Development of a Competitiveness Improvement Framework for Forensic Science Laboratories

This study presents a methodology used in developing the competitiveness improvement framework (CIF) for laboratories, in particular, Forensic Science Laboratories (FSLs). The cyclic nature of FSL processes allowed data collection for the purpose of identification of factors affecting FSL performance (cause factors). Flow charts were used to represent mathematical formulations for cause factor measurements and quantification of the baseline data on turnaround time (TAT), backlogs for case files (B g ), turnaround time in the supply chain (T sc ), and employee absenteeism (A b ). By quantifying the cause factors in addition to academic development coefficient (A d ) and complex longevity (L c ) for FSL employees, it was possible to establish the organizational design features requiring improvements. The relevance of cause factors to FSL stakeholders and means of improvement and sustainability were established. A detailed road map towards CIF was presented using D-MAIC methodology. The rated cause factors based on challenges in the FSL business environment were subjected to Pareto analysis to prioritize the challenges in order to improve FSLs’ competitiveness. The interrelationship between the three dimensions of competitiveness improvement (process, performance and planning) was presented in terms of the affected six cause factors. Also, the potential lean practices for improving competitiveness of FSL based on measured cause factors have been presented. This paper introduced methods and measures for improving operational competitiveness of laboratories. The CIF was finally presented in a form of a series of three flow charts summarizing all steps implemented in its development with inputs and cause factors involved.


Need for Developing the Competitiveness Improvement Framework for FSL
The FSL consists of a series of connected processes beginning at SRO's office (when samples arrive) and ending at the same area where clients collect their analytical reports. In this study, the critical performance measures and cause factors were identified in order to develop the CIF. The contribution of this study to the global knowledge is mainly on forensic science laboratory management, which is a new research area involving competitiveness improvement. After establishing the baseline performance data, the study scope was extended to locating areas of improvement and the approach.
The current structure of forensic science services in Tanzania is defined using the basic processes shown in Figure 1 (starting from a crime scene to judgment at a court of law). The study area started from the time samples/exhibits are submitted to the laboratory to the time when a report is released.
The competitiveness scenario of FSL focuses on differentiation of services from its competitors and elimination of customer complaints due to delayed reports (extended TAT and backlogs). It was important to use the lean principles (LPs) based on essential characteristics and power to identify the non-value added activities (NVAs) [1]. can be minimized by continued management effort in backlog elimination [6] [7] [8].

Extended Turnaround Time during Case-File and Sample Processing
TAT is the total time interval from when a request for laboratory analysis is received until when the results are collected by the client [9] [10]. The performance of the FSL is affected by extended TAT in the case-file and sample processing steps. This has not been subjected to intensive research, necessitating critical analysis [11] [12] [13] [14]. The total TAT was obtained as the sum of measured time interval for each work station, six of which were studied [15] [ 16]. 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 Y2014 and Y2015) to enable implementation of corrective actions [14]. Six case-file processing steps were identified for which starting and completion times were recorded in dates, giving TAT values in days. The overall turnaround time (TTAT) was the highest for forensic biology/DNA compared to forensic chemistry and toxicology (137.7, 76.4 and 54.5 days on average, respectively). The laboratory analysis time (TAT 2 ) was the longest of all six case-file processing steps (being 94.9, 31.8 and 10.4 days, for biology/DNA, toxicology highest in PQSD at 26.91%. Female employees show higher A bh (at 17.54%) and lower A bd (at 20.36%) than male employees (at 12.93% and 20.36%, respectively), while accountants show lowest absenteeism (at −4.94%) compared to other professions (12.58% and 36.58% for chemists and technologists, respectively). Employees in professional level had highest absence rate (24.0%) compared to skilled (13.6%), semi-skilled (12.7%) and unskilled employees (9.5%). Most working hours were lost during September-December compared to the rest of the year. The results reveal higher employee absenteeism in the FSL affecting its overall performance as lost productivity [26] [27] [28].

Employee Longevity and Academic Development
A detailed analysis of academic development index and longevity among laboratory employees was conducted aimed at improving organizational performance [29]- [35]. Data were collected from human resource database involving 171 The employment trend analysis indicated that the work force has been diversified from Y2004 to Y2016 leading to improved management of finance, procurement and human resource in the laboratory. As a result of a training pro- and competitiveness of any laboratory as they lead to human performance improvement (HPI) [36] [37].

Turnaround Time for Supply Chain Management Processes
The Supply Cycle Time (SCT) denoted as T sc , depends on all factors that affect the turnaround times across its key components (the user, supplier, tender board, the procurement management unit performance). Baseline study revealed wide variations in T sc , giving a mean of 105.6 days, which is extended. Reduction in T sc must involve the FSL management and all key players in the SCM processes. However, the reduction of SCT by elimination of wastes in the processes is difficult as most of the SCM steps in between are pre-requisite in the said process and the steps are mandatory. Thus, step by step reduction in the T sc is required. Lack of knowledge on procurement processes by staff in the user departments is another challenge to the FSL competitiveness. The T sc is also extended due to the fact that most of the laboratory suppliers involve a relationship with third party vendors, which implies a complex relationship with the FSL.

