The book is written by practitioners to share industry best practices and hands-on experiences. It provides more than 200 pages with guidance how to start a project, key learnings and challenges, pitfalls & failures. Twelve operational use cases are presented by experts from multiple industries and functions.
Lars Reinkemeyer (Editor)
Thomas H. Davenport
Lars Reinkemeyer (Editor)
Thomas H. Davenport
Chapters & Abstracts (Click2Read)
Fundamentals such as event logs, cases, activities and process variants are explained. Concrete examples show, how Process Mining can be used for business transparency and value. Allowing full transparency based on event logs, the implications of this important change - away from perception based towards a fact-based process management - are discussed. The metaphor of an MRT is used to explain possibilities, benefits and limitations of Process Mining.
One of the most common questions raised during discussions, presentations and initiation of projects is the question how to start a successful project. Experience and market research show that many Process Mining projects fail. Exaggerated promises and unrealistic expectations, unspecific targets, reluctant teams and insufficient digital traces can be some reasons for failure. While there is no silver bullet, experience shows that – besides the three factors Purpose, People and Processtraces which will be explained in the following chapters - ten general aspects such as quick start, expectation management and which process to start with are crucial.
Purpose implies a clear understanding, what Process Mining shall be used for, i.e. which use case shall be investigated. Like for any other tool, an idea is to be formulated first, what shall be achieved and how the tool can contribute. The chapter starts with typical questions from functional departments and reflecting challenges from process owners, along the value chain. Examples for purpose, which Process Mining can support, are explained with 22 standard use cases.
While innovative IT tools can be great enabler, it is a key success factor to get the right people on board. All smart data, insights and transparency will be useless if the process experts or process owners do not appreciate and support the approach. Similar to applying MRT technology, the affected people must be determined to pursue a therapy and strive for improvement. The chapter shares operational experiences, challenges and organizational setups which have proven successful.
Processtraces are comparable to raw Oil: they are hard to find, the collection causes technical challenges and the refinement is laborious. But once all these obstacles have been overcome, it can be used in an amazing variety of different forms and fuel impressive results. The chapter discusses not only best practices for identification and customization of processtraces from raw data, but also a structured approach, which includes technical aspects and challenges. The chapter covers technical architecture as well as user related aspects such as data access, security and user experiences.
As the evolution of Process Mining has not always been on the happy path, the idea of learning from failure has been adopted as a guiding principle for this book, applicable to this chapter as well as to the use cases in Part II. This chapter presents ten samples, reflecting challenges which were posed, pitfalls which were learned hands-on as well as failures which have been experienced. Samples range from data availability to process conformance checking and shall help the reader to avoid similar experiences.
With digital transformation as one of the hottest topics of today’s business, digital tools such as Process Mining and Robotics Process Automation (RPA) see a spike while Business Process Management (BPM) might be considered as a more traditional approach for operational efficiency. This chapter describes the differences between these technologies, how they correlate and can complement each other e.g. with the RPA Scout, and touches on the concept of a Digital Twin of an Organization (DTO).
As a summary for Part I, this chapter comprises ten key learnings on a one pager.
Global change that sticks in a complex organization is not an easy task, yet this has been achieved in only one year with a lean team of three people within Siemens Digital Industries. Using the innovative technology of process mining and equipped with frontline experience as well a distinct mindset, automation and digitalization had leaped forward tremendously on a global scale. This is the call for action, because everyone can achieve the same as the secret sauce is simply the combination of head, heart and hands.
Process Mining has allowed Uber’s Customer Support teams to uncover insights across their processes that touch more than 700 cities across 65 countries on six continents. This capability allows Uber to understand variation in customer support and target large scale multi-million dollar efficiency gains through process harmonization and increased customer satisfaction though global process benchmarking. Internally, Uber has used the power of Process Mining to help foster a culture of continuous improvement by providing a deeper level of business process visibility.
It has been about three years since BMW Group first started using Process Mining – besides several other fields - in an area where probably no other company had used it in such depth and with such an impact before: in manufacturing/production. When Nicolas Größlein and I introduced Process Mining at BMW Group with the great support of our former CIO, Klaus Straub, and our Vice President Connected Vehicle, Digital Backend and Big Data, Kai Demtröder, we were not driven by the desire to be particularly innovative or creative. Our main driver was to ensure world class production and the best possible quality of our cars for our customers. Because premium products require premium production processes!But is Process Mining at production really a new driver for innovation that can bring production processes to the next level? Or is it just a hype, a buzzword that will be replaced by the next one pretty soon? For BMW Group it has turned into a game changer, as it is shown in this use case for the example of production processes.
