Indicator identification and data collection
The selection of indicators for this case study was meticulously justified through an extensive literature review, pinpointing the key dimensions critical for evaluating the capabilities of countries within the semiconductor supply chain (Chang et al., 2021; Hsiao et al., 2015; Kuo et al., 2019; Mönch et al., 2018a; Saif M. Khan et al., 2021; Sugano, 1994). Stability, research and development (R&D), technology infrastructure, and economic efficiency were identified as pivotal. Stability is crucial as it affects investment decisions and the operational environment essential in semiconductor production. R&D was chosen for its role in fostering innovation and technological advancement, which are vital for maintaining competitiveness in this rapidly evolving industry. Technology Infrastructure is integral for supporting sophisticated manufacturing processes characteristic of semiconductor production. Lastly, Economic Efficiency assesses how effectively countries convert their technological capabilities into productive outputs, a vital factor for sustaining competitive advantage in the high-tech sector. These indicators collectively provide a robust framework for assessing each country’s potential and strategic fit within the global semiconductor supply chain, ensuring the study’s alignment with industry-specific requirements and strategic objectives.
In this study, the data collection process was meticulously designed to ensure reliability and accuracy, crucial for the robust assessment of Southeast Asian countries’ capabilities in the semiconductor supply chain. Data were systematically gathered from transparent, trustworthy, and open databases published by recognized international organizations such as the World Bank, the UNESCO Institute for Statistics, the World Intellectual Property Organization, and the United Nations Industrial Development Organization. These sources provided up-to-date and standardized data across various indicators including economic performance, technological infrastructure, educational achievements, and innovation capacities. The time frame for the data collection spanned the most recent full calendar year available to ensure relevance and applicability to current conditions. Each data source was vetted for its credibility and consistency, with data extraction being carefully documented to facilitate reproducibility and transparency in research methods. The detailed breakdown of the data sources and the specific indicators sourced from each is systematically presented in Table 4 of the study. Despite the high quality of the sources, potential biases and limitations in the data collection were also considered. These include the inherent lag in data publication, which may not fully capture the most recent industry developments or economic shifts. Additionally, while these international databases provide extensive coverage, variations in data reporting standards across countries could introduce discrepancies. Recognizing these challenges, the study undertook additional verification steps where possible and acknowledges these limitations in interpreting the findings. This thorough approach to data collection not only strengthens the credibility of the research but also provides a solid foundation for analyzing the dynamics of the semiconductor supply chain in Southeast Asia. Accordingly, fifteen indicators are considered in this study to assess the opportunities of Southeast Asia countries to participate in the global semiconductor supply chain as summarized in Table 4. The description, units of the indicators are presented in Table A1, Table A2, Table A3, and Table A4 in the supplementary information. Because statistical data for countries are published at unequal intervals, this study uses the most recent published data for each country. This assumption is seen as a major limitation of this study.
Indicator weighting
The result of the data collection process is the formation of a decision matrix as shown in Table 5. The purpose of this phase is to determine the weights of the indicators objectively based on the nature of the data. The min-max normalization process is used to transform raw data with different scales and units into a common scale according to Eq. (3). The purpose of min-max normalization is to bring all the criteria to a comparable range between 0 and 1, without distorting the relative relationships among the criteria. Accordingly, the min-max normalized decision matrix is construct as shown in Table 6.
Then, the standard deviation of each indicator is calculated according to Eq. (5). As shown in Fig. 3, it seems that the Global Occurrences from Natural Disasters (I2) has the highest standard deviation (0.490), suggesting that the data points for this indicator are more widely spread from the mean compared to others, indicating higher variability. The government effectiveness (I3), GDP (I11), E-participation Index (I12), ICT Development index (I13), and Logistics performance index (I14) have relatively low standard deviations (ranging from 0.306 to 0.331), implying that these indicators have less variation, and the data points are closer to the mean. The other indicators have moderate standard deviations, indicating a moderate amount of variation in their respective datasets.
The next factor affecting the amount of information in the data is the correlation between indicators. As mentioned in Step II-4, the linear correlation coefficient between indicators’ data is determined as shown in Fig. 4. Those correlation coefficient measures the strength and direction of a linear relationship between two variables. It ranges from −1 to +1, where −1 indicates a perfect negative linear relationship, +1 indicates a perfect positive linear relationship, and 0 indicates no linear relationship.
