What are scenario products?
Scenario products are dummy or hypothetical products in Pickler. They are used when you want to calculate the expected footprint of a product that does not yet exist, is not part of your own inventory or is based on assumptions rather than complete proof.
They are useful in sales, product development and internal analysis. For example, a customer might ask whether a lighter product, recycled material, different supplier or alternative product structure would reduce impact. Instead of giving a generic answer, your team can create a scenario product and compare the expected carbon footprint, eco-costs and lifecycle hotspots.
Scenario products versus inventory products
Pickler separates scenario products from inventory products. Inventory products represent real products based on data you own and can prove. These are the products you should use for product passports, widgets, external communication and marketing claims.
Scenario products are different. They are based on assumptions and are clearly marked as scenarios. They are meant to support decisions and conversations, not to become the primary proof for public claims. If a scenario becomes a real product, it should be converted into an inventory product with the right underlying data and evidence.
How teams use scenario products
Scenario products follow the same practical workflow as other products in Pickler. They can be created manually, imported with spreadsheets, created through the API or made by duplicating an existing inventory product. From there, teams can change variables such as material type, material share, weight, supplier assumptions, transport or end-of-life setup.
The strongest use case is comparison. By placing scenario products next to inventory products or other scenarios, teams can see which option performs better, where the difference comes from and which assumptions matter most. This makes scenario products especially useful for sales teams, product managers, procurement and sustainability teams that want to bring footprint data into earlier decisions.
