COMMENTARY
AI tools against the complete logistics dataset and offer timely predictive analysis to commanders at echelon.
STEP 3: STANDARDIZED FORMAT Standardize the logistics common operating format at echelon and have it fed by cloud-based data.
COLLECTION IS CRUCIAL
Currently, data isn’t collected at every opportunity—that hinders AI integration and is a hurdle the Army needs to clear. (Image by Getty Images/Laurence Dutton)
Logistics commanders, from the tactical to the enterprise, track metrics on a logistics common operating picture to inform decisions and help see the enemy and themselves. Te formats for these logistics common operating pictures are not in fact, common, but are all unique to each unit despite displaying the same information. Te lack of standardized format forces staffs to spend hours formatting PowerPoint and Excel documents to track latent data. Te Army has a great tool in the Army Readiness – Common Operating Picture with potential to advance the integration of informing decisions utilizing AI. Army Readiness – Common Operating Picture displays information from GCSS- Army logically to allow leaders to “see themselves.” While Army Readiness – Common Operating Picture is a great visual tool, it only displays historical data for maintenance and supply without providing useful predictive analysis and it is not useful at the tactical level.
GCSS-Army that would include the ability to track ammunition, fuel and transportation. Providing the visibility offered for main- tenance and supply in GCSS-Army to other areas of logistics is essential. Tis demonstrates that the Army sustainment enterprise has a foundation on which to build AI platforms.
Te Army must integrate this capability and the data collection efforts now. While it is largely agreed upon that the implemen- tation of GCSS-Army was successful, the transformation took 10 years: six years of operational assessment at Fort Irwin from 2007 to 2013, and four years for phase I and phase II integra- tion from 2013 to 2017. A similar timeline is likely to integrate data collection for transportation, fuel and ammunition. Estab- lishing a consistent data set and the data collection process for these areas of logistics is crucial. RAND found that a hindrance to AI integration is that data is not collected and stored at every opportunity. Tis is currently true for transportation, ammuni- tion, fuel and water—this is the hurdle the Army must clear to integrate tracking of commodities in GCSS-Army and to apply AI to inform commanders across all facets of logistics. Once the Army is able to consistently collect data from all areas of logis- tics and integrate that data into GCSS-Army, the Army can apply
As described by Fogg in his 2019 article “Building the Army Readiness – Common Operating Picture,” Army Readiness – Common Operating Picture needs to provide commanders with analytics. We think those analytics must be fully informed by AI and useful at echelon. To do so, we need to incorporate the expansion data from additional logistics commodities in GCSS- Army and apply AI against the data for predictive analysis in Army Readiness – Common Operating Picture to inform deci- sions. Tis will enable commanders to have timely predictions and forecasts rooted in data, and staffs will have the ability to focus on the future state rather than working to confirm the current state.
STEP 4: EXPAND TRAINING Expand training for enterprise resource platforms and data analysis at all levels of professional military education.
In a recent meeting on the future application of data within United States Special Operations Command, Chief Data Officer Thomas Kenny briefed that the Defense Logistics Agency saved 130,000 man-hours last year by automat- ing all displays. This removed the requirement for briefing on PowerPoint and freed staff to conduct analysis.
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