WO2008100571A1 - Distributed decision making for supply chain risk assessment - Google Patents
Distributed decision making for supply chain risk assessment Download PDFInfo
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- WO2008100571A1 WO2008100571A1 PCT/US2008/001960 US2008001960W WO2008100571A1 WO 2008100571 A1 WO2008100571 A1 WO 2008100571A1 US 2008001960 W US2008001960 W US 2008001960W WO 2008100571 A1 WO2008100571 A1 WO 2008100571A1
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- data
- decision
- data elements
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
Definitions
- This present application relates generally to methods for analyzing supply chain information, and in more particular applications, to risk assessment for supply chain management.
- the "risk relevant” information can be extracted from order data, production data, digital commercial invoice data, transportation partner data, supplier cargo bookings, at origin data, in transit data, and fright location data.
- supply chain data tends to be “siloed” or stored in a single location in space and time.
- Real-time intelligence into these globally “distributed data silos” can allow accurate and timely visibility on risk vulnerability for supply chain stakeholders.
- current decision support systems are inadequate and characterized by data warehouse based architectures, with main operational challenges concentrated on data integration steps (i.e., batch-mode, not real-time, not privacy preserving, etc).
- a method for determining supply chain risks including the steps of: providing a plurality of data locations, each data location having an agent and data elements; performing distributed data mining by each of the agents using the data elements at the respective data location to produce a candidate decision for the respective location; determining a global decision from the candidate decisions, the global decision covering the data elements at all of the data locations; and generating predictive risk scores for the data elements from the global decision.
- a method for determining supply chain risks including the steps of: providing a plurality of data locations, each data location having an agent and data elements; performing distributed data mining by each of the agents using the data elements at the respective data location to produce a candidate decision for the respective location; passing each of the candidate decisions from the respective data location to a central mediator; determining a global decision by the mediator based on the candidate decisions; and generating predictive risk scores for the data elements from the global decision.
- a method for determining supply chain risks including the steps of: providing a plurality of data locations, each data location having an agent and data elements; performing distributed data mining by a first agent using the data elements at a first data location to produce a first candidate decision; passing the first candidate decision to a second data agent at a second location; performing distributed data mining by the second agent using the data elements at the second data location to produce a second candidate decision; determining a global decision from the candidate decisions, the global decision covering the data elements at all of the data locations; and generating predictive risk scores for the data elements from the global decision.
- the step of performing distributed data mining utilizes a decision tree.
- steps a performing distributed data mining and determining a global decision are performed by a synchronized decision-making process.
- the steps a performing distributed data mining and determining a global decision are performed by a sequential decision-making process.
- the data elements include information specific to shipping containers such that the risk scores are generate for each specific shipping container.
- the method further includes the step of reporting a high-risk score.
- the data elements include information related to at least one of: seller data, merchandise description, location, quantity, weight, date, parties associated with a shipment, vessel, crew, customs manifest and proof of delivery.
- a system for determining supply chain risks includes at least one memory unit at each of a plurality of locations, a processing unit at each of the plurality of locations and a mediator.
- the at least one memory unit is for storing data elements.
- Each processing unit including an agent configured to perform distributed data mining using the data elements at the respective data location to produce a candidate decision for the respective location.
- the mediator is configured to determine a global decision from the candidate decisions, the global decision covering the data elements at all of the data locations, the mediator also being configured to generate predictive risk scores for the data elements from the global decision.
- the mediator is a central processing unit.
- the mediator is at least one of the processing units at one of the plurality of locations.
- FIG. 1 is a diagrammatic representation of one form of a distributed data mining method and system
- Figure 2 is a diagrammatic representation of an agent-mediator communication mechanism
- Figure 3 is a diagrammatic representation of one form of mediation between two decision trees
- Figure 4 is a diagrammatic representation of one form of a synchronized decision-making process
- Figure 5 is a diagrammatic representation of one form of sequential decision-making process
- Figure 6 is a diagrammatic representation of another form of a sequential decision- making process.
- FIG. 7 is diagrammatic representation of an example of a sequential decision-making process for risk scoring containerized traffic.
- Supply chain risk assessment can be performed in a variety of manners using data analysis techniques.
- distributed data mining is utilized as part of the supply chain risk assessment method.
- Figure 1 illustrates one basic form of distributed data mining.
- distributed mining is accomplished via a synchronized collaboration of agents 10 as well as a mediator component 12.
