About one month from now, on Wednesday 18 June 2014, this year’s Process Mining Camp takes place again in Eindhoven, the Netherlands. The camp is the annual community meeting of the process mining family and the only conference worldwide that is focused exclusively on the practical application of process mining — and we are sure that you don’t want to miss it!
The program is now complete, and we think it is absolutely fabulous. We have a great lineup of practice talks, with speakers from diverse backgrounds and industries all over the world. Our workshops are more focused this year, and we have found five experienced process mining practitioners that will give you a deep dive into a topic of your choice. To complement our keynotes, we have assembled a top-notch panel for a closing discussion on stage, as our little experiment for this year.
Take a look at what you can expect from this year’s process mining camp:
- In 6 Practice Talks you can learn from experienced practitioners who will tell you all about their successes, the difficulties they faced, and their best tips and tricks. This year’s practice talks include speakers from the Rabobank (NL), ING (NL), MLP Financial Services (DE), the Dutch Statistics Institute (NL), CKM Advisors (USA), and KPMG (NL).
- In 5 different Workshops you can dive deeper into topics such as How to get management buy-in for process mining, Managing complexity in process mining, Data science tools that complement process mining, Process mining and customer experience, and Process mining and lean.
Book your favorite workshop during registration and be quick to make sure that you get into the workshop you find most interesting.
- In the opening and closing Keynotes you will hear from us about the trends and future directions we see for process mining, and from Wil van der Aalst, the founding father of process mining, about how process mining fits into the data science topic that everyone talks about.
- Brand new this year is that Process Mining Camp will conclude with a Panel Discussion to discuss the state of the field from different perspectives. Next to our panelists representing the research, company practitioner, consulting, and software perspective, we are thrilled to have also secured two world-renowned analysts, Neil Ward-Dutton from MWD Advisors and Marc Kerremans from Gartner, for you to complement the panel discussion with their industry insights.
- If you are coming from farther away and want to make the most of your trip, you have the opportunity to combine Process Mining Camp with a Process Mining Training the day after camp. Our popular process mining trainings are held every month, but the June edition is reserved exclusively for our process mining camp visitors. We even throw in the Process Mining Camp entrance fee for those of you who attend the training. Note that seats are very limited.
- Finally, and most importantly, you will get the chance to dive into process mining for a full day and meet the process mining community. We are sure that you will learn a lot from our practice talks, workshops, keynotes, and the panel. But the highlight of Process Mining Camp every year is to get together with other process mining experts and fanatics, share your experiences and thoughts over a cup of coffee, and make new friends. Our annual Process Mining Camp T-Shirt is a symbol of this community and will serve you as a reminder of a great day for years to come. If you sign up by 8 June, we guarantee that you get your camp T-Shirt (but you sure don’t want to wait that long, because camp tickets are very likely to sell out well before that date…).
Last year’s camp was attended by just above 100 process miners, and since that is just about the number of people we can fit in the Zwarte Doos campsite legally, we expect this year’s Process Mining Camp tickets to be gone quickly. To make sure you don’t miss out, sign up now and secure your ticket.
We can’t wait to see you at camp!
This year’s BPI Challenge, the annual process mining competition (this year with a dedicated student competition and mentorship program!), has started and many people have already begun to look into the data and the process questions that are provided by the challenge.
As always, we have provided a Disco project file with the BPI Challenge data that can be opened right with the demo version of Disco.
If you open the provided project file, you see the imported data sets and two additional views as shown in the screenshot above. But what should you do next? How can you be as successful as possible in the challenge?
Here are some tips to help you get started.
1. Read the documentation
It’s easy to jump ahead and go dive in the data without taking the time to properly read all the information that is provided. Don’t make this mistake. Understanding the meaning of the data fields and the process context is critical to derive the right conclusions in your process mining analysis.
Read the description provided on the BPI Challenge website and in the quick reference guide from the Rabobank. Print out the data model overview and put it up at the wall.
If you are not yet familiar with ITIL processes, do some research and learn more about the IT Services Management practices and processes that form the context for this year’s challenge.
