When you start out with process mining, it is often not so easy to know where to start. Which process should you pick first? And which process might be less suitable for your process mining project?
Data availability and process awareness
One way to look at multiple candidates is to assess the data availability and the level of process awareness for each potential project (see the two axes in the image at the top of this article).
Data availability refers to whether there is an IT system that supports the process and collects data about the process steps that were performed. Process awareness refers to the degree to which people know what the process is and how much they follow it.
In the picture above, we describe the four main situations that you can encounter.
1. Bottom-left: Low data availability and low process awareness
For some processes, there is not much data that can be found in the IT systems about the process steps that are performed and the process itself is not very defined either.
For example, an annual planning process that determines the strategy for the coming year may be repeated every year. However, the process is executed via a series of meetings and the outcome is documented in meeting notes and an updated strategy brief for the company.
For processes that should be moved out of this ad-hoc stage into a more structured approach, the focus is on defining the process and the involved process steps first. This definition is a prerequisite to either make the process more repeatable (moving it upwards) or to introduce an IT system that supports the steps of the process in a more direct way (moving it to the right).
Suitability for process mining: Ad-hoc processes are less suitable for process mining. They typically not only lack the data that is needed for the process mining analysis but also the volume and process understanding to make such an analysis worthwhile.
2. Top-Left: Low data availability and high process awareness
Some processes are well-defined already but are still happening in a manual way.
For example, imagine a building permit process at a municipality that has not been digitized yet. The process is clear, documented, and people follow it. However, there is no data available in an IT system that reflects the steps that were taken by the municipality.
The focus for processes in this stage is often on the digitization, which means that an IT system (e.g., a workflow system, a case handling system, or an ERP system) is introduced that supports the execution of the process.
Suitability for process mining: Because of the lack of data, manual processes are not directly applicable for process mining analyses. However, because of the high process awareness it is worthwhile to:
Already collect some data manually. For example, in his Process Mining Camp presentation, Jan Vermeulen from Dimension Data talked about the sales process and how they temporarily collected data in a manual way to gain more visibility into this process.
Make sure that in its digitization trajectory the right data will be collected, so that process mining analyses are possible in the future.
3. Bottom-Right: High data availability and low process awareness
Some processes are already supported by IT systems (i.e., they are digitized) but they are quite unstructured and the system allows for a lot of flexibility in the process.1
For example, many hospitals have introduced electronic healthcare systems (e.g., an EHR system) that facilitates and documents the individual steps that are performed by the medical and administrative staff in the hospital. However, the patient flows themselves are highly individual and the doctors have the full flexibility in this process.
If the interest is there to better understand and improve such unstructured processes, then process mining is an excellent way to discover the actual processes that are performed. This discovery is then the basis to take the process and further define and standardize parts of it.
Suitability for process mining: Unstructured processes are a very interesting application area for process mining. The data is already available, but it is crucial that people with the right domain knowledge are involved to help deal with the complexity and to separate the different types of process that exist within the overall process domain.
4. Top-Right: High data availability and high process awareness
Once a process is fully defined and supported by an IT system, you can consider it to be an automated process. However, there are different degrees of automation that you will find here.
For example, a newly digitized ERP process may be still happening through mostly manual activities (performed by an employee via the IT system) but the routing of which step is next is defined in a relatively fixed way. Another scenario is a Straight Through Processing (STP) workflow process that fully automates the handling of travel expense reimbursements for most employees but has manual steps that are performed for some of the cases (e.g., based on a random sampling or particular process rules). Finally, a completely automated production process might not involve any manual activities anymore at all.
The focus in this stage is on the (further) optimization of the process. Typically, this is an iterative process that happens in multiple improvement cycles.
Suitability for process mining: Process mining is very applicable for such processes, because both the data is available and the necessary process awareness is available to define new goals and further improvements. However the more automated a process already is, the less interesting the process mining analysis often becomes due to the diminishing returns in the actual process improvement. Once the process is fully automated, the focus shifts into monitoring and goes away from the understanding and improving of the process as process mining supports it.
In addition, you can ask yourself the following questions:
Why do we need to do something now? Find a project, which has a strong sense of urgency. This will help to get the buy-in of the people who are involved and, therefore, can help you to get the necessary resources.