Linear correlation coefficients among evaluation indicators derived by CRITIC method.
The results show that most indicator pairs have positive correlation coefficients, suggesting that they have a positive linear relationship. This means that when one indicator increases, the other tends to increase as well, and vice versa. The strongest positive correlation is between Logistics performance index (I14) and Competitive Industrial Performance Index (I15), with a correlation coefficient of 0.965, indicating a very strong positive linear relationship between them. Some indicator pairs exhibit negative correlation coefficients, indicating a negative linear relationship. This means that when one indicator increases, the other tends to decrease, and vice versa. The strongest negative correlation is between Patents applications (I8) and Logistics performance index (I14), with a correlation coefficient of −0.892, indicating a very strong negative linear relationship between them. There are also some weakly correlated indicator pairs, as seen in low correlation coefficient values (close to 0).
Based on standard deviation and correlation coefficient, the nature of the data reflects the amount of information content of the indicators, which are calculated according to Eq. (7) and presented in Table 7. The more information, the greater the weight. According to Eq. (8), the indicator weighting is performed and presented as Fig. 5.
Weights assigned to each indicator based on CRITIC method analysis.
According to the results, they can be classified into three distinct categories based on their assigned weight values. The categorization of indicators by weight values into high (above 10%), moderate (between 5% and 10%), and low (below 5%) was strategically chosen to reflect the varying levels of impact these indicators have on the overall assessment of countries’ capabilities within the semiconductor supply chain. This threshold-based categorization is rooted in statistical analysis and the principle of diminishing returns, where the most significant factors are assumed to have a disproportionately higher impact on the outcome relative to others.
The high weight threshold of over 10% was designated to identify indicators that are critical drivers of success in the semiconductor industry. These indicators, such as Patents Applications, Global Occurrences from Natural Disasters, and Graduates in Science and Engineering, are considered essential because they directly influence a country’s innovation capacity, resilience to disruptions, and availability of skilled manpower, respectively. These factors are pivotal in determining a country’s ability to sustain and grow within the highly competitive and innovation-driven semiconductor sector.
The moderate weight category (between 5% and 10%) encompasses indicators that, while significant, do not singularly dictate the outcome of the assessment but collectively contribute to a nuanced understanding of a country’s industrial and economic landscape. Indicators like GDP, High-tech Export, and ICT Goods Exports fall into this category as they reflect economic health and technological prowess, which are important but not the sole determinants of success.
The low weight threshold (below 5%) includes indicators that provide contextual and supporting insights which are useful in a holistic evaluation but are less critical in directly influencing the primary outcomes of the assessment. These indicators, such as the Rule of Law and Logistics Performance Index, while important, are supplementary in the context of this specific analysis focusing on the semiconductor industry’s dynamics.
The implications of these weight classifications are profound, as they influence the prioritization of strategies and policy formulations. For instance, a high weighting on innovation-related indicators suggests that policies encouraging R&D and intellectual property protection could yield significant competitive advantages. Conversely, the lower weighting on logistics performance may indicate that while necessary, improvements in this area alone will not be sufficient to dramatically enhance a country’s position in the semiconductor industry without simultaneous advancements in higher-weighted areas.
This structured approach to weighting not only ensures transparency and objectivity in the assessment process but also aligns the evaluation with the strategic objectives of enhancing regional capabilities in the semiconductor supply chain. It allows policymakers and stakeholders to focus their resources and interventions on areas that will have the most substantial impact on their strategic goals, thereby optimizing outcomes based on a clear understanding of priority areas.
Semiconductor industry opportunity assessment
In this phase, the opportunity to participate in the semiconductor supply chain of Southeast Asian countries is assessed. Because the computational procedures in this phase are based on the concept of distance, the decision matrix is normalized in a different way. As described in Eq. (10), the vector normalized decision matrix is established and presented in Table 8. In the next step, the influence of weighted indicators’ is applied to the decision matrix according to Eqs. (11, 12). The weighted vector normalized decision matrix is presented as Table 9. Then, the ideal and negative ideal solutions are defined as discussed in Eqs. (13–16).
