WO2005057833A2 - System and method for analyzing relationships between sourcing variables - Google Patents
System and method for analyzing relationships between sourcing variables Download PDFInfo
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- WO2005057833A2 WO2005057833A2 PCT/US2004/041293 US2004041293W WO2005057833A2 WO 2005057833 A2 WO2005057833 A2 WO 2005057833A2 US 2004041293 W US2004041293 W US 2004041293W WO 2005057833 A2 WO2005057833 A2 WO 2005057833A2
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- sourcing
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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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
Definitions
- the present invention relates generally to material sourcing, and more particularly, to a system and method for analyzing relationships between sourcing variables.
- costs of sourcing materials and services comprise 30-70% of revenue and drive the business' gross margin.
- Material costs, inventory, and availability are key sourcing related business performance metrics.
- the business must constantly balance costs with inventory and availability. For example, the business may have an option of purchasing a particular material at a relatively low price. However, if the business cannot turn around and sell the material or a by-product of the material relatively quickly, the business is then required to store the material, leading to storage costs and reduced working capital. Alternatively, if the business has reason to believe that future availability of the item is low and thus will result in an increased price for the material, the high storage cost and reduced capital may be acceptable and more feasible in the long term.
- One method for reducing sourcing uncertainty is for a business to evaluate sourcing variables. Specifically, the relationships and interactions between sourcing circumstances, objectives, decisions, and performance should be clearly understood before, during, and/or after implementation of these sourcing agreements. Further, these variables are also important to understand with respect to implementation of sourcing agreement utilization policies (also referred to as "sourcing opportunity utilization policies"). However, relationships and interactions between these sourcing variables are complex due to their multi-dimensional nature. Thus, a method is needed for effectively analyzing the relationships between sourcing variables. Such a method will be extremely valuable because the understanding provided can be used to improve decisions and to more accurately assess determinants and consequences.
- the present invention provides in various embodiments a system and method for analyzing relationships between sourcing variables.
- a condition is defined utilizing one or more sourcing variables.
- one or more sourcing performance scenarios that satisfy the condition are identified.
- At least one relationship between the one or more sourcing variables is defined for analysis.
- the relationship is then analyzed utilizing the one or more identified sourcing performance scenarios.
- a data generation engine defines a condition of interest utilizing one or more sourcing variables and defines at least one relationship between the sourcing variables.
- the data generation engine also identifies one or more sourcing performance scenarios that satisfy the condition.
- a database coupled to the data generation engine stores the one or more variables and the one or more sourcing performance scenarios.
- an analytical engine coupled to the database analyzes the relationship between the one or more sourcing variables utilizing the one or more identified sourcing performance scenarios.
- FIG. 1 is a schematic diagram showing an exemplary architecture for analyzing relationships between sourcing variables;
- FIG. 2 is a schematic diagram showing an exemplary scenario generation engine;
- FIG. 3 is a schematic diagram showing an exemplary analytical engine;
- FIG. 4 is a schematic diagram showing an exemplary analysis output engine; and
- FIG. 5 is a flowchart describing a process for analyzing relationships between sourcing variables, according to an embodiment of the present invention.
- the present invention provides a system and method or analyzing relationships between sourcing variables. These sourcing variables may be utilized in the sourcing opportunity utilization policy system of related U.S. patent application no. 10/269,794.
- FIG. 1 a schematic diagram of an exemplary architecture 100 for analyzing relationships between sourcing variables is shown.
- the exemplary architecture 100 comprises a data generation engine 102, at least one database 104, an analytical engine 106, and an analysis output engine 108.
- the architecture 100 may also comprise a user interface 110 which allows the user to view and refine the results of the sourcing variable analysis.
- Each of the engines of FIG. 1 will be discussed in further detail below.
- the data generation engine 102 creates data and/or selects existing data (e.g., sourcing performance scenarios) from the database 104 based on user supplied criteria. These supplied criteria define conditions of interest. For example, a user may wish to evaluate all sourcing performance scenarios that achieve less than 30 days of inventory under lower demand conditions. If new or additional sourcing performance scenarios or other data need to be generated that satisfy the condition of interest, the database generation engine 102 or attached systems (e.g., U.S. patent application no. 10/269,794, "System and Method for
- the database 104 comprises various data including sourcing variables.
- Sourcing variables may comprise properties or characteristics of sourcing circumstances, objectives, decisions, performance, and functions related to other sourcing variables.
