| Abstrakt: |
Transportation activities have witnessed a significant increase in recent years, with industrial evolution directly impacting the pillars of sustainable development. The literature demonstrates a surge in transport activities, with, for instance, a 20% rise in environmental impact and a 15% increase in overall economic influence. Recognizing this, businesses understand the importance of developing long-term plans for road transportation, considering its substantial impact on sustainability within logistics operations. Consequently, it becomes crucial to construct decision support models capable of analyzing the sustainability of supply chain and logistics performance. Therefore, the objective of this study is to create a prediction model utilizing an Artificial Neural Network (ANN) to approximate the global multidimensional sustainable performance value of the supply chain in the context of road freight transport. This approach combines the key dimensions of sustainability, including economic, social, and environmental aspects, along with operational and stakeholder considerations, for the first time in pursuit of this goal. Prior to the machine learning phase, a minimum condition algorithm was utilized to calculate sustainable performance as an initial design step. This algorithm assigns to a dimension the lowest level among the fields within the same dimension. This study presents a unique technique for predicting the global multidimensional sustainable performance within the logistics industry, which can also be adapted for other sectors. As a result, it offers valuable insights to managers regarding strategic development options. The sustainable performance value provides an indication and quantification of a company's sustainable performance level corresponding to its adherence to and achievement of objectives. [ABSTRACT FROM AUTHOR] |
