Patent Application
20110055041
1. Technical Field
This invention relates generally to electronic classification of data and more particularly, but not by way of limitation, to a system and method for facilitating redeployment of human resources from one workforce sector to another.
2. History of Related Art
For a variety of reasons, human resources frequently make a workforce transition from a first workforce sector to a second workforce sector. One common example is that of military personnel migrating from the public sector to the private sector when a service commitment ends or as a temporary transition to gain experience in the private sector before returning to the public sector. Typically, in a public-sector entity such as, for example, a military branch, a form of worker classification may be used internally to classify or describe skills and experience of human resources. In the private sector, however, numerous other nomenclatures and taxonomies may be utilized to classify or describe skills and experience of human resources. Workforce transitions from the first workforce sector to the second workforce sector are thus exceedingly difficult, particularly when human resources migrate back and forth between the first workforce sector and the second workforce sector as part of a career plan.
In one embodiment, a method includes configuring a labor pool, the labor pool including a plurality of human resources in a first workforce sector. The configuration of a labor pool includes storing human-resource information. In addition, the method includes configuring a plurality of business entities operating in a second workforce sector that is distinct from the first workforce sector. The configuration of a plurality of business entities includes storing job-opening information for a plurality of job openings. Further, the method includes normalizing the labor pool to a human-capital management (HCM) taxonomy via at least a portion of the human-resource information. Additionally, the method includes normalizing the plurality of job openings to the HCM taxonomy via at least a portion of the job-opening information. The method also includes facilitating a redeployment of at least one human resource in the normalized labor pool from the first workforce sector to the second workforce sector. The facilitating includes matching the at least one human resource to at least one job opening in the normalized plurality of job openings. The method is performed via one or more computers having a processor and memory.
In another embodiment, a computer-program product includes a computer-usable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed to implement a method. The method includes configuring a labor pool, the labor pool including a plurality of human resources in a first workforce sector. The configuration of a labor pool includes storing human-resource information. In addition, the method includes configuring a plurality of business entities operating in a second workforce sector that is distinct from the first workforce sector. The configuration of a plurality of business entities includes storing job-opening information for a plurality of job openings. Further, the method includes normalizing the labor pool to a human-capital management (HCM) taxonomy via at least a portion of the human-resource information. Additionally, the method includes normalizing the plurality of job openings to the HCM taxonomy via at least a portion of the job-opening information. The method also includes facilitating a redeployment of at least one human resource in the normalized labor pool from the first workforce sector to the second workforce sector. The facilitating includes matching the at least one human resource to at least one job opening in the normalized plurality of job openings.
The above summary of the invention is not intended to represent each embodiment or every aspect of the present invention.
A more complete understanding of the method and apparatus of the present invention may be obtained by reference to the following Detailed Description when taken in conjunction with the accompanying Drawings wherein:
Various embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The invention may, however, be embodied in many different forms and should not be constructed as limited to the embodiments set forth herein; rather, the embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The flow 200 typically begins with an input record 22 for ingestion and classification. In various embodiments, the input record 22 may be either a structured record or an unstructured record. As used herein, a structured record is a record with pre-defined data elements and known mappings to the vector space for the subject-matter domain. Conversely, as used herein, an unstructured record is a record that lacks pre-defined data elements and/or known mappings to the vector space. Thus, the input record 22 may be, for example, a database, a text document, a spreadsheet or any other means of conveying or storing information. Substantively, the input record 22 typically contains information that it is desirable to classify, in whole or in part, into a master taxonomy 218. In one embodiment, for example, résumés, job descriptions and other human-capital information may be classified into a human-capital-management (HCM) master taxonomy.
A parsing-and-mapping engine 24 typically receives the input record 22 and operates to transform the input record 22 via the language library 28. The parsing-and-mapping engine 24 is typically similar to the parsing-and-mapping engine 14 of
The dictionary-stewardship tool 210 generally operates to identify and flag “noise words” in the input record 22 so that the noise words may be ignored. Noise words may be considered words that have been predetermined to be relatively insignificant such as, for example, by inclusion in a noise-words dictionary. For example, in some embodiments, articles such as ‘a’ and ‘the’ may be considered noise words. In a typical embodiment, noise words are not removed from the input record 22 but instead are placed in a language quarantine 212 and ignored for the remainder of the flow 200.
The dictionary-stewardship tool 210 also is typically operable to place into the language quarantine 212 linguistic units that are not able to be enriched by the language library 28. In some embodiments, these linguistic units are not able to be enriched because no pertinent information concerning the linguistic units is able to be obtained from the language library 28. In a typical embodiment, the dictionary-stewardship tool 210 may track the linguistic units that are not able to be enriched and a frequency with which the linguistic units appear. As the frequency becomes statistically significant, the dictionary-stewardship tool 210 may flag such linguistic units for possible future inclusion in the language library 28.
The parsing-and-mapping engine 24 generally projects the linguistic unit onto the vector space to produce a multidimensional vector 206. Each dimension of the multidimensional vector 206 generally corresponds to a subject dictionary from the set of subject dictionaries in the language library 28. In that way, each dimension of the multidimensional vector 206 may reflect one or more possible meanings of the linguistic unit and a level of confidence in those possible meanings.
A similarity-and-relevancy engine 26, in a typical embodiment, is operable to receive the multidimensional vector 206, reduce the number of possible meanings for the linguistic units and begin classification of the linguistic units in the master taxonomy 218. The similarity-and-relevancy engine is typically similar to the similarity-and-relevancy engine 16 of
Additionally, each node in the plurality of nodes 216 may generally be measured as a vector in the vector space of the subject-matter domain. In various embodiments, the vector may have direction and magnitude in the vector space based on a set of master data. The set of master data, in various embodiments, may be data that has been reliably matched to ones of the plurality of nodes 216 in the master taxonomy 218 by experts in the subject-matter domain. One of ordinary skill in the art will appreciate that, optimally, the set of master data is large, diverse and statistically normalized. Furthermore, as indicated by a node construct 230, each node in the plurality of nodes 216 may have a label 232, a hierarchy placement 234 that represents a position of the node in the master taxonomy 218 and attributes 236 that are relevant to the subject-matter domain. The attributes 236 generally include linguistic units from data in the set of master data that have been reliably matched to a particular node in the plurality of nodes 216.
The similarity-and-relevancy engine 26 typically uses a series of vector-based computations to identify a node in the plurality of nodes 216 that is a best-match node for the multidimensional vector 206. In addition to being a best match based on the series of vector-based computations, in a typical embodiment, the best-match node must also meet certain pre-defined criteria. The pre-defined criteria may specify, for example, a quantitative threshold for accuracy or confidence in the best-match node.
In a typical embodiment, the similarity-and-relevancy engine 26 first attempts to identify the best-match node at the family level 228. If none of the nodes in the plurality of nodes 216 positioned at the family level 228 meets the predetermined criteria, the similarity-and-relevancy engine 26 may move up to the class level 226 and again attempt to identify the best-match node. The similarity-and-relevancy engine 26 may continue to move up one level in the master taxonomy 218 until the best-match node is identified. As will be described in more detail below, when the master taxonomy is based on a large and diverse set of master data, it is generally a good assumption that the similarity-and relevancy engine 26 will be able to identify the best-match node at the family level 228. In that way, the similarity-and-relevancy engine 26 typically produces, as the best-match node, a node in the plurality of nodes 216 that comprises a collection of similar species at the species level 238 of the master taxonomy 218. In a typical embodiment, the collection of similar species may then be processed by an attribute-differential engine 21.
