Extensive decision support capabilities at all levels – from individual gate decisions to a strategic review of the total portfolio. GenSight incorporates advanced business analytics functions that enable real-time multi-dimensional analysis – OLAP, data cubes and multi-criteria scorecards – to be presented in a user friendly way to decision makers.
Real Time Analytical Support to Make More Informed Decisions
The information captured in the online business cases is automatically fed into GenSight’s decision support models to enable consistent and objective assessment of investment proposals. GenSight’s decision support models enable you to combine both hard and soft data in order to:
- Prioritize projects and investment proposals according to the expected return to the business
- Support Go/Kill decisions
- Visualize and data mine through large quantities of data to identify key insights
- Quantify probability of success – eg. for risk factoring of expected returns
- Analyze risks according to fundamental factors driven by nature and goals of projects – eg. complexity, external uncertainties, marketing and competitive risks, technology risks etc.
- Use multi criteria scorecards to systematically analyze many dimensions of the business – eg. Scorecards can cover strategic alignment, market attractiveness, competitive advantage, financial potential, risk, innovation etc.
GenSight enables you to combine hard and soft data. The exact combination of parameters is flexible and configurable according to your business needs. The following give typical examples of hard and soft data that you may wish to utilize.
Examples of hard data in GenSight:
- Cost estimates, either as total cost or broken out into expense categories (eg. capex, operating, admin, program spend etc), and if required broken down over time.
- Sales forecast data, either point forecasts or projections over time
- Profitability and cash flow forecasts, can be calculated automatically from input data
- Financial data extracted from ERP accounting systems
- Financial ratios and metrics calculated by GenSight from your raw data – eg. NPV, ROI, economic profit etc.
- Resource estimates and associated costs
- Market and competitive data – eg. Market size, growth, market share
- Other metrics or key performance indicators, including basic numeric data or timing information
- Any calculated metrics derived using GenSight’s flexible calculation engine.
Examples of soft data in GenSight:
- Classification schemas configured to your organization – you can have any number of these to categorize projects/initiatives into areas such as business unit, market, technology, location, project type, stage etc.
- Linkage to strategic goals – what goal(s) does each project support, and to what degree
- Structured questions with multiple choice answers – to capture judgemental information in a repeatable way
- Utility scores associated with different situations/characteristics identified by structured questions (it’s a bit like psychological profiling for projects)
- Semi quantitative data – eg. Range estimates such as ‘Market size is in the range $10-20 million’.
Scorecards that combine multiple criteria with weights, and can be nested inside other scorecards to build composite models.
MULTI DIMENSIONAL PORTFOLIO ANALYSIS
The GenSight software supports mult-layered analysis of different and potentially inter-related business elements such as projects, markets, strategies etc. Each portfolio can span multiple business dimensions (such as region, category or division) with each dimension having many elements in the portfolio. Individual elements and whole portfolios can be analyzed using up to 1,000 configurable criteria.
The screenshot to the right shows one of the many types of user-defined tabular and graphical views that enable the user to view, enter and analyze data across mutliple dimensions.
|MULTI DIMENSIONAL PORTFOLIO ANALYSIS CAPABILITIES|
|Hierarchical||Maintains multiple levels of parent-child relationship between business elements.|
|Multi-Level Aggregation||GenSight uses the hierarchy to compute summarized portfolio metrics at each level. Aggregation algorithms support sum, average and weighted average methods to suit different criteria and configurations. For example, the system can calculate the overall risk of a portfolio from the average risk of each portfolio element weighted by investment level.|
|Multi-Dimensional Aggregation||GenSight can filter, aggregate and present multi-dimensional data. The system supports dynamic “slice and dice” aggregation with real time calculations that enable the user to interactively analyze the portfolio and explore the effects of “what-if” assumptions.|
|Interlinked Portfolios||Criteria can be connected between portfolios. Analytics from one portfolio can be cross-referenced and used in another. For example, a profitability measure in a product portfolio can be linked into a research project portfolio to update the priority of projects in the most profitable product areas.|
|Intelligent Data Management||A highly optimized memory-resident cache provides fast access to multi-dimensional data held in a relational database. This data management technology further optimizes efficiency by performing calculations in real time while data is being stored or retrieved.|