Does a manufacturing company’s sales director actually know how many days pass between a customer placing an order and making the final payment? In most cases, the answer is no. The order enters the system, the shipment goes out, the invoice is issued, and collection is awaited — but no one has a clear view of how many days are spent at each link in that chain. ERP systems hold the data, accounting software holds the records, yet these sources show only a snapshot of the current state, not the real flow of the process. In Turkey’s 2019 business environment — shaped by currency pressure and persistent inflation — this lack of visibility carries a measurable financial cost. When collection is delayed, financing costs rise. When customer complaints are handled late, the relationship deteriorates. Process mining has entered the corporate agenda as an analytical approach that uses data to surface the true bottlenecks in this chain.
Process mining reconstructs how a process actually flows by reading the event logs generated by ERP, CRM, or accounting systems. Every step from order intake to invoicing to collection already carries a timestamp in these logs. Process mining tools take this raw data, map the real process, and highlight the steps that deviate from the intended design — measured and visualised, not estimated. For a mid-sized Turkish company running SAP, Logo, Netsis, or Mikro, these logs already exist in the system; no additional data collection infrastructure is required. The real work is reading that data with the right tool. Celonis, Minit, and ProcessGold are among the platforms that have gained traction in this space, while open-source alternatives are also used in academic and pilot settings.
In the order-to-cash chain, bottlenecks tend to cluster at three points: the order approval process, the invoice issuance timing, and the collection follow-up. An analysis at a Turkish textile exporter might reveal that the average time between a customer order being entered into the system and shipment approval is three business days, while the process design assumes one. Multiplied across hundreds of orders, that two-day gap translates into weeks of delay and a significant volume of tied-up cash. Process mining surfaces this gap not through assumption but through actual transaction data. The same analysis can show which customer segment, which product group, or which sales representative’s orders are driving the most delay — giving management the precision to intervene in the right place rather than applying blanket fixes.
Invoice issuance timing has a direct and often underestimated impact on the cash cycle. The number of days between shipment and invoice creation determines when the collection clock actually starts. For companies within the scope of Turkey’s mandatory e-invoice system, this step is partially standardised — but the gap between shipment confirmation and e-invoice generation remains a live issue in many firms. In a process mining analysis, the delta between the shipment approval timestamp and the e-invoice creation timestamp is measured statistically across the entire operation. A two-day average delay on a thirty-day payment term represents roughly a six percent extension of the cash cycle. Multiply that by the cost of short-term financing, and the result is a concrete improvement case that can be brought to senior management with numbers rather than intuition.
Collection follow-up is the most complex and human-dependent link in the chain. Which customer was contacted, when, by whom, and how many days after that contact did payment actually arrive — this information typically lives in scattered Excel files or accounting notes rather than in a structured, queryable format. Process mining imposes structure on this scatter: it automatically identifies cases (variants) where the collection process deviates from the standard flow. For example, an analysis might show that for certain customers, an average of eight days passes before a payment reminder is sent, while for others the contact happens the same day. This inconsistency is not random; it reflects a gap in process design. In periods when Turkish SMEs face cash constraints — as many did through 2018 and into 2019 — the cost of these gaps compounds quickly.
The limitations of process mining must be stated plainly. The tool analyses the data generated by an existing process; it does not explain why the process was designed that way or what reasoning drives individual human decisions. More importantly, the quality of the analysis is entirely dependent on the quality of the underlying data. Inconsistent record entry in the ERP, missing timestamps, or transactions handled outside the system — by phone, email, or verbal approval — will distort the output. In many mid-sized Turkish companies, data hygiene has not yet reached the level required for reliable process mining results. Before launching such a project, a data quality assessment and remediation phase is not optional. Beyond the technical dimension, the findings must be adopted by process owners to drive real change. Producing a report and changing an organisation are separated by a management challenge that no software resolves on its own.
A manager considering process mining for the order-to-cash chain should start with one question: are our system event logs clean and complete enough to support this analysis? If the answer is no, the first phase of the project may be six months of data quality work — which is itself a worthwhile investment. If the answer is yes, starting with a single customer segment or product group as a pilot delivers concrete learning before the full process is in scope. The information needed to shorten the cash cycle, reduce customer complaints, and improve collection efficiency already exists in your systems. Process mining makes that information visible. What happens next is a management decision.
This article was originally written in Turkish by Gökhan MERCANOĞLU on May 27, 2019 and has been automatically translated into English and other languages using machine translation.