- agents 10 as well as a mediator component 12.
- the mediator component 12 facilitates the communication among agents 10.
- each agent 10 has access to its own local database 14 and is responsible for mining the data contained by the database 14.
- Distributed data mining results in a set of rules generated through a tree induction algorithm.
- the tree induction algorithm determines the feature which is most discriminatory and then it dichotomizes (splits) the data into classes categorized by this feature.
- the next significant feature of each of the subsets is then used to further partition them and the process is repeated recursively until each of the subsets contain only one kind of labeled data.
- the resulting structure is called a decision tree, where nodes stand for feature discrimination tests, while their exit branches stand for those subclasses of labeled examples satisfying the test.
- a tree is rewritten to a collection of rules, one for each leaf in the tree. Every path from the root of a tree to a leaf gives one initial rule. The left-hand side of the rule contains all the conditions established by the path and thus describe the cluster.
- the rules are extracted from a decision tree.
- Each agent 10 then starts the process of mining its own local data by finding the feature (or attribute) that can best split the data into various training classes (i.e. the attribute with the highest information gain).
- the selected attribute is then sent as a candidate attribute to the mediator 12 for overall evaluation.
- the winner agent 10 i.e. the agent whose database includes the attribute with the highest information gain
- the winner agent 10 will then continue the mining process by splitting the data using the winning attribute and its associated split value. This split results in the formation of two separate clusters of data (i.e. those satisfying the split criteria and those not satisfying it).
- the associated indices of the data in each cluster are passed to the mediator 12 to be used by all the other agents 10.
- the other (i.e. non-winner) agents 10 access the index information passed to the mediator 12 by the winner agent 10 and split their data accordingly.
- the mining process then continues by repeating the process of candidate feature selection by each of the agents 10.
- the mediator 12 is generating the classification rules by tracking the attribute/split information coming from the various mining agents 10. The generated rules can then be passed on to the various agents 10 for the purpose of presenting them to the user through advanced 3D visualization techniques.
- the decision model used for analyzing supply chain risk is a decision tree.
- the decision-making analysis can be performed in a variety of manners such as synchronized (as described above) and sequential decision-making.
- one leaf may lead to a high risk condition warranting an alert to government personnel.
- Figure 3 depicts the mediation process that searches for a globally unique decision ID by matching local data, represented by dark circles 20 and light circles 22 to two decision trees 24,26 located at Location 1 performed by agent 28 and Location 2 performed by agent 30 respectively.
- Each circle 20,22 on the tree represents a decision point, while the leafs, depicted as shaded boxes 31, represent the final decision class with one of two possible values: A or B.
- a prediction module is used to match the testing data with an existing model. All the existing agents 28,30 perform a prediction for each example in the following manner. All the agents 28,30 have the same decision tree, such as decision tree 24 or 26, but do not have all the attributes needed to pass through the decision tree. Hence, while passing through the tree, it goes down the appropriate branch, if it has a value for that attribute, else it goes through both the branches. Finally, each agent 28,30 creates a list 32,34 of leaf nodes it reached and sends this list to the mediator. The mediator makes a decision by finding the common leaf node among all the lists. There will always be only one common leaf node among all the lists 32,34, since there is always a unique path when all the attributes are known for the decision tree.
- the decision at any given node involves the test of some attribute, the outcome of which determines how the object under consideration is sorted down the tree (i.e. which decision path is taken).
- each agent 28,30 since each agent 28,30 only has access to its own local database, it can only partially resolve the decisions to be made at decision points down a given path.
- the agent 28 at Location 1 can only test the attributes at decision nodes represented by circles 20. For example, based on the value of the attribute at the root node, the agent 28 has decided that the decision path lies on the right hand side of the node. However, at the next decision point, represented by circle 22, the agent 28 can not determine the exact decision path, as it lacks access to the attribute under consideration (i.e. the value of this attributes resides in Location 2).
- the agent 28 should follow the decision path on both side of this particular decision node.
- This leads to a leaf node 31 (LID 4) with decision class B and another sub-tree to be further explored by the agent 28.
- the agent 30 at Location 2 is only able to resolve the decisions at the nodes represented by circles 22 and ultimately arrives at its own final list 34 of possible decision leafs, here LID 4, 8, 9, and 1 1. It is then the job of a mediator 36 to come up with a final decision by finding the common decision leaf ID between the lists 32,34 generated by the two agents 28,30.
