Reducing supply chain complexity can significantly increase an organization’s performance. One approach to achieve this is through understanding the value-added characteristics of the supplier. This article arises from a case where a new industrial project made use of the relationship with their supplier to overcome shortcomings in the coordination of the purchasing department and operational planning. In this, the use of exact, heuristics and meta-heuristic methods are explored to solve a 1D Cutting Stock Problem (1DCSP) with multiple objects and varied items. Particularly noteworthy is the development of a Genetic Algorithm (GA) for solving more complex scenarios, given the NP-Hard nature of the problem. A comparative study was conducted between constructive heuristics and a mix of exact methods and the genetic algorithm, demonstrating the development of each of these tools according to the problem’s characteristics. The approach proved fruitful, resulting in a significant reduction in wasted materials, the creation of working methods for the purchasing department to avoid overstock or stockouts, and the establishment of a system that enabled considerable anticipation in the coordination of operational activities related to the cutting sector.