The semiconductor industry is crucial for global technological advancement and economic growth. The semiconductor supply chain is complex and globalized, with disruptions having significant economic consequences. While research exists on global supply chain dynamics, a focused analysis of Southeast Asia's potential within this industry is lacking, especially considering recent geopolitical shifts. This study aims to fill this gap by employing a rigorous MCDM approach, combining CRITIC and TOPSIS methods, to evaluate the potential of Southeast Asian countries to participate in the semiconductor supply chain. The chosen methods offer an objective, data-driven approach, minimizing bias in strategic decision-making. The study uses data on fifteen indicators, identified through literature review and stakeholder consultations, to assess each country's readiness for semiconductor supply chain integration. The expected outcome is a ranking of Southeast Asian countries based on their potential, highlighting strengths and weaknesses, and providing insights for policymakers and investors.

Existing literature extensively covers the global semiconductor supply chain, exploring its structure, challenges, resilience, and the impacts of disruptions. Studies have examined coordination mechanisms, risk management strategies, and the effects of geopolitical tensions and demand volatility. Research also focuses on collaboration, sustainability practices, and the influence of technological advancements. Within Southeast Asia, studies have analyzed the region's potential, competitiveness, and supply chain capabilities, examining factors like strategic location, investment climate, skilled labor, and government initiatives. However, a gap exists in applying MCDM methods to specifically analyze Southeast Asian countries' semiconductor supply chain integration potential, which this study addresses.

This study uses a two-phase MCDM framework integrating the CRITIC and TOPSIS methods. Phase I involves a comprehensive literature review, indicator identification (fifteen indicators related to stability, R&D, technology infrastructure, and economic efficiency), and data collection from reputable international organizations (World Bank, UNESCO, WIPO, UNIDO). Phase II focuses on data normalization using the min-max method, which scales data to a 0-1 range. CRITIC method objectively determines indicator weights based on their standard deviation (contrast intensity) and correlation coefficients (conflict). TOPSIS ranks countries based on their closeness to the ideal solution and distance from the negative-ideal solution. The selected normalization and weighting methods enhance the analytical rigor, ensuring objectivity and transparency. The calculations detailed in the study include equations (1-19) to guide the min-max normalization, standard deviation calculation, correlation coefficient matrix generation, information content estimation, indicator weighting, vector normalization, ideal and negative ideal solution identification, Euclidean distance calculation, and overall score computation.

The study's data collection resulted in a decision matrix (Table 5) showing each country's performance across the fifteen indicators. The min-max normalized decision matrix (Table 6) standardized the data for comparison. CRITIC analysis revealed indicator weights (Figure 5), with 'Patents Applications' and 'Global Occurrences from Natural Disasters' receiving high weights. TOPSIS analysis yielded overall scores (Figure 7), ranking countries based on their proximity to the ideal solution. Singapore achieved the highest score, followed by Brunei Darussalam and Malaysia. Vietnam also showed strong potential, while the Philippines and Indonesia scored lower, indicating challenges in infrastructure and business environment. The findings suggest that countries with strong technological infrastructure, stable economies, and supportive government policies have a greater chance of successful semiconductor supply chain participation. Specific findings included: Singapore's strong performance across all indicators; Malaysia's established manufacturing base and supportive policies; Vietnam's competitive labor costs and improving infrastructure; and the Philippines and Indonesia's lagging infrastructure and business environments as hindrances to greater integration.

The findings align with existing literature emphasizing Singapore's leading role in Southeast Asia's semiconductor industry, while also highlighting the emerging potential of Brunei Darussalam and Lao PDR. The study corroborates the observations of other researchers about the strengths of Malaysia and Vietnam, but also reveals challenges facing the Philippines and Indonesia. By providing a detailed, quantitative analysis using the CRITIC and TOPSIS methods, the study contributes a nuanced understanding of each country's strengths and weaknesses within the context of the semiconductor supply chain. The results highlight the importance of strong governmental support, investment in R&D, and the development of robust infrastructure in driving a country's success in the semiconductor industry.

This study provides a novel, data-driven assessment of Southeast Asian countries' potential in the semiconductor supply chain using an integrated CRITIC-TOPSIS MCDM approach. The findings offer actionable insights for policymakers and investors, identifying countries with high potential (Singapore, Malaysia, Vietnam) and those requiring further development (Philippines, Indonesia). Future research could expand on this work through longitudinal studies tracking industry changes and comparative analyses with other regions. Further research should also explore the dynamic nature of the semiconductor industry and incorporate qualitative data to enhance the robustness of future assessments.

The study's findings are limited by the selection of indicators and their weights, which may evolve over time. The static nature of the data may not fully capture dynamic industry changes. The focus on Southeast Asia limits the generalizability of the findings to other regions. The reliance on objective weighting methods using CRITIC might overlook qualitative factors that can influence a country's success in the semiconductor industry. Future research should address these limitations by incorporating dynamic modeling, qualitative data, and cross-regional comparisons.