Process Mining and Workflow Intelligence for Project Organizations Intensive Course

Course Introduction

Process mining and workflow intelligence are transforming how project organizations understand, monitor, and improve the way work is performed across complex delivery environments. The Process Mining and Workflow Intelligence for Project Organizations Intensive Course provides participants with practical methods for discovering actual workflows from event logs, analyzing process performance, identifying bottlenecks, and translating operational data into measurable project delivery improvements. The course bridges process management, project controls, data analytics, and continuous improvement to help organizations move beyond assumptions about how work should operate toward evidence-based understanding of how work actually flows.

Project organizations frequently manage interconnected workflows involving project approvals, procurement, engineering reviews, design changes, document control, contract administration, financial approvals, resource allocation, risk escalation, and progress reporting. These processes often cross departmental boundaries and rely on multiple digital systems, creating delays, duplicated effort, inconsistent handovers, and limited visibility into end-to-end performance. Participants learn how process mining can reconstruct workflow paths from operational records, reveal deviations from established procedures, quantify waiting times, and identify the underlying causes of inefficiency across project lifecycle activities.

The course examines the relationship between event data, process models, workflow performance indicators, and project management objectives. Participants explore event-log structures, case identifiers, activity timestamps, resource attributes, process variants, conformance checking, bottleneck analysis, cycle-time measurement, and rework detection. They learn how to prepare and interpret process data from enterprise resource planning systems, project management platforms, procurement applications, document management systems, enterprise service platforms, and other operational databases while accounting for incomplete records, inconsistent identifiers, timestamp errors, and fragmented information.

Effective workflow intelligence requires more than producing process maps or performance dashboards. Project organizations must understand why workflows deviate, which constraints are controllable, how process variations affect delivery outcomes, and what interventions are likely to generate sustainable improvements. The course develops practical capabilities in root-cause analysis, process conformance assessment, performance benchmarking, workflow segmentation, queue analysis, exception management, and evidence-based redesign. Participants also examine how process mining complements established approaches such as Lean, Six Sigma, business process management, project controls, value-stream mapping, and continuous improvement.

Emerging technologies are expanding the possibilities for workflow intelligence through artificial intelligence, machine learning, automated process discovery, predictive analytics, task mining, process simulation, and intelligent workflow orchestration. The course explores how these capabilities can support early identification of approval delays, forecast procurement cycle times, detect emerging rework patterns, identify recurring control failures, and prioritize process interventions. It also addresses the limitations of automated analysis, including incomplete event data, misleading correlations, process variability, privacy concerns, access controls, model explainability, and the need for human validation before operational or management decisions are made.

By completing the Process Mining and Workflow Intelligence for Project Organizations Intensive Course, participants will be equipped to assess process maturity, develop reliable event logs, discover actual workflow behavior, evaluate process performance, and design practical improvement initiatives grounded in operational evidence. They will learn to connect workflow findings with project cost, schedule, quality, risk, compliance, and stakeholder objectives while establishing governance for continuous process monitoring. The course supports project leaders, process analysts, transformation professionals, PMO teams, and digital operations specialists seeking to improve project execution through transparent, data-driven, and increasingly intelligent workflows.

Duration

10 days

Who Should Attend

• Project managers responsible for improving project execution, coordinating cross-functional workflows, reducing delays, and strengthening operational performance.

• PMO directors and project controls professionals seeking greater visibility into approval cycles, reporting processes, schedule dependencies, and delivery bottlenecks.

• Business process analysts responsible for discovering, documenting, measuring, redesigning, and optimizing operational processes within project organizations.

• Process mining specialists and data analysts seeking to apply event-log analysis, process discovery, conformance checking, and performance diagnostics to project environments.

• Digital transformation leaders overseeing workflow automation, enterprise process modernization, operational analytics, and data-driven improvement initiatives.

• Project controls engineers and planning professionals interested in analyzing workflow delays, decision latency, rework, handover performance, and schedule impacts.

