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 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
a)
Value Chain Balance - Understand the importance of a balanced Value
Chain
b) Demand Chain and Supply chain
c) Metrics and Goal Alignment
d) Unbiased Demand Plan – the Holy Grail
e) Value chain Measurement Process
2)
Review of Customer Service
Metrics
a)
Measuring Customer Service
b)
First time fill Rate
c)
Order Complete
d)
Decomposing Service Failure
3)
Inventory and Production Metrics
a)
Inventory Turns and Inventory Coverage
b)
Value of Inventory Coverage Metric
c)
Slow-moving and non-moving inventory
d)
Manufacturing Schedule Adherence
4)
Forecast Performance Metrics
a)
Measuring Forecast Error
b)
Mean Absolute Percent Error
c)
Effects of Demand Volatility
d)
What are the alternatives to MAPE?
e)
Forecast Bias and Attainment
5)
Introduction to Exception Management
a)
What do we learn from Forecast Error?
b)
Decompose forecast error to understand major drivers of error
c)
Define process and systems to understand exception management
d)
Illustrations of sample exception reports
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