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One of the first things that you learn in the process mining methodology is how to filter out incomplete cases to get an overview about what the regular end-to-end process looks like.
Incomplete cases are process instances that have not finished yet. They are somewhere “in the middle” of the process. You typically remove such incomplete cases, for example, when you analyze the average case duration because the case duration of incomplete cases is misleading. The time between the first and the last event in an incomplete case can appear to be very fast, but in fact only a fraction of the process has taken place so far: If you would have waited a few more days, or weeks, then more activities would likely have taken place.
But what if you are exactly interested in those incomplete cases?
For example, you may want to know how long they have been open, how long nothing happened since the last activity or status change, and which statuses accumulate the most and most severe open cases without any action afterwards? These may be cases, where the customer—unnoticed by the company—has been already waiting for a long time and is about to be disappointed.
In this article, we show you how you can include the perspective of open cases in your process mining analysis. We provide detailed step-by-step instructions (download Disco if you have not done so yet) to follow along.
1. Apply filter to focus on incomplete cases
As a first step, we need to filter our data set to focus on the incomplete cases. One typical way to do that is to use the Endpoints filter, where you can first select the expected endpoints, and then invert the selection (by pressing the half-filled circle next to the search icon in the upper right corner of the filter settings).
Another way to filter incomplete cases is to focus on whether neither of the expected milestones in the process has been reached using the Forbidden mode in the Attribute filter. For example, in a customer refund process, these milestones may be activities such as ‘Canceled’, ‘Order completed’, and ‘Payment issued’, because they indicate that the refund order is not open anymore for the customer (see below – click on the screenshot to see a larger version).
The difference between using the Attribute filter and using the Endpoints filter is that with the Forbidden mode of the Attribute filter we do not care about what exactly the last step in the process was. Instead, we want to base our incompleteness evaluation on the fact that a specific activity has not (yet) occurred. Read The Different Meanings of “Finished” to learn more about the differences between these definitions for complete cases.
For the refund process, we use an Attribute filter in Forbidden mode, in which we select the milestone activities that indicate a completion, a cancellation, a payment, or a rejection of the refund request. This removes all cases that have reached one of these milestones somewhere in the process. In addition, we combine this Attribute filter with an Endpoints filter that removes all refund requests for which we are currently waiting for the customer in the ‘Missing documents requested’ activity (see screenshot below).
2. Export the filtered data set as a CSV file
The result is a process view that contains only those cases that are still open. As we can see, ca. 36% of the cases are incomplete in this data set (see screenshot below).
In this view, you can already see what the last activities were for all these open cases: The dashed lines leading to the endpoint indicate how many cases performed that particular activity as the last step in the process so far. For example, we can see that 20 times the activity ‘Invoice modified’ was the very last step that was performed.
However, what you cannot see here is for how long they have already been waiting in this state. The problem is that when you measure the case duration in process mining, then you always look at the time between the very first and the very last event in each case, irrespective of how long ago that “last event” was performed.
To find out how long these open cases have been idle after the last step (and for how long they have been open in total), we are going to use a trick and simply add a “Today” timestamp to the data. To do that, first export the incomplete cases data set using the ‘Export CSV file…’ button (see lower right corner in screenshot above).
3. Export the list of cases as a CSV file
We will need to add this artificial “Today” timestamp to the end of each of the open cases. To quickly get a list of the case IDs, switch to the ‘Statistics’ tab, right-click somewhere in the Cases overview table and choose the ‘Export to CSV…’ option (see screenshot below).
This will export a list of all open cases in a CSV file, one row per case.
4. Copy the Case IDs from the exported list of cases
Now, open the list of Case IDs that you just exported in Excel and select and copy the case IDs in the Case ID column to the clipboard (see screenshot below).
5. Append the Case IDs and add ‘Today’ timestamp
Paste the case IDs from the clipboard below the last row in the exported data file from Step 2 (see screenshot below).
Then, type the activity name “Today” in the activity column for the first newly added row. Furthermore, add a ‘Today’-timestamp to the timestamp column (see screenshot below). Make sure that you use exactly the same date and time pattern format as the other timestamps in your data set.
Which ‘Today’-timestamp should you use? If you have extracted your data set fairly recently (and you would assume that most cases that appear to be open in the data set are still open now), you can actually simply use your current date. Otherwise, look up the latest timestamp of the whole data set via the End timestamp in the overview statistics and use that date and timestamp to be precise. For example, 24 January 2012 was the last timestamp in the customer refund process.
Finally, copy the “Today”-activity name and the timestamp cells and copy them to the remaining newly added rows (see screenshot below).
6. Re-import the data to analyze open cases
If you now save your file and import it again into Disco, you will see that a new “Today” activity has appeared at the very end of the process (see screenshot below).
The main difference, however, will be in the performance analysis.
For example, if you switch to a combination of total and mean duration1 in the performance view of the process map (see screenshot below), then you will see that one of the major places in the process where cases are “stuck” is after the ‘Shipment via logistics partner’ activity. On average, open cases have been inactive in this place for more than 13 days.
Another example is the case duration statistics, which now reflect the accurate time that these incomplete cases have actually been open so far (see screenshot below). For example, the average time that incomplete cases have been open in this data set is 24.9 days.
