Execution and results
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The first output provided by the OCRA digital tool is an overview of the partial scores for each task, used to highlight the incidence of each working task on the overall value of the OCRA index. As shown in the sample table below, in the Output 1 sheet, which is created after one code run, the absolute and % values of the ATA and RTA of each task are computed; in addition, a partial risk feedback at the task level is provided:
Lastly, the desired outcome of the OCRA methodology is provided in the Output 2 sheet: the overall value for the light and left part of the upper limbs of the OCRA Index and the correspondent Risk Range:
The OCRA toolkit has been tested in two implementation modes:
• Independent OCRA execution with all data loaded by the operator
• Semi-Automated OCRA execution with task processing time estimation through MOST suite integration on python.
The second mode allows an operator to derive the OCRA index in two steps:
Loading task data on the MOST input file transferring the Processing times and Technical actions list on the OCRA excel file
Loading shift & postural data, by copying and pasting values from the FPRpy file created ad hoc by the OCRA-MOST link python code, and run the OCRA toolkit to get the OCRA index and the Cycle Total Risk
In this way the data collection time is significantly reduced, providing a faster toolkit implementation given that the user has already been trained for handling the MOST toolkit too. It's crucial to specify that the MOSt-OCRA link is represented by the FPRpy sheet that is created ad hoc for this implementation mode once the OCRA-MOST data transfering code has been run. In particular, the columns of task code, task name, move type and task duration have all their values set as links to the FPRpy sheet.
This guide provides key pillars for understanding the main theoretical concepts behind the OCRA methodology; furthermore it supports the user in the correct interpretation of the different OCRA sheets in excel, highlighting the main constraints and providing examples of the outputs of the toolkit.
The properly take advantage of this methodology the user should bear in mind that:
This toolkit is based on assumptions that, despite being reasonable, don't perfectly reflect the theoretical concepts of the OCRA method
The OCRA method works efficiently with tasks whom processing time are not equally distributed along a cycle
To avoid uncontrollable errors in the python code execution, the user shouldn't change the names in any column of each excel sheet;
To avoid wrong reading of the data in python, the user should check that all the columns that have not been filled with data, have been left with empty values (not null values). 200 rows have been set for the FPRdata sheet and the AdMdata sheet.