In a typical embodiment, each node at the species level 238 may have a product key 248 that defines the node relative to a spotlight attribute. The product key 248 may include, for example, a set of core attributes 250, a set of modifying attributes 252 and a set of key performance indicators (KPIs) 254. The spotlight attribute, in a typical embodiment, is an attribute in the set of core attributes 250 that is of particular interest for purposes of distinguishing one species from another species. For example, in a human-capital-management master taxonomy for a human-capital-management subject-matter domain, the spotlight attribute may be a pay rate for a human resource. By way of further example, in a life-insurance master taxonomy for a life-insurance subject-matter domain, the spotlight attribute may be a person's life expectancy.
The core attributes 250 generally define a node at the species level 238. The modifying attributes 252 are generally ones of the core attributes that differentiate one species from another. The KPIs 254 are generally ones of the modifying attributes that significantly affect the spotlight attribute and therefore may be considered to statistically drive the spotlight attribute. In a typical embodiment, the attribute-differential engine 21 is operable to leverage the KPIs 254 in order to compare an unclassified vector 242 with each species in the collection of similar species. The unclassified vector 242, in a typical embodiment, is the multidimensional vector 206 as modified and optimized by the similarity-and-relevancy engine 26.
In a typical embodiment, the attribute-differential engine 21 is operable to determine whether the unclassified vector 242 may be considered a new species 244 or an existing species 246 (i.e., a species from the collection of similar species). If the unclassified vector 242 is determined to be the existing species 244, the unclassified vector 242 may be so classified and may be considered to have the spotlight attribute for the existing species 244. If the unclassified vector 242 is determined to be the new species 246, the new species 244 may be defined using the attributes of the unclassified vector 242. A spotlight attribute for the new species 244 may be defined, for example, as a function of a degree of similarity, or distance, from a most-similar one of the collection of similar species, the distance being calculated via the KPIs 254.
In a typical embodiment, the HCM master dictionary 356 is a superset of the abbreviation dictionary 362, the inference dictionary 360 and the plurality of subject dictionaries 358. In that way, the HCM master dictionary 356 generally at least includes each entry present in the abbreviation dictionary 362, the inference dictionary 360 and the plurality of subject dictionaries 358. The HCM master dictionary 356 may, in a typical embodiment, include a plurality of Boolean attributes 356a that indicate parts of speech for a linguistic unit. The plurality of Boolean attributes 356a may indicate, for example, whether a linguistic unit is a noun, verb, adjective, pronoun, preposition, article, conjunction or abbreviation. As illustrated in
In a typical embodiment, the HCM master dictionary 356, the abbreviation dictionary 362, the inference dictionary 360 and the plurality of subject dictionaries 358 may be created and populated, for example, via a set of HCM master data. The set of HCM master data, in various embodiments, may be data that has been input into the HCM language library 38, for example, by experts in the HCM subject-matter domain. In some embodiments, standard dictionary words and terms from various external dictionaries may be integrated into, for example, the plurality of subject dictionaries 358.
In various embodiments, the HCM master taxonomy 418 and the HCM language library 38 are configured and pre-calibrated, via HCM subject-matter expertise, to a set of HCM master data in manner similar to that described with respect to the language library 28 and the master taxonomy 218 of
At spell-check step 704, the parsing-and-mapping engine 74 may perform a spell check of a linguistic unit from the linguistic units that were parsed at the step 702. At an abbreviation step 706, if the linguistic unit is an abbreviation, the parsing-and-mapping engine 74 attempts to identify one or more meanings for the abbreviation. At an inference step 708, the parsing-and-mapping engine 74 identifies any inferences that may be made either based on the linguistic unit or products of the steps 704 and 706. At step 710, as a cumulative result of steps 702, 704, 706 and 708, the linguistic unit is categorized, for example, into one or more of a plurality of subject dictionaries such as, for example, the plurality of subject dictionaries 358 of
However, if an input record such as, for example, the input record 22 of
Linguistic parsing may be used to parse an unstructured record when, for example, template parsing is either not feasible or not preferred. In a typical embodiment, linguistic parsing may involve referencing a HCM language library such as, for example, the HCM language library 38 of
At step 804 of
At step 902, the parsing-and-mapping engine 74 may perform a character-standardization algorithm on the parsed linguistic unit. For example, one of ordinary skill in the art will appreciate that an “em dash,” an “en dash,” a non-breaking hyphen and other symbols are frequently used interchangeably in real-world documents even though each is a distinct symbol. In various embodiments, performing the character-standardization algorithm operates to translate the parsed linguistic unit into a standard character set that removes such ambiguities. In that manner, the efficiency and effectiveness of the spell-check flow 900 may be improved.
At step 904, the parsing-and-mapping engine may select a subject dictionary for searching. In a typical embodiment, the subject dictionary selected for searching may be one of a plurality of subject dictionaries such as, for example, the plurality of subject dictionaries 358 of
Depending on a particular objective, various orders may be utilized. For example, in some embodiments, the parsing and mapping engine 74 may check the plurality of subject dictionaries 358 in the following order: the job dictionary 358(4), the product dictionary 358(3), the organization dictionary 358(2), the place dictionary 358(1), the calendar dictionary 358(5) and the person dictionary 358(6). In these embodiments, if an exact match for the parsed linguistic unit is found in the job dictionary 358(4), that match is used and no further dictionaries are searched. In that way, computing resources may be preserved.
At step 906, the parsing-and-mapping engine 74 may attempt to identify an exact match for the parsed linguistic unit in the subject dictionary selected for searching at the step 904. In a typical embodiment, the parsing-and-mapping engine 74 of
If, at the step 906, an exact match is found for the parsed linguistic unit in the subject dictionary selected for searching, in a typical embodiment, the spell-check flow 900 proceeds to step 908. At the step 908, the exact match is kept and no other spell-check algorithm need be performed with respect to that dictionary. Additionally, the exact match may be assigned a match coefficient of one. The match coefficient will be discussed in more detail below. From the step 908, the spell-check flow 900 proceeds directly to step 914.
If the exact-match algorithm returns a zero for the parsed linguistic unit at the step 906, the spell-check flow 900 proceeds to step 910. At the step 910, the parsing-and-mapping engine 74 may identify top matches in the subject dictionary selected for searching via a match coefficient. As used herein, a match coefficient may be considered a metric that serves as a measure of a degree to which a first linguistic unit linguistically matches a second linguistic unit. As part of calculating the match coefficient, an edit-distance-ratio algorithm and a metaphone-ratio algorithm may be performed.
