AHP-Driven Strategic Decision Framework for Selecting Optimal Radiopharmaceutical Facility Locations
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Given the increasingly important role of radiopharmaceutical products in medical diagnosis and treatment, choosing a strategic location for a new radiopharmaceutical facility is an important decision for pharmaceutical companies. This research develops a multi-criteria decision-making framework for selecting optimal radiopharmaceutical facility locations. The framework begins by generating alternative locations and criteria by using a PESTEL analysis to assess the external environment. Five key criteria were then identified for evaluation: customer proximity, transportation accessibility, operational support, public acceptance, and legal acceptance, with each criterion further broken down into specific sub-criteria. The Analytical Hierarchy Process (AHP) method was used to weigh these criteria and sub-criteria based on expert and stakeholder input, and to evaluate the alternative locations. Results of the AHP analysis showed that proximity to customers and accessibility to transportation were the most important criteria, leading to the selection of Cikarang as the optimal location due to its proximity to hospitals, convenient transportation access, and compliance with regulations and permits. Sensitivity analysis confirmed the robustness of this choice against variations in criteria weights. This framework provides a structured approach for pharmaceutical companies in strategic decision-making regarding the siting of radiopharmaceutical facilities.
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Introduction
The rapid development of nuclear medicine technology, particularly the use of radioisotopes, has transformed the diagnosis and treatment of various diseases, paving the way for more precise and effective care. Radioisotopes, unstable atoms that emit radiation, are essential for imaging organs and tissues, as well as targeted cancer therapies. Radiopharmaceuticals are chemical compounds that contain radioisotopes and can be used to visualize organ function using imaging techniques such as Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT).
In Indonesia, the demand for radioisotope-based diagnostic and therapeutic services continues to increase in line with the increase in cases of diseases such as cancer. There are around 408,661 new cases of cancer diagnosed in Indonesia, with the number of deaths reaching 242,988 cases (Ferlayet al., 2024). Accordingly, the use of radiopharmaceuticals is increasing along with increasing public awareness of the benefits of nuclear medicine and a wider availability of facilities. This is driving the growth of the radiopharmaceutical industry, with increasing demand for high-quality and affordable products.
Radiopharmaceutical facilities are used to produce radioisotopes, especially those with short half-lives such as fluorine-18 (F-18), which is utilized in PET scan radiopharmaceuticals for cancer diagnosis. F-18 plays an important role in the formation of fluorodeoxyglucose (FDG), a molecule used in PET scans to detect the metabolic activity of cancer cells. The growing number of hospitals investing in nuclear medicine technology, particularly for cancer diagnosis and therapy, is a key driver of the increasing demand for radiopharmaceutical facilities.
PT Green Pharma, a pharmaceutical company in Indonesia, realizes the vast potential of the growing radiopharmaceutical market. As part of a strategic diversification effort, the company aims to enter this sector and establish a cyclotron facility in the high-demand DKI Jakarta, West Java, and Banten regions. Optimizing the location of this facility is crucial to ensure operational efficiency, cost-effectiveness, and a competitive edge in the market.
However, Green Pharma faces challenges in selecting an optimal location due to the absence of a comprehensive decision-making framework for new business locations. This gap could lead to suboptimal choices, jeopardizing the company’s successful expansion into the radiopharmaceutical industry. Therefore, a systematic, data-driven approach is essential to navigate the complexities of location selection and ensure the chosen site aligns with the company’s strategic goals and operational requirements.
The selection of a strategic location for the radiopharmaceutical facility is a crucial decision that will determine the success of the company’s expansion into the radiopharmaceutical sector. This decision will have a significant impact on operational efficiency, the company’s ability to serve customers, project financial viability, regulatory compliance, and the company’s competitiveness in an increasingly competitive radiopharmaceutical market. However, several business issues needed to be addressed, including:
1. Lack of Confidence in Choosing the Best Location: The company has identified three potential locations but lacks confidence in determining the most suitable site for the radiopharmaceutical facility. Thus, a more comprehensive evaluation of existing alternatives, and potentially the exploration of new ones, is necessary.
2. Customer Service and Accessibility Challenges: There are potential challenges in achieving optimal service levels, especially related to mileage, traffic congestion, and transportation infrastructure.
3. Financial Feasibility Concerns: A comprehensive financial analysis for each alternative location is critical, considering the initial investment costs, operating costs, potential revenue, and associated financial risks.
4. Compliance with Permit Regulations: Difficulty in ensuring each alternative site’s compliance with regulatory standards, especially the time required to obtain environmental permits.
5. Competitive and Time-to-Market Pressure: The presence of potential competitors increases the pressure to choose the best location and accelerate time to market, making it a crucial factor for success.
Based on the identified business needs, the objective of the research is to:
1. Analyze the geographic and regulatory landscape in the provinces of DKI Jakarta, West Java, and Banten to identify potential locations for radiopharmaceutical facilities.
2. Define a set of criteria for selecting optimal locations for a radiopharmaceutical facility and develop a structured framework for evaluating and prioritizing potential locations.
3. Evaluate the identified alternative locations and determine the most optimal location for the radiopharmaceutical facility using the AHP method.
4. Develop implementation plans and follow-up strategies to ensure the successful construction and operation of radiopharmaceutical facilities at selected locations.
Green Pharma needs to conduct an in-depth analysis of the accessibility of each alternative location to the target hospital. In addition, companies also need to consider potential logistical issues, such as the availability of adequate transportation and other supporting infrastructure, to ensure efficient radiopharmaceutical distribution and customer satisfaction.
Literature Review and Conceptual Framework
The selection of a production facility’s location is a crucial strategic decision for the success of an industry. The right location can provide competitive advantages, operational efficiency, and better market access (Farahani & Hekmatfar, 2009). Various theories and models have been developed to assist decision-makers in determining optimal locations, considering factors such as cost, accessibility, resources, and regulations.
Classical location theories, such as Weber’s (1929) Location Theory, provide a basis for understanding the importance of minimizing total transportation costs. This theory assumes that the locations of resources and markets are known and transportation costs are proportional to the distance and weight of the material transported. Meanwhile, the Central Place Theory expressed by Christaller, highlights the spatial distribution of markets and competition. This theory explains how service centers are distributed hierarchically in an area and how this can influence location selection to reach the market optimally (Getis & Getis, 1966).
