Analytics United Inventory Optimizer
Invenotry Optimization: Challenges and OpportunitiesWhile most companies can setup their safety stock using either desired customer service level or days of supply approach, they experience the common problems:
- Difficult to Predict Service Level It’s difficult to predict what service level we will get until a few months later. This is because inventory policy will not impact inventory till the purchasing lead time passes (that is, when the inventory is received)
- Unforeseeable Inventory Problem It’s hard to foresee how the demand and supply planning will fare until a few month later. By that time, any damage (over stock or out of stock) will be done. And user will need to re-adjust the inventory policy and then wait again to see the results
- Hard to Suit Demand Planning System Every demand planning tools are different. How can we adjust the safety stock policy so that it fits best for the demand planning tool in use
- Tough to Obtain Problematic and Opportunity Item List It’s tough to go over tens or hundreds of thousands of items to list the problematic items ( and opportunity items) based on current inventory policy
Solution
Analytics United developed a unique machine learning Inventory Optimizer and uses historical service level and inventory performance to optimize the safety stock settings. Specifically
1.Conducts auto-piloting using real life data. This is NOT theory based as all other softwares do2.Optimization is tailored to demand forecasting tool and supply planning tool3.Simulate what will happen using the current safety stock policy, identify issues and problems before they can happen4.Identify problematic items and opportunity items so that we can focus on them during demand and supply planningResults
- Customer one achieved 32% on inventory reduction while still meeting the current customer service level
- Customer two achieved 16% on inventory reduction while meeting high customer service level by focusing demand forecasting on the top 10% problematic items