Purchase-to-Pay (P2P) Process Mining has the objective to visualize process flows, identify process weaknesses and support process improvements. It allows to monitor and manage any process in global and complex organizations in an unprecedented form and efficiency. This includes: • Visualization of P2P processes based on live data from SAP ERP systems. Time stamps for duration between relevant process steps. • Identification of P2P process weaknesses, e.g. with low degree of automation or multiple approval steps. • Support process improvements with immediate review of process adjustments and interactive remediation. In a nutshell P2P Process Mining provides the answers to “how can I increase operational efficiency?” and “how can I optimize my working capital by reducing cash out towards external suppliers?” within the P2P process.
athenahealth’s Technology Enabled Services – Service Outcomes team is responsible for the optimization and scaling of healthcare administration transactions – one in which we complete millions of transactions each day on our customers behalf. athenahealth was looking to create technology tooling to gain a better understanding of total process workflows across these service lines – both legacy and new services included. With the legacy service lines, the processes were more set-in place (well known happy paths, built out homegrown tooling, known pain points and exceptions, etc.) and with the newer service, developments were shifting frequently. From all of this, athenahealth turned to Process Mining as the tool to gain clean process insights, to help improve our customer’s experience and bring more value to their practice.
Energias de Portugal (EDP) is aiming to become a digital utility provider. Process Mining plays a pivotal role in the digital transformation journey and helps to transform the sales to dept cycle includingonboarding, billing, debt management and customer care. It provides insights in real-world activities and customer behaviorsthat help to reshape the way to do business. Customer experience visualization and cross-silo transparency allows new ways to analyze actual processes and provides a foundation to boost business efficiency.
ABB is a global technology company in the sector of Power and Automation and is using Process Mining technology for improving its performance towards fourkeyperformance indicators: Care, Customer, Cost and Cash. In order to get the maximum out of this, our ultimate goal is to use Process Mining not just for analytics purposes, but ultimately to use it as a technology, supporting the business in early identification of opportunities and risks, so moving from Mining processes towards Driving processes.Starting with lead times and on time deliveries, Process Mining has expanded at ABB towards hundreds of analytics cases: from logistics to finance to manufacturing. This innovative technology is used extensively throughout the organization and supported with an elaborate governance model, assuring continuous improvements.
As a large corporation and global player, BOSCH has several Process Mining use cases implemented, in different business divisions, across different continents and various process types for example P2P, O2C, production and ticketing. The BOSCH Process Mining set up can be characterized as a top management driven central approach. Planning and execution is steered by a cross-divisional team consisting of the inhouse consultancy, central IT and divisional coordinators of the participating business units.
As a medium sized distributor for electronic components, Schukat electronic must leverage technological innovation to stay competitive. Operational efficiency is crucial to stay competitive and secure a position as distributor in the value chain. For a better understanding of actual processes, Process Mining was deployed to gain transparency regarding actual order processing. Process Mining providing unprecedented transparency and insights, Schukat is now amidst a data driven continuous changing process which affects the whole organization.
Process Mining applications were adopted in the already productive business intelligence platformto support the constantly developing Computed Tomography (CT) product and service portfolio. The approach was driven by innovation management,recognizing the unique opportunity to optimize the CT product design and software workflow based on the real interaction between human and machine. For typical questions such as “how performant and user-friendly are the CT devices in clinical routine?”, “are programmed / predefined workflows accepted?” or “does the new innovative tablet control improve patient workflow?”Process Mining provides unprecedented transparency and thus the basis for strategic improvement.
Internal Audit at Bayer AG was an early adopter of the innovative Process Mining technologythough - at first glance - an audit organization does not seem to be predestined to be the typical point of entry. Two factors favored this step: On the one hand, Bayer AG has been using SAP globally in its core processes for many years; this systemic homogeneity is not to be underestimated for the implementation of a Process Mining application. On the other hand, the majority of the audits performed by Internal Audit at Bayer AG, particularly in the commercial area, are strongly process-driven. Although proven and extensive table-based toolboxes were available, it is very difficult to describe and interpret a global e2e process using tabular analyses and to audit it in a risk-oriented manner. Intense search discovered visual Process Mining as the perfect solution. In the start-up hall of the international IT-Fair CEBIT in 2012 the first meeting with an innovative young company took place. On the personal wish not to endure PowerPoint presentations, but only to see live data of real existing systems, it quickly became clear that the product came very close to what we had been looking for since many years. Our use case outlines the challenges of implementing Process Mining and how it drove the digital transformation of Internal Audit at Bayer.