As one of ordinary skill in the art will appreciate, a formula for calculating an edit-distance ratio between a first linguistic unit (i.e., ‘A’) and a second linguistic unit (i.e., ‘B’) may be expressed as follows:
Max_Length=Max(A.Length,B.Length)
Edit-Distance Ratio(A,B)=(Max_Length−Edit Distance(A,B))/Max_Length
An edit distance between two linguistic units may be defined as a minimum number of edits necessary to transform the first linguistic unit (i.e., ‘A’) into the second linguistic unit (i.e., ‘B’). A length of the first linguistic unit (i.e., ‘A’) may be defined as the number of characters contained in the first linguistic unit. Similarly, a length of the second linguistic unit (i.e., ‘B’) may be defined as the number of characters contained in the second linguistic unit. One of ordinary skill in the art will recognize that the only allowable “edits” for purposes of calculating an edit distance are insertions, deletions or substitutions of a single character. One of ordinary skill in the art will further recognize that the formula for edit-distance ratio expressed above is exemplary in nature and, in various embodiments, may be modified or optimized without departing from the principles of the present invention. In that way, an edit-distance ratio between the parsed linguistic unit and a target linguistic unit in the subject dictionary selected for searching may be similarly calculated.
As one of ordinary skill in the art will appreciate, a formula for calculating a double-metaphone ratio may be expressed as follows:
Double-Metaphone Ratio(A,B)=Edit-Distance Ratio(A.Phonetic_Form,B.Phonetic_Form)
As one of ordinary skill in the art will appreciate, the double-metaphone ratio algorithm compares a phonetic form for the first linguistic unit (i.e., ‘A’) and the second linguistic unit (i.e., ‘B’) and returns a floating number between 0 and 1 that is indicative of a degree to which the first linguistic unit and the second linguistic unit phonetically match. In various embodiments, the double-metaphone ratio algorithm may vary as, for example, as to how A.Phonetic_Form and B.Phonetic_Form are determined and as to how an edit-distance ratio between A.Phonetic_Form and B.Phonetic_Form are calculated. In that way, a double-metaphone ratio between the parsed linguistic unit and a target linguistic unit in the subject dictionary selected for searching may be similarly calculated.
For example, as one of ordinary skill in the art will recognize, the double-metaphone algorithm may determine a primary phonetic form for a linguistic unit and an alternate phonetic form for the linguistic unit. Therefore, in some embodiments, it is possible for both the parsed linguistic unit and a target linguistic unit in the subject dictionary selected for searching to each yield a primary phonetic form and an alternate phonetic form. If the primary phonetic form and the alternate phonetic form for both the parsed linguistic unit and the target linguistic unit in the subject dictionary selected for searching are considered, one of ordinary skill in the art will recognize that four edit-distance ratios may be calculated. In some embodiments, the double-metaphone ratio may be a maximum of the four edit-distance ratios. In other embodiments, the double-metaphone ratio may be an average of the four edit-distance ratios. In still other embodiments the double-metaphone ratio may be a weighted average of the four edit-distance ratios such as, for example, by giving greater weight to ratios between primary phonetic forms.
In some embodiments, greater accuracy for the double-metaphone algorithm may be achieved by further considering a double-metaphone ratio for a backwards form of the parsed linguistic unit. The backwards form of the parsed linguistic unit is, in a typical embodiment, the parsed linguistic unit with its characters reversed. As discussed above, the double-metaphone ratio for the backwards form of the parsed linguistic unit may be considered via, for example, an average or weighted average with the double-metaphone ratio for the parsed linguistic unit in its original form. One of ordinary skill in the art will recognize that any formulas and methodologies for calculating a double-metaphone ratio expressed above are exemplary in nature and, in various embodiments, may be modified or optimized without departing from the principles of the present invention.
Still referring to the step 910 of
Match Coefficient(A,B)=(Exact-Match(A,B)+Edit-Distance Ratio(A,B)+Double-Metaphone Ratio(A,B))/3
As one of ordinary skill in the art will recognize, by virtue of reaching the step 910, no exact match for the raw linguistic typically exists in the dictionary selected for searching. Therefore, “Exact-Match (A, B)” will generally be zero.
In various embodiments, a result of the step 910 is that the parsing-and-mapping engine 74 identifies the top matches, by match coefficient, in the subject dictionary selected for searching. In a typical embodiment, any matches that have a match coefficient that is less than a dictionary coefficient for the subject dictionary selected for searching may be removed from the top matches. The dictionary coefficient, in a typical embodiment, is a metric representing an average edit distance between any two nearest neighbors in a dictionary. For example, a formula for the dictionary coefficient may be expressed as follows:
Dictionary Coefficient=(½)+(Average_Edit_Distance(Dictionary)/2)
In this manner, in terms of edit distance, it may be ensured that the top matches match the parsed linguistic unit at least as well as any two neighboring linguistic units in the subject dictionary selected for searching, on average, match each other.
In a typical embodiment, after the step 910, the spell-check flow 900 proceeds to step 912. At the step 912, the parsing-and-mapping engine 74 may determine whether, for example, others of the plurality of subject dictionaries 358 of
At the step 914, the parsing-and-mapping engine 74 may perform statistical calculations on a set of all top matches identified across, for example, the plurality of subject dictionaries 358 of
In a typical embodiment, a local frequency may be calculated for each top match of the set of all top matches. As mentioned above with respect to
From the step 914, the spell-check flow 900 proceeds to step 916. At the step 916, the parsing-and-mapping engine 74 may compute a weighted score for each top match in the set of all top matches. In various embodiments, the weighted score may be calculated as follows:
Weighted Score=Match Coefficient*Local_Frequency/Total Frequency
One of ordinary skill in the art will note that the weighted score yields a value between 0 and 1. In that way, the parsing-and-mapping engine may weight a particular top match's match coefficient based on a frequency of that top match relative to frequencies of other top matches.
From step 916, the spell-check flow 900 proceeds to step 918. At the step 918, the parsing-and-mapping engine 74 may identify overall top matches in the set of all top matches. In a typical embodiment, the overall top matches in the set of all top matches are those matches that meet one or more predetermined statistical criteria. An exemplary pre-determined statistical criterion is as follows:
Local Frequency>=Max_Frequency−(3*Standard_Deviation(Local_Frequencies))
Thus, in some embodiments, the overall top matches may include each top match in the set of all top matches for which the local frequency meets the exemplary pre-determined statistical criterion. After the step 918, the spell-check flow 900 ends. In a typical embodiment, the process 900 may be performed for each of the plurality of parsed linguistic units produced by the parsing flow 800 of
At step 1004, the parsed linguistic unit and each of the overall top matches are mapped to any possible abbreviations listed, for example, in the abbreviation dictionary 362 of
As shown in Table 3, the inference dictionary 360 of
At step 1104, each of the source linguistic units are mapped to any possible inferences, or inferred linguistic units, from the inference dictionary 360. In a typical embodiment, “IS-A” relationships and synonym relationships are each given a rank of one. Additionally, in a typical embodiment, frequency-based relationships are ranked from one to n based on, for example, a frequency number provided in the inference dictionary 360. The inferred linguistic units are, in a typical embodiment, retained and stored with the source linguistic units, that is, the parsed linguistic unit, the overall top matches from the spell-check flow 900 of
In various embodiments, the multidimensional vector 1202 represents a projection of the plurality of parsed linguistic units produced in the parsing flow 800 of
In a typical embodiment, each of the overall top matches from the spell-check flow 900 of
Typically, the highest-weighted possible meaning is identified for each parsed linguistic unit in the plurality of parsed linguistic units produced in the parsing flow 800 of
Various performance optimizations may be possible with respect to the step 1302. For example, one of ordinary skill in the art will recognize that a master taxonomy such as, for example, the HCM master taxonomy 418 may conceivably include thousands or millions of nodes. Therefore, in various embodiments, it is beneficial to reduce a number of nodes for which a node-category score must be calculated. In some embodiments, the number of nodes for which the node-category score must be calculated may be reduced by creating a stop condition when, for example, a node-category score is zero. In these embodiments, all nodes beneath a node having a node-category score of zero may be ignored under an assumption that the node-category score for these nodes is also zero.
