Operations Analyst
Accountable for forecasting demand, diagnosing process exceptions and planning capacity and queue routing from operational data.
4tasks in this job
42skills it uses
11tools named
The skills this job leans on most
- 1Converting demand into capacity requirementsNeeded in 3 of 4 tasks
- 2Evaluating service performance and quality trade-offsNeeded in 3 of 4 tasks
- 3Diagnosing operational handoffs and flowNeeded in 3 of 4 tasks
- 4Analysing queue behaviour, capacity and service constraintsNeeded in 3 of 4 tasks
- 5Investigating anomaliesNeeded in 2 of 4 tasks
ToolsMicrosoft ExcelSAP S/4HANASAP Integrated Business Planning (IBP) and APOMicrosoft Power BIMicrosoft Dynamics 365 Business CentralMicrosoft SQL Server (with SSMS)RELEXBlue YonderKinaxis RapidResponseNICE CXone and NICE IEX WFMCalabrio WFM (formerly Teleopti)
All 4 tasks, where people get stuck and all 42 skills
Tasks
- Diagnose operational and fulfilment exceptions and agree corrective ownership
- Answer a commissioned business question with reproducible query-based analysis
- Forecast demand statistically, test accuracy and agree the demand plan
- Plan service capacity, schedules and routing against service levels
Where people get stuck
- Forecasting operational demand at interval and site grain
- Forecasting business trends and turning points for planning
- Diagnosing forecast accuracy and bias and comparing methods fairly
All skills
- Analysing queue behaviour, capacity and service constraints
- Converting demand into capacity requirements
- Diagnosing operational handoffs and flow
- Investigating anomalies
- Analysing event sequences, states and durations
- Assessing data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Balancing territories and quotas from account potential
- Calculating and interpreting percentages, rates and ratios
- Choosing and justifying missing-data treatments
- Comparing groups
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing efficient and reproducible investigations
- Diagnosing data quality exceptions
- Diagnosing incorrect query and calculation results
- Eliciting needs through interviews, workshops and observation
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Exploring data to find patterns that survive validation
- Interpreting trends and seasonality
- Judging whether a change exceeds normal variation
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Profiling data
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Selecting charts for evidence and decisions
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
Paths to start
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Diagnostic Investigation and Root Cause Analysis
Resolve contradictory evidence and find, verify and prevent the causes of business and data anomalies.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonService Operations Analytics and Capacity Planning
6tasks in this job
79skills it uses
17tools named
The skills this job leans on most
- 1Forecasting operational demand at interval and site grainNeeded in 3 of 6 tasks
- 2Converting demand into capacity requirementsNeeded in 3 of 6 tasks
- 3Investigating anomaliesNeeded in 3 of 6 tasks
- 4Forecasting business trends and turning points for planningNeeded in 1 of 6 tasks
- 5Diagnosing forecast accuracy and bias and comparing methods fairlyNeeded in 1 of 6 tasks
ToolsMicrosoft ExcelSAP S/4HANASAP Integrated Business Planning (IBP) and APOCelonisMicrosoft Power BIMicrosoft Dynamics 365 Business Central+ 11 more
All 6 tasks, where people get stuck and all 79 skills
Tasks
- Diagnose operational and fulfilment exceptions and agree corrective ownership
- Answer a commissioned business question with reproducible query-based analysis
- Forecast demand statistically, test accuracy and agree the demand plan
- Plan service capacity, schedules and routing against service levels
- Deliver and sustain a trusted BI report or dashboard
- Analyse a current business process from event and operational data and define the future-state process
Where people get stuck
- Evaluating service performance and quality trade-offs
- Testing competing explanations with discriminating evidence
All skills
- Converting demand into capacity requirements
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Forecasting business trends and turning points for planning
- Forecasting operational demand at interval and site grain
- Investigating anomalies
- Analysing event sequences, states and durations
- Analysing queue behaviour, capacity and service constraints
- Assessing data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Balancing territories and quotas from account potential
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Calculating and verifying KPIs against their specification
- Challenging probability-weighted sales forecasts
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing groups
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing incorrect query and calculation results
- Diagnosing operational handoffs and flow
- Discovering process variants from event logs
- Eliciting needs through interviews, workshops and observation
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating service performance and quality trade-offs
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Governing shared data definitions
