Supply Chain Data Analyst
SOC 13-1081.00 · ESCO 2421 · OSCA 223434
Role snapshot
Overview
Analyses logistics and inventory data to identify bottlenecks, forecast demand, and recommend improvements that reduce costs and delivery times. Builds dashboards and models that give supply chain managers real-time visibility into operations. This role involves deep dives into data, utilizing statistical methods and data visualization tools to optimize complex supply chain processes, from procurement to delivery.
Optimizes operational efficiency, reduces waste, improves on-time delivery, and enhances decision-making across the supply chain, directly contributing to cost savings and customer satisfaction.
On the job
- Collect, clean, and analyze large datasets related to inventory, transportation, and warehousing.
- Develop and maintain dashboards and reports using business intelligence tools to monitor key performance indicators (KPIs).
- Identify trends, patterns, and anomalies in supply chain data to pinpoint areas for improvement.
- Build predictive models for demand forecasting, inventory optimization, and risk assessment.
- Present data-driven insights and recommendations to supply chain managers and stakeholders.
Tools & technology
Average salary
Job outlook
GrowingJob growth is expected to be above average over the next five years.
Education & training
Bachelor's degree in Supply Chain Management, Logistics, Business Analytics, Statistics, or a related quantitative field. Master's degree can be an advantage.
AI impact outlook
Note — this is our current view. AI is moving fast, so we revisit these ratings.
Show how this was assessed Hide the detail
Note — this is our current view. AI is moving fast, so we revisit these ratings.
Show how this was assessed Hide the detailWhy this role received this rating
Core task exposure
high
How much of the role’s important work could AI perform?
Tasks like data collection, cleaning, statistical analysis, and model building are highly amenable to AI and machine learning techniques.
End-to-end automation
moderate
Can AI complete the work without substantial human involvement?
AI can perform extensive data processing and insight generation, but human interpretation, strategic application, and validation remain crucial.
Adoption pressure
high
How likely are employers to introduce AI into this work?
There is very high employer pressure to adopt AI to extract deeper insights and optimize complex supply chain operations.
Human dependence
moderate
How much does success depend on human judgement, relationships and accountability?
Success requires human judgment for interpreting nuanced data, providing business context, and translating insights into actionable strategic recommendations.
Protective — a higher rating lowers the overall score.
Role adaptability
strong
How easily can the role evolve as AI takes on more tasks?
The role can readily adapt by focusing on complex problem framing, validating AI models, and translating insights into strategic narratives for stakeholders.
Shown for context — not part of the score.
What AI may take on
These are the parts of the role most likely to be automated or significantly accelerated.
- Automating the collection and cleaning of large datasets from various sources
- Developing predictive models for demand forecasting and inventory optimization
- Generating dashboards and reports with key performance indicators
- Identifying trends, patterns, and anomalies in supply chain data
- Performing statistical analysis to find correlations and causal relationships
Where people remain essential
These parts continue to depend heavily on human judgement, relationships and accountability.
- Framing complex business problems that AI models should address
- Interpreting the nuanced output of AI models in business context
- Communicating data-driven insights and recommendations to non-technical stakeholders
- Validating the accuracy and fairness of AI-generated models and predictions
- Designing and iterating on data visualization to tell a compelling story
- Assessing and managing risks identified through data analysis
- Developing new analytical methodologies beyond current AI capabilities
How the role may evolve
Less time on data preparation. More on interpreting insights and framing strategic questions.
The role will shift from manual data manipulation to leveraging AI for rapid analysis, allowing analysts to concentrate on higher-value activities like strategic problem-solving, model validation, and effective communication of insights.
Strengthen your future fit
- Advanced proficiency in AI/ML tools and platforms for data analysis
- Strong critical thinking and problem-framing skills
- Expertise in data storytelling and communicating complex findings
- Deep understanding of supply chain operations and business context
- Ethical considerations and bias detection in AI models
- Assessment horizon
- 3–7 years
- Confidence
- High
- Last reviewed
- August 2026
- Methodology
- v1.0
This assessment reflects current AI capabilities and expected adoption patterns. Actual impacts will vary by industry, employer and the way each role is performed.
Career pathways
WHERE YOU COULD GO
CURRENT ROLE
Supply Chain Data Analyst
Logistics & Supply Chain
ADJACENT MOVES
STARTING POINTS
Who thrives here
Interest profile
conventional · CEI
People who enjoy working with data, systems, and processes to solve practical business problems, often involving influencing decisions through insights, tend to thrive in this role.
Personality characteristics
Analytical
Enjoys dissecting complex problems and finding data-driven solutions.
Methodical
Prefers structured approaches to data handling and problem-solving, ensuring accuracy.
Curious
Driven to explore data, uncover hidden patterns and understand underlying causes.
Resilient
Can maintain focus and effectiveness when dealing with complex data challenges or tight deadlines.
Influential
Capable of communicating complex data insights clearly to persuade stakeholders and drive action.
