Anne23 May
Imagine the situation of a manufacturing company which needs to handle the repair and exchange of faulty products with its customers. To save costs, the process should be as efficient as possible. But as a customer-facing process, the speed and convenience for the consumer is also important.
If the customer service process is handled poorly, the consumers might badmouth the manufacturer in the internet and among friends, which can lead to significant brand damage and a loss in future sales. But if repairs and exchanges are handled very well, the consumers might be delighted and actually increase their brand loyalty.
Customer service example
In the picture below you see an anonymized, simplified example of the planned process for the shipment of replacement products (left) compared with the actual process (right). While the goal is to complete each case within 3 days, in a fairly sequential process, it actually takes 6 days on average (and in several instances much longer than that) and the real process is much more complicated.
But the main problem of the company is not the inefficient or complicated process.
The actual problem
The real problem is that the actual process is not visible to the service manager in the company in the first place. It is impossible to improve when there is no insight into what is actually happening.
Process mining
Because you can’t improve what you can’t measure, the biggest benefit of process mining is that it can make the real processes visible based on existing log data in the IT systems. Only when you can see what is happening, you can get to the root causes of problems and take action.
Conformance and Performance
The gained transparency can be used for both improving the conformance and the performance of the process.
Conformance. Deviations from the intended process can reveal hidden activities or process flows (see below). Deviations do not need to be a problem. But if these deviations are not visible in the first place, then it is impossible to detect illegal workarounds that are a serious problem for an organization.
Performance. The same holds for inefficiencies. For example, if the overall product replacement process takes too long and the customers are unhappy, then one needs to be able to see
where exactly time is lost, where more resources should be assigned, and so on.
Process mining can help to make that first step: Creating transparency about what is actually happening.
Where do you wish that processes would be more transparent? And how would it help you? Let us know in the comments.
Anne16 May
For those of you who want to become creative in the first BPI Challenge, it will be good news that the deadline has been extended to 20th of June. So, there is still more than one month left to do some wild process mining.
Participants of the challenge are provided with a real-life event log and asked to analyze the data using whatever techniques available. Their findings can be documented in one of two ways:
- The participants can focus on a specific aspect of interest and analyze this aspect in great detail. Here, one can choose for example to focus on specific models, such as control-flow models, social network models, performance models, predictive models, etc.
- The participants may report on a broader range of aspects, where each aspect does not have to be developed in full detail. The report submitted in this category will be judged on its completeness of analysis and usefulness for the purpose of a real-life business improvement setting.
We think that the BPI Challenge is a great initiative and, being among the jury members, we are already very curious about the results!
Christian7 May
About ten years ago, when Anne and myself were still studying for our Master’s degree at the HPI Potsdam in Germany, we first heard about process mining in a BPM seminar given by Mathias Weske. We became so fascinated with this new technology, and with the BPM domain itself that, by the end of 2004, we moved to Eindhoven to start working on a PhD in process mining with Wil van der Aalst and his outstanding research group.
With this background, saying that we owe a lot to the academic community is rather an understatement. Also, as former academics we are of course still very passionate about research and education, and we wanted to give something back to the academic community.
This is why today we are proud to announce the “Fluxicon Academic Initiative for Process Mining“, our new program to support research and education in process mining around the world!
What we have to offer
For our academic partners we provide:
- Free tickets for our tool Nitro for all researchers and students.
Nitro fills the gap between real-life logs and ProM. It helps researchers to focus on their case study, or a new process mining algorithm. In an education setting, it allows students to experience the whole scope of a process mining project, starting from raw CSV data.
- Slides and instructions for a process mining hands-on tutorial based on Nitro and ProM.
We have given this tutorial multiple times, and it has been used successfully at several universities already. It is a great starting point for including process mining in BPM courses or seminars.
We hope to grow the set of teaching materials over time with the help of our academic partners. We are convinced that it is essential to support education in process mining, a technology that we believe will shape the future of BPM.