There are situations, in which the activity name is only captured on a very technical level by the IT system (e.g., as an action code, a transaction number, or some other cryptic label). This is a problem. Not only because it makes it difficult for business users to understand the process map, but also because it becomes close to impossible for the process mining analyst to interpret what they are seeing. Therefore, we recommend to always take the time to enrich such technical activity labels by human-readable activity names.
For example, take a look at the following data set extracted by a Brazilian IT Service Management department (see below). The ‘task sequence’ column represents the status changes of the tickets in the IT Service Management system.
When you import the data into Disco to discover the process map1, you find that the activity names are shown as numbers (see below). For example, the first activity at the top is shown as ’10’, the second one as ’20’, etc. (click on the process map to see a larger version).
This is not practical, because—unlike you are so familiar with the IT system that you “think in” task sequence codes yourself—you will have a hard time to understand and interpret this process.
Even having a translation table on your desk and looking up individual activities (to see which activity belongs to which status code) is not a good idea, because the process maps that you discover with process mining get complicated very quickly already by themselves. You need to be able to build up a mental model of the process to deal with this complexity in your analysis.
So, in this article we show you step by step how you can add meaningful activity names to a data set that only has cryptic activity labels.
Step 1: Export the activities
First, you can export the list of all the different activities that are contained in your data set. To do this, you can go to the ‘Activities’ view in the ‘Statistics’ tab in Disco. Simply right-click somewhere in the activity statistics table and use the ‘Export CSV…’ option to save the activity statistics as a CSV file (see below).
You can then open the exported file in Excel (see below).
The ‘Frequency’ and ‘Relative frequency’ statistics are not needed for this use case and you can delete those columns.
Step 2: Mapping the activities
In the next step, you can add a new column and give the Excel sheet to the IT administrator of the system from which you extracted the data. Ask them to add a short description for each of the technical activity labels in your list.
Alternatively, you can also fill in a meaningful activity name yourself by looking at example cases and the process map together with a domain expert.
For example, for the IT Service Management process from before a column ‘ActivityLabel_PT’ has been added with the Portuguese and another column ‘ActivityLabel_EN’ for the the English activity name (see above).
Step 3: Apply the new mapping to your dataset
Now that we have the mapping, we need to apply it to the source data. Here, we show you two simple ways of how to do this in Excel. We will share alternative ways of relabeling activity names for data sets that are too large to be manipulated in Excel in an upcoming article.
The easiest way is to just use the ‘Find and Replace’ functionality in Excel (see below).
Copy and paste the column with the technical activity code. Choose a new heading for the new column to indicate that this is the new activity name.
Select the new column (to make sure only fields in this column are being replaced) and open the ‘Find and replace’ tool in Excel.
Don’t forget to check the ‘find entire cells only’ options, otherwise you may only replace part of the text.
Copy and paste the first technical activity code in the ‘Find’ and its new human-readable name in the ‘Replace with’ field.
Press ‘Replace All’.
Continue until all technical activity codes in the new column have been replaced.
The ‘Find and Replace’ method becomes a bit tedious if you have a large number of different activities. In such situations you can better use the VLOOKUP function in Excel.2
To do this:
Add a new tab called ‘Mapping’ to the source Excel file and copy the result from Step 2 above (without headings) to this new tab.
Then, go back to your source data tab and add a new column including a heading for the relabeled activity.
Add the following formula =VLOOKUP(C2,Mapping!A:C,2,FALSE) in the first cell of the newly created column.
You can then automatically apply this formula to all the rows in the new column by double-clicking on the bottom right corner of this cell.
In the screen above both the Portuguese as well as the English activity names have been added to the data in this way.
Step 4: Import the data with the new label
Now, you can save the result from the previous step as a CSV file from Excel and import the CSV file into Disco.
For the IT Service Management data set we can choose whether we want to see the Portuguese or the English activity names in the process map (see below).
You can still also use the technical activity label as the activity name if you want to. To do this, simply configure both columns as ‘Activity’ during the import step. For example, in the screen above we have included both the ‘task sequence’ column as well as the ‘ActivityLabel_EN’ column into the activity name.
The resulting process map contains activity names with the combination of both column values as shown below.
Finally, validate if your process after the mapping is the same as before. The relabeling should not change the process itself (just the names of the activities).
For example, the process map above is exactly the same as the one that we got in the very beginning. The only difference is that we have now meaningful activity names displayed in the process map.
Note that the process map has been simplified and, therefore, the numbers do not add up to 100%. You can learn more about when and how complex process maps can be simplified in our guide on Simplification Strategies for Process Mining. ↩
The VLOOKUP method also has the advantage that you can create more complicated mappings. For example, the original IT Service Management data set from this example actually had different activity names for the same task sequence codes depending on the IT Service Category. In such a situation, you can define the mapping as a combination of fields rather than a 1:1 mapping. ↩
For eight years, it has been an amazing experience for us to welcome process miners from all over the world at the annual Process Mining Camp. Also this year’s camp was fantastic! The atmosphere was great and there were a lot of inspiring talks by process mining professionals from many different areas.
Here is a short summary of this year’s camp. Sign up at the camp mailing list to be notified about next year’s camp and to receive the video recordings once they become available.
Opening Keynote
Anne Rozinat, co-founder of Fluxicon, opened the camp by emphasizing that it is an exciting time to be a process miner. The field is growing faster than ever before on a global scale. Fluxicon is very proud that professionals from 40 (!) different countries joined camp over the years to share best practices. It is also exciting to see that our academic initiative has exceeded the 600 universities mark.