For example, if a node-category score of zero is obtained for a node at the job-domain level 420, all nodes beneath that node in the HCM master taxonomy 418, in a typical embodiment, may be ignored and assumed to similarly have a node-category score of zero. In various embodiments, this optimization is particularly effective, for example, at domain, category and subcategory levels of a master taxonomy such as, for example, the master taxonomy 418. Additionally, in various embodiments, utilization of this optimization may result in faster and more efficient operation of a similarity-and-relevancy engine such as, for example, the similarity- and relevancy engine 1326. One of ordinary skill in the art will recognize that other stop conditions are also possible and are fully contemplated as falling within the scope of the present invention.
In various embodiments, performance of the step 1302 may also be optimized through utilization of bit flags. For example, in a typical embodiment, a node in the HCM master taxonomy 418, hereinafter a flagged node, may have a bit flag associated with a node attribute for the flagged node. In a typical embodiment, the bit flag may provide certain information regarding whether the associated node attribute may also be a node attribute for the flagged node's siblings. As one of ordinary skill in the art will appreciate, all nodes that immediately depend from the same parent may be considered siblings. For example, with respect to the HCM master taxonomy 418 of
In a typical embodiment, the bit flag may specify: (1) an action that is taken if a particular condition is satisfied; and/or (2) an action that is taken if a particular condition is not satisfied. For example, in various embodiments, the bit flag may specify: (1) an action that is taken if the associated node attribute matches, for example, a dimension of the multidimensional vector 1202 of
For example, as shown in Table 4, in a typical embodiment, the similarity-and-relevancy engine 1326 may utilize an attribute-only-exists bit flag, an attribute-must-exist bit flag, an attribute-can-exist bit flag and an attribute-must-not-exist bit flag. In some embodiments, every node in a master taxonomy such as, for example, the HCM master taxonomy 418 may have bit flag associated with each node attribute. In these embodiments, the bit flag may be one of the four bit flags specified in Table 4.
In a typical embodiment, the attribute-only-exist bit flag indicates that, among the flagged node and the flagged node's siblings, only the flagged node has the associated attribute. Therefore, according to the attribute-only-exist bit flag, if the associated node attribute matches, for example, a dimension of the multidimensional vector 1202 of
In a typical embodiment, the attribute-must-exist flag indicates that, in order for the flagged node or any of the flagged node's siblings to be considered to match a dimension of a multidimensional vector such as, for example, the multidimensional vector 1202 of
In a typical embodiment, the attribute-can-exist bit flag indicates that the associated node attribute may exist but provides no definitive guidance as to the flagged node's siblings. According to the attribute-can-exist flag, if the associated node attribute matches, for example, a dimension of the multidimensional vector 1202 of
In a typical embodiment, the attribute-must-not-exist bit flag indicates that neither the flagged node nor the flagged node's siblings have the associated node attribute. Therefore, according to the attribute-must-not-exist bit flag, if the associated node attribute matches, for example, a dimension of the multidimensional vector 1202 of
Following the step 1302, the process 1300 proceeds to step 1304. At the step 1304, an overall node score may be calculated for each node of the HCM master taxonomy 418 of
Overall_Node_Score=Square-Root((C*S1)̂2+(C*S2)̂2+ . . . +(C*Sn)̂2)
In the formula above, C represents a category weight, S1 and S2 each represent a node-category score and ‘n’ represents a total number of node-category scores for the particular node. In a typical embodiment, a category weight is a constant factor that may be used to provide more weight to node-category weights for certain dimensions of the multidimensional vector 1202 of
From the step 1304, the process 1300 proceeds to step 1306. At the step 1306, the similarity-and-relevancy engine 1326 may calculate a node lineage score for each node at a particular level, for example, of the HCM master taxonomy 418 of
Node_Lineage_ScoreNode=Square-Root((Node_Level_WeightNode*Overall_Node_ScoreNode)̂2+ . . . +(Node_Level_WeightDomain*Overall_Node_ScoreDomain)̂2)
As part of the formula above, calculating the node lineage score for a particular node (i.e., Node_Lineage_ScoreNode) may involve calculating a product of a node-level weight for the particular node (i.e., Node_Level_WeightNode) and an overall node score for the particular node (i.e., Overall_Node_ScoreNode). Typically, as shown in the formula above, a product is similarly calculated for each parent of the particular node up to a domain level such as, for example, the job-domain level 420. Therefore, a plurality of products will result. In a typical embodiment, as indicated in the formula above, each of the plurality of products may be squared and subsequently summed to yield a total. Finally, in the formula above, a square-root of the total may be taken in order to obtain the node lineage score for the node (i.e., Node_Lineage_ScoreNode).
In various embodiments, as indicated in the exemplary formula above, the node lineage score may utilize a node-level weight. The node-level weight, in a typical embodiment, is a constant factor that may be used to express a preference for overall node scores of nodes that are deeper, for example, in, the HCM master taxonomy 418. For example, Table 6 lists various exemplary node-level weights that may be used to express this preference. One of ordinary skill in the art will recognize that other node-level weights may also be utilized without departing from the principles of the present invention.
From the step 1306, the process 1300 proceeds to step 1308. At the step 1308, the similarity-and-relevancy engine 1326 may calculate a distance between the maximum node-lineage score identified at the step 1306 and each sibling of a node having the maximum node-lineage score. For simplicity of description, the node having the maximum node-lineage score will be referenced as a candidate node and a sibling of the candidate node will be referenced as a sibling node. In various embodiments, an objective of the step 1306 is to use the distance between the candidate node and each sibling node to help ensure that the candidate node more closely matches, for example, the multidimensional vector 1202 of
In a typical embodiment, for a particular sibling node, the step 1308 generally involves processing node attributes of the particular sibling node as a first hypothetical input into the similarity-and-matching engine 1326 solely with respect to the candidate node. In other words, the step 1302, the step 1304 and the 1306 may be performed with the hypothetical input in such a manner that ignores all nodes except for the candidate node. The first hypothetical input, in a typical embodiment, yields a first hypothetical node-lineage score that is based on a degree of match between the node attributes of the sibling node and the candidate node.
Similarly, in a typical embodiment, the step 1308 further involves processing node attributes of the candidate node as a second hypothetical input into the similarity-and-matching engine 1326 solely with respect to the candidate node. In other words, the step 1302, the step 1304 and the 1306 may be performed with the second hypothetical input in such a manner that ignores all nodes except for the candidate node. The second hypothetical input, in a typical embodiment, yields a second hypothetical node-lineage score based on a degree of match between the node attributes of the candidate node and the candidate node.
