Process Mining: An Analysis of Key Industries and Core Business Processes

Process mining is an intelligent method for analyzing business processes based on event logs. These logs display the actual, not the idealized, flow of business processes in detail. Each process event must have at least three attributes: an event identifier, an action name, and a timestamp. Since modern information systems already contain this data, process mining can be used in almost any area where processes are digitized.

In practice, several key industries are actively implementing process mining. As of 2024, these include fintech, sales, procurement, logistics, and telecommunications.

Key technology trends shaping the future

The field is evolving rapidly beyond simple process mapping. Three key trends are defining the next generation of process mining:

• Generative AI integration: This is the most significant recent development. Leading platforms now integrate Generative AI to transform analysis. Instead of just showing a bottleneck, the AI provides natural language explanations for its root cause and suggests specific, actionable solutions, democratizing data-driven decision-making.

• Object-centric process mining (OCPM): Traditional process mining tracks a single object, like an order. OCPM provides a more holistic view by tracking multiple interacting objects simultaneously—such as the customer, the order, the inventory, and the shipment. This is a game-changer for understanding complex, end-to-end operations in areas like supply chain and customer journey management.

• Cloud-native platforms: The vast majority of process mining solutions are now cloud-based. This shift offers greater scalability, flexibility, and cost-efficiency, making the technology more accessible to small and medium-sized enterprises (SMEs), not just large corporations.

Industries leading in process mining implementation

While adoption is broadening, several key sectors remain at the forefront.

Leaders: Fintech, telecom, supply chain, retail, and logistics continue to be dominant adopters. The complex, highly regulated processes and vast data pools in these industries make them ideal candidates for process mining.

High-growth adopters: Healthcare and Life Sciences are showing the highest growth potential. The critical need to optimize patient journeys, streamline clinical trials, and manage electronic health records (EHR) is a major driver.

Emerging sectors: Government, Education, and Utilities are increasingly adopting the technology to improve citizen services, optimize the student lifecycle, and enhance regulatory compliance.

The trend in these industries is emerging due to the prevalence of standard processes that are ideally suited for automated processing:

• Order to Cash (O2C): The process that starts with receiving a customer order to ends with the final payment, transforming orders into revenue.
• Procure-to-Pay (P2P): The process that starts with procuring goods or services and ends with the final payment, regulating all procurement activities.

Optimizing these standard processes with process mining allows companies to extract maximum benefit from existing opportunities in the shortest possible time, achieving a "quick win" effect.

The practical effects of implementation

The impact of process mining is best illustrated with real-world examples:

• FinTech: Credit company BridgeLoan has integrated a process mining tool into its ERP system to identify the bottlenecks in their loan application processes. After recreating their processes, the system started to progress 40% faster and the company can now process 30,000 applications in a month.
• Retail: In the retail and commerce sector, process mining is used to optimize core financial and operational processes that support the customer experience. To understand the root cause of its high product return and cancellation rates, Swiss luxury retailer Globus employed process mining. This analysis uncovered a hidden inefficiency caused by a lack of real-time communication between its different sales systems. Specifically, it was possible for one customer to reserve an item online while another customer bought the very same item in a store. This conflict forced Globus to cancel orders, leading to customer dissatisfaction. By addressing this system flaw, Globus successfully cut its cancellation rate by 20% and implemented a new dashboard for real-time visibility into its logistics.
• Supply Chain: In supply chain and procurement, process mining is used to optimize the Procure-to-Pay process. This allows companies to identify unreliable suppliers, prevent violations of delivery terms, and minimize operational errors. For example, after a major acquisition, European energy giant E.ON used process mining to analyze and harmonize the procurement processes across its many subsidiaries. This data-driven approach allowed them to create a standardized, more efficient process model, reducing costs and improving compliance across their entire supply chain.
• Logistics: The logistics industry uses process mining to identify bottlenecks in transportation, import/export, internal movement, and storage. A prime example is the Deutsche Post DHL Group, which applied process mining to its complex customs clearance procedures. By analyzing the data, they were able to pinpoint delays and deviations in the process, enabling them to streamline operations and reduce the time required for customs brokerage, leading to faster and more reliable international deliveries.
• Telecommunications: Telecom companies use process mining to manage sales and marketing and to optimize service delivery. For example, Deutsche Telekom analyzed its service processes to better understand the customer journey from order to activation. The insights helped them identify the root causes of delays and service disruptions, allowing them to redesign their workflows. This resulted in faster service delivery, fewer customer complaints, and a significant improvement in customer satisfaction scores.
• Industry/Manufacturing: According to analysts, the greatest effect from process mining is expected in industry, where it can identify shortcomings in the technological cycle. For instance, global industrial powerhouse Siemens used process mining within its Digital Industries division to analyze and streamline its complex Order to Cash process. This provided a transparent, data-based view of their operations, allowing them to pinpoint bottlenecks, increase automation by 24%, and reduce manual rework by 11%, which led to a more efficient, harmonized, and on-time delivery cycle for their industrial products.

High-level process analysis

There are six top-level business processes, as shown in the figure:

Among these, a significant impact from implementing process mining is expected in the following four process types:

• Opportunity to Order (O2O): This process, common in e-commerce, covers the customer journey from initial interest to placing an order. Implementing process mining here can make the ordering and payment procedure as user-friendly as possible, minimize the number of clicks required, avoid redundant information entry, and eliminate loops.
• Issue to Complete (I2C): From problem to completion, this process is typical for production environments in various industries. In this case, process mining can identify non-optimal operations in the production cycle. Correcting them can save raw materials, electricity, and optimize equipment depreciation.
• Concept to Launch (C2L): Spanning from initial concept to product launch, this process is crucial for innovation and new product development. Process mining in this sphere allows companies to identify bottlenecks in launching new products and services by analyzing past processes. This optimization helps develop new products much faster and with lower financial and labor costs.
• Sustain to Retain (S2R): Focused on the customer lifecycle from service maintenance to retention, this process is used in marketing and customer service. An in-depth analysis of these processes allows a company to identify effective measures for retaining customers, determine the most effective marketing strategies, and develop optimal business procedures.

Megaladata process mining solution

To implement process mining in various industries and business processes, our company has developed a ready-made solution based on the Megaladata analytical low-code platform.

Megaladata Process Mining (MPM) is a modular solution for automated process analysis based on digital footprints—the electronic traces left in a company's IT systems, such as timestamps, user actions, and transaction records. MPM is designed to restore how business procedures actually happen, rather than relying on idealized or theoretical models. The solution is applicable in any sector of the economy, including finance, distribution, retail, manufacturing, medicine, and telecom.

The practical effects of MPM include:

• a reduction in the number of routine operations
• a reduction of operating costs
• an increase in labor productivity
• the ability to scale efficient process execution methods

To know more about the available Megaladata solutions, contact us for a presentation.

See also:

Shewhart Charts: A Tool to Control Business Processes

Sustain and Retain: Building Customer Loyalty