Some of the following examples of machine learning in business may be more applicable to some verticals than others, but each one can be a game-changer.

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SwissCognitiveMachine learning in business didn’t take long to go from a technological fantasy to a practical and accessible reality. Companies all over the world can begin using ML today to augment or replace many of their processes. The result is a leaner organization with more effective and efficient processes.

Some of the following examples of machine learning in business may be more applicable to some verticals than others, but each one can be a game-changer.

Monitoring Supply Chains for Supply and Demand

The economy is more global than ever and consumer expectations are higher than ever. Balancing supply and demand within supply chains was already a challenge. With the addition of shipped foods and other perishable goods, the stakes are even higher. E-commerce sales for consumables and foodstuffs rose by 21.7% in 2018, reaching $58 billion.

Keeping grocery stores and climate-controlled warehouses stocked practically demands machine learning. Therefore, companies increasingly rely on ML to monitor the reality on the ground and deliver actionable — oftentimes proactive — insights. These insights may be based on:

  • Historical and real-time sales data
  • Weather and seasonal forecasts
  • Geopolitical shifts
  • Vendor activities or supply chain disruptions
  • Real-time store or online traffic

ML programs can study cause-and-effect more thoroughly and quickly than humans. As a result, they help vested parties reach decisions about quantities of products to stock, promotions to run, store hours to adjust, warehouses to relocate, and many other mission-critical factors.

Nobody needs reminding just how complicated and legally fraught the business world can be. In the worlds of business law and risk management, machine learning is increasingly indispensable.

One way ML programs provide aid is through their ability to study large numbers of lengthy legal documents. These can include insurance policies, business contracts, and invoices. With machine learning capable of parsing natural language across hundreds or thousands of documents, companies can better understand or find errors within contracts or invoices. The advantage over manual business or process audits can be substantial.

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Machine learning in business yields insurance opportunities as well. ML algorithms can study the factors which may result in liability across a whole company. The result is a more accurate and complete picture of that company’s risk portfolio, which potentially means savings on insurance policies compared with a blanket policy that provides more protection than is necessary.

Predicting Equipment Failure and Optimizing Maintenance Intervals

Anticipating failure in critical equipment is a longstanding engineering problem. Traditionally, companies relied on the break-fix model. This is where the equipment had to show obvious signs of struggling — such as longer takt times or more manufacturing errors — or fail completely before intervention.

This model is far from efficient. It results in unnecessary downtime and avoidable wear and tear on the equipment. Thanks to embedded sensors and logic boards, companies can now engage in predictive maintenance with edge computing.

For a typical example of predictive maintenance in action, consider the problem of keeping an industrial pump online:

  • Pumps often operate in difficult-to-reach areas, including in oil and gas fields, underwater or in large holding or mixing tanks. Proactive manual inspections can be costly and time-consuming.
  • Sensors embedded within the pump capture real-time data on signs of impending failure, such as lost pressure, lack of suction, or leaks.
  • From here, trained machine learning models compare the state of the pump against known fault states, such as leaking valves, worn-out bearings, or blocked plungers.

With real-time data on the condition of the equipment and historical benchmarks for “satisfactory” and “ideal” operation, the ML system can alert engineers about failures before they happen.

ML can also make recommendations on ideal machine maintenance intervals. Doing so keeps industrial systems running at maximum efficiency with a minimum amount of wear, thereby optimizing replacement cycles and helping investments go further.


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