Inventory Excellence: Illuminating Opportunities for a DoD Logistics Organization Using Data-Driven Decision Making

Frustrated by over-reliance on anecdotal decision making, a DOD sustainment executive turned to Censeo to conduct a data-driven assessment of inventory operations. Censeo helped the client tap into their data and identify over $1B of inventory that could be liquidated, enough to clear out 30 warehouse bays.

Situation

Having worked with Censeo nearly ten years earlier, a DOD sustainment executive recalled the team’s successful data-driven approach and contacted them again to support an extensive emerging project.

The client organization’s primary mission is weapon system sustainment. In support of this mission, they control several billion dollars worth of spare parts used in weapon system repairs and modifications.

Some of the inventory-related challenges faced by this organization included:

• A large percentage of their inventory was dormant and could likely be divested
• Many planning processes used to identify future inventory needs were broken or underdeveloped
• Poor technology was in place to support the management of these inventories, and critical information was fragmented across multiple systems

With this in mind, Censeo launched a study with the objective of identifying opportunities to improve the organization’s effectiveness, efficiency, and cost of inventory operations.

Approach

1. Developed a baseline of on-hand inventory: As a starting point, the Censeo team collected data from several different inventory systems and integrated them to build the most complete and accurate picture possible of what existed in the organization’s warehouses. They spoke with stakeholders with subject matter expertise across the organization to understand gaps, overlaps, and other intricacies that would be important to consider in interpreting the data. Finally, they documented the “known unknowns,” i.e. important information that was not captured in any system but should be in the future.

2. Identified items with excess stock: After compiling a complete snapshot of inventory on hand, Censeo analyzed each item’s historical demand patterns and future outlook to determine which ones the organization might be carrying in excess. They collaborated with material management staff on the ground to validate the conclusions and ensure that the major items on the excess stock list were not needed for a maintenance job in the foreseeable future.

3. Identified items with deficient stock: In addition to quantifying areas of excess, the team also looked for items or categories where the organization had historically low material availability, meaning they could not fulfill customer orders on time. To complete this analysis, they reviewed backorder trends, interviewed stakeholders with first-hand knowledge, and identified root causes that could be addressed in the future through improved planning processes.

4. Built total cost picture: At the start of this study, the client organization had limited visibility into the total costs associated with moving, storing, and managing their inventory. Since costs were incurred across multiple organizations that operated with different budgets, they had yet to trace the costs of each inventory bucket through the various parties that touched it to build a total cost picture. Censeo collected bills from suppliers, internal resource breakdown structures, and historical transaction information to piece together this cost model and clearly identify the key drivers that were impacting the overall cost. In doing so, they found several opportunity areas to reduce cost in the future without decreasing service quality.

5. Benchmarked performance against peers: Finally, to put these numbers in context, Censeo reached out to peer organizations to collect benchmark data, insights, and best practices. Some of the key areas that were benchmarked included cost, resource efficiency, material availability, and forecast accuracy. To ensure that these data elements were interpreted properly, the team met with key stakeholders from the peer organizations to review the data and ensure a common understanding. See figure 1 for an example of the findings.