Digital Strategy for Operations Management Training Course

Participants can assess current digital operations maturity and identify capability gaps.

• Maturity dimensions assess process discipline, data, technology, people and governance
• Evidence collection combines interviews, observation, system records and performance data
• Capability heat maps expose gaps between current and target operating states
• Scoring calibration reduces inconsistent ratings across sites, functions and assessors
• Benchmark references used carefully without treating maturity scores as investment cases
• Gap statements connect missing capabilities to operational risks and missed value
• Assessment findings converted into priorities, dependencies and improvement hypotheses

Participants can map operational flows and locate digital intervention points.

• Value Stream Mapping traces material, information and decision flows end-to-end
• SIPOC defines suppliers, inputs, process boundaries, outputs and customers
• BPMN models handoffs, exceptions, approvals and system interactions precisely
• Customer demand patterns distinguish stable, seasonal, volatile and event-driven workloads
• Little's Law connects work-in-progress, throughput and cycle-time performance
• Waste analysis identifies waiting, rework, motion, overprocessing and unused capacity
• Digital intervention points prioritise recurring delays, errors and information gaps

Participants can define a target operating model for digitally enabled operations.

• Target operating model defines processes, roles, controls, technology and performance measures
• Service blueprints connect front-stage service actions with back-office operations
• Decision rights allocated through RACI for operational and technology responsibilities
• Standard work redesigned around alerts, exceptions and human-machine handoffs
• Control spans tested against automation levels, site complexity and service hours
• Shared-service options assessed for planning, analytics, maintenance and support activities
• Future-state process maps identify changed roles, controls and data requirements

Participants can specify data requirements and governance for operational decisions.

• Critical data elements defined for orders, assets, inventory, quality and labour
• Data lineage maps source systems, transformations, reports and decision uses
• Master data governance assigns ownership for products, locations, assets and suppliers
• Data-quality rules test completeness, validity, timeliness, consistency and uniqueness
• Common data models reduce conflicting definitions of operational performance measures
• Metadata catalogues document data meaning, ownership, sensitivity and refresh frequency
• Data stewardship routines resolve exceptions and monitor recurring quality failures

Participants can build a performance framework supporting digital operations decisions.

• KPI trees connect strategic outcomes with process drivers and daily measures
• Overall Equipment Effectiveness separates availability, performance and quality losses
• Balanced scorecards align financial, customer, process and capability measures
• Leading indicators identify conditions preceding service failures, defects and downtime
• Statistical process control distinguishes common-cause variation from special-cause variation
• Control charts establish escalation thresholds for operational intervention
• Dashboard design prioritises decisions, exceptions, ownership and action timing

Participants can outline an integration architecture for operational digital initiatives.

• IT and OT boundaries clarify ownership, reliability requirements and change controls
• ISA-95 layers connect enterprise planning with manufacturing operations and control
• Integration patterns compare APIs, event streaming, middleware and batch interfaces
• System landscapes map ERP, MES, WMS, CMMS, CRM and data platforms
• Interoperability requirements specify identifiers, protocols, latency and data ownership
• Vendor architecture claims tested against interface documentation and operating constraints
• Technical debt registers record unsupported systems, customisations and integration risks

Participants can evaluate automation and industrial IoT opportunities against operational needs.

• Automation candidates screened for task stability, volume, risk and exception rates
• Industrial IoT architectures connect sensors, gateways, edge devices and platforms
• Sensor selection considers accuracy, sampling rate, environment and maintenance burden
• Robotic process automation suits rules-based administrative operational transactions
• Human factors analysis identifies workload, usability, safety and override requirements
• Proof-of-value scope defines baseline, hypothesis, success measures and exit criteria
• Scaling criteria address reliability, support capacity, standards and site readiness

Participants can frame analytics and AI use cases with appropriate controls.

• Analytics use cases distinguish descriptive, diagnostic, predictive and prescriptive decisions
• Problem framing defines decision owner, action window, data and success metric
• Predictive maintenance models estimate failure probability from condition-monitoring data
• Demand forecasting compares baseline methods, error measures and forecast bias
• Machine-learning model governance addresses validation, drift, explainability and approval
• Human-in-the-loop controls specify review thresholds and override accountability
• AI use-case registers document risks, benefits, owners and monitoring requirements

Participants can determine when simulation or a digital twin supports operational decisions.