Team-oriented
Works effectively with colleagues from different departments to achieve common goals.
Best for
- Individuals who enjoy working with large datasets to uncover actionable insights.
- Those who are driven to optimize processes and improve efficiency through quantitative methods.
- People who thrive on presenting data-driven recommendations and seeing their impact on business operations.
- Professionals seeking a blend of technical data analysis and business problem-solving.
Watch out for
- Requires continuous learning of new tools and analytical methods to stay current.
- Success depends on translating complex data into understandable insights for non-technical stakeholders.
- May involve dealing with imperfect or incomplete data, requiring patience and creative problem-solving.
A week in the life
A representative working week for a Supply Chain Data Analyst — where the deep work, meetings, and admin actually land.
Real people. Real results.
Thousands of people
can't be wrong.
Frequently asked questions about Supply Chain Data Analyst roles
What does a Supply Chain Data Analyst do?
A Supply Chain Data Analyst analyses logistics and inventory data to identify bottlenecks, forecast demand, and recommend improvements that reduce costs and delivery times. Builds dashboards and models that give supply chain managers real-time visibility into operations. This role involves deep dives into data, utilizing statistical methods and data visualization tools to optimize complex supply chain processes, from procurement to delivery. Optimizes operational efficiency, reduces waste, improves on-time delivery, and enhances decision-making across the supply chain, directly contributing to cost savings and customer satisfaction.
How much does a Supply Chain Data Analyst earn?
A Supply Chain Data Analyst earns a median of $95,000 per year in the US, typically ranging from $70,000 to $130,000.
What qualifications do you need to become a Supply Chain Data Analyst?
To become a Supply Chain Data Analyst, bachelor's degree in Supply Chain Management, Logistics, Business Analytics, Statistics, or a related quantitative field. Master's degree can be an advantage.
What personality suits a Supply Chain Data Analyst?
Supply Chain Data Analyst roles tend to suit people who are highly conscientious — precise, organised and strong on follow-through (Conscientiousness 85/100) and steady under pressure — deadlines and setbacks do not rattle them easily (Emotional Stability 72/100). The traits that matter most in the role are Analytical, Methodical, Curious and Resilient. Enjoys dissecting complex problems and finding data-driven solutions. On interests, Supply Chain Data Analyst maps to a CEI Holland Code profile — people who enjoy working with data, systems, and processes to solve practical business problems, often involving influencing decisions through insights, tend to thrive in this role.
Who does a Supply Chain Data Analyst role suit?
A Supply Chain Data Analyst role is usually a strong fit for these reasons. Strong Conventional affinity: the role demands precision, organization, and systematic data analysis. High Conscientiousness: success comes from meticulous attention to detail and structured problem-solving. Opportunity to influence strategic decisions through data-driven recommendations.
What are the downsides of being a Supply Chain Data Analyst?
Supply Chain Data Analyst roles come with trade-offs worth weighing up. Requires continuous learning of new tools and analytical methods to stay current. Success depends on translating complex data into understandable insights for non-technical stakeholders. May involve dealing with imperfect or incomplete data, requiring patience and creative problem-solving.
What is the work environment like for a Supply Chain Data Analyst?
Work as a Supply Chain Data Analyst is mostly office-based with hybrid arrangements common, semi-structured — a mix of set processes and self-directed work, a moderate pace and high exposure to clients or stakeholders. Around 61% of the week is focused deep work.
What skills do you need to be a Supply Chain Data Analyst?
Core skills for a Supply Chain Data Analyst include Data analysis, Statistical modeling, Demand forecasting, Supply chain optimization, Data visualization and Problem-solving.
How do you become a Supply Chain Data Analyst?
Common entry routes into Supply Chain Data Analyst roles include Junior Data Analyst, Logistics Coordinator and Business Analyst.
What career progression is there for a Supply Chain Data Analyst?
From a Supply Chain Data Analyst role, common next steps include Senior Supply Chain Data Analyst and Supply Chain Manager; lateral moves include Data Scientist and Operations Research Analyst.
What is the job outlook for Supply Chain Data Analyst roles?
The outlook for Supply Chain Data Analyst roles is currently rated growing. Job growth is expected to be above average over the next five years.
Will AI replace Supply Chain Data Analyst roles?
Traitstack rates automation risk for Supply Chain Data Analyst roles at 70 out of 100, which is strong. AI could automate much of the data collection, cleaning, and model building, allowing analysts to focus on interpreting complex insights and strategic storytelling. AI is most likely to take on automating the collection and cleaning of large datasets from various sources, developing predictive models for demand forecasting and inventory optimization and generating dashboards and reports with key performance indicators. Framing complex business problems that ai models should address, interpreting the nuanced output of ai models in business context and communicating data-driven insights and recommendations to non-technical stakeholders stay with people. Less time on data preparation. More on interpreting insights and framing strategic questions. That score measures how much of the work could change, not the likelihood the job disappears. It is Traitstack's current view, revisited as AI capability moves.