Rawstron (1958), a leading economic geographer, added three principles governing industrial location: Physical Restrictions (availability of natural resources), Economic Restrictions (transportation costs, labor costs, and market access), and Technical Constraints (impact of technological advancements). Additionally, Reilly’s (1931) Gravity Model predicts the flow of goods or people between two locations based on population size and the distance between those locations, providing insight into potential interactions between production facilities and markets.
However, the complexity of site selection demands a more modern and comprehensive approach. A multi-criteria approach is sometimes a more relevant and feasible solution because it recognizes that location selection involves considering various criteria that often conflict with each other (Drezner & Hamacher, 2004). One effective method in this approach is the Analytical Hierarchy Process (AHP) (Saaty, 2008), which allows decision-makers to compare and prioritize various criteria based on their relative importance. AHP can help break down complex problems into simpler hierarchies, making it easier to evaluate and select optimal alternatives.
The pharmaceutical industry has unique characteristics that demand special attention in selecting the location of its facilities. One of the main characteristics is very strict regulation, covering all stages from research and development to production, distribution, and marketing of products (Towse & Danzon, 2010). This regulation aims to ensure the safety, efficacy, and quality of pharmaceutical products to protect consumers. In addition, complex supply chains, from raw material procurement to finished product distribution, are also an important consideration in site selection.
Radiopharmaceuticals have unique characteristics that are time-sensitive products because they have a short half-life. Therefore, proximity to hospitals or medical centers requiring regular radioisotope supplies is a crucial factor, in addition to general factors such as cost and regulations. In this case, Central Location Theory and Gravity Models can provide initial insight into the potential of strategic locations. However, a multi-criteria approach such as AHP is still needed to integrate various other relevant criteria, such as the availability of skilled labor, supporting infrastructure, and regulations, as well as to consider trade-offs between different criteria.
Empirical studies have demonstrated the effectiveness of AHP in site selection in the pharmaceutical and healthcare industries. Arslan (2020) shows AHP’s application to determine the optimal pharmaceutical warehouse in Turkey considering infrastructure, transportation conditions, and regional economic conditions are important factors in selecting a location. Dachyaret al. (2019) also applied AHP to determine the best location for Original Equipment Manufacturers (OEMs) in the traditional medicine industry in Indonesia, focusing on factors like raw material availability, transportation access, and labor costs are the main considerations in choosing an OEM location. Another study by Şahinet al. (2019) also demonstrated the application of AHP in hospital location selection in Muğla Province, Türkiye. In this research, criteria such as demand, accessibility, competition, government support, related industries, and environmental conditions were evaluated using AHP.
Nuclear Medicine is a branch of medical science that uses radioactive materials, such as radioisotopes, for the diagnosis and therapy of disease (Saha, 2017). PET (Positron Emission Tomography) and SPECT (Single Photon Emission Computed Tomography) are diagnosis techniques by visualize the function of organs and body tissues, as well as detect abnormalities such as tumors, infections, or organ dysfunction (Cherryet al., 2012). In therapy, nuclear medicine uses radioisotopes to deliver radiation directly to cancer cells or diseased tissue, destroying them while minimizing damage to surrounding healthy tissue (Saha, 2017).
Nuclear medicine and radiopharmaceuticals have a complementary and interdependent relationship. Nuclear medicine relies on radiopharmaceuticals developed and produced by radio pharmacists to carry out diagnosis and therapy (Decristoforoet al., 2017). Radiopharmaceutical is a drug that contains a radioactive form of a chemical element called a radioisotope. Used in nuclear medicine for diagnosis and therapy. Depending on the type of radiation produced by the radioisotope, this drug can be used to diagnose or treat several medical conditions (Galindo, 2024).
One of the most common radioisotopes used in PET scan radiopharmaceuticals is Fluor-18 (F-18). The F-18 has a half-life of approximately 110 minutes, which means its radioactive activity will be reduced by half in that time (International Atomic Energy Agency (IAEA), 2018). Due to its relatively short half-life, the F-18 must be produced close to where it is used. The cyclotron is a tool used to produce F-18 and other radioisotopes (International Atomic Energy Agency (IAEA), 2018). F-18 is then used to synthesize fluorodeoxyglucose (FDG), a glucose analog that can be tracked by PET scans. FDG accumulates in metabolically active cells, such as cancer cells, thereby enabling the detection and localization of tumors with a high degree of accuracy (Saha, 2017).
Strict regulations are enforced in the radiopharmaceutical industry to ensure radiation safety and quality of radiopharmaceutical products as Fig. 1 (BAPETEN Regulation No. 3 Year 2021). In Indonesia, the Nuclear Energy Regulatory Agency (BAPETEN) is responsible for supervising and licensing the construction and operation of radiopharmaceutical facilities and the distribution of radiopharmaceutical products (BAPETEN Regulation No. 3 of 2021) (Badan Pengawas Tenaga Nuklir, 2021). As a medicinal product, radiopharmaceuticals must meet the rules of CPOB Annex 9 which is supervised by the Food and Drug Administration (BPOM). Radiopharmaceutical facilities also need to obtain an environmental permit from the environmental agency based on Law No. 32 of 2009 (Republik Indonesia, 2009) and a building permit (PBG) from DPMPTSP per Government Regulation No. 16 of 2021 (Republik Indonesia, 2021).
Fig. 1. Licensing flow of radiopharmaceutical facilities.
The conceptual framework in this research aims to systematically describe how the process of selecting optimal locations for radiopharmaceutical facilities is carried out. This research framework, depicted in Fig. 2, combines several relevant business strategy models and approaches.
Fig. 2. Conceptual research framework in selecting optimal locations for radiopharmaceutical facilities.
Research Methodology
This research uses mixed methods, an approach that combines qualitative and quantitative data to obtain a more comprehensive and in-depth picture of the phenomenon being studied. Mixed methods were chosen because they can utilize the advantages of each approach, namely interviews to collect in-depth qualitative data and questionnaires to collect quantitative data that can be analyzed using the Analytical Hierarchy Process (AHP). The use of these two types of data aims to complement and strengthen research results, as well as provide a broader context for the findings obtained.
