Performance Measurement

Accurate and timely demand plans are a vital component of an effective supply chain. Inaccurate demand forecasts typically would result in supply imbalances when it comes to meeting customer demand and hence Demand Forecast accuracy at the SKU level is critical for proper allocation of resources. In designing a holistic measurement process, it is important to understand that the demand metrics are the key driver and hence ownership of these metrics require careful deliberation.

Web Presentation of Forecast Accuracy and Safety Stock Planning Presentation of Forecast Accuracy and Safety Stock Planning is a useful resource to define, structure and use demand forecast metrics with in a supply chain process.

Supply chain metrics include customer service metrics (fill rates), inventory metrics (inventory turns, dead and near-dead inventory, inventory coverage), manufacturing planning (production volatility, manufacturing schedule adherence), and demand metrics (forecast accuracy and forecast bias. Once defined, the question is who will own these measures. All the measures are inter-related. By observing the metrics, we can understand the effect of organizational bias.

Assessing the metrics process and creating a new value-enhancing process generally involves the following steps:

1) Value Chain Balance - Overview

• Value Chain Balance - Understand the importance of a balanced Value Chain
• Demand Chain and Supply chain
• Metrics and Goal Alignment
• Unbiased Demand Plan – the Holy Grail
• Value chain Measurement Process

2) Review of Customer Service Metrics

• Measuring Customer Service
• First time fill Rate
• Order Complete
• Decomposing Service Failure

3) Inventory and Production Metrics

• Inventory Turns and Inventory Coverage
• Value of Inventory Coverage Metric
• Slow-moving and non-moving inventory
• Manufacturing Schedule Adherence

4) Forecast Performance Metrics

• Measuring Forecast Error
• Mean Absolute Percent Error
• Effects of Demand Volatility
• What are the alternatives to MAPE?
• Forecast Bias and Attainment

5) Introduction to Exception Management

• What do we learn from Forecast Error?
• Decompose forecast error to understand major drivers of error
• Define process and systems to understand exception management
• Illustrations of sample exception reports