Academic Partners
We are excited and immensely grateful that 20 universities are joining us as launching partners of our Academic Initiative, including some of the most excellent institutes for BPM and process mining research and education around the globe:
- Eindhoven University of Technology (Eindhoven, The Netherlands)
- Hasso Plattner Institute for IT Systems Engineering (Potsdam, Germany)
- Queensland University of Technology (Brisbane, Australia)
- Stevens Institute of Technology (Hoboken, USA)
- Ulm University (Ulm, Germany)
- Universitat Politècnica de Catalunya (Barcelona, Spain)
- Instituto Superior Técnico (Lisbon, Portugal)
- University of Padua (Padua, Italy)
- Katholieke Universiteit Leuven (Leuven, Belgium)
- Penn State University (University Park, USA)
- Gent University (Gent, Belgium)
- University of Tartu (Tartu, Estonia)
- University of Vienna (Vienna, Austria)
- Frankfurt School of Finance & Management (Frankfurt, Germany)
- University of Innsbruck (Innsbruck, Austria)
- Universidad de la República (Montevideo, Uruguay)
- University of Pretoria (Pretoria, South Africa)
Vrije Universiteit Brussel (Brussels, Belgium)
- Technische Universität Darmstadt (Darmstadt, Germany)
- Universidad de Castilla-La Mancha (Ciudad Real, Spain)
But we don’t want you to think of this as an elitist circle — if you can’t find your university on this list, please get in touch with us and let’s make it happen!
Anne6 May
Good process improvement can achieve both an increase of quality and lower cost at the same time. Efficient healthcare processes are very relevant, because patient treatments pose a significant burden on our aging societies.
But could hospitals be run more efficiently? Or would this necessarily mean a decrease in quality? A few weeks ago, I attended a Lean Six Sigma seminar in which a very interesting case study was presented: A process improvement team in the general hospital Reinier de Graaf groep had reduced the time patients had to stay in the hospital for a hip operation from six to only three days while improving patient satisfaction at the same time.
This example suggests that there is much more room for improvement than one might think.
Process Mining
Process mining is a revolutionary new technology for process improvement. Process mining does not start at the whiteboard to make an existing business process visible. Instead, it leverages existing log data that are collected by the many IT systems that are supporting enterprises around the world: ERP systems like SAP, but also legacy systems, CRMs, and so on, record very detailed information about the activities that have been performed, when, and by whom.
For any process improvement, determining the current ‘As-is’ process is the first necessary step. Using process mining, one can automatically and accurately visualize the actual process flows based on objective data. This transparency allows organizations to continuously monitor and improve their processes in ways that were not possible before.
But is process mining also applicable to processes in healthcare?
Challenges
Healthcare processes are either diagnosis / treatment processes or of organizational nature (such as the scheduling of appointments). The biggest challenges for applying process mining to healthcare processes are their complexity, their multi-disciplinarity, that they are changing often, and the log data from the IT systems.
1. Heterogeneity
A lot of the complexity of healthcare processes comes from the heterogeneity of the patients that are treated. After all, every one of us runs through the same process when we apply for a new passport, but we are unique and complex human beings when it comes to medical conditions.
As a consequence, the individual treatment processes of different patients even with the same illness are often unique. For process mining techniques—which generalize the common process based on the individual process executions that happened in practice—this lack of similarity can be quite a challenge.
2. Multi-disciplinarity
Hospitals departments are highly specialized in their respective fields but need to work together across their disciplines. For example the doctor of a cancer patient might send the patient to the radiology department, which needs to return the results to the oncology department.
While the multi-disciplinary nature of healthcare processes adds to the complexity, it is also an indicator for improvement opportunities. Generally, process inefficiencies often emerge at the boundaries of different functional units because people oversee their part of the job quite well but lack insight into what happens before and after and why.
3. Changing fast
The medical knowledge evolves continuously. As a consequence, the corresponding medical procedures and processes change as well.
Again, this adds to the difficulty of process improvement initiatives (Try to hit a moving target!). But at the same time it increases the attractiveness of process mining because it allows to make the current process visible automatically, just based on the history logs in the IT systems.
4. Data collection
The log data is the basis for process mining. So, of course the availability and quality of data is key to be able to apply process mining techniques.
On the plus side, detailed records are kept in healthcare processes for billing purposes. And new developments such as the Electronic Patient Record will increase the availability and quality of the data.
At the same time, there are still many manual activities that are not observable. Furthermore, data entries are often made manually after an activity has actually occurred. Over time, hospital information systems will evolve and with increasing integration and automation, the data availability and quality will improve.
More to come
To give you an update on the current state of the art in process mining research in healthcare, I plan to write up a few case studies that have been performed in this area in future blog posts.
