The cutting stock problem consists of allocating smaller items into larger objects without overlap, while respecting specific constraints and pursuing a defined objective, such as minimizing the number of bins used. Although it has been studied since the 1960s and has significant economic relevance in sectors such as sheet metal and furniture, the literature on the topic remains fragmented in two aspects: the lack of comprehensive systematic reviews and the rarity of real-world applications, especially in Small and Medium-sized Enterprises (SMEs). To address these gaps, this dissertation proposes two complementary articles. The first conducts a systematic literature review on the two-dimensional cutting and packing problem, using the Methodi Ordinatio method to select and rank the most relevant studies, analyzing 116 articles out of 447 classified as eligible. The main findings include: the predominance of exact methods (45 articles) over metaheuristics (23 articles); the lack of real industrial applications (only 14 out of the 116 articles); and the near absence of studies applied to SMEs (only three articles). Based on the review findings, the second article proposes a Genetic Algorithm for the two-dimensional cutting problem with threestage guillotine cuts, an unlimited number of bins, and no item rotation, applied both to instances from the literature and to real instances from a Brazilian SME in the furniture sector. Compared with the literature, the algorithm achieves a global gap of 1.89%, with performance ranging from 0% in Class 9 instances to 6.36% in Class 6 instances. For the real instances, the algorithm obtains results equivalent to human planning in all tested cases, with an average reduction of 94.06% in solution time. The method is executed through a command-line interface with an Excel file as input, dispensing with advanced computational infrastructure or specialized technical knowledge, making it directly applicable to the context of SMEs. Taken together, the two articles demonstrate that the methodological sequence systematic review-development-industrial application constitutes a rigorous and replicable approach for advancing the field of cutting and packing problems.

The growing convergence between digital transformation and sustainability has increased the need for integrated approaches capable of supporting organizations in the development of more resilient, efficient, and environmentally responsible supply chains. In this context, this dissertation aimed to develop an integrated approach to understand, structure, and evaluate the contribution of Industry 4.0 to sustainability in green supply chains. To achieve this objective, the research was structured into five complementary and interdependent studies, combining theoretical foundations, structural modeling, and the development of managerial artifacts. Initially, a narrative literature review was conducted to examine the evolution of Supply Chain Management, highlighting its transition from a predominantly operational approach to a strategic model driven by sustainability, resilience, and digitalization. Subsequently, a Systematic Literature Review (SLR) was carried out following the PRISMA guidelines, enabling the identification of research trends, theoretical gaps, and critical factors related to the integration of Industry 4.0 and sustainability. As a result, the Industry 4.0 Framework for Integrated Sustainability (IRIS Framework) was proposed, integrating technological, environmental, economic, social, and governance dimensions associated with sustainable digital transformation. In the third stage, an integrated approach based on the Decision-Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Modeling (ISM), and Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) methods was applied to identify, analyze, and hierarchically structure the main barriers to the implementation of sustainable Industry 4.0 in green supply chains. The findings revealed that professional qualification and resistance to change are the primary causal factors within the system, exerting strong influence over the remaining barriers and playing a strategic role in enabling sustainable digital transformation. Subsequently, from a Design Science Research (DSR) perspective, the Sustainable Industry 4.0 Supply Chain Maturity Index (SI4SCMI) was developed as a managerial artifact designed to assess organizations’ sustainable and digital maturity levels. The index was structured around six dimensions: organizational, economic, technological, environmental, social, and sustainable supply chain integration. A pilot application of the instrument revealed higher maturity levels in the technological, social, and organizational dimensions, whereas sustainable supply chain integration presented the lowest performance levels. Finally, the Green Chain Index (GCI) was proposed as a composite indicator designed to measure the sustainability performance of supply chains operating within the Industry 4.0 context, enabling monitoring, benchmarking, and strategic decision-making support. Taken together, the results demonstrated that sustainable digital transformation depends not only on the adoption of advanced technologies but also on overcoming organizational, human, and strategic barriers. As its main contribution, this dissertation provides an integrated set of frameworks, structural models, maturity indices, and performance indicators capable of supporting organizations in the assessment, planning, and implementation of sustainability- and Industry 4.0-oriented initiatives. These contributions advance scientific knowledge while fostering the development of more intelligent, resilient, and sustainable supply chains.