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How Capital-Intensive Businesses Maximize Return on Assets with AI [5 Examples]

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Artificial intelligence (AI) is a tool that can be used by nearly any business, regardless of sector or size. The technology has particular value, though, for capital-intensive businesses.

As you’ll recall from Econ 101, in labor-intensive businesses, the majority of costs are related to employee or contractor work, whether in the form of physical labor (cooking, cleaning, assembling) or something more cerebral (design, bookkeeping, coding). In capital-intensive businesses, however, the majority of costs stem from investments in property, machinery, heavy equipment, or other fixed assets required to produce a good or service.

Because these assets represent such a sizable ongoing expense, businesses that rely on them need to optimize their operations to reduce costly inefficiencies. That’s why AI has proven to be so valuable, as it enables sophisticated asset optimization solutions to be implemented without requiring large-scale data science support. The results are increased operational visibility, continuous operational improvements, and more efficient and informed operational decision-making.

In fact, 49% of manufacturers say that artificial intelligence, machine learning, and advanced analytics initiatives will be the single most important factor in their competitiveness in the next two to three years according to Accelerate Your Data-Driven Transformation, a commissioned study conducted by Forrester Consulting on behalf of RapidMiner.

5 Ways Capital-Intensive Businesses Maximize Return with AI

So how are capital-intensive businesses leveraging the power of AI to improve their bottom lines? Let’s look at five specific examples of how AI is being leveraged today.

1. Improving quality control

Even in capital-intensive industries like manufacturing, human labor has always been vital for quality control tasks like inspecting completed products on an assembly line. Even the sharpest set of eyes can still get tired and distracted, though, which is why costly flaws and imperfections can still happen.

Thankfully, smart cameras and other AI-enabled technology are helping manufacturers optimize their quality inspection procedures for greater speeds, efficiency, and above all accuracy. Image recognition programs are able to identify prominent product features (such as color, curves, corners, etc.) that are relevant to the inspection process. Then a rule-based system for evaluation is created that determines how narrowly a product has to fall within given parameters for said features (how red does an apple need to be, for instance) in order to pass.

Every product is different, of course, but these programs have amazing flexibility and can accurately process almost any manner of good. In fact, a McKinsey study found that image recognition programs may increase defect detection rates by up to 90% compared to human inspection.

2. Optimizing production

Companies with heavy assets have been “digitizing” their plants for decades now, but until that digital data is plugged into AI, it’s of limited value. Sensors can describe what’s occurring, but they can’t tell you why you’re getting certain outputs or how that process could be improved. 

AI programs, though, can sort through vast amounts of machine data looking for patterns and relationships that humans could never recognize. This functionality can be extremely beneficial for improving production procedures, as you can train AI to monitor and analyze any number of relevant variables, including:

  • quantities processed
  • cycle times & lead times
  • environmental data (like temperature)
  • production errors
  • machinery downtime

AI will consider production as a holistic process, noting how changes in one variable impact other variables and outcomes. This analysis can be performed using historical datasets alone, but it’s even more beneficial when historical figures are combined with real-time data generated by sensors or other digital devices, which can be represented by a digital twin.

Looking to drive real business impact with AI?

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3. Maximizing employee efficiency

Even the most capital-intensive businesses still rely on human labor. With AI, though, you can limit the time employees spend on routine tasks, especially physical ones performed in highly structured and predictable environments. A McKinsey study of the German manufacturing sector, for example, found that around 55% of all activities currently conducted by humans have the potential to be automated.

This doesn’t mean your workers suddenly become redundant. Every job includes various types of activities, each of which has different requirements for automation. By equipping AI to handle time-consuming and often monotonous tasks, your employees can free up time for value-added activities better suited to their talent sets.

For example, many businesses employ AI to detect and deter computer security intrusions, but these tools have not eliminated the jobs of IT security professionals. Rather, AI has freed up time that would otherwise be spent reviewing mundane network activity, enhancing their analysis capabilities, and better positioning them to deal with increasingly sophisticated attacks.

4. Maximizing machine efficiency

AI can help machinery operate more efficiently in several ways, most notably through predictive maintenance. Right now, machines are generally serviced either (a) when a component becomes nonfunctional, or (b) when regular maintenance is scheduled, whether needed or not. The latter option is certainly better than the former, but still represents a costly disruption in production.

Thankfully, AI presents a third option. Predictive maintenance models are developed using real-time data from IIoT sensors, which can be analyzed along with historical operational data and environmental data. These models thus provide a comprehensive overview of how machines are operating at the moment as well as how they are expected to perform in the future. Components can then be replaced as soon as any signal of failure occurs or when aging criteria (based on usage and predicted lifespan) is met.

When compared to other maintenance approaches, predictive maintenance models significantly reduce machine downtime. In fact, a McKinsey study found that AI-based predictive maintenance can generate as much as a 10% reduction in annual maintenance costs, 25% reduction in downtime, and 25% reduction in inspection costs.

AI can boost efficiency in other ways as well—for instance, by identifying periods of time when energy-intensive machinery operation is more cost-effective due to electrical rates.

5. Conducting research & development

AI can be used as a research tool for projects in any domain regardless of industry or organizational size. And AI-based methodologies can be leveraged to identify goods and services that are most likely to be marketable and increase efficiencies within developmental processes.

Siemens, for example, relies on AI to help with the planning, installation, and optimization of railway equipment product lines and systems. During one such project in China, AI was able to determine the optimal configuration (out of 1090 possible combinations) to secure complex interlockings and maintain the highest level of train control. 

A recent report by the National Bureau of Economic Research goes as far as to argue that AI “has the potential to change the innovation process itself.” And the aforementioned McKinsey study that AI can generate a 10 to 15% productivity gain in R&D projects, anywhere from a 10 to 40% time-to-market acceleration, and an overall decrease in development risk.

Wrapping Up

Artificial intelligence is the key to transitioning from mass data collection to strategic data management and analysis. That’s why capital-intensive businesses in any industry (industrial, agricultural, pharmaceutical, transportation, etc.) can leverage AI to become more operationally efficient and better able to compete among workforce shifts and market volatility.

If you want to get a sense of how to measure the value of your capital-intensive AI projects, take a look at our whitepaper Talking Value: Impactful Machine Learning Models for Industry 4.0. We’ll walk you through how to think about assessing value and applying solutions, step by step.

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Additional Reading

Chris Doty

Chris Doty

Chris has a PhD in linguistics, and has previously worked on ML projects for Amazon's Alexa. As RapidMiner's Content Marketing Manager, he works to evangelize for the power of AI and ML to upskill and empower people in a changing world. When he isn't working, he enjoys learning languages and drawing.