• Procurement and supply chain professionals seeking to understand requisition-to-purchase-order workflows, tendering delays, contract approvals, and supplier onboarding performance.

• Contract administrators and commercial managers responsible for correspondence, claims, variations, approvals, payment certification, and contract administration workflows.

• Finance professionals involved in project budgeting, expenditure approvals, invoice processing, payment certification, financial controls, and cost reporting.

• Quality assurance and continuous improvement specialists applying Lean, Six Sigma, root-cause analysis, and performance measurement to project delivery processes.

• Enterprise architects and IT professionals integrating workflow data from ERP, project management, document management, procurement, and business process platforms.

• Data engineers and business intelligence developers responsible for extracting, transforming, validating, and preparing event data for process analysis.

• Risk, compliance, and internal audit professionals interested in workflow conformance, control execution, segregation of duties, exception detection, and auditability.

• Artificial intelligence and automation professionals developing predictive workflow analytics, intelligent routing, process recommendations, and automated exception handling.

• Senior executives and operational leaders seeking evidence-based insights into organizational productivity, workflow reliability, process governance, and project performance.

Course Objectives

• Develop a comprehensive understanding of process mining, workflow intelligence, process discovery, and their applications in improving project organization performance across complex operational environments.

• Identify suitable project workflows for process mining by assessing business value, data availability, process stability, operational significance, and opportunities for measurable improvement.

• Design and validate event logs containing appropriate case identifiers, activities, timestamps, resource attributes, and supporting business information for reliable process analysis.

• Apply process discovery techniques to reconstruct actual workflows from enterprise system records and compare observed execution patterns with documented procedures and expected process models.

• Measure workflow performance using cycle time, waiting time, throughput, rework frequency, bottleneck concentration, process variants, and other indicators relevant to project delivery.

• Conduct conformance checking to identify deviations from approved workflows, missed control steps, unauthorized transitions, inconsistent approvals, and recurring operational exceptions.

• Analyze relationships between workflow behavior and project outcomes, including schedule delays, procurement performance, cost impacts, quality problems, resource utilization, and stakeholder response times.

• Apply process improvement techniques to identify root causes, redesign inefficient workflows, eliminate unnecessary handovers, clarify decision rights, and improve process consistency.

• Integrate process mining with project controls, Lean management, Six Sigma, business process management, workflow automation, and continuous improvement frameworks.

• Evaluate process mining and workflow intelligence technologies, data integration methods, visualization approaches, analytical capabilities, and implementation considerations for project organizations.

• Apply predictive and prescriptive analytics to anticipate workflow delays, estimate completion times, prioritize interventions, and support more proactive operational decisions.

• Establish a sustainable workflow intelligence framework incorporating data governance, performance ownership, privacy safeguards, change management, continuous monitoring, and measurable benefits realization.

Comprehensive Course Outline

Module 1: Foundations of Process Mining and Workflow Intelligence

• Examine the principles of process mining, workflow intelligence, business process management, operational analytics, and their relevance to project delivery organizations.

• Differentiate process discovery, conformance checking, process enhancement, task mining, workflow analytics, and conventional business intelligence.

• Explore project organization workflows across initiation, planning, procurement, design, execution, change control, financial management, reporting, and project closure.

• Identify high-value use cases by evaluating process complexity, operational pain points, stakeholder impact, data readiness, and potential benefits.

Module 2: Project Workflow Architecture and Process Identification

• Map the relationships between project lifecycle stages, business processes, operational systems, organizational responsibilities, decision points, and workflow handovers.

• Develop process inventories and prioritize workflows according to cycle time, transaction volume, failure rates, business criticality, and improvement opportunities.

• Define process boundaries, case concepts, start and end events, activity definitions, subprocesses, exception paths, and relevant performance measures.

• Establish baseline process documentation through stakeholder interviews, system analysis, existing procedure reviews, process workshops, and operational observation.

Module 3: Event Logs, Data Sources, and Process Data Architecture

• Understand the structure of event logs, including case identifiers, activity names, event timestamps, resource identifiers, lifecycle transitions, and business attributes.

