at least one processor programmed with a set of non-transitory computer-readable instructions to collectively execute:

receiving, from a first data source, an indication associated with a set of products that relates to a first entity that sells the set of products and a second entity that purchases the set of products, wherein the set of products is associated with at least one of a set of manufactured goods or a set of raw materials;

in response to receiving the indication associated with the set of products, automatically predicting a baseline cost of at least one of purchasing or selling the set of products at a future point in time based on the indication;

in response to predicting the baseline cost, automatically retrieving a futures cost from a second data source, at a current point in time, of a futures contract for an obligation to the at least one of purchasing or selling the set of products for at least one of delivery or performance of the set of products at the future point in time;

in response to retrieving the futures cost, automatically executing a smart contract for the futures contract based on the baseline cost and the futures cost; and

in response to executing the smart contract for the futures contract, orchestrating the at least one of delivery or performance of the set of products at the future point in time by automatically triggering a transfer of the set of products from the first entity to the second entity; and

an autonomous system that is at least partially incorporated in each respective product of the set of products or packaging of each product of the set of products,

wherein the autonomous system receives data from a set of sensors that detects whether each respective product has experienced negative effects that include at least one of: being exposed to an adverse environmental condition or incurring damage,

wherein the set of non-transitory computer-readable instructions includes:

generating, via a machine-learned model, a prediction that a product of the set of products requires replacement based on the data from the set of sensors; and

automatically triggering a transfer of a replacement product from the first entity to the second entity in response to the machine-learned model generating the prediction,

wherein the machine-learned model is trained at least in part with simulation data generated by executing simulations via a digital twin simulation system, and

wherein executing the simulations via the digital twin simulation system includes performing a set of stress tests associated with a set of workflows of the set of products.