Impact of Process Variables on FSL Competitiveness
The pillars of the FSL competitiveness include processes and organizational design. All these areas have been studied in details, before developing the CIF.
Turnaround time analysis was accomplished by identifying action points to be assessed [9] [38] [39], followed by detailed data collection. Moreover, the backlogged case-files, N cb , were also repeatedly captured at different action points by determining the number of case-files received and reported [5].
The laboratory turnaround time can also be defined differently according to the test type or analysis requested. The "total testing cycle" describes TAT as a syndicate of eight stages: collection, identification, transport, preparation, analysis, reporting, interpretation, and action [40].
Supply chain management (SCM) is an integrative philosophy to manage the total flow of a distribution channel from supplier to the ultimate user [41] [42].
In this study, SCM analysis comprised of: determination of the TAT components in SCM; determination of procurement performance measurement system; interaction between the user, procurement management unit (PMU), accounts and supplier, and determination of the key metrics for supply chain management (financial and other metrics).
Organizational design analysis included case-file flow steps, supply chain management and human resources, and the actual human resource management (HRM). HRM involves forecasting and placement, staff experience and longevi-

Resource-Based Competitiveness
Researchers have advanced various methods for improving performance of laboratories including competitiveness, for example, a resource-based method [45] [46]. The term resource is meant anything which could be thought of as strength of a laboratory as an organization. According to this broad definition, resources can be tangible or intangible. In this case, organizations perform poorly because either they possess resources that are not in demand, own resources that are not scarce or do not own such resources [46] [47]. Improving performance of a laboratory requires understanding the core processes and factors hindering these processes. Intervention under this method would comprise of searching for resources that are in demand, scarce and appropriated by the organization, such as working areas, machinery, skilled and well developed staff, motivated staff, etc. Improving performance should adopt an industry-based method, which relies on the principles that laboratory success is based on its ability to harness opportunities and tame threats that exist in the business environment [48] [49].

Effect of Extended Turnaround Time in Supply Chain Management on FSL Competitiveness
Supply chain management (SCM), is among the factors affecting FSL competitiveness. In this study, supplier-laboratory interactions have been assessed based on T sc and financial metrics, etc. In FSL, the role and importance of the supply chain is mainly understood as evidence supply chain or the storage of evidence at the FSL. In this study, however, SCM focuses on material and services flow from suppliers. One of the methods which can be used to assess the key performance of supply chain management in the laboratory setting is consideration of issues affecting supply chain [50]. However, there is a need of having supply chain operators responsible for managing inventory flow to ensure that the goods arrive at the right place at exactly the right time, that is, the procurement management unit (PMU).
Supply chain performance measures (SCPM) serve as indicators of how well the supply chain system is functioning. Measuring SCP can facilitate a greater understanding of the supply chain and allow improving its overall performance [51] [52]. Different perspectives of SCPM are cost and non-cost perspective, strategic, tactical or operational focus [53] business process perspective and financial perspective [54]. In this study, the focus was on the challenges especially the turnaround time at every stage of product supply consideration and hence the total turnaround time, T sc . The focus of SCM was the availability of services, consumables, reagents, chemicals, instruments or equipment for all laboratory settings.

Models for Improving Organizational Competitiveness
Competitiveness is a multidimensional concept, observed from different levels: national and organizational or firm level. Competitiveness means involvement in a business opposition for markets, in this study used to describe economic strength of entity laboratory with respect to its competitors [55]. Laboratory or firm-level competitiveness is defined as the capacity of the laboratory to design, produce and or sell its products or services superior to those offered by competitors, considering the price and non-price qualities.
Researchers suggest different framework designs for competitiveness. While others suggest a framework with three folds: performance, potential, and the management processes, others suggest a framework that involves a combination of assets and processes, where assets are either inherited (natural resources) or created (infrastructure) and processes transform assets to achieve economic gains from sales to customers [56]. Other authors present an approach which emphasizes the role of factors internal to the laboratories such as strategy, structures, competencies, capabilities to innovate, and other tangible and intangible resources for their competitive success [57] [58]. In order to provide customers with a greater value of satisfaction than their respective competitors, laboratories must be operationally efficient, cost effective, and quality conscious [59].
Competitiveness improvement process seeks to identify the important factors and performance of core processes including the human resource processes, operations management processes (that is, laboratory processes based on case-file management and supply chain management) and planning or strategic management processes. Balancing the core processes will enhance the ability of laboratory to compete more effectively. Researchers view sources of competitiveness as those assets within the laboratory organization that provide advantage which can be tangible or intangible [60].
Business design for FSL, therefore, has two principal requirements: sustaining casework productivities in a timely and efficient manner, whilst adhering to quality standards and the timelines of the investigatory and judicial processes.
The design involves also developing and executing major programs of infrastructure investment and process re-engineering. Competitiveness is the ability of a laboratory or country to offer services and products that meet the quality standards of the local and global markets at prices that are competitive and pro- oped covers also multidimensional aspects of organizations namely, organizational processes, design, culture and politics [64]. The need to consider the environment is also taken into account in particular the stakeholder demand of services, and challenges in the laboratory business environment [48].

