Prepare input data

In this section we will discuss the format of the OCRAinput excel spreadsheet, highlighting the main characteristics of each table, the constraints on the data that the operator should load and how to load the outputs by running the python code.

### Organisational data: the Shiftdata Sheet

Inside the Shiftdata sheet, data involving the organisational parameters are loaded by the operator who generally has good knowledge of his/her work schedule and his/her daily working behaviour in terms of unofficial and scheduled breaks. All data regarding time measures are expressed in minutes and the excel file will automatically compute in the same sheet:
• The Total net repetitive work time in the shift (D): derived by subtracting to the shift duration the values of the three rows below it (look at the section on D of the chapter 1.1:"The number of actual technical actions performed in a shift: ATA" for the formula explanation).
• The Duration Multiplier (DuM): computed through the following if condition implemented on excel: \$SE(B10<=120;2;SE(B10<=180;1,7;SE(B10<=240;1,5;SE(B10<=300;1,3;SE(B10<=360;B10;SE(B10<=420;1,1;SE(B10<=480;1;SE(B10<=540;0,83;SE(B10<=600;0,66;SE(B10<=660;0,5;SE(B10<=720;0,35;0,25)))))))))))\$.
This function replicates the DuM table provided in the section on "DuM" of chapter 1.1. B10 is the cell reference for the figure "D". (Language in the excel solver: ITA)
• Recovery Multiplier (RcM): computed through an if condition on excel that reflects the RcM table provided in the section "RcM" of chapter 1.1: \$SE(B6<=0,5;1;SE(B6<=1;0,9;SE(B6<=1,5;0,85;SE(B6<=2;0,8;SE(B6<=2,5;0,75;SE(B6<=3;0,7;SE(B6<=3,5;0,65;SE(B6<=4;0,6;SE(B6<=4,5;0,52;SE(B6<=5;0,45;SE(B6<=5,5;0,3;SE(B6<=6;0,25;SE(B6<=6,5;0,17;0,1)))))))))))))\$ where B6 is the cell reference for the figure "Number of hours without a recovery period" provided as input by the operator.
• Organizational Risk Score (ORS): computed through an if condition that reflects the working rythm of the operator relative to the one of the machine (look at "ORC" section in the chapter 1.2) : \$SE(B9=0;1;SE(B9=1;8;SE(B9=2;12;"Errore")))\$
where B9 represents the ORC code considered in the toolkit and inserted by the operator:
• 0: Free work pace
• 1: Work pace set by machine but with buffers
• 2: Work pace set entirely by the machine

### Force and Postural data: the FPRdata Sheet

The FPRdata sheet represents the core of the ergonomic risk assessment of each operator's task; as shown in the table above, the sheet gathers data regarding:
• The Technical Actions performed by the operator inside one cycle, with the correspondent code that identifies them in the process in order to avoid misunderstandings when a same T.A. is executed more times in different tasks;
• The correspondent Task Code that allows the python code to group the T.A. in one task during the ergonomic risk assessment;
• The Duration of each Technical Action involved in the cycle which is used to derive the Cycle Time, required for both the RTA and the ATA computation;
• The Force Load measured in each T.A. execution, expressed in values of the Borg CR-10 scale. Bear in mind that, due to the presence of ad hoc constraint on the data insertion, the values of the force load can be either null or the ones of the Borg scale.
• The Posture Codes for the 4 Upper Limb areas have been defined in this way:  Constraints on the data insertion for all these codes are already built in excel in order to respect data consistency and to support rapid data processing in python.

The AdMdata sheet collects, at task level, information regarding the possible physio-mechanical (P.M.) risk conditions of the operator. Due to the simplicity of the industrial case on which this toolkit has been validated, (a set of mounting/dismounting operations not requiring the usage on any peculiar tool a part from screwdrivers) we have formulated the following weak assumption, that might be dropped in future improvements of the toolkit to consider more general industrial cases:
Assumption: Considering a single task, at most one among the PM code can be assigned by the operator i.e. at most one Physio-mechanical risk condition can happen. Following the logic of building a consistent machine-readable input file for our toolkit, we have considered 8 PM codes reflecting the possible P.M. risk conditions shown in the literatur and in the "AdM" chapter 2.3.

### The hidden sheets: Shiftpy and PSTpy

Two additional sheets are worth of mention in the Excel input file for the OCRA methodology:
• Shiftpy sheet: Not seen by the user interface, this hidden sheet as the role of supporting the python toolkit to read easily the column names in the correspondent Shiftdata sheet, ensuring a one-to-one correspondence of values and avoiding errors in the reading phase.
• PSTpy sheet: This specific sheet has no meaning for the user but it's crucial to speed up the phase of Posture scores computation for all the T.A. in the cycle. It represents in a single table the set of scores for all the posture areas of the body, with an increasing value that is functional to the %time spend in the posture: 