Respondents in this study consisted of internal and external parties of the company who have relevant knowledge and experience in the radiopharmaceutical industry and production facility site selection. The selection of respondents was based on the results of a stakeholder analysis that identified parties with significant interests and influence on site selection decisions.
The data collection in this research involves both qualitative and quantitative for the analysis of radiopharmaceutical facility location selection. Qualitative data was collected through semi-structured interviews with predetermined respondents. The interview questions focused on identifying the criteria and sub-criteria in radiopharmaceutical facility site selection, as well as their perceptions of the existing site alternatives. Quantitative data was collected through a questionnaire administered to respondents to conduct pairwise comparisons between criteria and sub-criteria. The questionnaire used a standardized AHP rating scale with nine levels of preference.
The data collection in this research involves both primary and secondary sources, employing both qualitative and quantitative methods, for a comprehensive analysis of radiopharmaceutical facility location selection. Primary data collection includes:
1. Semi-structured interviews: Conducted with predetermined respondents, including top management, operational teams, business and finance teams from Green Pharma, as well as external experts such as government representatives, community leaders (Nuclear physicians), and engineering consultants. The interview questions focused on identifying the relevant criteria and sub-criteria in radiopharmaceutical facility site selection, as well as their perceptions of the existing site alternatives.
2. Brainstorming sessions: Conducted with key internal stakeholders (top management and operational teams) to generate additional insights and potential alternative locations that were not initially considered.
Secondary data collection includes:
1. Internal company reports: Such as feasibility studies, and market provide valuable information on the company’s strategic objectives and constraints.
2. Government regulations: Laws and regulations related to the radiopharmaceutical industry, land use, environmental permits, and building permits from relevant agencies such as BAPETEN, the Ministry of Health, and local government authorities.
3. Academic journals and publications: Research papers on radiopharmaceutical facility location, AHP methodology, and related topics provide theoretical frameworks and empirical evidence for analysis.
4. Statistical data: Demographic data, healthcare statistics, and economic indicators for the DKI Jakarta, West Java, and Banten provinces, offering insights into the market potential and operational environment for the facility.
Quantitative data was collected through a questionnaire administered to respondents to conduct pairwise comparisons between criteria and sub-criteria. The questionnaire used a standardized AHP rating scale with nine levels of preference.
Qualitative data from the interviews were analyzed descriptively to identify the criteria and sub-criteria most frequently mentioned by respondents. Quantitative data from the questionnaire was analyzed using the AHP method. The steps of the AHP analysis include:
1. Pairwise Comparison Matrix
2. Matrix Normalization
3. Calculation of Eigenvector, Lamda Max, CI, and CR
4. Evaluation of Local and Global Weighting
5. Evaluation of Alternative
6. Sensitivity Analysis
The location with the highest weighted score in the AHP analysis will be recommended as the optimal location for the construction of a new radiopharmaceutical facility. The results of the sensitivity analysis will be used to confirm the robustness of the site selection to changes in criteria weights.
Result and Discussion
Stakeholder analysis begins with identifying all parties involved in or affected by the location selection decision. Subsequently, their level of interest and influence/power over this decision is assessed. The analysis focuses on individuals or groups with a comprehensive understanding of the radiopharmaceutical business process to ensure the relevance of the data and information gathered. This approach aims to ensure that the perspectives and needs of all key stakeholders are considered proportionally throughout the decision-making process.
Stakeholder mapping is carried out to visually depict the relationship between stakeholder interests and influence as presented in Fig. 3. This mapping helps in understanding stakeholder priorities and engagement strategies. The results of the stakeholder analysis provide valuable information regarding the parties who have a significant interest and influence on the radiopharmaceutical facility development project. This analysis identifies top management and operational time as “Key Players” with high importance and influence, so they should be the focus in decision-making. Local governments and finance teams, which fall into the “Meeting Their Needs” category, are also important because of their high influence, although their interests are more specific. Meanwhile, the community and consultants, who are in the “Keep Informed” quadrant, although their influence is lower, must still be involved to ensure support and smooth project implementation. This stakeholder mapping is the basis for selecting the suitable group for each data collection method so that the data obtained can provide a comprehensive and accurate picture of the various aspects relevant to selecting the location of a radiopharmaceutical facility.
Fig. 3. Stakeholder mapping of selection for radiopharmaceutical facility.
All alternative locations that have been determined are filtered based on must criteria obtained from interviews. Two criteria that must be met by all alternatives are 1) Located in the provinces of DKI Jakarta, West Java, or Banten and 2) Using corporate asset land. Alternative locations that do not meet one or both criteria must be eliminated from further analysis by wanted criteria. The four locations that passed the initial screening and will be reviewed based on the want criteria are Cibitung, Cikarang, Cikampek, and Banjaran as presented in Table I.
Alternative | Location | MC1 | MC2 | Go/No Go |
---|---|---|---|---|
Initial | Grogol Petamburan, DKI Jakarta | ✓ | ✗ | No Go |
Cibitung, West Java | ✓ | ✓ | Go | |
Cikarang, West Java | ✓ | ✓ | Go | |
Proposed | Cikampek, West Java | ✓ | ✓ | Go |
Banjaran, West Java | ✓ | ✓ | Go | |
Sidoarjo, West Java | ✗ | ✓ | No Go |
PESTEL analysis served as the foundation for developing interview questions that explored potential factors influencing site selection. These interviews provided valuable insights into defining the want criteria, which were then validated by experts to ensure their relevance and accuracy. The interviews revealed several critical external factors necessitating careful consideration. In the political sphere, regulatory stability and local government support emerged as crucial elements. Economically, operational costs and market potential in the target region were deemed highly significant. Socially, public perception of radiopharmaceutical facilities and the availability of skilled personnel were identified as key concerns. Technologically, access to cutting-edge technology and robust supporting infrastructure were emphasized. Environmentally, the potential impact on the local ecosystem and adherence to environmental regulations were highlighted. Legally, licensing requirements and compliance with relevant laws were deemed essential. A comprehensive summary of these interview findings is presented in Table II, providing a detailed PESTEL analysis of the radiopharmaceutical manufacturing industry.