For the professional, having a good tool for process mining is essential, but developing process mining as a discipline is the key to unlock the true potential. Besides extracting, preparing, and validating the data, you need to identify the best candidate process for process mining. Furthermore, you need to consider the impact and the ethical aspects of such an initiative. Then, you start your analysis by exploring the data and discovering the process, but you also have to choose the right moment to move into a more targeted analysis. Finally, being able to translate the insights into a solid business case and actual process change is crucial to realize the improvement opportunities.
For us at Fluxicon it is still amazing to see how people react when they first find out about process mining. It brings us back to the days when we were experimenting and could see our ideas work in practice for the first time. It is wonderful to see that process mining keeps on spreading across the globe; it is literally (almost) everywhere.
Process Miner of the Year
Kevin Joinson from GlaxoSmithKline was awarded the Process Miner of Year Award. He developed a new approach for cost deployment in manufacturing.
Cost deployment is a method from World Class Manufacturing, where an industrial engineering approach is taken to understand the cost of losses within an organisation (based on 100% of the cost). A key success factor was the involvement of the Subject Matter Experts (SMEs) and the initial segmentation of the data.
One of the results of Kevin’s work is that the processing time of the quality management processes could be improved by 22%. We will share his winning contribution with you in more detail in an upcoming, dedicated article.
Freerk Jilderda – ASML, The Netherlands
Freerk Jilderda from ASML kicked off with the first talk of the day. ASML is the leading developer of photolithography systems for the semiconductor industry. The machines are developed and assembled in Veldhoven and shipped to customers all over the world. Availability of the machines is crucial and, therefore, Freerk started a project to reduce the recovery time.
A recovery is a procedure of tests and calibrations to get the machine back up and running after repairs or maintenance. The ideal recovery is described by a procedure containing a sequence of 140 steps. After they identified the recoveries from the machine logging, they used process mining to compare the recoveries with the procedure to identify the key deviations. In this way they were able to find steps that are not part of the expected recovery procedure and improve the process.
Jozef Gruzman & Claus Mitterlehner – Raiffeisen Bank International, Austria
The second speakers were Claus Mitterlehner and Jozef Gruzman from Raiffeisen Bank International. They started process mining 12 months ago as a part of their smart automation portfolio to derive insights from process-related data at the bank. Since then, they were able to apply process mining on various processes such as: corporate lending, credit card and mortgage applications, incident management and service desk, procure to pay, and many more.
Based on their experience they have developed a standard approach for black-box process discoveries. Using process mining, they first explore and review the processes on their own (prior to the in-depth analysis with the subject matter experts). They illustrated their approach and the deliverables they create for the business units based on the customer lending process.
Zvi Topol – MuyVentive, United States
Zvi Topol from MuyVentive, was the third speaker of the day. He explored process mining for a completely new use case: The improvement of conversational interfaces.
Chatbots and voice interfaces such as Amazon Echo and Google Home are changing the way we interact with computers. Using natural language processing and machine learning, data scientists can detect the intents during the course of a conversation. Zvi added process mining on top of the detected intents to visualize the conversational flows. He showed how the discovery of conversational patterns can help to improve the customer experience of these conversational interfaces.
Bas van Beek & Frank Nobel – PGGM, The Netherlands
As the fourth speakers of the day, Bas van Beek and Frank Nobel showed how they made an impact with process mining at the Dutch pension provider PGGM. The process lies at the heart of most of their process improvement initiatives and it is always a multi-disciplinary effort. However, the nature of each initiative is quite different.
Some projects are more focused on the redesign or implementation of an IT solution. Others require extensive involvement from the business to change the way of working. Frank showed the difference in approach by two examples. Afterwards, Bas showed an example where they used process mining for compliance purposes. Because they were able to demonstrate that certain individual funds actually follow the same process, they could group these funds and simplify the audits by using generic controls.
Mark Pijnenburg & Carmen Bratosin – Philips Healthcare & ESI, The Netherlands
The fifth speakers, Mark Pijnenburg and Carmen Bratosin, applied process mining to the usage of MRI machines by physicians. Understanding the actual usage patterns in the field is especially interesting to improve the system requirements and to increase the test coverage based on real-life behavior for these machines.
But it is not easy, because the technical logging produced by the MRI machines is only available on a technical log level used for debugging. Furthermore, each physician has their own preferences regarding the machine setup for certain exams (adding to the complexity).
However, this did not stop Carmen and Mark. They started to select the key activities in the technical log, then aligned them with the user interface elements, and finally matched them with the steps described by the American College of Radiology to get them onto the abstraction level a radiology expert would understand. Following this approach, they were able to compare the actual usage with pre-defined exam cards.
Sudhendu Rai – AIG, United States
Sudhendu Rai, lead scientist and head of data driven process optimization at AIG, was the sixth speaker. He developed a ‘Process Wind Tunnel’ framework to evaluate and optimize process structure and parameters using real-world data prior to committing to a final process design. Not to test the aerodynamic qualities of aircraft models, but to test the qualities of future state processes.
The initial model needs to reflect the reality as closely as possible. Process mining is a great way to discover the key steps that need the be part of the simulation model. Furthermore, process mining helps to determine the probabilities of transitions and the distribution of the process times to populate the model.
