Excessive throughput times have a negative effect on process costs and cash flow.

With Process Mining, you can analyze the different throughput times of your end-to-end processes by customer or product group, for example, and identify the potential effects on cash flow. You can also analyze individual cases in detail and identify general bottlenecks that cause fluctuations in throughput time. Meanwhile, further key figures will help you perform more specific analyses in order to gain transparency regarding costs that have arisen and the cash flow involved.

Delayed deliveries can have very serious consequences.

Process Mining identifies such cases where deliveries arrive late. With it, you can also identify unreliable freight forwarders or specific material groups that are associated with delayed deliveries. On the basis of these findings, you'll be able to take appropriate measures to support the planning, measurement, and improvement of your order management.

A significant portion of work is still carried out manually, which greatly increases costs.

Luckily, you can gain the transparency you need regarding the automation level of individual process steps.

With this knowledge, you can identify optimization potential and avoid manual activities – for example, in creating purchase orders, entering goods receipts, or processing invoices. This will enable you to significantly reduce throughput times and costs. 

Manual checking of credit blocks has a negative effect on working capital and process costs.

With Process Mining, you can identify long-running credit checks or automatically remove credit blocks for customers with a history of on-time payment.

Constant changes to customer orders and frequent corrections are time-consuming and costly.

Process Mining enables you to analyze which customers frequently make certain kinds of changes to their purchase orders. Identify the causes of recurring order changes or corrections that have a negative effect on your order processing.

Delayed or incomplete deliveries frequently result in irritated customers.

Process Mining helps you precisely analyze customer orders for which delivery times have not been met or only partial deliveries have been made. This ensures that you can increase your on-time-in-full (OTIF) rate and thus improve your customer satisfaction over the long term.

When performed by people, root cause analyses are time-consuming, costly, and often subjective.

The combination of process mining technology and machine learning means you can forensically analyze the digital data of your operations in order to obtain findings that a human analysis of the same data could never achieve. Risk analyses and early recommendations can be made based on the product, service, or country in question; the historical payment behavior of the customer; and the most recent deviations.