- sourcing variables may represent any feature or aspect related to sourcing performance. These sourcing variables may be at individual points in time, averaged values, changes over time (e.g., trends, cycles, or measures of variance), and performance relative to benchmark values. Any type of sourcing performance variable is within the scope of the present invention.
- the sourcing variable may be a circumstance such as demand, supply price, supply availability, capacity, and quality.
- Further sourcing variables may comprise supplier status and performance (e.g., financial status or delivery capability), inventory (e.g., storage costs and capacity), and shortage (e.g., cost of delivery delay, lost sale, or damaged customer relationship).
- the sourcing variable may be buyer or supply decisions or performance (e.g., timing and amount of orders placed or shipped, quantity of material commitment not honored by supplier or buyer).
- the sourcing variable may be a combination of factors.
- the sourcing variable may be a combined inventory and shortage measure.
- Other existing data in the database may comprise sourcing performance scenarios.
- all of the data about sourcing variables as described above will be attached to sourcing performance scenarios.
- Sourcing performance scenarios may be represented mathematically as stochastic processes.
- sourcing performance scenarios represent data given a specified set of parameters.
- a sourcing performance scenario may be data regarding a demand for pork bellies from Northern California between January 2001 to January 2002, and associated values of related sourcing variables.
- any sourcing performance scenario suitable for use with the present invention may be employed.
- the sourcing performance scenarios are generated according to the system and method described in co-pending U.S. patent application no. 10/269,794 entitled "System and Method for Automated Analysis of Sourcing Agreements and Performance".
- This system and method stores the necessary information in a database allowing the data to be transferred directly to the database 104. Additionally the data may be entered into the database 104 directly as a result of output of prior analyses done by the analytic engine 106 or manually entered by the user. Finally, data such as forecasts and historical data may be captured from other databases through system integration mechanisms.
- data stored in the database 104 may comprise sourcing performance scenarios, values of associated sourcing variables, and conditions of interest.
- Conditions of interest represent conditions and circumstances that a user may choose to research, and are discussed further in connection with FIG. 2.
- Sourcing variables, sourcing performance scenarios, and conditions of interest may be selected from existing data in the database 104, or they may be generated by the data generation engine 102 or other functionally equivalent engine, and stored in the database 104.
- the user selects or creates variables pertinent to the particular sourcing performance scenario, condition of interest, etc. Examples of sourcing performance scenarios and conditions of interest that incorporate variables are discussed herein.
- any type of data may be stored in the database 104 according to the present invention.
- the exemplary architecture 100 of FIG. 1 illustrates one embodiment.
- Alternative embodiments may comprise more, less, or other functionally equivalent engines.
- database 104 is shown in FIG. 1, alternative embodiments may comprise a plurality of databases (e.g., one database for storing conditions of interest and one database for storing sourcing performance scenarios).
- FIG. 2 a schematic diagram of an exemplary data generation engine 102 is shown.
- the exemplary data generation engine 102 comprises a sourcing performance variable identification module 202, a condition identification module 204, a sourcing performance scenario identification module 206, and a relationship identification module 208.
- Alternative embodiments may comprise more modules, less modules, other modules, and/or functionally equivalent modules.
- the exemplary sourcing performance variable identification module 202 identifies sourcing variables by selecting the sourcing variables from the existing data in the database 104 (FIG. 1) and/or by creating sourcing variables defined as a function of one or more sourcing variables in the database 104.
- the user can select the sourcing variables from a pre-defined set of sourcing variable made available to him/her in the sourcing performance variable identification module 202.
- the sourcing performance variable identification module 202 can enable the user to define the sourcing variable by using a predefined set of functions and restricted by available data in the system. For example, the use may wish to define new sourcing variables (e.g., averages over time and materials, or quarterly performance instead of monthly performance).
- the sourcing variables selected by the sourcing performance variable identification module 202 are then utilized by the condition identification module 204 to identify a condition of interest according to one embodiment of the present invention. More than one condition of interest may be identified in accordance with the present invention. As discussed herein, a condition of interest may represent any condition, scenario, or question that the user wishes to examine. In one embodiment, a particular condition of interest may be selected from a list of conditions of interest identified and presented to the user via the user interface 110 (FIG. 1). Alternatively, the user may select or enter the condition of interest via the user interface 110.
