1. Automated Process Discovery
Logic Extraction: We ingest raw event logs from your ERP, CRM, or Legacy Database to generate an objective process map. We do not rely on interviews; we rely on timestamps and transaction IDs.
Process Mining & Algorithmic Diagnostics
Metanow Process Mining Services bridge the gap between your intended strategy and your actual execution.
Process Mining & Algorithmic Diagnostics
Metanow Process Mining Services bridge the gap between your intended strategy and your actual execution.
Process Mining & Execution Forensics
Algorithmic reconstruction of operational reality using existing event log data. We validate your "As-Is" state before architecting the "To-Be" solution.
2. Conformance Checking
Governance Validation: We overlay your documented SOPs against actual data flow. This highlights every instance where execution deviates from the designed "Happy Path."
Compliance Audit
SOP Validation
3. Latency & Bottleneck Analysis
Cycle Time Measurement: We quantify the exact duration of every step. We isolate stages where cases stagnate, identifying resource constraints, system latency, or trapped capital.
O2C Optimization
Waste Detection
4. Pre-Automation Optimization
Logic Refinement: Consistent with ITIL, we eliminate waste before writing code. We strip redundant loops and non-value-added steps to ensure high-precision AI or RPA deployment.
ITIL Principles
AI-Ready Logic
1. Automated Process Discovery
Logic Extraction: We ingest raw event logs from your ERP, CRM, or Legacy Database to generate an objective process map. We do not rely on interviews; we rely on timestamps and transaction IDs.
Digital Twin
Shadow IT Detection
2. Conformance Checking
Governance Validation: We overlay your documented SOPs against actual data flow. This highlights every instance where execution deviates from the designed "Happy Path."
Compliance Audit
SOP Validation
3. Latency & Bottleneck Analysis
Cycle Time Measurement: We quantify the exact duration of every step. We isolate stages where cases stagnate, identifying resource constraints, system latency, or trapped capital.
O2C Optimization
Waste Detection
4. Pre-Automation Optimization
Logic Refinement: Consistent with ITIL, we eliminate waste before writing code. We strip redundant loops and non-value-added steps to ensure high-precision AI or RPA deployment.
ITIL Principles
AI-Ready Logic
End-to-End Diagnostic Methodology
Data Ingestion & Extraction
The Input: Secure, read-only connectivity to core systems (SQL, REST APIs, or CSVs).
The Logic: We map Case IDs and Timestamps across disparate systems, adhering to the ITIL principle "Start Where You Are."
The Output: A normalized Event Log ready for algorithmic processing.
Automated Process Discovery
The Input: The normalized Event Log.
The Logic: Algorithms reconstruct flow based on timestamps to generate a "Spaghetti Diagram," visualizing loops, rework, and deviations.
The Output: An objective "As-Is" process map revealing operational reality vs. the SOP.
Conformance Checking
The Input: "As-Is" process map vs. "Target Model" (SOP).
The Logic: We overlay intended design onto actual data to calculate percentage of cases violating governance rules.
The Output: A "Compliance Gap Report" quantifying risk and points of failure.
Performance Mining
The Input: Cycle time data between nodes.
The Logic: We pinpoint exact waiting times and resource saturation to identify "Technical Debt" or manual friction.
The Output: A heatmap of inefficiencies, prioritized by economic impact (e.g., cost of delay).
The Optimization Blueprint
The Input: Validated inefficiencies.
The Logic: We re-engineer workflow to eliminate bottlenecks first. Only then do we define specs for automation or AI Agents.
The Output: A technical specification document ensuring code is written only for a validated process.
Data Ingestion & Extraction
The Input: Secure, read-only connectivity to core systems (SQL, REST APIs, or CSVs).
The Logic: We map Case IDs and Timestamps across disparate systems, adhering to the ITIL principle "Start Where You Are."
The Output: A normalized Event Log ready for algorithmic processing.
Automated Process Discovery
The Input: The normalized Event Log.
The Logic: Algorithms reconstruct flow based on timestamps to generate a "Spaghetti Diagram," visualizing loops, rework, and deviations.
The Output: An objective "As-Is" process map revealing operational reality vs. the SOP.
Conformance Checking
The Input: "As-Is" process map vs. "Target Model" (SOP).
The Logic: We overlay intended design onto actual data to calculate percentage of cases violating governance rules.
The Output: A "Compliance Gap Report" quantifying risk and points of failure.
Performance Mining
The Input: Cycle time data between nodes.
The Logic: We pinpoint exact waiting times and resource saturation to identify "Technical Debt" or manual friction.
The Output: A heatmap of inefficiencies, prioritized by economic impact (e.g., cost of delay).
The Optimization Blueprint
The Input: Validated inefficiencies.
The Logic: We re-engineer workflow to eliminate bottlenecks first. Only then do we define specs for automation or AI Agents.
The Output: A technical specification document ensuring code is written only for a validated process.
Stop guessing. Validate your operational reality..
Contact MetanowStop guessing. Validate your operational reality..
Contact MetanowFrequently Asked Questions
How does Process Mining differ from traditional Business Intelligence (BI)?
BI is static; Process Mining is dynamic. BI dashboards report what happened (e.g., "Sales dropped 10%"). Process Mining explains why and how it happened by reconstructing the sequence of events (e.g., "Sales dropped because the approval step in the ERP takes 4 days longer than the SOP mandates"). We move from key performance indicators (KPIs) to root cause analysis.