Utilizing the Cyclic Nature of the FSL Processes
(CIF) applies repetitive business processes such as the case flow management (CFM) assessed thoroughly in this research. In the FSL model setup, each case-file passes through several action points and complete processing is achieved before passing to another point. Whenever any action is completed time taken was noted while, if the case files are not completed in seven days, the case file is regarded as back logged. When another request for analysis of samples arrive, the same process is repeated at varying performance, measured using TAT, case-file backlogs (B g ), coupled with assigning staff of different traits (A d , L c and A b ), leading to a dynamic cyclic process of different characteristics. Figure 2 shows the decision diagram for conducting CIF analysis by observing designated events at different action points. Table 1 summarizes the parameters or cause factors assessed after capturing data in this study.
In forensic science laboratory, the repeating business takes place within the organization design or in the processes taking place within the laboratories. All the six parameters, or cause factors for FSL competiveness can be tracked in a repeating mode using the case-files as a unit of measure for laboratory issues (B g , TAT), purchase order processing (T sc ) and absence/presence of staff with different traits (A d , L c ) based on assignment case files (number of case files per analyst per year) and biometric database on daily basis (A b ). However, intervals among action points differ from one case-file to another which was measured leading to wide data volume for TAT and B g . The processing time was also different for each purchase order, leading to data on T sc .

Capturing Variations in the Total Turnaround Time and Backlogs during Case-File Management
Variations were identified in the turnaround time (TAT) for sample analysis.  Case-file backlogs were studied as function of time for case-files received and processed in Y2014, Y2015 and Y2016, as a function of time for all 52 weeks of the calendar years, and also as a function of laboratory disciplines within the FSL.
T sc All purchase orders are initiated and completed leading to a spread in the values of T sc .
Turnaround time between different action points within the FSL TAT for internal and external SCM processes during order processing.

TAT
All case files go through the process from receiving to collection.
TAT studied as function of time for case-files received and processed in Y2014 and Y2015, and also in different laboratory units.

A b
All staff report to work daily, punch in and punch out leaving traces of absence or presence which can be measured.
Absenteeism studied as a function of time for three months periods of Y2016. The studied covered factors affecting absenteeism such as departmental setups, zonal laboratories, skill levels, professions and gender. The identified six components of the total turnaround time (TTAT) were also identified, which in turn vary from one case-file to another [14]. Each time interval between action points was determined as the difference between receiving and completion of processing time in the same action points. The initial and final times were recorded in dates giving TAT values in days, as shown in Figure  3. For simplicity, during TAT analysis, some of the time intervals were combined to give one delay reducing the number of time intervals or action points from 20 to 6. Meanwhile, some actions could proceed in parallel (where the reference date remains the same). Figure 3, the number of backlogged case-files was determined for each laboratory discipline, by comparing case-files in and case-files out, in a specified time interval, say 7 days [5] [14].

Capturing Variations in the SCM Performance Measures
Turnaround time variations were also captured in the supply chain management. The supply chain cycle time, denoted as T sc which measures the total time required to complete the order. The methodology used to establish the T sc is complex due to wide involvement of staff and stakeholders, as well as large  The supply cycle time, T sc , including payments acknowledgement, was determined as difference between date payments are made and acknowledged, T pa and the date purchase order is requested by the user department, T os . Table 2 summarizes the time variations by years studied and purchase order sampling details for determination of T sc . Each purchase order processing undergoes the same cycle, based on which, variations in the cycle times were captured.

Variations in Staff Absenteeism
Measurements of absenteeism ratio, A b , started with collecting data for individual employee on daily basis using the biometric system, from which, the ratio was expressed in terms of hours or days absent. For 192 staff logged into the system, this is a complex data set to manage [25]. Thus, the data was grouped into three time intervals (4 months), departments, units, gender, professions and skill levels to characterize and compare the average values. The determination of A b is characterized by long time interval (3 months in this case), data screening to eliminate staff with special absence like study leave, maternity leave or sick leave, wrong data entry and errors, which reduces the sample size. The mathematical formulations implemented for absenteeism analysis can be summarized using a flow chart as shown in Figure 4, which shows two different outcomes of absenteeism data analysis, that is, day-based and hour-based absenteeism (A bh and A bd , respectively) [25].

