Description

This curriculum spans the technical analysis phases of a multi-workshop business process redesign program, covering the same depth of systems integration, data analysis, and governance activities typically addressed in enterprise advisory engagements focused on automation and operational transformation.

Module 1: Assessing Process Maturity and Readiness for Redesign

Conducting process mining to extract actual workflow sequences from ERP or CRM system logs, identifying deviations from documented procedures.
Selecting between AS-IS process mapping techniques (e.g., BPMN vs. value stream mapping) based on stakeholder technical fluency and integration requirements.
Determining the scope of redesign by analyzing transaction volume, error rates, and compliance exposure across subprocesses.
Establishing data quality thresholds for log extraction, including handling of missing timestamps, inconsistent user IDs, and system-generated events.
Deciding whether to exclude shadow IT systems from initial analysis based on their integration risk and user dependency.
Setting criteria for process retirement versus redesign based on alignment with core business capabilities and automation feasibility.

Module 2: Data-Driven Identification of Process Bottlenecks

Configuring performance counters in workflow engines to capture cycle time, wait time, and rework loops at task level.
Applying statistical process control (SPC) to distinguish between common-cause and special-cause variation in process throughput.
Integrating queueing theory models to estimate resource contention in shared service pools (e.g., finance approvals).
Mapping handoff points between departments and quantifying latency due to communication mode (email vs. system alerts).
Using regression analysis to isolate the impact of specific variables (e.g., document completeness) on processing delays.
Validating bottleneck hypotheses through targeted time-motion studies on high-variance process segments.

Module 3: Technical Feasibility Analysis of Automation Opportunities

Evaluating API availability and stability across source systems to determine RPA versus embedded automation approaches.
Assessing UI volatility in legacy applications to estimate RPA script maintenance overhead and exception handling needs.
Calculating ROI thresholds for automation based on FTE reduction, error cost avoidance, and exception handling frequency.
Mapping data lineage from input capture to downstream systems to identify transformation points requiring human validation.
Defining exception escalation paths and fallback procedures for automated tasks that encounter unstructured inputs.
Coordinating with IAM teams to provision non-human identities with least-privilege access across target systems.

Module 4: Designing Process Logic with Decision Modeling

Translating business rules into DMN decision tables, ensuring traceability to regulatory or policy sources.
Resolving conflicts between departmental policies by establishing centralized rule ownership and version control.
Designing fallback mechanisms for decision services when external data sources (e.g., credit checks) are unavailable.
Integrating predictive scoring models into decision flows while maintaining auditability of logic paths.
Specifying rule testing protocols using boundary value analysis and edge case simulations.
Implementing rule performance monitoring to detect degradation due to data drift or policy changes.

Module 5: Integrating Process Redesign with System Architecture

Negotiating data ownership boundaries between process teams and application owners during integration design.
Selecting event-driven versus request-response patterns for cross-system process coordination based on latency requirements.
Designing compensating transactions for long-running processes that span systems with differing rollback capabilities.
Implementing idempotency in process steps to prevent duplication during retry scenarios.
Defining payload schemas for process events to balance flexibility and validation rigor.
Configuring retry policies and dead-letter queues for asynchronous process steps in distributed environments.

Module 6: Performance Measurement and Control Frameworks

Defining lead and lag indicators for redesigned processes, ensuring alignment with operational SLAs and strategic KPIs.
Implementing real-time dashboards with drill-down capabilities to isolate performance degradation to specific nodes.
Establishing baseline thresholds for process health metrics before go-live to enable meaningful variance detection.
Configuring alerting rules that minimize false positives by incorporating trend analysis and seasonal adjustments.
Mapping process metrics to organizational accountability structures to ensure ownership of performance outcomes.
Conducting root cause analysis on recurring process exceptions using structured techniques like 5 Whys or fishbone diagrams.

Module 7: Change Management and Operational Transition

Sequencing process changes to avoid overloading shared resources during parallel run periods.
Designing rollback procedures that preserve data consistency when reverting to legacy workflows.
Developing role-specific training materials based on task frequency and error-proneness in pilot runs.
Implementing phased cutover plans that align with business cycles (e.g., avoiding month-end close periods).
Establishing hypercare support protocols with defined escalation paths and resolution time targets.
Documenting tacit knowledge from super-users before decommissioning legacy process variants.

Module 8: Sustaining Improvements through Governance and Evolution

Forming cross-functional process governance boards with authority to approve changes to standardized workflows.
Implementing version control for process models and linking them to change management systems.
Conducting periodic process health audits to detect drift from designed logic and compliance requirements.
Establishing feedback loops from frontline staff to surface emerging bottlenecks or workarounds.
Integrating process performance data into continuous improvement backlogs prioritized by business impact.
Updating process documentation automatically through integration with workflow execution platforms.