| CPC G06Q 30/0206 (2013.01) [G05B 13/048 (2013.01); G06F 9/4881 (2013.01); G06N 5/043 (2013.01); G06N 10/60 (2022.01); G06N 10/80 (2022.01); G06N 20/00 (2019.01); G06Q 10/0631 (2013.01); G06Q 10/06315 (2013.01); G06Q 10/087 (2013.01); G06Q 30/0201 (2013.01); G06Q 30/0625 (2013.01); G06Q 40/04 (2013.01)] | 25 Claims |
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1. A raw material system comprising:
memory hardware configured to store computer-executable instructions; and
processor hardware configured to execute the computer-executable instructions,
wherein the processor hardware and the memory hardware collectively execute:
a product manufacturing demand estimation system programmed to calculate an expected demand for a product at a future point in time;
an environment detection system configured to identify at least one of: an environmental condition or an environmental event;
a raw material production system programmed to estimate a raw material availability at the future point in time based on the expected demand and the at least one of: the environmental condition or the environmental event;
a raw material requirement system programmed to calculate a required raw material amount to manufacture the product at the future point in time based on the expected demand and on the at least one of: the environmental condition or the environmental event;
a raw material procurement system programmed to autonomously configure a futures contract for procurement of at least a portion of the required raw material amount in response to the required raw material amount calculation exceeding the raw material availability; and
a risk tolerance system configured to:
retrieve a pre-determined risk tolerance of an entity that procures a raw material; and
generate a risk threshold based on the pre-determined risk tolerance of the entity that procures the raw material,
wherein the pre-determined risk tolerance indicates a difference between a baseline cost of the raw material and a futures cost of the raw material,
wherein the raw material procurement system includes a robotic process automation (RPA) service configured to provide a set of instructions for autonomously configuring the futures contract,
wherein the RPA service trains a neural network on a training data set to provide the set of instructions for autonomously configuring the futures contract,
wherein the training data set includes at least one of: historical data, feedback from outcomes, or human interactions involved in contract negotiations,
wherein the RPA service provides the set of instructions that include performing a set of de-risking algorithms to configure a set of terms and conditions for the futures contract,
wherein the raw material procurement system is further programmed to autonomously configure the futures contract based at least in part on the risk threshold and the set of instructions from the RPA service,
wherein the raw material procurement system is further configured to execute a smart contract for the futures contract,
wherein the smart contract is implemented on a blockchain,
wherein the raw material procurement system executes the smart contract in response to a risk exceeding the risk threshold,
wherein the risk is associated with a possibility of the futures cost of the raw material exceeding a maximum price the entity that procures the raw material is willing to pay,
wherein the RPA service includes an interface for an entity associated with the executed smart contract to provide feedback on the executed smart contract,
wherein the neural network is retrained based on (i) an outcome of the executed smart contract and (ii) the feedback received from the entity associated with the executed smart contract, and
wherein the retraining of the neural network includes placing gates on the neural network to render it a gated neural network that balances learning with a need to diminish certain inputs.
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