2. Focus on a question
Once you have a good overview about the processes and the data, be clear on which question you would like to focus on first. This can be one of the questions posed by the process owner, or it can be a question that you have posed yourself.
3. Prepare data if needed
Then, think about the view(s) that you need on your process to answer your question. As you know, the meta model for process mining means that you take a process view on your data. Depending on the question that you want to answer, you can take different perspectives on the same data.
4. Document all steps
Process mining is an inherently interactive and explorative activity. This is a big advantage but at the same time poses the risk that you get lost. To stay on track, focus on your question and document all the steps that you are taking and note down observations or open questions to follow-up later.
(In Disco, you can keep your notes right in the project view for each data set copy that you create, but writing them down on paper or in a Word document works just as well).
5. Clearly describe assumptions
In a real process mining project, you would have access to the process owner to ask questions about the process and the data when they come up. In the BPI Challenge, the data descriptions and process owner questions provide you as much context as possible, but most likely you will come across doubts and missing pieces.
When you present your analysis results for your BPI Challenge submission, make sure that you clearly describe the business questions that you tackled and your assumptions along the way. For the judges, it is very important to understand what you did and why. For example, if a question is a bit vague then clearly define how you understood it. This will greatly improve the quality of your submission and is a good practice to get into for any data analysis activity anyway.
Do you have more tips? Share them in the comments!
6. Reminder: BPI Challenge Webinar this Thursday
Of course, asking the process owner for clarifications always beats writing down assumptions.
Don’t miss the chance to ask all your questions about the BPI challenge directly to the process owner and challenge organizer this Thursday
Sign up for the webinar here
This is your opportunity to get answers directly from the data and process owners and get an optimal start with your process mining analysis. See you on Thursday!
Are you planning to participate in the BPI Challenge this year? This is an excellent chance to do a process mining analysis with a real data set and last year’s participants have told us they have learned a lot. So, if you are still thinking about it we recommend that you give it a try!
This year, the challenge data set is kindly provided by the Rabobank Group ICT. Normally, the data and the questions from the process owner are your only reference point when participating in the challenge, and we know that last year several people would have loved to ask more questions.
This year, you can!
We have invited both the challenge organizers and the data and process owner from the Rabobank for a webinar session on Thursday, 15 May, at 17:00 CET. The following people will answer your questions:
Boudewijn van Dongen is assistant professor at Eindhoven University of Technology and the main organizer of the BPI Challenge.
Martin Leijen is a BI and data consultant at the Rabobank Group ICT and has prepared the data set for this year’s challenge.
Anne Rozinat is co-founder of Fluxicon, host of this webinar, and organizer of the BPIC Student Challenge mentorship program.
Frank van Geffen is Process Innovator at the Rabobank and chairman of the SIG Process Mining at the Dutch industry association Ngi-NGN. The SIG supports the BPI Challenge.
Sign up now!
This is your chance to answer all your questions about the process, the data, or the challenge in itself. Get yourself a smooth start and increase your chances of winning by delivering relevant analysis results:
Join the webinar and register here
The webinar takes place this Thursday, 15 May, at 17:00 CET. Sign up now to make sure you don’t miss it!
Update: The webinar went great. There was a lot of interest and many detailed questions about the data set were asked and answered. Here is a summary with the questions and answers from the webinar.
Do you think that process mining was one of the most interesting things you learned in your studies this year? Are you thinking about doing your Bachelor or Master project on a process mining topic? Could you see yourself as a process mining analyst at a big company, or in a consultancy, after you graduate? Then the BPIC 2014 Student Challenge is the perfect opportunity for you to see how a real-life process mining project looks like.
Every year, the BPI Challenge brings the top process mining professionals and researchers around the world together, as they compare their data analysis skills and innovative approaches. This public process mining competition is based on a real life data set, provided by a large company together with relevant analysis questions, so we are not talking about a theoretical exercise here.
Everyone is welcome to participate — You can take a look at the reports from 2013 and 2012 to get more of an idea of what the challenge is about.
This year, for the first time, there is a special student competition. As a student, this means that, while you will analyze the same data as the professional business analysts and process mining researchers in the main track, your submission will be judged only in comparison to other students to give you a more level playing field.