Therefore, in various embodiments, a distance between the candidate node and the particular sibling node may be considered to be the first hypothetical node-lineage score divided by the second hypothetical node-lineage score. Similarly, in various embodiments, a distance between, for example, the multidimensional vector 1202 of
From the step 1308, the process 1300 proceeds to step 1310. At the step 1310, a best-match node, for example, for the multidimensional vector 1202 of
At the step 1406, a set of KPIs may be determined. In a typical embodiment, the set of KPIs may be similar to the set of KPIs 254 of
At the step 1408, the attribute-differential engine 1421 is operable to determine whether, for example, the multidimensional vector 1202 of
In some embodiments, it may be beneficial to utilize, for example, various embodiments described with respect to
Solely by way of example, as one of ordinary skill will appreciate, military personnel in the United States and other jurisdictions may make a workforce transition, or redeployment, from the public sector to the private sector either when a service commitment ends or as part of an overall career plan in the public sector. Typically, in a public-sector entity such as, for example, a military branch, a form of worker classification may be used internally to classify or describe skills and experience of human resources. In the private sector, however, numerous other nomenclatures and taxonomies may be utilized to describe skills and experience of human resources. In various embodiments, as described with respect to
Additionally, continuing the above example, a military branch and other public-sector entities often are buyers in a project-work sphere. The project-work sphere, as used herein, refers to a practice of outsourcing projects to one or more outside entities such as, for example, business entities in the private sector. For example, a military branch or another related public-sector entity, as buyers in a project-work sphere, may outsource projects related to aerospace or weaponry. One of ordinary skill in the art will appreciate that such projects frequently result in substantial economic benefits, for example, for business entities in the private sector to which project work for the projects is awarded.
In various embodiments, it may be advantageous to leverage an entity's status as a buyer in a project-work sphere to encourage entities in another workforce sector to accept redeployment of the buyer's human resources that are scheduled for redeployment, for example, out of the public sector into the private sector. For example, in various embodiments, a military branch may employ human resources that, pursuant to a service commitment or plan or other agreement, periodically or semi-permanently transition to employment in the private sector. In a typical embodiment, the transition to employment in the private sector may be temporary with a planned transition back to the public sector or semi-permanent with the caveat that the human resources may be recalled to the public sector on an as-needed basis.
In addition, in a typical embodiment, the workforce transition to employment in the private sector may be part of a career plan for the human resources. A career plan, in a typical embodiment, is a plan to progressively develop various skills and/or experience in order to gradually transition a human resource into one or more different roles or positions in a workforce sector. A career plan may be utilized in order to help the human resources develop skills that are useful to a role or a prospective role in the public sector. In this manner, in various embodiments, the military branch would benefit from an ability to track and control how human resources are redeployed and to ensure that the human resources are in fact redeployed to the private sector. Thus, in various embodiments, a status as a buyer in a project-work sphere may be leveraged to increase speed and effectiveness of redeployment in another workforce sector such as, for example, the private sector.
The labor pool 1504 may include a plurality of human resources that are employed in or otherwise participating within the prior workforce sector 1502. The prior workforce sector 1502 may be, for example, the public sector. In a typical embodiment, participation in the public sector may involve employment by an entity in the public sector such as, for example, employment pursuant to a service commitment for a military branch. Typically, the prior workforce sector 1502 is centrally controlled such as, for example, by a government, a board, or other similar leadership.
The applicant tracking system 1506, in a typical embodiment, is operable to configure and manage the labor pool 1504. As part of configuring the labor pool 1504, the applicant tracking system 1506 typically stores and maintains human-resource information that describes, for example, skills, experience and career plans for each of the plurality of human resources in the labor pool 1504. As part of managing the labor pool 1504, the applicant tracking system 1504 typically adds or removes human resources to the labor pool 1504 as needed.
One of ordinary skill in the art will appreciate that the configuration of the labor pool 1504 may be a dynamic and continuous process. As noted above, transitions or redeployments into the private sector, in various embodiments, may be temporary or semi-permanent so that a return to the public sector is planned or at least possible. Therefore, in a typical embodiment, as the plurality of human resources in the labor pool 1504 develop additional skills and experience in either the prior sector 1502 or the private sector, those skills and the experience may be integrated into the human-resource information for the labor pool 1504. In this manner, in a typical embodiment, the applicant tracking system 1506 is operable to maintain, via the human-resource information, a current snapshot of the labor pool 1504. Thus, as a result, the plurality of human resources in the labor pool 1504 may be more effectively redeployed to the private sector and more effectively utilized in the public sector upon a return to the public sector.
The PDS 1508, in a typical embodiment, is operable to normalize the human-resource information for each of the plurality of human resources in the labor pool 1504 to a master taxonomy such as, for example, the HCM master taxonomy 418 of
The plurality of FTE employers 1510(1) generally includes business entities in another workforce sector such as, for example, the private sector. The PDS 1508 typically configures and manages the business entities in the plurality of FTE employers 1510(1). Configuring the business entities in the plurality of FTE employers 1510(1), in a typical embodiment, involves obtaining and storing information related to the business entities that is normalized, for example, to the HCM master taxonomy 418. Management of the business entities typically involves adding and deleting business entities in the plurality of FTE employers 1510(1) and ensuring that the information related to the business entities in the plurality of FTE employers 1510(1) is current.
The plurality of FTE employers 1510(1) and the plurality of FTE employers 1510(2) are depicted separately in
The plurality of FTE employers 1510, in a typical embodiment, may interact with the outsource system 1518, for example, in order to bid for and obtain the project work 1516. The plurality of FTE employers 1510, in a typical embodiment, may further utilize the services of the plurality of vendors 1514 of contingent or temporary labor in order to staff the project work. In various embodiments, interactions between the PDS 1508, the project-work sphere 1512 and the outsource system 1518 operate to optimize redeployment of the plurality of human resources in the labor pool 1504 into the private sector, as described in more detail below.
In various embodiments, the outsource system 1518 may be utilized as leverage to award the project work 1516 to ones of the plurality of FTE employers 1510 that employ the plurality of human resources in the labor pool 1504 either as full-time employees or as temporary labor to staff the project work 1516. In that way, in a typical embodiment, redeployment of the plurality of human resources 1504 in the private sector may be optimized via interactions between the PDS 1508, the outsource system 1518 and the project-work sphere 1512. Examples of the optimization will be described in more detail with respect to
In a typical embodiment, the applicant tracking system 1606 is operable to configure the labor pool 1604. In a typical embodiment, configuration of the labor pool 1604 may involve obtaining and storing, for each of a plurality of human resources in the labor pool 1604, human-resource information. The human-resource information may include personal-profiling information 1606a, skills/experience information 1606b, career-development plans 1606c and assignment logistics 1606d. As discussed above with respect to the labor pool 1504 of and the applicant tracking system 1506 of
The personal-profiling information 1606a may include, for example, objective information regarding strengths, abilities and employment inclinations. The personal-profiling information 1606a may, in some embodiments, be extracted from results of a personal-profiling test administered to the plurality of human resources in the labor pool 1604. The skills/experience information 1606b may include résumé-type information that includes, for example, skills developed, for example, inside and outside of the prior workforce sector 1502 and work history and experience inside and outside of the prior workforce sector 1502. The career-development plans 1606c may include, for example, information related to career goals and desires for the plurality of human resources in the labor pool 1604. In various embodiments, the career goals and desires may be constructed with the benefit of consultation with a career-development counselor or other similar professional. The assignment logistics 1606d may include, for example, one or more desired locations for employment and dates of availability.
