How Process Mining Compliments Productivity Management at WACKER
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Wacker is a German chemical company that produces silicone rubbers, polymer products, biotech solutions, and polysilicon and wafers for the semiconductor industry. Minh Chau Nguyen is a digitalization manager and Klaus Kühnel is a project and program management practitioner at Wacker. Minh Chau and Klaus have seen how process mining complements existing analysis tools in the toolbox of a productivity manager in the era of digitized industries. They showed two concrete cases from analyzing the polysilicon production process at Wacker. One of the projects has resulted in saving the company 17 million Euros per year and the other one lead to a cost reduction of 400 thousand Euros per year.
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Jozef Gruzman is a digital and innovation enthusiast working in RBI, focusing on retail business, operations & change management. Claus Mitterlehner is a Senior Expert in RBI’s International Efficiency Management team and has a strong focus on Smart Automation supporting digital and business transformations.
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Process Mining Machine Recoveries to Reduce Downtime
Process Mining Machine Recoveries to Reduce DowntimeProcess mining Evangelist
ASML provides chip makers with everything they need to mass-produce patterns on silicon, helping to increase the value and lower the cost of a chip. The key technology is the lithography system, which brings together high-tech hardware and advanced software to control the chip manufacturing process down to the nanometer. All of the world’s top chipmakers like Samsung, Intel and TSMC use ASML’s technology, enabling the waves of innovation that help tackle the world’s toughest challenges.
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Skills for Successful Process Scientists
Skills for Successful Process ScientistsProcess mining Evangelist The final speaker at Process Mining Camp 2018 was Wil van der Aalst, the founding father of process mining. In his closing keynote, Wil talked about the updated skill set that process and data scientists need today. Since process mining research was starting up in Eindhoven in the late 90s, the availability of suitable data has increased tremendously, which makes it even more important that this data can and will be used in an appropriate and responsible manner.
This requires dedicated capabilities from the process miner in each stage of the analysis pipeline: Processing and analyzing data, being responsible about the effects on people, and on business models. When you look for people who are skilled in all of these technical areas, as well as in soft skills like communication and ethics, you start looking for (as they would say in the Netherlands) a “sheep with 5 legs”, or something that is very rare. Becoming a data scientist requires a lot of effort to learn all the skills that are needed to live up to these high expectations.
As ambassadors of process mining, we also have the responsibility to use the right terms. Wil sees a clear a distinction between Artificial Intelligence (AI), machine learning, and data mining. At the same time, one could argue that process mining is data mining, but the underlying techniques are very different. So, saying that process mining is part of data mining, or AI, doesn’t make any sense.
There are incredible expectations around AI and Big Data, which is very dangerous as we have seen in past “AI winters”. We should be careful not to overpromise and try to be realistic. The incredible successes of machine learning techniques like deep learning are, for example, still limited to very specific fields.
Some in the media and big technology companies use terms like Artificial Intelligence, Machine Learning, and Deep Learning interchangeably. They might argue that you don’t need process mining as you can just put an event log into a deep neural network and a process model will come out. There is, however, not one deep learning algorithm that can discover a process model. Instead, when we look at process mining it combines the fields and methods of ‘process science’ and ‘data science’. This makes it even more challenging for us to cover all required skills.
But do you, as a professional, need to know how a car works internally in order to drive it? It depends on what you want to accomplish. For example, if you need to drive fast around the Nurburg Ring, it can be very useful. Also, if you need to select a car then it would certainly be useful to know about its internals. Process mining is a relatively young technology. Therefore, it is useful to know how it works in order to select the right tool, and to use it to maximum effect.
So what kind of skills do you need as a process miner? You need to be able to extract and clean the data, spend time on the analysis, and interpret the results. This is not easy.
Process Mining and Jira - Air France KLM
Process Mining and Jira - Air France KLMProcess mining Evangelist
The sixth speaker at Process Mining Camp 2018 was Marc Tollens, a digital product owner at Air France KLM. In order to compete in the aviation market, KLM focuses on providing an excellent customer experience. They adopted the agile methodology to be able to reduce the time to introduce new touch points in the traveler’s journey. The performance of the agile teams is crucial to get the most out of each sprint.
As a Sunday afternoon project, Marc collected data from Jira, a project management software for (agile) software development, to see how process mining could be applied to help the teams learn from each other. Surprisingly, the flow of each team was quite different.