But there’s even more than that. You can get exclusive access and support from one of the most experienced professional business analysts around the world (many of them participating themselves in the BPI Challenge) through our mentorship program:
Fluxicon is organizing a mentorship program, where each student team will be matched with a professional with process mining experience to get support in preparing their submission. You will see that domain knowledge and an understanding of the process are very important to draw the right conclusions from your analysis, and your mentor will be able to help you if you get stuck.
You can apply until 31 May, and we will match your student group to a mentor in your region, on a first-come first-serve basis.
We explicitly try to match you to a professional in your region, so that you can more easily communicate and – who knows – maybe even get a chance to meet up. But don’t wait too long, there are only a limited number of mentors available!
Why you should participate
Are you ready to find a fellow student to form a team for the competition? Here is why we think you should participate:
- Build your skills. Data scientists are one of the most sought after talent today. Start sharpening your skills early, add valuable and unique assets to your profile, and open up a whole new world of interesting job opportunities.
- Build relationships. Having a mentor will help you to produce a better submission for the competition, but on top of that you might learn a thing or two from your mentor besides the challenge itself.
- Because it’s fun. Process mining is one of the most interesting data analysis techniques today. It’s data-based and visual, and with the BPIC 2014 Student Challenge you get the chance for a deep-dive into how a process mining project looks like in real life.
- There are also prizes. The winning team will receive one iPad, sponsored by Eindhoven University of Technology. Also, for one member of the best Dutch team the Process Mining SIG of the Dutch industry association Ngi-NGN will sponsor the trip to the BPM conference and award ceremony in Haifa, Israel, in September.
We recommend that you form teams of 2-4 people. You can of course also participate just by yourself, but we think that working on the challenge as a team will be much more fun.
Deadline: July 12, 2014, 23:59 CET
A submission should contain a pdf report of at most 30 pages, including figures, using the LNCS/LNBIP format specified by Springer (available both as a Word and as LaTeX template). Appendices may be included, but should only support the main text.
Further details can be found at the BPI Challenge website and this previous blog post.
Help Us Spread the Word!
Are you not a student, but do you know students who could be interested? Or do you know someone who knows students?
Please help us spread the word about the student competition in this year’s BPI Challenge and forward this announcement. Thank you!
The BPIC 2014 Student Challenge is supported by the Rabobank, Fluxicon, Eindhoven University of Technology, the IEEE Task Force for Process Mining, and the SIG Process Mining of the Ngi-NGN.
The BPI Challenge is an annual process mining competition, which takes place for the fourth time this year. The goal of the challenge is to give both researchers and practitioners the opportunity to do process mining analyses on real-life data (read our interview with Boudewijn, where he tells the story of how the BPI Challenge came to life).
In this competition, anonymized but real data is provided and can be analyzed by anyone using any tools. Submissions can be handed in until July 12, 2014 and the winner will be awarded a prestigious prize!
As always, we make our process mining software Disco available for anyone for the purpose of this challenge. Read on to see what this year’s challenge is about and how you can get started.
This year’s data is provided by the Rabobank Group ICT.
Similar to other ICT companies, Rabobank Group ICT has to implement an increasing number of software releases, while the time to market is decreasing. Rabobank Group ICT has implemented the ITIL-processes and uses the Change process for implementing these so called planned changes.
The data is provided for the following service desk processes: Interaction Management, Incident Management, Change Management (see below).
As you can see in the illustration, a problem reported to the service desk (for example, a slow internet connection) may evolve from an Interaction (an agent from the service desks troubleshoots the reported issue) to an Incident (the issue cannot be solved on the phone but someone has to look into it) up to a Change request (repeated problems of the same kind lead to a structural change that should prevent issues in the future).
The following detailed information is provided about each stage.
In order to manage calls or mails from customers (Rabobank colleagues) to the Service Desk concerning disruptions of ICT-services, a Service Desk Agent (SDA) logs calls/mails in an Interaction-record and relates it to an Affected Configuration Item (CI). The SDA can either resolve the issue for the customer directly (First Call Resolution) or create an Incident-record to assign the issue to an Assignment Group with more technical knowledge to resolve the service disruption.
