Process discovery plays a pivotal role in enabling data-driven process excellence programs. This comprehensive guide provides technology decision-makers an insightful overview of the landscape of automated process discovery solutions.
We will cover everything from working principles and benefit case studies through AI risks and mitigations to help you make the right platform choice aligned with long-term optimization needs.
An Introduction to Automated Process Discovery
Before exploring solutions, let‘s level set – what is automated process discovery?
Process discovery refers to automatically mapping flows based on system events versus traditional workshop-based process documentation.
Specialized tools observe real-life processes via access logs, database extracts, screenshots and other sources to create detailed process models identifying variants, bottlenecks etc. without any manual analysis.
Benefits include:
- 80% lower effort than flowcharting via interviews
- Broader visibility spanning systems vs siloed understanding
- Fact-based analysis circumventing biases or blind spots
- Near real-time insights enabling agile optimization
- Foundations for AI via standardized event log formats
Forrester projects solutions maturity to create a robust process discovery software market exceeding $1.46 billion globally by 2025.
Classification of Discovery Solutions
| Category | Description | Examples |
|---|---|---|
| RPA | Robotic process automation tools that replicate user actions to develop process maps | UiPath Process Mining, Automation Anywhere Process Discovery |
| BPM Suites | BPM/workflow platforms providing analytics on current step flows | IBM Automation Process Insights, Appian Process Mining |
| Analytics | Data analytics software leveraging logs to recommend process enhancements | Qlik Process Mining, SAS Process Mining |
| Pure-Play Mining | Specialized process discovery and mining tools with advanced analytics | Celonis Execution Management System, myInvenio |
Comparative Evaluation Framework
| Criteria | Description |
|---|---|
| Data Connectors | Prebuilt APIs and adapters to ingest logs from different systems |
| Visualization | Interactive process flow diagramming with filtering, search, localization |
| Simulation | Ability to playback as-is process executions |
| AI Powered | Embedded predictive/prescriptive algorithms |
| Conformance Checking | Measure alignment between actual flows v/s designed models |
| Developer Community | Ability to customize algorithms, visualizations via open interfaces |
| Commercial Model | License structure (named user, consumption based etc.) and TCO |
Gartner estimates close to 70% of organizations now have formal process analytics initiatives – validating how discovery sits at the core of process transformation.
Techniques Enabling Automated Process Mining
Process discovery solutions rely on varied approaches – let‘s analyze the common techniques empowering automated flows mapping:
Robotic Process Automation
Tools like UiPath robots replicate employee actions via screen scraping and machine vision while tracking system events to develop detailed process logs for mining. Forrester notes close to 60% of RPA programs also leverage process discovery for enhancement opportunities.
System and Application Logging
Platforms provide adapters and APIs to pull activity logs from multiple backends like ERP, CRM, email servers into a consolidated data lake for sequencing and analytics.
AI and ML Algorithms
Supervised and unsupervised learning algorithms identify complex patterns across lengthy noisy event logs to classify process steps, variants – especially effective for unstructured data.
Process Simulation
Recorded user sessions are replayed visually in a sandbox environment providing an interactive, immersive understanding of current flows while protecting data privacy.
Advanced Analytics
Statistical techniques help drive use cases like – correlation of deviations with risk metrics, predictive modeling of outcomes given process variants, employee productivity measurement normalized by case complexity.
Text Mining and NLP
Semi-structured data in databases, forms and documents gets mined via OCR, entity recognition and topic modelling to capture process context and insights.
Computer Vision
As employees interact with applications, on-screen actions are tracked and scripted automatically via vision algorithms into software-driven task flows.
No single approach can address all discovery use cases – leading vendors allow configuring hybrid methods. Let‘s explore the relative pros and cons.
Comparative Evaluation of Discovery Techniques
| Approach | Benefits | Limitations |
|---|---|---|
| RPA | Handles unstructured UIs, insights on exceptions | High implementation effort, prone to script breaks |
| Log Analysis | Broad system coverage, standards-based | Complex integrations, post-facto vs real-time |
| AI / ML | Adaptable, predictive insights | Data prep constraints, interpretability concerns |
| Simulation | Secure, easily reproducible | Partial coverage risks |
| Analytics | Quantifiable rankings, drill-downs | Model degradation over time |
| Text Mining | Uncovers hidden steps from documents | Brittle for highly dynamic content |
| Computer Vision | Precise tracking at user level | Costly data storage needs |
The right combination depends on your ecosystem constraints – pilots help make optimal capability trade-offs.
