Technological and methodological advances in Big Data management, analytics and cognitive computing (i.e. the tools at the base of artificial intelligence) are bringing revolution to factories and offices around the world, improving workflows and company performance.
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Such as? We think of a work ecosystem where it is possible to obtain maximum visibility on all processes and machines. It has a widespread control over both operational and decision-making functionalities. Every device involved in production and business activities has intelligence, or the ability to detect information through sensors. They send it thanks to connectivity and through the network to analytical systems in the cloud or on premise. Here the data is crossed with a myriad of structured information from multiple sources (ERP and CRM solutions, Internet sites, social networks, digital apps and services, e-commerce portals, etc.). Then the data is processed to obtain hidden evidence useful for business operations.
Artificial Intelligence accelerate decisions, trigger automatisms
Thanks to insights, it is possible to improve and accelerate decision making, making it more aware, accurate and reliable. This is because it is based on large volumes of objective data. This also triggers automatisms to correct inefficiencies and optimize processes, accelerating continuous improvement paths. In the latter case, therefore, the same intelligent devices will receive instructions from analytical systems. This will adjust workflows to eliminate anomalies or bottlenecks, maximizing results.
http://eecoswitch.com/compliance/ Algorithms: the beating heart of Artificial Intelligence
But throughout this process of continuous improvement based on artificial intelligence, a fundamental ingredient has not been explained. Algorithms, or “mathematical” formulas to correctly transform raw data into value information to make decisions and activate automatisms.
Algorithms are the heart, the true essence of artificial intelligence that simulates the human capacity for learning, reasoning and problem solving. They allow us to understand the cause-effect relationships, hypothesize future scenarios, predicts events and, therefore, formulate solutions in the short and long term.
How Artificial intelligence improves Business
For example, let’s consider the case of a production plant dotted with smart devices. They detect environmental parameters and information on the state of health and operation of machinery. All this data is sent to the analytical engines, make it possible to identify any malfunctions and correct them before they can cause damage. This is the so-called predictive maintenance. The algorithms allow to calculate the risk of failure based on information gathered in real-time directly from the production lines. This allows an exact, punctual planning and without wasting maintenance interventions.
With these available data, the analytical systems can also identify possible corrections to be made to the manufacturing and logistic processes based on the type of production. For example, they can send inputs to the drive technologies to regulate the speed of the conveyor belts. This is according to the goods to be moved, or remotely adjust the temperature of the cold rooms to best preserve certain foods.
In the manufacturing sector, think of the importance of artificial intelligence to anticipate consumption and forecast work orders in order to optimize production volumes. It is possible to predict future orders and adjust production rates, avoiding waste, reducing inventories and accelerating time-to-market.
However, it is not only on a purely productive level that algorithms can make a significant contribution to improving business performance. Analyzing data coming from the web and apps, from consumer interactions with smart devices, from use by users of digital services, marketing managers can intercept consumption habits and product satisfaction. Thus anticipating customer needs and the evolution of demand.
Roadmap for AI implementation
In short, the advantages of artificial intelligence to improve the company are decisive and evident. However, the roadmap to realize the potential linked to Big Data Analytics and cognitive computing is not so obvious. First of all, it is necessary to limit the application field and the problems that must be solved. Hence the second step, which turns out to be a fundamental key to decree the success of the project. In fact, it is a matter of identifying the data needed for processing. Also the algorithms that allow finding the correct answers to the questions.
We then proceed to the phase of planning and realization of the intelligences, or rather the mathematical models and the cognitive computing functionalities specific for the application field are developed. Thus ensuring the ability to identify solutions and triggering automatisms. Finally, it follows the actual implementation phase within the system, remembering to think from the perspective of continuous improvement. The algorithms must be continually refined and challenged in order to obtain increasingly accurate and punctual answers. This minimizes the margins of error.
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