Marc started comparing the flows of the three teams to identify key differences in behavior and the resulting effects. In this way he could see if a team was able to deliver what they promised and whether they started testing too early or too late. He shared his observations with the teams during the retrospective and returned for the next sprint to see how the improvement worked out.
Understanding Complex Development Processes
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She applied process mining to the process from development to release at the Component and Data Management IT division. It looks like a simple process at first, but Daniel explains that it becomes increasingly complex when considering that multiple configurations and versions are developed, tested and released. It becomes even more complex as the projects affecting these releases are running in parallel. And on top of that, each project often impacts multiple versions and releases.
After Olga obtained the data for this process, she quickly realized that she had many candidates for the caseID, timestamp and activity. She had to find a perspective of the process that was on the right level, so that it could be recognized by the process owners. In her talk she takes us through her journey step by step and shows the challenges she encountered in each iteration. In the end, she was able to find the visualization that was hidden in the minds of the business experts.
Controlling Financial Processes at a Municipality
Controlling Financial Processes at a MunicipalityProcess mining Evangelist
The fourth speaker at Process Mining Camp 2018 was Wim Kouwenhoven from the City of Amsterdam. Amsterdam is well-known as the capital of the Netherlands and the City of Amsterdam is the municipality defining and governing local policies. Wim is a program manager responsible for improving and controlling the financial function.
A new way of doing things requires a different approach. While introducing process mining they used a five-step approach:
Step 1: Awareness
Introducing process mining is a little bit different in every organization. You need to fit something new to the context, or even create the context. At the City of Amsterdam, the key stakeholders in the financial and process improvement department were invited to join a workshop to learn what process mining is and to discuss what it could do for Amsterdam.
Step 2: Learn
As Wim put it, at the City of Amsterdam they are very good at thinking about something and creating plans, thinking about it a bit more, and then redesigning the plan and talking about it a bit more. So, they deliberately created a very small plan to quickly start experimenting with process mining in small pilot. The scope of the initial project was to analyze the Purchase-to-Pay process for one department covering four teams. As a result, they were able show that they were able to answer five key questions and got appetite for more.
Step 3: Plan
During the learning phase they only planned for the goals and approach of the pilot, without carving the objectives for the whole organization in stone. As the appetite was growing, more stakeholders were involved to plan for a broader adoption of process mining. While there was interest in process mining in the broader organization, they decided to keep focusing on making process mining a success in their financial department.
Step 4: Act
After the planning they started to strengthen the commitment. The director for the financial department took ownership and created time and support for the employees, team leaders, managers and directors. They started to develop the process mining capability by organizing training sessions for the teams and internal audit. After the training, they applied process mining in practice by deepening their analysis of the pilot by looking at e-invoicing, deleted invoices, analyzing the process by supplier, looking at new opportunities for audit, etc. As a result, the lead time for invoices was decreased by 8 days by preventing rework and by making the approval process more efficient. Even more important, they could further strengthen the commitment by convincing the stakeholders of the value.
Step 5: Act again
After convincing the stakeholders of the value you need to consolidate the success by acting again. Therefore, a team of process mining analysts was created to be able to meet the demand and sustain the success. Furthermore, new experiments were started to see how process mining could be used in three audits in 2018.
Mining a Global Trade Process with Data Science - Microsoft
Mining a Global Trade Process with Data Science - MicrosoftProcess mining Evangelist
The third speaker at Process Mining Camp 2018 was Dinesh Das from Microsoft. Dinesh Das is the Data Science manager in Microsoft’s Core Services Engineering and Operations organization.
Machine learning and cognitive solutions give opportunities to reimagine digital processes every day. This goes beyond translating the process mining insights into improvements and into controlling the processes in real-time and being able to act on this with advanced analytics on future scenarios.
Dinesh sees process mining as a silver bullet to achieve this and he shared his learnings and experiences based on the proof of concept on the global trade process. This process from order to delivery is a collaboration between Microsoft and the distribution partners in the supply chain. Data of each transaction was captured and process mining was applied to understand the process and capture the business rules (for example setting the benchmark for the service level agreement). These business rules can then be operationalized as continuous measure fulfillment and create triggers to act using machine learning and AI.