- the sourcing performance scenario identification module 206 identifies at least one sourcing performance scenario that satisfies the condition of interest identified by the condition identification module 204 and/or selected by the user. Many sourcing performance scenarios under which the condition is satisfied may be identified. For example, there may be a variety of sourcing performance scenarios that satisfy the condition "less than 5% shortages under high demand conditions.”
- the exemplary relationship identification module 208 defines a relationship between the sourcing variables to be analyzed utilizing the sourcing performance scenarios identified by the sourcing performance scenario identification module 206.
- the relationship between sourcing variables can be an interaction between the sourcing variables, circumstances, objectives, and/or performance that impact the sourcing variables, for example. Any type of relationship between sourcing variables is within the scope of the present invention.
- the analytical engine 106 comprises a variable value module 302 and a scenario confirmation module 304.
- the exemplary variable value module 302 evaluates the values of the sourcing variables identified by the sourcing performance variable identification module 202 (FIG. 2).
- the evaluation comprises organizing (e.g., averaging, repackaging, etc.) the variable values according to the condition of interest. For example, if the variable values received from the sourcing performance variable identification module 202 are based on monthly results and the condition of interest is concerned with quarterly performance, then the variable value module 302 will reorganize the variable values into quarterly results.
- the evaluations are performed to error check and pre-process the variable values and sourcing performance scenarios before actual analysis begins.
- the scenario confirmation module 304 confirms that the sourcing performance scenarios identified by the sourcing performance scenario identification module 206 (FIG. 2) represent the condition of interest identified by the condition identification module 204 (FIG. 2) based on the evaluation of the sourcing variable values by the variable value module 302. If the sourcing performance scenarios identified do not accurately represent the condition of interest, additional, replacement, and/or a subset of sourcing performance scenarios can be identified by the refinement module (FIG. 4). For example, the user or system reviews a "first cut" set of sourcing performance scenarios identified to represent the condition of interest.
- the process proceeds to the refinement module to define a new, more appropriate condition.
- the user may specify a condition that average shortages must be less than 5%.
- a query may return 50 scenarios, some of which have an average of 5% but a worst-case of 20% and others that have an average of 5% and a worst case of 10%.
- the user may add the additional condition that the worst case scenario cannot exceed 10%.
- FIG.4 a schematic diagram of an exemplary analysis output engine 108 is shown.
- the analysis engine 108 comprises a refinement module 402 and a configuration module 404.
- the exemplary refinement module 402 utilizes the results from the analytical engine 106 (FIG. 1) to determine whether other areas of analysis are appropriate.
- the refinement module 402 may identify further conditions of interest, supplemental sourcing performance scenarios, and so forth. Additionally, the refinement module 402 may also extend analysis to include a broader spectrum by including the supplemental sourcing performance scenarios, conditions of interest, etc. For example, assuming that the initial analysis from the analytical engine 106 reveals that under high demand, the objective of limiting expedited material orders to 10% or less of purchases is binding a large percentage of the time, the further question "are shortages more common and/or larger in size on the sourcing performance scenarios where the constraint is binding than when it is not?" may need to be answered.
- the configuration module 404 may configure the data analyzed for output.
- the configuration module 404 may select an output presentation type and formats the data according to the presentation type. For example, if the data analyzed is in graph and/or chart format, the configuration module 404 conforms the data to the graph and/or the chart format for display via the user interface 110 (FIG. 1). In a further example, in order to create a historgram to compare multiple sourcing performance scenarios, the configuration module 404 will have to visually optimize the graph by creating data buckets and by sizing an axis. [0031] Referring now to FIG.
- At step 502 at least one condition is defined utilizing one or more variables.
- the user may either define the condition directly, or select from a list of pre-defined conditions.
- the at least one condition is defined by the condition identification module 204 (FIG. 2).
- Each condition may be an interaction or a relationship between sourcing variables, or a series of interactions or relationships between variables, which is of interest to a user.
- the user may have an interest in a "demand is high" condition.
- the user has an interest in understanding the relationship and/or interaction between variables when demand for a material, service, product, etc. is high.
- One or more sourcing variables may be selected to represent the condition of interest and the associated relationship (s) or inter action(s) of interest (discussed in more detail at step 508).
- sourcing variables may be properties and/or characteristics of circumstances, objectives, decisions, and/or performance associated with sourcing.
- the condition of interest and the associated relationship or interaction of interest may be defined by specified values of the variable(s) or by functions of the variable(s).
