Drummondville Virtual Twin: Driving Sustainable Industrial Planning for our Eco-Park

Planning Drummondville’s new industrial eco-park required addressing a unique level of complexity. Three main challenges defined the project: 1. Managing a vast array of interrelated criteria — Urban planning parameters, economic development goals, environmental preservation targets, and spatial constraints all had to be addressed simultaneously. Each dimension brought its own regulations, technical requirements, and stakeholder priorities, making the planning process inherently multi-layered. 2. Visualizing and understanding interdependencies between urban planning rules — To achieve the city’s development objectives, it was essential to design zoning, infrastructure, and environmental protection measures as a coherent system. However, traditional planning methods made it difficult to foresee how modifying one regulation could affect others, potentially causing delays, inefficiencies, or unintended environmental impacts. 3. Identifying cause-and-effect links between development criteria and economic outcomes — The city needed to understand how land-use decisions, infrastructure investments, and environmental safeguards would influence the eco-park’s economic performance. This included forecasting the return on investment, job creation potential, and long-term industrial attractiveness. Addressing these challenges required a collaborative, data-driven approach capable of integrating diverse datasets, simulating scenarios, and making complex cause-and-effect relationships visible and understandable for decision-makers, investors, and citizens alike.

1. Managing a vast array of interrelated criteria The Virtual Twin brought together urban planning, economic, environmental, and spatial data into a single interactive model. Instead of working in silos, all datasets were layered and contextualized, allowing planners to see the complete picture at once. This ensured that zoning rules, infrastructure capacity, environmental protections, and industrial layouts were evaluated together, not in isolation. 2. Visualizing and understanding interdependencies between urban planning rules The platform’s simulation capabilities enabled the automated generation of hundreds of possible eco-park configurations. Stakeholders could instantly see how changes to one rule — for example, increasing buffer zones — would affect developable land, infrastructure costs, and environmental performance. These visual cause-and-effect demonstrations helped align decisions with the city’s development objectives while avoiding unintended regulatory conflicts. 3. Identifying cause-and-effect links between planning criteria and economic outcomes By integrating business intelligence tools, the Virtual Twin connected land-use variables to economic markers such as potential job creation, industrial attractiveness, and projected return on investment. This made it possible to quantify how specific planning choices — like road placement or conservation zones — would influence both short-term development costs and long-term economic benefits. In short, the solution transformed a highly complex planning process into a data-driven, transparent, and collaborative exercise, resolving each of the three core challenges in a measurable and actionable way.