Process Mining and the P2P Process

The Procurement-to-Pay Process, or P2P process, connects procurement and the entire supply chain processes within a company from the goods receipt process to payment to the supplier. Since the transaction volume in the P2P process is extremely high and often very complex (cross-functional, with many different approvals, process requirements, and suppliers), every step within the process represents a potential source of errors. This makes P2P processes an excellent target for Process Mining, which brings transparency to the entire end-to-end process, identifies the causes of problems (e.g., incorrect purchases), enables improvement measures, and unlocks automation possibilities.

The P2P process is a sequence of operations conducted within a company from procurement to payment of an invoice, including all approval processes and documents involved. It begins with the purchase requisition, goes through ordering, order confirmation, delivery, and ends with invoice processing. P2P processes span across many different departments and involve many individuals in a variety of individual micro-processes. At the core of all these processes is the transaction of data between customers and suppliers, as well as examination and approval processes within companies. Such a complex process is susceptible to errors. Pitfalls in a non-automated Purchase-to-Pay process are, unfortunately, not uncommon. When the process is primarily handled by people, the time and personnel required are very high, and the susceptibility to errors is significant. The complexity of these processes increases with the size of the company. The larger the company, the more individuals, departments, and business partners are involved, leading to more process variants and even more sources of errors. When a P2P process is intertwined with so many different departments, a mistake can trigger a chain reaction that causes the company to lose time and money. Therefore, many companies have placed a significant focus on improving their P2P process in recent years. Typical areas for improving the P2P process and their points include:

Reducing long processing times

  • Lost invoices.
  • Manual approval procedures for invoice approval.
  • Unnecessary additional work for employees.
  • Limited supplier integration.

Increasing transparency

  • Bridging information gaps between procurement requests, orders, and invoices.
  • Lack of order concepts.
  • Management of supplier master data.
  • High number of suppliers.
  • Low level of supplier management.

More efficient use of discounts

  • Quick invoice payments.
  • Short credit terms.

But while the areas for improvement are clear, the “improvement strategy” often isn’t. In fact, companies face a significant challenge before they can even begin to think about improvement measures. How can they map and understand their entire end-to-end process? And how can they find the most critical problems and their causes? Usually, the individuals involved in parts of the entire P2P process are only familiar with their specific micro-process. There’s also an understanding of how the overall process should run. However, the complete end-to-end process includes a large number of steps that are not always executed in the correct order or at the right time—and every process change, error, or rework extends lead times and increases costs. Process Mining is definitely a solution to this challenge. (Here you can find more information about: What is Process Mining?) Companies dealing with Process Mining often face the challenge of integrating it into their systems and the time it takes to get data into the Process Mining tool and then visualize it. For companies looking for an easy entry and quick results, the ValueData Miner Sprint is the best starting point for great P2P processes. We’ll show you how quickly the optimization potential in your P2P business processes becomes visible within just 10 days. Within 10 days, we analyze the “digital footprints” of your processes within your IT system landscape and reconstruct


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