This high-speed, real-time "chess game" is the real value of combining IoT and ML. For example, if you have a warehouse full of robots, the optimal solution for the enterprise may involve some robots taking convoluted routes to free space for others engaged in high-priority tasks. Thus far, the focus of IoT has been on communicating with individual devices so we can control them, but the real value lies in the ability to get all your devices to work together for your company’s goals and, in doing so, become much greater than the sum of their parts.Īchieving this unification requires two things: an immediate and accurate view of the environment as it was less than a second ago and the ability to use ML in real time to orchestrate all your devices at the same time to pursue a common goal. The Internet of Things (IoT) will revolutionize many industries, especially manufacturing and logistics, but this goes far beyond adding sensors to things. Command And Control For The Internet Of Things Similarly, a logistics company can use machine learning to analyze real-time streaming data to determine the best routes and better predict demand, which can then help them maintain optimal staffing levels to reduce costs and maximize margins. Organizations can use this information to figure out how to automate repetitive tasks and streamline recurring work processes.įor example, a manufacturer can use machine learning algorithms to monitor production lines, identify potential bottlenecks and suggest or even autonomously trigger corrective actions. When machine learning algorithms are fed a steady stream of real-time data, they can quickly detect bottlenecks, anomalies and other operational inefficiencies. What might your company be able to do with that much more cash? This is another area where real-time data and machine learning can make a massive impact. Improved Operational EfficiencyĪccording to one study, operational inefficiency can eat up as much as 30% of an organization’s revenue. This information can then be used to do things like prevent fraud and create hyper-personalized customer experiences. Machine learning algorithms can rapidly analyze vast sums of data, uncovering patterns, insights and trends. These capabilities become even more pronounced when real-time decision making is coupled with machine learning.
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