Product logistics system for autonomous replacement
A system for product replacement includes a product logistics system for a product in a product condition. The system includes an exposure data collection system configured to collect exposure data indicating at least one of an event or an environmental condition that may impact the product condition of the product. The system includes a replacement determination system programmed to calculate a probability for the need to replace the product based on the at least one of the event or the environmental condition. The system includes a replacement procurement system programmed to autonomously configure an option-type futures contract for replacement of the product based on the probability for the need to replace the product.
1 . A system for product replacement, comprising:
a product logistics system comprising:
a plurality of environmental sensors disposed near at least one of a product or a transport vehicle and configured to monitor one or more environmental parameters during product transport;
an exposure data collection system configured to collect exposure data from the plurality of environmental sensors during the product transport; and
a product assessment system configured to determine a product condition using an artificial intelligence (AI) system with a machine learned model, that is trained on one or more historical data sets correlating environmental exposure conditions with product replacement outcomes, and the collected exposure data during the product transport;
a replacement determination system programmed to calculate a probability for a need to replace the product based on the determined product condition;
a robotic process automation (RPA) system trained to autonomously execute contract configuration operations based on the calculated probability;
a replacement procurement system programmed to autonomously configure, via the robotic process automation (RPA) system, an option-type futures contract for replacement of the product based on the calculated probability for the need to replace the product, wherein the option-type futures contract is configured while the product is still in transport; and
a smart contract system programmed to autonomously configure, via the robotic process automation (RPA) system, a smart contract to secure replacement of the product based on the option-type futures contract, wherein the smart contract system configures the smart contract to have a duration of option based on estimating a time until delivery completion.
2 . The system of claim 1 , wherein the calculated probability for the need to replace the product includes calculating a probability of catastrophic loss, and wherein the smart contract system configures the smart contract to have the duration of option further based on the probability of catastrophic loss.
3 . The system of claim 1 , further comprising a replacement alternatives system programmed to configure an alternative smart contract that offers alternatives to replacement of the product to at least one of: a purchaser of, an owner of, or an insurer with a security interest in the product.
4 . The system of claim 3 , wherein the replacement alternatives system is programmed to configure the alternative smart contract that offers at least one of: a refund of a purchase price of the product, alternative goods or services, or incentives to accept a delayed delivery of the product.
5 . The system of claim 1 , further comprising a future price renegotiation system programmed to renegotiate a set of future prices based on a current market state and on the exposure data.
6 . The system of claim 5 , wherein the future price renegotiation system is further programmed to renegotiate the set of future prices in response to the exposure data indicating a likelihood of widespread supply chain disruptions for goods or services associated with the product.
7 . The system of claim 1 , wherein the AI system is trained to predict an impact of the need for replacement based on at least one of: an impact of delays or reduced supply on pricing.
8 . The system of claim 1 , further comprising a digital twin system configured to create a product digital twin of the product.
9 . The system of claim 8 , wherein the product digital twin is used to simulate an impact of the exposure data on the product.
10 . The system of claim 1 , wherein the machine learned model is trained prior to deployment in the product assessment system using training examples including historical environmental exposure data during product transport and corresponding historical product replacement determinations.
11 . The system of claim 1 , wherein the one or more environmental parameters monitored by the plurality of environmental sensors include at least one of: a temperature parameter, a humidity parameter, a pressure parameter, a vibration parameter, or a shock parameter.
12 . The system of claim 1 , further comprising a digital twin system configured to create a product digital twin of the product and simulate an impact of the exposure data on the product condition using the product digital twin.
13 . The system of claim 1 , wherein the robotic process automation (RPA) system is trained on a training set including expert procurement professional interactions with contract configuration tasks.
14 . A computerized method for product replacement of a product in a product condition, the computerized method comprising:
training a machine learned model on one or more historical data sets correlating environmental exposure conditions with product replacement outcomes;
collecting exposure data during a product transport from a plurality of environmental sensors disposed near at least one of the product or a transport vehicle and configured to monitor one or more environmental parameters;
determining the product condition using an artificial intelligence system with the machine learned model and the collected exposure data during the product transport;
calculating a probability for a need to replace the product based on the determined product condition;
utilizing a robotic process automation (RPA) system that has been trained to autonomously execute contract configuration operations based on the calculated probability;
autonomously configuring, via the robotic process automation (RPA) system and while the product is still in transport, an option-type futures contract for replacement of the product based on the calculated probability for the need to replace the product; and
autonomously configuring, via the robotic process automation (RPA) system, a smart contract to secure replacement of the product based on the option-type futures contract, wherein the smart contract is configured to have a duration of option based on estimating a time until delivery completion.
15 . The computerized method of claim 14 , wherein the configuring the smart contract includes configuring the smart contract to have the duration of option further based on a probability of catastrophic loss indicated by the probability for the need to replace the product.
16 . The computerized method of claim 14 , further comprising configuring an alternative smart contract that offers alternatives to replacement of the product to at least one of: a purchaser of, an owner of, or an insurer with a security interest in the product.
17 . The computerized method of claim 16 , wherein the configuring the alternative smart contract includes configuring the alternative smart contract that offers at least one of: a refund of a purchase price of the product, alternative goods or services, or incentives to accept a delayed delivery of the product.
18 . The computerized method of claim 14 , further comprising creating a product digital twin of the product.
19 . The computerized method of claim 18 , further comprising simulating an impact of the exposure data on the product using the product digital twin.
20 . The computerized method of claim 14 , wherein the calculating the probability for the need to replace the product is further based on at least one factor of: duration of exposure to the environmental condition, severity of the environmental condition relative to product tolerance thresholds, or historical replacement rates for products exposed to similar environmental conditions.