Operations Manager
Accountable for an operating unit such as fulfilment, logistics or a finance shared service and for the team that runs it.
6tasks in this job
67skills it uses
9tools named
The skills this job leans on most
- 1Testing competing explanations with discriminating evidenceNeeded in 5 of 6 tasks
- 2Diagnosing operational handoffs and flowNeeded in 3 of 6 tasks
- 3Investigating anomaliesNeeded in 2 of 6 tasks
ToolsMicrosoft ExcelMicrosoft Power BISAP S/4HANAServiceNowEcoOnlineWorkdayMicrosoft PowerPointSafetyCulture (iAuditor)IA-systemet
All 6 tasks, where people get stuck and all 67 skills
Tasks
- Investigate variance of actuals against budget or forecast
- Diagnose operational and fulfilment exceptions and agree corrective ownership
- Plan service capacity, schedules and routing against service levels
- Produce an accountable operating budget
- Prepare a periodic performance or risk report for a management or governance committee
- Analyse health and safety incidents and inspection data and track corrective actions to closure
Where people get stuck
- Converting demand into capacity requirements
- Evaluating service performance and quality trade-offs
- Analysing queue behaviour, capacity and service constraints
All skills
- Diagnosing operational handoffs and flow
- Investigating anomalies
- Testing competing explanations with discriminating evidence
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing pay equity with raw and adjusted pay gaps
- Analysing queue behaviour, capacity and service constraints
- Analysing working-capital drivers and cash conversion
- Assigning and operating data ownership and stewardship
- Balancing territories and quotas from account potential
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- Converting demand into capacity requirements
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing auditable decision models
- Designing efficient and reproducible investigations
- Diagnosing data quality exceptions
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Discovering process variants from event logs
- Eliciting needs through interviews, workshops and observation
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating service performance and quality trade-offs
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Facilitating evidence-led retrospectives and corrective actions
- Forecasting business trends and turning points for planning
- Forecasting operational demand at interval and site grain
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Judging the materiality of defects and deficiencies for the intended use
- 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
- Presenting and defending evidence-based recommendations to decision-makers
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising misleading numerical claims
- 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
- Testing investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing verifiable requirements and acceptance criteria
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
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
9tasks in this job
116skills it uses
10tools named
The skills this job leans on most
- 1Investigating anomaliesNeeded in 4 of 9 tasks
- 2Diagnosing operational handoffs and flowNeeded in 3 of 9 tasks
- 3Converting demand into capacity requirementsNeeded in 2 of 9 tasks
- 4Evaluating service performance and quality trade-offsNeeded in 2 of 9 tasks
- 5Analysing queue behaviour, capacity and service constraintsNeeded in 2 of 9 tasks
ToolsMicrosoft ExcelMicrosoft Power BISAP S/4HANAServiceNowEcoOnlineWorkday+ 4 more
All 9 tasks, where people get stuck and all 116 skills
Tasks
- Investigate variance of actuals against budget or forecast
- Diagnose operational and fulfilment exceptions and agree corrective ownership
- Answer a commissioned business question with reproducible query-based analysis
- Plan service capacity, schedules and routing against service levels
- Produce an accountable operating budget
- Prepare a periodic performance or risk report for a management or governance committee
- Automate a recurring reporting or data task with controls and a named owner
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Analyse health and safety incidents and inspection data and track corrective actions to closure
Where people get stuck
- Evaluating service performance and quality trade-offs
- Testing competing explanations with discriminating evidence
All skills
- Analysing queue behaviour, capacity and service constraints
- Converting demand into capacity requirements
- Diagnosing operational handoffs and flow
- Evaluating service performance and quality trade-offs
- Investigating anomalies
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- Balancing territories and quotas from account potential
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- 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 auditable decision models
- 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 forecast accuracy and bias and comparing methods fairly
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- 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 and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Forecasting business trends and turning points for planning
- Forecasting operational demand at interval and site grain
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- 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 budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether uncertainty changes the decision
- 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
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- 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
- Specifying AI transparency and explanation duties to affected people
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Excel
- Microsoft Power BI
- SAP S/4HANA
- ServiceNow
- EcoOnline
- Workday
- Blue Yonder
- Microsoft PowerPoint
- SafetyCulture (iAuditor)
- IA-systemet
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
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonService Operations Analytics and Capacity Planning
10tasks in this job
122skills it uses
10tools named
The skills this job leans on most
- 1Investigating anomaliesNeeded in 6 of 10 tasks
- 2Diagnosing operational handoffs and flowNeeded in 5 of 10 tasks
- 3Converting demand into capacity requirementsNeeded in 2 of 10 tasks
- 4Evaluating service performance and quality trade-offsNeeded in 2 of 10 tasks
- 5Analysing queue behaviour, capacity and service constraintsNeeded in 2 of 10 tasks
ToolsMicrosoft ExcelMicrosoft Power BISAP S/4HANAServiceNowEcoOnlineWorkday+ 4 more
All 10 tasks, where people get stuck and all 122 skills
Tasks
- Investigate variance of actuals against budget or forecast
- Diagnose operational and fulfilment exceptions and agree corrective ownership
- Define and govern a KPI metric contract
- Answer a commissioned business question with reproducible query-based analysis
- Plan service capacity, schedules and routing against service levels
- Prepare a periodic performance or risk report for a management or governance committee
- 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
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Analyse health and safety incidents and inspection data and track corrective actions to closure
Where people get stuck
- Evaluating service performance and quality trade-offs
- Testing competing explanations with discriminating evidence
All skills
- Analysing queue behaviour, capacity and service constraints
- Converting demand into capacity requirements
- Diagnosing operational handoffs and flow
- Evaluating service performance and quality trade-offs
- Investigating anomalies
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- Balancing territories and quotas from account potential
- 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
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- 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 auditable decision models
- 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 forecast accuracy and bias and comparing methods fairly
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- 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 and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Forecasting business trends and turning points for planning
- Forecasting operational demand at interval and site grain
- 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 budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether uncertainty changes the decision
- 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
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- 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 AI transparency and explanation duties to affected people
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- 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 verifiable requirements and acceptance criteria
All tools
- Microsoft Excel
- Microsoft Power BI
- SAP S/4HANA
- ServiceNow
- EcoOnline
- Workday
- Blue Yonder
- Microsoft PowerPoint
- SafetyCulture (iAuditor)
- IA-systemet
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
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonService Operations Analytics and Capacity Planning
About the role
Accountable for an operating unit such as fulfilment, logistics or a finance shared service and for the team that runs it. Decides staffing and capacity, approves process changes and automation, and reviews performance against service and cost targets. Data and technology judgement sits in reading operational measures correctly and deciding where automation is safe to use.
Also called Finance Operations Manager, Purchase-to-Pay Manager, Accounts Payable Manager, Logistics Manager