Site Safety Analytics 

 Challenge & Selection 

 One of our manufacturing sites is considered as the most dangerous work place . There are thousands of   risk events every year caused by accidents knowingly or unknowingly . These accidents not only cause   severe health / safety issues but also lead to huge financial loss . 

 Our team grouped together with the operations team to analyze the problem and realized that quality   data was being captured for the above risk events . We then decided to use the Learning Launch tool to   tackle this problem and prevent the occurrence of similar accidents and promote workplace safety as per   below assessment : 

 a. Analysis of historical accidents is crucial . Based on the results of root cause analysis , proper 

 actions can be taken by safety professionals and management to eliminate or lessen the identified   causes . 

 b. It is also noted that one major factor contributing to the risk of an accident is the presence of 

 destructive items such as distorted & damaged equipment , sharp articles nearby . Mitigating   strategies can be made accordingly after identification of such matters . 

 Application 

 To come up with a prototype application , we started analyzing the problem from two perspectives : one   from management ’s policies and other one was technology driven . We have presented our ideas and got   acceptance by Leadership for further action .      Dedicated web portal was designed to enhance the safety management processes such as safety   education and interactive training focusing on individual / organizational characteristics such as workers ’   attitudes towards safety to increase their awareness .     This portal was even integrated with visualization based on large number of disaster study reports   generated after a fatal accident providing a complete description of the accident in the form of   unstructured text data that were manually analyzed to classify the causes with an objective to find hidden   patterns and present the trends that emerge from the data .      Insight & Approach 

 Our application had helped the sites to understand the causal factors of the risk events with intuitive   visualization which clearly illustrated that the accidents are considered an outcome of interaction among   physical conditions of the work site , environmental factors , administrative issues and human error .   Feedback received was that the effect of this strategy for preventing injuries was limited due to their   reactive nature . 

 We would like to move towards the AI based risk model that emphasizes more on automatic classification   of incidents from a massive number of reports into different cause categories which will help in better   understanding of reports , predict and prevent the occurrence of site accidents . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Site Safety Analytics 

 Challenge & Selection 

 One of our manufacturing sites is considered as the most dangerous work place . There are thousands of   risk events every year caused by accidents knowingly or unknowingly . These accidents not only cause   severe health / safety issues but also lead to huge financial loss . 

 Our team grouped together with the operations team to analyze the problem and realized that quality   data was being captured for the above risk events . We then decided to use the Learning Launch tool to   tackle this problem and prevent the occurrence of similar accidents and promote workplace safety as per   below assessment : 

 a. Analysis of historical accidents is crucial . Based on the results of root cause analysis , proper 

 actions can be taken by safety professionals and management to eliminate or lessen the identified   causes . 

 b. It is also noted that one major factor contributing to the risk of an accident is the presence of 

 destructive items such as distorted & damaged equipment , sharp articles nearby . Mitigating   strategies can be made accordingly after identification of such matters . 

 Application 

 To come up with a prototype application , we started analyzing the problem from two perspectives : one   from management ’s policies and other one was technology driven . We have presented our ideas and got   acceptance by Leadership for further action .      Dedicated web portal was designed to enhance the safety management processes such as safety   education and interactive training focusing on individual / organizational characteristics such as workers ’   attitudes towards safety to increase their awareness .     This portal was even integrated with visualization based on large number of disaster study reports   generated after a fatal accident providing a complete description of the accident in the form of   unstructured text data that were manually analyzed to classify the causes with an objective to find hidden   patterns and present the trends that emerge from the data .      Insight & Approach 

 Our application had helped the sites to understand the causal factors of the risk events with intuitive   visualization which clearly illustrated that the accidents are considered an outcome of interaction among   physical conditions of the work site , environmental factors , administrative issues and human error .   Feedback received was that the effect of this strategy for preventing injuries was limited due to their   reactive nature . 

 We would like to move towards the AI based risk model that emphasizes more on automatic classification   of incidents from a massive number of reports into different cause categories which will help in better   understanding of reports , predict and prevent the occurrence of site accidents .
### Output: 