For now, join the discussion and let me know what you think about process mining in healthcare: Do you feel there is room for improvement in our hospitals? And do you think that mining (anonymized) patient history data for process improvement purposes is legitimate?
Christian5 May
The Lean Startup methodology has helped us a lot to learn faster about our customers. So we are insanely proud to have been selected the official Eindhoven simulcast location for Eric Ries’ Startup Lessons Learned conference!
The goal for this event is to give practitioners and students of the lean startup methodology the opportunity to hear insights from leaders in embracing and deploying the core principles of the lean startup methodology. The day-long event will feature a mix of panels and talks focused on the key challenges and issues that technical and market-facing people at startups need to understand in order to succeed in building successful lean startups.
We’ll all be getting together on 23 May to watch the live stream of the Startup Lesson Learned conference at the Fluxicon HQ in Eindhoven starting at 18:00. Please register at this website so we know how many people to expect.
This simulcast is brought to you by Fluxicon and our friends from UXSuite. Our location is a private and cozy place, so there won’t be professional catering. We will provide some basic caffeination, but please feel free to bring some snacks, drinks, and whatever you require to make it through a night of non-stop lean startup action!
If you are building a startup in Eindhoven, or thinking about it, join us for the simulcast at 23 May, 18:00 in Eindhoven! Please register here: http://sll2011eindhovensimulcast.eventbrite.com/
Confirmed speakers include:
- Eric Ries (The Lean Startup)
- Brad Smith (President and CEO, Intuit)
- Mitch Kapor
- Steve Blank
- Suneel Gupta (VP Product, Groupon)
- Drew Houston (Co-Founder and CEO, Dropbox)
- …and many more.
Anne19 Apr
Together with Prof. Wil van der Aalst, Fluxicon is involved in a Master’s project, in which existing, commercial process mining tools are evaluated for different use case scenarios. It is important to consider the context of use for a process mining tool because, for example, an auditor has quite different requirements than a typical process analyst.
Our Master student Irina has compiled a first list of use cases in a survey here. She did a great job in defining a short but comprehensive list of process mining functionalities. But you can help her to rank them in importance and identify missing use cases.
→ Take the survey here
Someone’s going to get lucky!
Among all people filling out this survey, we will randomly draw one lucky soul to win a Process Mining Instant Expert Kit, including:
Take the survey now and be a winner!
Please forward and share this invitation with anyone who could contribute to the survey. Thank you!
Anne18 Apr
Prof. dr. Wil van der Aalst is widely regarded the “godfather” of process mining. He started process mining research at the Technical University in Eindhoven about twelve years ago. Recently, he published the first book on this topic, which is aptly titled “Process Mining”.
We had the privilege of reading drafts of this book, and it is really hard not to recommend it for everyone interested in process mining. Wil is one of the fewer academics writing in an accessible and down-to-earth manner, without skimping on clarity or scientific rigor, and without hyperbole. The book covers the fundamentals and basics of process mining, and gives a comprehensive overview about the state of the art of the field.
Wil was kind enough to answer five questions about his new book for us. He explained why BI is not really intelligent, who this book is for, and why you should read it.
Interview with Wil van der Aalst
Anne: This is the first book on process mining. I know that both academics and professionals have been waiting for a book on process mining. For whom did you write this book?
Wil: The initial goal was to write a shorter less technical book primarily focusing on professionals. However, while writing it became clear that the topic cannot be introduced without giving concrete definitions and examples. Therefore, the book does not shy away from technical details. As Einstein said: “Everything should be made as simple as possible, but no simpler”. As a result the book is interesting for both academics and professionals.
Anne: You make the point that most Business Intelligence systems are rather un-intelligent. What do you mean by that?
Wil: The problem of new technologies and tools in the field of Business Process Management (BPM) and Business Intelligence (BI) is that they are presented as silver bullets able to solve notoriously difficult problems with little effort. In reality such technologies seldom live up to their expectations as there is no such thing as a free lunch.
BI tools tend to be data-centric while providing only reporting and dashboard functionality. They can be used to monitor and analyze basic performance indicators (flow time, costs, utilization). However, they do not allow users to look into the end-to-end process. Moreover, despite the “I” in BI, most of the mainstream BI tools do not provide any intelligent analysis functionality.
Anne: You distinguish between ‘Lasagna’ processes, which are more structured, and ‘Spaghetti’ processes, which are unstructured. Where do you find them and how is process mining different for these two types of processes?