In a typical embodiment, the PDS 1608 is operable to configure the plurality of FTE employers 1610. In a typical embodiment, configuration of the plurality of FTE employers 1610 involves performing and storing information related to business-entity general-business profiling 1608a, business-entity skill-utilization profiling 1608b, business-entity technology/equipment profiling 1608c and business-entity management 1608d. Configuration of the plurality of FTE employers 1610, in a typical embodiment, may further involve obtaining and storing business-entity job postings 1608e and generating deployment business intelligence (BI) 1608g. The business-entity job postings 1608e generally include job-opening information for job openings for the plurality of FTE employers 1610. The job-opening information may include, for example, information regarding required skills and experience, job location and other job requirements or descriptions. The PDS 1608 also may include a deployment-processing module 1608f.
The information related to the business-entity general-business profiling 1608a may include any type of data related to a business entity, such as, for example, a business-entity type, geographical locations, industry segmentation, size, spend capacity, etc. The information related to the business-entity skill-utilization profiling 1608b, in a typical embodiment, may include information related to specific skills and sets of skills historically required by a business entity. The information related to the business-entity technology/equipment profiling 1608c may include, for example, information regarding equipment, technologies and products utilized by employees of a business entity in the plurality FTE employers 1610. In a typical embodiment, the information related to the business-entity technology/equipment profiling 1608c may be similar to information contained within the product dictionary 358(3) of
The information related to the business-entity skill-utilization profiling 1608b and the information related to the business-entity technology/equipment profiling 1608c, in a typical embodiment, may be acquired over time by ingesting job descriptions such as, for example, the business-entity job postings 1608e and classifying the job descriptions into a HCM master taxonomy such as, for example, the HCM master taxonomy 418 of
The business-entity management 1608d may include, for example, functionality to add or delete business entities in the plurality of FTE employers 1610. In various embodiments, the business-entity management 1608d may further include functionality to maintain an accuracy and currency of information related to the business entities in the plurality of FTE employers 1610. For example, in a typical embodiment, the PDS 1608 may periodically prompt a business entity in the plurality of FTE employers 1610 to confirm or update the information related to business-entity general-business profiling 1608a for that business entity. In a typical embodiment, the PDS 1608 may also periodically prompt a business entity in the FTE employers 1610 to update, for example, the business-entity job postings 1608e.
The business-entity job postings 1608e typically represent, for example, job openings for which the plurality of human resources in the labor pool 1604 may apply. In some embodiments, the business-entity job postings 1608e may be provided directly by business entities in the plurality of FTE employers 1610. In other embodiments, the business-entity job postings 1608e may be provided by periodicals (e.g., newspapers, magazines, etc.), Internet web sites and other sources. In a typical embodiment, job-opening information related to the business-entity job postings 1608e may be ingested and classified into a master HCM taxonomy such as, for example, the HCM master taxonomy 418 of
The deployment-processing module 1608f, in a typical embodiment, is operable to normalize human-resource information for each of the plurality of human resources in the labor pool 1604 to a master taxonomy such as, for example, the HCM master taxonomy 418 of
In a typical embodiment, via the normalizations described above, the deployment-processing module 1608f is operable to match human resources in the labor pool 1604 with ones of the business-entity job postings 1608e while considering, for example, the assignment logistics 1606d. The deployment-processing module 1608f is further typically operable to track and record information related to hires, or completed redeployments, from the plurality of human resources in the labor pool 1604 based on a job posting in the business-entity job postings 1608e. In a typical embodiment, the deployment processing module 1608f shares the recorded information related to hires with the applicant tracking system 1606 and the business-entity management 1608d. In that way, in a typical embodiment, the applicant tracking system may update the human-resource information for the labor pool 1504 and a fulfilled job posting may be removed from the business-entity job postings 1608e.
In a typical embodiment, the deployment-processing module 1608f enables the configuration of the labor pool 1604 described above with respect to the applicant tracking system 1606 to be a dynamic and continuous process. For example, transitions or redeployments into the private sector, in various embodiments, may be temporary or semi-permanent so that a return, for example, to the prior sector 1502 of
The deployment business intelligence (BI) 1608g, in a typical embodiment, may include analytics related to various activities of the PDS 1608. In a typical embodiment, the PDS 1608 is operable to develop the deployment BI 1608g based on, for example, the information related to business-entity general-business profiling 1608a, business-entity skill-utilization profiling 1608b, business-entity technology/equipment profiling 1608c and business-entity management 1608d, the business-entity job postings 1608e and recorded hires. Other information may also be utilized. In that way, in a typical embodiment, the deployment BI 1608g may include analytics such as, for example, metrics identifying a degree to which business entities in the FTE employers 1610 utilize skills represented in the labor pool 1604. In various embodiments, the deployment BI 1608g may further include analytics such as, for example, metrics identifying a degree to which the business entities in the plurality of FTE employers 1610 hire from the labor pool 1604.
In various embodiments, the deployment BI 1608g may further drill down to more specific analytics such as, for example, from among the business entities in the plurality of FTE employers 1610 that utilize certain skills represented in the labor 1604 (e.g., a family or species in the HCM master taxonomy 418 of
The plurality of outsource projects 1720 may be projects, for example, from multiple discrete entities within a prior workforce sector such as, for example, the prior workforce sector 1502 of
The outsource system 1718, in a typical embodiment, may include a bid-template and statement-of-work (SOW) module 1722, a PDS integrated-resource module 1724, a spend-management module 1726, a sub-contracting-management module 1728, a project-tracking module 1730 and a payment module 1732. The bid-template and SOW module 1722, in a typical embodiment, may include functionality, for example, to control a competitive bid process for purposes of outsourcing the project work 1716 to one or more of the plurality of FTE employers 1710. Further, for performing any project, the plurality of FTE employers 1710 may utilize contingent labor provided by the plurality of vendors/suppliers 1714. In a typical embodiment, the bid-template and SOW module 1722 may allow, for example, the outsource system 1718 to mandate utilization of, for example, ones of the plurality of human resources in the labor pool 1604 of
The sub-contracting-management module 1728, in a typical embodiment, is operable to extend functionality of the bid-template and SOW module 1722 to downstream contractors (i.e., subcontractors). For example, one of the plurality of FTE employers 1710 may be awarded the project work 1716 and subcontract portions of the project work 1716 to a subcontractor such as, for example, another one of the plurality of FTE employers 1710 or one of the plurality of vendors/suppliers 1714. One of ordinary skill in the art will recognize that multiple layers of subcontracting may occur. In a typical embodiment, the sub-contracting management module 1728 may allow, for example, the outsource system 1718 to mandate utilization of, for example, ones of the plurality of human resources in the labor pool 1604 of
The project-tracking module 1730, the spend-management module 1726 and the payment module 1732, in a typical embodiment, are operable to perform functionality for an awarded project such as, for example, one of the plurality of outsource projects 1720. The spend-management module 1726 generally manages and tracks all financial aspects for the awarded project through completion of project work defined by the awarded project. The payment module 1732 may manage, for example, payment for project work, for example, according to terms of a purchase requisition for the awarded project. The project-tracking module 1730 may track, for example, a completion status of project activities, generate project-performance metrics and manage project-activity scheduling. The project-tracking module 1730, in a typical embodiment, may confirm and enforce any required utilizations, for example, of the labor pool 1603 in staffing the project work 1716.