Using the process mining insight, the main variants are translated into Visio process maps for monitoring. The tracking of the performance of this process happens in real-time to see when cases become too late. The next step is to predict in what situations cases are too late and to find alternative routes.
As an example, Dinesh showed how machine learning could be used in this scenario. A TradeChatBot was developed based on machine learning to answer questions about the process. Dinesh showed a demo of the bot that was able to answer questions about the process by chat interactions. For example: “Which cases need to be handled today or require special care as they are expected to be too late?”. In addition to the insights from the monitoring business rules, the bot was also able to answer questions about the expected sequences of particular cases. In order for the bot to answer these questions, the result of the process mining analysis was used as a basis for machine learning.
Process Mining at Deutsche Bank - Journey
Process Mining at Deutsche Bank - JourneyProcess mining Evangelist Niyi started with process mining on a cold winter morning in January 2017, when he received an email from a colleague telling him about process mining. In his talk, he shared his process mining journey and the five lessons they have learned so far.
Deciding Surgery Room Allocations in a Hospital
Deciding Surgery Room Allocations in a HospitalProcess mining Evangelist The first speaker at Process Mining Camp 2018 was Fran Batchelor from UW Health in the United States. Fran is a Nursing Informatics Specialist who supports the surgical services at three of UW Health’s hospitals. She used process mining to analyze the flow of urgent and emergent surgical cases added to the schedule. What did she find?
The operations for most cases are scheduled and planned well in advance. For these patients the room is being prepared and the patient is transported to the room and after the operation the patient is transported to the recovery room. At the hospital they have 27 operation rooms available.
There are other patients that require urgent care, for which additional ‘hold’ rooms are reserved. However, sometimes there are more emergent cases than available operating rooms, such that schedules need to be adjusted. The smooth flow is critical for emergent cases and the challenge is to allocate the operating space for these patients.
At the hospital, two additional operation rooms were to be opened for the emergent cases and a project was started to determine how these rooms could be best allocated. Neurosurgery had made a case for all available new space and was in line to receive it. However, Peripheral Vascular also voiced a need. A team was assembled to provide information regarding the decision making. How many add-on cases are there without a dedicated hold room being available? How are they moving through the process and are they still meeting the internal performance metrics?
From the database the team extracted the data for each step in the process and developed the logic to identify the add-on cases. By visualizing the process using process mining they were able to see how add-on cases behave. They were able to see that 70% of the cases were scheduled and of the 30% unscheduled cases 12% didn’t have dedicated hold rooms.
When looking at the flow of the add-on cases, they realized that not all cases have the same urgency. By giving the cases a priority, they were able to distinguish between the different levels of urgency. Especially when focusing on the emergent cases of Neurosurgery and Peripheral Vascular they found that 43% of the cases that took longer than 1 hour to get to the operating room belonged to the Peripheral Vascular surgery (a higher volume compared to Neurosurgery). So, it was most logical to allocate the additional rooms for both of these procedures.
Process mining reduced the political and emotional components when taking these decisions. By looking at the data and the visualization it was possible to tell the story more easily. Without process mining it would not have been possible to make such a clear-cut case and the decision would have been made differently.
However, it was not easy and took two years to get to this point. First of all, it was a challenge to set up the project and get access to the right data.
Mining the Customer Journey, accelerating New Product Introduction
Mining the Customer Journey, accelerating New Product IntroductionProcess mining Evangelist Mick is a supply chain manager at Veco, a precision metal manufacturer. With process mining, Mick found a technique to radically accelerate the New Product Development cycle and convert this to an opportunity for faster growth.
Veco had started using process mining three years ago as an addition to their Lean Six Sigma methodology, which helped them to reduce the lead time of production orders significantly. However, you often see that when you solve one problem, you uncover another.
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.
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Feature Engineering for Electronic Health Record Systems
Feature Engineering for Electronic Health Record SystemsProcess mining Evangelist Oak Ridge National Laboratory (ORNL) is a leading science and technology laboratory under the direction of the Department of Energy.
Hilda Klasky is part of the R&D Staff of the Systems Modeling Group in the Computational Sciences & Engineering Division at ORNL. To prepare the data of the radiology process from the Veterans Affairs Corporate Data Warehouse for her process mining analysis, Hilda had to condense and pre-process the data in various ways. Step by step she shows the strategies that have worked for her to simplify the data to the level that was required to be able to analyze the process with domain experts.