In Step III-4, the Euclidean distance to ideal and negative ideal of alternatives are determined according to Eqs. (17, 18). As shown in Fig. 6, The country with the smallest distance to the ideal solution is Singapore, followed closely by Brunei Darussalam and Vietnam. These countries perform well according to the indicator and are considered closer to the ideal solution. On the other hand, the country with the largest distance to the ideal solution is the Philippines, indicating that it is relatively farther from the ideal solution based on the given indicator. Conversely, the country with the smallest distance to the negative ideal solution is the Philippines, implying that it is the closest to the worst-performing country in terms of the indicator. The country with the largest distance to the negative ideal solution is Indonesia, suggesting that it performs relatively better compared to the worst-performing country based on the given indicators.
Euclidean distances to ideal and negative ideal solutions across countries.
The overall scores of countries obtained by the proposed approach are visualized in Fig. 7. These results indicate that Singapore has the highest overall score, making it the top-performing country in terms of the opportunity to participate in the semiconductor supply chain among the Southeast Asian countries considered. Singapore has emerged as a prominent player in the semiconductor industry in Southeast Asia. It has a well-developed infrastructure, a skilled workforce, and a business-friendly environment that attracts semiconductor companies. The country’s strategic location, excellent logistics capabilities, and strong government support for research and development also contribute to its high overall score. As a result, Singapore is likely to offer attractive opportunities for companies looking to participate in the semiconductor supply chain.
Overall ranking and assessment results of Southeast Asian countries by TOPSIS method.
It is followed closely by Brunei Darussalam and Lao PDR, which also have relatively high overall scores, suggesting good potential for participation in the semiconductor supply chain. Brunei Darussalam also shows promising potential for participation in the semiconductor supply chain. While it may not have the same level of infrastructure and established semiconductor industry as Singapore, its geographic location and proximity to major markets in Asia could present advantageous opportunities. However, further developments in the semiconductor ecosystem and government initiatives to attract investments may be required to enhance its competitiveness. Lao PDR’s relatively high overall score suggests that it could be an emerging player in the semiconductor supply chain in Southeast Asia. Its low labor costs and growing economy might be appealing to semiconductor companies seeking cost-effective manufacturing and assembly options. However, infrastructure development and support for education and skill development will be crucial for sustaining and growing its participation in the semiconductor industry.
In the middle, Malaysia has been a significant player in the semiconductor industry for many years. It offers a well-established manufacturing base, skilled labor force, and a supportive business environment. Additionally, the government has actively promoted the semiconductor industry through various incentives and policies. Malaysia’s overall score reflects its attractiveness for semiconductor supply chain participants, although it may face increasing competition from other regional players. Vietnam has witnessed significant growth in the semiconductor sector in recent years. Its young and large labor force, competitive labor costs, and improving infrastructure have contributed to its overall score. The Vietnamese government’s efforts to promote the semiconductor industry and attract foreign investments have been instrumental in its growth. As the country continues to develop its capabilities, it is likely to become an even more appealing destination for semiconductor supply chain activities. Cambodia’s overall score indicates that it holds potential as a participant in the semiconductor supply chain. While it may not be as developed as some of its neighbors, it offers a relatively lower cost of doing business, making it attractive for companies seeking cost efficiencies. To enhance its competitiveness, investments in infrastructure and skill development initiatives will be essential.
Myanmar’s overall score reflects moderate potential for participation in the semiconductor supply chain. As the country opens to foreign investment and economic development, opportunities may arise in the semiconductor sector. However, it is essential to consider factors such as political stability and labor regulations before making investment decisions in Myanmar. Thailand has a well-established semiconductor industry and a supportive business environment. Its overall score suggests that it remains an attractive destination for semiconductor supply chain participants. The country’s strong manufacturing capabilities, skilled workforce, and strategic location in Southeast Asia contribute to its competitiveness.
On the other hand, the Philippines and Indonesia have lower overall scores compared to other countries in the list, indicating that they might have relatively less favorable conditions for participation in the semiconductor supply chain. Indonesia’s overall score indicates that it may have some challenges in attracting semiconductor supply chain participants compared to other Southeast Asian countries. While it is a large and growing market, infrastructure and bureaucratic hurdles might impact its appeal to semiconductor companies. However, Indonesia’s size and potential as a consumer market may still present opportunities for companies looking to participate in the supply chain. The Philippines receives the lowest overall score among the countries listed, suggesting that it may face significant challenges in attracting semiconductor supply chain activities. The country has struggled to keep up with some of its neighbors in terms of infrastructure development and ease of doing business. However, the Philippines’ skilled labor force and potential for domestic market growth could still offer some opportunities for niche segments in the semiconductor industry.
