Now organizations are registering greater amounts of data to analyze processes. Unfortunately, traditional data mining and business intelligence techniques aren’t enough to analyze business processes, as they do not have clear process orientation techniques.
Process Mining, on the other hand, combines data mining and model-based process analysis and therefore drives improvement of such business performance factors as operational efficiency.
By using Process Mining software, enterprises can achieve greater financial and operational efficiency.
If you are looking for ways to improve operational efficiency, you have definitely asked yourself:
What really occurs in a specific process?
Are there deviations from the intended process and if so, what are they?
What are the bottlenecks?
How can the process be optimized?
All these questions can be answered by using Process Mining.
The Benefits of Process Mining
At the present time Process Mining generates valuable insights that improve current processes.
Intuitive and easy to use, process visualization greatly speeds up insight generation time. It lets you investigate what the process really was. It is a fast and easy technique to control and improve your processes.
Process Mining tools help to detect issues like supply chain delays and understaffing earlier, hence increasing efficiency.
Therefore, process variations are easily discoverable, which leads to realizing best practices and complete alignment of processes.
As a result, the operational efficiency and quality of processes such as audit increases tremendously.
Analyze, adjust and track results
When analyzing Process Mining outcomes, right actions can be taken to improve efficiency. With help of advanced Process Mining tools these actions can in turn be easily tracked. In the finaly analysis a business analyst can compare process models’ variations and visually observe the impact of adjustments.
As a result of increased operational efficiency there can be more finn the financial savings gained through acting on insights into business processes.
Process Mining vs. Traditional process discovery
In traditional process discovery, companies turn to external management consultants to discover their processes via workshops and interviews.
Why is doesn’t always work?
Because it is time consuming. Besides, it binds resources, since the interviewees cannot perform other work. Therefore, we end up with a risk of political deadlock.
In addition such methods provide an incomplete picture and can be very costly. Replication is also difficult, as analyzing in this way comes along with inherent subjectivity and bias.
Process Mining on the contrary does not have these drawbacks. It is time efficient and actually frees up resources. Providing objective results, it cuts through political deadlock, and the results represent what actually happened.
In short, it generates a complete picture.
With Process Mining, there is no need for a team of expensive external management consultants to manually discover business processes.
The control over your business is now in your hands.
Continuous Monitoring of Processes
Again, Process Mining isn’t just a one-time exercise, it’s designed for continuous optimization of processes. It acts as automated process advice: automatically scanning processes for improvement opportunities and making proactive recommendations to end users.
It can even uniquely predict if a due date will be exceeded, using historical and current data. Not to mention predictions of the length of expected throughput time.
Management teams can accurately analyze their core operations and quickly identify any root causes of delays or bottlenecks. It addresses inefficiencies and non-compliance issues at their core and allows users to make more informed decisions about which areas need the most improvement.
Enterprises can avoid previously unforeseeable issues and are better positioned to meet or surpass customer expectations.
Using tags for functional insights
The dataset can be enriched through the use of tags, which add business logic to the data and are exclusively available in our platform.
Let say you want to discover the process KPI’s that are relevant to the business.
People will often tell you about their business rules when delivering a dataset. If these are not available, think about industry standards that can be applied to the process.
Tags allow you to equally use both case and event information.
Tag types like inefficiency, rework or violation can also be added. An example of an inefficiency tag type could be that the contract conditions were not checked properly. When multiple final checks of an invoice were needed, it could be a rework tag type. A service level agreement violation could be a violation tag type.
With Process Mining, these tags can be seen faster and easier, so that unwanted events can be prevented. Besides, you can add ranks of importance or influence among tags, which will allow you to find cases that score high for specific tag types and pinpoint where to optimize them.
Business Information Systems
Many companies use Business Information Systems to collect, process, store and distribute information in so-called ‘event logs’ throughout your company. Among those you may find CRM, Accounting, ERP, BPM, deverse data bases and other tools.
This results in large amounts of data stored in your organization on who did what and when. Systems log massive amounts of information to compute process graph in a feasible way.
Imagine how much golden information is there.
With the right tool this big data can be used to gain insights in unexpected business processes, and find inefficiencies using Process Mining.
Process Mining tools help businesses highlight process deviations and pinpoint possibilities for improvement within their core operations.
Again, these deviations can be in terms of time, result or order. Hence, it uses the digital traces left behind by every IT-driven operation in a company.
With all these advantages, why wouldn’t every business use Process Mining to improve operational efficiency?
In recent years, computation power has increased exponentially.
Data storage continues to grow.
It’s obvious – the need for Process Mining techniques has well and truly arrived!