How to Set Up Process Mining in a Decentralized Organization?
How to Set Up Process Mining in a Decentralized Organization?Process mining Evangelist
The history of a.s.r. begins 1720 in “Stad Rotterdam”, which as the oldest insurance company on the European continent was specialized in insuring ocean-going vessels — not a surprising choice in a port city like Rotterdam. Today, a.s.r. is a major Dutch insurance group based in Utrecht.
Nelleke Smits is part of the Analytics lab in the Digital Innovation team. Because a.s.r. is a decentralized organization, she worked together with different business units for her process mining projects in the Medical Report, Complaints, and Life Product Expiration areas. During these projects, she realized that different organizational approaches are needed for different situations.
For example, in some situations, a report with recommendations can be created by the process mining analyst after an intake and a few interactions with the business unit. In other situations, interactive process mining workshops are necessary to align all the stakeholders. And there are also situations, where the process mining analysis can be carried out by analysts in the business unit themselves in a continuous manner. Nelleke shares her criteria to determine when which approach is most suitable.
Analyzing the Customer Lifecycle - Total Gas & Power
Analyzing the Customer Lifecycle - Total Gas & PowerProcess mining Evangelist Analyzing the customer journey is all about shifting the focus from an internal department view to a customer perspective. To achieve that, Business Intelligence analyst Carmen Vermeer and Marketing Manager Noortje Groenendaal used process mining for their analysis of the customer lifecycle at Total.
The departments who are responsible for customer service, billing, sales, and marketing, are all involved in this customer lifecycle. Instead of looking at these individual departments one by one, Carmen and Noortje determined what a successful and what an unsuccessful customer journey looks like from a customer’s point of view. Then, they analyzed the effect of actions that were taken to increase customer satisfaction and the timing of these actions.
Theory of Constraints in Parts Repair - Lufthansa
Theory of Constraints in Parts Repair - LufthansaProcess mining Evangelist Christian Pohle is Head of Process at Lufthansa Technik. His process mining journey started off with an unusual situation: They had great data. Their ERP system collects very detailed and accurate information about all the steps that happen in the parts repair process. So, they were in an excellent position to use that data for their process mining analysis.
With his background in Lean Six Sigma, Christian could use the lean methodologies of continuous improvement and combine them with process mining. One interesting tool that they used is the Theory of Constraints, which is a technique that focuses on finding the biggest bottleneck and resolving it, then finding the next-biggest bottleneck and resolving it, etc.
Instead of working on individual transformation projects, they used an Agile approach to implement and review the improvements in sprints. This led to an openness and honesty that were appreciated throughout the company. In his remarkable story, Christian brings to life how process mining helps to move discussions “from the belly to the brain” through objectification of gut instinct.
Responsible Data Science for Process Miners
Responsible Data Science for Process MinersProcess mining Evangelist Wil van der Aalst gave the closing keynote at camp. He started with giving an overview of the progress that has been made in the process mining field over the past 20 years. Process mining unlocks great potential but also comes with a huge responsibility. Responsible data science focuses on positive technological breakthroughs and aims to prevent “pollution” by “bad data science”.
Wil gave us a sneak peek at current responsible process mining research from the area of ‘fairness’ (how to draw conclusions from data that are fair without sacrificing accuracy too much) and ‘confidentiality’ (how to analyze data without revealing secrets). While research can provide some solutions by developing new techniques, understanding these risks is a responsibility of the process miner.
Process Mining Meets Football - JADS (Hadi Sotudeh)
Process Mining Meets Football - JADS (Hadi Sotudeh)Process mining Evangelist Sometimes, an application of process mining comes along that nobody thought of before. Hadi Sotudeh, PDEng student at JADS, had such an example when he applied process mining to data from the 2018 World Cup in football.
After transforming the data into an event log suitable for process mining, he was especially interested in how a football team possesses the ball on the pitch. This would give coaching staff great insights into interesting patterns of play to develop counter strategies for an opponent, or for a team to learn from mistakes.
The challenge is that the football interactions do not follow a typical (standard) process. Therefore, finding the right perspective is not easy. Hadi shows that there is not just one perspective, but that the same data can be molded to explore many different angles. Each of these perspectives can give different insights. For example, next to sequences of types of actions, he also looked at interactions between individual players, zones in the field, and patterns for particular outcomes (e.g., goal or throw-in).
