Incremental enhancements can only take operational efficiency so far. To enable step-change improvements in speed, cost, and quality, leading organizations are building intelligent operations leveraging emerging process improvement technologies.
This guide provides operations and technology leaders an in-depth look at four digital innovations to drive transformational gains across the enterprise:
- Process Mining
- Robotic Process Automation
- Business Process Management and Automation
- Data Extraction
Combined, these solutions accelerate processes, unlock hidden capacity, and imbue operations with sensing, insight, and autonomous decision skills. The collective impact establishes the foundations for the Intelligent Enterprise of the future.
Process Mining: The X-Ray for Intelligent Operations
Process mining melds data science, process analysis, and event log processing to create a digital twin of enterprise operations. This delivers unparalleled visibility into actual processes revealing waste, variants, bottlenecks and risks traditional methods miss.
Armed with this high fidelity view, organizations can identify and simulate targeted enhancements. Companies using process mining to guide transformation average 15-25% cost reduction and over 50% cycle time acceleration in the first year.
Process mining combines event log replay, conformance checking, performance metrics, and process models to deliver unprecedented visibility. Source: QPR Process Mining
Let‘s examine how global packaging leader Metsä Board unlocked supply chain transformation using process mining.
Supply Chain Breakthrough: Metsä Board
Metsä Board produces premium paperboard packaging for leading CPG and retail brands worldwide. Though operating 5 mills and 10 distribution centers, supply chain performance lackedVisibility into actual process constraints.
Deploying process mining software Celonis provided this missing visibility by reconstructing supply chain processes from 12 months of ERP data encompassing 300K order line items.
Process mining unearthed over 300 unmonitored supply chain process variants concealed within legacy systems. Source: Celonis
The analysis quantified the business impact of this process variability. One deviation alone caused €600K in excess annual product holding costs.
Armed with this granular insight, Metsä simulated and implemented corrections around purchase order handling, delivery consolidation, and stock level policies. Management also utilized process mining capability to continuously monitor improvements.
"Process mining enabled breakthrough performance improvement well beyond what our previous incremental efforts could deliver." – Mika Joukio, CEO, Metsä Board
In the first year, the company reduced supply chain costs by €2.2M annually and improved service levels from 92% to 99%. This transformation continues as Metsä utilizes process mining to hone competitive advantage.
Impact
- 15% supply chain cost reduction
- 8% increase in service levels
- 63% improvement in supply chain flexibility
Learn More: Supply Chain Process Mining
Next Frontier: Continuous Improvement AI
Process mining provides unmatched visibility. But turning insights into action still requires substantial data science skill. New AI capabilities are changing this equation.
Continuous Improvement AI solutions analyze massive process data to automatically:
- Classify hundreds of process variants
- Identify root causes of deviations
- Predict impacts of process changes
- Recommend data-driven optimizations
For example, leading platform Celonis introduced Automatic Insights Engine that eliminates hours of manual analysis via intelligent process assistants. These enable business leaders to cut cost and friction through contextual recommendations.
As AI augmentation progresses, process mining becomes an exponentially more powerful transformation accelerator requiring less specialized data skills. Expect AI-assisted analysis to proliferate across mining solutions.
"AI doesn‘t replace human creativity but eliminates rote analysis allowing people to focus on higher judgement tasks." – Alex Rinke, Co-CEO, Celonis
Learn More: Expert Guide on Process Mining
RPA: Automating White Collar Work
Robotic Process Automation (RPA) provides a fast lane to automate repetitive human tasks by deploying software robots. These bots interface with systems through standard UIs to interpret data, trigger responses and communicate with other systems just like humans but without human limitations like fatigue.
This allows companies to eliminate inefficient manual effort and shift highly-paid staff to judgement intensive work aligned with strategic goals versus repetitive tasks. Leading organizations expect to automate over 40% of back office transactional tasks with bots by 2025.
Let‘s examine how America‘s largest home improvement retailer leverages RPA to transform operations…
RPA automates repetitive tasks across departments like Finance, HR and Customer Service freeing staff to focus on value-added work. Source: Blue Prism
The Home Depot – Delivering on Customer Service
With millions of customers and suppliers worldwide, Home Depot contended with high volumes of queries across order status, inventory, and deliveries. Resolving these issues consumed thousands of manual hours annually that could shift to improving customer experience.
By deploying RPA across its commerce operations, Home Depot automated >80% of routine transactional tasks that plagued call centers and stores. Bots now process thousands of order status and availability queries without human involvement while seamlessly escalating exceptions.
This transformed support capacity allowing staff to focus on higher-touch customer engagements and exception management. Customer Effort Score (CES) improved 12% following RPA deployment.
"RPA helps remove the grunt work so our teams can spend more time with customers." – Tim Wong, VP Innovation, The Home Depot
The Bot Workforce Goes Cognitive
Today RPA provides major returns but is limited to predictable digital tasks. Cognitive RPA supercharges bots with technologies like computer vision, natural language processing and machine learning. This enables bots to handle far more dynamic and complex work.
For example, leading RPA platform Automation Anywhere released IQ Bot which leverages ML to extract and comprehend semi-structured data from documents – a task previously requiring human judgement. This unlocks automation for entirely new categories of high-volume processes.
As RPA adopts AI, the scope for automated white-collar labour expands exponentially. Gartner predicts a 5X increase in business process tasks automated by 2024. RPA will become a mainstay technology across banking, insurance, retail and more.
"AI makes the full promise of automation finally attainable." – Prince Kohli, CTO, Automation Anywhere
Learn More: RPA vs AI – The Rise of Intelligent Automation
BPM & BPA: Creating Adaptable Digital Operations
Business Process Management (BPM) provides methods and software platforms for continuous optimization, while Business Process Automation (BPA) digitizes execution of processes through robotic workforce and integration tools.
Combined, BPM and BPA break functional silos to create flexible digital operations where integrated data and bots enable processes to adapt independently based on business context. This creates unprecedented operational responsiveness.
Let‘s examine how Spanish banking group BBVA utilized BPM and automation to accelerate agility…
BBVA – Turbocharging Lending with Adaptive Processes
Amid rapidly evolving customer expectations and risk models, BBVA sought to digitally transform lending for greater customization and speed.
The bank implemented web-based BPM enabling line of business teams to continuously modify lending workflows through low code tools without deep IT skills. Staff access and input applications from mobile devices enabling real-time applicant support. Software robots handle known tasks like application data capture freeing staff for complex decision making.
With dynamic self-updating processes, BBVA slashed lending decision cycles by 50% while lowering cost-income ratio below 30% – creating efficiency buffer to fund further digital innovation.
"BPM allows us to achieve flexibility at scale that once seemed impossible" – Carlos Kuchovsky, CDO, BBVA
Impact
- 50% acceleration in lending decision response times
- 20% increase in lending volume
-
30% reduction in lending operations costs
Learn More: BPM Implementation Stages
The Autonomous Digital Enterprise
Leading BPM platforms like Appian now integrate robotic workforce management, AI analytics, and auto-monitoring capabilities – delivering exponentially greater business agility.
For example, Appian helps global pharmaceutical giant Flex dynamically orchestrate its clinical trials leveraging real-time visibility into site readiness, patient risk factors and progress data. This accelerates time-to-market for new therapies by upwards of 30%
As technology barriers disappear, expect large complex processes like drug R&D and insurance claims to become data-driven, self-optimizing workflows which continuously match supply with demand.
"The autonomous enterprise represents the next frontier in operational excellence." – Matt Calkins, CEO, Appian
Dive Deeper: BPM and Low Code Technology Guide
Data Extraction: Unlocking Hidden Efficiency
Data extraction tools utilize AI and machine learning to automatically convert raw, unstructured data from documents, contracts, surveys, email and human conversations into usable, structured data.
This extraction of previously trapped data powers unified analytics and harmonization of workflows touching the converted content – driving major efficiency gains upwards of 35%.
Let‘s examine how AI-based extraction accelerated efficiency within accounts payable process at global accounting leader PKF Cooper Parry.
PKF Cooper Parry – Boosting Accounting Efficiency
High invoice volumes and unique layouts created friction and disputes in payables processing. AP staff painfully deciphered thousands of monthly supplier invoices converting details into company ERP.
AI-based data extraction classifies documents and extracts fields for transaction processing without human effort. Source: HyperScience
Deploying AI software automated over 90% of this document conversion work by classifying invoices and precisely extracting key details needed for payments.
This improved payables cycle efficiency 30% allowing resources redeployed to value-added exceptions management and supplier experience programs. Efficiency gains funded discounts incentivizing early supplier payments capturing $500K in first-year savings.
"Extraction transformed efficiency constraints we assumed were fixed ceilings." – Gary Simon, CEO, PKF Cooper Parry
Impact
- 90% document conversion work automated
- 30% acceleration in accounts payable cycle
- $500K savings from early payment discounts
Learn More: Guide to AI-Based Data Extraction
The Rise of DigitalOps
By automatically generating structured data inputs, AI extraction layers intelligence into formerly manual workflows – enabling emergence of "DigitalOps".
In DigitalOps, task outcomes, process metrics, applications, robotic workflows, and staff actions feed intelligent auto-monitoring dashboards that provide visibility with built-in root cause diagnostics.
For example, when invoice processing exceptions spike, alerts trigger assignments to aide bots while workflows automatically adjust to smooth resource allocation between bots and staff.
As extraction enters mainstream use, DigitalOps will compound exponential efficiency gains breaking constraints of static policies and org structures designed for human-only teams. Extraction becomes a multiplier effect to scale intelligent process improvement.
"DigitalOps marks the convergence of data, decisions and actions within increasingly autonomous operations." – Anand Swaminathan, CEO, AllyO
Comparing Process Technology Alternatives
While all driving transformational impact, these process technologies each offer unique capabilities for different improvement scenarios:
| Process Mining | RPA | BPM + BPA | Data Extraction | |
|---|---|---|---|---|
| Best For | Understanding and optimizing complex processes |
Automating repetitive digital tasks |
Coordinating workflows with frequent change |
Turning analog data digital |
| Key Enablers | Process models + event logs | Software bots + UI automation | Low code + integration tools + RPA |
AI/ML document classification + entity recognition |
| Efficiency Gain | 15-25% process cost reduction >50% cycle time acceleration |
60%+ task capacity increase >30% operations savings |
20-50% increased process agility |
35%+ disbursements efficiency |
| Lead Innovators | Celonis, Signavio, Minit | Automation Anywhere UiPath, Blue Prism |
Appian, Pega, IBM | Hyperscience, Nanonets Robocorp, Amazon Textract |
Comparison of market-leading process improvement technologies highlighting focus areas
While distinct in primary capabilities, we see growing convergence across solutions:
- RPA taps process mining to discover new automation opportunities
- Mining integrates RPA bots executing optimization steps
- BPM utilizes mining and RPA to enable autonomous workflows
Combined intelligently, these exponential technologies enable unprecedented transformation.
Emerging Innovations: Cutting Edge Results
Beyond accelerating capability convergence, we see cutting-edge process innovations unlocking entirely new realms of operational efficiency:
Hyperautomation infuses AI across interconnected automation tools creating automated operations requiring only high-level human guidance. Gartner notes hyperautomation delivered up to 7X ROI for early adopters.
Intelligent Document Processing (IDP) melds AI data extraction with process mining and RPA to automate unstructured data tasks achieving over 90% straight through processing on previously human-reliant processes like mortgage underwriting and claims adjustment.
Edge Process Innovation combines above techniques to unlock exponential efficiency gains within previously constrained contexts:
- Real-time supply chain optimization – Shipedge mines logistics processes then layers in RPA track-and-trace bots and simulations to enable dynamic re-routing around delays
- Frictionless banking – Clinc uses NLP bots to categorize customer queries then utilizes mining on outcomes to continuously tune query-resolution processes
- Precision medicine – Inato links genetics analysis RPA with computational BPM empowering automated dynamic treatment plans tuned to patient biomarkers
"We are just scratching the surface of what becomes possible combining these exponential technologies." – Maureen Fleming, Program VP of Intelligent Process Automation, IDC
Key Takeaways
- Digital process innovation is achieving orders-of-magnitude (not incremental) returns through stepped-change productivity and system-wide coordination.
- Leaders are early combining process mining, RPA, BPM, and data extraction to develop sensing, intelligent operations greater than the sum of parts.
- AI integration layers like Continual Improvement Bots and Hyperautomation multiply impact, enabling previously unfeasible levels of efficiency at scale.
- Failing fast in adoption risks displacement as rivals utilize these technologies to achieve superior cost economies and customer intimacy. Assessing applicability and piloting shifts from luxury to necessity.
"Companies not aggressively exploring process technology combinations risk competitive relevance." – Michael Feindt, Founder, Blue Prism
To assess process technology readiness for your operations, connect with an automation expert.