Data Warehouse for Enterprise Process Benchmarking

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Solution Overview

Problem

Large enterprises face challenges in improving overall business process outcomes due to restricted end-to-end views, insufficient understanding of process metrics, and limited access to granular benchmarks, leading to suboptimal or illusory efficiency gains and value leakage at interfaces.

Innovation Solution

A system utilizing a data warehouse with multiple databases to collect and compare benchmark, metric, and result data, enabling users to determine strategies for improving process outcomes through the Define-Build-Analyze-Solve-Refine (DBASR) methodology, which establishes linkages between business outcomes, performance measures, and drivers using proprietary databases and cross-industry benchmarks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If enterprises focus on gaining efficiency in specific processes, then apparent efficiency gains are achieved, but overall process outcomes do not improve correspondingly

Engineering Contradiction:

Improveprocess efficiencyVSAvoidend-to-end process view

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent segments the overall business process into multiple operational subprocesses and stores data for each in separate databases within the data warehouse. This segmentation allows detailed analysis of individual subprocess efficiency while maintaining the ability to view and analyze the complete end-to-end process through integrated data access, thereby resolving the contradiction between focused efficiency improvement and holistic process understanding.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The data warehouse acts as an intermediary system that collects, stores, and integrates data from multiple operational subprocesses. It enables decision-makers to access granular benchmark data and compare subprocess performance against standards, providing both detailed efficiency metrics and comprehensive end-to-end process outcomes simultaneously, thus eliminating information loss.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Ease of manufacture

If organization and supply chain silos are maintained, then operational simplicity is preserved, but value leakage occurs at interfaces

Engineering Contradiction:

Improveoperational simplicityVSAvoidvalue leakage

Core Design Contradiction:

Ease of manufactureVSLoss of energy

Solution Approach 1:

The data warehouse serves multiple functions simultaneously: it stores data for individual operational subprocesses, provides benchmarking capabilities, enables end-to-end process analysis, and supports decision-making across different organizational units. This universal system allows siloed operations to maintain their simplicity while eliminating value leakage through integrated data access and comprehensive process visibility.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Device complexity

If granular benchmarks are not accessible, then decision-making complexity is reduced, but process performance measurement capability is disabled

Engineering Contradiction:

Improvedecision-making complexityVSAvoidprocess performance measurement

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The system performs preliminary action by pre-collecting and storing granular benchmark data for multiple operational subprocesses in the data warehouse before they are needed for decision-making. This advance preparation allows decision-makers to access ready-to-use, precise benchmark data without experiencing complexity, as the data collection, validation, and organization work has already been completed.

Inventive Principle:

Principle #10Preliminary action

4Extent of automation

If technology leverage is increased, then process automation is improved, but unrealized value from process rigor and analytics occurs

Engineering Contradiction:

Improveprocess automationVSAvoidprocess insights

Core Design Contradiction:

Extent of automationVSLoss of information

Solution Approach 1:

The data warehouse implements feedback mechanisms by collecting actual process performance data from automated operational subprocesses and comparing it against benchmark standards. This feedback loop provides decision-makers with actionable insights and analytics about process performance, ensuring that technology automation delivers realized value through rigorous measurement and continuous improvement opportunities.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS8660983B2System and method for improving outcomes in enterprise level processes

Publication Date: 2014.02.25 GENPACT USA INC

AI summary

A method and system for using a data warehouse to improve results of enterprise level processes are provided. The data warehouse typically includes industry-wide empirical data relating to corresponding operational practices, metrics, and outcomes. The method focuses on actual process results by taking a holistic, end-to-end view of the process in conjunction with using the data in the data warehouse to enable effective process improvements.