In 2016 Deutsche Telekom Services Europe decided to improve the analytics capabilities in one of the most important internal e2e processes. As a shared service center is typically focused on e2e process performance, one major attempt was the implementation of a process mining software in order to further improve the efficiency. The idea was to investigate our core processes, to find out where to shorten lead times, reduce complexity and make the processes more efficient. During the implementation it then turned out that our shared service could benefit far more from this technology: We built up operational steering capabilities, which led to concrete savings. We were able to bring our reporting and analytics capabilities on a new level. And we helped to position our shared services internally as a driver for digitalization. Of course, the road towards this was paved with a lot of challenges like workers’ council negotiations, internal constraints and technical challenges – just to mention a few of them. At the end that all paid-off – we saved a lot of money in our operations, were able to establish a new digital steering solution and have now a flexible and powerful reporting solution at hand.
Written by Wil van der Aalst, this chapterreflects on the adoption of traditional process mining techniquesand the expansion of scope, discussed with five trends.An inconvenient truth explains, why – despite a considerable progress in Process Mining research - commercial tools tend to not use the state-of-the-art and make "short-cuts" instead that seem harmless at first, but inevitably lead to problems at a later stage. Seven novel challenges provide an outlook on open research topics. In a final appeal, Wil coins the term of “Process hygiene” to make Process Mining the “new normal”.
The business outlook is written by the editor andconsiders the dimensions business expectations, potentials and benefits, technological developments, market trends and developments of a digital workforce. The chapter has been structured on a timeline from present trends to short, mid and long term outlook, concluding in a Vision of a Digital Enabled Organization. The chapter aims to initiate thought processes, stir discussions, stimulate technical developments and further enhance the power of Process Mining.
10 Key Learnings
#10 Process Mining and RPA can complement each other
Testimonials
“This book, written by practitioners for practitioners, provides an excellent overview on Process Mining principles and a broad variety of use cases. Each practitioner shares individual experiences and the multitude of outlooks from practitioners and academics will foster future development, innovations andincrease business value. Thus, leaving Process Mining as an academic and innovative field that could well be one of the key technologies driving the Digital Transformation.“
Hannes Apitzsch (CEO Global Business Services, Siemens AG)
Klaus Straub (CIO: 2014-2019 BMW, 2004-2012 AUDI, 2002-2004 SiemensVDO)
Thomas H. Davenport (Distinguished Professor, Babson College and Research Fellow, MIT Initiative on the Digital Economy); Author of Process Innovation, Competing on Analytics, and The AI Advantage
Seungjin Whang (Professor of Operations, Information & Technology at Stanford Graduate School of Business)
Jianwen Su (Professor of Computer Science at University of California, Santa Barbara)
Sustainability
Authors
Gia-Thi Nguyen | Siemens AG, Digital Industries, Head of Operational Excellence |
Martin Rowlson | Uber, Global Head of Process Excellence, Community Operations |
Dr. Patrick Lechner | BMW Group, Head of Process Mining & Robotic Process Automation (RPA) |
Khaled El-Wafi | Siemens AG, P2P Service Manager |
Corey Balint | Athenahealth, Business Process Improvements Manager, Process Mining Insights |
Ricardo Henriques | EDP Comercial, Deputy Director of Business Enablement & Transformation |
Heymen Jansen | ABB Group VP Global Business Services – Head of Advanced Process Analytics |
Christian Buhrmann | Bosch Group, Director Bosch Management Consulting |
Georg Schukat | Schukat electronic, Co-owner and Co-CEO |
Jutta Reindler | Siemens Healthineers, Innovation Manager for Business Intelligence |
Arno Boenner | Bayer AG, Head of Audit Intelligence of Internal Audit |
Gerrit Lillig | Telekom Deutschland GmbH, Head of program Digital and Agile Transformation of Finance and VP Steering Instruments Consumer business |
Prof. Dr. Ir. Wil van der Aalst | RWTH Aachen University |
Editor
Dr. Reinkemeyer joined Siemens AG in 1994, right after he earned a Master degree in Business Administration and a PhD from the University of Cologne, from which he graduated summa cum laude. He joined Siemens as Product- and Regional Manager and was delegated to Siemens Australia as International Account Manager in 1996. In 1998, he signed on as General Manager at Oztrak Europe GmbH, gaining some hands-on start-up experiences. In 2000, he joined Atoss Software AG as a Director of International Sales. Dr. Reinkemeyer rejoined Siemens AG in 2001, where he held various international leadership positions in strategy, compliance and IT. He is a guest speaker at Stanford Graduate School of Business and regular speaker on international conferences.