If similar calls/mails are received by the Service Desk, a SDA can decide to relate multiple Interaction-records to one Incident-record. Further logging of Activities to resolve the service disruption will be done in the Incident-record.
Based on an estimated Impact and Urgency, done by the SDA, an Incident-record is prioritized and gets a deadline to resolve the service disruption. A Team leader within the Assignment Group assigns the records to an Operator. The Operator resolves the issue for the customer, or reassigns the record to a colleague if other or more knowledge is needed. After solving the issue for the customer, the Operator relates the Incident-record to the Configuration Item (CausedBy CI) that caused the service disruption. After closing the Incident-record, the customer receives an email to inform that the issue is resolved.
If particular service disruptions reoccur more often than usual, a Problem investigation is started, which will lead to an analysis and improvement plan to prevent the service disruption to happen again. The improvement plan leads to a Request for Change (RfC) on the CausedBy CI. All CI’s are related to a Service Component, Risk Impact Analysis is done by an Implementation Manager assigned to changes related to the specific Service Component.
The Data Set
As in any process mining analysis, the data needs to be linked to a Case ID, Activity, and Timestamp.
The data that you will analyze in this BPI Challenge stems from the IT Service Management (ITSM) software that is used in the service desk to handle the processes described above. Activities and timestamps are recorded within the ITSM system for the processed interactions, incidents, and changes.
An additional difficulty this year is that the data is provided in four pieces for the different processes. For the process mining analysis, the data needs to be combined and this can be done in different ways. It is part of the analysis to understand and prepare the data according to the questions and goals of the analysis.
If you click on the picture below, you can see the data fields that are contained in the four files:
We have imported these four files and already created two additional views for you in a Disco project file that you can simply open with the freely available demo version of Disco.
You can download both the Disco project file and the raw data and data model explanation here:
Download the raw data files in a Zip file (CSV files and explanation reference about the data model)
Download the Disco project file that can be opened with the freely available demo version of Disco
We think that many of you will want to create additional views by combining or importing the data in different ways. The two views that we created (integrated incidents and a more detailed view on the change process) are just an example and a starting point.
If you have created another combination of the data that you want to analyze in Disco as well, you can send the file to firstname.lastname@example.org and we will add your view to the project file. You will be mentioned as the creator of the new data view and updates to the project file will be made available here on this blog post for the community.
New This Year: Student Challenge
View Process Mining Research Institutes in a larger map
Students all over the world are starting to learn about process mining. However, learning about it in theory and applying process mining in practice are quite a different story.
To give students the possibility to develop their process mining skills, this year’s BPI Challenge, for the first time, includes a separate student competition. Student groups are invited to participate in the challenge, and their submissions will be evaluated separately from the regular submissions.
Because students normally do not have any experience with process analysis and improvement work at companies, we decided to pair them with a mentor who is a practitioner and can guide them and be available for questions. This way, the student teams will learn more and deliver better analyses. More than a dozen practitioners in Europe, Scandinavia, the US, and South America have already volunteered to mentor a student team.
To be matched with a mentor, students can email email@example.com by 31 May 2014.
Update: Please apply to the mentorship program through the form at the BPIC 2014 Student Challenge website.
Do you want to be a mentor as well? This in no way hinders your own participation in this year’s BPI challenge (the student challenge is completely separate). Let us know and we will try to match you with a student team in your geographical region.
Now, there is one more bonus attached to the student competition: Two extra prizes are available for the winners.
Questions About the Process
One of the challenges of a process mining project is that you need a starting point to understand the process context and what business questions and goals are relevant for the analysis. Otherwise it is really easy to get lost in the data.
This is why the data in the BPI Challenge is not just dropped over the fence, but the data providers are encouraged to provide questions that can be the starting point for the people who are participating in the challenge.
Here is what the Rabobank ideally would like to know about the data:
Rabobank Group ICT is looking for fact-based insight into sub questions, concerning the impact of changes in the past, to predict the workload at the Service Desk and/or IT Operations after future changes.
