This paper introduces the ongoing research conducted on enabling industrial greenhouse growers to optimize production using multi-agent systems and digital twin technology. The project seeks to develop a production process framework for greenhouses, based on several case studies, that can be applied to different greenhouse facilities to enable a broad implementation in the industrial horticulture sector. The research will incorporate AI technology to support the production process agent in forecasting and learning optimal operating conditions within set parameters that will be feedback to the grower through a common information model. Furthermore, the production agent will communicate with other process agents to co-optimize the essential aspects of production. In turn, this allows the growers to optimize the production cost with minimal risk to product quality while aiding in upholding grid stability. The findings in this research project may be beneficial for developing industry-specific energy flexibility solutions incorporating product and process constraints.