Develop an integrated energy optimisation plan for SD42 and SD403 that: reduces energy consumption (gas + electricity) per tonne of product,increases process stability, provides input for the long-term energy transition (e.g. reduced gas dependency), is directly usable for decision-making.

Smit & Zoon lacks precise insights into where and when the demand for leather chemicals will peak, potentially leading to inefficient production planning and unnecessary costs. Challenge: Develop an AI model that performs predictive analyses to forecast the future demand for leather chemicals in various world regions. Utilize public data and supply chain data to identify patterns and make accurate predictions. The AI tool should deliver month-by-month planning for different world regions, including EMEA, Asia, and the Americas. Additionally, it should be adjustable to input data and capable of learning from historical sales data to improve its accuracy over time. We need to have better insight in our production planning and warehousing to have quicker deliveries we are working on installation of on internal integrated planning tool, but would love to add the external data too for more precise control.