Modernizing Army SSA Metrics

Industry Lessons and Data-Driven Solutions

By CPT Danielle M. Turner and CPT Timothy R. Maginn

Article published on: January 29, 2026 in the Army Sustainment Winter 2026 Issue

Read Time: < 7 mins

Supply support activities (SSAs) are critical components of Army sustainment and the supply chain. However, outdated metrics can hinder their ability to optimize operations, identify bottlenecks, and forecast resource allocation. What if Army warehouses could operate with the same efficiency as Amazon Logistics?

This research explores how lessons from industry and data-driven methodologies can modernize SSA performance metrics. By introducing tailored key performance indicators (KPIs) like transfer order performance (TOP) and leveraging real-time dashboards, this research demonstrates how SSAs can achieve greater transparency, effectiveness, and responsiveness. A proof of principle using one year’s worth of data from an Army SSA highlights the practical applications of these recommendations, offering a roadmap for improving sustainment operations across the Army.

Why Current Metrics Fall Short

The Army currently uses customer wait time (CWT) as a primary metric for evaluating SSA performance. CWT measures the time it takes for a requisition to be fulfilled, from order submission to receipt of material. While this provides an adequate overall view of supply chain health, it often fails to capture the nuances of warehouse operations.

For example, the outbound delivery (OBD) segment of CWT involves 18 factors, only eight of which are directly managed by the SSA. This means that delays in OBD may be caused by external factors, making it difficult for SSA managers to pinpoint and address issues.

The lack of granularity in CWT creates challenges for decision making. SSA managers require metrics that focus on processes they can control, such as picking, cross-docking, and issuing. Without these insights, it becomes difficult to identify bottlenecks, allocate resources effectively, or improve workflows.

Lessons from Industry

Private-sector warehouses, such as those operated by Amazon Logistics and Virginia Commonwealth University (VCU) Health, offer valuable lessons for improving SSA performance. These organizations’ practices were observed, and key leaders were interviewed, to identify tailored KPIs to track specific processes and outcomes.

For example, Amazon monitors on-time dispatch and first-day delivery success using real-time dashboards that allow managers to make immediate on-the-floor adjustments. Similarly, VCU Health tracks dead stock to ensure critical supplies are available when needed. These custom-built KPIs are tailored to the needs of the organization, allowing for constant surveillance of critical sub-metrics and materials. In the case of VCU Health, monitoring deadstock is of the upmost importance. Deadstock is defined as stock in a supply chain that has high unsold, unusable, or obsolete inventory that sits in storage. Because of the mission-critical nature of the healthcare industry, it is vitally important for the supply chain to identify stock that is not being used or is expired. These characteristics are very similar to the needs of the Army’s supply chain needs.

The key takeaway from industry research is the importance of actionable metrics. High-performing warehouses measure what they can directly influence, using these insights to drive operational improvements. This approach inspired the development of TOP and other actionable KPIs as proposed metrics for Army SSAs.

A Data-Driven Approach to SSA Metrics

TOP is a time-based metric that tracks the execution speed of warehouse actions, such as picking, cross-docking, and issuing. This metric was born from the research conducted and developed by the authors of this article. Unlike CWT, which is influenced by several external factors, TOP focuses solely on processes within the SSA’s scope of physical responsibility. This makes it a more reliable indicator of warehouse performance.

To validate the effectiveness of TOP, a proof of principle was conducted using data from an active-duty Army installation SSA from March 2022 to March 2023. The analysis revealed that 76.47% of out-of-tolerance (over one day from initiation to execution) transfer orders were attributed to pick rates, highlighting a bottleneck in warehouse operations. It is important to note that the data does not account for non-duty days. The data was further analyzed using visualization tools, which allowed for drill-down capabilities to isolate specific issues.

The data can be shown over time to exhibit correlations between a unit’s major movements (deployment, redeployment, major training events, etc.) and SSA processing performance (with zero open transfer orders during a duty day being the standard). While some KPIs might reveal that an SSA is not meeting a certain standard, tailored KPIs like TOP show leaders the precise location, process, or personnel that is contributing to a specific weakness. High put-away times are also correlated with high inventory loss, which is another way TOP can be used by SSA leaders.