Variations in the Staff Academic Development
Another variation within the laboratory was identified in the staff academic development, experience and longevity which originate from individual employee data. The cyclic nature on this case stems from the fact that during employment

Methodology for Implementation of Pareto Analysis
Given the six cause factors measured and implemented in the CIF development process, each factor was assessed on its relationship with: a) challenges in the FSL business environment and b) applicability of the lean practices for improving FSL competitiveness. The business environment challenges studies were 8 while the lean practices for CIF were 5, the so called variants. Let N v = number of variants, that is, number of challenges in the business environment or number of lean practices (N v = 8 and 5 respectively), and N cf = number of cause factors measured and assessed for CIF development, that is, N cf = 6. Then, using a scoring system of N s = 1, 2 or 3 for low, medium and high connection between the cause factors and the challenges or practices respectively, the total score, S tot , for a given variant, was established based on Equation (1): The total score values for each variant, were then used to establish the percentage frequency distribution, using Equation (2).
Finally, Pareto analysis was conducted by analyzing the F d values.

Variations in the Turnaround Time
Based on TAT analysis, the performance of FSL disciplines shows strong variations between Y2014 and Y2015 as presented in Figure 6. For forensic chemistry,  the number of case-files completed within one day of submission increased from 2% in Y2014 to 45% in Y2015. For biology/DNA, on the other hand, an opposite scenario was observed, as the performance decreased leading to very few case files completed in a given time of 50 days from 50% to only 35% of case files. For forensic toxicology, improved performance was observed, for instance, at a given TAT 2 of 50 days, 60% of case files were completed in Y2014 which increased to 85% in Y2015. Cases where performance dropped require intervention if the FSL is to remain at its competitive edge.

Variations in the Backlogged Case-Files
According to Figure 7, variations in the laboratory performance with time for each discipline are obvious. The laboratory backlog data was assessed for each calendar year, from Y2014 to year Y2016. Figure 7 shows the cumulative distribution for B g data for 52 weeks in each year. By drawing a vertical line at B g = 10, using the collected data in three consecutive years, it is evident for DNA laboratory that the backlogs increased between Y2014 and Y2015 and decreased between Y2015 and Y2016. For forensic toxicology, backlogs increased between Y2014 and Y2015 while in forensic chemistry, backlogs decreased. Thus, variations in cause factors have been captured using data collection tools applied to the cyclic nature of the processes, allowing for identification of root causes and taking action to improve and sustain.

Harnessing Variations in Cause Factors for Competitiveness Improvement
One of the first steps towards FSL competitiveness improvement was to establish where the FSL stands among other organizations, that is, bench marking. The six  parameters or cause factors affecting competitiveness were studied in details and quantified between Y2014 and Y2016 so that improvements and sustainability can be measured in the next few years or later. This study is thus used as a defin- itive guide on what needs to be improved. Table 3 gives what is needed to improve based on the pre-determined performance factors and how to approach the improvements. In details, gives the key steps applicable to each cause factor starting from relevance of each factor to FSL, stakeholders and baseline or current status are given. Means of improving and sustaining competitiveness via these cause factors are also presented in Table 3. Finally, future plans for sustaining competitiveness are also summarized.
Not shown in Table 1

Dimensions of the FSL Competitiveness
The competitiveness improvement framework involves individual staff, product (laboratory reports) and FSL services (receiving/processing exhibits, expert witness and training for sampling officers). Single measures of competitiveness (e.g. finance, SCM, etc.), do not capture all the elements of the research issue, it was necessary to examine performance, potential and management processes (shown in Figure 8), in order to evaluate critically the changes or level of competiveness and interrelationship. The Venn diagram in Figure 8 shows the interrelationship between the competitiveness dimensions based on the cause factors measured and analyzed in this study.

Competitiveness Improvement by Managing Performance in the Laboratory
Under this component, the study measured the status of A d , L c and A b which are related to staff capability or tools to enable them push the organization towards These factors identified and shown in Figure 8 and Table 3 and Table 4 demand a lot from the human resource team, all of which are based on performance management issues. Thus, performance management is much of a comprehensive and a complex function, as it incorporates activities such as mutual goal setting, continuous progress assessment and regular communication, response and training for improved performance and implementation of employee development plans. In addition, FSL employees should continually be seeking ways to improve their own performance, to take ownership for their work, and reinforce team working, thereby improving worker motivation.

Improving Competitiveness via Management of Laboratory Processes
Another aspect of CIF portrayed in Figure 9 is process management, a concept that integrates quality, performance and excellence during accomplishing the tasks in the laboratory that connects the organization with clients (processes).
The process parameters identified in this study include TAT, B g , and T sc . These parameters should be managed while focusing at CIF as they connect with the outside customers or stakeholders.
Process management consist of process design or engineering (which is the development of new processes), process definition (narrative of the current processes), process documentation, analysis and control and process improvement. Process design and definition include describing the essential procedures to accomplish the tasks followed by describing the process using flowcharts, process maps or checklists. This will enable the process pertinent data to be collected, analyzed and improved. There are many process analysis tools, including cause-and-effect diagrams, statistical process control, and trend analyses. In this study, simple statistical analysis using PDFs and CDFs, Pareto charts were able to indicate areas requiring improvements (Tables 1-3). Process improvement may result from improvements based on many, small changes rather than few

Customers of the process
It is about understanding the customers, their needs and how to provide the services. The customers of a process are the people who require the products and services. Classified as: external customers (or people who consume the products and/or services of the FSL) and internal customers (the owners of the next phases in the process within the FSL).