Wil: Lasagna processes are relatively structured and the cases flowing through such processes are handled in a controlled manner. Therefore, it is possible to apply all of the process mining techniques presented in the book (also more advanced techniques such as prediction and short-term simulation). Spaghetti processes are the counterpart of Lasagna processes. Because Spaghetti processes are less structured, only a subset of the process mining techniques described in the book are applicable. However, the potential process improvements may be much more substantial.
Spaghetti processes are typically encountered in product development, service, resource management, and sales/CRM. Lasagna processes are typically encountered in production, finance/accounting, procurement, logistics. The structuredness of processes also varies from industry to industry, e.g., processes in healthcare tend to have more variability than processes in manufacturing.
Anne: Which aspect of process mining deserves more space than it gets in your book and why?
Wil: The initial goal was to write a book of 200 pages. In the end the book was more than 350 pages. As a result, the book is comprehensive and self-contained. Although the book shows various examples of process mining results based on numerous real-life event logs, it would have been good to present a few case studies in more detail. Moreover, the relationship to Visual Analytics could have been discussed in more detail.
Anne: If someone is completely new to process mining, what would you hope is the biggest take-away point for that person?
Wil: Event data is omnipresent, thus enabling evidence-based BPM. Process mining combines techniques from data mining and process modeling and analysis. As a result, it is possible to analyze and improve business processes based on facts rather than fictive PowerPoint diagrams.
The threshold to start a process mining project is low. Therefore, it is best to experience the “magic” of process mining using data from your own organization. The book shows how this can be done and provides pointers to the software needed to start discovering and improving processes based on facts rather than fiction.
Preview and additional material
If you want to take a closer look at the book, here is the table of contents and an online preview of the book.
You can also download slides for every chapter in the book. Furthermore, all event logs and models that are used in the book are available here.
Update: Take a survey now and win the book!
Anne14 Apr
I have been invited to talk about Process Mining in healthcare at the Dutch Ngi event De mens en IT in de Zorg in Utrecht next Tuesday. The Ngi is an association for IT professionals in the Netherlands and is regularly organizing events around a variety of topics.
Process mining in healthcare is an exciting topic. Having good processes in place is relevant for society from both a cost-saving as well as from a quality perspective. At the same time, the complex nature of the processes in the healthcare domain leads to interesting challenges for process mining.
For those of you who are located in the Netherlands, let me know if you want to join. Normally, non-members pay 10 Euros but I will see if I can organize an invitation. You can RSVP at LinkedIn here.
Christian13 Apr
It is one of my more pleasurable duties here at Fluxicon to announce updates to Nitro. We just released Nitro 2.1, and so again it’s time for me to walk you through the changes and new features in this version. As always, you can download installer packages for Windows and Mac OS X at http://fluxicon.com/nitro.
Simplified configuration
You are probably familiar with our interface to configure the columns of CSV and Excel files. We have made two changes to this interface, and we think that they make it even easier to quickly get your data converted.
We have extended the preview shown in the table to 1,000 rows, so that both you and Nitro have a better idea of the data you are configuring, and what it means. We have also added row numbering, so that even with that bigger preview, you always know what you are looking at.
Previously, you had to configure for each timestamp column whether that timestamp signified the start or the completion of the activity described by that table row. Actually, we found that Nitro can figure out pretty well on its own which timestamp is the first one, and which is the last. So, starting from version 2.1, Nitro will use the earliest timestamp per row as the activity’s start timestamp, and the latest will be used as the completion timestamp. One less thing to worry about, n’est-ce pas?
Events with duration
So far, Nitro’s event model followed that of the MXML and XES standards. That means we counted the starting and finishing of each activity as separate events. For academics that viewpoint may be pretty sound, but we found that for many practitioners it makes more sense to see an event as one execution of an activity — which, consequently, has a start and an end time.
For Nitro 2.1, we have reengineered the Octane layer to automatically correlate start and end timestamps for each event, no matter whether you load your log from a CSV, Excel, MXML, or XES file. For one thing, this means that if you have two timestamps per event in your log, the number of events Nitro will show you in the Statistics view will now be half of what you have seen before. But don’t worry, Nitro still has all your data, and it knows even more.
Since Nitro now knows the start and end timestamp for each event it now knows about the duration of activities. Consequently, we have extended the statistics views for Activity and Resource event classes with some additional information.