Sudhendu then developed “What-If” scenarios that reflected alternative process re-designs of the current process. Using discrete event simulation he tested the impact of each scenario before making the decision to implement a change in the actual process. In this way he was able to find the best scenario and could reduce the cycle time from 12 days to 5 days, increasing the throughput by over 30%.
Boris Nikolov – Vanderlande, The Netherlands
The seventh speaker, Boris Nikolov, presented the application of process mining in logistic process automation. As a process improvement engineer, Boris supports customers by solving problems and by implementing new systems for baggage handling or parcel sorting and routing.
One of the customers in the parcel distribution center called Boris to solve a problem of recirculating parcels. Normally, parcels entering the system are scanned and routed to the right locations. However, a percentage of parcels kept circulating. Using the standard checks, he was not able to find the problem quickly and therefore tried to analyze it using process mining. In this way his was able to find that the lookup of the location of the parcels in the ERP was delayed and not known in time to be routed to the right location.
Besides solving problems, he also used process mining in the design stage of new baggage handling systems for airports. In order to save time, they develop simulation models to test if the design meets customer requirements. Data produced by the simulation models provided great insight when testing failure scenarios and helped to improve standard operating procedures.
Hadi Sotudeh – JADS, The Netherlands
Sometimes, we see an application of process mining that nobody thought of before. Hadi Sotudeh, PDEng student at JADS, had such an example when he applied process mining to data from the 2018 World Cup in football.
After transforming the data, he was able to explore the actions of the players but found that there was not one dominant pattern. He took various approaches to take other perspectives to discover patterns. He was able to look at interactions with individual players, zones in the field, and to see the patterns for a particular outcome (goal or throw-in). Because the football interactions do not follow a typical (standard) process, finding the right level is one of the challenges to get insights. Taking various perspectives can help to learn new things about the opponent pattern of play, or for a team to learn from mistakes.
Wil van der Aalst – RWTH Aachen, Germany
Wil van der Aalst gave the closing keynote at camp. He started with giving an overview of the progress that has been made in the process mining field over the past 20 years. Process mining unlocks great potential but also comes with a huge responsibility. Responsible data science focuses on positive technological breakthroughs and aims to prevent “pollution” by “bad data science”.
Wil gave us a sneak peek at current responsible process mining research from the area of ‘fairness’ (how to draw conclusions from data that are fair without sacrificing accuracy too much) and ‘confidentiality’ (how to analyze data without revealing secrets). While research can provide some solutions by developing new techniques, understanding these risks is a responsibility of the process miner.
Second Day: Workshops
The majority of the campers stayed for the second day to join one of the four workshops. In the workshops, (1) Rudi Niks explained how to improve digital processes when using process mining in each of the stages in the Lean Six Sigma improvement methodology. (2) Wesley Wierz and Rick van Buuren guided the workshop participants though the steps of extracting event logs from an ERP. (3) Andrés Jiménez Ramírez and Hajo Reijers discussed the combination of Robotics Process Automation (RPA) and process mining in their workshop. (4) Anne Rozinat taught the participants how to answer 20 typical process mining questions.
And, of course, during the breaks people got the chance to discuss and learn from each other.
We would like to thank everyone for the wonderful time at camp, and we can’t wait to see you all again next year!
The final speaker at Process Mining Camp 2018 was Wil van der Aalst, the founding father of process mining. In his closing keynote, Wil talked about the updated skill set that process and data scientists need today. Since process mining research was starting up in Eindhoven in the late 90s, the availability of suitable data has increased tremendously, which makes it even more important that this data can and will be used in an appropriate and responsible manner.
This requires dedicated capabilities from the process miner in each stage of the analysis pipeline: Processing and analyzing data, being responsible about the effects on people, and on business models. When you look for people who are skilled in all of these technical areas, as well as in soft skills like communication and ethics, you start looking for (as they would say in the Netherlands) a “sheep with 5 legs”, or something that is very rare. Becoming a data scientist requires a lot of effort to learn all the skills that are needed to live up to these high expectations.
As ambassadors of process mining, we also have the responsibility to use the right terms. Wil sees a clear a distinction between Artificial Intelligence (AI), machine learning, and data mining. At the same time, one could argue that process mining is data mining, but the underlying techniques are very different. So, saying that process mining is part of data mining, or AI, doesn’t make any sense.
There are incredible expectations around AI and Big Data, which is very dangerous as we have seen in past “AI winters”. We should be careful not to overpromise and try to be realistic. The incredible successes of machine learning techniques like deep learning are, for example, still limited to very specific fields.
Some in the media and big technology companies use terms like Artificial Intelligence, Machine Learning, and Deep Learning interchangeably. They might argue that you don’t need process mining as you can just put an event log into a deep neural network and a process model will come out. There is, however, not one deep learning algorithm that can discover a process model. Instead, when we look at process mining it combines the fields and methods of ‘process science’ and ‘data science’. This makes it even more challenging for us to cover all required skills.
But do you, as a professional, need to know how a car works internally in order to drive it? It depends on what you want to accomplish. For example, if you need to drive fast around the Nurburg Ring, it can be very useful. Also, if you need to select a car then it would certainly be useful to know about its internals. Process mining is a relatively young technology. Therefore, it is useful to know how it works in order to select the right tool, and to use it to maximum effect.
