{"id":196,"date":"2023-10-26T14:29:09","date_gmt":"2023-10-26T14:29:09","guid":{"rendered":"http:\/\/adamaisolutions.local\/?page_id=196"},"modified":"2024-02-20T20:27:15","modified_gmt":"2024-02-20T20:27:15","slug":"product","status":"publish","type":"page","link":"https:\/\/www.adamaisolutions.com\/?page_id=196","title":{"rendered":"Product"},"content":{"rendered":"\n
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Presto<\/h1>\n<\/div>\n\n\n\n
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As a risk predictive tool PRESTO
is the \ufb01rst of its kind<\/h5>\n<\/div>\n\n\n\n
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REQUEST DEMO<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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PRESTO goes beyond traditional risk tools that offer static assessments based on historical data.<\/strong><\/p>\n\n

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It dynamically understands and predicts risks by considering various attributes that influence risk levels, offering prescriptive recommendations to proactively address potential events.<\/p><\/p>\n\n<\/div>\n<\/div>\n\n\n\n

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PRESTO employs machine learning algorithms to assess and manage individual workers’ Occupational Health and Safety risks, taking into account past incidents, training history, and years in the position.<\/strong><\/p>\n\n

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Additionally, it considers real-time factors such as weather conditions, the worker’s physical state (monitored through wearables), social media interactions, and implicit risks associated with specific operations to provide a comprehensive risk assessment.<\/p>\n\n<\/div>\n\n\n\n

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PRESTO calculates a numeric risk index based on its assessments.<\/strong><\/p>\n\n\n

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When this index surpasses a predefined threshold, the system triggers alerts and suggests preventive actions to mitigate incidents in situations where the risk level is considered excessive.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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PRESTO calculates a numeric risk index based on its assessments.<\/strong><\/p>\n\n\n

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When this index surpasses a predefined threshold, the system triggers alerts and suggests preventive actions to mitigate incidents in situations where the risk level is considered excessive.<\/p>\n<\/div>\n\n\n\n

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Talk to our Team<\/h2>\n

Learn more about the range of our services.<\/p>\n<\/div>\n

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CONTACT US<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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Safety & Sustainability<\/h2>\n\n\n\n
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Data Discovery and Analitycs<\/h4>\n\n\n\n

PRESTO represents a leap in industrial safety for the railway sector, specifically tailored to the needs of Italy’s leading railway engineering company.

In the demanding and dynamic environment of railway engineering, PRESTO’s predictive capabilities are pivotal. The tool harnesses real-time biometric data to craft risk indices, providing a nuanced understanding of workplace hazards. This digital advancement ensures heightened worker safety by anticipating potential incidents, thereby embedding a culture of prevention over reaction.<\/p>\n<\/div>\n\n\n\n

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\"prestoData<\/figure>\n<\/div><\/div>\n<\/div>\n\n\n\n
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How to make sense of your safety and sustainability data sets?

\nUsing our 100+ years\u2019 combined expertise and applying artificial intelligence and machine\nlearning techniques we will provide useful insights for your safety and sustainability\nmanagement and support your performance improvement.

\nADAM provided data discovery and anaytics services to several large manufacturing\ncompanies to identify incident patterns and improve data management for prevention.
br>\nADAM supported Finscience in the definition of an alternative ESG score to help investors\nand companies looking to measure sustainability performances.<\/p>\n<\/div>\n\n\n\n

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Case Study<\/h4>\n\n\n\n

Presto application to a railway construction site<\/p>\n

From November 2021 to November 2022, Italferr, alongside Elettri-Fer and Salc,\ntrialed PRESTO to enhance on-site safety. The approach involved comprehensive\ninitial assessments, including a review of work plans and extensive field\nobservations. Workers were integral to the process, from initial consultations about\nthe project and devices to providing vital feedback on the system's efficacy. By\nleveraging wearables and IoT systems, PRESTO offered a dynamic predictive model\nthat informed decision-making, minimized human error, and cultivated a secure\nworking environment. The tangible benefits were seen in improved risk\ncommunication and a more informed, safety-conscious workforce.

\nPRESTO may represent a leap in industrial safety for the railway sector, specifically tailored\nto the needs of Italy’s leading railway engineering company.

