Downtime for today’s big, multi-layered companies is more than a basic inconvenience. The cost of interruptions, particularly when employees are kept from doing their jobs, can be tremendous. A recent Gartner study indicated that a large business may lose $540,000 per hour or more from an avoidable technical failure.
Service businesses that quickly perform equipment upkeep and fixes can significantly reduce downtime and save hundreds of thousands of dollars. A major challenge is achieving this reality is not having the sufficient knowledge or tools on hand. Service businesses don’t have the inventory to allow for every technician to bring every tool or part.
Without a comprehensive evaluation of equipment wear and dependability, scheduled maintenance may be insufficient to stop downtime, as service schedules are based on experience, intuition and luck. Service businesses are now progressively seeking to embrace predictive analytics to get rid of time wasted and optimize profits.
A new wave of predictive analytics businesses is looking to address this multi-billion-dollar problem. Using predictive AI, these companies have created algorithms that foresee where and when service will be required and which tools the technician should have on hand.
Below are a few ways AI is being used to minimize downtime.
Uncertainty is a major driver of downtime. Predictive AI can eliminate uncertainty by assessing statistical information at a scale that’s impossible for humans. By analyzing a variety of pertinent variables that can result in failure, maintenance schedules can then be created that are effective and designed to save time and money.
This tactic has already been utilized to great effect. Imaginea is one company that has been using predictive analytics in the healthcare industry to overcome staffing and other human resource issues by examining resource availability, schedules, operating hours, historical records and other factors.
Among the most powerful cases for predictive AI in almost any industry is the capacity to make smarter, data-driven company decisions. Predictive analytics has already been used to optimize production and crucial operations and enhance customer service. Predictive AI will scour all available information and offer actionable insights for a company. Insights can be delivered to executives and supervisors alike.
According to Gartner, at least 50 percent large companies around the world are already using advanced analytics and unique algorithms to boost profits.
Among the biggest areas where service businesses lose profit is when they neglect to optimize pricing. Service businesses can use real-time pricing optimization founded on parts availability, demand and other factors to maintain profitable margins. A combination of AI and machine learning can calculate all available information to allow a company to more precisely know how much to charge each distinctive customer.
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