Process Mining in Logistics - Vanderlande
Process Mining in Logistics - VanderlandeProcess mining Evangelist Vanderlande is the global market leader for value-added logistic process automation at airports, and in the parcel market. The company is also a leading supplier of process automation solutions for warehouses.
Boris Nikolov is a Process Improvement Engineer at Vanderlande. He supports customers by solving problems and by implementing new systems for baggage handling or parcel sorting and routing.
One of the customers in the parcel distribution center called Boris to solve a problem of recirculating parcels. Normally, parcels entering the system are scanned and routed to the right locations. However, a percentage of parcels kept circulating. Using the standard checks, he was not able to find the problem quickly. So, he tried process mining and found out that the root cause was that the lookup of the location of the parcels in the ERP was delayed. He also used process mining in the design stage of new baggage handling systems for airports.
Process wind tunnel for improving business processes
Process wind tunnel for improving business processesProcess mining Evangelist With roots that trace back to 1919, AIG is a global insurance company with operations in more than 80 countries and jurisdictions. Sudhendu Rai is a Lead Scientist and Head of Data-Driven Process Optimization in the COO Office of AIG’s Investments organization.
Sudhendu presents their ‘Process Wind Tunnel’ framework to evaluate and optimize process structure and parameters prior to committing to a final process design. In the same way as wind tunnels are used to test the aerodynamics of a physical object, a discrete-event simulation can be used to explore alternative “What-If” scenarios for a process without implementing them (yet) in the real world.
A big problem of using simulation is that the initial model needs to reflect the reality as closely as possible. Process mining is a great way to discover the key steps and process flows that need the be part of the simulation model. Furthermore, process mining helps to determine the probabilities of transitions and the distribution of the process times to populate the model. Based on this initial model, alternative process re-designs of the current process can be tested in the simulation tool. After finding the best scenario, Sudhendu could reduce the cycle time from 12 days to 5 days, increasing the throughput by over 30%.
Building Operational Profiles for Philips MRI Machines
Building Operational Profiles for Philips MRI MachinesProcess mining Evangelist Philips Healthcare is a global maker of many healthcare products, among which are imaging systems such as X-Ray, CT, Fluoroscopy and Magnetic Resonance Imaging (MRI) machines. ESI is an independent research organisation for high-tech embedded systems design and engineering.
Carmen Bratosin is a research fellow at ESI and Mark Pijnenburg is a Clinical Verification Lead at Philips Healthcare. Mark and Carmen showed how process mining can be used to analyze the system usage of an MRI machine. Understanding the actual usage patterns in the field is especially interesting to improve the system requirements and to increase the test coverage based on real-life behavior for these machines.
Preparing the data for process mining is not easy, because the logging produced by the MRI machines is only available on a technical log level used for debugging. Furthermore, each physician has their own preferences regarding the machine setup for certain exams, which adds to the complexity. They started by selecting the key activities in the technical log and then aligned them with the user interface elements. Finally, they matched the logged events with the steps described by the American College of Radiology to get them on the abstraction level that a radiology expert would understand. In this way, they were able to compare the actual usage of the MRI system with the expected (and designed) behavior.
How to make impact with process mining? - PGGM
How to make impact with process mining? - PGGMProcess mining Evangelist PGGM is a non-profit cooperative pension administration organization. They are founded by social partners in the care and welfare sector and serve four million participants.
Bas van Beek is a process consultant and Frank Nobel is a process and data analyst at PGGM. Instead of establishing process mining either in the data science corner or in the Lean Six Sigma corner, they approach every process improvement initiative as a multi-disciplinary team with people from both groups.
The nature of each initiative can be quite different. For example, some projects are more focused on the redesign or implementation of an IT solution. Others require extensive involvement from the business to change the way of working. In a third example, they showed how they used process mining for compliance purposes: Because they were able to demonstrate that certain individual funds actually follow the same process, they could group these funds and simplify the audit by using generic controls.
Process Mining as Enabler for Digital Transformations
Process Mining as Enabler for Digital TransformationsProcess mining Evangelist Raiffeisen Bank International (RBI) is a leading Retail and Corporate bank with 50 thousand employees serving more than 14 million customers in 14 countries in Central and Eastern Europe.
