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] .

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 longevity, academic and professional development, staff absenteeism, staff responsibilities and interactions [25] [43] [44] .

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] .

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.

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 provide adequate returns on resources employed [46] [61] . Competitiveness of a laboratory is measured in terms of quality of service, level of productivity, quality of human resource, price, and ability to innovate [31] [32] [35] [62] [63] .

Though several performance improvement methods have been proposed by scholars, yet they possess inherent deficiencies. Most literature discusses performance factors in singular. Therefore, in this study, competitiveness is discussed as multidirectional and multifactor concept observed at laboratory or firm-level. The multi-factors considered include the strategies of reducing backlogs, turnaround time and fewer customer complaints. The framework developed 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] .

(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.

Variations were identified in the turnaround time (TAT) for sample analysis.

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).

Based in 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] .

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 number of steps or action points. Repeating cycles in the form of orders were assessed and characterized as purchase orders were initiated and processed. For simplicity, the complex interactions among different SCM parameters were grouped together and the time between the order request and final payments were recorded. For further investigation on the effect of external environment on FSL competitiveness, the analysis was conducted separately for cycle times covering internal and external operations on purchase orders. Given the total

number of orders in the period studied, N_to, all the order processes falls in the cyclic nature and repetition, which fits well into the development of the CIF.

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.

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] .

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

and working life, each individual staff is subjected to academic development and longevity, data of which was captured not only once but at different times (Y2014 and Y2016) for comparison [44] . Successful management of any laboratory requires a great sense of self-awareness, commitment or engagement and the presence of the staff in the processes where academic development index, A_d, and complex longevity, L_c, play a vital role in performance and hence, competitiveness of the whole FSL as an organization. The parameters used to assess staff development (academic and professional development) are summarized in Figure 5. These variations arise among employees and also change with time for individual employees, specific FSL units or departments (total complex longevity or academic development index), which were used to develop the CIF. Thus variations in employee trails were captured as time possess while individual employee data offers another variation analysis opportunity by comparing individual staff, laboratory discipline, or zonal setting. The sample size (number of employees in each period) used to study variations in the academic development and longevity is also summarized in Figure 5.

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):

S t o t = ∑ i = 1 N c f N s , i (1)

The total score values for each variant, were then used to establish the percentage frequency distribution, using Equation (2).

F d = S t o k , k ∑ k = 1 N v S t o t , k (2)

Finally, Pareto analysis was conducted by analyzing the F_d values.

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 definitive 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 are the responsible directors, managers or departments and units towards sustainability of the competitiveness improvement methods identified which can be done internally, as it depends on the organizational structure of the laboratory. With six cause factors identified, improvement of competitiveness is not a minor task since requirements span into many directions and above all, time consuming and costs money. Maintaining the competiveness is a difficult task because of many factors that influence the FSL and the market or clients, some of which are not controllable by FSL management (e.g. components of SCM played by suppliers) necessitating efforts in backstopping. Factors like technological advancement or lack of specialized equipment could put the laboratory out of competitive advantage. If improvements are made across the six cause factors, the resulting competiveness improvement will be achieved through reduced backlogged case-files, reduced TAT and improved QMS.

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.

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.

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, it

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).

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

should be scheduled with specific dates, e.g., daily, monthly, yearly, etc. Also, the improvement measures and other related processes have to be documented. Figure 11 presents a flow chart for competitiveness improvement based on a detailed D-MAIC methodology using 15 steps presented in Table 9.

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. Table 10 is a template for continuously measuring the potential lean practices of FSL, where six lean processes related to the cause factors affecting FSL competitiveness have been analyzed. Where there was no relationship between the cause 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.

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.

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.