Analytics-Based Enterprise Performance Management

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 presentation will describe how to complete the full vision of analytics-based enterprise performance management. Topics include:

How strategy maps and their companion balanced scorecards communicate strategic objectives with target-setting to help cross-functional employee teams align their behavior to the strategy and better collaborate.
Why measures of channel and customer profitability and customer value are now superseding profit and service-line measures – and shifting from product to customer-focused organizations including future potential value – customer lifetime value.
How activity-based cost management (ABC/M) provides not only accurately traced calculated costs (relative to arbitrary broad-averaged cost allocations), but more importantly provides cost transparency back to the work processes and consumed resources, and to what drivers cause work activities.
Reforming the broken annual budgeting process with performance-based budgeting that links strategy to operations and is process volume sensitive rather than simply incremental at each cost center.
Why business analytics, with emphasis on predictive analytics and pro-active decision making, is becoming a competitive advantage differentiator and an enabler for trade-off analysis.
How all levels of management can quickly see and assess how they are doing on what is important – typically with only a maximum of three key performance indicators (KPIs).
How to integrate performance measurement scorecards and ABC/M data with:

Strategy formulation.
Process-based thinking and operational productivity improvement.
Channel/customer profitability and value analysis and CRM.
Supply chain management.
Quality and lean management (Six Sigma, cost of quality).

Session Objectives:

How to view enterprise and corporate performance management (EPM/CPM) as the seamless integration of managerial methods rather than as a process.
Understand how business analytics is an advance over business intelligence and where Big Data fits in.
How to identify and differentiate strategic KPIs in a balanced scorecard and operational performance indicators (PIs) in dashboards.
How to properly calculate product, service-line, channel, and customer profitability for analysis, insights and actions.
How to perform “predictive accounting” for capacity-sensitive driver-based budgets / rolling financial forecasts, what-if analysis, and outsourcing decisions
How to overcome implementation barriers such as behavioral resistance to change and fear of being held accountable.

Primary Instructor - Gary Cokins, CPIM: Gary is an internationally recognized expert, speaker and author in advanced cost management and performance improvement systems. Gary began his management consulting career first with Deloitte and then with KPMG. Subsequently, he headed the National Cost Management Consulting Services for Electronic Data Systems (EDS) and worked for SAS, a leading provider of enterprise performance management and business analytics and intelligence software. Gary received a BS degree with honors in Industrial Engineering/Operations Research from Cornell University and his MBA from Northwestern University’s Kellogg School of Management.