Integrated business planning

Integrated business planning (IBP) refers to the technologies, applications and processes of connecting the planning function across the enterprise to improve organizational alignment and financial performance. IBP accurately represents a holistic model of the company in order to link strategic planning and operational planning with financial planning.

By deploying a single model across the enterprise and leveraging the organization’s information assets, corporate executives, business unit heads and planning managers use IBP to evaluate plans and activities based on the true economic impact of each consideration.

History of IBP

The roots of IBP date back to 1996 where Dr. Robert Whitehair, working at the University of Massachusetts, developed technology for capturing and exploiting expert knowledge. Dr. Whitehair worked in close collaboration with several colleagues, including Professor Igor Budyachevsky of the Russian Academy of Science, to develop a technology now called COR (Constraint Oriented Reasoning). COR technology was used to capture expert knowledge; then embed it in applications that allow users to leverage it through a natural language interface.

Using grant funding from corporate giants such as Chase, DuPont, General Electric, PricewaterhouseCoopers, Shell and the Williams Company, Dr. Whitehair captured expert knowledge from numerous disciplines and introduced an application for business analysis that empowered decision makers.

Components of IBP

As illustrated right, planning is integrated across the enterprise, which enables decision makers to identify the activities that deliver the greatest financial impact across the company.

Recent developments and successes in the areas of business intelligence and performance management are accelerating the adoption of integrated business planning. While IBP has been a vision for many years; the technology required for modeling, optimization and scaling was non-existent. Dr. Robert C. Whitehair of River Logic, considered to be the father of IBP[citation needed], used constraint-oriented reasoning (COR) and knowledge-based rules engines to generate mathematical representations of planning constraints and variables; thereby making IBP a reality.

Dr. Whitehair, working in collaboration with scientists in the U.S. and the Russian Academy of Science, solved the problem of scaling real-life situations in mathematical equivalents. Today, IBP software easily runs thousands of analyses of a mathematical representation of ~1,000,000 equations, each in excess of 1,000,000 variables, in a typical solve.

In broad terms, the use of mathematical representations and extensive knowledge bases enable users to build the massive, multivariable models required for Integrated Business Planning.

Analyses

Companies use IBP to translate insight into financial impact by providing analyses such as: Identification of top financial (profit) drivers

Answers to “what-if” questions

Simulation

Optimization to any variable or ratio, including balance sheet, profitability, NPV, cash flow, etc

Intelligent sensitivity analysis

Modeling infeasibilities

Understanding of unique performance driver relationships

Opportunity costs and marginal economic value

Benefits

IBP transforms planning into a decisive competitive advantage by:

Providing an integrated planning platform across marketing, operations and finance

Generating a holistic understanding of performance drivers

Quantifying the financial impact and interdependencies across planning alternatives

Optimizing strategic planning and resource allocation

Balancing sales and operations planning for profitability

Quantifying financial risk

Increasing business flexibility

IBP Applications

IBP has been used to successfully model and integrate the planning efforts in a number of applications, including:

Product profitability

Customer profitability

Capital expenditures

Manufacturing operations

Supply chain

Business processes (human and information-based)

Business policy

Market demand curves

Competitive strategy

(source:wikipedia)