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EP-4314985-B1 - LOGISTICS COMMUNICATION FLOW SYSTEMS AND METHODS

EP4314985B1EP 4314985 B1EP4314985 B1EP 4314985B1EP-4314985-B1

Inventors

  • HUDICKA, Joseph

Dates

Publication Date
20260506
Application Date
20220329

Claims (12)

  1. A logistics communication flow system, comprising: a processor; a memory adapted to implement communications via a cloud-based platform; and the cloud-based platform having a demand and capacity maximizer module configured to: receive a capacity dataset, a demand dataset, and a conditions dataset, wherein the capacity dataset comprises a logistics partner capacity dataset, a logistics partner contract dataset, a logistics partner booking dataset, a logistics partner sub-contractor dataset, a logistics partner event dataset, a logistics partner financial dataset and a logistics partner qualitative dataset; configure a decision making algorithm based on the capacity dataset, the demand dataset, and the conditions dataset; determine a prediction data based on the configured decision making algorithm using an artificial intelligence (Al) block, wherein the prediction data corresponds to an on-time prediction, an in-budget prediction, a loss prediction, a contract conversion prediction, a demand volatility prediction, a sustainability prediction and a happiness prediction, wherein determining the prediction data based on the configured decision making algorithm using the Al block comprises generating a predictive capacity algorithm, a predictive demand algorithm, a prescriptive booking algorithm, a prescriptive performance algorithm, and a prescriptive sustainability algorithm, wherein the Al block implements a deep learning neural network trained on historical data from the capacity dataset, demand dataset, and conditions dataset to generate the predictive capacity algorithm, predictive demand algorithm, prescriptive booking algorithm, prescriptive performance algorithm, and prescriptive sustainability algorithm, and wherein the deep learning neural network adapts its weights based on feedback data to finetune the decision making process; generate a feedback by using each of the predictive capacity algorithm, the predictive demand algorithm, the prescriptive booking algorithm, the prescriptive performance algorithm, and the prescriptive sustainability algorithm; finetune a decision making process based on the generated feedback; and recommend a logistics supply chain to at least one user based on the determined prediction data and the decision making process wherein the recommendation includes transmitting a configured logistics supply chain recommendation to a user interface of a user terminal over a network to optimize asset utilization and shipment flow.
  2. The logistics communication flow system of Claim 1, wherein the demand and capacity maximizer module is configured to: learn at least one feedback data over a period of time using the Al block; and modify the prediction data using the at least one feedback data.
  3. The logistics communication flow system of Claim 1, wherein the on-time prediction corresponds to transportation time, transportation cost, delivery time corresponding to a transportation, a logistics partner, a shipper and a logistics supply chain based on a past event, wherein the in-budget prediction predicts a budget based on a current price along with a current market value and generates a score based on a variance based on the current price along with the current market value, wherein the loss prediction corresponds to predicting a loss or damage of goods during the logistics supply chain, wherein the contract conversion prediction defines that a shipper is going to be held accountable for a volume that the shipper promised in a contract during a supply chain, wherein the demand volatility prediction generates a confidence level on a demand forecast at the shipper based on at least one event, wherein the sustainability prediction generates a score on an overall environmental footprint for all products that are being created, shipped, delivered and sold to an end customer, wherein the happiness prediction indicates a qualitative input and an end customer feedback, wherein the predictive capacity algorithm predicts reliability of a capacity of the logistics partner, wherein the predictive demand algorithm predicts reliability of a demand that the shipper is promising to the end customer, wherein the prescriptive booking algorithm determines all bookings handled by the logistics communication flow system, wherein the prescriptive performance algorithm determines performance of a capacity side and a demand side based on an activity history, a relationship of the end customer, and a deliverability data, and wherein the prescriptive sustainability algorithm collects all the data about retailers, manufactures, companies, logistics, and the supply chain, and based on the collected data the prescriptive sustainability algorithm advertises a product and determines an environmental impact of the product the end customer is purchasing.
  4. The logistics communication flow system of Claim 1, wherein the demand dataset comprises a shipper demand dataset, a shipper contract dataset, a shipper booking dataset, a shipper sub-contractor dataset, a shipper event dataset, a shipper financial dataset and a shipper qualitative dataset.
  5. The logistics communication flow system of Claim 1, wherein the conditions dataset comprises a weather dataset, a traffic dataset, an economy dataset, a customs dataset, a world events dataset, a sustainability dataset and an index dataset.
  6. The logistics communication flow system of Claim 1, wherein the at least one feedback data comprises a supplier feedback data, a manufacturer feedback data, a consumer products group feedback data, a distributor feedback data, a logistics partner feedback data, and a retailer feedback data.