Factors | Analysis |
---|---|
Political | • Regulations for producing radiopharmaceuticals are stricter than producing drugs because it involves 2 regulators, namely BAPETEN and BPOM. • Radiopharmaceutical facilities require tighter and longer licensing due to the presence of radioisotopes. • The government through the Ministry of Health supports companies that will build radiopharmaceutical facilities because of its alignment with the ministry’s program related to cancer detection. |
Economic | • Region with strong healthcare infrastructure drive higher demand for radiopharmaceutical products. • Region with balance characteristic between labor cost and skilled labor availability are preferable. • Region with Special Economic Zones (SEZs) or Bonded Zone offer financial benefits such as tax rebates and streamlined customs clearance, thus providing operational advantages. • The proximity between the location of the radiopharmaceutical facility and the hospitals is one of the key points on site selection. |
Social | • Regions with high population density and high disease prevalence increase demand for healthcare services and radiopharmaceutical therapies. • Urban areas have higher acceptance of modern medical treatments, including acceptance of radiopharmaceutical products. • Public acceptance of radiopharmaceuticals and environmental concerns can influence local government approvals. |
Technological | • Regions that have proximity to related research institutions such as the Nuclear Reactor Technology Research Center, have an advantage in supporting the operation of radiopharmaceutical facilities. • Regions that have proximity to universities that have nuclear education programs, such as UI and UNPAD, have an advantage in supporting the radiopharmaceutical business ecosystem. |
Environmental | • Proper waste management of radiopharmaceutical production is critical for minimizing environmental contamination and assuring regulatory compliance. • Industrial Estates offer better waste management in terms of governance and stricter regulations, making them suitable for radiopharmaceutical facilities. • Industrial Estates generally already have environmental permits from the government so the process of applying for environmental permits for radiopharmaceutical facilities in Industrial estates can be a shorter process. |
Interview results are used to identify desired criteria that can provide added value (want criteria). Based on the results of the interview validated by the problem owner, the wanted criteria that must be considered in selecting the location of a radiopharmaceutical facility are shown in Table III.
Want criteria | Explanation (Sub criteria) |
---|---|
Supply chain | Related to travel distance, travel time, and number of hospitals that can be served. |
Operational support | Related to the number of workers and minimum wage in the radiopharmaceutical facility area, also the existence of universities with nuclear study programs and nuclear research institutes |
Transportation accessibility | Related to the distance to the nearest, main road, toll gate and airport |
Public acceptance | Related to the distance to public areas and distance to the nearest other buildings |
Legal approval | Related to the speed/ease of obtaining environmental approval and building approval (PBG) |
After defining research objectives, criteria, and sub-criteria, as well as identifying prospective location options, the following phase in this study is to create a decision hierarchy. The hierarchy for selecting the location of a radiopharmaceutical facility (Fig. 4) describes the relationship between the destination, the criteria, the sub-criteria, and the alternative location to be assessed using the AHP method.
Fig. 4. AHP Hierarchy Structure for selection of radiopharmaceutical facility.
The assessment of each alternative location against the sub-criteria will be carried out using the appropriate numerical scale and then normalized to ensure equality of comparison between locations. In some sub-criteria, such as distance and travel time, smaller values indicate better performance, so an inverse score will be performed before normalization. The results of this normalized assessment will then be multiplied by the priority weight of each sub-criteria to get a weighted score. This weighted score will be used to compare the performance of each alternative location against each criterion, and ultimately determine the most optimal location for the construction of radiopharmaceutical facilities.
To determine the degree of importance of each main criteria and sub-criteria, a pairwise comparison is carried out using the AHP method. The results of pairwise comparison are then converted into a pairwise comparison matrix and normalized by dividing each element in a column by the number of columns, as shown in Tables IV and V. Next, the eigenvector (priority weight) is calculated by finding the average of each row in the normalized matrix. The lambda max value is calculated as the average of the results of multiplying the initial matrix by the eigenvector. Consistency Index (CI) and Consistency Ratio (CR) are calculated to test the consistency of respondents’ assessments. If the CR value is less than 0.1, then the pairwise comparison is considered consistent and reliable.