• Identify relevant data sources such as ERP platforms, procurement systems, document management tools, project scheduling applications, ticketing systems, and workflow engines.

• Design event-log schemas that support reliable process reconstruction, cross-system case correlation, activity sequencing, and consistent analytical interpretation.

• Examine data quality challenges involving duplicate events, missing timestamps, inconsistent identifiers, incorrect activity labels, timezone differences, and incomplete process histories.

Module 4: Data Extraction, Transformation, and Event-Log Preparation

• Develop practical approaches for extracting workflow records from databases, application programming interfaces, data warehouses, spreadsheets, and enterprise reporting systems.

• Apply data transformation techniques to standardize event names, normalize timestamps, resolve identifiers, remove invalid records, and preserve meaningful process attributes.

• Establish data validation procedures covering case completeness, event ordering, duplicate detection, timestamp consistency, process coverage, and source-to-target reconciliation.

• Prepare analytical datasets using appropriate SQL, Python, spreadsheet, or data preparation tools while maintaining traceability, security, and reproducibility.

Module 5: Process Discovery and Workflow Reconstruction

• Apply process discovery concepts to reconstruct actual workflow models from event logs and understand how observed execution differs from documented process designs.

• Examine process maps, activity sequences, transition frequencies, parallel paths, decision branches, loops, and recurring variants across project operations.

• Compare discovery algorithms and model-selection approaches by considering interpretability, model complexity, noise tolerance, and process behavior coverage.

• Interpret discovered process models to identify unexpected routing, unnecessary approvals, repeated handovers, fragmented responsibilities, and inefficient execution paths.

Module 6: Workflow Performance Measurement and Bottleneck Analysis

• Establish workflow performance indicators covering throughput, average and median cycle time, waiting time, processing time, queue duration, rework, and exception frequency.

• Analyze bottlenecks by examining activity-level delays, resource constraints, approval queues, handover latency, workload distribution, and process variation.

• Segment workflow performance by project, department, supplier, geography, transaction type, risk category, or organizational unit to reveal meaningful differences.

• Develop operational dashboards and analytical summaries that translate process behavior into actionable information for project managers and process owners.

Module 7: Conformance Checking, Controls, and Process Compliance

• Compare observed event sequences against reference process models, approved procedures, business rules, and defined workflow requirements.

• Identify deviations involving skipped approvals, unauthorized transitions, missing documentation, incorrect sequencing, duplicate processing, and control exceptions.

• Examine techniques for measuring conformance, prioritizing deviations, distinguishing legitimate exceptions from control weaknesses, and investigating recurring patterns.

• Establish evidence-based workflows for exception review, control remediation, audit support, and ongoing monitoring while accounting for justified process variations.

Module 8: Root-Cause Analysis and Workflow Improvement

• Apply root-cause analysis to determine why specific workflow variants, handover delays, approval queues, and recurring rework patterns occur.

• Combine process mining evidence with interviews, process observation, value-stream mapping, cause-and-effect analysis, and operational performance data.

• Design workflow improvements that simplify decision paths, remove unnecessary activities, improve responsibility assignments, reduce duplication, and strengthen information quality.

• Develop prioritized improvement backlogs with defined owners, implementation milestones, measurable targets, expected benefits, and verification arrangements.

Module 9: Process Mining for Project Controls and Schedule Performance

• Analyze workflows associated with design reviews, technical approvals, document submissions, change requests, issue resolution, and project reporting.

• Examine how workflow waiting times, rework loops, late decisions, and unresolved dependencies contribute to schedule slippage and delivery uncertainty.

• Connect process performance indicators with project schedules, milestone data, risk registers, and progress reports to improve schedule diagnostics.

• Develop interventions for reducing decision latency, strengthening handover discipline, improving constraint resolution, and increasing the predictability of project execution.