What are the process related questions? Define early what the most important questions are that should be answered about each process. This will help to make sure that process mining is actually the right tool to answer these questions.
What is the smallest scope that still makes a significant impact? The first project should not be too big (see also this list of process mining success factors here). However, the scope should also not be too small to not be relevant anymore. Pick a project and a scope that allows you to create a success story that paves a way for your future projects.
Do you already have some ideas about which processes you could pick for your first process mining project? Start now and download the worksheet here to plot your candidates!
Note that the fact that a process is supported by an IT system does not mean that it is fully automated. There are many systems that record data about what is happening, but the process itself is fully driven by the people performing the process steps in it. ↩
This is a guest article by Carmen Bratosin from TNO. If you have a guest article or process mining case study that you would like to share as well, please contact us via anne@fluxicon.com.
Understanding how the customer uses the system, and how its behavior deviates from the expected (and designed) behavior, is the main question that Philips MR wanted to answer by usage profiling. Philips MR is a division of Philips Healthcare that builds systems for magnetic resonance imaging (MRI). MRI is a non-invasive diagnostic imaging method.
MR systems (see above) are heavily parametrized. This means that scan parameters like position, orientation, etc. can have different values configured for different applications. Furthermore, new methods appear constantly and guidelines for the usage of the MRI with respect to a particular diagnostic are vague most of the time.
Therefore, usage profiling for an MR system starts with answering how one can define usage. To be able to define ‘system usage’ in a way that it can be understood by the application specialists, we needed to overcome two main challenges:
1. The low-level scan parameters had to be translated into meaningful activities.
2. The ability of process mining to look at sequences of these activities was crucial to analyze the usage profiles in the context of the medical guidelines.
Data Abstraction
The MR system records very detailed information about which functions are used on the device and when. From a process mining perspective, the case ID is the so-called exam ID corresponding to a patient examination. The timestamps that are needed for process mining are also there. However, for the activity name this event data is too detailed (and too technical) for the application specialists who need to interpret the usage of the system from a medical perspective.
To bridge this gap, we took a step back and looked at how an application specialist looks at the usage process. An MRI examination is defined by its purpose (the diagnostic part) and by the applied methods. Therefore, we chose to abstract the purpose in terms of the anatomic region (the body part) that needs to be imaged. In terms of the method, practitioners use a set of scans to produce multiple images that will later on provide evidence for/against a particular diagnosis. So, from the many recorded events we only needed the actual scans.
For the scan events there were also a lot of parameters recorded. For example, the orientation or the contrast of the image can be configured differently for two different scans. Each scan is in fact defined by these parameters from a medical perspective. Different parameter combinations can be stored and configured when the machine is set up (and later during the usage period) to be re-used for different applications.1
So, the usage of an MRI system is defined by the performed examinations. At the lowest level, the usage is thus represented by the parameters of a scan. However, when trying to use all parameters used for a scan to define a scan we realize that comparing two scans becomes a highly complex task for two reasons: 1) for a specific scan, in average, less than 10% of the parameters are used and, 2), the parameter types are highly heterogeneous: categorical, numerical and Boolean.
A solution to the above challenges was found by mapping the logged parameters to so-called “tags” defined by MRI literature and, at the same time, selecting a reduced number of tags to represent a scan. For the mapping and selection, we used input from medical guidelines and practitioners.
This approach made scan parameters easily understandable by practitioners and facilitated an exam analysis based on expected behaviour and medical guidelines.
From Scan Parameters to Profiles
Figure 2 shows the implemented workflow to define and analyze the usage profiles. First, we defined a mapping from the actual scan parameters to “tags”. We use domain-specific language (DSL) technology (represented by a combination of Xtext/Xtend) to allow Philips specialists to define the mapping. Once such a mapping is created, the framework automatically generates python code that tags the extracted data.
Figure 2: Processing workflow for creating usage profiles
This processed data could now be analyzed with process mining techniques, because the activities were on the right level that MRI specialists could understand.
The big benefit of process mining is that to understand the usage profile of an MRI application you actually need to look at the sequence of scans (not just an individual scan). There could be same type of scan used in the context of a knee MRI as well as for a spine MRI, but the sequence will be different. So, to judge the usage profile one needs to look at the sequences of scans and this is what process mining now allows the application specialists to do.
