Covid-19 has proved to be one of the most significant challenges the global economy has had to face in the recent period. The protracted crisis has highlighted the importance of knowing how to manage business resilience processes to mitigate the risks that threaten organizations. The ability to withstand adversity, while maintaining key workflows, and to protect employee health has become the focus of a business survival strategy. It’s not just pandemics that threaten business development. Adversity in the weather, cuts in energy supplies and attacks on cybersecurity can also affect the business of a company and require a quick and secure response.
Investing in the Internet of Things allows companies to be competitive and maintain productivity even in a complex period such as the one we are experiencing due to the pandemic. The application of smart solutions to the supply chain and production processes allows to increase the level of automation. Smart sensors, installed on machines and in the workplace, acquire useful information to increase production, safety and energy efficiency. The IoT can be used for remote monitoring of production plants and for predictive maintenance, reducing the intervention of employees in the event of a breakdown.
Going beyond the opportunities offered by IoT-based solutions, it is possible to apply a combination of technology, data analytics and behavioral science: the Internet of Behavior. By collecting data not only from connected devices, but also from information about users’ online actions, we get much more insight into their behavior patterns. This information can help address instability problems, predicting people’s decisions and promptly adjusting the business to them. In large-scale distribution or in stores, it is possible to monitor customer flows within the store to learn more about purchasing behavior and identify the points of greatest interest in the store, where specific commercial offers can be entered. It is also possible to use facial recognition to determine the gender, age and mood of consumers to propose personalized promotions. In goods transport, sensors on board vehicles can provide information on the driver’s driving style and make information available to improve safety, fuel consumption and vehicle performance, checking the wear of tires and other components that need maintenance.
The risk assessment processes are linked to the collection, management and analysis of data. Artificial intelligence can support big data analytics and generate real-time scenarios to identify and investigate risks more accurately and in significantly shorter time frames. It also allows you to design more thoughtful disaster recovery plans, studying vulnerabilities in the organizational infrastructure and simulating the worst outcome. AI is also used to increase productivity by reducing waste. For example, by installing a vision system with Machine Learning algorithms on a painting line, it is possible to progressively “learn” from the detected defects, trace the causes and make corrections to improve quality. Similar applications can be used in welding, where measurement systems verify the work done by robots, which can self-adjust based on the indications received to reduce defects and rejects.
Automation is not just about industrial equipment; it is also about using software to perform tiring and repetitive tasks that previously required human intervention. Robotic process automation can be used in many situations, from simple data entry jobs, such as copying and pasting rows of data from a database to a spreadsheet, to preparing invoices, to more complex tasks such as customer support and the resolution of specific problems. By using these solutions in administrative / accounting and management reporting processes, workers can free themselves from data entry and control and dedicate most of their time to analyzing business results and more strategic assignments.
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