Pairwise Matrix | Normalized Matrix | Λmax | CI | CR | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CP | OS | TA | PA | LA | CP | OS | TA | PA | LA | Weight | |||||
CP | 1 | 5.662 | 1.870 | 2.806 | 1.829 | CP | 0.382 | 0.328 | 0.474 | 0.263 | 0.346 | 35.87% | 5.127 | 0.032 | 0.028 |
OS | 0.177 | 1 | 0.204 | 0.565 | 0.254 | OS | 0.068 | 0.058 | 0.052 | 0.053 | 0.048 | 5.57% | |||
TA | 0.535 | 4.892 | 1 | 4.852 | 1.509 | TA | 0.205 | 0.283 | 0.254 | 0.456 | 0.285 | 29.65% | |||
PA | 0.356 | 1.770 | 0.206 | 1 | 0.701 | PA | 0.136 | 0.103 | 0.052 | 0.094 | 0.132 | 10.35% | |||
LA | 0.547 | 3.937 | 0.663 | 1.428 | 1 | LA | 0.209 | 0.228 | 0.168 | 0.134 | 0.189 | 18.56% | |||
Sum | 2.615 | 17.261 | 3.943 | 10.650 | 5.293 |
Pairwise Matrix of Costumer Proximity (CP) | Normalized Matrix of Costumer Proximity (CP) | Λmax | CI | CR | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CP-01 | CP-02 | CP-03 | CP-01 | CP-02 | CP-03 | Weight | |||||||
CP-01 | 1 | 4.331 | 0.768 | CP-01 | 0.395 | 0.422 | 0.39 | 40.21% | 3.02 | 0.01 | 0.017 | ||
CP-02 | 0.231 | 1 | 0.203 | CP-02 | 0.091 | 0.097 | 0.103 | 9.71% | |||||
CP-03 | 1.302 | 4.935 | 1 | CP-03 | 0.514 | 0.481 | 0.507 | 50.08% | |||||
Sum | 2.533 | 10.267 | 1.971 | ||||||||||
Pairwise Matrix of Operational Support (OS) | Normalized Matrix of Operational Support (OS) | Λmax | CI | CR | |||||||||
OS-1 | OS-2 | OS-3 | OS-4 | OS-1 | OS-2 | OS-3 | OS-4 | Weight | |||||
OS-1 | 1 | 1.869 | 4.331 | 2.058 | OS-1 | 0.444 | 0.531 | 0.38 | 0.338 | 42.32% | 4.074 | 0.025 | 0.028 |
OS-2 | 0.535 | 1 | 3.808 | 2.594 | OS-2 | 0.238 | 0.284 | 0.334 | 0.426 | 32.04% | |||
OS-3 | 0.231 | 0.263 | 1 | 0.441 | OS-3 | 0.103 | 0.075 | 0.088 | 0.072 | 8.43% | |||
OS-4 | 0.486 | 0.386 | 2.266 | 1 | OS-4 | 0.216 | 0.11 | 0.199 | 0.164 | 17.20% | |||
Sum | 2.252 | 3.517 | 11.405 | 6.093 | |||||||||
Pairwise Matrix of Transportation Accessibility (TA) | Normalized Matrix of Transportation Accessibility (TA) | Λmax | CI | CR | |||||||||
TA-1 | TA-2 | TA-3 | TA-1 | TA-2 | TA-3 | Weight | |||||||
TA-1 | 1 | 0.803 | 1.967 | TA-1 | 0.363 | 0.335 | 0.422 | 37.36% | 3.015 | 0.008 | 0.013 | ||
TA-2 | 1.246 | 1 | 1.69 | TA-2 | 0.452 | 0.418 | 0.363 | 41.09% | |||||
TA-3 | 0.508 | 0.592 | 1 | TA-3 | 0.185 | 0.247 | 0.215 | 21.55% | |||||
Sum | 2.754 | 2.394 | 4.657 | ||||||||||
Pairwise Matrix of Public Acceptance (PA) | Normalized Matrix of Public Acceptance (PA) | Λmax | CI | CR | |||||||||
PA-1 | PA-2 | PA-1 | PA-2 | Weight | |||||||||
PA-1 | 1 | 1.261 | PA-1 | 0.558 | 0.558 | 55.77% | – | – | – | ||||
PA-2 | 0.793 | 1 | PA-2 | 0.442 | 0.442 | 44.23% | |||||||
Sum | 1.793 | 2.261 | |||||||||||
Pairwise Matrix of Legal Acceptance (LA) | Normalized Matrix of Legal Acceptance (LA) | Λmax | CI | CR | |||||||||
LA-1 | LA-2 | LA-1 | LA-2 | Weight | |||||||||
LA-1 | 1 | 1.139 | LA-1 | 0.532 | 0.532 | 53.24% | – | – | – | ||||
LA-2 | 0.878 | 1 | LA-2 | 0.468 | 0.468 | 46.76% | |||||||
Sum | 1.878 | 2.139 |
After obtaining weights for each criterion and sub-criteria, the next step is to calculate the global weight for each sub-criteria as shown in Table VI. Global weight was used to evaluate location alternatives at a later stage. The evaluation results reveal that proximity to customers and transportation accessibility are the most important factors in choosing radiopharmaceutical facility locations, with the sub-criteria “Number of Served Hospitals” and “Travel Time” ranking first. This emphasizes the need to have a strategic position to reach potential consumers, particularly hospitals, as well as transportation access to provide effective radiopharmaceutical delivery. Meanwhile, the low ranking of operational support sub-criteria indicates that variables such as minimum wages, labor availability, and proximity to nuclear research institutions are valued less than proximity to customer and transportation accessibility.
Local weighting | Global weighting | Ranking | |||
---|---|---|---|---|---|
Criteria | Priority | Sub criteria | Priority | ||
Costumer Proximity (CP) | 35.87% | (CP-1) Travel time | 40.21% | 14.43% | 2 |
35.87% | (CP-2) Travel distance | 9.71% | 3.48% | 10 | |
35.87% | (CP-3) Number of served hospital | 50.08% | 17.96% | 1 | |
Operation Support (OS) | 5.57% | (OS-1) Labor minimum wage | 42.32% | 2.36% | 11 |
5.57% | (OS-2) Labor population | 32.04% | 1.78% | 12 | |
5.57% | (OS-3) Universities with nuclear programs | 8.43% | 0.47% | 14 | |
5.57% | (OS-4) Nuclear research institutes | 17.20% | 0.96% | 13 | |
Transportation Accessibility (TA) | 29.65% | (TA-1) Distance to nearest main road | 37.36% | 11.08% | 4 |
29.65% | (TA-2) Distance to nearest toll gate | 41.10% | 12.18% | 3 | |
29.65% | (TA-3) Distance to nearest airport | 21.55% | 6.39% | 7 | |
Public Acceptance (PA) | 10.35% | (PA-1) Distance to nearest public space | 55.77% | 5.77% | 8 |
10.35% | (PA-2) Distance to nearest building | 44.23% | 4.58% | 9 | |
Legal Acceptance (LA) | 18.56% | (LA-1) Environmental permit | 53.24% | 9.88% | 5 |
18.56% | (LA-2) Building permit | 46.76% | 8.68% | 6 |
Each alternative location was evaluated using the same scale as the pairwise comparison scale in AHP. According to the calculations in Table VII, the alternate ranking order is 1) Cikarang, 2) Cikampek, 3) Cibitung, and 4) Banjaran. These findings suggest that Cikarang is the best location for a radiopharmaceutical facility, followed by Cikampek, Cibitung, and Banjaran. Cikarang has the greatest weighted score, meaning that it has performed the best overall in terms of meeting the required criteria and sub-criteria.