Why should we perform Process Mining before automating or deploying AI Agents?
Automating a broken process simply creates broken results faster. Per the ITIL principle "Optimize and Automate," we must first use Process Mining to identify and remove inefficiencies (loops, rework, waste). We only apply automation to a logic flow that has been proven to be efficient.
Can Process Mining detect "Shadow IT" or off-system work?
Yes, by inference. If a transaction timestamp jumps from "Step A" to "Step C" without a record of "Step B," the mining algorithm flags a "Gap." This usually indicates work being done outside the system (e.g., via Excel or email). We map these gaps to identify where your system fails to support the user.
What is the expected ROI of a diagnostic phase?
The ROI comes from immediate waste reduction. We typically identify rework rates of 20-30% in unoptimized processes. By eliminating these redundant steps and ensuring compliance, the cost-per-transaction decreases significantly before any new software is purchased.
Does this require a "Rip and Replace" of our current systems?
No. We adhere to the ITIL principle "Start Where You Are." Process Mining is a non-invasive layer that sits on top of your existing infrastructure. We extract read-only logs from your current ERP or CRM. No migration or system replacement is required to perform the diagnosis.
What data requirements are necessary to start?
We require three specific data points from your event logs: a Case ID (e.g., Order Number), an Activity Name (e.g., "Order Approved"), and a Timestamp. Most SQL-based systems (Odoo, SAP, Salesforce, Legacy ERPs) generate these automatically.
How do you handle data privacy and GDPR during analysis?
We apply pseudonymization at the source. Sensitive fields (Customer Names, Employee IDs) are hashed or tokenized before ingestion. The algorithms analyze the process flow and timings, not the personal identity of the actors.
We have a custom Legacy ERP with no API. Can you still mine it?
Yes. If direct API connectivity is unavailable, we utilize flat-file extraction (CSV/XLS) or direct SQL database queries. As long as the system records a transaction history, we can reconstruct the process map.
How long does the "Discovery Phase" take?
The standard timeline is 2 to 3 weeks. Week 1 is dedicated to data extraction and validation (cleaning the logs). Week 2-3 involves the algorithmic analysis and the generation of the "As-Is" process model.
Is Process Mining a one-time project?
It begins as a diagnostic project but functions best as a continuous monitoring tool. Once the data pipeline is built, we can set up "Continuous Improvement" dashboards that alert you immediately when process execution deviates from the agreed-upon standard.
How does Process Mining differ from traditional Business Intelligence (BI)?
BI is static; Process Mining is dynamic. BI dashboards report what happened (e.g., "Sales dropped 10%"). Process Mining explains why and how it happened by reconstructing the sequence of events (e.g., "Sales dropped because the approval step in the ERP takes 4 days longer than the SOP mandates"). We move from key performance indicators (KPIs) to root cause analysis.
Why should we perform Process Mining before automating or deploying AI Agents?
Automating a broken process simply creates broken results faster. Per the ITIL principle "Optimize and Automate," we must first use Process Mining to identify and remove inefficiencies (loops, rework, waste). We only apply automation to a logic flow that has been proven to be efficient.
Can Process Mining detect "Shadow IT" or off-system work?
Yes, by inference. If a transaction timestamp jumps from "Step A" to "Step C" without a record of "Step B," the mining algorithm flags a "Gap." This usually indicates work being done outside the system (e.g., via Excel or email). We map these gaps to identify where your system fails to support the user.
What is the expected ROI of a diagnostic phase?
The ROI comes from immediate waste reduction. We typically identify rework rates of 20-30% in unoptimized processes. By eliminating these redundant steps and ensuring compliance, the cost-per-transaction decreases significantly before any new software is purchased.
Does this require a "Rip and Replace" of our current systems?
No. We adhere to the ITIL principle "Start Where You Are." Process Mining is a non-invasive layer that sits on top of your existing infrastructure. We extract read-only logs from your current ERP or CRM. No migration or system replacement is required to perform the diagnosis.
What data requirements are necessary to start?
We require three specific data points from your event logs: a Case ID (e.g., Order Number), an Activity Name (e.g., "Order Approved"), and a Timestamp. Most SQL-based systems (Odoo, SAP, Salesforce, Legacy ERPs) generate these automatically.
How do you handle data privacy and GDPR during analysis?
We apply pseudonymization at the source. Sensitive fields (Customer Names, Employee IDs) are hashed or tokenized before ingestion. The algorithms analyze the process flow and timings, not the personal identity of the actors.
We have a custom Legacy ERP with no API. Can you still mine it?
Yes. If direct API connectivity is unavailable, we utilize flat-file extraction (CSV/XLS) or direct SQL database queries. As long as the system records a transaction history, we can reconstruct the process map.
How long does the "Discovery Phase" take?
The standard timeline is 2 to 3 weeks. Week 1 is dedicated to data extraction and validation (cleaning the logs). Week 2-3 involves the algorithmic analysis and the generation of the "As-Is" process model.
Is Process Mining a one-time project?
It begins as a diagnostic project but functions best as a continuous monitoring tool. Once the data pipeline is built, we can set up "Continuous Improvement" dashboards that alert you immediately when process execution deviates from the agreed-upon standard.