Figure 3: Usage profile created through process mining software Disco
Figure 3 shows a process map that was created based on the tagged data. Each activity is defined by a combination of tags2. The top-most activity node consists of the tags “T2”, “SAG”, and “TSE”, which each refer to parameter configuration in the scan. If the parameter configuration is different than the tag will be different. For example, “T1” and “T2” are two different tags referring to different configurations of the same parameter in the scan.
Once the usage profile is obtained, a practitioner can compare the workflow with known medical guidelines (such as the ones provided by American College of Radiology – ACR).
Figure 4 shows an excerpt of the medical guideline for the MRI of an adult spine. This is the medical guideline that belongs to the usage profile shown in Figure 3. For example, the “T1” and “T2” in the medical guideline refer to the the same tag that has been matched from the event data in the discovered process map.
Note that the thickness of the edges in the process map in Figure 3 is correlated to the number of direct relations between the scans. The thicker the edge, the more frequently the relation is observed in the data.
It is easy to observe that most typical workflow is the one indicated in the guidelines: T1 Sagittal => T2 Sagittal => T2 Transversal (or Axial). However, a number of deviations are observed. These deviations are currently investigated by practitioners to understand whether there are special workflows employed by certain practitioners or there are anomalies due to system/user error.
Note that the set of parameters available for a scan depend on the characteristics of a particular system. Therefore, we decided to focus our investigation on a particular system release. ↩
To combine multiple columns into the activity name, these columns are all configured as ‘Activity’ during the import. ↩
This is a guest article by René Peter from Warehouse Totaal and by Joris Keizers from Veco. The article previously appeared in Dutch here. If you have a guest article or process mining case study that you would like to share as well, please contact us via anne@fluxicon.com.
Before he knew it, he was one of the three finalists. “Yes, and once you’re on stage, you obviously want to win as well.” In April 2018, Group Operations Manager Joris Keizers (45) became Logistics Manager of the Year with the application of a data analysis technique called Process Mining. Jury chairman René de Koster on this technology: “We think this is a fantastic tool that you can use in many organizations.”
“You are looking for the gold in a smart way.” With ‘gold’ Keizers is referring to the insights about where improvements can be made in the logistical process.
How do you apply such a process mining tool and what practical tips can be given to warehouse managers about it?
Congratulations on the title. How did you experience the election night?
Keizers: “I enjoyed it. The title is a very nice recognition for the work you do. Everyone in the company was also very happy. When I entered my office everything was decorated. Then you realize that it is indeed quite special what we have achieved together. ”
Did you think you would have a chance to win the title right from the start?
“Well, I saw from the beginning that all finalists had a very different profile, all with their own strong points. We are not a company with a large warehouse where trucks drive on and off continuously. We have a tiny 30 square meter warehouse. At the end of the day, a courier comes by to collect everything in one go.
Here in Eerbeek we make very small precision products from nickel, such as the sight of a shotgun, atomizers for medicines, or coding discs for robot arms. Only at the very end we see the result of the production process.
Therefore, it is very important for us to have as short as possible lead times. In order to achieve this, I started to study the application of Big Data & Data Science in the supply chain.”
Do you think that too little work is being done with Big Data in internal logistics?
“Absolutely. If you see what kind of data is already being collected in automated warehouses, I think there is still too little done with it. With every scanned barcode a timestamp is recorded along with a lot of other useful information: Who does what & when?
When you hear Big Data you may perhaps only think of big tech companies like Amazon, Google and Facebook that collect more data than they can process. The trick is to apply smart methods to get things out of the data based on which you can actually do something.
Many managers arrive with an ISO book when you ask how their processes are running. But that is only how it was once invented and does not guarantee that it will happen like that in the workplace. It obscures your view of the performance of the entire chain if you look no further.”
Is process mining such a smart method?
“Yes, it is a technique that allows you to make use of the available data in a smart way. It makes the performance of your process transparent.
With process mining, I can characterize all operations within our company, stored by our ERP system, with three different parameters. First the number of the production order it belongs to, second the workstation where it was executed, and thirdly when exactly the operation happened. With one production order many more workstations are involved than with others.
By letting smart algorithms have a go at this data, insightful patterns can be discovered. I can see which workstations always or never follow each other. It gives real insight into the problems in your business processes. The technique can show you how your process really works and whether it deviates from how it was designed in the beginning.”
