Ilyas Masudin

The rapid expansion of hydrogen supply chains introduces complex governance, financial, and operational risks that challenge sustainable transition pathways, particularly within urban and peri-urban industrial ecosystems. This study aims to identify the structural drivers of these risks and develop contradiction-based strategies to enhance systemic robustness. An integrated DEMATEL–ISM–TRIZ framework is employed to map causal interdependencies, construct hierarchical risk structures, and translate systemic tensions into innovation-oriented design principles. DEMATEL identifies cause-and-effect relationships, ISM establishes multilevel structural positioning, and TRIZ resolves critical contradictions through inventive principles. The results reveal that institutional coordination, regulatory adaptability, and strategic investment logic function as foundational drivers of cascading vulnerabilities. Hierarchical modeling confirms that many operational and social risks, including community acceptance and environmental conflicts, are dependent outcomes rather than root causes, with significant implications for urban governance and social legitimacy. Mapped to governance levels, the foundational drivers (R6, R9) operate mainly at the national and supply-chain-wide levels, the regulatory transmission risks (R10–R12) link national rule-setting with regional and municipal implementation, and the dependent social risks (R13–R15) materialize primarily at the urban/local level. TRIZ analysis indicates that robustness is achieved through adaptive governance, phased investment, modular infrastructure design, and socially embedded implementation sequencing. The TRIZ-derived inventive principles are subsequently contextualized as a strategic governance framework for city and regional policymakers, providing a roadmap for orchestrating a just and resilient hydrogen transition within urban and peri-urban industrial ecosystems. The integrated framework demonstrates that hydrogen supply chain sustainability emerges from structural redesign rather than incremental risk mitigation. This study contributes a coherent methodological architecture that links structural diagnosis with contradiction-driven governance innovation for sustainable energy transitions in cities.

This study explores the integration of sustainable value stream mapping (SVSM) and the DMAIC approach to improve performance in Indonesian SMEs within the food industry, focusing on the significance of fostering a culture of organisational learning. In the measure phase, sustainability metrics were established across economic, environmental and social dimensions. The economic metric indicated that the frying process, characterised by a double frying method, had the longest cycle time, adversely affecting production efficiency. Environmental analysis revealed significant raw material wastage in the frying line, underscoring the need for ongoing process optimisation through collective learning. Additionally, social metrics identified elevated occupational health and safety risks associated with the frying line, highlighting the importance of learning from work-related accidents to enhance safety measures. The analysis phase utilised current sustainable value stream mapping (CSVSM) to pinpoint critical process inefficiencies, leading to targeted improvement actions, such as streamlining the frying process to a single cycle and minimising product breakage. The future sustainable value stream mapping (FSVSM) demonstrated substantial potential reductions in cycle time and raw material waste. Overall, the findings illustrate how SVSM and DMAIC can drive sustainable practices and operational efficiency, while emphasising the role of organisational learning in developing replicable models for other SMEs aiming to enhance sustainability performance.

The high variability of consumer demand makes the development of inventory strategies crucial, especially regarding operational inventory resilience. Combining defect prediction with inventory strategies is crucial amidst uncertainty related to quality. Conventional Demand-Driven Material Requirements Planning (DDMRP) strategies are sensitive to shifts in consumer demand based on buffers for replenishment. However, this strategy has the disadvantage of not considering losses due to defective production output quality. This study develops a hybrid inventory model by combining DDMRP with Order-Up-To-Level (OUTL) replenishment management, and defect rate prediction. Production output is assessed from estimated defect rates converted into yield factors. OUTL is used for conditional quantity setting by determining the amount of excess replenishment. Defect rate prediction uses a manufacturing defect dataset along with production volume, supplier quality, maintenance hours, time percentage, and worker productivity. The initial predictive element in inventory simulation using a random forest regressor configuration achieved an R² value of 0.7208. Numerical experiments to evaluate the inventory model used 24 scenarios over a 200-day daily review period. Scenarios were conducted by integrating various demand patterns, production process conditions, and production capacity limitations. The DDMRP-OUTL hybrid strategy model can reduce the Bullwhip Effect Ratio (Ratio of Echelon Logistics - REL) compared to conventional DDMRP for various scenarios, and the most significant reduction is close to 24% under intermittent demand. Furthermore, it demonstrates a higher average inventory increase as a trade-off between replenishment stability and inventory load. Stockout events are not consistently reduced across all scenarios, although the integration of defect rate prediction and the DDMRP-OUTL hybrid model leads to replenishment stability, and inventory load and service reliability must be balanced when implementing this policy.