Customer/supplier relationship
Concepts relating to client/supplier relationships and satisfaction are the right to expect quality products and services to internal or external client. The FSL employees are considered as the next phase in the process employees or the internal client. Also, each team (individual and team performance) should treat one another as valuable to bring improvement. In addition, the customer shall determine the product or service satisfaction level and the value expected from the supplier.
Relationship to quality, schedule and cost Evaluating and improve processes by establishing process baselines for quality, schedule, and cost. It is about highest quality products and services on or ahead of schedule and at the lowest possible price which are interdependent. In order to satisfy and retain its external customers, FSL should be competitive based on analysis and supply chain turnaround time by providing real-time information to internal and external customers. Design and simplify with minimal non-value activities for the customers such as defects or constraints. Modify the models based on progressive decision making, with coexisting engineering management. In addition, empower workers to undo time-wasting bureaucracy.
The process owners The process owners are the analysts (chemists and technologist) who understand about the processes which accomplish all activities in action point and accept accountability. Thus, process assessment and development should be done per day so as TAT reduction to become a reality.
How to improve Knowledgeable and accountable employees of the process are process owners. However, criticism from customers and suppliers contributes an unlimited need for improvement.
Responsible for improvement process Improvement is gradual and continuous, intense process redesign or re-engineering, and should be the basic part of process management and improvements. radical changes. Ideas for such changes come from the workers themselves based on the talents of the existing workforce and therefore easier to implement. Also, such changes involved in process management should not require major capital investment, consultants or expensive equipment.
For the purpose of CIF development, detailed review and conceptualization of laboratory processes were done using conceptual frameworks [5] [14] [25]. Given that a process is a series of connected steps or actions with a beginning and an  [14]. The FSL was viewed as a set or hierarchy of laboratory processes that yield analytical reports and services of value to the criminal justice system and investigative science, as well as a set of functions such as chemical or genetic profiling, expert witnessing, accounting, procuring of supplies or services, training and capacity building. Table 4 summarizes the process remarks with respect to the cause factors in relation to arrangement of processes.
For the laboratory to experience the highest levels of success and hence competitiveness, external and internal customers must be satisfied. Each laboratory staff (chemists, accountants and technologists) have the duty to understand their roles as suppliers to internal and external customers. Principally, customers want to be their suppliers' first priority. They deserve perfect analytical reports, which delivered on or ahead of schedule (client's charter). They expect suppliers to be in the improvement mode of operation so that the criminal justice system and the investigative science field are assured of a competitive third opinion expert advice to bring justice.
Process management has potential for improvement. The FSL management should focus on current issues to avoid performing analysis using old methods which are of no use to the criminal justice system. Furthermore, focus may be on quality, whereas reduced turnaround time is crucial. In addition, determining the supplier performance using rating system based on quality, capabilities, conformance to requirements is an important role in process performance measuring.

The Role of Policies, Procedures and Plans on Competitiveness Improvement
The third component of CIF presented in Figure 3 is the role of policies, procedures, programs and plans (strategic plans, staff training program, improvement programs, expenditure framework, etc.). The competitiveness and service viability of forensic science services (FSS) industry as well as its disciplines can potentially be enhanced by planning strategically for future. Laboratory strategic planning, especially in the selected priority disciplines can help set the stage for appropriate responses to the many dynamic changes and driving forces that impact the FSS industry. The FSS strategic planning must take into consideration the laboratory complexities, necessitating comprehensive perception. FSS faces continuous challenges. It is therefore required to have a constant and enduring strategic planning in a dynamic manner so as to encounter challenges in rapidly changing technologies within the FSS industry and dynamic personnel skills requirement. Planning strategically requires to be focused over the future for a period of years. The planning process must be effective in a continuous manner capturing FSL conditions, external environment, challenges and changing priorities. Regular updates are essential. Areas of emphasis for the FSS strategic planning will change over a period of time as the laboratory service industry priorities undergo transformation.

Identification and Elimination of Wastes in the FSL Processes
Anything that doesn't increase value in the laboratory processes and in the perception of the customers must be considered as waste and every effort should be made by the management to eliminate that waste. By understanding the different types of waste within the laboratory it is possible to eliminate or reduce impact of such steps to the performance and competitiveness. In this study, such wastes were reached at after detailed analysis of the cause factors, as summarized in Table 5.