The PDS integrated-resource module 1724, in a typical embodiment, is operable to integrate resources of the PDS 1708 and the outsource system 1718 in a manner that optimizes both the PDS 1708 and the outsource system 1718. For example, in a typical embodiment, the PDS integrated-resource module is operable to inform the bid-template and SOW module 1722 and the sub-contracting-management module 1728 regarding the deployment BI 1608g of
The bid request data 1840 is formatted by the project bid management system 1830 and transmitted as a bid request 1800 to one or more vendors 1810a . . . 1810n for solicitation of respective bid responses 1820. For example, the vendor 1810 can be business entities in the plurality of FTE employers 1710 of
In accordance with embodiments of the present invention, the project bid management system 1830 can be implemented within a computer system 1900, as is shown in
A bid web server 1920 enables vendors 1810, buyers 1850, contractors 1915 and administrators 1980 to interface to a database system 1950 maintaining data related to the vendors 1810, buyers 1850, contractors 1915 and administrators 1980. The data related to each of the vendors 1810, buyers 1850, contractors 1915 and administrators 1980 can be stored in a single database 1955, in multiple shared databases 1955 or in separate databases 1955 within the database server 1950 for security and convenience purposes, the latter being illustrated. For example, the database system 1950 can be distributed throughout one or more locations, depending on the location and preference of the buyers 1850, vendors 1810, administrators 1980 and contractors 1915.
The user interface to the vendor users 1905 is provided by the bid web server 1920 through a vendor module 1917. For example, the vendor module 1917 can populate web pages pushed to the vendor browser 1920b using the data stored in the particular vendor database 1955b. The user interface to the buyer users 1905 is provided by the bid web server 1920 through a buyer module 1910. For example, the buyer module 1910 can populate web pages pushed to the buyer browser 1920a using the data stored in the particular buyer database 1955a. The user interface to the contractor users 1905 is provided by the web server 1920 through a contractor module 1930. For example, the contractor module 1930 can populate web pages pushed to the contractor browser 1920c using the data stored in the contractor database 1955c. The user interface to the administrative users 1905 is provided by the bid web server 1920 through an administrative module 1935. For example, the administrative module 1935 can populate web pages pushed to the administrative browser 1920d using the data stored in the administrator database 1955d. It should be noted that the vendor module 1917, buyer module 1910, contractor module 1930 and administrative module 1935 can each include any hardware, software and/or firmware required to perform the functions of the vendor module 1917, buyer module 1910, contractor module 1930 and administrative module 1935, and can be implemented as part of the bid web server 1920, or within an additional server (not shown).
The computer system 1900 further provides an additional user interface to administrative users 1905 through an administrative web server 1925. The administrative web server 1925 enables administrators 1980 to interface to a top-level database 1960 maintaining data related to the vendors 1810, buyers 1850 and contractors 1915 registered with the computer system 1900. For example, the top-level database 1960 can maintain vendor qualification data 1962, buyer-defined vendor criteria data 1964 and contractor re-deployment data 1966.
To access information related to vendors 1810, the administrative web server 1925 uses a vendor module 1945 to push web pages to the administrative browser 1920d related to vendors 1810. For example, the vendor module 1945 can access vendor qualification information 1962 to qualify vendors 1810 for a particular buyer 1850 or for a particular industry. Likewise, the administrative web server 1925 can push web pages to the administrative browser 1920d related to the buyer-defined vendor criteria information 1964 through a buyer module 1940 in order to qualify vendors 1810 for a particular buyer 1850. The buyer-defined vendor criteria information 1964 may include information such as, for example, a predetermined threshold in hiring from the labor pool 1604 of
A contractor module 1948 enables administrators 1980 to access contractor re-deployment data 1966 entered by contractors 1915 through the bid server 1920 and retrieved into the top-level database 1960 from a contractor database 1955. The re-deployment data 1966 can include, for example, an indication of the mobility of the contractor, desired geographical areas, contractor skills, desired pay and other contractor information that can be used to assist administrators 1980 in qualifying vendors 1810 for buyers 1850.
In another embodiment, as shown in
To create a bid template 2040, the bid template creation tool 2080 accesses the buyer database 1955a to retrieve bid items 2030 within a bid item list 2094 and provides the buyer with the bid item list 2030 via the buyer module 1910, web server 1920, data network 1990 and buyer browser 1920a for the buyer to choose from. The bid items 2030 are associated with specific types of information to be solicited from the buyer, vendor or both. From the list of bid items 2030, the buyer selects and provides one or more bid item selections 2035 for inclusion in a bid template 2040. Depending on buyer configurations, one or more of the bid items 2030 may be mandatory for the bid template 2040, such as the name of the buyer, location of the work to be performed, type of project work requested and a required utilization of a labor pool such as, for example, the labor pool 1704 of
To create a bid request 1800, the bid request creation tool 2085 accesses the buyer database 1955a to retrieve the bid templates 2040 stored within the bid template list 2090 and provides a list of bid templates 2040 to the buyer via the buyer module 1910, web server 1920, data network 1990 and buyer browser 1920a for the buyer to choose from. Upon selecting an appropriate bid template 2040, the buyer provides bid request data 2010 to the bid request creation tool 2085 for inclusion in a bid request 1800 of the bid template 2040 type. For example, the buyer can enter bid request data 2010 into provided fields for each bid item selection 2035 that requires information from the buyer within the bid template 2040. By way of example, but not limitation, the bid request data 2010 could include the location of work to be performed, the timing of the project and the specific vendor qualifications necessary for the project.
The bid request creation tool 2085 further interfaces with the buyer database 1955a to access a vendor list 2058 for the buyer and determine the appropriate vendors to receive the bid request. The appropriate vendors can be selected based on the bid template 2040 type and any other vendor qualifications included within the bid request 1800 itself. Thus, the vendor list 2058 can be separated into pre-qualified vendors for bid template 2040 types to further reduce processing time when submitting bid requests 1800. For example, the vendor qualifications may include a predetermined threshold in hiring from the labor pool 1604 of
Vendor bid responses 1820 received from solicited vendors (as shown in
The flow 2100 begins at step 2102, at which step rules are configured by the buyer relative to an SCE. At step 2104, the SCE is enabled and program-configured supplier-load requirements are set. At step 2106, the SCE is affiliated with one or more approved suppliers. At step 2108, an approved supplier receives a request for quote/request for proposal/request for bid (RFx) from the buyer. At step 2110, the approved supplier decides to issue a daisy-chain quotation. The term daisy chain is used in the context of handling and transmission of work flow elements between entities in which one or more records are parsed from a master record. The parsed records are then transmitted from one entity to another, such that a receiving entity has access to the parsed and transmitted records. Upon further processing, the parsed transmitted records may be reintegrated back into the master record for further handling as part of the work flow. A daisy-chain quotation and a daisy-chain acquisition are examples of how a daisy-chain concept may be used in the context of the work flow process described herein. In response to the decision, at step 2110, to issue a daisy-chain quotation, at step 2112, the approved supplier selects desired bid response items to include in the daisy-chain quotation. The items included in the daisy-chain quotation must include at least one selection of a billable services/goods item. The items included in the daisy-chain quotation may specify, for example, a required utilization of the labor pool 1704 of
From step 2112, execution proceeds to step 2114. At step 2114, the approved supplier selects desired affiliated SCEs to which to post the daisy-chain quotation. At step 2116, the approved supplier posts the daisy-chain quotation and standard solution notifications are initiated. Standard solution notifications include, for example, so-called on-line dashboard notifications as well as e-mail notifications. From step 2116, execution proceeds to step 2118. At step 2118, a receiving SCE executes applicable buyer agreements to gain access to the daisy-chain quotation. Upon execution of the applicable buyer agreements at step 2118, execution proceeds to step 2120. At step 2120, the SCE completes applicable quotation items. At step 2122, the SCE posts the completed daisy-chain quotation back to the supplier.