\nIn the demanding and dynamic environment of railway engineering, PRESTO's predictive\ncapabilities are pivotal. The tool harnesses real-time biometric data to craft risk indices,\nproviding a nuanced understanding of workplace hazards. This digital advancement ensures\nheightened worker safety by anticipating potential incidents, thereby embedding a culture of\nprevention over reaction.<\/p>\n<\/div>\n<\/div>\n\n\n

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Safety data analytics<\/p>\n

We helped a large chemical company based in Italy to understand where there is\nroom to improve their safety management and performance based on their past\nincident and near miss data.\nIn the data discovery phase we used rule based techniques and topic mining\nalgorithms to develop insights to prioritise future safety programs and initiatives.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n<\/div>\n\n

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References & collaborations<\/h2>\n \n
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Research<\/h2>\n
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DYNAMIC SAFETY RISK PROFILING \u2013 A PILOT PROJECT<\/strong><\/h6>\n

La profilazione dinamica del rischio sicurezza \u2013 resoconto di un progetto pilota in ambito ferroviario<\/p>\n

by Mario Foresta Pierluigi Lozi Michele Villa, 2023<\/p>\n <\/a> \n <\/div>\n

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WORK ACCIDENT PREVENTION<\/strong><\/h6>\n

The power of safety data to prevent work related accidents: empirical evidences from pilot projects in Italy<\/p>\n

by M. G. Gnoni, M. Villa, A. Donvito, A. Guglielmi, G. Campo, D. De Merich, D. de Santis, A. Leva, M. Pellicci, 14th International Conference on Applied Human Factors and Ergonomics (AHFE), S. Francisco, USA 2023<\/p>\n <\/a> \n <\/div>\n

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OCCUPATIONAL ACCIDENTS IN TUNNEL CONSTRUCTION<\/strong><\/h6>\n

Infortuni sul lavoro nel settore delle Gallerie – Analisi dei dati e proposte operative<\/p>\n

by Giuseppe CAMPO Diego DE MERICH Daniele DE SANTIS Antonia DONVITO Armando GUGLIELMI Antonio LEVA Mauro PELLICCI Michele VILLA, 2021
\n An analysis of injury and operational proposals for prevention<\/p>\n <\/a> \n <\/div>\n

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ESG ALTERNATIVE SCORE<\/strong><\/h6> \n

Reviewing Finscience ESG Alternative Score<\/p>\n

by Shanshan Zhu, Antonia Donvito (Finscience); Michele Villa (ADAM), 2020
A first analysis of the ESG score applied to S&P500<\/p>\n <\/a>\n <\/div>\n

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STRESS DETECTION AND WEARABLES<\/strong><\/h6>\n

Stress detection with wearable devices and Machine Learning<\/p> \n

by Christopher Ottesen, 2019
An automated machine learning approach to create a stress monitoring system with data from wearable devices<\/p>\n <\/a> \n <\/div> \n

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HOW TO TRACK YOUR HRV<\/strong><\/h6>\n

Heart rate variability: A new way to track well-being<\/p>\n

by Marcelo Campos, Harward Health Publishing, 2017
\n Tracking HRV to measure well-being and motivate behavioural change<\/p>\n <\/a> \n <\/div>\n

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PERSONAL STRESS AND WORKER SAFETY<\/strong><\/h5>\n

Stress and worker safety<\/p>\n

by Tom Musick, Safety+Health, 2016
Stress may lead to workplace injuries<\/p>\n <\/a> \n <\/div>\n

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WORKER SAFETY IN THE CONSTRUCTION SECTOR<\/strong><\/h6>\n

Ridurre gli incidenti nel settore delle costruzioni: uno strumento a supporto delle decisioni<\/p>\n

by Danilo Bucchieri, Alberto Rosso, Michele Villa, Convegno Societa\u2019 Italiana Gallerie, SAIE Bologna Ottobre 2020<\/p>\n <\/a> \n <\/div> \n <\/div> \n <\/div> ","protected":false},"excerpt":{"rendered":"

Presto As a risk predictive tool PRESTOis the \ufb01rst of its kind PRESTO goes beyond traditional risk tools that offer static assessments based on historical data. It dynamically understands and predicts risks by considering various attributes that influence risk levels, offering prescriptive recommendations to proactively address potential events. PRESTO employs machine learning algorithms to assess […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-196","page","type-page","status-publish","hentry"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t