No abstract

A detailed analysis of the measured absenteeism data for employees in a forensic science laboratory (FSL) is presented. About 134 employees out of 172 were used as a sample. The factors assumed to affect absenteeism included differences in gender, departments/units, period of the year, skill-levels and professions. The data were collected throughout a calendar year, using a biometric system infrastructure incorporating fingerprint capturing device, IP camera, database server and software (Bio Star Version 1.62). The calendar was used to establish the total working days and hours while employee absenteeism data in percentage were established by computation of days and hours absent for a given period, based on days or hours attended. Equations were derived to determine the absenteeism ratios expressed in percentage (A bd and A bh). The absenteeism ratio, A bh , was observed to be the most appropriate parameter. The business development department (BDD) shows the lowest A bh compared to other departments. Female employees show higher A bh and lower A bd than male employees, while accountants show lowest absenteeism compared to Employees in professional level who had highest absence rate compared to skilled, semiskilled and unskilled employees. Most hours were lost during September-December compared to the rest of the year. The results reveal higher employee absenteeism in the FSL affecting its performance.

Case-file backlogs were identified as one of the cause factors affecting the competitiveness of the forensic science laboratory (FSL). Backlogs represent case-files that remain unprocessed or unreported within a selected time interval (year, week or month) which leads to increased customer complaints, rework, cost of analysis, degradation of biological samples, etc. Case-file backlogging was quantified in three consecutive years (2014 to 2016), using the following parameters: case-files received and case-files processed, difference of which gives case-files backlogged. There was a need to define time interval for a case-file to be regarded as backlogged (that is, one week), results of which can translate into backlogged case-files per month or year. A data collection tool was established and used for three work stations (forensic chemistry, biology/DNA and toxicology laboratories). The tool includes starting and ending date for each time interval, in which the numbers of case-files received and processed were entered followed by computing the backlogs. It was observed that, case-files reported increased between 2014 and 2016 leading to a decrease in backlogged case-files. The annual percentage of the case-files backlogged was highest for forensic toxicology. The highest number of case-files backlogged was observed for forensic chemistry, followed by forensic biology/DNA. The number of case-files backlogged per analyst per year was highest in 2014 and dropped continuously towards 2016, being comparably higher in forensic biology/DNA and chemistry. Probability density functions (PDFs) and cumulative distribution functions (CDFs) of backlogs data indicated that a large number of backlogs created in previous weeks were eliminated. It was concluded that the effect of case-file backlogging on FSL competitiveness can be minimized by continued management effort in backlog elimination.

Turnaround time (TAT), is the total time interval from when a request for forensic laboratory analysis is received until when the results are collected by the client. The performance of the forensic science laboratory (FSL) is affected by extended TAT in the case-file and sample processing steps necessitating critical analysis reported in this paper. The total TAT was obtained as the sum of measured time interval for each work station (six of which were studied). Extended TAT leads not only to customer complaints, but also paves way for customers to seek for services from competitors, leading to lost competitive edge for the FSL. This study was conducted to establish the baseline data on TAT (between 2014 and 2015) to enable implementation of corrective actions. Six casefile processing steps were identified for which starting and completion times were recorded in dates, giving TAT values in days. The TAT data for each step was collected as each case file is processed and analyzed separately using statistical analysis while comparing the data for the two years (Y2014 and Y2015) and among three forensic science laboratory disciplines (biology/DNA, chemistry and toxicology). The overall turnaround time (TTAT) was the highest for forensic biology/DNA compared to forensic toxicology and chemistry. The analysis time (TAT 2) was the longest of all six case-file processing steps. Using Pareto analysis, the three major steps necessitating root-cause analysis and intervention to minimize TAT were analysis turnaround time (TAT 2), report collection time (TAT 6) and report review time (TAT 4). It was concluded that the causes for extended TAT are within control by the FSL management, although financial and human resources are required.

This paper presents the detailed analysis of academic development index and longevity among forensic science laboratory (FSL) employees as the key factors for improving organizational performance based on human capital development. The data were collected from human resource database involving 171 (88%) employees out of 195. New mathematical formulations were developed for academic development index A d , a measure of the time delay in academic development while working within FSL, simple longevity (L s) and complex longevity (L c), based on years served and academic certificates attained. The values of A d , L s and L c were compared for different units and departments including zonal laboratories between Y2014 and Y2016. Both total and average values of L s , L c and A d indicated an imbalance in the distribution of staff in different laboratories, necessitating reallocation to improve performance. The employment trend analysis shows that the work force has been diversified from Y2004 to Y2016 leading to improved management of finance, procurement and human resource in the FSL. As a result of a training program, the percent of staff with MSc has been increasing from about 3.5% in Y2004 to 9% in Y2015. The average values of longevity and academic development index were observed to be the better parameters for comparing laboratories or units than the total values. Results show a balanced staff distribution based on L c and A d is inevitable for improved performance. It was concluded that the new indices (A d and L c) are important tools for describing the development of the workforce and competitiveness of the FSL.