Method and apparatus for business process analysis and optimization

• US 20060053039A1
• Filed: 09/03/2004
• Published: 03/09/2006
• Est. Priority Date: 09/03/2004
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Ramachandran, Bala, Gamarnik, David, Jengte, Nitin P., Squillante, Mark Steven, Lu, Yingdong
• Application Number:
  US10/933,544
• Publication Number:
  US 20060053039A1
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/06393 Score-carding, benchmarking or key performance indicator [KPI] analysis

Abstract

A methodology for business process analysis and optimization. This enables firms to analyze business processes using stochastic processing network models to estimate process key performance indicators. Based on these indicators, alternate process models can be further developed and analyzed, in order to optimize business objectives. The analysis methodology can be used for business process design (at design time) and for business process management (at run time).

Claims

1. A method for analyzing and optimizing business processes comprising the steps of:

identifying a family of inputs subject to defined constraints, that together describe the business process;

operating upon the family of inputs according to at least one of the techniques selected from the class of fluid flow models, Brownian models, Markov processes, general probability models and so on, and general optimization to produce a set of outputs which satisfy defined constraints; and

operating recursively on said inputs and resulting outputs, in order to apply the above method to the entire business process

2. The method according to claim 1, wherein the analysis is performed on different alternatives of the business process topology to determine an impact on overall business performance indicators.

3. The method according to claim 1, wherein the analysis is

performed on different alternatives of the business process

parameters to determine an impact on overall business

performance indicators.

4. The method according to claim 1, wherein the outputs of the analysis are used to determine the business process topology for deployment.

5. The method according to claim 1, wherein the outputs of the analysis are used to determine the attributes of the business process for deployment.

6. The method according to claim 1, wherein the method is used at design time for Business Process Modeling and Design, with the inputs for the analysis being obtained from Business process modeling systems.

7. The method according to claims 1, wherein the method is used at runtime for Business Performance Management, with the inputs for the analysis being obtained from Business Performance Management systems.

8. The method according to claim 1, wherein the method is incorporated in a computer system or any other suitable electronic device.

9. A method for determining an optimal topology for a business process comprising the steps of:

formulating a stochastic processing network model for a business process;

identifying constraints on topology of the business process model;

identify a set of objectives and key performance indicators for the business process

identifying a cost structure for the topology, wherein the cost is a function of the topology;

identifying the optimal topology by finding the topology which optimizes the key performance indicators (including costs) for the business process, among the space of topologies satisfying the constraints.

10. A method for managing a business process through mathematical analysis of stochastic processing networks with a given topological structure comprising the steps of:

converting the business process to its underlying stochastic processing network description;

solving the stochastic processing network, including calculating the key performance indicators for the network;

identifying parameters and structures of the business process that are essential to an implementation of the results obtained from the solution of the stochastic processing network; and

adjusting and tuning the parameters and structures of the business process according to the results obtained from the solution of the stochastic processing network.

11. A computer system for business process analysis and optimization, at design-time, comprising the steps of:

modeling a business process using different business process constructs;

formulating a stochastic processing network model for a business process;

solving the stochastic processing network, including calculating the key performance indicators for the network;

modifying the business process model based on the results obtained from the solution of the stochastic processing network.

12. A computer system for business process analysis and optimization, at run-time, comprising the steps of:

modeling a business process using different business process constructs;

formulating a stochastic processing network model for a business process;

solving the stochastic processing network, including calculating the key performance indicators for the network;

identifying parameters and structures of the business process that are essential to an implementation of the results obtained from the solution of the stochastic processing network; and

adjusting and tuning the parameters and structures of the business process according to the results obtained from the solution of the stochastic processing network.