Assessment of Human Resource Utilization A bank has foreclosed on Core-Line Software, a software…

Assessment of Human Resource Utilization A bank has foreclosed on Core-Line Software, a software development company. Although Core-Line has a strong level of sales, its costs are far too high and it cannot repay its loans. The bank had hoped to line up a buyer before foreclosing; however, none was willing to pay the banks price. As a result, the bank has been forced to foreclose and operate the company until a buyer can be found. In order to recoup as much of its loans as possible, the bank has decided to seek advice on how to improve the operating efficiency of the company. With
Assessment of Human Resource Utilization A bank has foreclosed on Core-Line Software, a software development company. Although Core-Line has a strong level of sales, its costs are far too high and it cannot repay its loans. The bank had hoped to line up a buyer before foreclosing; however, none was willing to pay the banks price. As a result, the bank has been forced to foreclose and operate the company until a buyer can be found. In order to recoup as much of its loans as possible, the bank has decided to seek advice on how to improve the operating efficiency of the company. With increased efficiency, the bank hopes to obtain a higher price. Because the banks staff has no experience in the software business, it has retained a consulting firm to advise it on how to improve the efficiency of the companys operations. Because labor costs constitute the bulk of software firms expenses, Core-Lines utilization of human resources is an obvious area in which to look for potential improvement. Like most software companies, Core-Line has a full array of specialists, including programmers, graphic artists, systems analysts, project managers, systems architects, and the like. The consulting firm has decided to begin by assessing Core Lines efficiency in the utilization of programmers by comparing its utilization level with other software developers. In order to obtain data for its recommendations to the bank, the consulting company has conducted a survey of other software developers. The survey was designed to obtain information on several business practices, including questions about staffing levels and human resource utilization. (The consulting firm has already determined that Core-Lines salaries are average for the industry.) Unfortunately, although the consulting firm has done some work with software developers and has a good reputation, many firms refused to supply data. Consequently, only 40 percent of the questionnaires were returned, for a total of 65 responses. Nonetheless, the consulting firm has decided to proceed with an analysis of the data. The analysis of the data will employ a comparative approach in which each responding companys staffing level of programmers (dependent variable) will be compared with potential explanatory variables (independent variables). In order to control the influence of each independent variable, the consulting firm has decided to use the statistical technique of multiple regression analysis. Each of the 65 responses will constitute a separate observation in the analysis. Thus, in the regression analysis, the dependent variable (Y) is the number of programmers. The independent variables are the dollar volume of annual software sales (X1), average number of hours worked each year by programmers including overtime (X2), average years of experience of programmers (X3), a rating of the quality of programming supervision (X4), and a rating of the complexity of the software produced by each company (X5). The rating of the quality of supervision is a five-interval scale ranging from 1 (poor) to 5 (good). Similarly, the software complexity rating scheme is a five-interval scale ranging from 1 (simple) to 5 (complex). The regression model is Y = c + d1X1 + d2X2 +d3X3 +d4X4 + d5X5 + g, where c is the intercept or constant; d1, d2, d3, d4, and d5 are the respective regression coefficients derived in the model; and g is the error term. In order to derive the values for c, d1, d2, d3, d4, and d5, the consulting firm will run the regression analysis, with each observation being composed of matched data from one of the 65 different software companies. (Under most conditions, the value for g is assumed to be 0.) If the model is adequately specified, statistically significant, and a substantial amount of variance in the dependent variable is accounted for, the consulting firm will have confidence in the statistical justification for using its parameters. The adequacy of explanatory power is indicated by the R 2 and the overall models F values. (For the purposes of this case, assume that the model is statistically adequate on all of these indicators.) To predict the number of programmers that Core-Line should be using, which is the value for _, the consulting firm will plug into the regression model Core-Lines X values for annual sales, average annual hours worked by programmers, average experience of programmers, rating of the quality of programmer supervision, and rating of software complexity. Using this model or equation, the consulting firm will then compute the number of programmers (_ value). This predicted value or industry standard will then be compared with CoreLines actual utilization of programmers in order to determine if it is overstaffed. Further, for each independent variable having a statistically significant relationship with the dependent variable, the positive or negative sign of the regression coefficient will provide direction for potential solutions to utilization problems. Questions 1. What are the implications of using data from a survey of other companies in which only 40 percent of the sample chose to respond? How could one determine whether there is any potential bias in the returned questionnaires? 2. What potential variables has the consulting firm left out of its model? How would any omitted variables affect the results? 3. Because the consulting firm has employed a cross sectional approach, it cannot pick up any productivity trends. How might this be a problem? 4. What other methods should be used to determine whether Core-Line is utilizing its programmers efficiently? 5. How is the process of benchmarking similar to the process employed by the consulting firm?