The challenge is to design a (draft) predictive model, which can be used to implement in a BI environment. The purpose of this predictive model will be to support Business Change Management in implementing software releases with less impact on the Service Desk and/or IT Operations.
We have prepared several case-files with anonymous information from Rabobank Netherlands Group ICT for this challenge. The files contain record details from an ITIL Service Management tool called HP Service Manager. We provide you with extracts in CSV with the Interaction-, Incident- or Change-number as case ID. Next to these case-files, we provide you with an Activity-log, related to the Incident-cases. There is also a document detailing the data in the CSV file and providing background to the Service Management tool.
- Identification of Impact-patterns: We expect there to be a correlation between the implementation of a change and the workload in the Service Desk (SD) and/or IT Operations (ITO), i.e. increased/decreased volume of Closed Interactions and/or increased/decreased volume of Closed Incidents. Rabobank Group ICT is interested in identifying any patterns that may be visible in the log for various service components to which a configuration item is related, in order to predict the workload at the SD and/or ITO after future changes.
- Parameters for every Impact-pattern: In order to be able to use the results of prior changes to predict the workload for the Service Desk directly after the implementation of future changes, we are interested in the following parameters for every impact-pattern investigated in sub question 1:
- What is the average period to return to a steady state?
- What is the average increase/decrease of Closed Interactions once a new steady state is reached?
- Change in Average Steps to Resolution: Since project managers are expected to deliver the same or better service levels after each change implementation, Rabobank Group ICT is looking for confirmation that this challenge is indeed being met for all or many Service Components.
- Creativity challenge: Finally, we challenge the creative minds, to surprise Rabobank Group ICT with new insights on the provided data to help change implementation teams to continuously improve their Standard Operation Procedures.
What can you do if these questions are not interesting or not feasible for you? After all, you may need quite some data mining skills to fully address the questions above.
Don’t worry. Like explained above, the questions from the process owner are intended to provide you with a starting point. The BPI Challenge gives you the chance to practice your process mining skills on real data and there are many ways to do this. Think of a process question that would be relevant for you, for your clients, or – if you are a researcher – what insights would your fantastic new algorithm add in this situation?
What is important is that you clearly state the questions and the reasoning behind your analysis. Motivate why the question is relevant and describe how you approach the analysis in sufficient detail, so that others can understand what you did and why.
How to Submit
You can submit your challenge contribution through the EasyChair system at https://www.easychair.org/conferences/?conf=bpic2014.
A submission should contain a pdf report of at most 30 pages, including figures, using the LNCS/LNBIP format specified by Springer (available both as a Word and as LaTeX template). Appendices may be included, but should only support the main text.
Submission deadline: July 12, 2014, 23:59 CET
Announcement of winners: at the 10th Workshop on Business Process Intelligence (BPI 14), Haifa, Israel, 8th September 2014
Join us for a webinar, where we have invited both the challenge organizers and a process expert from the Rabobank. This is your chance to answer all your questions about the challenge and about the data set.
The tentative date for the webinar is 15 May at 17:00 CET. Sign up now to make sure you don’t miss it!
This is a guest post by Vladimir Rubin. Vladimir shares his experience from applying process mining to software processes for a tourism company.
If you have a process mining case study that you would like to share as well, please contact us at firstname.lastname@example.org.
Why Software Process Mining?
Building flexible, adaptive software systems is becoming more and more important, because businesses need to be able to change rapidly. Especially agile methods and processes are becoming extremely popular, since they naturally deal with business change by decreasing the length of iteration lifecycles and getting quicker responses from the end-users. Additionally, concepts such as continuous integration and delivery support the dynamic rollout of software to customers and enable short user feedback loops.
By using these agile approaches, the end-user becomes a part of the software development life-cycle. His experience and his way of working with the software become accessible and essential for subsequent iterations of software development. This is the point where process mining comes into play.
We have successfully applied process mining, which is normally used more for the analysis of traditional business processes, to the area of software development. Both user interaction and system’s internal behavior can be analyzed with the help of process mining. The results of this analysis can significantly influence the architecture, design, testing, and development of the software system.