For activities and resources, Nitro 2.1 now has three new charts (each both as Pareto charts or classical histograms):
- Mean duration of each activity / resource.
- Aggregate duration of each activity / resource (i.e., the complete time spent by each activity or resource over the whole log)
- Duration range for each activity / resource (showing you the differences in variation for each item)
The same information is also shown in the table view. We have added one column with an inline histogram that allows you to compare the mean duration of each activity / resource. Further, we have introduced a new inline histogram column which shows you the duration range for each item.
The “fat” bar in each row shows the range, i.e. its left edge shows the minimum duration, and its right edge the maximum duration of each item. Within this bar is another, ligher and “thinner” bar ending in a vertical line. This indicator shows you the mean duration, giving you more information about the actual distribution of durations.
Log explorer
Once you have loaded your log into Nitro 2.1, you will notice that the familiar Statistics view is now one of two alternative views on your log. We have added the Log explorer view, which allows you to view the actual cases in your log.
On the very left side of the log explorer view, you can see a list of case variants. Nitro now automatically organizes the cases in your log in such a way, that all cases that feature the same sequence of activities are grouped together in a so-called variant. So, the set of variants is the set of unique activity sequences in your log.
The second column from the left shows you the list of all cases that are grouped together in the selected variant. When you select a case from that list, information about that case, including its precise sequence of events, will be shown in the right part of the log explorer view.
On the top of that case view, you will see a chart indicating when and over which timeframe in the log the selected case occurs. The chart shows the density of cases over the whole log’s timeframe as a blue curve area, with the selected case’s timeframe highlighted in red. This allows you to intuitively spot when in the log your case occurs, and how long it has been executing.
To the right of this chart, Nitro shows some statistics for the selected case:
- Number of events in the selected case.
- Starting date and time of the case.
- Duration of the case.
- Active time of the case (as a percentage of the complete duration).
The active time indicates the time spent executing the case’s activities, in relation to the complete case runtime, as a percentage. It allows you to get a grasp of the efficiency, or the relative waiting time, for a specific case.
On the lower part of the case view you can see the actual sequence of events in the selected case. This view can be switched between a graph view allowing for a quick overview, and a table view that allows you to see the values of attributes for each event.
Bugs and fixes
Nitro 2.1 contains all bug fixes up to and including version 2.0.8, plus additional bug fixes. At this point we would like to thank Michael Westergaard and Jan Claes for pointing out bugs that could occur in our configuration screen, and George Varvaressos for alerting us to an issue with the modification time for compressed MXML files. And of course a big thanks to all of you who sent us further bug reports and suggestions — we hear you and we like what we hear, keep it coming!
While we have tested Nitro 2.1 extensively, the changes under the hood are quite dramatic. Should you run into a bug or problem, please let us know at support@fluxicon.com, and we will fix it ASAP.
Epilogue
We hope you are as excited about the new features in Nitro 2.1. as we are. Make sure to download your copy right away at http://fluxicon.com/nitro.
By the way, how do you like these release write-ups so far? Do they help you to keep on track for where we’re going with Nitro, and do they give you a good idea of what you can do with Nitro? Are they too long and rambling, or would you like them even more in detail? I’d like to make this as useful for you as possible, so if you have any suggestions or feedback, please let me know in the comments!
Anne3 Apr
Processes that are primarily based on information flows are particularly challenging to analyze because they are inherently invisible. When I told Mathew, a Lean Six Sigma practitioner, about process mining he thought that was fantastic. And he made a comparison that I really like. He said:
Making information flows visible is the equivalent of walking on a factory floor.
In an assembly line, you can move from one step to the next step in the process and easily observe what is happening. But information-based processes usually don’t pass around piles of papers anymore. That means you simply can’t see what is going on. Making the process flows visible based on IT data is therefore really valuable.
Since 2009, we have used the X-ray metaphor to explain process mining. Wil often uses the metaphor of a TomTom navigation system. Now we have the factory floor metaphor. Which one do you like best? Which other metaphors have you seen? Let us know in the comments.
Meet us in Utrecht on 6 April
If you are in The Netherlands, come to the Lean Six Sigma Seminar in Utrecht next week Wednesday. We will have a product booth there and would love to talk to you in person!
There are no comments for this article yet. Add yours!