• Digital twin types distinguish asset, process, system and enterprise representations
• Discrete-event simulation models queues, resources, routing rules and variability
• Scenario design tests capacity, demand, downtime and policy changes
• Model assumptions documented with source data, owners and validation evidence
• Sensitivity analysis identifies inputs that materially alter investment decisions
• Simulation outputs translated into service, utilisation, inventory and cost implications
• Twin governance defines model updates, version control and decision authority

Participants can identify digital improvements for planning, inventory and fulfilment processes.

• Demand and supply planning workflows connect forecasts, constraints and replenishment decisions
• Sales and Operations Planning aligns commercial demand with operational capacity
• ABC-XYZ analysis segments inventory by value, variability and control approach
• Safety-stock calculations reflect demand variability, lead time and target service levels
• Control-tower designs define alerts, exceptions, escalation paths and decision rights
• Warehouse data captures location accuracy, pick performance and inventory discrepancies
• Transport visibility requirements specify milestones, event data and carrier accountability

Participants can integrate digital initiatives with quality and asset management systems.

• DMAIC structures data-led improvement for chronic quality and reliability problems
• Failure Mode and Effects Analysis prioritises failure risks before automation changes
• Condition-based maintenance triggers work from monitored asset health indicators
• ISO 55000 principles connect asset value, risk, lifecycle and decision-making
• Computerised maintenance management system workflows govern work orders and history
• Digital quality records strengthen traceability, deviation handling and corrective actions
• Root-cause analysis combines Pareto charts, five whys and fault-tree logic

Participants can incorporate cybersecurity and resilience controls into operations digital strategy.

• OT threat modelling identifies assets, attack paths, consequences and controls
• NIST Cybersecurity Framework categories structure identify, protect, detect, respond and recover activities
• Network segmentation separates critical control systems from enterprise environments
• Access management applies least privilege, multifactor authentication and privileged-account reviews
• Third-party risk assessments examine remote access, support obligations and incident reporting
• Business continuity plans address manual fallback, spare capacity and recovery priorities
• Risk registers assign treatment actions, residual risk ratings and accountable owners

Participants can plan adoption activities, capability development and workforce transition.

• Stakeholder mapping identifies influence, impact, concerns and engagement approaches
• ADKAR plans address awareness, desire, knowledge, ability and reinforcement
• Role-impact assessments identify changed tasks, controls, skills and decision rights
• Training needs analysis maps competency gaps to roles and implementation stages
• Frontline feedback loops capture usability problems, workarounds and safety concerns
• Change champion networks support local communication, coaching and issue escalation
• Adoption metrics track active use, process compliance, proficiency and benefits attainment

Participants can develop a governed investment portfolio and phased implementation roadmap.

• Benefits maps link initiatives to operational outcomes, financial measures and owners
• Total cost of ownership includes implementation, licences, support, integration and retirement
• Discounted cash-flow analysis tests net present value, payback and internal rate of return
• Sensitivity analysis examines uncertainty in benefits, costs, adoption and timing
• Prioritisation matrices compare strategic fit, value, feasibility, risk and dependencies
• Roadmap sequencing reflects foundations, pilots, scaling waves and platform decisions
• Portfolio governance sets stage gates, funding decisions, reporting and escalation routes

Participants can build, review and present a complete digital operations strategy pack for a defined operation.

• Operating challenge framed with baseline performance, customer needs and strategic objectives
• Maturity assessment and value-stream findings consolidated into priority capability gaps
• Target operating model specifies processes, roles, data, controls and technology
• Use-case portfolio scored using benefits, feasibility, risk and dependency criteria
• Architecture principles define integration, data governance, cybersecurity and vendor requirements
• Phased roadmap sequences quick wins, foundations, pilots, scaling and governance gates
• Investment case quantifies costs, benefits, assumptions, owners and sensitivity scenarios
• Executive presentation defends recommendations, trade-offs, risks and next-step decisions