‘Defragment' your business via Process Mining: gain speed, cash flow, and profitability in days
Do you remember defragmenting a computer? For those less IT-inclined: think of it as the computer playing perfect Tetris on its own hard drive. Every gap is closed, files are packed neatly together, and when it is finished, the machine runs as it always should. Faster, cleaner, without the accumulated clutter of years of improvised workarounds.
Now imagine doing the same thing to your business processes.
A story that makes the point better than any abstract explanation
Two years before this post was written, a large company's sales team in one of its country markets was watching competitors consistently beat them to the shelf. Products arrived late, replenishment times were poor, and customers who got tired of waiting simply moved on.
The local country manager - let's call him Nasir - was incentivised on sales and getting desperate. During a coffee break with the Corporate Head of Supply Chain, he heard about something the corporate team was piloting called process mining: software that combed through all system databases automatically, surfacing gaps, delays, and deviations from defined processes - what Lean practitioners would call muda, Japanese for waste.
With nothing to lose, Nasir gave the instruction. The next day, the corporate supply chain team had access to the local systems. The process mining software reviewed every activity performed for the affected products.
Two days later, the problem was identified. Order entry by field sales staff was routed through two local managers for approval before execution could begin. When questioned, the managers explained: "We had issues with wrong order entries in the past. We wanted to check first. Since we were understaffed, we figured that as long as we batched the orders before the month-end report, no one would notice." Orders were sitting in an internal approval queue for up to three weeks.
The fix was immediate. The team switched from a pre-approval to a post-control model: orders were processed directly, with one manager receiving the daily report to validate. Since the regional warehouse delivered in two days, and the three-week internal delay was now gone, customers received their products approximately 90% faster than before. Cash flow improved immediately. Customer buying behaviour has shifted. Sales increased 30% within three months, and by year-end had stabilised at 270% above the pre-change baseline.
Nasir then ran the software across all order-to-cash processes and products, finding and fixing hundreds of additional gaps. The following spring, his team was named Most Improved Player before the global management meeting. They achieved the highest increase in sales and customer satisfaction ever recorded in the company.
What process mining actually is
Process mining is Lean and Six Sigma digitalised and accelerated into the 21st century. It works because every enterprise system - ERP, MES, S&OP, finance, HR - maintains transaction logs: a chronological record of every action taken, who took it, and when. Process mining software reads those logs and maps the actual process flows that occurred, not the process flows that were supposed to occur. The gap between the two is where the value lives.
Decades of Lean analysis across thousands of companies have established a reliable benchmark: value-adding activities - actions that directly increase the benefit of a product or service to a customer - account for only 20 to 40% of total fulfilment time. The rest is waiting, checking, approving, re-planning, correcting errors, and improvising workarounds that nobody documented. Process mining makes all of that visible in days rather than months.
Where does your organisation stand?
Six questions to assess the current state honestly:
Is process mining software actively used across the company, and is management trained to interpret results and act on them?
Do you operate on cloud infrastructure - Microsoft Azure, AWS, or equivalent - across products, customer interfaces, and internal operations?
Have you created dedicated roles for data scientists and machine learning engineers embedded in business functions, not just in IT?
Does the organisation genuinely understand the distinction between data, information, and insight - and act accordingly?
Is there a zero-tolerance policy for unapproved shadow processing outside of your core ERP, MES, and S&OP systems?
Bonus: Have you extended your data architecture to incorporate relevant data from key suppliers and customers to identify further optimisation potential?
A clear yes to most of those means the foundation is in place. If most are no or uncertain, the gap between current and potential performance is significant - and the financial case for closing it is straightforward.
The size of the prize
The specifics depend on your starting point, but the questions below give a practical framework for estimating your own opportunity:
What would a 50% reduction in lead times mean for your cash flow, revenue, and margin? What would pruning your product portfolio - eliminating the long tail of low-volume derivatives that add complexity without proportionate revenue - do to your production efficiency? What would a cleaner view of customer profitability mean for resource allocation and overdue performance? And at the most sophisticated level: if you could augment internal process data with relevant macroeconomic variables to improve forecasting accuracy, what would smoother, more predictable revenue patterns do to your cost of capital and investor confidence?
A company operating across 100 countries with over one million SKUs generates a digital twin data footprint of around ten terabytes. The cost of maintaining that twin is currently in the low millions of dollars annually - not a rounding error, but not a barrier either for any company for whom the upside calculation justifies the investment.
Setting up the programme
The timeline is 24 months. The board sponsor is the CEO or COO; the operational lead is the COO or a senior project leader with both IT and Lean or Six Sigma backgrounds. The combination matters - process mining sits at the intersection of data architecture and operational knowledge, and leaders who only understand one side tend to under-deliver on both.
The right sequence: start with a board-level demonstration session featuring companies already working in this space, ideally with C-level customer references who can speak to actual results rather than vendor claims. Run a short pilot on real company data to validate the approach and the software choice before committing to a full programme. Then expand systematically across sales, operations, procurement, finance, and service - each tightly supported by internal and external expertise.
Only after the pilot has generated credible results should the full programme blueprint be locked. This sequencing matters. Process mining sceptics - and there will be some - are much harder to argue with before they have seen results. Show them Nasir's story first.
On software selection: do not attempt to replicate process mining capability internally. This is exactly the kind of front-end expertise that belongs in the market. Evaluate established providers, ask for references in your industry, and choose based on demonstrated outcomes rather than feature lists.
The IT team supporting the programme should be small but genuinely capable. A few exceptional data scientists and machine learning engineers embedded directly in business functions will outperform a large team of average performers every time. Finding that talent is harder than funding it - plan accordingly.
Watch out for
Data black holes are the primary risk: local Excel files and shadow databases where data is manipulated before being batched into the ERP, creating multiple versions of operational reality rather than a single source of truth. Process mining is only as good as the data it reads. Dirty data puddles will produce misleading results, and misleading results will kill the programme's credibility before it delivers.
Cover-ups when gaps are found are the cultural equivalent. The entire value of process mining is in surfacing what is actually happening. A culture that treats exposed gaps as failures rather than opportunities will suppress the findings that matter most. Gaps should be celebrated as the starting point for improvement, not buried.
Run repeat defragmentations regularly - quarterly at minimum - and consider rotating software providers occasionally to get a genuinely fresh perspective on the same data. Ensure the executive committee reviews findings as a standing agenda item, not an occasional update.
Summary
Want to grow revenues, accelerate response times, and improve margins simultaneously? Play perfect Tetris with your processes via process mining. It shows you - without leaving your desk - how your company was designed to work, and exactly where the accumulated clutter of years of workarounds has slowed it down.
The technology is available. The financial case is clear. The only remaining question is who in your competitive landscape moves first.
Stay safe. Be bold.
Daniel
The views expressed in this post are my personal professional opinions, based on research and publicly available information. They reflect analysis of industry trends and practices, not assertions of fact about specific companies or individuals. Nothing in this post constitutes legal, financial, or investment advice.