So what kind of skills do you need as a process miner? You need to be able to extract and clean the data, spend time on the analysis, and interpret the results. This is not easy. All the involved parties need to invest the time to determine what the process maps actually mean, so that they can really trust their interpretation. The sheep with five legs would be the ideal process miner, but in most cases this is not realistic.
Traditionally, you will often rely on collaboration between a data-driven expert and a business/domain-driven expert. However, you can also think about more hybrid process mining profiles. Some experts can integrate technological skills into their domain knowledge, while other data scientists can be process mining experts which are especially skilled to perform specific types of analysis in a particular domain.
Do you want to know what kind of process miner you could become? Watch Wil’s talk now!
The sixth speaker at Process Mining Camp 2018 was Marc Tollens, a digital product owner at Air France KLM. In order to compete in the aviation market, KLM focuses on providing an excellent customer experience. They adopted the agile methodology to be able to reduce the time to introduce new touch points in the traveler’s journey. The performance of the agile teams is crucial to get the most out of each sprint.
As a Sunday afternoon project, Marc collected data from Jira, a project management software for (agile) software development, to see how process mining could be applied to help the teams learn from each other. Surprisingly, the flow of each team was quite different.
Marc started comparing the flows of the three teams to identify key differences in behavior and the resulting effects. In this way he could see if a team was able to deliver what they promised and whether they started testing too early or too late. He shared his observations with the teams during the retrospective and returned for the next sprint to see how the improvement worked out.
The fifth talk at Process Mining Camp was given by Olga Gazina and Daniel Cathala from Euroclear. As a data analyst at the internal audit department Olga helped Daniel, IT Manager, to make his life at the end of the year a bit easier by using process mining to identify key risks.
She applied process mining to the process from development to release at the Component and Data Management IT division. It looks like a simple process at first, but Daniel explains that it becomes increasingly complex when considering that multiple configurations and versions are developed, tested and released. It becomes even more complex as the projects affecting these releases are running in parallel. And on top of that, each project often impacts multiple versions and releases.
After Olga obtained the data for this process, she quickly realized that she had many candidates for the caseID, timestamp and activity. She had to find a perspective of the process that was on the right level, so that it could be recognized by the process owners. In her talk she takes us through her journey step by step and shows the challenges she encountered in each iteration. In the end, she was able to find the visualization that was hidden in the minds of the business experts.
Do you want to see how Olga was able to find the right perspective in her spaghetti process? Watch Olga’s talk now!
The fourth speaker at Process Mining Camp 2018 was Wim Kouwenhoven from the City of Amsterdam. Amsterdam is well-known as the capital of the Netherlands and the City of Amsterdam is the municipality defining and governing local policies. Wim is a program manager responsible for improving and controlling the financial function.
A new way of doing things requires a different approach. While introducing process mining they used a five-step approach:
Step 1: Awareness
Introducing process mining is a little bit different in every organization. You need to fit something new to the context, or even create the context. At the City of Amsterdam, the key stakeholders in the financial and process improvement department were invited to join a workshop to learn what process mining is and to discuss what it could do for Amsterdam.
Step 2: Learn
As Wim put it, at the City of Amsterdam they are very good at thinking about something and creating plans, thinking about it a bit more, and then redesigning the plan and talking about it a bit more. So, they deliberately created a very small plan to quickly start experimenting with process mining in small pilot. The scope of the initial project was to analyze the Purchase-to-Pay process for one department covering four teams. As a result, they were able show that they were able to answer five key questions and got appetite for more.
Step 3: Plan
During the learning phase they only planned for the goals and approach of the pilot, without carving the objectives for the whole organization in stone. As the appetite was growing, more stakeholders were involved to plan for a broader adoption of process mining. While there was interest in process mining in the broader organization, they decided to keep focusing on making process mining a success in their financial department.
Step 4: Act
After the planning they started to strengthen the commitment. The director for the financial department took ownership and created time and support for the employees, team leaders, managers and directors. They started to develop the process mining capability by organizing training sessions for the teams and internal audit. After the training, they applied process mining in practice by deepening their analysis of the pilot by looking at e-invoicing, deleted invoices, analyzing the process by supplier, looking at new opportunities for audit, etc. As a result, the lead time for invoices was decreased by 8 days by preventing rework and by making the approval process more efficient. Even more important, they could further strengthen the commitment by convincing the stakeholders of the value.
Step 5: Act again
After convincing the stakeholders of the value you need to consolidate the success by acting again. Therefore, a team of process mining analysts was created to be able to meet the demand and sustain the success. Furthermore, new experiments were started to see how process mining could be used in three audits in 2018.
For Wim process mining is a discipline, not only a tool. Therefore, you need to find the right balance between the process, the tools and the people. Firstly, if you focus too much on your own results you will limit the learning experience organization wide. Secondly, the more pressure we put on others the less results will be achieved for the organization as a whole. Finally, you need to inspire others and let process mining grow.
Do you want to learn from the best practices from the City of Amsterdam and grow your own processes? Watch Wim’s talk now!
The third speaker at Process Mining Camp 2018 was Dinesh Das from Microsoft. Dinesh Das is the Data Science manager in Microsoft’s Core Services Engineering and Operations organization.
Machine learning and cognitive solutions give opportunities to reimagine digital processes every day. This goes beyond translating the process mining insights into improvements and into controlling the processes in real-time and being able to act on this with advanced analytics on future scenarios.
