Manufacturing companies must uncover opportunities to reduce inventory to stay competitive. One way to reduce excess inventory (without the risk of stocking-out of important parts) is to determine an optimal replenishment cycle. This can be done by identifying which parts would benefit from an eKanban loop replenishment system.
An effective way to determine optimal replenishment for inventory is by a computer simulation of historical inventory data. First, assessing inventory savings (via simulation or any other method) requires that the definitions of all measures are presented in a mutually understood Supply Chain Analyst Dashboard.
What to Measure
Accurate inventory analysis requires measurements including consumption volume, actual on-hand inventory for each day based on current replenishment methodology, and projected on-hand inventory for each day. This last element is a scenario process as if an eKanban replenishment methodology were already utilized. Using historical data, the simulation identifies realized and foregone inventory savings, while simultaneously predicting future part shortages based on anticipated manufacturing activity. The simulation highlights the potential weakness in current replenishment methodologies. A good simulation tool should also measure the effect of changes in supplier lead time, lot size, and safety stock.
The simulation should include an easy to read dashboard, preferably with clear visuals. The visuals should quantify the variability of actual consumption, provide visual clues on the amount of inventory being carried on-hand, and the amount of safety stock held. Only through this approach can manufacturers quickly determine which parts are good candidates for eKanban replenishment based on potential savings and (S/X, Std Deviation/Mean) variability of consumption.
A last measure impacted from simulation includes impacts on supplier performance. Changes in supplier lead-time, lot size, and safety stock and the corresponding warehouse capacity constraints can be correlated to each supplier and performance metrics compared and contrasted. These simulated reports can then be shared with other managers as well as categorized for planning and archival purposes.
Simulating changes in supplier lead time, lot size, and safety stock are extraordinarily valuable data. The ability to simulate consumption and compute on-hand inventory for each day addresses consumption volume for each day of the interval as well as actual on-hand inventory for each day based on current replenishment methodology. Such simulation allows manufacturers across a wide variety of industries to project on-hand inventory for each day based on if used Kanban replenishment methodology.
The simulation highlights the potential weakness in current replenishment methodologies; the Min-Max methodologies typically used by ERP systems with reorder points are the primary cause of excess inventory and material shortages. Since reorder points are not maintained, they go out of sync resulting in greater on-hand inventory.
The use of simulation drives provides a high-level summary of the health of the supply chain plan. It displays primary measures for supply and demand, resources, and exceptions. It also enables the supply chain analyst to compare an archived version of a plan against a current version, or compare two or more plans.
The simulation quickly and clearly provides a logical path to supply-chain effectiveness. Determining which parts are good candidates for eKanban replenishment, thus reducing inventory, can have a major impact to a company’s bottom-line. The simulation estimates these potential inventory savings at an aggregate level and then drills down to savings by part or by supplier for real analytics that can be shared with suppliers.
Ultriva’s Inventory Optimization Simulation Tool integrates with many existing ERPs such as Oracle’s E-Business Suite and NetSuite Cloud ERP. Historical data from the ERP can be easily transmitted to the Ultriva Inventory Optimization Tool, and easy to read reports with actionable business intelligence can be generated to include recommendations that will improve inventory management and supply chain performance, while freeing-up operating capital to redeploy into the manufacturing business.