The Smart Forecaster
Pursuing best practices in demand planning,
forecasting and inventory optimization
We are often asked what the difference is between these two important performance metrics for inventory planning. While they are both important for measuring how successful a business is in meeting demand, their meaning is very different. If not understood and incorporated into the strategic inventory planning process, inventory will be inefficiently allocated resulting in lower customer service and higher carrying costs. We’ve illustrated the difference in this 4 minute recording using Microsoft Excel.
Smart Operational Analytics automatically calculates historical service levels & fill rates across any item. To see how you calculate these and other operational metrics including inventory turns, supplier performance, and more register below to watch a five minute demonstration. The demo will show how our cloud platform continuously calculates and reports these metrics across thousands of items helping you identify opportunities for service level improvement and inventory reduction.
Uncover data facts and improve inventory performance
The best inventory planning processes rely on statistical analysis to uncover relevant facts about the data. When you have the facts and add your business knowledge, you can make more informed stocking decisions that will generate significant returns. You’ll also set proper expectations with internal and external stakeholders, ensuring there are fewer unwelcome surprises.
What Silicon Valley Bank Can Learn from Supply Chain Planning
If you had your head up lately, you may have noticed some additional madness off the basketball court: The failure of Silicon Valley Bank. Those of us in the supply chain world may have dismissed the bank failure as somebody else’s problem, but that sorry episode holds a big lesson for us, too: The importance of stress testing done right.
Do your statistical forecasts suffer from the wiggle effect?
What is the wiggle effect? It’s when your statistical forecast incorrectly predicts the ups and downs observed in your demand history when there really isn’t a pattern. It’s important to make sure your forecasts don’t wiggle unless there is a real pattern. Here is a transcript from a recent customer where this issue was discussed: