Let me let you in on a little secret – almost every company has hidden waste and bottlenecks buried within their processes. These inefficiencies sneak in over years of changing systems, compliance needs, mergers, and outdated ways of thinking. Left unaddressed, they slowly erode profits and competitive agility.
The good news? New innovations in process mining provide business leaders with the missing visibility needed to continuously improve performance. Read on as we uncover what process mining is, why it matters, and how to make it work for your organization.
What is Process Mining and Why Now?
Process mining uses data science to analyze system event logs in order to automatically model real-life business processes. Think of it as an X-ray machine giving you a detailed diagnosis on exactly how work gets done cross-departmentally.
The unlocked transparency helps uncover:
- Process bottlenecks
- Variants and deviations
- Automation opportunities
- Impacts on KPIs like cost, quality and speed
Armed with this intelligence, companies can confidently transform processes for optimal efficiency, compliance and competitiveness – the lifeblood of high-performance operations.
Forrester predicts nearly $20B in global investment towards process analytics by 2023 as firms realize the performance potential hidden within their existing processes. IDC similarly forecasts rapid growth, with analytics-driven solutions delivering over $10B in cost savings and productivity gains over the next four years.
Clearly, leading enterprises understand process mining and related process excellence capabilities allow them to tap into some of their largest remaining pools of untapped efficiency.
Bringing Facts and Data to Process Improvement
Process improvement programs traditionally rely on interviews and workshops to map current state processes. But manual process mapping suffers from three flaws:
1. Time-intensive: Significant consultant hours spent running discovery workshops.
2. Subjective: Models limited by biases, availability and recollection of participants.
3. Disconnected: No linkage between qualitative diagrams and actual quantitative process data.
Process mining overcomes these barriers by automatically constructing as-is process models from real-time data recorded in transactional systems like ERPs, CRMs and case management tools.
This brings unmatched speed, accuracy and auditability to process excellence initiatives relative to traditional methods. Data and algorithms replace guesswork with facts.
Inside the Technology
The automation starts with specialized process mining algorithms that replay system events sequentially to construct full process flows. This works because almost every digital process leaves a data trail across multiple systems allowing the sequence of steps to be reconstructed retroactively.
But process mining goes far beyond just modeling reality. Advanced analytics quantify process performance by calculating bottlenecks, variants, service levels and more for each step and in aggregate. The downstream visualizations and reports turn model data into actionable insights.
For example, consider the procure-to-pay process below analyzed through process mining:
We can clearly see:
- The most common process paths by percentage
- Steps with the longest duration
- Key metrics like case cycle time and rework rate
- Top root causes behind inefficiencies
And this is just the tip of the iceberg of the intelligence surfaced through process mining.
Real Business Impact Through Use Cases
Process mining delivers value across functions. Here are a few examples:
Manufacturing: Global manufacturer uses process mining for overall equipment effectiveness (OEE) to boost plant productivity by $31M and reduce conversion costs by 2.1%.
Insurance: Top insurance firm analyzes claims process to identify over $125M in potential bottom-line savings and transform customer experience.
Banking: International bank avoids over €3M in wasted costs by revealing unnecessary manual rework in lending processes through end-to-end analysis.
Technology: Fortune 500 tech giant consolidates disjointed IT processes post-merger to save $19M, decommission redundant tools and improve SLAs.
The use cases share a common thread – leveraging process analytics to find invisible leakage across fragmented processes and take targeted action. One bank recovered $8M in revenue leakage in under 3 months. Another insurer identified over 200 hours of unnecessary employee wait times per week.
It‘s no wonder multiple studies confirm 20-30% typical improvement in process efficiency and effectiveness within the first year of process mining adoption.
Get Started in 5 Steps
Hopefully you‘ve realized process mining‘s potential for maximizing process excellence. Here are 5 tips for getting started:
1. Identify initial use case: Avoid overly complex processes in the beginning. Start where reliable event logs are available for high-impact processes.
2. Evaluate tools: Leading products include Celonis, UiPath Process Mining and Signavio. Seek automated discovery and monitoring functions.
3. Clean event data: Work backwards from your questions to the data needed. Clean it through parsing, filtering, aggregating etc.
4. Interpret insights: Don‘t just analyze – act. Contextualize findings through workshops with stakeholders to drive change.
5. Expand scope: Once proven, scale process mining across other areas, train citizen data scientists and continually optimize.
As with any capability, focus on quick returns through priorities that align to current process improvement roadmaps. The goal is to amplify existing efforts – not replace them.
Over time, we see process analytics becoming ubiquitous; merging with traditional techniques to form the foundation for next-generation, self-optimizing processes. The innovation race comes down to realizing trapped potential before competitors. Now is the time to put process mining on your radar.
Special thanks to the process mining experts interviewed for this article for providing their guidance and perspectives on the current state of the market.