  7. A method for logistics communication flow, the method comprising: receiving, by a logistics communication flow system, a capacity dataset, a demand dataset, and a conditions dataset, wherein the capacity dataset comprises a logistics partner capacity dataset, a logistics partner contract dataset, a logistics partner booking dataset, a logistics partner sub-contractor dataset, a logistics partner event dataset, a logistics partner financial dataset and a logistics partner qualitative dataset; configuring, by the logistics communication flow system, a decision making algorithm based on the capacity dataset, the demand dataset, and the conditions dataset; determining, by the logistics communication flow system, a prediction data based on the configured decision making algorithm using an artificial intelligence (Al) block, wherein the prediction data corresponds to an on-time prediction, an in-budget prediction, a loss prediction, a contract conversion prediction, a demand volatility prediction, a sustainability prediction and a happiness prediction, wherein determining the prediction data based on the configured decision making algorithm using the Al block comprises generating a predictive capacity algorithm, a predictive demand algorithm, a prescriptive booking algorithm, a prescriptive performance algorithm, and a prescriptive sustainability algorithm, wherein the Al block implements a deep learning neural network trained on historical data from the capacity dataset, demand dataset, and conditions dataset to generate the predictive capacity algorithm, predictive demand algorithm, prescriptive booking algorithm, prescriptive performance algorithm, and prescriptive sustainability algorithm, and wherein the deep learning neural network adapts its weights based on feedback data to finetune the decision making process: generating, by the logistics communication flow system, a feedback by using each of the predictive capacity algorithm, the predictive demand algorithm, the prescriptive booking algorithm, the prescriptive performance algorithm, and the prescriptive sustainability algorithm; finetuning, by the logistics communication flow system, a decision making process based on the generated feedback; recommending, by the logistics communication flow system, a logistics supply chain to at least one user based on the determined prediction data and the decision making process, wherein the recommending includes transmitting a configured logistics supply chain recommendation to a user interface of a user terminal over a network to optimize asset utilization and shipment flow.
  8. The method of Claim 7, wherein the method further comprises: learning, by the logistics communication flow system, at least one feedback data over a period of time using the Al block; and modifying, by the logistics communication flow system, the prediction data using the at least one feedback data.
  9. The method of Claim 7, wherein the on-time prediction corresponds to transportation time, transportation cost, delivery time corresponding to a transportation, a logistics partner, a shipper and a logistics supply chain based on a past event, wherein the in-budget prediction predicts a budget based on a current price along with a current market value and generates a score based on a variance based on the current price along with the current market value, wherein the loss prediction corresponds to predicting a loss or damage of goods during the logistics supply chain, wherein the contract conversion prediction defines that a shipper is going to be held accountable for a volume that the shipper promised in a contract during a supply chain, wherein the demand volatility prediction generates a confidence level on a demand forecast at the shipper based on at least one event, wherein the sustainability prediction generates a score on an overall environmental footprint for all products that are being created, shipped, delivered and sold to an end customer, wherein the happiness prediction indicates a qualitative input and an end customer feedback, wherein the predictive capacity algorithm predicts reliability of a capacity of the logistics partner, wherein the predictive demand algorithm predicts reliability of a demand that the shipper is promising to the end customer, wherein the prescriptive booking algorithm determines all bookings handled by the logistics communication flow system, wherein the prescriptive performance algorithm determines performance of a capacity side and a demand side based on an activity history, a relationship of the end customer, and a deliverability data, and wherein the prescriptive sustainability algorithm collects all the data about retailers, manufactures, companies, logistics, and the supply chain, and based on the collected data the prescriptive sustainability algorithm advertises a product and determines an environmental impact of the product the end customer is purchasing.
  10. The method of claim 7, wherein the demand dataset comprises a shipper demand dataset, a shipper contract dataset, a shipper booking dataset, a shipper sub-contractor dataset, a shipper event dataset, a shipper financial dataset and a shipper qualitative dataset.
  11. The method of claim 7, wherein the conditions dataset comprises a weather dataset, a traffic dataset, an economy dataset, a customs dataset, a world events dataset, a sustainability dataset and an index dataset.
  12. The method of claim 7, wherein the at least one feedback data comprises a supplier feedback data, a manufacturer feedback data, a consumer products group feedback data, a distributor feedback data, a logistics partner feedback data, and a retailer feedback data.