- the user through the user interface 110 (FIG. 1), the user first defines the sourcing variables of interest and then specifies the conditions (the definition of the conditions may depend on what type of variable the user defines) to be met, according to one embodiment.
- the condition is first identified and then the sourcing variables of interest are defined.
- the exemplary system may allow for both embodiments to be performed (i.e., determination of condition or sourcing variables of interest first followed by the determination of the sourcing variable or condition of interest, respectively).
- at least one scenario that represents the condition of interest is identified.
- the variable values for each scenario of the relevant sourcing performance analysis are analyzed to identify those scenarios, if any, on which the variable values satisfy the condition of interest as defined.
- the scenario identification is performed by the sourcing performance scenario identification module 206 (FIG. 2). Using the example above, one or more scenarios that satisfy the "demand is high" condition are identified.
- a relationship between the one or more variables is defined for analysis utilizing the at least one scenario identified in step 504.
- the relationship is defined by the relationship identification module (FIG. 2). For example, under definitions of the "demand is high" condition, such as the "demand at a specified point in time exceeds a specified level," the one or more sourcing variables used to define these relationships or interactions may not involve any of the one or more sourcing variables used to define the condition, itself.
- the one or more sourcing variables used to specify the relationship or interaction may include specifically the one or more sourcing variables also utilized to define the condition.
- both the one or more sourcing variables utilized to define the condition as well as one or more additional sourcing variables may be used to specify the relationship or interaction.
- the relationshi (s) or interaction(s) of interest are analyzed for each such scenario using the sourcing variable values for those scenarios or functions thereof, as appropriate.
- the relationship of interest may be supplier performance on alternative forms of supply agreements when demand is high or material availability levels when demand is high.
- the variables utilized to define the condition may be the same variables utilized to define the relationship or they may be different variables, as discussed herein.
- the relationship analysis is a three step process performed by the relationship identification unit 208 (FIG. 2) to identify the relationship, the variable value module 302 (FIG. 3) to compute the metric (i.e., sourcing variable), and the configuration module 404 (FIG. 4) to generate charts that demonstrate the relationships.
- step 510 the results of the analysis in step 508 may be utilized to identify an alternative representation of the condition or of the relationship (s)/ interaction(s), to guide the refinement of the analysis of the prior condition or relationship/interaction, or to identify at least one other condition or relationship (s)/interaction(s) of interest.
- step 510 is performed by the refinement module 402 (FIG. 4).
- the user may choose to identify an alternative representation of the condition or the relationship (s)/interaction(s), if the results of the analysis of the condition or relationship (s)/interaction(s) as currently represented reveal that an alternative representation would have greater or incremental value.
- the results may suggest that representing the "demand is high" condition using average demand over the analysis period is not sufficiently specific, and that evaluating the condition and relevant relationship (s) and interaction(s) for specific time intervals or points in time would provide greater or incremental value.
- the results may suggest that the lead time of supply agreements has a greatest impact on material availability when demand is high, and as a result that revising or refining the relationship (s) or inter action(s) being evaluated to better explore this relationship would be valuable.
- the user can accomplish the revision or refinement of either the condition or the relationship(s) or interaction(s) by repeating steps of the flowchart 500 to continue the refinement process.
- the user can return to step 502 in order to define the new or different condition.
- the user can return to step 506 to define a new relationship of interest, and then continue the analysis process.
- the user can evaluate more relationships and interactions associated with the sourcing variables.
- additional scenarios that satisfy the condition can be generated using the sourcing performance analysis, and the additional scenarios can be used to supplement the analysis. Relevant relationship(s) or interaction(s) can then be analyzed over the expanded set of scenarios.
- the user can generate more scenarios that meet the high demand condition and then calculate the values of the relevant variables for those scenarios.
- This type of iteration provides an ability to dynamically tailor statistical accuracy of results to desired levels.
- the definition of the condition may be refined by utilizing at least one of the one or more variables.
- the definition of the "demand is high" condition may be further refined by specifying that the condition occurs at a specific point in time, over a specific time interval or set of time intervals, and so forth.
- the user may want to understand the "demand is high" condition during the summer months. Another way of stating this is that the user wants to understand what other circumstances, objectives, performance, decisions, etc.
- the condition "demand is high” may be defined to occur whenever the value of demand at a specified point in time exceeds a specified level.
- this condition may be defined to occur whenever the average level of demand over a specified time interval is in the top 10% of all such average levels of demand over the specified time interval.
