Process Analytics

Transform process data into actionable intelligence with advanced analytics. Quantify cycle times, identify root causes, measure automation potential, and build the business case for targeted optimization investments.

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Key Capabilities

Cycle Time Analytics

Decompose end-to-end cycle time into processing time, wait time, rework time, and queue time to pinpoint exactly where time is lost.

Root Cause Analysis

Statistical analysis that identifies the factors driving process delays, errors, and cost overruns with quantitative evidence.

Throughput Analytics

Measure and optimize process throughput: how many cases can be processed per unit of time under various conditions.

Cost Analytics

Calculate actual cost-per-case by summing labor time, system costs, error correction, and overhead for each process variant.

Automation Potential Scoring

Analyze each process step for automation potential based on volume, complexity, and rule-based decision patterns.

Implementation Roadmap

Data Preparation

Clean, enrich, and structure process data for analytical depth and accuracy.

Descriptive Analytics

Measure current performance: cycle times, costs, error rates, and throughput baseline.

Diagnostic Analytics

Identify root causes of poor performance using statistical and ML techniques.

Prescriptive Analytics

Generate specific optimization recommendations with projected impact and ROI.

Use Cases

Cycle Time Reduction

Analyze which process steps contribute most to cycle time and model scenarios for targeted reduction.

Error Rate Analysis

Identify the process steps, teams, and conditions most correlated with errors for targeted quality improvement.

Capacity Planning

Analyze throughput data to forecast capacity needs and optimize resource allocation.

Vendor Performance

Analyze vendor-related process steps to quantify supplier impact on overall process performance.

Tools & Technology

Celonis Python R Power BI Tableau Process Mining Tools Statistical Software

FAQ

Process analytics focuses specifically on how work flows through operations, using event-level data to measure process performance. Business analytics is broader, covering financial, marketing, and strategic metrics.

Process analytics works at the individual case and step level. You can analyze performance by team, time period, case type, geography, or any attribute captured in your event data.

Not necessarily. Modern process mining platforms provide built-in analytics with user-friendly interfaces. For advanced statistical analysis and custom ML models, data science expertise adds value.

We focus on decision-oriented analytics: every insight includes a specific recommendation, expected impact, and next steps. We avoid analysis paralysis by prioritizing findings by actionability and impact.

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Our process optimization experts will analyze your current workflows and deliver a detailed improvement roadmap.

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