Prioritizing Proposal Development Under Resource Constraints

• US 20160004985A1
• Filed: 07/02/2014
• Published: 01/07/2016
• Est. Priority Date: 07/02/2014
• Status: Active Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Blomberg, Jeanette L., Boyette, Neil, Chandra, Anca A., Oh, Sechan, Strong, Hovey Raymond Jr.
• Application Number:
  US14/322,199
• Publication Number:
  US 20160004985A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/06313 Resource planning in a project environment
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling

Abstract

Methods, systems, and articles of manufacture for prioritizing proposal development under resource constraints are provided herein. A method includes clustering multiple items of historical proposal development data into clusters based on one or more parameters, wherein said historical data comprise multiple prior proposal requests; generating a logistic regression model for time sensitivity associated with each of the prior proposal requests within each cluster; simulating each of the prior proposal requests (i) based on the corresponding logistic regression model and (ii) under each of multiple prioritization policies to determine an expected revenue measure for each of the prior requests under each of the prioritization policies; selecting a prioritization policy from the multiple prioritization policies based on said expected revenue measures; and computing a priority score for each proposal request in a given set of proposal requests based on application of the selected prioritization policy to each request in the given set.

Claims

1. A method comprising:

clustering multiple items of historical proposal development data into one or more clusters based on one or more parameters, wherein said historical proposal development data comprise multiple prior proposal requests;

generating a logistic regression model for time sensitivity associated with each of the prior proposal requests within each of the one or more clusters;

simulating each of the prior proposal requests (i) based on the corresponding logistic regression model and (ii) under each of multiple prioritization policies to determine an expected revenue measure for each of the prior proposal requests under each of the prioritization policies;

selecting a prioritization policy from the multiple prioritization policies based on said expected revenue measures; and

computing a priority score for each proposal request in a given set of proposal requests based on application of the selected prioritization policy to each proposal request in the given set;

wherein at least one of said clustering, said generating, said simulating, said selecting, and said computing is carried out by a computing device.

2. The method of claim 1, comprising:

prioritizing execution of said given set of proposal requests based on said computed priority scores.

3. The method of claim 1, comprising:

storing each of the multiple prior proposal requests in a repository.

4. The method of claim 3, wherein each of the multiple prior proposal requests is represented in the repository as a vector in a vector space based on one or more factors.

5. The method of claim 4, wherein said one or more factors comprise proposal request context.

6. The method of claim 4, wherein said one or more factors comprise value associated with each proposal request.

7. The method of claim 4, wherein said one or more factors comprise proposal request receipt date.

8. The method of claim 4, wherein said one or more factors comprise proposal development start date.

9. The method of claim 4, wherein said one or more factors comprise proposal completion date.

10. The method of claim 1, wherein said generating comprises:

utilizing (i) a proposal request context measure, (ii) a value associated with a given proposal request, and (iii) a response time measure of the given proposal request as inputs; and

generating a win probability of the given proposal request as output.

11. The method of claim 10, wherein said response time comprises the time period between a request receipt date and a proposal completion date.

12. The method claim 1, wherein said simulating comprises computing available capacity for proposal development for a duration of simulation based on a proposal development start date and a completion date of each of the multiple prior proposal requests.

13. The method of claim 12, comprising:

computing, for each day of simulation, a priority score for each request awaiting capacity allocation using the selected prioritization policy.

14. The method of claim 12, comprising:

assigning available capacity to one or more proposal requests based on priority score.

15. The method of claim 1, wherein said multiple prioritization policies comprise a marginal penalty policy that determines a priority score based on a reduction in expected revenue of a given proposal request caused by delaying capacity allocation to the given proposal request by a given temporal interval.

16. A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computing device to:

cluster multiple items of historical proposal development data into one or more clusters based on one or more parameters, wherein said historical proposal development data comprise multiple prior proposal requests;

generate a logistic regression model for time sensitivity associated with each of the prior proposal requests within each of the one or more clusters;

simulate each of the prior proposal requests (i) based on the corresponding logistic regression model and (ii) under each of multiple prioritization policies to determine an expected revenue measure for each of the prior proposal requests under each of the prioritization policies;

select a prioritization policy from the multiple prioritization policies based on said expected revenue measures; and

compute a priority score for each proposal request in a given set of proposal requests based on application of the selected prioritization policy to each proposal request in the given set.

17. The computer program product of claim 16, wherein said multiple prioritization policies comprise a marginal penalty policy that determines a priority score based on a reduction in expected revenue of a given proposal request caused by delaying capacity allocation to the given proposal request by a given temporal interval.

18. The computer program product of claim 16, wherein the program instructions executable by the computing device further cause the computing device to:

prioritize execution of said given set of proposal requests based on said computed priority scores.

19. A system comprising:

a memory; and

at least one processor coupled to the memory and configured for;

clustering multiple items of historical proposal development data into one or more clusters based on one or more parameters, wherein said historical proposal development data comprise multiple prior proposal requests;

generating a logistic regression model for time sensitivity associated with each of the prior proposal requests within each of the one or more clusters;

simulating each of the prior proposal requests (i) based on the corresponding logistic regression model and (ii) under each of multiple prioritization policies to determine an expected revenue measure for each of the prior proposal requests under each of the prioritization policies;

selecting a prioritization policy from the multiple prioritization policies based on said expected revenue measures; and

computing a priority score for each proposal request in a given set of proposal requests based on application of the selected prioritization policy to each proposal request in the given set.

20. A method comprising:

clustering multiple items of historical transaction data into one or more clusters based on transaction type, wherein said historical transaction data comprise multiple prior proposal requests;

generating a logistic regression model for time sensitivity associated with each of the prior proposal requests within each of the one or more clusters;

simulating each of the prior proposal requests across the one or more clusters (i) based on the corresponding logistic regression model and (ii) under each of multiple prioritization policies to determine an expected revenue measure for each of the prior proposal requests under each of the prioritization policies;

selecting a prioritization policy from the multiple prioritization policies for each transaction type based on said expected revenue measure for each of the proposal requests across the one or more clusters; and

computing a priority score for each request in a given set of requests based on (i) identification of a transaction type associated with each request and (ii) implementation of the selected prioritization policy for said transaction type associated with each request;

wherein at least one of said clustering, said generating, said simulating, said selecting, and said computing is carried out by a computing device.