At step 2124, the approved supplier accesses the SCE daisy-chain quotation. From step 2124, execution may proceed to either step 2126 or 2128. If, as step 2124, the approved supplier determines that an optional quotation analysis tool is to be used, execution proceeds to step 2126. If, however, the approved supplier does not want to use the quotation analysis tool, execution proceeds to directly to step 2128. At step 2126, the approved supplier may enable the optional quotation analysis tools. The quotation analysis tools permit daisy-chain quotation grading and scoring to occur. At step 2128, the approved supplier may accept or decline the SCE daisy-chain quotation. If the approved supplier accepts the SCE daisy-chain quotation, execution proceeds to step 2130.
At step 2130, the approved supplier selects daisy-chain quotation response items. For example, the approved supplier may select all or less than all of the daisy-chain quotation response items received from an SCE when certain response items are acceptable to the approved supplier and others are not. The selected response items must include at least one voucherable services/goods item. In response to selection of the desired daisy-chain quotation response items, execution proceeds to step 2132. At step 2132, a determination is made as to whether all necessary validations have been passed. For example, a validation could fail if the approved supplier were to attempt to select multiple bid response items for the same bid item. If it is not determined that all necessary validations have been passed, execution returns to step 2130. If, however, it is determined that all necessary validations have passed, execution proceeds from step 2132 to step 2134.
At step 2134, a supplier bid response is updated with the daisy-chain quotation values validated at step 2132. At step 2136, applicable status changes to the SCE daisy-chain quotation and supplier bid response are made. For example, once the selected daisy-chain quotation response items have been accepted by the supplier, the status of those items may be changed from pending to accepted. At step 2138, standard notifications are issued to the SCE. At step 2140, the approved supplier may optionally edit pricing of the SCE to reflect applicable supplier mark-ups. In various embodiments of the invention, the editing of SCE pricing by the approved supplier must not reduce the SCE pricing and must comply with configured allowable mark-up percentages as set by the buyer. From step 2140, execution proceeds to step 2142. At step 2142, the approved supplier submits the bid response to the buyer.
At step 2144, the buyer accesses the bid response. At step 2146, the approved buyer may optionally access via a user interface all SCE affiliated bid response details. At step 2148, the buyer processes the bid responses. At step 2150, the buyer awards the bid.
One of ordinary skill in the art will appreciate that the applicant tracking system 1506 of
In various embodiments, the plurality of public-sector entities 2234 may each store separate representations of skills and experience for human resources such as, for example, the plurality of human resources in the labor pool 1704 of
In a typical embodiment, the HCM data warehouse 2238 may be developed, for example, by extracting data from the plurality of data stores 2236, transforming the data into a normalized format such as, for example, via the HCM master taxonomy 418 as described with respect to
In a typical embodiment, the applicant tracking system 2306 may be similar to the applicant tracking systems 1506, 1606, 1706 and 2206 of
In a typical embodiment, the applicant tracking system 2306 may interact with the HCM data warehouse 2338 as described with respect to the applicant tracking system 2206 and the HCM data warehouse 2238 of
The decision-support system 2338, in a typical embodiment, may be utilized by the public-sector entity 2334, for example, to develop business intelligence and analytics based on information provided by the EDRS. For example, the decision-support system 2338 may utilize information, for example, from the HCM data warehouse 2338 or the PDS 2308 to project human-resource availability for particular skill sets, develop budgets or develop other analytics. One of ordinary skill in the art will recognize that the decision-support system 2338 may be utilized in many ways to enhance decision-making activities and other activities of the public-sector entity 2334.
In a typical embodiment, the applicant tracking system 2406 may obtain and store the career-development plan 2406c in a manner similar to that described with respect to the career-development plans 1606c of
In a typical embodiment, the career-development plan 2406c may specify a series of steps, or a career path, for the human resource 2404 to take in order to progressively advance towards one or more career goals. In various embodiments, the one or more career goals may be personal goals of the human resource 2404. In some embodiments, the one or more career goals may also factor in the needs, for example, of a public-sector entity in the public sector. Solely as an example, the career-development plan 2406c is depicted in
The series of exemplary career-path steps 2406c(1)-(7) of
Still describing the series of exemplary career-path steps 2406c(1)-(7), following a period of time working as a warehouse supervisor in the public sector, at a step 2406c(5), the human resource 2404 may be redeployed to a job opportunity in the private sector as a procurement specialist. Following a period of time working as a procurement specialist in the private sector, at a step 2406c(6), the human resource 2404 may be redeployed to another job opportunity in the private sector as a procurement manager. At a step 2406c(7), the human resource may be recalled to the public sector based on unexpected needs in the public sector. At the step 2406c(7), the PDS 2408 may match one of the unexpected needs in the public sector using the career-path and the career-path plan 2406c of the human resource 2404. One of ordinary skill in the art will recognize that, although specific steps and employment positions are described above, the steps, a sequence of the steps and the employment positions are solely exemplary in nature.
Although various embodiments of the method and apparatus of the present invention have been illustrated in the accompanying Drawings and described in the foregoing Detailed Description, it will be understood that the invention is not limited to the embodiments disclosed, but is capable of numerous rearrangements, modifications and substitutions without departing from the spirit of the invention as set forth herein.
This application claims priority from, and incorporates by reference the entire disclosure of, U.S. Provisional Application No. 61/237,924 filed on Aug. 28, 2009. This application incorporates by reference the entire disclosure of U.S. patent application Ser. No. 10/412,096, filed on Apr. 10, 2003, U.S. patent application Ser. No. 12/342,116, filed on Dec. 23, 2008, U.S. patent application Ser. No. 12/492,438, filed on Jun. 26, 2009, U.S. patent application Ser. No. 12/692,937, filed on Jan. 25, 2010, U.S. patent application Ser. No. 11/351,835, filed on Feb. 10, 2006, and U.S. patent application Ser. No. 11/698,603, filed on Jan. 25, 2007.
| Number | Date | Country | |
|---|---|---|---|
| 61237924 | Aug 2009 | US |