Is it still too complicated for many logistics companies to apply?
“Perhaps it is also that the need is not really felt enough to do something with it. Yet the intralogistics world is very well suited to leverage Big Data. People who work there think in processes. On the other hand, it is not always easy: You can sometimes get dozens of different results out of your analysis.
I think that in internal logistics faster machines and robotics are mainly thought of instead of processes as a whole.”
How do you convert that ton of data into practical tools?
“I transfer the data from our ERP system to the process mining software of ‘Disco’. With the help of the algorithms from this program, the production orders are analyzed. I can run a replay of a certain time period and see through an animation how the orders run through the factory.
Where I see a delay, I can filter which orders this concerns. In such an animation I saw that almost all orders go through our measuring room. When I showed it to my team, it became a lot more insightful.
It appeals much more to the imagination than a graph or statistic. It communicates much easier, so you also appeal to a broader solution area for employees. ”
Where are the points for improvement?
“After such an analysis you can look much more focused at where you can improve. That is often not in the speed of machines, but especially in displacements, administrative preparation, waiting time, etc.
So you can put a lot of time into making machines faster, but then I may be optimizing only 20 percent of the whole process. So, I can better look at that 80 percent, trying to shorten the waiting time.”
Where is the biggest challenge for warehouse managers who want to get started with process mining?
“People with an analytical background can learn this fairly quickly. You can easily go through an order picking process through the software to see where the bottlenecks are.
An important condition is that you have as much unity as possible in the type of data that you import. In the future, I expect that such plugins for analysis can be integrated into WMS and ERP systems.
My message to logistics managers is also to look for new techniques to apply. Process mining is just one of the tools that you can use.”
Every year, it is a truly amazing experience for us to welcome process miners from all over the world at the annual Process Mining Camp. This year, people came together from 16 different countries!
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 process mining is more than just a tool — it is developing into a discipline. Over the years, more than 2000 process miners have been trained to apply process mining in practice. After completing the training, they understand the depth of the skills that are needed, and they often ask: “How can I become really good at this?”. Training is a good starting point, but you need to put your knowledge into practice to further develop your process mining skills.
Successful process miners have skills in four key areas (data, process, management, and leadership), based on which we have now developed a process mining certification framework. Knowledge about preparing your data, and being able to analyze your data from a process perspective, lie at the heart of the process mining skillset. But you also need to be able to lead the way to drive the business change to make a significant impact. Furthermore, managerial skills are crucial to realize and sustain the benefits within an organization.
Frank van Geffen (Rabobank) and Lucy Brand-Wesselink (ALFAM) are both leaders in the process mining field. They have managed to complete complex process mining projects with significant benefits, and they could prove that they fulfil all requirements to obtain the Process Mining Master certification. This excellence was also affirmed by their sponsors who co-signed their certification.
Fran Batchelor — UW Health, United States
Fran Batchelor was the first speaker of the day. Fran used to be a nurse practitioner specialized in surgery operation for many years. Today, she works as a nursing information specialist at UW Health, where she improves the surgical operations. One of her challenges was to find a way to best allocate operating room space for urgent and emergent surgical cases (so-called ‘Add on’ cases) which have to be handled on top of the scheduled surgical care. Some of the specialty services have dedicated hold rooms for their add-on cases while others have to fit them into their regular schedule. When two new operating rooms were built, departments were competing about who should get access to them as a hold room for add-on cases. With process mining, Fran analyzed the process flows of the add-on cases for these departments. She could show the impact that having a dedicated hold room has on meeting the internal performance metrics and, as a result of her process mining analysis, the decision about how to allocate these new resources was made differently than initially planned.
Niyi Ogunbiyi — Deutsche Bank, United Kingdom
Niyi Ogunbiyi was the second speaker. As a Six Sigma Master Black Belt in the Chief Regulatory Office (CRegO) Operational Excellence Team, he shared five lessons he learned when introducing process mining at Deutsche Bank. One of the lessons was that you need to be aware and communicate clearly what process mining can do — but also what it can’t do. For example, Process mining will help you to find bottlenecks quickly, but you need additional techniques to find and address the root cause. Improving processes goes further than just pointing out the problem. Another lesson was that you need to have a balance between explorative analyses and finding answers to defined questions. Process mining has the advantage that it enables you to discover valuable things that you did not even know you were looking for. But such untargeted exploration can also be very time consuming. Niyi showed some examples of how they structured their targeted analyses around business questions and recommended to spend about 30% of your time on untargeted and 70% on the targeted exploration of your data in your project.
