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Foreword |
6 |
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Foreword |
8 |
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Process Mining in a Volatile World |
8 |
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Preface |
11 |
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Outline of the Book |
12 |
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Acknowledgments |
15 |
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Contents |
16 |
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Abbreviations |
18 |
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Part I: Principles and Value of Process Mining |
20 |
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1: Process Mining in a Nutshell |
21 |
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2: How to Get Started |
29 |
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3: Purpose: Identifying the Right Use Cases |
33 |
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4: People: The Human Factor |
44 |
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5: Processtraces: Technology |
48 |
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6: Challenges, Pitfalls, and Failures |
53 |
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7: Process Mining, RPA, BPM, and DTO |
57 |
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8: Key Learnings |
61 |
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Part II: Best Practice Use Cases |
63 |
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9: Siemens: Driving Global Change with the Digital Fit Rate in Order2Cash |
64 |
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Challenge |
64 |
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Use Case |
65 |
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Impact |
68 |
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Technology |
69 |
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Lessons Learned |
70 |
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Outlook |
71 |
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Links |
72 |
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10: Uber: Process Mining to Optimize Customer Experience and Business Performance |
73 |
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Challenge |
73 |
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Use Case |
74 |
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Impact |
75 |
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Technology |
75 |
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Lessons Learned |
75 |
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Outlook |
76 |
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11: BMW: Process Mining @ Production |
78 |
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Challenge |
78 |
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Use Case |
80 |
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Impact and Technology |
81 |
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Lessons Learned |
82 |
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Outlook |
85 |
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Links and Further Reading |
86 |
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12: Siemens: Process Mining for Operational Efficiency in Purchase2Pay |
87 |
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Challenge |
87 |
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Segregation of Duty Versus e2e Transparency |
88 |
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Cornerstones and Definitions for P2P Process Mining Cases |
89 |
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Use Cases |
91 |
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P2P Direct Material Focus Areas and Data Models |
91 |
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P2P Overview: Direct Material |
92 |
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Rework: Direct Material |
93 |
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P2P Indirect Material Focus Areas (SRM) |
94 |
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Payment Terms |
98 |
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EDI Rate Versus Rework Rate |
99 |
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Robotic Process Automation |
101 |
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Impact: Potential Business Benefits and Savings |
103 |
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Benefits of Increasing Operational Efficiency |
104 |
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Benefits of Reducing Throughput Times |
104 |
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Benefits of Optimizing Working Capital |
105 |
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Benefits of Increasing Digitalization |
105 |
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Technological Concepts of Process Mining P2P |
106 |
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Technological Prerequisites |
106 |
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P2P Data Model Configuration Template |
107 |
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Lessons Learned and Outlook |
108 |
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13: athenahealth: Process Mining for Service Integrity in Healthcare |
109 |
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Challenge |
109 |
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Why Process Mining? |
110 |
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Use Cases |
110 |
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Use Case #1 |
111 |
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Use Case #2 |
111 |
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Implementation of Use Case #1 |
111 |
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Implementation of Use Case #2 |
113 |
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Technology |
115 |
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Impact |
115 |
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Lessons Learned |
116 |
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Macro |
117 |
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Micro |
118 |
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Key Takeaways |
118 |
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Outlook |
119 |
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Links and Background Information |
119 |
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14: EDP Comercial: Sales and Service Digitization |
121 |
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Challenge |
121 |
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Use Case |
122 |
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Impact |
123 |
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Medium- and Long-Term Impacts: Accelerating the Positive Impact on Climate Change |
124 |
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Customer Care Insights |
124 |
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How Process Mining Is Contributing to Break Silos? |
126 |
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Analyzing Within the Processes |
126 |
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Technology |
127 |
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Lessons Learned |
127 |
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About People |
127 |
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About Processes |
128 |
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How to Engage? |
128 |
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Outlook |
128 |
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The Main Role of Process Mining |
128 |
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Public Sector Benefits |
129 |
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Process Mining Requires New Skills |
129 |
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Technological Context |
129 |
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Organic Context |
129 |
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Is the Global Market Mature? |
129 |
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Links (Fig. 14.4) |
130 |
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15: ABB: From Mining Processes Towards Driving Processes |
131 |
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Challenge |
131 |
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Use Case |
133 |
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Impact |
134 |
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Technology |
136 |
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Lessons Learned |
138 |
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16: Bosch: Process Mining-A Corporate Consulting Perspective |
141 |
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Challenge |
141 |
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Use Case |
142 |
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Impact |
143 |
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Technology |
144 |
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Lessons Learned |
144 |
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Outlook |
145 |
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17: Schukat: Process Mining Enables Schukat Electronic to Reinvent Itself |
146 |
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Challenge |
146 |
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Use Case |
147 |
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Bottom-Up-Analyze First |
147 |
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Top-Down-Design First |
148 |
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Loop |
149 |
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Trigger Live Action |
149 |
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Impact |
150 |
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Technology |
150 |
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Lessons Learned |
150 |
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Outlook |
151 |
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Links |
153 |
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18: Siemens Healthineers: Process Mining as an Innovation Driver in Product Management |
154 |
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Challenge |
154 |
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Use Case |
156 |
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Impact |
158 |
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Process Mining Enhanced by New Types of Visualizations |
161 |
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Exam Throughput per Hour |
161 |
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Trending of Examination Times |
161 |
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Visualization of Individual System Settings |
162 |
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Technology |
163 |
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Event Log and Data Architecture |
163 |
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Process Mining App |
164 |
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Hosting |
164 |
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Self-Service Process Mining |
165 |
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Lessons Learned |
165 |
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Challenge of Gathering Data |
166 |
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Structural and Organizational Challenges |
166 |
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Outlook |
166 |
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Links and Further Reading |
167 |
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19: Bayer: Process Mining Supports Digital Transformation in Internal Audit |
169 |
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Challenge |
169 |
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Use Cases |
171 |
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Impact |
174 |
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Technology |
174 |
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Lessons Learned |
175 |
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Outlook |
177 |
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Links |
178 |
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URLs |
178 |
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20: Telekom: Process Mining in Shared Services |
179 |
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Challenge |
179 |
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Use Case |
181 |
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Step 1: The First Implementations |
182 |
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Step 2: Center of Excellence |
182 |
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Step 3: Integration |
183 |
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Step 4: Machine Learning |
184 |
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Impact |
184 |
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Technology |
185 |
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Lessons Learned |
186 |
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Outlook |
188 |
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Links |
188 |
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Part III: Outlook: Future of Process Mining |
189 |
|
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21: Academic View: Development of the Process Mining Discipline |
190 |
|
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Adoption of Traditional Process Mining Techniques |
191 |
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Expanding the Scope of Process Mining |
192 |
|
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An Inconvenient Truth |
195 |
|
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Novel Challenges |
200 |
|
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Process Hygiene |
204 |
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References |
204 |
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22: Business View: Towards a Digital Enabled Organization |
206 |
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Present Trends |
207 |
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Near Future |
208 |
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Midterm Future |
212 |
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Long-Term Future |
213 |
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Vision of a Digital Enabled Organization |
214 |
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About the Editor |
216 |
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