Module 10: Procurement, Contract Administration, and Financial Workflow Intelligence

• Analyze procurement workflows from requisition through sourcing, tendering, evaluation, approvals, purchase orders, delivery, and contract closure.

• Investigate contract administration processes involving correspondence, technical submissions, variations, claims, payment certificates, invoice verification, and approvals.

• Examine financial workflows covering budget authorization, commitment recording, expenditure approvals, invoice processing, payment scheduling, and reconciliation.

• Identify opportunities to reduce procurement cycle times, improve contract response performance, strengthen financial controls, and improve visibility into outstanding transactions.

Module 11: Workflow Intelligence for Quality, Risk, and Operational Resilience

• Analyze quality management workflows involving nonconformities, inspection requests, corrective actions, defect resolution, and acceptance approvals.

• Identify process patterns associated with recurring operational risks, repeated control failures, delayed incident resolution, and ineffective corrective actions.

• Assess workflow resilience by examining exception recovery, alternative process paths, workload surges, dependency failures, and continuity arrangements.

• Integrate workflow findings with risk registers, quality indicators, audit evidence, and operational resilience assessments to support targeted risk reduction.

Module 12: Process Mining Tools, Platforms, and Technical Implementation

• Evaluate process mining platforms and supporting technologies according to event-log compatibility, discovery capabilities, analytical depth, visualization, integration, and governance features.

• Explore technical implementation architectures that connect source systems, data pipelines, transformation layers, process analysis engines, and reporting interfaces.

• Examine the roles of SQL, Python, data warehouses, APIs, process modeling standards, and business intelligence tools in a process mining solution.

• Develop platform evaluation criteria covering scalability, security, interoperability, usability, deployment models, licensing considerations, and long-term maintainability.

Module 13: Predictive Analytics and AI-Enabled Workflow Intelligence

• Examine how machine learning and predictive analytics can estimate workflow completion times, identify likely delays, and forecast process performance.

• Develop analytical approaches for detecting anomalous event sequences, predicting bottlenecks, classifying exceptions, and prioritizing cases requiring management attention.

• Explore AI-assisted process interpretation, automated process documentation, natural language querying, and intelligent recommendations for workflow improvement.

• Evaluate model quality, explainability, data drift, false positives, human oversight, and the limitations of using predictive signals for operational decisions.

Module 14: Workflow Automation, Orchestration, and Process Simulation

• Identify suitable opportunities for workflow automation by assessing process repeatability, decision complexity, exception rates, data quality, and operational risk.

• Explore integration between process mining, robotic process automation, business process management suites, workflow engines, and intelligent task-routing solutions.

• Apply process simulation concepts to compare proposed workflow designs, estimate capacity requirements, test alternative routing, and evaluate potential performance changes.

• Establish controlled automation practices involving human approval, exception handling, rollback procedures, audit trails, and post-deployment performance monitoring.

Module 15: Governance, Privacy, Change Management, and Benefits Realization

• Establish governance arrangements defining process ownership, data stewardship, analytical responsibilities, decision rights, access controls, and improvement accountability.

• Address privacy, confidentiality, employee monitoring concerns, data minimization, retention, security, and applicable organizational or legal requirements.

• Develop stakeholder engagement and change management approaches that build trust, explain analytical findings, and support adoption of redesigned workflows.

• Create benefits realization frameworks that measure baseline performance, implementation costs, realized savings, cycle-time improvements, quality gains, and sustained operational outcomes.

Module 16: Capstone Project and Enterprise Workflow Intelligence Roadmap

• Select a real or simulated project workflow and define its business problem, analytical objectives, process boundaries, stakeholders, and expected performance improvements.

• Prepare an event-log dataset, conduct data quality checks, reconstruct the workflow, and identify major process variants, bottlenecks, and conformance issues.

• Develop a prioritized improvement proposal supported by process evidence, root-cause analysis, practical interventions, performance targets, and implementation responsibilities.

• Present an enterprise workflow intelligence roadmap covering technology, data readiness, governance, capability development, pilot selection, scaling strategy, and continuous improvement.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.