Effect of Laboratory Business Environment on Measured Cause Factors
Services offered that differentiate FSL from other organizations are those that are associated with criminal justice. While the rest of laboratory services are subjected to competition. Commercial or private laboratories, research cum training laboratories, other government laboratories, are likely to give stiff competition to the FSL. Any laboratory is regarded as competitive if its services consider priority to regular customers, priority services with additional charges for faster services, discount on the cost, and continuous improved/innovative quality services. Also, quick and short delivery of services at reduced waiting time through a provision of mistake-proof or error-free services is a key to competitiveness. The CIF parameters shown in Table 6 were identified through data collection and application of the AHP methodology in ranking the factors. Table 6 shows the importance of the cause factors towards competitiveness and category of indicators to be utilized. The categories denoted as A, B and C have the following extended description: A-Outcome indicators that capture the final objectives of policy; B-Fundamental factor of competitiveness that structurally drives outcomes and which are core levels for policy intervention that can have sustainable impact; and, C-Control indicators that capture potential imbalance that have the potential to create high short term costs even if they don't drive outcomes in the long run. During development of the CIF, it was important to characterize the cause factors in relation to whether improvements can lead to increased customer awareness and satisfaction, increased number and strength of customer specifications or increased purchase power of customers. Moreover, the cause factors were assessed and rated in relation to coping with fast changing technology, large number of competitors, newer business models and practices, and also on the need for improved business infrastructure. Newer business models in the public laboratory like FSL are difficult to implement, giving chance to competitors in implementing such models like private sector and research institutions, laboratories. The last rating was focused on whether the cause factors are affected by frequently changing government policies or increased cost of manpower in order to improve, as shown in Table 7.
Using the total score determined from summation of the rating values for each challenge found in the business environment, as presented in Table 7  Analysis work in the process has stopped due to bottlenecked operations, equipment changeover and services Also, system response time, approvals from the laboratory manager may take long depending on the type of case and samples involved (such as challenging samples) Administrative duties for Director may delay approval of reports Lack of supplies-reagents and other consumables may cause delay in laboratory processes  Equipment reliability such as Genetic analyzer, LCMS/MS through Total Productive Maintenance (TPM) with service contract as part of SCM  Adequate staffing at the bottlenecked operations such as Backlog Reduction  Improve system reliability-scientific report capacity building  Push decision-making down to lower levels, that includes the users, technologists  Cross-train employees so that work can continue in absence (e.g. top managers)  Reduce batch sizes and run them more frequently, ultimately shooting for a batch size of one  Make sure all supplies are available  Increase number of identification equipment Overproduction  Printing analytical report before completing analyzing all the data  Purchasing items such as reagents and other perishables that are consumed seasonally or before they are needed  Producing reports that are not needed  Purchasing equipment that are rarely used  Over staffing in some areas  High sample influx and too many case-files arriving in short period of time  Establish a flow sequence to satisfy the downstream customer or supervisor (manager)  Create workplace SOPs, guidelines and regulations for each process  Create signal devices to prevent over processing, e.g.

FIFO lanes
Defects  Cross contamination, wrong labeling or overheating in re-amplification due to power interruption.  Multiple profile, contaminated STR profile  Mischaracterization of drugs of abuse  Defective or degraded biological samples  Inoperative machines lying without service or maintenance  Error-proof steps-working in set of two individuals  Decontamination of working bench and the FS laboratory processes  Good laboratory practices especially Checklists  Stocktaking of functioning instruments and equipment  Establishing and maintaining the service contract  Preventive maintenance schedule adhered according to manufacturer and installation engineer Under-utilized human resources  People's creativity, ideas, and abilities are not fully tapped  Limited employee authority and responsibility for basic tasks, management command and control  Losing ideas, skills, and improvements by not listening to employees  Institute the academic development program and consider tapping the employees with high complex longevity  Initiate employee suggestion systems  Form teams to solve process problems Excess processing  Processing of more samples or duplicate samples of the same case-file  Taking unneeded steps to process the samples that have shown to have no trace of searched chemical or low DNA  Inefficient processing due to poor tool and product design  Also, re-entering data, extra copies, unnecessary or excessive reports  Lack of statistical or arbitrary resampling techniques for similar items such as pellets or sachets of drugs of abuse  Perform preliminary/presumptive tests before undergoing into the major processes  Remove unnecessary steps  Use design for as manufactured for the specific instrument with the specified ratios  Stop unnecessary signoffs and reviews

Continued
Transportation  Movement of work or paperwork, sample, from one step to the next step in the analytical process or from one place to the other  Long distance movement between buildings or time consumed to reach the next location for processing  Sample management-proper packaging and storage for sample transportation  Make the distance over which something is moved as short as possible  Consider work cells and co-located teams  Establish chain of possession forms and adhere chain of custody and sample integrity Inventory  Any supply that is in excess or less, hence performing stocktaking to avoid pending cases, creating backlogging. Any form of batch processing  Producing more profiles, analytical or computing statistical data, than customer demand or submission  Movement of people, staff reallocation and placement  Purchase only enough to satisfy your downstream sample process and what is submitted to the laboratory  Ensure that work arrives at the downstream process when it is required and does not stay pending or put away for storage  Reducing batch sizes eventually to a batch size of one where necessary, thus reducing the amount of reagent usage  Create print on demand processes for reports and documents for the specific sample/exhibit of the case-file reducing stationary usage Motion  Movement of analysts during processing Use of network for data transfer  Limitation of staff from different work areas into other restricted or private  Arrange work areas to reduce movement  Consider cell type processing (each process to have its own cubicle)  Part trays located close to the worker  Provide extra fax, copy machines and computers and locate files at work stations  Use color codes as much as possible to differentiate processing areas or laboratory coats Tracking of procured supplies such as reagents, and instruments to obtain proper resources and provide quality and timely forensic services. Starting from the time the order request is placed by the user department within FSL to the time the order is supplied and payment is made and acknowledged by the supplier. It is the overall efficiency of the supply chain.
A, B, C  was possible to arrange the challenges in descending order, and apply Pareto analysis, as summarized in Figure 9. Based in Figure 9, there are five challenges in the business environment that the laboratory needs to address first in order to improve its competitiveness by 80% while utilizing 20% of the rather limited resources. These challenges are: increased purchase power of customers; frequently changing government policies; increased cost of manpower; fast changing technology; large number of competitors; and, increased customer awareness, in that order of importance. The last three challenges, contribute only 20% of the problems facing the competitiveness of the laboratory.
Furthermore, Table 8 shows the means of which the above nine challenges in the business environment affect FSL via the six cause factors identified and measured in this study. The business environment challenges are also arranged in the same order as presented in the Pareto chart ( Figure 9).