Jozef Gruzman is a digital and innovation enthusiast working in RBI, focusing on retail business, operations & change management. Claus Mitterlehner is a Senior Expert in RBI’s International Efficiency Management team and has a strong focus on Smart Automation supporting digital and business transformations.
Together, they have applied process mining on various processes such as: corporate lending, credit card and mortgage applications, incident management and service desk, procure to pay, and many more. They have developed a standard approach for black-box process discoveries and illustrate their approach and the deliverables they create for the business units based on the customer lending process.
Process Mining Machine Recoveries to Reduce Downtime
Process Mining Machine Recoveries to Reduce DowntimeProcess mining Evangelist
ASML provides chip makers with everything they need to mass-produce patterns on silicon, helping to increase the value and lower the cost of a chip. The key technology is the lithography system, which brings together high-tech hardware and advanced software to control the chip manufacturing process down to the nanometer. All of the world’s top chipmakers like Samsung, Intel and TSMC use ASML’s technology, enabling the waves of innovation that help tackle the world’s toughest challenges.
The machines are developed and assembled in Veldhoven in the Netherlands and shipped to customers all over the world. Freerk Jilderda is a project manager running structural improvement projects in the Development & Engineering sector. Availability of the machines is crucial and, therefore, Freerk started a project to reduce the recovery time.
A recovery is a procedure of tests and calibrations to get the machine back up and running after repairs or maintenance. The ideal recovery is described by a procedure containing a sequence of 140 steps. After Freerk’s team identified the recoveries from the machine logging, they used process mining to compare the recoveries with the procedure to identify the key deviations. In this way they were able to find steps that are not part of the expected recovery procedure and improve the process.
Skills for Successful Process Scientists
Skills for Successful Process ScientistsProcess mining Evangelist The final speaker at Process Mining Camp 2018 was Wil van der Aalst, the founding father of process mining. In his closing keynote, Wil talked about the updated skill set that process and data scientists need today. Since process mining research was starting up in Eindhoven in the late 90s, the availability of suitable data has increased tremendously, which makes it even more important that this data can and will be used in an appropriate and responsible manner.
This requires dedicated capabilities from the process miner in each stage of the analysis pipeline: Processing and analyzing data, being responsible about the effects on people, and on business models. When you look for people who are skilled in all of these technical areas, as well as in soft skills like communication and ethics, you start looking for (as they would say in the Netherlands) a “sheep with 5 legs”, or something that is very rare. Becoming a data scientist requires a lot of effort to learn all the skills that are needed to live up to these high expectations.
As ambassadors of process mining, we also have the responsibility to use the right terms. Wil sees a clear a distinction between Artificial Intelligence (AI), machine learning, and data mining. At the same time, one could argue that process mining is data mining, but the underlying techniques are very different. So, saying that process mining is part of data mining, or AI, doesn’t make any sense.
There are incredible expectations around AI and Big Data, which is very dangerous as we have seen in past “AI winters”. We should be careful not to overpromise and try to be realistic. The incredible successes of machine learning techniques like deep learning are, for example, still limited to very specific fields.
Some in the media and big technology companies use terms like Artificial Intelligence, Machine Learning, and Deep Learning interchangeably. They might argue that you don’t need process mining as you can just put an event log into a deep neural network and a process model will come out. There is, however, not one deep learning algorithm that can discover a process model. Instead, when we look at process mining it combines the fields and methods of ‘process science’ and ‘data science’. This makes it even more challenging for us to cover all required skills.
But do you, as a professional, need to know how a car works internally in order to drive it? It depends on what you want to accomplish. For example, if you need to drive fast around the Nurburg Ring, it can be very useful. Also, if you need to select a car then it would certainly be useful to know about its internals. Process mining is a relatively young technology. Therefore, it is useful to know how it works in order to select the right tool, and to use it to maximum effect.
So what kind of skills do you need as a process miner? You need to be able to extract and clean the data, spend time on the analysis, and interpret the results. This is not easy.
Process Mining and Jira - Air France KLM
Process Mining and Jira - Air France KLMProcess mining Evangelist
The sixth speaker at Process Mining Camp 2018 was Marc Tollens, a digital product owner at Air France KLM. In order to compete in the aviation market, KLM focuses on providing an excellent customer experience. They adopted the agile methodology to be able to reduce the time to introduce new touch points in the traveler’s journey. The performance of the agile teams is crucial to get the most out of each sprint.