Criteria | Sub criteria | Global weighting | Location evaluation score | Location evaluation weight | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Cikarang | Cibitung | Cikampek | Banjaran | Cikarang | Cibitung | Cikampek | Banjaran | |||
(CP) | (CP-1) | 14.43% | 0.287 | 0.288 | 0.253 | 0.172 | 0.041 | 0.042 | 0.037 | 0.025 |
(CP-2) | 3.48% | 0.310 | 0.317 | 0.230 | 0.143 | 0.011 | 0.011 | 0.008 | 0.005 | |
(CP-3) | 17.96% | 0.400 | 0.343 | 0.171 | 0.086 | 0.072 | 0.062 | 0.031 | 0.015 | |
(OS) | (OS-1) | 2.36% | 0.223 | 0.223 | 0.221 | 0.333 | 0.005 | 0.005 | 0.005 | 0.008 |
(OS-2) | 1.78% | 0.328 | 0.328 | 0.143 | 0.201 | 0.006 | 0.006 | 0.003 | 0.004 | |
(OS-3) | 0.47% | 0.235 | 0.252 | 0.135 | 0.378 | 0.001 | 0.001 | 0.001 | 0.002 | |
(OS-4) | 0.96% | 0.195 | 0.210 | 0.131 | 0.464 | 0.002 | 0.002 | 0.001 | 0.004 | |
(TA) | (TA-1) | 11.08% | 0.258 | 0.164 | 0.403 | 0.175 | 0.029 | 0.018 | 0.045 | 0.019 |
(TA-2) | 12.18% | 0.264 | 0.441 | 0.236 | 0.059 | 0.032 | 0.054 | 0.029 | 0.007 | |
(TA-3) | 6.39% | 0.295 | 0.305 | 0.203 | 0.197 | 0.019 | 0.019 | 0.013 | 0.013 | |
(PA) | (PA-1) | 5.77% | 0.502 | 0.037 | 0.285 | 0.176 | 0.029 | 0.002 | 0.016 | 0.010 |
(PA-2) | 4.58% | 0.589 | 0.109 | 0.257 | 0.045 | 0.027 | 0.005 | 0.012 | 0.002 | |
(LA) | (LA-1) | 9.88% | 0.400 | 0.100 | 0.400 | 0.100 | 0.040 | 0.010 | 0.040 | 0.010 |
(LA-2) | 8.68% | 0.429 | 0.071 | 0.429 | 0.071 | 0.037 | 0.006 | 0.037 | 0.006 | |
Sum | 0.350 | 0.243 | 0.276 | 0.130 | ||||||
Rank | #1 | #3 | #2 | #4 |
The sensitivity analysis showed no change in the ranking of alternative locations, even when the weights of the three most important criteria, CP-1 (Table VIII), CP-3 (Table IX), and TA-2 (Table X) were altered by ±10%. This suggests that the optimal location selection in this study is quite robust or resistant to minor changes in decision-maker preferences. This means that, despite minor discrepancies in the assessment of the relative importance of the criteria, the results are consistent, with Cikarang being the most optimum location. Several factors could explain this outcome:
Sub criteria | −10% for CP-1 | +10% for CP-1 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Global weight | Cikarang | Cibitung | Cikampek | Banjaran | Global weight | Cikarang | Cibitung | Cikampek | Banjaran | |
(CP-1) | 4.43% | 0.013 | 0.013 | 0.011 | 0.008 | 24.43% | 0.070 | 0.070 | 0.062 | 0.042 |
(CP-2) | 4.25% | 0.013 | 0.013 | 0.010 | 0.006 | 2.71% | 0.008 | 0.009 | 0.006 | 0.004 |
(CP-3) | 18.73% | 0.075 | 0.064 | 0.032 | 0.016 | 17.19% | 0.069 | 0.059 | 0.029 | 0.015 |
(OS-1) | 3.13% | 0.007 | 0.007 | 0.007 | 0.010 | 1.59% | 0.004 | 0.004 | 0.004 | 0.005 |
(OS-2) | 2.55% | 0.008 | 0.008 | 0.004 | 0.005 | 1.01% | 0.003 | 0.003 | 0.001 | 0.002 |
(OS-3) | 1.24% | 0.003 | 0.003 | 0.002 | 0.005 | −0.30% | −0.001 | −0.001 | 0.000 | −0.001 |
(OS-4) | 1.73% | 0.003 | 0.004 | 0.002 | 0.008 | 0.19% | 0.000 | 0.000 | 0.000 | 0.001 |
(TA-1) | 11.85% | 0.031 | 0.019 | 0.048 | 0.021 | 10.31% | 0.027 | 0.017 | 0.042 | 0.018 |
(TA-2) | 12.95% | 0.034 | 0.057 | 0.031 | 0.008 | 11.41% | 0.030 | 0.050 | 0.027 | 0.007 |
(TA-3) | 7.16% | 0.021 | 0.022 | 0.015 | 0.014 | 5.62% | 0.017 | 0.017 | 0.011 | 0.011 |
(PA-1) | 6.54% | 0.033 | 0.002 | 0.019 | 0.012 | 5.00% | 0.025 | 0.002 | 0.014 | 0.009 |
(PA-2) | 5.35% | 0.032 | 0.006 | 0.014 | 0.002 | 3.81% | 0.022 | 0.004 | 0.010 | 0.002 |
(LA-1) | 10.65% | 0.043 | 0.011 | 0.043 | 0.011 | 9.11% | 0.036 | 0.009 | 0.036 | 0.009 |
(LA-2) | 9.45% | 0.041 | 0.007 | 0.041 | 0.007 | 7.91% | 0.034 | 0.006 | 0.034 | 0.006 |
SUM | 100.00% | 0.356 | 0.237 | 0.276 | 0.132 | 100.00% | 0.345 | 0.250 | 0.277 | 0.129 |
Rank | #1 | #3 | #2 | #4 | #1 | #3 | #2 | #4 |
Sub criteria | −10% for CP-3 | +10% for CP-3 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Global weight | Cikarang | Cibitung | Cikampek | Banjaran | Global weight | Cikarang | Cibitung | Cikampek | Banjaran | |
(CP-1) | 15.20% | 0.044 | 0.044 | 0.038 | 0.026 | 13.66% | 0.039 | 0.039 | 0.035 | 0.023 |
