Industrial manufacturing processes, particularly energy-intensive batch operations, face significant challenges due to volatile electricity prices and stringent sustainability requirements. This paper proposes a data-driven batch scheduling optimization framework designed to minimize electricity costs by aligning high-resolution energy consumption profiles with dynamic electricity pricing. The methodology integrates synthetic data generation, precise cost modeling at 10 s resolution, and a rigorous combinatorial optimization procedure that ensures optimal scheduling solutions within discrete intervals. Validation through a real-world case study from a Danish foundry demonstrates substantial cost savings, achieving an overall weekly electricity expense reduction of approximately 16,783 DKK across multiple batches. The proposed approach distinctly advances existing literature by guaranteeing global optimality, explicitly incorporating real operational constraints, and employing realistic industrial data for validation. This research provides practical, robust decision-support capabilities, enabling industries to effectively navigate market fluctuations, reduce operational costs, and enhance environmental sustainability without additional capital investment.