see customers in the last mile of operations, the technology provider can generate a new, profitable revenue stream.
Some of the notable impact areas of this use case are to reduce the facility’s footprint, reduce the time required to extract oil for the first time, reduce production downtime and reduce capital expenditures.
2. AI-driven equipment and process optimization to achieve cost savings through increased efficiency
AI can also help eliminate quality skewed payments. For example, manufacturers catering to quality-conscious customers, such as those in the industrial adhesives industry, face the urgent challenge of inconsistent product quality within their specification groups. Due to the associated recovery costs, they have to eliminate all skewed quality batches, causing losses to spiral out of control.
Manufacturers strive to achieve the gold standard in product quality for All The batch can do this using artificial AI. A custom-designed AI solution can help them achieve three main goals:
1- Activating the economic potential: Industrial AI solutions can accurately measure the quality of past output at the factory (at the batch or line level) and then issue automatic alerts when KPI (Key Performance Indicator) scores drop below an acceptable threshold value.
2. Smart Diagnosis: By assigning scores to different criteria, hundreds of quality indicators can be measured. Moreover, Root Cause Analysis (RCA) can be done with a single click using AI.
3. Real time forecasts: AI can accurately predict the quality of the output during production, making immediate recommendations for improvement and absorbing suggestions from experienced end users for future use.
Significantly reducing customer complaints and shoddy products can lead to annual savings.
3. Accelerate Net-Zero results profitably and sustainably using AI
For conglomerates working in energy and infrastructure, the combination of profitability and net-zero targets is one of the future specific use cases for AI.
For example, if a company encounters frequent contamination (heat buildup) in its heat exchangers, this will lead to a significant increase in energy consumption and inefficiency of the process. As a result, the additional energy consumed will not only reduce the company’s profitability, but it will also affect the company’s net zero goals due to increased carbon emissions.
First, a digital twin or digital copy of the system needs to be implemented for real-time equipment monitoring and live diagnostics. Second, an ML-based process simulator must be developed to detect the relevant underlying patterns.
This way, field engineers can be alerted immediately (of heat exchanger contamination in this case) so that they can quickly take necessary corrective action.
By using AI, conglomerate can achieve higher quality of output flows, higher productivity, lower energy consumption, and a significant reduction in the unit’s carbon footprint.
From AI to ROI: How (and where) the journey begins
1. Accurate identification and budgeting of major operational problems
A great way to keep ROI at the heart of everything you do is to break down operational challenges into simple, measurable, and budgeted goals.
Some examples are:
• Reduce invisible downtime in initial drilling operations by 8%.
• Chemically reducing batch defects in an industrial process by 5%.
2. Mapping the data landscape for AI
Being AI ready (and ready for ROI) requires you to have a comprehensive data map so you can make the most of all the data at your disposal to create intelligent, customized solutions to mission-critical problems.
Whether it’s sensor data in SCADA systems, historical systems, or downtime data from maintenance card systems, we recommend that you collect it all into a usable data map for industrial AI solutions for maximum return on investment.
3. Classify use cases according to their potential impact on the balance sheet
Choosing the right AI industrial use case for your business is critical to achieving a positive ROI. By giving more weight to use cases that improve the cost side of the balance sheet and/or generate new revenue streams, you can consciously approach those use cases first that have the greatest potential for ROI.
For companies looking to take advantage of AI, the three combinations discussed here will be a great starting point. By working closely with industry leaders, we hope to continue our endeavors to demonstrate that profitability and sustainability are possible with AI.