Dinesh sees process mining as a silver bullet to achieve this and he shared his learnings and experiences based on the proof of concept on the global trade process. This process from order to delivery is a collaboration between Microsoft and the distribution partners in the supply chain. Data of each transaction was captured and process mining was applied to understand the process and capture the business rules (for example setting the benchmark for the service level agreement). These business rules can then be operationalized as continuous measure fulfillment and create triggers to act using machine learning and AI.
Using the process mining insight, the main variants are translated into Visio process maps for monitoring. The tracking of the performance of this process happens in real-time to see when cases become too late. The next step is to predict in what situations cases are too late and to find alternative routes.
As an example, Dinesh showed how machine learning could be used in this scenario. A TradeChatBot was developed based on machine learning to answer questions about the process. Dinesh showed a demo of the bot that was able to answer questions about the process by chat interactions. For example: “Which cases need to be handled today or require special care as they are expected to be too late?”. In addition to the insights from the monitoring business rules, the bot was also able to answer questions about the expected sequences of particular cases. In order for the bot to answer these questions, the result of the process mining analysis was used as a basis for machine learning.
Do you want to know more about the combination of process mining and machine learning? Watch Dinesh’s talk now!
Have you always wanted to meet other process miners in person? Perhaps you followed the MOOC and would like to share your experiences with people who are also just starting out. Or you have already worked with process mining for several years and now you want to learn from other organizations about how they made the next step?
For the eighth time, process mining enthusiasts from all around the world will come together in the birth place of process mining1. We are already super excited to meet you all, and we are very proud of the fact that Process Mining Camp is just as international as the process mining community itself. Over the past years, people from 34 different countries have come to camp to listen to their peers, share their ideas and experiences, and make new friends in the community.
The first day (Thu 20 June) will be a day full of inspiring practice talks from different companies, as you have seen at previous camps.
We are excited to tell you that the following speakers will share their experiences in their practice talks at this years’ camp:
Freerk Jilderda — ASML, The Netherlands
ASML provides chip makers with everything they need to mass produce patterns on silicon, helping to increase the value and lower the cost of a chip. The key technology is the lithography system, which brings together high-tech hardware and advanced software to control the chip manufacturing process down to the nanometer. All of the world’s top chipmakers like Samsung, Intel and TSMC use ASML’s technology, enabling the waves of innovation that help tackle the world’s toughest challenges.
Freerk Jilderda is a project manager running structural improvement projects in the Development & Engineering sector. In this talk he will outline the use of data analytics and process mining to analyze and improve lithography system start and calibration sequences, resulting in higher system availability.
Sudhendu Rai — AIG, United States
With roots that trace back to 1919, AIG is a global insurance company with operations in more than 80 countries and jurisdictions. AIG provides a range of insurance products to support clients in business and in life, including: general property/casualty, life insurance, and retirement and financial services through General Insurance, Life and Retirement and Investments business units.
Sudhendu Rai is a Lead Scientist and Head of Data-Driven Process Optimization in the COO Office of AIG’s Investments organization. In his talk, Sudhendu will discuss their ‘Process Wind Tunnel’ framework that utilizes data analytics, visualization, process mining and discrete-event simulation optimization for improving insurance business processes within AIG.
Carmen Bratosin & Mark Pijnenburg — ESI & Philips Healthcare, The Netherlands
ESI is an independent research organisation for high-tech embedded systems design and engineering. Philips Healthcare is a global maker of many healthcare products, among which are imaging systems such as X-Ray, CT, Fluoroscopy and Magnetic Resonance Imaging (MRI) machines.
Carmen Bratosin is a research fellow at ESI and Mark Pijnenburg is a Clinical Verification Lead at Philips Healthcare. Mark and Carmen will show how process mining can be used to analyze the system usage of an MRI machine. It helps to understand how the customer (the physician) uses the MRI system, and how its behavior deviates from the expected (and designed) behavior. But to get to the actual process mining analysis, the low-level technical system log data of the MRI machine first needs to be prepared in several ways.
Jozef Gruzman & Claus Mitterlehner — Raiffeisen Bank International, Austria
Raiffeisen Bank International (RBI) is a leading Retail and Corporate bank with 50 thousand employees serving more than 14 million customers in 14 countries in Central and Eastern Europe.
Jozef Gruzman is a digital and innovation enthusiast working in RBI, focusing on retail business, operations & change management. Claus Mitterlehner is a Senior Expert in RBI’s International Efficiency Management team and has a strong focus on Smart Automation supporting digital and business transformations. Together they will show how RBI started its process mining journey, how process mining fits into their Smart Automation portfolio, and in which areas of the Bank they have made discoveries so far. Based on a concrete Use Case Josef and Claus will show you how they assess and discuss their process mining findings.
Boris Nikolov — Vanderlande, The Netherlands
Vanderlande is the global market leader for value-added logistic process automation at airports, and in the parcel market. The company is also a leading supplier of process automation solutions for warehouses. Vanderlande’s baggage handling systems move 3.7 billion pieces of luggage around the world per year, in other words 10.1 million per day. Its systems are active in 600 airports including 13 of the world’s top 20.
Boris Nikolov is a Process Improvement Engineer at Vanderlande. In this talk, he will tell us how they use process mining to gain insight on how to validate and optimize test scenarios during some of the most critical phases of a project — acceptance testing and operational trials.