Description

CLAIM OF PRIORITY, IDENTIFICATION OF RELATED APPLICATIONS This Non-Provisional Patent Application claims priority from US Provisional Patent Application No. 63/167,637 filed on the 29th March 2021 entitled Global Transportation and Logistics Shipper Demand Flow Platform, to common inventor Joseph Hudika. TECHNICAL FIELD The present invention generally relates to logistics communications platform, and more specifically to logistics communication flow systems and methods. Document US 2014/058775 A1 discloses providing data and analysis useful in recognizing an investment and supply chain, involving forecasting a set of tasks related to the set of transportation vehicles and the set of cargo types to inter future supply of cargo types. Document US 2016/335593 A1 refers to providing efficient matching of shipments with carriers and real-time online tracking of shipments. PROBLEM STATEMENT AND HISTORY INTERPRETATION CONSIDERATIONS This section describes technical field in detail and discusses problems encountered in the technical field. Therefore, statements in the section are not to be construed as prior art. DISCUSSION OF HISTORY OF THE PROBLEM The logistics industry is a large industry comprising complex communications channels among shippers, carriers, freight forwarders, traders, for example. Currently, the logistics industry does not allow micro-level customization of communication flow among all the users and requires more transparent understanding of global shipper demand in order to optimize the flow of goods, and the cost of moving them. That is, the logistics industry is well overdue to make optimization pivot from a capacity model to one which is driven by the global shipper demand. There is presently no solution to these drawbacks. Accordingly, the present invention provides such a solution. SUMMARY The above objective is achieved by logistics communication flow systems and methods as defined in claims. The logistics communication flow system comprises a processor, a memory and a demand and capacity maximizer module coupled with the processor and the memory. The demand and capacity maximizer module is configured to receive at least one capacity dataset, at least one demand dataset, and at least one conditions dataset and configure a decision making algorithm based on the at least one capacity dataset, the at least one demand dataset, the at least one conditions dataset. The at least one capacity dataset comprises a logistics partner capacity dataset, a logistics partner contract dataset, a logistics partner booking dataset, a logistics partner sub-contractor dataset, a logistics partner event dataset, a logistics partner financial dataset and a logistics partner qualitative dataset. The at least one demand dataset comprises a shipper demand dataset, a shipper contract dataset, a shipper booking dataset, a shipper sub-contractor dataset, a shipper event dataset, a shipper financial dataset and a shipper qualitative dataset. The at least one conditions dataset comprises a weather dataset, a traffic dataset, an economy dataset, a customs dataset, a world events dataset, a sustainability dataset and an index dataset. The demand and capacity maximizer module further determines at least one prediction data based on the configured decision making algorithm using an artificial intelligence (AI) block, wherein the prediction data corresponds to an on-time prediction, an in-budget prediction, a loss prediction, a contract conversion prediction, a demand volatility prediction, a sustainability prediction and a happiness prediction. The demand and capacity maximizer module learns at least one feedback data over a period of time using the AI block and modifies the at least one prediction data using the at least one feedback data, wherein the at least one feedback data comprises a supplier feedback data, a manufacturer feedback data, a consumer products group feedback data, a distributor feedback data, a logistics partner feedback data, and a retailer feedback data. Of course, the present is simply a Summary, and not a complete description of the invention. BRIEF DESCRIPTION OF THE DRAWINGS Various aspects of the invention and its embodiment are better understood by referring to the following detailed description. To understand the invention, the detailed description should be read in conjunction with the drawings. Figure 1 illustrates a logistics communication flow system.Figure 2 illustrates various inputs and outputs in the logistics communication flow system.Figure 3 is a flow diagram illustrating an inventive logistics communication flow method.Figure 4 continues the flow diagram of Figure 3 illustrating an inventive logistics communication flow method.Figure 5 continues the flow diagram of Figure 3 illustrating an inventive logistics communication flow method.Figure 6 continues the flow diagram of Figure 3 illustrating an inventive logistics communication flow method. DESCRIPTION OF AN EXEMPLARY PREFERRED E