As a Sunday afternoon project, Marc collected data from Jira, a project management software for (agile) software development, to see how process mining could be applied to help the teams learn from each other. Surprisingly, the flow of each team was quite different.
Marc started comparing the flows of the three teams to identify key differences in behavior and the resulting effects. In this way he could see if a team was able to deliver what they promised and whether they started testing too early or too late. He shared his observations with the teams during the retrospective and returned for the next sprint to see how the improvement worked out.
Understanding Complex Development Processes
Understanding Complex Development ProcessesProcess mining Evangelist The fifth talk at Process Mining Camp was given by Olga Gazina and Daniel Cathala from Euroclear. As a data analyst at the internal audit department Olga helped Daniel, IT Manager, to make his life at the end of the year a bit easier by using process mining to identify key risks.
She applied process mining to the process from development to release at the Component and Data Management IT division. It looks like a simple process at first, but Daniel explains that it becomes increasingly complex when considering that multiple configurations and versions are developed, tested and released. It becomes even more complex as the projects affecting these releases are running in parallel. And on top of that, each project often impacts multiple versions and releases.
After Olga obtained the data for this process, she quickly realized that she had many candidates for the caseID, timestamp and activity. She had to find a perspective of the process that was on the right level, so that it could be recognized by the process owners. In her talk she takes us through her journey step by step and shows the challenges she encountered in each iteration. In the end, she was able to find the visualization that was hidden in the minds of the business experts.
Controlling Financial Processes at a Municipality
Controlling Financial Processes at a MunicipalityProcess mining Evangelist
The fourth speaker at Process Mining Camp 2018 was Wim Kouwenhoven from the City of Amsterdam. Amsterdam is well-known as the capital of the Netherlands and the City of Amsterdam is the municipality defining and governing local policies. Wim is a program manager responsible for improving and controlling the financial function.
A new way of doing things requires a different approach. While introducing process mining they used a five-step approach:
Step 1: Awareness
Introducing process mining is a little bit different in every organization. You need to fit something new to the context, or even create the context. At the City of Amsterdam, the key stakeholders in the financial and process improvement department were invited to join a workshop to learn what process mining is and to discuss what it could do for Amsterdam.
Step 2: Learn
As Wim put it, at the City of Amsterdam they are very good at thinking about something and creating plans, thinking about it a bit more, and then redesigning the plan and talking about it a bit more. So, they deliberately created a very small plan to quickly start experimenting with process mining in small pilot. The scope of the initial project was to analyze the Purchase-to-Pay process for one department covering four teams. As a result, they were able show that they were able to answer five key questions and got appetite for more.
Step 3: Plan
During the learning phase they only planned for the goals and approach of the pilot, without carving the objectives for the whole organization in stone. As the appetite was growing, more stakeholders were involved to plan for a broader adoption of process mining. While there was interest in process mining in the broader organization, they decided to keep focusing on making process mining a success in their financial department.
Step 4: Act
After the planning they started to strengthen the commitment. The director for the financial department took ownership and created time and support for the employees, team leaders, managers and directors. They started to develop the process mining capability by organizing training sessions for the teams and internal audit. After the training, they applied process mining in practice by deepening their analysis of the pilot by looking at e-invoicing, deleted invoices, analyzing the process by supplier, looking at new opportunities for audit, etc. As a result, the lead time for invoices was decreased by 8 days by preventing rework and by making the approval process more efficient. Even more important, they could further strengthen the commitment by convincing the stakeholders of the value.
Step 5: Act again
After convincing the stakeholders of the value you need to consolidate the success by acting again. Therefore, a team of process mining analysts was created to be able to meet the demand and sustain the success. Furthermore, new experiments were started to see how process mining could be used in three audits in 2018.
Mining a Global Trade Process with Data Science - Microsoft
Mining a Global Trade Process with Data Science - MicrosoftProcess mining Evangelist
The third speaker at Process Mining Camp 2018 was Dinesh Das from Microsoft. Dinesh Das is the Data Science manager in Microsoft’s Core Services Engineering and Operations organization.