Bas van Beek & Frank Nobel — PGGM, The Netherlands
PGGM is a non-profit cooperative pension administration organization. They are founded by the social partners in the care and welfare sector and serve 750.000 employees and pensioners.
Bas van Beek is process consultant and Frank Nobel is process and data analyst at PGGM. In their talk, they will show how process mining goes further than unveiling the bottlenecks in their processes. Discovering and analyzing the process is often the starting point to develop a solution. They show how the goal and approach of the analysis are slightly different when you decide to start a Lean Six Sigma or compliance initiative compared to, for example, the goal of automating tasks, developing a data science or robotics process automation solution.
Zvi Topol — MuyVentive, United States
MuyVentive, LLC is an advanced analytics R&D company focusing on AI/ML and Conversational Analytics work.
Zvi Topol is a Data Scientist and CEO at MuyVentive. In his talk, Zvi will show how to leverage process mining techniques to improve natural language interfaces. Based on an example using the Microsoft Cognitive Services LUIS API, Zvi will show you how conversational data from chatbot interactions with customers can be transformed into structured data, which in turn can then be analyzed further with process mining techniques.
Keynote by Wil van der Aalst
At the end of the first day, prof. Wil van der Aalst will give a closing keynote about the topic of Responsible Data Science for Process Miners.
Wil van der Aalst — RWTH Aachen University, Germany
Data Science techniques can run the risk of enabling systematic discrimination based on data, invasion of privacy, non-transparent life-changing decisions, and inaccurate conclusions. We use the term “Green Data Science” for technological solutions that enable individuals, organizations, and society to reap the benefits from the widespread availability of data while ensuring fairness, confidentiality, accuracy, and transparency.
Wil’s keynote will give you a sneak peek into the latest research in responsible data science. He will show the results from two ongoing research projects that focus on fairness in the process mining analysis and on the analysis of anonymized data.
Wil van der Aalst is the founding father of process mining. He started to work on “workflow mining”, as it used to be called, way back when nobody even thought the necessary data existed. As a full professor at RWTH Aachen University, Wil has supervised countless PhD and Master students on the topic and is head of the IEEE Task Force on Process Mining. He is the author of the book “Process Mining: Data Science in Action” and the creator of the popular Process Mining MOOC.
Day 2: Workshops on 21 June
On the second day (Fr 21 June), we will have a hands-on workshop day. Here, smaller groups of participants will get the chance to dive into various process mining topics in depth, guided by an experienced expert.
Participation in workshops is of course optional, but if you want to hone your craft and focus on your topic of choice with a group of like-minded process miners, you will fit right in! The workshops take place in the morning and all four workshops will run in parallel (so you need to pick one).
Workshop 1 · How to improve processes in the digital age?
Rudi Niks, Fluxicon
Digital transformation does not only impact the expectation of the customer. It also impacts the techniques and methods that companies use to delight customers every day. The DMAIC (Define, Measure, Analyze, Improve and Control) improvement cycle lies at the heart of the Six Sigma methodology. Process mining is a great addition for the Lean Six Sigma practitioner to understand and analyze the real complexity of the value streams.
In this workshop we will go step by step through a typical Lean Six Sigma project and experience together how process mining can be used in each stage of the DMAIC.
Rudi Niks has been one of the first process mining practitioners. He has over ten years of experience in creating value with process mining as a Lean Six Sigma Black Belt. At Fluxicon he ensures that Disco miners are the best process miners in the world.
Workshop 2 · From ERP system to dataset: How do I prepare a useful event log?
Wesley Wiertz and Rick van Buuren, Sifters
Preparing a high-quality eventlog from an ERP-system can be a considerable challenge for organizations. Retrieving the raw data from the system or its database is often the first hurdle. Then, when the data is available, its amount and complexity can be overwhelming. Finding the relevant pieces of information is like finding a needle in the haystack. Finally, you need to make sure that the data correctly reflects the process, which is essential to be able to rely on the findings and to convince stakeholders.
How to overcome these challenges is the topic of this workshop. You will be guided through the process of data preparation and we will demonstrate the pitfalls and best practices step by step.
Wesley Wiertz and Rick van Buuren have extensive experience in the fields of financial audits, IT audits, and business intelligence. With a strong focus on compliance and traceability, they now focus on helping clients with the extraction of relevant data and the preparation of high-quality, validated event logs for process mining.
Workshop 3 · How can I combine process mining with RPA?
Andrés Jiménez Ramírez, Universidad de Sevilla and Hajo Reijers, Utrecht University
The lifecycle of any Robotic Process Automation (RPA) project starts with the analysis of the process that should be automated. This is a very time-consuming phase, which in practice often relies on the study of process documentation and on interviews with subject matter experts. Process mining can help to discover the actual process based on IT data, but the data that is collected from the IT systems is often too detailed to be used directly.
We will walk you through a possible transformation of low-level screen-mouse-key-logger data (a sequence of images, mouse actions, and key actions stored along with their timestamps) into a UI log that can then be analyzed with process mining techniques. We will also discuss the different scenarios in which it makes sense (and in which it does not make sense) to apply process mining in RPA projects.
Andrés Jiménez Ramírez is assistant professor at Universidad de Sevilla and Hajo Reijers is a full professor at Utrecht University. They have a lot of experience with process mining and applied process mining in several real-life RPA projects.
Workshop 4 · What questions can I answer with process mining?