(CP-2) | 4.25% | 0.013 | 0.013 | 0.010 | 0.006 | 2.71% | 0.008 | 0.009 | 0.006 | 0.004 |
(CP-3) | 7.96% | 0.032 | 0.027 | 0.014 | 0.007 | 27.96% | 0.112 | 0.096 | 0.048 | 0.024 |
(OS-1) | 3.13% | 0.007 | 0.007 | 0.007 | 0.010 | 1.59% | 0.004 | 0.004 | 0.004 | 0.005 |
(OS-2) | 2.55% | 0.008 | 0.008 | 0.004 | 0.005 | 1.01% | 0.003 | 0.003 | 0.001 | 0.002 |
(OS-3) | 1.24% | 0.003 | 0.003 | 0.002 | 0.005 | −0.30% | −0.001 | −0.001 | 0.000 | −0.001 |
(OS-4) | 1.73% | 0.003 | 0.004 | 0.002 | 0.008 | 0.19% | 0.000 | 0.000 | 0.000 | 0.001 |
(TA-1) | 11.85% | 0.031 | 0.019 | 0.048 | 0.021 | 10.31% | 0.027 | 0.017 | 0.042 | 0.018 |
(TA-2) | 12.95% | 0.034 | 0.057 | 0.031 | 0.008 | 11.41% | 0.030 | 0.050 | 0.027 | 0.007 |
(TA-3) | 7.16% | 0.021 | 0.022 | 0.015 | 0.014 | 5.62% | 0.017 | 0.017 | 0.011 | 0.011 |
(PA-1) | 6.54% | 0.033 | 0.002 | 0.019 | 0.012 | 5.00% | 0.025 | 0.002 | 0.014 | 0.009 |
(PA-2) | 5.35% | 0.032 | 0.006 | 0.014 | 0.002 | 3.81% | 0.022 | 0.004 | 0.010 | 0.002 |
(LA-1) | 10.65% | 0.043 | 0.011 | 0.043 | 0.011 | 9.11% | 0.036 | 0.009 | 0.036 | 0.009 |
(LA-2) | 9.45% | 0.041 | 0.007 | 0.041 | 0.007 | 7.91% | 0.034 | 0.006 | 0.034 | 0.006 |
SUM | 100.00% | 0.344 | 0.231 | 0.285 | 0.141 | 100.00% | 0.357 | 0.255 | 0.268 | 0.120 |
Rank | #1 | #3 | #2 | #4 | #1 | #3 | #2 | #4 |
Sub criteria | −10% for TA-2 | +10% for TA-2 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Global weight | Cikarang | Cibitung | Cikampek | Banjaran | Global weight | Cikarang | Cibitung | Cikampek | Banjaran | |
(CP-1) | 15.20% | 0.044 | 0.044 | 0.038 | 0.026 | 13.66% | 0.039 | 0.039 | 0.035 | 0.023 |
(CP-2) | 4.25% | 0.013 | 0.013 | 0.010 | 0.006 | 2.71% | 0.008 | 0.009 | 0.006 | 0.004 |
(CP-3) | 18.73% | 0.075 | 0.064 | 0.032 | 0.016 | 17.19% | 0.069 | 0.059 | 0.029 | 0.015 |
(OS-1) | 3.13% | 0.007 | 0.007 | 0.007 | 0.010 | 1.59% | 0.004 | 0.004 | 0.004 | 0.005 |
(OS-2) | 2.55% | 0.008 | 0.008 | 0.004 | 0.005 | 1.01% | 0.003 | 0.003 | 0.001 | 0.002 |
(OS-3) | 1.24% | 0.003 | 0.003 | 0.002 | 0.005 | −0.30% | −0.001 | −0.001 | 0.000 | −0.001 |
(OS-4) | 1.73% | 0.003 | 0.004 | 0.002 | 0.008 | 0.19% | 0.000 | 0.000 | 0.000 | 0.001 |
(TA-1) | 11.85% | 0.031 | 0.019 | 0.048 | 0.021 | 10.31% | 0.027 | 0.017 | 0.042 | 0.018 |
(TA-2) | 2.18% | 0.006 | 0.010 | 0.005 | 0.001 | 22.18% | 0.059 | 0.098 | 0.052 | 0.013 |
(TA-3) | 7.16% | 0.021 | 0.022 | 0.015 | 0.014 | 5.62% | 0.017 | 0.017 | 0.011 | 0.011 |
(PA-1) | 6.54% | 0.033 | 0.002 | 0.019 | 0.012 | 5.00% | 0.025 | 0.002 | 0.014 | 0.009 |
(PA-2) | 5.35% | 0.032 | 0.006 | 0.014 | 0.002 | 3.81% | 0.022 | 0.004 | 0.010 | 0.002 |
(LA-1) | 10.65% | 0.043 | 0.011 | 0.043 | 0.011 | 9.11% | 0.036 | 0.009 | 0.036 | 0.009 |
(LA-2) | 9.45% | 0.041 | 0.007 | 0.041 | 0.007 | 7.91% | 0.034 | 0.006 | 0.034 | 0.006 |
SUM | 100.00% | 0.358 | 0.220 | 0.278 | 0.144 | 100.00% | 0.343 | 0.266 | 0.275 | 0.117 |
Rank | #1 | #3 | #2 | #4 | #1 | #3 | #2 | #4 |
1) One criterion, CP-3, has such dominance over the others that a relatively slight change in its weight is insufficient to modify the ranking of alternative locations.
2) Because the weighted scores across alternative sites differ significantly, modifications in criteria weights are insufficient to affect the ranking order.
3) Criteria compensation, which means that a loss in alternative location performance on one criterion can be offset by an improvement in performance on another criterion.
The Kepner-Tregoe Potential Problem Analysis (PPA) method is used to identify and address potential issues that may develop during the construction and operation of the radiopharmaceutical facility at the chosen location. The PPA comprises a systematic assessment of potential problems, their potential causes, risk occurrence, risk mitigation, residual risks, and contingency plans, as indicated in Table XI. This proactive approach tries to minimize potential disruptions and ensure the project executes smoothly.
