Starting in August 2021, the Army Talent Management Task Force (ATMTF) partnered with the Combined Arms Support Command and Army Logistics University to develop a pilot build for the Career Mapping and Succession Planning Tool (CM/SP-T). The logistics branch is ideal for piloting this tool as it is both an operational and specialty branch with broad skill sets within three converging branches along with a high density of diverse backgrounds.
CM/SP-T Objectives
The CM/SP-T provides employees and leaders with a digital tool that puts the power of talent data in their hands. Through the tool, Soldiers will see how their talents align to positions, can visualize potential career paths, identify talent gaps, and pursue interventions to close them. Leaders also will be able to harness the power of aggregated talent data to make informed decisions about training, leader development, and succession planning.
Leveraging innovative assessments and utilizing artificial intelligence and machine learning, the CM/SP-T is designed to integrate with Integrated Personnel and Pay System-Army (IPPS-A) to inform and support data-rich talent management applications that span the entire human resource lifecycle.
The CM/SP-T applies the Army Talent Attribute Framework (ATAF) to identify talent demands at the position level, visualize best-fit talent alignment, and serve as a developmental tool to increase retention and readiness while defining new talent data requirements for IPPS-A. Our immediate objective for the logistics branch pilot build is to operationalize the ATAF as a proof of concept using an interactive career mapping capability based on a competency alignment algorithm that will leverage knowledge, skills, behaviors, and preferences (KSB-Ps) data.
CM/SP-T Building Blocks–KSB-Ps
KSB-Ps are unique to the individual Soldier and serve as inputs when aligning talent to positions across an Army career. KSB-P data will allow the CM/SP-T to illustrate potential matches or talent fit across positions, allow the individual to create one or more unique career paths (i.e., individual development plan), identify talent gaps and interventions, and enable developmental conversations with their leadership, mentors, or coaches. A comprehensive framework and robust talent data are required to document knowledge, skills, and behaviors (KSB) demands across positions. The ATAF is the common currency that will enable the development of algorithms for talent-based analytic tools.
The CM/SP-T competency alignment algorithm will show a dynamic comparison of the KSBs possessed by an individual at a point in time against the minimum KSB talent requirements needed to execute a potential job assignment(s) effectively. The CM/SP-T, at its foundation, is a mechanism to capture measurable and quantifiable KSB-P data that will:
- Allow Soldiers to visualize KSB fits and pathways to further career advancement.
- Allow branches, leaders, and career managers to see talent gaps in real-time and leverage talent data to develop officers, build teams, and support individual talent alignments.
- Enhance the algorithms’ ability to illustrate talent alignment over time for individuals and organizations.
CM/SP-T Status
The team closed out Phase 1 (July to December 2021), the project’s study phase, which included the deliverable skills matrix – underlining the preliminary KSB proficiency and criticality requirements for each of the 25 identified logistics branch positions across ranks of lieutenant through colonel. During Phase 2a (Jan. to June 2022), the team worked on the pilot development of a minimum viable product (MVP), which included the following workstreams and incorporated focus group efforts with Logistics Captains Career Course (LOG-C3) participants and subject matter expert (SME) inputs. It included:
- Competency Alignment Algorithm: work with Logistics Management Proponency Office SMEs on algorithm logic aligning KSB requirements to positions, requiring the KSB Self-Report Survey for LOG-C3 students and KSB Supervisor Survey for LOG-C3 senior leaders.
- Preferencing Identification: work with LOG-C3 students and SMEs to identify drivers that influence Soldier’s decisions at different career junctions during their tenure, resulting in a Preference Survey (LOG-C3 participants) to
- facilitate focus group discussions for refinement.
- Succession Planning Discovery: building out use case wireframes for SME inputs to develop a leadership dashboard to support succession planning.
- CM/SP-T MVP Release 1 (Jun 2022): test with a pilot group (LOG-C3 participants).
- Phase 2b (July to Dec. 2022): migrate the instance to set up in the ArmyGovCloud for further testing and refining for MVP releases 2 and 3 with a tentative target group in mind—Command and General Staff College participants to be determined.
- Phase 2c (Jan. to June 2023)data: integration with IPPS-A and scaling MVP to other branches and components.
For more information, visit https://talent.army.mil or contact Talent Management at usarmy.army-talent-management@army.mil.
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Maj. Jung (Jon) S. Lee currently serves as a data scientist assigned to the Army Talent Management Task Force and the project lead for the Career Mapping and Succession Planning Tool. He holds a Master of Science in Operations Research from the Florida Institute of Technology and a Bachelor of Science in Mathematics and Statistics from Rutgers University.
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This article was published in the Summer 2022 issue of Army Sustainment.
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