Machine learning and cognitive solutions give opportunities to reimagine digital processes every day. This goes beyond translating the process mining insights into improvements and into controlling the processes in real-time and being able to act on this with advanced analytics on future scenarios.
Dinesh sees process mining as a silver bullet to achieve this and he shared his learnings and experiences based on the proof of concept on the global trade process. This process from order to delivery is a collaboration between Microsoft and the distribution partners in the supply chain. Data of each transaction was captured and process mining was applied to understand the process and capture the business rules (for example setting the benchmark for the service level agreement). These business rules can then be operationalized as continuous measure fulfillment and create triggers to act using machine learning and AI.
Using the process mining insight, the main variants are translated into Visio process maps for monitoring. The tracking of the performance of this process happens in real-time to see when cases become too late. The next step is to predict in what situations cases are too late and to find alternative routes.
As an example, Dinesh showed how machine learning could be used in this scenario. A TradeChatBot was developed based on machine learning to answer questions about the process. Dinesh showed a demo of the bot that was able to answer questions about the process by chat interactions. For example: “Which cases need to be handled today or require special care as they are expected to be too late?”. In addition to the insights from the monitoring business rules, the bot was also able to answer questions about the expected sequences of particular cases. In order for the bot to answer these questions, the result of the process mining analysis was used as a basis for machine learning.
Process Mining at Deutsche Bank - Journey
Process Mining at Deutsche Bank - JourneyProcess mining Evangelist Niyi started with process mining on a cold winter morning in January 2017, when he received an email from a colleague telling him about process mining. In his talk, he shared his process mining journey and the five lessons they have learned so far.
Deciding Surgery Room Allocations in a Hospital
Deciding Surgery Room Allocations in a HospitalProcess mining Evangelist The first speaker at Process Mining Camp 2018 was Fran Batchelor from UW Health in the United States. Fran is a Nursing Informatics Specialist who supports the surgical services at three of UW Health’s hospitals. She used process mining to analyze the flow of urgent and emergent surgical cases added to the schedule. What did she find?
The operations for most cases are scheduled and planned well in advance. For these patients the room is being prepared and the patient is transported to the room and after the operation the patient is transported to the recovery room. At the hospital they have 27 operation rooms available.
There are other patients that require urgent care, for which additional ‘hold’ rooms are reserved. However, sometimes there are more emergent cases than available operating rooms, such that schedules need to be adjusted. The smooth flow is critical for emergent cases and the challenge is to allocate the operating space for these patients.
At the hospital, two additional operation rooms were to be opened for the emergent cases and a project was started to determine how these rooms could be best allocated. Neurosurgery had made a case for all available new space and was in line to receive it. However, Peripheral Vascular also voiced a need. A team was assembled to provide information regarding the decision making. How many add-on cases are there without a dedicated hold room being available? How are they moving through the process and are they still meeting the internal performance metrics?
From the database the team extracted the data for each step in the process and developed the logic to identify the add-on cases. By visualizing the process using process mining they were able to see how add-on cases behave. They were able to see that 70% of the cases were scheduled and of the 30% unscheduled cases 12% didn’t have dedicated hold rooms.
When looking at the flow of the add-on cases, they realized that not all cases have the same urgency. By giving the cases a priority, they were able to distinguish between the different levels of urgency. Especially when focusing on the emergent cases of Neurosurgery and Peripheral Vascular they found that 43% of the cases that took longer than 1 hour to get to the operating room belonged to the Peripheral Vascular surgery (a higher volume compared to Neurosurgery). So, it was most logical to allocate the additional rooms for both of these procedures.
Process mining reduced the political and emotional components when taking these decisions. By looking at the data and the visualization it was possible to tell the story more easily. Without process mining it would not have been possible to make such a clear-cut case and the decision would have been made differently.
However, it was not easy and took two years to get to this point. First of all, it was a challenge to set up the project and get access to the right data.
Mining the Customer Journey, accelerating New Product Introduction
Mining the Customer Journey, accelerating New Product IntroductionProcess mining Evangelist Mick is a supply chain manager at Veco, a precision metal manufacturer. With process mining, Mick found a technique to radically accelerate the New Product Development cycle and convert this to an opportunity for faster growth.
Veco had started using process mining three years ago as an addition to their Lean Six Sigma methodology, which helped them to reduce the lead time of production orders significantly. However, you often see that when you solve one problem, you uncover another.
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.
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