What is it about?

Many organizations are far from where they want and need to be with improving performance, and they apply intuition, rather than hard data, when making decisions. Enterprise performance management (EPM) is now viewed as the seamless integration of managerial methods such as strategy execution with a strategy map and its companion balanced scorecard (KPIs) and operational dashboards (PIs); enterprise risk management (ERM); capacity-sensitive driver-based budgets and rolling financial forecasts; product / service / channel / customer profitability analysis (using activity-based costing [ABC] principles); customer lifetime value (CLV); lean and Six Sigma quality management for operational improvement; and resource capacity spending planning. Each method should be embedded with business analytics of all flavors, such as correlation, segmentation and regression analysis, and especially predictive analytics as a bridge to prescriptive analytics to yield the best (ideally optimal) decisions. This text describes how to complete the full vision of analytics-based enterprise performance management.

Featured Image

Why is it important?

Most organizations implement enterprise and corporate performance management (EPM/CPM) methods, including the Balanced Scorecard, in isolation of each other. There is synergy when an organization seamlessly integrates them, and even more power when one imbeds business analytics of all flavors (e.g., regression, correlation analysis) into each method.

Perspectives

Many organizations use practices from the 1960s. They need to get into the 21st century using progressive EPM/CPM methods. These methods can be easily implemented in weeks, not months, using a rapid prototyping with iterative remodeling approach which is a consulting offering I provide. Crawl, walk, run, and fly. Start with the “crawl” to accelerate learning and buy-in.

Mr Gary Cokins
Analytics-Based Performance Management LLC

Read the Original

This page is a summary of: Performance Management, October 2015, Wiley,
DOI: 10.1002/9781119202875.ch1.
You can read the full text:

Read

Contributors

The following have contributed to this page