- LID 4 is determined to be the final decision leaf which in turn returns a value of B as the final decision class.
- Decision-making for supply chain risk assessment can be performed in a variety of manners using decision trees. For example, this decision-making can be performed in a synchronized process or it may be performed in a sequential process. Each of these processes will be described in more detail below.
- a decision model 40,42 containing a set of conditional rules describing the A and B elements of distributed data record is maintained at each data locale 44,46. These data elements are matched to the predictive risk model to generate a set of candidate decisions, as shown in Figure 3.
- Sets of candidate decisions are sent to the mediation process 48 that finds a globally unique decision 50 for the globally distributed data records.
- the candidate decisions set is computed first at the data locale A by a software agent 52. This step is followed by the step in which the locale B agent 54 computes its set of candidate decisions, reads the candidate decisions from the agent 54 at the data locale B and starts the mediation process in a centralized coordinated server that assesses the risk patterns from database A and B.
- Figure 6 depicts this sequential decision-making with more then two data locales 60, 62,64, 66.
- the mediation process finds the current set of candidate decisions based on the previously received contributions from the risk prediction software agents 68,70,72,74. This can be seen as the disambiguation process in which as more data is matched to the global model during subsequent steps, the mediation process eliminates candidate decisions from the set until it finds the globally unique one model that assembles risk scores from multiple data sources.
- Figure 7 depicts the application scenario of the sequential decision-making to the supply chain.
- the following three layers can be distinguished in this scenario:
- a supply chain layer 80 represents actual sequence of events from placing an order to the point of container arrival at Customs. For the illustrative purpose, this process starts on May 2, 2006 and completes on June 29, 2006.
- this layer 82 includes three data silos 84,86,88, that is, database sources which can be modeled for risk scoring.
- Data Silo represented by reference number 84
- 2006 Data Silo represented by reference number 86
- June 29, 2006 Data Silo, represented by reference number 88 may include a number of data elements 94 such as customs manifest and proof of delivery.
- a decision risk scoring layer 96 includes a plurality of decision agents 98 and decision risk models 100. It should be understood that some of the models 100 may be high risk detection models while others are low risk models.
- the above example is an application of one form of the present method and system. It should be understood that variations of the method are also contemplated as understood by those skilled in the art. Furthermore, it should be understood that the methods described herein may be embodied in a system, such as a computer, network and the like as understood by those skilled in the art.
- the system may include one or more processing units, hard drives, RAM, ROM, other forms of memory and other associated structure and features as understood by those skilled in the art. It should be understood that multiple processing units may be used in the system such that one processing units performs certain functions at one data locale, a second processing unit performs certain functions at a second data locale and a third processing unit acts as a mediator.
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CA002678351A CA2678351A1 (en) | 2007-02-14 | 2008-02-14 | Distributed decision making for supply chain risk assessment |
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US90139107P | 2007-02-14 | 2007-02-14 | |
US60/901,391 | 2007-02-14 |
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WO2008100571A1 true WO2008100571A1 (en) | 2008-08-21 |
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Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20030229526A1 (en) * | 2002-04-04 | 2003-12-11 | Gallacci Jeffery K. | Computer-implemented system and method for assessing supply chain solutions |
US20040059627A1 (en) * | 2000-03-24 | 2004-03-25 | Robert Baseman | Method for integrated supply chain and financial management |
US20050021360A1 (en) * | 2003-06-09 | 2005-01-27 | Miller Charles J. | System and method for risk detection reporting and infrastructure |
US20070033060A1 (en) * | 2005-08-02 | 2007-02-08 | Accenture Global Services, Gmbh | System and method for location assessment |
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2008
- 2008-02-14 WO PCT/US2008/001960 patent/WO2008100571A1/en active Application Filing
- 2008-02-14 CA CA002678351A patent/CA2678351A1/en not_active Abandoned
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20040059627A1 (en) * | 2000-03-24 | 2004-03-25 | Robert Baseman | Method for integrated supply chain and financial management |
US20030229526A1 (en) * | 2002-04-04 | 2003-12-11 | Gallacci Jeffery K. | Computer-implemented system and method for assessing supply chain solutions |
US20050021360A1 (en) * | 2003-06-09 | 2005-01-27 | Miller Charles J. | System and method for risk detection reporting and infrastructure |
US20070033060A1 (en) * | 2005-08-02 | 2007-02-08 | Accenture Global Services, Gmbh | System and method for location assessment |
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