Discussion
Recent studies, such as those by Thadani and Allen (2023) and Prabheesh and Vidya (2024), have laid a significant groundwork in understanding the semiconductor supply chain dynamics within Southeast Asia (Prabheesh & Vidya, 2024; Thadani & Allen, 2023). This discussion aims to juxtapose our study’s findings against these established narratives to highlight both corroborations and deviations in the regional assessment of semiconductor supply chain capacities.
This study’s findings underscore Singapore’s preeminent status within Southeast Asia’s semiconductor supply chain, corroborating observations by Thadani and Allen, who highlight the region’s significant concentration of ATP facilities and OSAT providers. Consistent with these findings, our research confirms that Singapore’s advanced infrastructure, strategic positioning, and strong governmental support have cemented its role as a pivotal hub in the semiconductor industry. This alignment supports the notion that well-established industry bases combined with proactive government policies are critical drivers of success in the semiconductor sector. Moreover, our study identifies Brunei Darussalam and Lao PDR as emerging players with considerable potential, a perspective less commonly detailed in the existing literature. While Thadani and Allen focus primarily on the major hubs, our analysis suggests that these smaller markets may possess untapped potential due to strategic geographical positioning and growing economic frameworks. This introduces a nuanced layer to the regional analysis of semiconductor supply chains, suggesting that peripheral countries are gradually enhancing their capabilities and participation in this sector. In contrast, Prabheesh and Vidya underscore the centrality of Singapore in the semiconductor trade network, while noting the increasing integration of Vietnam, Malaysia, Thailand, the Philippines, and Indonesia. Our findings complement this perspective by detailing the specific attributes contributing to each country’s capacity and potential challenges within the supply chain. For instance, Malaysia’s established semiconductor base and supportive policy environment align with our identification of its sustained attractiveness to supply chain participants. Similarly, the emerging significance of Vietnam, highlighted by its robust growth and governmental promotion in Prabheesh and Vidya’s study, is mirrored in our analysis, which points to Vietnam’s competitive labor costs and improving infrastructure.
Conversely, the challenges faced by the Philippines and Indonesia, as noted in this study, reflect their lower rankings in the regional semiconductor network. Prabheesh and Vidya suggest these countries have improved their network participation, yet our findings indicate ongoing infrastructural and bureaucratic hurdles that could hinder their fuller integration into the supply chain. This divergence necessitates a closer examination of how each country’s unique socio-economic and political landscapes impact their roles within the semiconductor industry.
Lastly, the economic implications of the COVID-19 pandemic, as discussed by Prabheesh and Vidya, resonates with our observations of disrupted trade flows and its impacts on regional connectivity. This shared challenge across studies emphasizes the need for robust supply chain strategies that can withstand global disruptions and highlights the critical role of resilience planning in maintaining supply chain efficacy.
The managerial implications
Policymakers are advised to bolster R&D infrastructures and educational programs, particularly in emerging countries like Brunei Darussalam and Lao PDR, to catalyze technological advancements and align educational outcomes with industry needs. Investors should focus on countries with robust infrastructure and stable economies like Singapore and Malaysia but also consider emerging markets such as Vietnam and the Philippines for diversified investment opportunities due to their growth potential and governmental support. Industry stakeholders could benefit from diversifying their supply chains to include new regional players, thereby enhancing resilience and reducing dependency risks highlighted by recent global disruptions. Additionally, fostering collaborative projects and joint ventures can tap into regional innovations, driving mutual growth across the industry.
Future research should explore longitudinal studies to track changes in the semiconductor industry over time and conduct comparative analyses with other regions to deepen the understanding of global supply chain strategies. These efforts will not only provide a richer analytical framework but also enable stakeholders to make more informed decisions that align with evolving market conditions and technological trends.
Discover more from PressNewsAgency
Subscribe to get the latest posts sent to your email.