This granular analysis enables SSA managers to implement targeted corrective actions, such as reallocating personnel to areas experiencing slower-than-expected pick rates. When paired with CWT, TOP provides a more complete picture of warehouse performance. While CWT offers a macro-level view of supply chain health, TOP delivers actionable insights at the micro (SSA/warehouse) level, empowering managers to address inefficiencies in real time.

Other KPIs were also organized into a tiered hierarchy aligned with leadership roles and levels of supply chain responsibility. The tiers include Up and Outs, Down and Ins, and Both. The category identifies metrics that are intended to be monitored by SSA accountable officers (AOs) and briefed at the brigade level. This structure creates a clear micro-to-macro system of accountability and enables leaders across the sustainment enterprise to quickly drill down to the source of performance issues.

Recommendations for Improvement

To modernize SSA performance metrics, this research proposes three key recommendations:

1. Adopt a Tiered KPI Hierarchy: A tiered approach links micro-level metrics, such as TOP, to macro-level outcomes like readiness. This structure allows SSA managers to monitor granular details while providing senior leaders with a high-level view of performance. For example, pairing TOP with CWT offers both detailed and broad insights, enabling more effective decision making. It also allows leaders to hold parties accountable for shortfalls.
2. Prioritize Actionable Metrics: SSA evaluations must focus on metrics that fall within the direct control of the AO and their staff. Metrics like TOP and picking productivity present immediate means for operational improvement, unlike broader measures such as authorized stockage list composition, which are generally managed at higher organizational levels. AOs can also use tailored KPIs to monitor the effectiveness of their workforce to target area weak points within their SSAs and Soldiers who might need additional assistance, retraining, or digital resources.
3. Standardize KPI Dashboards: The Army must continue to develop standardized dashboards that integrate directly with systems like Global Combat Support System–Army. These dashboards simplify data visualization and enable real-time decision making, reducing the burden on SSA managers and promoting consistency across units. Whether it is the Sustainment Enterprise Analytics tool or a dashboard built in Army Vantage (where the research for this paper was conducted) it is recommended that these tools are consolidated and standardized to provide solidarity and a shared understanding across the force.

Areas for Future Research

While this study demonstrates the value of metrics like TOP and visualization tools, it also highlights a critical gap in the Army’s ability to generate, analyze, and standardize data at the unit level. SSA managers often lack the bandwidth, resources, or technical expertise to manipulate raw data and create actionable insights on demand. This limitation underscores the need for trained data analysts within units to bridge the gap between raw data and decision-making tools.

For example, VCU Health successfully paired supply chain managers with data analysts to create dashboards that track critical metrics like deadstock. This collaboration enabled managers to focus on operational improvements without the burden of mastering complex visualization platforms.

Additionally, further studies could examine the integration of predictive analytics into SSA metrics. By linking KPIs to demand forecasting models, SSAs could provide early warning signals for inventory shortages or overages, allowing for proactive adjustments that sustain readiness.

Investing in data-centric capabilities at the unit level will enhance SSA performance and ensure the Army remains adaptive and prepared to meet the challenges of modern warfare.

Conclusion

Modernizing SSA performance metrics is not just about improving efficiency; it is about transforming the Army’s approach to sustainment in the face of today’s data-driven battlefield. The ability to make rapid, informed decisions based on actionable insights is critical to maintaining readiness and operational agility. Metrics like TOP, when paired with real-time dashboards, provide the transparency and granularity needed to optimize warehouse processes and empower leaders at all levels.

As the Army continues to adapt to the demands of modern warfare, embracing data-centric methodologies will be imperative. By investing in tools, training, and personnel to support data-driven decision making, the Army can ensure that its sustainment practices remain agile, efficient, and capable of meeting the challenges of large-scale operations. These efforts will enhance SSA performance and strengthen the Army’s ability to sustain combat readiness in an increasingly complex operational environment.