Powering your SKU’s with Data
Automated Statistical Analysis
Drives Inventory Management
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.
Get the Article!
Pursuing best practices in demand planning, forecasting and inventory optimization
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.
In the supply chain planning world, the most fundamental decision is how to balance item availability against the cost of maintaining that availability (service levels and fill rates). At one extreme, you can grossly overstock and never run out until you go broke and have to close up shop from sinking all your cash into inventory that doesn’t sell.
Many of our customers that saw demand dry up during the pandemic are now seeing a significant demand surge. Other customers in critical industries like plastics, biotech, semiconductors and electronics saw demand surges starting as far back as last April. For suggestions about how to cope with these situations, please read on.