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This paper presents a novel approach to the strategic planning of the supply chain for lime cultivation. The uncertainty associated with lime production due to factors such as transportation, crime, and water availability, among others, represents different challenges. To address these challenges, the authors propose the use of machine learning tools coupled with deterministic optimization to obtain optimal solutions for the supply chain for future years. This paper highlights the use of justice schemes to ensure an equitable distribution of lime production between farmers and learning machines to quantify the insecurity impact on lime production in Michoacan. The problem can be solved using a Mixed-Integer Linear Programming (MILP) model that focuses on maximizing economic benefits, minimizing the use of irrigation water in crops, and minimizing Carbon Dioxide Equivalent (CO 2eq) emissions within the supply chain. The results demonstrate that the proposed approach can effectively address the challenges facing the lime production supply chain, resulting in an optimized and equitable lime production and distribution between farmers using the Social Welfare Justice scheme. Scenario A presents a utopian viewpoint with maximum benefits and minimum CO 2eq generated emissions, corresponding to $ 4.08 × 109 and 1.94 × 106 tons of CO 2eq , respectively. This paper contributes to the growing body of literature on the use of Artificial Intelligence (AI) in supply chain management, specifically in the optimization of supply chain processes. [Display omitted] [ABSTRACT FROM AUTHOR]

The textiles and apparel industry is a major contributor to economic development while at the same time being one of the most polluting industries due to its lengthy supply chain and resource intensive production operations. To address these sustainability challenges, digitalization is seen as one of the potential solutions. Using the lens of sustainability and digitalization in Supply Chain Management (SCM), this paper analyses the sustainability and digitalization status of Dutch textile and apparel firms. We used a mixed methodology of quantitative text mining of 94 Dutch textile and apparel firms as well as qualitative thematic and coding analysis of experts' views and opinions on sustainability and digitalization in the Dutch textiles and apparel industry. Quantitative analysis of website data shows that Dutch textile and apparel firms predominantly communicate the environmental, to a lesser extent social, and least of all economic sustainability factors. Keyword analysis also shows that the use of technological keyword indicators is less prominent, while certain technologies such as IoT, sensors and blockchain correlate mostly to environmental sustainability factors. Moreover, qualitative analysis reveals that to address sustainability via digitalization, it is important to link sustainability goals to Key Performance Indicators, which requires data for traceability. We recommend firms to: (1) re-evaluate their business models and assess the extent traceability can be incorporated in their sustainability strategy; (2) enhance stakeholder collaboration within and outside the supply chain to utilize traceability; and (3) proactively use traceability information to improve transparency and accountability to meet legal requirements and address greenwashing. This study contributes to literature by showing the importance of traceability for (a) linking sustainability and digitalization in SCM, b) achieving the ultimate goals of transparency and accountability, and c) predicting demand and supply to address overproduction and waste in the textiles and apparel sector. • This study analyses the sustainability and digitalization status of Dutch textile and apparel firms. • Firms predominantly communicate environmental, followed by social and least of all economics sustainability, in their websites. • To address sustainability via digitalization, it is important to link sustainability goals to Key Performance Indicators. • Traceability data can improve transparency and accountability, as well as predictability of demand and supply in the chain. [ABSTRACT FROM AUTHOR]