- Other sets of variables may be selected for defining the condition further and/or for introducing additional factors relating to the condition, relationship (s), or interaction(s) of interest.
- Other sets of variables may introduce additional defining circumstances, such as shortage cost or inventory levels, or defining decisions, such as how materials are sourced under the high demand conditions. These sets of variables may be utilized to refine the relationship(s) or interaction(s) initially defined. As discussed herein, these variables can serve as, or be used to construct, conditions and/or relationships and interactions of interest.
- the present invention allows for the determination of causes and consequences of decisions regarding sourcing conditions. Relationships between inputs and/or outputs can be evaluated to present information to the user that helps the user to better understand options and outcomes associated with specified sourcing conditions.
- the variables to be considered and analyzed with respect to the condition may relate to circumstances, objectives, decisions, performance, or any combination thereof. Any variable suitable for use with the present invention may be included for analysis. Further, the user may specify any level of detail desired with respect to the analysis. For example, management may be more interested in summary metrics such as average performance over an extended time period, across an aggregated value of multiple performance measures, or with agreement negotiations, may want to drill down to look at more detailed sourcing variables, such as performance at specific points in time or on individual performance measures, etc. Moreover, the level of detail may change depending on the results output to the user. Accordingly, the user can manage the details in order to enhance the user's understanding of the results and potential continued analysis.
Abstract
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Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
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EP04813599A EP1698088A2 (en) | 2003-12-09 | 2004-12-09 | System and method for analyzing relationships between sourcing variables |
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US52845403P | 2003-12-09 | 2003-12-09 | |
US60/528,454 | 2003-12-09 |
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WO2005057833A2 true WO2005057833A2 (en) | 2005-06-23 |
WO2005057833A3 WO2005057833A3 (en) | 2007-01-18 |
WO2005057833A8 WO2005057833A8 (en) | 2007-05-03 |
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PCT/US2004/041293 WO2005057833A2 (en) | 2003-12-09 | 2004-12-09 | System and method for analyzing relationships between sourcing variables |
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Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5615109A (en) * | 1995-05-24 | 1997-03-25 | Eder; Jeff | Method of and system for generating feasible, profit maximizing requisition sets |
US6006192A (en) * | 1997-03-12 | 1999-12-21 | International Business Machines Corporation | Method for production planning in an uncertain demand environment |
US6157915A (en) * | 1998-08-07 | 2000-12-05 | International Business Machines Corporation | Method and apparatus for collaboratively managing supply chains |
US6308162B1 (en) * | 1997-05-21 | 2001-10-23 | Khimetrics, Inc. | Method for controlled optimization of enterprise planning models |
US6493682B1 (en) * | 1998-09-15 | 2002-12-10 | Pendelton Trading Systems, Inc. | Optimal order choice: evaluating uncertain discounted trading alternatives |
US6671673B1 (en) * | 2000-03-24 | 2003-12-30 | International Business Machines Corporation | Method for integrated supply chain and financial management |
-
2004
- 2004-12-09 EP EP04813599A patent/EP1698088A2/en not_active Withdrawn
- 2004-12-09 WO PCT/US2004/041293 patent/WO2005057833A2/en not_active Application Discontinuation
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5615109A (en) * | 1995-05-24 | 1997-03-25 | Eder; Jeff | Method of and system for generating feasible, profit maximizing requisition sets |
US6006192A (en) * | 1997-03-12 | 1999-12-21 | International Business Machines Corporation | Method for production planning in an uncertain demand environment |
US6308162B1 (en) * | 1997-05-21 | 2001-10-23 | Khimetrics, Inc. | Method for controlled optimization of enterprise planning models |
US6157915A (en) * | 1998-08-07 | 2000-12-05 | International Business Machines Corporation | Method and apparatus for collaboratively managing supply chains |
US6493682B1 (en) * | 1998-09-15 | 2002-12-10 | Pendelton Trading Systems, Inc. | Optimal order choice: evaluating uncertain discounted trading alternatives |
US6671673B1 (en) * | 2000-03-24 | 2003-12-30 | International Business Machines Corporation | Method for integrated supply chain and financial management |
Also Published As
Publication number | Publication date |
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WO2005057833A8 (en) | 2007-05-03 |
EP1698088A2 (en) | 2006-09-06 |
WO2005057833A3 (en) | 2007-01-18 |
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