Application of D-MAIC Methodology in Competitiveness Improvement
In this study, D-MAIC methodology was selected as a tool to provide a roadmap that can be followed for competitiveness improvement for the laboratory. The term D-MAIC comes from the five principle steps in this process which include Define, Measure, Analyze, Improve, and Control as shown in Figure 10. Based in Figure 10, the effects and impacts of the cause factors on FSL competitiveness were established in specific details, to increase the probability of designing or remodeling for a better improvement. After identifying the cause factors, measurements and quantification of the variations were made to determine the current status of the cause factors as a study parameter in the specific discipline of the laboratory. Analyze implies quantification of the current status, followed by analysis and presentation of the data collected. In this study, data collected on TAT, A b , B g , A d , L c , and T sc were analyzed and presented in understandable   format, [5] [14] [25] [44] showing the criticality of current situation and action required. At this point, it is important to establish a need to redesign or re-model the existing conceptual model of the FSL system or there is a specific cause factor that has to be corrected. Analysis was followed by remodeling or optimization, before improvement and control. In this case control means maintaining the improved system to prevent the system from going back to the initial poor state (sustainability). Table 9 shows the competitiveness improvement framework developed using the detailed D-MAIC methodology for the FSL.
Based in Table 9, the competitiveness improvement framework has been presented in five key steps of D-MAIC but also using 15-step processes with highly detailed process analysis for each step in relation to the cause factors established.
The possible measures for improving competitiveness can then be introduced or an optimum can be used for the existing situation suggested as per concepts in Table 9. Also, trial statistics can be designed for the purpose of prioritization as which cause factor gives the highest improvements towards competiveness depending on the client's demands. An example of such analysis is AHP application, used in this study to rank the factors, keeping in mind that the cause factors are interdependent. Subsequently, as stated above implementation of the improvement measures requires controls to be placed so that they can confirm sustainability and prevent recurrence. The control measurements for improvement    Table 9.

Lean Practices for Competitiveness Improvement
A firm's competitiveness advantage grows fundamentally out of the value it can provide to its clients. For instance, TAT, and T sc are the major cause factors that affect strongly or directly the laboratory-client relationship, which can be reduced to improve the competitive edge. The samples and case file processing and management is a unique product which should be managed properly in a way, not offered by any competitor which creates a strong relationship with clients.
Lean process is about FSL being effective and efficient. It begins from the point of understanding the customer requirements, standards and prerequisites and performs the best way to provide the analytical reports and expert opinion. factors and the lean practices no score was inserted, equivalent to zero score. The total scores presented in Table 10 were further analyzed using Pareto principle, as shown in Figure 12, based on which, the higher the score the more effective the practice in bringing positive change to the complex and dynamic laboratory business environment. Using Pareto principle, there are three lean practices that can be applied in order to improve competitiveness of the FSL by 80%, while utilizing 20% of the resources, as shown in Figure 12. These lean practices are: continuous improvement of services, customer satisfaction and record keeping. Thus, to fasten competitiveness improvement for the laboratory,    it is important to understand how the competitors benefit from the bottlenecks or problems facing the organization and also to find out how the external competitive environment affects the measured factors.

