One of our core enterprise resource planning products is a logistics categorization model where we determine which items are critical to business success. We call this supply categorization model the Quadrant Model .
The Quadrant Model is a supply categorization model that aggregates inventory items of a supply system into quadrants. The quadrant an item is placed in determines the procurement and inventory practices that an organization will use for that item. The quadrants of the model are referred to as the Bottleneck, Critical, Leveraged, and Routine quadrants. The quadrant traits are:
Routine: Low mission value, and low risk/uniqueness items are found where the buyer has many options available from potential suppliers.
Bottleneck: These items have the same low mission value as the routines, but possess an element of risk in that there are few suppliers available, and/or the particular item has little or no potential for being substituted.
Leveraged: Low risk or uniqueness, and high mission value characterize leveraged items which some also refer to as commodities.
Critical: High mission value, and high risk or uniqueness by definition suggests a category of materials that buyers will need to manage carefully.
The focus of the Quadrant Model is on inventory, vendor relations, and prioritization:
- Manage critical products/services based on criticality
- Develop vendor relationships based on mission value and product uniqueness
- Reduce unnecessary stockage levels
- Reduce transportation costs
- Push forward critical items
- Leverage improved information & distribution systems to provide responsive support to the customer
Available logistics data sets are gathered and analyzed for quality. The data is then categorized as a risk/uniqueness data set, or a mission value data set. The data is then normalized and summed along each axis data set, to give each item a position on the X-Y coordinate plane. The analysis of data, and set aggregation varies according to the items you’re putting into the model. Quadrant models have been constructed for the Marine Corps that contrast the criticality of:
- Serial vehicle priorities for rebuild/depot maintenance
- Parts within a vehicle/end item
- End item comparison for prioritization of maintenance funding
Mission value, and risk/uniqueness characterization of a group of items, depends on what we are using the comparison for. The case where we are sending a vehicle to a depot for rebuilding dictates that I should prioritize the fleet of vehicles by sending the “Hanger Queens” first. Comparison of a fleet of vehicles is done by using data from a maintenance management database, and aggregating the data by serial number to find:
1) Large number of repairs for a serial number
2) Low Mean Time Between Failures (MTBF)
3) Lengthy repairs/High Mean Time To Repair (MTTR)
4) Expensive repairs
Data that describes the above characteristics is normalized, and then aggregated into the mission value (X) or risk/uniqueness axis (Y) axis. Failure frequency and MTBF are plotted on the X-axis on the Hanger Queen model, and affect the mission value of the item. High MTTR and more expensive repairs are plotted along the Y-axis and affect the risk of owning the item. The end result is the serial numbers that have frequent, and expensive failures that take the longest to repair are prioritized to go through the depot first. The serials that are have infrequent breakdowns that are relatively inexpensive to repair go through the depot last.
Another use of the Quadrant Model is a situation where we have a group of end items, and we wish to prioritze maintenance, and/or acquisition funding among that group. We can use similar metrics, along with some supply data, and usage data to determine which of our end items are more mission valuable or unique. In this manner we can compare a group of dissimilar items such as armored vehicles, trucks, and howitzers.