An operations director at a manufacturing company says the order fulfillment cycle averages three days. The accounting team knows it sometimes stretches to ten. The sales team, fielding customer complaints, sees yet another picture. Three teams, one company, one process — three different realities. This is not a communication problem; it is a structural blind spot built into traditional process management. Process mining addresses exactly this gap: it reads the data already recorded in the company’s information systems and makes visible how a process actually flows, where it stalls, and why deviations occur. As digital transformation conversations mature in Turkey, the growing interest in this discipline is not coincidental.
The core logic of process mining is straightforward. Enterprise systems such as ERP, CRM, and accounting platforms log every transaction with an event record. When an order is created, approved, shipped, or invoiced, the system stores each step with a timestamp. Process mining software takes these event logs, reconstructs the actual sequence of steps for each case, and maps the flow visually. The resulting picture almost always diverges — often significantly — from the flowcharts drawn in meeting rooms. Tools such as Celonis, Minit, and SAP’s own process mining capabilities have been gaining traction internationally. In Turkey, the relatively high penetration of SAP and Oracle ERP among large and mid-sized companies simplifies one of the biggest challenges in any process mining project: the data is already there, waiting to be read.
Why is interest accelerating now? Several forces are converging. First, ERP systems in Turkey have reached sufficient maturity in corporate adoption — many large and mid-sized businesses have accumulated over a decade of transactional data. That data is the raw fuel for process mining. Second, the spread of RPA (robotic process automation) investments has delivered an unexpected lesson: automation projects that start without understanding how the target process actually behaves tend to stumble badly. A customer service automation initiative at a Turkish bank illustrated this clearly — the automation design was built around the standard process path, but real cases were flowing through ten different variants, not the standard one. Had process mining been applied first, the cost and timeline of that project would have been materially lower. Third, the currency turbulence Turkey experienced in 2018 has made operational efficiency a necessity rather than a preference. The tolerance for wasteful processes has narrowed sharply.
Process mining delivers measurable value in three concrete areas. The first is compliance verification. Under Turkey’s e-invoice and e-ledger obligations, companies must ensure that invoicing processes follow specific rules. Process mining validates whether the actual flow meets those rules by reading system data directly — it surfaces deviations that manual audits cannot reliably catch. The second is bottleneck identification and root cause analysis. With traditional methods, tracing the source of a delay in a process typically requires weeks of interviews and observation. Process mining compresses this to hours, showing at which step, in which department, and under which conditions delays accumulate. The third is process variant management. No real-world process flows through a single path. Process mining reveals how many variants exist, what each variant costs, and which variant consistently produces the best outcome — information that is practically invisible without event log analysis.
The limitations of these tools deserve equal attention. Process mining is not a self-contained solution; it is an analytical method dependent on data quality. If event logs are incomplete, inconsistent, or scattered across disconnected systems, the analysis produces misleading results. In a significant share of mid-sized Turkish companies, ERP, accounting, CRM, and logistics systems still live in separate data silos. In those environments, a meaningful portion of a process mining project ends up being data integration and cleansing work before any analysis can begin — which adds both time and cost. Beyond data quality, the tool produces findings but does not make decisions. Understanding why a deviation exists, whether its roots are organizational, cultural, or structural, still requires human judgment. Without the internal capability to read, interpret, and act on process mining output, the software investment does not generate value.
For a Turkish executive considering an investment in this space, the practical starting criterion should be data readiness. Does your ERP system hold at least two years of consistent, step-level event logs for core processes? Are timestamps available at the individual transaction step — not just at the case level? If the answer is no, the first investment should go to data infrastructure, not to process mining software. If the answer is yes, the pilot scope should be kept narrow: one critical process rather than the entire organization. Order-to-invoice flow or the purchase approval cycle are natural starting points. If the pilot produces a measurable improvement, the case for scaling becomes much more defensible. For companies that genuinely want to see how their operations work — not how they were designed to work — process mining is a serious tool. But only when applied with clean data and realistic expectations.
This article was originally written in Turkish by Gökhan MERCANOĞLU on January 21, 2019 and has been automatically translated into English and other languages using machine translation.