In this blog article, we discuss two main use cases:
- The interaction of the end-user (or of a Beta-Tester) with the software system can be logged and, therefore, analyzed with the help of mining tools. Then, the analysis results are given to the business analysts, testers, architects, and developers in order to improve the usability, reliability, efficiency, and other properties of the software system.
- The sequence of services calls (calls of interfaces between components) is usually traced in order to provide developers information about system behavior and failures. This information can be imported in the process mining tool, which helps deriving the view of the software processes from a technical perspective by analyzing the performance and frequency of calls.
Both use cases were inspired by concrete requirements coming from a big European enterprise touristic project:
- The team wanted to analyze the productive behavior of the users in order to see the system failures, bottlenecks, and to gather statistics.
- Several critical performance challenges appeared with an increasing number of users, they had to be identified and solved.
To address these problems, we have written the user logs and the traces of the system. Then, we have imported them in the Disco tool for process mining.
Here is a short overview of the results. The data has been anonymized to protect the confidentiality of the client.
Case 1: User Activity Analysis
In Figure 1 we show the positive behavior of the user – the cases which were successfully finished in the production system. It is a convenient possibility to track the production state and to identify the frequency of the paths taken through the system by the user.
Figure 1: User positive behavior (Frequency View)
In Figure 2 we show the performance view of the negative behavior, i.e. the cases containing failures, and the time wasted.
Figure 2: User negative behavior (Performance View)
In Figure 3 the cases are clustered per variant and the typical behavior is shown. It is helpful for analyzing the individual user behavior patterns and the variety of the business processes.
Figure 3: Variety of Cases (Types of Behavior)
Case 2: System Performance Analysis
For the second case, we have taken the trace of system calls in order to analyze the system behavior. We could identify the most frequent service calls, the spreading of calls, and also the loops, as you can see in Figure 4.
Figure 4: Frequency Analysis
Moreover, we could also see the detailed statistics of calls and, thus, the most critical services from the performance point of view, as shown in Figure 5.
Figure 5: Frequency Statistics
After switching to the performance view of Disco and looking at the total time statistics, we could effectively identify the most time consuming calls in the system. Identifying these delays and increasing the performance had a high priority for the developer team, because a slow service would cause users to abandon the website and potentially leave to a competitor.
Figure 6: Performance Analysis
In this article, we have shown two successful applications of process mining in a concrete enterprise software project.
From our point of view, this is a very fruitful application domain, because productive software systems provide a big amount of data in form of logs and traces. This data can and should be analyzed in order to improve the software quality.
Last week, we published a new article about The Added Value of Process Mining at the respected BPM analyst platform BPTrends. You find a short abstract and a link to the original article below.
Process mining, just like data mining, is a generic technology and can be applied in many different ways. This is an advantage but at the same time it makes it difficult for you to understand what exactly the added value would be for your situation. Should you be interested in process mining and learn more about it? Which kinds of processes can be analyzed with process mining? What benefits would it bring?
In this Article, we give you a framework for the most common process mining use cases, so that you can see where you fit in.
Read the full article at the BPTrends website…
What do you think about the discussed use cases? Which are the ones you find most important? Which ones have we missed? Let us know in the comments.
Process mining can not only be used to analyze internal business processes, but also to understand how customers are experiencing the interaction with a company, and how they are using their products.
“Process Mining and Customer Journeys” was the topic of the first event of the new Special Interest Group (SIG) for Process Mining in the Dutch industry association Ngi-NGN. Fluxicon is on the board of this Ngi SIG group and was co-organizing the event, which took place yesterday on 25 March 2014 in Utrecht, at the Rabobank.
More than 50 people had signed up for the event and it went great. Below is a quick summary for everyone who could not be there.
Introduction Customer Journey
Jaap Rigter from VisionWaves first introduced the topic of customer journeys. He illustrated how customers interact with a company through multiple channels, and how understanding the customer experience across these different channels is critical in understanding the customer and improving her experience.
Introduction Process Mining
I then introduced process mining using the metaphor of sailing boat journeys from 150 years ago. For the people who were already familiar with process mining I had brought the first two applications of process mining to customer journeys, which are probably not what you might think (take a look at the slides to find out).