No. | Potential problems | Possible causes | Risk occurrence | Risk mitigation | Residual risk | Contingency plan |
---|---|---|---|---|---|---|
1 | Delay/denial of Environmental permit | Lack of document completeness | Possible | Prepare documents carefully and according to standards | Documentation is still lacking | Prepare alternative sites, increase risk mitigation, appeal |
2 | Delay/denial of building permit | Building design errors | Possible | Consultation with experts and authorities to ensure the design is up to standard | Design not approved | Redesign building, increase building specifications, appeal |
3 | Delay/denial of construction permit | Complex administrative requirements | Possible | Intensive coordination with authorities, apply for permits as early as possible | Bureaucratic red tape | Prepare alternative location, lobby |
4 | Delay/refusal of operational license | Non-compliance of facilities with safety standards | Unlikely | Ensure facilities are built according to BAPETEN and CPOB standards | Facilities are not up to standard | Improve facilities, conduct independent audit |
5 | Delay/refusal of distribution license | Administrative issues, unguaranteed product safety | Unlikely | Prepare complete documents and conduct product tests according to BPOM standards | Bureaucracy or product quality is not suitable | Improve product quality |
6 | Increased investment costs | Inflation, increase in material/equipment prices | Likely | Perform accurate cost estimation and allocate reserve funds | Estimates are way off | Find alternative supplier, renegotiate contract |
7 | Increased operational costs | Rising prices of raw materials, energy, labor wages | Likely | Conduct regular market analysis and cost prediction, look for alternative suppliers | Costs are not controlled | Operational efficiency, find alternative funding sources |
8 | Lower market demand | Intense competition, changes in medical trends | Possible | Conduct in-depth market research prior to development, develop effective marketing strategies | Product not in demand | Diversify products, offer competitive prices |
9 | Natural disaster | Earthquake, flood, etc. | Rare | Choose locations with low risk of natural disasters, design earthquake and flood resistant buildings | Disaster still occurs | Insurance, disaster recovery plan |
10 | Radiation risk to workforce | Equipment failure, human error | Likely | Design facilities according to specifications, regular training, close monitoring, use of PPE, develop strict SOPs | PPE is broken, SOPs are not followed | Routine equipment maintenance, strict sanctions for SOP violations |
11 | Radiation risk to society | Radiation leakage, improper waste handling | Possible | Design a layered security system, good waste management, regular environmental monitoring | NA | Evacuation, decontamination |
12 | Rejection from surrounding community | Lack of socialization, environmental/health impact concerns | Possible | Intensive and transparent socialization, involve the community in the decision-making process | Rejection still occurs | Re-evaluate location, offer compensation, offer CSR programs |
13 | Difficult to recruit skilled labor | Limited availability, competition with other industries | Likely | Offer competitive remuneration packages, training programs, recruitment from outside the region | NA | Cooperate with universities or research institutes |
14 | Late product delivery | Transportation issues, production constraints, product damage | Possible | Choose strategic locations, develop efficient logistics management systems-Design reliable production processes | NA | Prepare backup stock, increase fleet, use alternative transportation |
15 | Product dosing error | Human error, improper calibration of tools | Unlikely | Strict SOPs, periodic training, regular quality checks | NA | Product recall, SOP evaluation and improvement |
Using the Kepner-Tregoe method while developing implementation plans can give various benefits, including:
1. Improved implementation effectiveness: The Kepner-Tregoe technique helps improve project implementation effectiveness by detecting and addressing possible issues early on.
2. Risk reduction: The Kepner-Tregoe method can assist lower the risk of project failure by identifying and reducing potential hazards.
3. Improved efficiency: By picking the optimum solution based on preset criteria, the Kepner-Tregoe technique can assist boost resource efficiency.
4. Improving decision quality: The Kepner-Tregoe method, which employs a structured and systematic approach, can assist in improving the quality of decisions.
Based on the AHP analysis that identified Cikarang as the optimal location, the authors recommend a comprehensive implementation plan using the Kepner-Tregoe Potential Problem Analysis (PPA) approach. Table XI details potential problems during the construction and operation of the facility, along with the likelihood of occurrence, impact, and level of risk. Specific countermeasures have been formulated for each issue, including permit preparation, safety training, and environmental monitoring. Contingency plans are also in place to address risks that may remain. By implementing this PPA, PT Green Pharma is expected to anticipate and mitigate risks proactively, thus ensuring the smooth and successful construction of the radiopharmaceutical facility in Cikarang.
Conclusion
In conclusion, this study successfully developed a comprehensive and structured multi-criteria decision-making framework for radiopharmaceutical facility site selection. The framework integrates the Analytical Hierarchy Process (AHP) to evaluate five main criteria: customer proximity, transportation accessibility, operational support, public acceptance, and legal compliance, along with their associated sub-criteria. Data collected through interviews and questionnaires of experts and stakeholders were analyzed using AHP to determine the priority weights of each criterion and sub-criteria.
The AHP analysis revealed that proximity to customers and transportation accessibility are the most crucial criteria in selecting a radiopharmaceutical facility location. Based on the analysis, Cikarang was identified as the optimal location, demonstrating superior performance in terms of proximity to hospitals, good transportation access, and compliance with regulations and permits. Sensitivity analysis confirmed the robustness of this selection to changes in criteria weights.
This study has limitations in terms of its geographical scope, which is limited to the DKI Jakarta, West Java, and Banten regions. Additionally, in-depth financial analysis and social impact assessments were not included due to the limited scope of this study. Future research can be expanded to include other regions of Indonesia, conduct a more thorough financial analysis, and evaluate the social impact of the construction of radiopharmaceutical facilities on the surrounding community.
The framework developed in this study is expected to provide valuable contributions to PT Green Pharma and the radiopharmaceutical industry in general in making strategic decisions regarding the selection of radiopharmaceutical facility locations. By adopting this structured and comprehensive approach, it is hoped that the location selection process can become more objective, transparent, and effective. In addition, this study also provides insight into the key factors to consider in the site selection of radiopharmaceutical facilities in Indonesia, which can serve as a reference for future studies in this field.
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