Dinesh Das — Microsoft, United States
The third speaker of the day was Dinesh Das. Dinesh is the Data Science manager at Microsoft’s Core Services and Engineering and Operations unit. He shared his vision of how process mining can be used to accelerate the digital transformation. To illustrate his vision, Dinesh presented a Proof of Concept that he implemented for a Global Trade process, including a live demo. In the demo, he showed how process mining plays a crucial part in the implementation of a real-time monitoring solution by deriving the business rules for the monitoring. Furthermore, he demonstrated how Cognitive Analytics and other machine learning techniques can be integrated with the monitoring platform to interactively support the decision making for the people who are working in this process.
Wim Kouwenhoven — City of Amsterdam, The Netherlands
The fourth speaker was Wim Kouwenhoven, a program manager for the financial function of the municipality of Amsterdam. He introduced process mining as one of the initiatives to stabilize and improve the financial function. He learned that the adoption of a new technology like process mining requires a new approach. It starts with awareness and the involvement of the right people: Sponsorship at the right level is important to experiment and learn how to apply process mining in practice. Wim suggested to start small but focus on getting some tangible results quickly. After this first step, you need to take a step back and link your process mining experiences with the business objectives. Then you are ready to select the right initiatives and focus your process mining efforts on the most promising opportunities. Wim closed his presentation by sharing how process mining has helped them to address human project challenges, by reducing emotions and by increasing knowledge sharing between team members.
Olga Gazina — Euroclear, Belgium
Olga Gazina from Euroclear was the fifth speaker. Olga was accompanied by her colleague Daniel Cathala, who – as the process owner – explained the Software Configuration Management Lifecycle process in the Component & Data Management group at Euroclear. This is an important process for Euroclear to be able to update and release their IT services quickly and with high quality. Olga is a data analyst who works for the Internal Audit department. While wearing two hats in this project (data analyst and auditor), she worked with Daniel and his team to create a process mining-based view of their process. At camp, Olga shared the many iterations she had to go through to find the right representation of this complex process. ‘Right representation’ means a representation of that process that the team recognizes as their own. Finding this representation required asking a lot of questions for Olga and a change in thinking for the team. Ultimately, they succeeded and it opened up new perspectives and ideas for them.
Marc Tollens — KLM, The Netherlands
As the sixth speaker, Marc Tollens presented his Sunday afternoon pet project: As a product owner at KLM he had seen that some agile teams were completing less items than they initially planned. Because he knew process mining from previous projects, he had the idea that one could analyze the process that the teams follow in each sprint. Would he be able to learn something about their way of working that could help improve the development process? Marc extracted data from Jira, an agile project management tool, and started comparing the processes for multiple development teams. He observed that each of these teams had problems in different parts of the process: Some teams were very new, some started testing too late, and some made the scope too big to be achieved within the sprint. By discussing these insights based on the process maps with the teams, Marc could help them see what was blocking them and address the specific challenges each team had.
Process Miner of the Year 2018 awards
Every year, only one process process miner is awarded the title of Process Miner of the year. This year, David Baltar Boilève showcased an exceptional project that he completed at the Hospital Universitario Lucus Augusti in Spain. In this project, he analyzed how mouth cancer patients actually move through the hospital until they are diagnosed. We will share the case study describing David’s analysis and results in a dedicated article here on the blog in the coming weeks. So, stay tuned!
Wil van der Aalst — RWTH Aachen, Germany
Wil van der Aalst gave the closing keynote at camp. After receiving the prestigious Alexander von Humboldt professorship award, Wil continues his process mining research at his new Process and Data Science chair at the RWTH Aachen University. Currently, his research is focused on four main areas: (1) Foundations of process mining, (2) Dealing with different types of event data, (3) Automated operational process improvement, and (4) Responsible process mining.
In his keynote, Wil shared his view on the skills that data scientists need today and examined how others are defining the data science landscape. He also warned that we should all be careful not to overpromise what Artificial Intelligence can do to avoid another “AI winter”.