Vendor solutions fit broadly into five categories based on dominant techniques as discussed next.
Analysis of Leading Process Discovery Vendors
RPA-centric Mining Suites
Legacy RPA tools like Blue Prism and Automation Anywhere now offer integrated process discovery capabilities to transition clients from pure automation to intelligence.
- Blue Prism Process Intelligence – uses captured event logs from Robot actions for visualized insights into process metrics, variants and recommendations.
- Automation Anywhere Discovery Bot – auto-generates detailed process maps through robotic walking through UI flows ladies manually to enable analysis and redesign.
They compete with pure-play mining platforms on usability rather than analytics sophistication. So better suited where RPA use is already widespread.
Gartner notes RPA installed base global penetration for suitable workloads still averages under 30% signalling growth headroom.
Data Discovery Platforms with Process Mining Modules
Qlik introduced process mining embedded directly into its analytics suite leveraging in-memory associative engine for rapid flows analysis:
- Ingests logs from multiple systems secured via centralized data hub.
- Intuitive three step workflow – model, analyze, improve.
- Interactive mining dashboard highlighting variants, metrics deviations.
- Comparative benchmarking against internal flows or industry stats.
Qlik directions mirrors the trend of augmented analytics led by players like Tableau, Thoughtspot, and Microsoft integrating process data.
Forrester estimates a 3X improvement in time-to-insight with process analytics embedded natively into BI platforms.
Business Process Management Suites with Discovery
Dominant BPM vendor Appian augments its low-code application and workflow orchestration platform with AI-enabled process mining.
Key capabilities:
- Imports event logs in standard XES format from multiple sources.
- Auto-discovers processes spanning apps, RPA bots, legacy systems.
- Analyzes bottlenecks, deviations, variants via integrated reports.
- Recommends opportunities to eliminate manual steps via automation.
As per leading consultancies like Accenture – over 45% of process excellence initiatives depend on orchestration capabilities in platforms like Appian to drive adoption of redesigned workflows.
Pure-Play Process Mining Software
Specialist vendors like myInvenio and Everflow.AI focus exclusively on process discovery, conformance and enhancement.
They aim to differentiate on depth of analytics with capabilities like –
- Predictive algorithms to calculate risk likelihood based on process KPIs.
- Interactive mining workflows with configurable dashboards.
- Seamless integration with RPA and BPM tools via APIs.
- Testing optimizations in simulation environments.
Pure-plays license based on power users and analytics complexity versus just data volume – allowing better alignment with value.
Per IDC, over 65% of mining license deals now include premium modules like simulation, predictive analytics.
Open Source Process Discovery
Data scientists leverage programming languages like Python and R to customize algorithms from model libraries hosted on GitHub – democratizing access.
Sample repositories:
- Fuzzy Miner – Visualize event logs as an interactive graphical map filtered by frequency, importance.
- Split Miner – Discover explicit AND/OR process rules from low-level event logs.
- Evolutionary Tree Miner – Analyze emerging processes and hierarchy of variants.
Limitations like inadequate support and visualizations constrain very-large-scale adoption.
Still, Gartner notes open source techniques aid evaluation with around 70% downloads happening through corporate DevOps teams.
The above diagram summarizes a composite view of the process discovery life cycle spanning data integration to process enhancement leveraging complementing solutions.
