WHAT DOES BUSINESS PROCESS MINING MEAN?
Process Mining enables companies to visualize precisely how their business processes are operated on a daily basis, to control them in real time and to optimize them.
Process Mining solutions automatically capture information from event logs in IT systems (ERP, WMS, CRM, etc.). In concrete terms, logs are based on facts and provide time-stamped information that allows you to track the status, progress and path taken by each unit in your processes in real time.
Still today collected and structured with difficulty by humans, data can now be processed simply and in real time thanks to Process Mining.
THE MANAGEMENT OF YOUR BUSINESS PROCESSES BY PROCESS MINING IS CARRIED OUT IN 4 PHASES
PROCESS DISCOVERY
It provides a real-time visualization of the way your business processes are operated: steps, times, anomalies, non-conformities, rework, etc. You have a first view of the real performance of your organization.
BUSINESS PROCESS COMPLIANCE
From the discovery phase, you have a first level of analysis. It is often a matter of comparing your theoretical processes with your real processes. What are the pain points of your processes? How do they evolve over time and space? etc.
BUSINESS PROCESS AUTOMATION
Which parts of my process are redundant, most frequently used and time consuming? In short, which steps would benefit from an investment in RPA? Process Mining then enables you to measure the performance of this automation.
BUSINESS PROCESS PREDICTION
How can I use machine learning to predict, based on historical data, what will happen in my business processes? What will be the bottlenecks and future anomalies? These valuable insights will permit you to anticipate decision making and adapt your organization.
WHAT ARE THE BENEFITS OF PROCESS MINING?
According to IDC market research, companies lose more than 20% of their annual revenues due to inefficient processes. Process Mining provides a solution to mitigate these losses. The benefits of this technology are numerous and depend on the processes being managed. In particular, it enables you to:
- Have an end-to-end vision of all your processes in real time
- Detect anomalies, identify root causes, and eliminate operational inefficiencies
- Identify the best performing parts of your processes: suppliers, employees, etc.
- Carry out automation actions and measure the ROI
WHAT ARE THE FIELDS OF PROCESS MINING APPLICATION?
Address regulatory constraints (bank, P2P)
The banking environment, for example, is often constrained by security and compliance regulations. Process Mining allows you to highlight them easily. This also applies to procure-to-pay processes where the supplier’s payment is subject to late payment interest and this will be even more important with the new regulations to come (dematerialized invoices, real-time VAT declaration, etc.).
Respond to time constraints (supply chain, maintenance)
In supply chain, for example, Process Mining aims to optimize all the actors and key stages of the delivery process in order to reduce lead times and keep customer promises. This can also be applied to industrial maintenance processes: stopping certain equipment can represent a significant cost, the management of the maintenance process can reduce these latency times by optimizing the time and stages of maintenance.
Face operational efficiency issues (O2C, helpdesk)
In order-to-cash or even in helpdesk, Process Mining identifies rework, bottlenecks to better anticipate them or find different organizations to improve the efficiency of teams.
Raise customer satisfaction issues (customer experience)
The subscription or even moving process is very complex internally: the mining process allows to simplify these processes by highlighting recurrent anomalies and possible automations to make these processes more fluid and optimize them. (add part about customer journey)
WHICH RESULTS CAN BE EXPECTED?
Real-time control of business processes allows for more efficient and engaging execution for employees. For example, the following results can be observed:
Reduced costs
A multinational retailer and wholesaler with $20 billion in revenue wanted to decrease the number of incomplete shipments, save time on the global delivery process and determine a performance baseline during typical and rush periods. By revealing peak period inefficiencies, the company saved over $540k in less than six months.
Improved performance
A major automotive manufacturer needed to make their spare parts purchasing process more efficient. The company visualized its ERP’s procure-to-pay process to optimize a $12M cash flow and accelerate responsiveness in just three months.
Automated savings
A pharmaceutical company in North America wanted to optimize its supplier payment process. Within several months they increased contactless payments, saved $300k, eliminated a case of double payments and identified automation areas for future savings.
FAQS
1.How does process mining integrate with existing business intelligence tools?
Process mining integrates with business intelligence (BI) tools by leveraging event log data from enterprise systems such as ERP, CRM, and workflow management software. Many process mining platforms offer APIs and connectors to BI tools like Power BI, Tableau, and Qlik, enabling businesses to visualize and analyze process inefficiencies alongside other performance metrics. Additionally, process mining can enhance BI by providing a deeper understanding of how workflows operate in real-time, improving decision-making.
2. What are the common challenges faced during the implementation of process mining?
Some common challenges include:
- Data Quality & Availability: Process mining relies on structured event logs, but many organizations have incomplete or inconsistent data.
- Integration Complexity: Extracting data from legacy systems and different software platforms can be difficult.
- Resistance to Change: Employees and leadership may be hesitant to adopt process mining insights that require workflow changes.
- Interpreting Insights Correctly: While process mining identifies inefficiencies, companies must have the right expertise to interpret the results and implement improvements effectively.
- Compliance & Privacy Concerns: Process mining involves analyzing user actions, which may raise data security and compliance issues (e.g., GDPR, HIPAA).
3. How can process mining be applied to improve supply chain management?
Process mining enhances supply chain management by identifying bottlenecks, inefficiencies, and deviations in processes such as procurement, order fulfillment, and logistics. It helps organizations:
- Optimize Lead Times: By analyzing delays in supplier deliveries or production, businesses can adjust schedules for better efficiency.
- Improve Inventory Management: Process mining can reveal inefficiencies in stock replenishment and demand forecasting.
- Enhance Supplier Collaboration: Identifies inefficiencies in supplier interactions, helping businesses streamline procurement workflows.
- Detect Fraud & Compliance Issues: Ensures that transactions align with company policies and regulatory requirements.
4. What industries benefit the most from process mining?
While process mining is valuable across industries, some sectors benefit more due to their complex workflows:
- Manufacturing: Optimizes production lines, quality control, and supply chain logistics.
- Healthcare: Improves patient flow, billing, and compliance with regulations like HIPAA.
- Banking & Finance: Enhances fraud detection, compliance, and customer service processes.
- Retail & E-commerce: Streamlines order processing, returns management, and inventory control.
- Telecommunications: Helps in optimizing network operations, customer support, and billing systems.
5. How does process mining handle unstructured data within business processes?
Process mining primarily works with structured event logs, but recent advancements allow it to analyze unstructured data from emails, chat logs, PDFs, and other text-based sources. Techniques such as:
- Natural Language Processing (NLP): Converts text-based interactions into structured data for analysis.
- Optical Character Recognition (OCR): Extracts data from scanned documents or images.
- AI & Machine Learning: Helps categorize and interpret unstructured data to integrate with process mining workflows.
