Business Intelligence for Making Construction Safety Possible!

Hassaan
Hassaan
Sep 9, 2018 · 3 min read

Construction safety is the most important aspect for contractors consuming 8% of payroll. But even with this huge share in total construction cost, the serious injury rate of the construction sector is more than twice than rest of the sectors combined, according to WorkSafeBC. A Business Intelligence (BI) model not only help the senior management, executives, and clients to take informed decisions to save this 8% cost but can also reduce the serious injury rate.

Conventionally, WorkSafeBC reports safety as the incident type occurred at the site. For example, hand injury, eye injury, overexertion etc as shown below.

However, if this convention is modified and a Business Intelligence model is developed based on the frequency rate method, it not only save project cost but can also reduce the injury rate per person in the construction sector. Frequency rate method works on 5 different indices (which can be the five datasets in the safety database of project or program). Based on these 5 indices database, I developed my project progress report using SQL Server Reporting Services (SSRS) and MS Report Builder.

Database: Health, Safety, and Environment
Datasets — Total datasets=5:

Dataset 1: Loss Time Injury Frequency Rate (LTIFR)
The first dataset is for Loss Time Injury Frequency Rate (LTIFR) which is the number of lost time injuries occurring in a workplace per 1 million hours worked. This is calculated by multiplying Number of Loss Time Injury (LTI) happened with one million and then dividing it by the number of hours worked.
Fields:
(a) Number of Loss Time Injury (LTI) happened
(b) Number of hours worked

Dataset 2: Medical Injury Frequency Rate (MIFR)
The second dataset is for Medical Injury Frequency Rate (MIFR) which is the number of non-lost time injuries occurring in a workplace per 1 million hours worked. This is calculated by multiplying the number of non-loss time injury happened with one million and then dividing it by the number of hours worked.
Fields:
(a) Number of non-loss time injury happened
(b) Number of hours worked

Dataset 3: Total Recordable Injury Frequency Rate (TRIFR)
The third dataset is for Total Recordable Injury Frequency Rate (TRIFR) which is the number of fatalities, substitute work, and other injuries requiring treatment by a medical professional per million hours worked. This is calculated by multiplying the number of fatalities happened with one million and then dividing it by the number of hours worked.
Fields:
(a) Number of fatalities happened
(b) Number of hours worked

Dataset 4: Hand Injury Frequency Rate (HIFR)
The fourth dataset is for Hand Injury Frequency Rate (HIFR) which is the number of hand injuries happened per million hours worked. This is calculated by multiplying the number of hand injuries happened with one million and then dividing it by the number of hours worked.
Fields:
(a) Number of hand injuries happened
(b) Number of hours worked

Dataset 5: All Injury Frequency Rate (AIFR)
The fifth dataset is for All Injury Frequency Rate (AIFR) which is the total number of minor and major injuries happened on site per million hours worked. This is calculated by multiplying the number of injuries happened with one million and then dividing it by the number of hours worked.
Fields:
(a) Number of injuries happened
(b) Number of hours worked

After compiling all this data in database, I presented them in daily, bi-weekly, and monthly project status reports as shown below. I developed this report by using the construction database, particularly Safety database consisting of 5 datasets as described above. I analyzed the 5 datasets and preferred to present them in bar-chart and pie-chart using SSRS and MS Report Builder. All other report items are also developed using MS Report Builder which I’ll describe in separate articles.

Business Intelligence is a powerful tool for the improvement of construction project management. The field/site data can be collected with drones and recorded in the database of the project. The datasets can be generated from the database to form reports for the senior management, executives, and clients to make informed decisions.

Hassaan

Written by

Project management and business intelligence consultant for capital civil engineering, rail, and transit projects

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