The topic of digital transformation in supply chain optimization has garnered considerable attention in recent years due to its importance. The purpose of the study was to offer empirical evidence and insights into the advantages and obstacles linked with digital transformation in supply chain management. To investigate the effects of digital transformation on supply chain optimization, the research employs a hybrid methodology and comprehensive approach that includes a thorough literature review, the creation of a theoretical framework, and the presentation of empirical finings through various case studies using the predefined selection criteria. The case analyses highlight crucial elements that support effective digital transformations, including real-time data analytics, teamwork, blockchain technology, digital twin augmented and virtual reality and collaborative robots. The practical implications from the findings of this study, proffers insights that can be extremely helpful for professionals in various industrial sectors and businesses planning similar digital transformation journeys. This empirical study with regards to the implication of Digital transformation 5.0 on supply chain management is novel to the body of literature. It is however necessary to conduct more study to confirm the results, apply them to a wider range of businesses, and investigate different aspects of digital transformation in supply chain optimization.

Residual biomass is a reliable source of energy and hence requires effective supply chain management for optimal performance and sustainability. While there are various studies on this recent trend, a comprehensive review of the literature on simulation-based modeling of the supply chain for residual agroforestry biomass is lacking. This study aims to present a systematic review of relevant literature surrounding residual agroforestry supply chain simulation insights and directions. The systematic literature review was carried out in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 standards and intends to answer the research questions based on (1) Key Performance Indicators (KPI); (2) Simulation techniques; and (3) Efficient supply chain. A search of the Science Direct, SCOPUS, and UA EBSCO databases was conducted using the appropriate keywords combination. The databases were searched, and a total of 1617 papers were appraised automatically. Subsequently, the titles, keywords, and abstracts of 172 papers were examined. Following the full-text analysis, 20 papers in addition to 27 articles taken from other sources matched the requirements for study inclusion. The publications accessed reveals that simulation-based techniques will optimize the supply chain for residual biomass when applied.

As environmental sustainability gains importance, enhancing supply chains to minimize environmental hazards is essential, particularly in industries using residual biomass. This study tackles this by investigating the integration of sustainability criteria into supply chain optimization for a biomass energy company in Portugal, using a combination of simulation modeling through anyLogistix software (version: 2.15.3.202209061204) and multi-criteria decision-making. Four supply chain scenarios were designed and simulated, differing in their number of distribution centers, the adoption of green logistics, and split-by-ratio distribution strategies over a 305-day period. Through the weighted sum model, Scenario C emerged as the optimal configuration, achieving a balance between operational efficiency and sustainability by reducing CO₂ emissions by up to 90% and lowering transportation costs without compromising revenue. Sensitivity analysis further highlighted the trade-offs between cost efficiency, lead times, and environmental impact, showing that the strategic placement of distribution centers and the use of eco-friendly vehicles significantly improve the sustainability of the biomass supply chain. These findings provide practical insights for decision-makers, demonstrating how digital modeling tools can enhance supply chain management by optimizing environmental and operational goals simultaneously. This research contributes to the fields of sustainable logistics and supply chain management by validating the effectiveness of green logistics strategies and multi-criteria decision-making approaches in reducing environmental impact while maintaining economic viability.

Background: In the pursuit of sustainable energy sources, residual biomass has emerged as a promising renewable resource. However, efficiently managing residual biomass poses significant challenges, particularly in optimizing supply chain operations. Advanced modeling approaches are necessary to address these complexities. This study aims to develop a comprehensive methodological framework for creating simulation models tailored to agroforestry residual biomass supply chains. Methods: The study employs a hybrid simulation approach, integrating geographic information system mapping with a case study analysis. The simulation was conducted over a 365-day period, using the anyLogistix software (version 2.15.3.202209061204) to model various supply chain dynamics. The framework also accounts for financial, operational, customer satisfaction, and environmental metrics. Results: The simulation results showed a total expenditure of EUR 5,219,411.3, with transportation being the primary cost driver, involving 5678 trips and a peak capacity of 67.16 m³. CO₂ emissions were measured at 487.7 kg/m³. The model performed as expected, highlighting the need for sustainable logistics strategies to reduce costs, lower losses, and improve productivity. Conclusions: This study presents one of the first detailed methodological frameworks for simulating agroforestry residual biomass supply chains. It provides valuable managerial insights into the financial, operational, and environmental aspects of supply chain management. The findings may stakeholders make informed decisions to enhance the sustainability of biomass utilization in energy production.