The center of the first part of the evening was the case study presentation by Ellen van Molle and Bram Vanschoenwinkel from AE. They presented the results from a process mining mining analysis at an interim sector company, where employers were matched with employees.
By understanding how potential employees were using the job search application they could highlight the process areas, where people dropped out. Furthermore, by enhancing the data in a second iteration they were able to check hypotheses of the business such as “mostly elderly people have problems with the navigation in the system”.
The second part of the evening was an open discussion in small groups. As a starting point questions such as “What are the challenges of process mining for customer journeys?” and “What is the added value of process mining for customer journeys?” were provided to the groups. Afterwards, the results from the discussion were summarized.
Here are some of the discussion points I remember:
- One challenge is that the data need to be coupled across multiple channels / systems to get an integrated picture.
- Another challenge is that next to the analyst and the business one actually needs to involve the customer herself to understand the underlying root causes and motivations.
- While the rules for analyzing business processes are mostly well-defined, analyzing customer data is much more sensitive and privacy concerns play an important role.
- Potential benefits that were discussed are, for example, the saving of costs of customers calling the helpdesk by better adjusting the websites so that they find what they need.
- Another mentioned benefit was that by improving the customer experience, businesses can expect more revenue from their happy customers and more recommendations from their customers.
It was a well-attended and very lively event. Thank you all for coming! You can download all slides and more photos of the event at the Ngi-NGN event site here.
Photo Credit: 96dpi via Compfight cc
This is a guest post by Walter Vanherle from Crossroad Consulting in Belgium. Walter shares his experience from applying process mining to an operational process from a security provider.
If you have a process mining case study that you would like to share as well, please contact us at email@example.com.
Security Services companies are caught between the rising costs of operations and the downward price pressure due to direct and indirect competition. Further improvements in operational excellence together with service innovation are key in addressing these challenges.
Service delivery is always managed via agreements in the form of contractual obligations based on target performance. Not reaching pre-set targets has immediate financial implications. The service provider, therefore, actively manages these agreements in order to deliver the services efficiently, with costs/penalties managed in relation to the individual client expectation and priorities between clients.
The goal of the process mining project was to measure the performance of such a security services process and to create a reference base of Key Performance Indicators (KPIs).
The Intervention Management Process
Imagine a bank who is a customer of a security services company. Someone breaks a window and the security alarm goes off at the site of the security service provider: An intervention process is started.
The intervention management process has 2 stages (see also picture below). The first stage starts with a client intervention service request (T0). Then, the dispatching unit covering the confirmation activates the service request (T1), identifies an available agent (T2), and the agent confirms the acceptance of the mission (T3).
The second stage is the intervention itself. The execution of the intervention holds 4 subsequent steps: Effective departure to the location for the intervention (T4), Arrival at the location and start of the observations (T5), End of the Observation and documenting the intervention (T6). End of Mission (T7).
There are four KPIs that are relevant for this process. The most important one is the time from the initial client request to the arrival on site (T0-T5). Also important are the time from the client request to the confirmation (T0-T1), the time from the agent’s confirmation to the arrival on site (T3-T5), and the total time from the initial client request to the end of mission (T0-T7).
The service process execution is registered by a special service management software for security service providers by Risk Matrix Resultants. The anonymized event log held data over a period of 2 years containing all interventions for all clients. The dataset contained over 50.000 cases (missions) and 400.000 events.
The analysis below is based on the data from the missions for one client of the security service company over the timeframe of one year.
Process Mining Results
The expectation was that about 70% of the cases should follow the Straight Through Process (STP) flow with the 7 steps T0-T7 as explained above. Furthermore, the following four additional process variants were expected for the remaining 30% of the cases:
- T0-T1 (request is not confirmed)
- T0-T3 (solved, no intervention is needed)
- T0-T4 (aborted in the recording)
- T0-T7 but without T5 (no intervention is needed by accountable)
But how does the process look like in reality? Using the process mining software Disco, the real process flows could be discovered based on the data.