- Identifying keys and testing referential integrity
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting trends and seasonality
- Judging whether a change exceeds normal variation
- Maintaining requirements traceability and change control
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Profiling data
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing maintainable code for analytical and data work
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Excel
- SAP S/4HANA
- SAP Integrated Business Planning (IBP) and APO
- Celonis
- Microsoft Power BI
- Microsoft Dynamics 365 Business Central
- Microsoft SQL Server (with SSMS)
- RELEX
- Blue Yonder
- Kinaxis RapidResponse
- Python
- NICE CXone and NICE IEX WFM
- Calabrio WFM (formerly Teleopti)
- Esri ArcGIS (ArcGIS Pro, ArcGIS Online)
- QGIS
- Qlik Sense
- Tableau
Paths to start
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Diagnostic Investigation and Root Cause Analysis
Resolve contradictory evidence and find, verify and prevent the causes of business and data anomalies.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Forecasting and Scenario Planning
Build and challenge planning forecasts and scenarios.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonService Operations Analytics and Capacity Planning
7tasks in this job
89skills it uses
18tools named
The skills this job leans on most
- 1Forecasting operational demand at interval and site grainNeeded in 3 of 7 tasks
- 2Converting demand into capacity requirementsNeeded in 3 of 7 tasks
- 3Investigating anomaliesNeeded in 3 of 7 tasks
- 4Forecasting business trends and turning points for planningNeeded in 1 of 7 tasks
- 5Diagnosing forecast accuracy and bias and comparing methods fairlyNeeded in 1 of 7 tasks
ToolsMicrosoft ExcelSAP S/4HANASAP Integrated Business Planning (IBP) and APOCelonisMicrosoft Power BIMicrosoft Dynamics 365 Business Central+ 12 more
All 7 tasks, where people get stuck and all 89 skills
Tasks
- Diagnose operational and fulfilment exceptions and agree corrective ownership
- Answer a commissioned business question with reproducible query-based analysis
- Forecast demand statistically, test accuracy and agree the demand plan
- Plan service capacity, schedules and routing against service levels
- Deliver and sustain a trusted BI report or dashboard
- Automate a recurring reporting or data task with controls and a named owner
- Analyse a current business process from event and operational data and define the future-state process
Where people get stuck
- Evaluating service performance and quality trade-offs
- Testing competing explanations with discriminating evidence
All skills
- Converting demand into capacity requirements
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Forecasting business trends and turning points for planning
- Forecasting operational demand at interval and site grain
- Investigating anomalies
- Analysing event sequences, states and durations
- Analysing queue behaviour, capacity and service constraints
- Assessing data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Balancing territories and quotas from account potential
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Calculating and verifying KPIs against their specification
- Challenging probability-weighted sales forecasts
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing groups
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Discovering process variants from event logs
- Eliciting needs through interviews, workshops and observation
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating service performance and quality trade-offs
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting trends and seasonality
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Maintaining requirements traceability and change control
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Profiling data
- Reading and tracing unfamiliar code
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing maintainable code for analytical and data work
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Excel
- SAP S/4HANA
- SAP Integrated Business Planning (IBP) and APO
- Celonis
- Microsoft Power BI
- Microsoft Dynamics 365 Business Central
- Microsoft SQL Server (with SSMS)
- RELEX
- Blue Yonder
- Kinaxis RapidResponse
- Python
- NICE CXone and NICE IEX WFM
- Calabrio WFM (formerly Teleopti)
- Esri ArcGIS (ArcGIS Pro, ArcGIS Online)
- QGIS
- Qlik Sense
- Tableau
- Power Automate
Paths to start
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Diagnostic Investigation and Root Cause Analysis
Resolve contradictory evidence and find, verify and prevent the causes of business and data anomalies.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Forecasting and Scenario Planning
Build and challenge planning forecasts and scenarios.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonService Operations Analytics and Capacity Planning
About the role
Accountable for forecasting demand, diagnosing process exceptions and planning capacity and queue routing from operational data. Delivers demand forecasts, exception analyses and capacity plans that operations managers act on. Data judgement sits in choosing the right measures and testing forecasts against actuals.
Also called Demand Planner, Supply Chain Analyst, Supply Planner, Resource Planning Analyst, Workforce Planner