Model Description:
In this investigation, we began by setting up a multiple linear regression model of the collected data and encompassing all of the independent variables described below. The regression model was then analyzed with the aid of the MiniTab and SPSS computer based statistical analysis software packages. The outcome of the model investigation was then analyzed for significance and goodness of fit to the sample. In order to determine the relationship between the dependent variable “duration” and the various independent variables, the regression model was set up as follows:
Dependent Variable (yhat = DUR): Duration (measured in total calendar days by subtracting open date and time from closed date and time)
Independent Variables (x):
x1 = #FORMS = Number of Forms in Department (measured in terms of quantity each). The number of forms in the department is the most apparent and definitive measure of the “load” on the department and varies over time as indicated by Figure 1 in the Figures and Tables section of this report.
Note: Because the data for total number of forms in the department at any given time was not recorded by the Special Applications Database, a manual calculation procedure was performed to determine the number of forms in the department for the sample data set.
Department Personnel …. Made up of the Number of Engineers in the Department and the Number of Designers in the Department at any given time.
x2 = #ENG = Number of Engineers in the department
x3= #DES = Number of Designers in the department
As with the department load, represented by the number of forms in the department, the number of Engineers and Designers has also varied over time, as represented by Figure 2 in the Figures and Tables section of this report.
Because the personnel in a department is the truest measure of a departments “workload capacity”, we would expect both coefficients, b2 and b3, to be negative indicating that the more people working on forms/projects within the department, the faster the forms/projects would be completed.
Form Type …. Because the Form Type variable is qualitative, it must be converted into quantitative terms in order to be processed via the regression software. To make the conversion to quantitative terms, the Form Types ER and RFQ will be assigned a separate dummy variable as detailed below. Because the qualitative variable can only be one of the three values (ER, RFQ or DR), we only need two dummy variables to represent all three values (i.e. if neither dummy variable is true, then the third variable, by deduction, must be true):
x4 = ERFORM =1, for Form Type = ER
x4 = ERFORM = 0, for Form Type <> ER
x5 = RFQFORM = 1, for Form Type = RFQ
x5 = RFQFORM = 0, for Form Type <> RFQ
(Both dummy variables measured in True/False terms 1 or 0)
Because Form Type is a qualitative variable and the 3 possible values for this variable have been represented in this model by the 2 dummy variables x4 and x5, the contribution to the overall lead-time by the Drawing Request form type is buried in the y-intercept coefficient, bo, and we therefore assumed the coefficient b4 to be positive and b5 to be negative since the effort typically required of Drawing Requests in somewhere between an ER and an RFQ, with the RFQ typically requiring the least concentrated effort and therefore impacting the overall form duration/lead-time the least.
Category …. Because the Category variable is also qualitative, it must also be converted into quantitative terms in order to be processed in via the regression software. To make the conversion to quantitative terms, the Category variable will also be assigned two dummy variables as detailed below:
x6 = MODCAT = 1, for Category = M (Moderate)
x6 = MODCAT = 0, for Category <> M
x7 = CMPLXCAT = 1, for Category = C (Complex)
x7 = CMPLXCAT = 0, for Category <> C
(Both dummy variables measured in True/False terms 1 or 0)
As with the Form Type variable, the Category variable is also qualitative, represented by 2 dummy variables with the third and least significant Basic Category influence buried in the y-intercept coefficient, bo. Because of this, we would expect the coefficients, b6 and b7, for the moderate and complex dummy variables, x6 and x7, to be generally positive.
Based on the above coefficient definitions, the Estimated Multiple Linear Regression Model took the symbolic form:
yhat = bo + b1x1 + b2x2 + b3x3 + b4x4 + b5x5 + b6x6 + b7x7
= bo + b1#FORMS + b2#ENG+ b3#DES + b4ERFORM+ …
…+ b5RFQFORM+ b6MODCAT + b7CMPLXCAT