Improving Operational Competitiveness in the Laboratory
In relation to the CI process and the cause factors studies, it was possible to establish from literature, the methods for improving operational competitiveness in the laboratory, that is economic added value, total shareholder return, value curve, capacity maturity model, and assets processes performance framework. Table 11 defines each method in relation to the cause factors established in this study. Thus, directors and laboratory managers should be aware of the operational competitiveness requirements being placed on them at the conceptual level, whereby, attempts to reorient the way in which they operate shall be observed significantly and that a continuous improvement culture should be slowly accommodated within the FSL such that the gap between Government policy on the one hand and implementation on the other is narrowed.
After observing the cause factors that affect the FSL competitiveness, it is important to effect methods that can assist in improving the competitiveness. Table 12 shows how these methods, forces and strategies to achieve better performance can be integrated to improve competitiveness for each of cause factors in accordance with the four competitiveness paradigms. The features of operational competiveness are action-oriented in design and focus on critical factors that are impacting the FSL competitiveness. There is a need to improve operational competitiveness of the laboratory. The abbreviations used for different methods for improvement of operational competitiveness can be found in Table 11, which fall on financial analysis category.
Based on Table 12, the methods for improving operational competitiveness play an important role in ensuring that the laboratory makes an important progress in changing the way in which it operates and hence become more competitive. It should be noted that operational competitiveness improvement is strongly related to EVA, TSR, VC, CMM and APPF. Additional methods like Balanced Scorecard (BSC) and Integrated Value Management (IVM), respectively, are also suggested for CIF.

Presentation of the Complete Competitiveness Improvement Framework for Laboratories
Finally, methods for improving competitiveness in FSL were outlined by combining the competitiveness paradigms with the methods to improve operational competitiveness. At the end, the complete Competitiveness Improvement Framework in the FSL was presented using a flow chart presented in three sections shown in Figures 13(a)-(c). The CIF for FSL is developed using the cyclic nature of the FSL processes. Furthermore, variations in the backlogs, components of the total turnaround time, SCM performance measures, staff absenteeism and in the staff academic and professional development were utilized from baseline data collected, as summarized in Figure 13(a).
The flow chart for a complete competitiveness improvement framework consists of five layers or phases: first the baseline study, data analysis and presentation.    The second layer involves identification and elimination of wastes, followed by a third layer, that is, use of D-MAIC methodology. Layer four comprises of assessing the applicable challenges in the laboratory business environment in relation to cause factors and ranking by Pareto analysis, also shown in Figure 13(a).
Phase five involves grouping the cause factors according to measures for improving competiveness considering the CI dimensions (process, performance, and planning dimensions) and application of operational improvement methods (economic value, total shareholder, value curve, capability maturity model, and assets processes performance framework) to the cause factors followed by ranking of the methods using Pareto analysis as shown in Figure 13(b) and Figure   13(c).
The CIF developed provides a common reference for a diverse set of FSLs, to assist in organizing the competitiveness improvement process, re-framing the perspectives and design analytical FSS strategies to reduce TAT, T sc and B g . It also establishes a tool that will make complex FSL systems as simple as they need to be by structuring and prioritizing the workflow meanwhile reducing absenteeism. The CIF clarifies and creates focus thinking about complex relationships within FSS, thereby supporting communication across FSL disciplines, knowledge systems, and between forensic science and policy to increase staff longevity, professional and academic development.

Conclusions
The development of CIF based on captured variations in the laboratory processes including fluctuations in TAT between different case files, between different action points and different laboratory disciplines enabled a deep understanding of process variations, via statistical analysis (CDFs). While it is difficult to trace performance metrics in the laboratory due to difficulties in gathering data, this study shows that it is possible to capture data given well designed data collection tools. Variations in the backlogged case files with time revealed similar CDF shapes between different laboratory disciplines, but different nature of factors affecting case files inflow and reported or processed case files on weekly basis. The results show that the extent of backlogs is alarming necessitating action. The cause factors developed in this study were assessed for relevance to stakeholders, current status, means of improvements, low to sustain and future plans for sustainability.
The case factors studied were observed to be complex and interrelated based on dimensions of competitiveness (planning, process, and performance). A Venn diagram was used to represent the interrelationships between cause factors, followed by the need to establish the indicators of competitiveness, application of AHP methodology, improvement methods and sustainability analysis.
The study reveals that competitiveness improvement requires managing the dimensions of competitiveness (performance, process, and planning). The study shows the process management areas such as customers of the process, relationship between customers, between quantity, schedule and cost, identifying process owners, performing improvements, and the role of measuring the current status. CIF development includes identification followed by elimination of wastes (such as waiting, overproduction, defects, under-utilized human resources, excess processing, transportation distances and inventory). The measured parameters were also assessed for importance and their effects to the FSL competitiveness, using Pareto analysis, for ranking business environment challenges (after detailed analysis) and ranking lean practices for improving FSL competitiveness.
The CIF development necessitated application of D-MAIC methodology based on simple 5-step and intensive 15-step methodology while providing relationship between each D-MAIC step with the cause factors and concepts used for conducting analysis of each step. The need for improving operational competitiveness involved linking the requirements with the cause factors. Some of the requirements utilized and linked to the cause factors include analysis of economic added value, total shareholder return, value curve, capability maturity model, and assets processes performance framework. This step involved also linking the categories of competitiveness paradigm with cause factors, analyzing forces that determine competitiveness, strategies to achieve better performance, and methods to improve operational competitiveness in relation to cause factors.
Finally, based on the developed methodology, the complete competitiveness improvement framework was presented in the form of a flowchart, showing the necessary steps with inputs to each step. The flow chart starts with data collection to establish the current situation for all six cause factors and ends with the final steps presenting the measures for improving the competitiveness.