Second Day: Workshops
On the second day of camp, 128 process mining enthusiasts joined one of the four workshops. Marc Gittler and Patrick Greifzu explained how process mining fits in the different phases of the audit process from preparation to reporting. Eddy van der Geest guided the workshop participants through the steps to prepare data easily and efficiently with a state-of-the-art data analysis and ETL tool. Rudi Niks showed how to overcome the common challenges when applying process mining to analyze customer journeys. Anne Rozinat taught the participants how to answer 20 typical process mining questions.
We would like to thank everyone for the wonderful time at camp, and we can’t wait to see you all again next year!
Process Mining Camp is just one week away. All tickets are sold out by now and we look forward to welcoming all of you in Eindhoven very soon! If you were planning to come but have not registered yet, you can get on the waiting list here and we will let you know if a spot opens up.
The final speaker at Process Mining Camp 2017 was Mick Langeberg from Veco. Veco is a precision metal manufacturer and Mick is a supply chain manager. With process mining, Mick found a technique to radically accelerate the New Product Development cycle and convert this to an opportunity for faster growth.
As part of their growth strategy, Veco wanted to expand to new and existing customers outside of their existing part portfolio. When customers request a new product that they have not ordered before (so, it does not yet exist in the product catalogue), additional steps take place in the sales process: A sample first needs to be engineered, produced, and shipped to the customers before larger quantities are ordered.
To be able to close new deals quickly, Veco has the ambition to produce and deliver these samples within 15 days. However, by extracting data from the CRM and ERP system and analyzing it with process mining, they saw that it in fact took on average 52 days to deliver the samples to the customers. The longer the sample production process takes, the higher the risk that they may be losing these orders to the competition.
To understand the root cause of the delays, Mick and his team identified a new process ‘From Engineering to Order’ that was not managed before. In contrast to the production process of their regular catalogue parts, these sample parts required the involvement of the engineering department before they could be produced.
By involving members from both engineering and production, a “fast lane” was created for the engineering and production of these samples to speed up the ‘Engineering to Order’ process. By experimenting with this new process for a few weeks they were able to get “jaw-dropping” results. Within weeks, the director gave them the green light to implement this new way of working as the standard process, paving the way for future growth.
Do you want to learn more about how Veco discovered and improved their New Product Development process? Watch Mick’s talk now!
The fifth talk at Process Mining Camp was from Roel Blankers and Wesley Wiertz from VGZ. The health insurance cooperation VGZ is using operational visual management to track process performance every day. Lean has been adopted as the problem-solving methodology.
However, it took Roel a lot of time to map the existing processes during brown paper sessions before they were able to understand the real problem. When they analyzed the non-routine dental care claims process they decided to try a different approach: Process mining.
After extracting and preparing the data, they discovered that this process took 28 days to complete. In the discovered process maps they could see that a lot of the requests were being forwarded from the administrative teams to the medical advisors. Just by sharing the discovered process and asking why these claims needed to be forwarded to advisors, they found that a lot of these cases could actually be handled by the administrative teams. Therefore, it was proposed to set up an experiment to transfer the knowledge between the medical advisors and the administrative teams. Using process mining, they were able to validate that this new approach has in fact improved the lead time by almost 40%.
With process mining they were able to identify the problem quickly. They got a fact-based insight, which prevented them from jumping to conclusions. Process mining is a great addition to the Lean toolbox and a fun way to collaborate with domain experts to find opportunities to improve.
Do you want to learn from the best practices from VGZ to extend your Lean toolbox with process mining? Watch Roel and Wesley’s talk now!
The fourth speaker at Process Mining Camp 2017 was Gijs Jansen from Essent, a large energy supplier in the Netherlands. Gijs Jansen is a business intelligence specialist and one day he was asked to calculate the “snake plot” and “ping-pong factor” for the process of becoming and being a customer. He had no clue how to approach this, but he was eager to solve this problem.
The business intelligence department is responsible to report Key Performance Indicators (KPIs) for these processes. However, reporting a “snake plot” was something different compared to the existing reports they delivered. It required a deeper insight into how the customer passed though the different departments and the number of times each department touched each request.