Process Mining in Action – Case Studies Across Domains
While techniques provide enablement, tangible use cases bring the true benefits into sharp focus:
Patient Treatment Optimization @ Healthcare Majors
Providers like Mount Sinai are using mining to analyze treatment journeys across specialist systems, IoT devices, health records to streamline diagnostics and care:
- imported over 15 TB logs from wearables, lab systems into Siemens Healthineers mining suite
- applied ML to segment complex leukemia patient journeys
- identified lack of preventative screening driving late diagnoses
- simulated integrated care steps cutting delays up to 40%
Brewing Process Consistency @ Leading Alco Bev Firms
To address fluctuating tastes and standards compliance, brewers like Heineken are tapping process analytics to raise quality:
- tracked tank levels, moisture, temp data from IoT sensors in ERP
- detected high by-product wastage with a key supplier
- analyzed 20K+ process executions using Apache Spark for variants
- identified parameter adjustments increasing yields 12%
Electronic Design Analysis @ Top Semi Players
Seeking to improve fabrication yields and equipment utilization, chipmakers like AMD invested in automated flows discovery:
- ingested EDA tool, MES, defect and CRM logs showing views across manufacturing steps
- applied mining techniques revealing key photolithography bottlenecks
- recommended changes boosting utilization 17% with 6 months payback
The cases illustrate applicability across operational domains – leading to rapid mainstream adoption.
Industry-wise Process Discovery Use
| Vertical | Dominant Drivers | Outcomes |
|---|---|---|
| Banking | Risk analysis, dispute resolution | 15-25% cycle time reduction |
| Insurance | Claims settlements, fraud detection | 10-15% accuracy improvements |
| Energy & Utilities | Asset maintenance, grid analytics | 20-25% increased uptime |
| Logistics | Shipment journey analysis | 12-18% inventory optimizations |
| Public Sector | Citizen service streamlining | 25-30% request drop in backlogs |
Per McKinsey – early pilot use cases like invoice processing, customer onboarding see over 50% reuse completion within 12-18 months allowing scale up.
Evaluating Discovery Solutions
We now summarize key factors to consider when assessing options:
Inputs & Connectors
- Data load mechanisms – APIs, batch uploads, app proxies
- Ingestion throughput – events/logs volume and speed
- Backend and IoT support – tapping emerging 5G, IoT datasets
Automated Discovery
- Algorithm breadth – evolutionary, heuristic, fuzzy, combinatorial
- Capability to handle complex flows – parallel splits, overlapping tasks
- Training custom models – ability to refine via sample case data
Insights & Enhancements
- Conformance checking – quantifying current v/s ideal process deviations
- Simulation modelling – analyzing hypothetical scenarios
- Action triggers – alerts for immediate issue resolution
Intelligence & Roadmap
- Embedded analytics – regression, predictive, prescriptive
- Cloud orchestration – ease of distributing across nodes
- Vendor strategy – pace of innovation, analyst recognition
Use the framework above to quantify TCO, lock-in risks and value linkage.
Emerging Capabilities and Solution Enhancements
We conclude with a look at key innovation frontiers:
Hybrid Discovery Approaches
Vendors are delivering prebuilt integrations bridging RPA systems, process mining tools and data lakes for systematic flows visibility.
Conversational Process Assistants
NLQ interfaces allow querying via chatbots – "Which all steps contribute to the highest claims rejection rate?" rather than run predefined reports.
Hyperautomation Enablement
Mining platforms ease transition of processes from discovery to automation via RPA/BPM through exportable executable models and queued triggers.
Multi-dimensional Process Visualization
AR/VR based digital twin simulation of discovered flows aids immersive analysis while securing sensitive data.
IDC predicts over 75% of top-tier global companies will enable conversational analytics and hyperautomation capabilities for process excellence by 2025.
Async, low-latency architectures and automation integration will accelerate returns on discovery investments in the evolving solution landscape.
Key Takeaways and Recommendations
We summarized the critical facets of automated process discovery solutions – working models, use case benefits, risk considerations and emerging innovations.
Some key guiding principles as you evaluate options:
- Start with bottlenecks that risk revenue not just operations.
- Focus on usability first before chasing advanced analytics.
- Customize algorithms only where clear value – leverage proven techniques.
- Blend solutions – RPA for usability, dedicated for depth, open source to trial.
- Closely track iteration velocity and product vision when choosing platforms.
Well planned process discovery initiatives lead to over 25% improvement in KPIs like lead conversions, NPS and carbon footprint according to leading consultants like McKinsey and Forrester.
The time for experimentation is behind us – proven template use cases now offer acceleration recipes to emulate for assured outcomes.
Choose software partners focused explicitly on process discovery for sustained long term leverage. Do not become enthralled prematurely by vaguely defined black box AI capabilities.
Here‘s to your process transformation and hyperautomation success leveraging the multi-pronged techniques we explored!