The process map below has been filtered to show the discovered process only for the five expected variants in the process. What stands out is that the four additional variants are almost never taken compared to the standard, STP variant, which is followed by 1518 cases. The other four expected variants are only taken by 31, 13, 2, and 20 cases, respectively.
The problem is that – unlike assumed – the process does not follow just these five expected paths. The STP variants covers 78% of the cases (this is actually slightly more than expected) but the five expected variants together only make up about 82% in total. So, the question is what is happening in the other 18% of the cases?
If we look at the full process, which has 58 variants (more than ten times as much as expected), then we get the following process map. The STP path is still visible, but there is a lot more variation. So, the question is what are these other variants and why are they there?
If we look at the unexpected variants, then it turns out that there are two types of root causes:
- actual variation in the process
- data quality problems that affect timestamps
For example, if we look at the expected variant “T0-T7 but without T5″, then we see that in addition to the sequence T0,T1,T2,T3,T4,T6,T7 (wich occurred 20 times), there are some additional patterns in the process (see process map below):
- 28 times the process went from T3 straight to T6 without T4 (no departure)
- 21 times the process went from T4 directly to T7 without T6 (no arrival)
- 34 times T3 was directly followed by T7 (no observations at all)
At the same time, there were many variations that were caused by what is called “clock drift“. In this process, many different parties were recording events on different devices (which had different clocks). As a consequence, there were often confused orderings in the process step sequence that were purely caused by such a clock difference (that is, the steps were actually be performed in the right order, but due to the different clocks they appeared inverted).
One example case, where this happens, is shown in the picture below. It seems as if T3 was performed before T2, but actually there is just a 5 sec time difference that is caused by the different clocks of the registering parties.
Such data quality problems do not only make the process variant analysis difficult, but also pose the risk to distort your KPI measuring. For example one of the KPIs was defined as the time from T0-T1. What happens now if T1 has an earlier timestamp than T0 due to the clock drift? If you just measure the time between them in Excel, you would get a negative duration that would reduce the average duration between these steps, which of course is not true.
In the intervention management Process, clock drift can occur for the transactions generated by the hand-held devices (PDAs) used by the field service agents or between the alarm generating system (T0) and the dispatching /intervention management system (T1). When the system clocks of devices are not synchronized, the recorded time stamps can shift with seconds, even minutes influencing the effective SLA timings. Using the case monitoring capacity of Disco with process filtering and visualization techniques we were able to visualize outliers quickly and suggest corrections to the transaction file compensating the irregular observations. We suppressed or eliminated the most prominent outliers from the final process mining file for more accurate performance statistics.
After cleaning the data, the SLA analysis for the KPIs (see above) was performed. We exported the durations from Disco and used a template-based Tableau Software Visualization to produce a cumulative SLA spectrum analysis. You can see such an SLA spectrum analysis for the time from T0-T5 for the year 2012 below.
SLA spectrum analysis for a partial data set
The KPIs T0-T5 and T0-T1 are particularly important, because they are linked with financial compensation. For cost optimization and predictive analytics the process sequences T3-T5, T0-T7 were analyzed. We also filtered out groups of clients with similar or different execution patterns based on their type of service contract.
Furthermore, the following process analyses were performed:
- priority accounts treatment,
- work handover patterns (preferential treatment of agents),
- correct treatment of the intervention priority classes.
Benefits and Lessons Learned
The registration process is both machine and people driven. Our experience shows that service tracking is subject to involuntary and voluntary errors and an ongoing, critical, management component. However, after overcoming these data quality challenges, we could generate many important benefits for the Security Services Company:
- Insight in the process variants helped to focus the communication to the operations teams for more accurate recording of the activities.
- Both conformance and performance analysis showed immediate money on the table (value leakage).
- The provided insight is instrumental input for business strategy and tactics corrections such as adaptations in client segmentation (priority services) and the possibility for more granular time based SLA service pricing.
- More accurate information for better planning. Recommendation for geolocation based research. Process Steps T3-T5 is the critical path in reaching target SLAs.
- More and better information in preparation and planning for client acquisition tactics. The analysis are used in pre-sales and sales campaigns.
If you want to know more about this case study, you can get in touch directly with Walter Vanherle at firstname.lastname@example.org.
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