A colleague suggested that he could try process mining. Gijs first started a small process mining project to analyse the credit insurance process. It was a simple process that was expected to be automated for most cases. However, the process mining results showed the contrary: Gijs found that disputes on contracts required a manual intervention for many cases.
This experience gave him the confidence to attack the “snake plot” and “ping-pong factor” problem. It took some effort but Gijs was able to extract the data for the customer process and transform it into the right process mining format. He then analyzed the process maps and saw how each customer request was handled, which departments were involved, and how often each request was touched by which employee. This resulted into a new set of KPIs that were discussed monthly to reduce the lead time and to limit the number of touches.
Do you want to learn about to the full process mining journey that Gijs went through at Essent? Watch Gijs’ talk now!
The third speakers at Process Mining Camp 2017 were Wilco Brouwers and Dave Jansen from CZ. They shared their process mining experience as IT auditors. Also auditors see that the digital transformation is slowly impacting their way of working. Dave and Wilco believe that data analysis skills will become increasingly important for future IT auditors – not only to be more efficient, but also to be more effective.
As the frontrunners within their team, Wilco and Dave have developed a new approach for auditing their digital processes of the future. Process mining plays an important role in this new auditing approach. With concrete examples, they showed where they see differences compared to the traditional audit approach in the preparation, fieldwork, reporting, and follow-up steps in their audits.
We have started to release the videos from last year’s camp so that we can all relive the experience. If you have missed the video of Remco Bunder and Jacco Vogelsang from Nederlandse Spoorwegen (Dutch Railways), you can watch it here.
The second speaker at Process Mining Camp 2017 was project and change manager Sebastiaan van Rijsbergen from Nationale Nederlanden. As one of the largest financial service providers in the Netherlands, Nationale Nederlanden needs to accelerate the digital transformation to remain competitive.
Sebastiaan knows how to start something new and, step by step, impact the whole organization. From his process mining journey we can learn how involving multi-disciplinary teams, an iterative approach, and data governance are critical to scale up your process mining success.
Introducing process mining in an organization is often a bumpy road and requires you to power trough the “valley of despair” in the change curve. For example, process mining makes things more transparent than some people are comfortable with and Sebastiaan received quite some pushback from certain people in the company. However, one of the magical moments in Sebastiaan’s process mining journey was when these very same people came back to him later on and told him that, in fact, process mining had brought peace into a discussion that had been heated with opinions and conflict before.
Do you want to know what process mining challenges Sebastiaan faced and how he resolved them? Watch Sebastiaan’s talk now!
Are you coming to Process Mining Camp on 19 and 20 June this year? Tickets are going fast and some of the workshops have already sold out. So, if you have not registered yet make sure to sign up now.
Today, we are excited to announce two additional speakers for this year’s Process Mining Camp!
Wim Kouwenhoven from the City of Amsterdam will share how process mining has helped him in his role as a program manager and Wil van der Aalst will talk about the new skill set for process and data scientists in his closing keynote.
Practice Talk by Wim Kouwenhoven — City of Amsterdam, The Netherlands
Amsterdam is well-known as the capital of the Netherlands. The city itself has a population of more than 850,000, with about 1.5 million people living in the entire Amsterdam city region. The municipality is responsible for defining and enforcing local policies regarding areas like poverty, taxes, sports, parking, and many more.
Wim Kouwenhoven is a program manager for the financial function of the municipiality. Change management often plays a major role in the programs he is involved in. This was also the case with the introduction of process mining. Wim will share the change traits that were critical for them to get most out of their process mining projects, so that they could actually improve the financial function and get the city of Amsterdam on the move.
Closing Keynote by Wil van der Aalst — RWTH Aachen University, Germany
With innovation and research continuously advancing, professionals are under pressure to expand their knowledge and keep up. BPM practitioners, for instance, need to apply a more data-driven approach when analyzing processes. The same applies to Lean Six Sigma professionals, IT Auditors, Business Analysts, etc. Meanwhile, data scientists are often still focused too much on flat data rather than behavioral data. Process-oriented data analysis techniques like process mining can help both groups to bridge the gap to analyze processes based on data. Moreover, ethics and privacy questions need to be addressed with an ever-increasing urgency.
What are the skills that old and new professionals need to develop today to be ready for the new data science economy? Wil’s keynote will give you an overview about the spectrum of skills needed, and tell you how you can develop yourself further in these areas.
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.
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