Forecasting Benchmark 2pg

Kwaring

Description

Energy and RevenueForecast BenchmarkObjectives, Findings, and Conclusions• Governance & Integration –The overall goal of In 2003 Capgemini, in collaboration with The control ofExelon, conducted a benchmark of the policies, organization, and processes ofthe Energy andintermediate (month-ahead & annual) energy the forecasting function and its integrationRevenue Forecastingand revenue forecasting in the US. into the businessBenchmark was to Environment – The external conditionsdevelop an under- To accomplish the benchmark, ten utility impacting the forecast assumptions, data,standing of how various companies were surveyed to understand how and methodsfactors affect the ability four specific areas affect their ability to Uncertainty – Understanding the effect ofto accurately forecast accurately forecast intermediate energy and uncertainty on forecast precision and accuracyrevenue in the current business environment. in the current DDaattaa aanndd MMooddeellss –– The current state of databusiness environment. and models used by the industry forThe survey components included in the intermediate-term forecasting and gapsbenchmark were:versus best practice.Small sustainedFORECAST AREA IMPROVED FORECAST VALUE DRIVERimprovements to theintermediate Minimizing the need for additional supply or(month-ahead & Demand Forecasts generation capacityannual) forecast can Delaying the timing of capital projectscreate millions of dollars of value for ...

Sujets

• management
• risk management

Informations

• Publié par: Fevil
• Nombre de lectures: 12
• Langue: English

Extrait

Energy and Revenue Forecast Benchmark

Objectives, Findings, andConclusions

The overall goal of the Energy and Revenue Forecasting Benchmark was to develop an under standing of how various factors affect the ability to accurately forecast in the current business environment.

Small sustained improvements to the intermediate (monthahead & annual) forecast can create millions of dollars of value for medium to largesize utilities.

In 2003 Capgemini, in collaboration with Exelon, conducted a benchmark of intermediate (monthahead & annual) energy and revenue forecasting in the US.

To accomplish the benchmark, ten utility companies were surveyed to understand how four specific areas affect their ability to accurately forecast intermediate energy and revenue in the current business environment.

The survey components included in the benchmark were:

FORECAST AREA

Demand Forecasts

Energy Forecasts

Revenue Forecasts

•Governance & Integration – The control of the policies, organization, and processes of the forecasting function and its integration into the business • Environment – The external conditions impacting the forecast assumptions, data, and methods • Uncertainty – effect ofUnderstanding the uncertainty on forecast precision and accuracy • Data and Models – The current state of data and models used by the industry for intermediateterm forecasting and gaps versus best practice.

IMPROVED FORECAST VALUE DRIVER

• Minimizing the need for additional supply or generation capacity • Delaying the timing of capital projects

• Optimizing energy procurement and risk management • Identifying additional energy for longterm market commitments

• Improving accuracy in quarterly financial reporting & reducing variance in revenue forecast • Minimizing organization time spent on rebudgeting process and governance

Components Governance & Integration Only one of ten utilities reported forecast governance and performance monitoring initiatives which met our recommended standard for the utility industry. Noother surveyed utility reported any type of performance monitoring system that included policy, process, and reporting components. No surveyed utility reported using an integrated set of Key Performance Indices (KPIs) to measure overall energy and revenue forecast performance.

Of the utilities surveyed, half are faced with significant to severe forecast complexity in developing their intermediate energy and revenue forecasts (i.e. regulatory considerations, geographic diversity, customer choice).

Environment Utilities with customer switching face the most complex forecasting environment (30% of the utilities surveyed fall in this category). To compare forecast practices and performance, utilities should select peers based on multiple

2.

1.5%

forecast characteristics (similar scope, scale, regulatory structures, weather, economy, etc.) not just financial performance.

Uncertainty Especially for the larger utilities, achieving BestinClass forecast performance is critical in minimizing risk exposure (through reducing error and variance in forecast requirements).

Data and Models All surveyed utilities are confronted by poor quality of incoming data. This is primarily due to the lack of a forecast framework integrating data and information across company (and external) sources.

Conclusions Two fundamental conclusions were reached through the intermediate energy and revenue forecast benchmark:

1. SarbanesOxley necessitates immediate improvements in governance and accuracy in forecasting 2. Utilities can save millions to tens of millions of dollars annually through: a. Minimizing or delaying capital projects, and b. Optimizing procurement, trading and risk management activities

1 1.5% WCMAPE12 (0.5% Improvement from “Common” Utility Performance)

Forecast Complexity 1.0 Least Intermediate Highest 0.5% 0% Total Investment ($) WCMAPE12: Weather Corrected Maximum Absolute Percent Error (12 Month Average) 1

OUR CREDENTIALS Capgemini is one of the world's largest providers of Consulting, Technology and Outsourcing services. The company helps businesses implement growth strategies and leverage technology. The organization employs approximately 50,000 people worldwide and reported 2003 global revenues of 5 billion euros.

For further information, > please contact: Chris Grier Capgemini 8472261056 [e-mail]