Powering your SKU’s with Data
Automated Statistical Analysis
Drives Inventory Management
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Automated statistical analysis drives inventory management
By Thomas R. Willemain, Ph.D.
Inventory managers struggle with the conflicting priorities of customer satisfaction vs. cost control. Learn how to harness customer demand data to craft optimal inventory policies:
- Measure current inventory policy performance – including service levels, fill rates, inventory turns, and ordering costs.
- Identify improvement goals: Assess tradeoffs between inventory investment and the risk of running out. Where are you over- and under-stocked, and how can you do better?
- Find your optimal balance point, setting reorder points and order quantities that will achieve the results you require.
- Then make it so.
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Call an Audible to Proactively Counter Supply Chain Noise
You know the situation: You work out the best way to manage each inventory item by computing the proper reorder points and replenishment targets, then average demand increases or decreases, or demand volatility changes, or suppliers’ lead times change, or your own costs change.
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:
Correlation vs Causation: Is This Relevant to Your Job?
Outside of work, you may have heard the famous dictum “Correlation is not causation.” It may sound like a piece of theoretical fluff that, though involved in a recent Noble Prize in economics, isn’t relevant to your work as a demand planner. Is so, you may be only partially correct.