Anne Rozinat, Fluxicon
When you start out with process mining, it is often a bit of a chicken-and-egg problem: You are supposed to start with questions about your process, but which kinds of questions can you actually answer with process mining?
We will give you 20 typical process mining questions as a starting point and show you how to answer them. In this workshop, you will work hands-on with multiple data sets to understand the different approaches for measuring your process performance, analyzing compliance, and answering other process mining questions.
Anne Rozinat is the co-founder of Fluxicon and working with process mining every day. She has obtained her PhD Cum Laude in the process mining group at Eindhoven University of Technology and has given more than 100 process mining trainings over the past years.
Get your ticket now!
Process Mining Camp is not your run-of-the-mill, corporate conference but a community meet-up with a unique flair. Our campers are really nice people who do not just brag about their successes but also share their pitfalls and failures, from which you can learn even more than from stories that go well. In addition, you will get lots of ideas about new approaches and use cases that you have not considered before.
Tickets for both the camp day and for the workshops are limited. To avoid disappointment, reserve your seat right away.
Eindhoven is located in the south of the Netherlands. Next to its local airport, it can also be reached easily from Amsterdam’s Schiphol airport (convenient, direct train connection from Schiphol every 15 minutes, the journey takes about 1h 20 min). ↩
Process Mining Camp is coming closer! This year’s camp takes place on 20 & 21 June, so keep these days free in your agenda. The program will be announced shortly and you can sign up at the camp mailing list to be notified as soon as the registration opens.
Meanwhile, we have started to release the videos from last year’s camp. You can already watch the video of Fran Batchelor from UW Health here. The second speaker at Process Mining Camp 2018 was Niyi Ogunbiyi from Deutsche Bank in the United Kingdom. Niyi is a Six Sigma Master Black Belt in the Chief Regulatory Office at Deutsche Bank.
Niyi started with process mining on a cold winter morning in January 2017, when he received an email from a colleague telling him about process mining. After searching the internet, he started the MOOC and shared his ideas with the team. They also got very excited to see what they would be able to do with it. They started with their proof of concept in October the same year. In his talk, he shared his process mining journey and the five lessons they have learned so far.
1. Be Persistent & Inventive
His first lesson was to be persistent to get the right people on board to secure the required sponsorship and the funds to get started. Also, getting the data can be challenging. Therefore, you need to be inventive and sometimes try to find other ways to get your hands on a dataset to just get started.
2. Be Clear What Process Mining Can & Can’t Do
The second lesson was to understand what process mining can, but also what it cannot do. To figure this out, they needed to take a step back and look at their current approach. Traditionally, process discovery is done by conducting interviews, which can take a lot of time. Additionally, the resulting model does not always reflect the reality. They saw that process mining could contribute to perform these analyses more quickly and with a higher precision. Another benefit was that with process mining they could test their process for conformance more easily on large data sets instead of manually reviewing process conformance manually based on a smaller sample of cases. Nevertheless, they also learned that process mining would not answer all the process-related questions and that domain expertise is required to be able to translate the insights into actions.
3. Find The Right Balance Between Targeted vs. Untargeted Exploration
When performing their analysis, they found that they were initially spending a lot of time on explorative (untargeted) analysis. While this was fun, and while it revealed a few things about the process that they would not have even thought to ask questions about, the insights from these explorations were often difficult to translate into action and were more anecdotal. In order to become more focused in their analysis they developed templates to answer questions that were relevant to their stakeholders. For example, understanding the relations between the lead time and rework and the case variations. This approach helped them to keep focusing on the relevant factors with the biggest impact. Niyi recommends to spend not more than 30% of your time on untargeted, explorative analysis and 70% on targeted, question-focused analysis, which was the third lesson.
4. Relate Analysis Results To Stakeholders’ Pain
Fourthly, in order to make the insights actionable you need to be able to relate them to the stakeholder’s pains and gains. Ideally, you can relate the analysis results to problems that otherwise keep the process manager up at night. This will really help to make them care about your analysis and they will help you to drive the actual change that needs to happen after the process mining analysis to realize the benefits. But also give them a clear understanding about the overall opportunities to improve and help them to determine if they are working on the right improvement initiatives.
5. Celebrate Your Successes & Cut Your Losses
Finally, they were able to complete the proof of concepts and continue with a number of other projects. Often, when you have completed something you immediately move on to the next. But in order to build resilience, don’t forget to also take a moment to celebrate and pat yourself on the back. Also: Be realistic and cut your losses when things don’t work out or are just too ambitions.
As a next step Niyi and his team are selecting more processes for process mining. For example, they are looking into employee trading to check conformance and the combination of process mining with RPA. Process mining is great to identify which activities would be the best candidate to automate and to estimate the benefits. Finally, they also see great potential for process mining in fraud detection and are experimenting with this.
You are reading Flux Capacitor, the company weblog of Fluxicon. Here, we write mainly about Process Mining, the things we're up to, and anything really.
We make Disco, the most powerful, user-friendly, and popular process mining software in the world. You should check it out and download your free demo version here!
Whether you are a beginner, or an experienced process mining practitioner — you may want to join one of our popular Process Mining Trainings, given every few weeks by experienced guides. We hear they're pretty great.
And if you're more the book worm type, go and read your heart out with our brand new Process Mining Book, which has everything to get you started and much more!
info@fluxicon.com