INTERACTIVE SESSION: TECHNOLOGY Siemens Makes Business Processes More Visible Siemens AG is a German manufacturing conglom- occur through bottlenecks, unnecessary detours. erate that produces systems and components for and manual interventions, or where compliance industrial automation, healthcare, energy build- issues might arise. Some process mining software ing, and transportation markets. The company is including Celonis, enables users to drill down to headquartered in Munich and Berlin, with 372,000 view the individual documents associated with a employees worldwide, and global revenue of 83 process billion approximately US $99 billion) in fiscal Celonis has capabilities for comparing users 2017. Siemens is the largest industrial manufac target operating models to the as-ts process. pro- turing company in Europe, with branch offices viding an automated fit-gap analysis. Celonis ana- abroad. This is clearly a company that prizes in- lyzes root causes for deviations and performance novation and continuous improvement of the effi- loss, highlighting the issues that have the greatest ciency and quality of its business processes. impact on process performance. At the touch of Siemens has thousands of business processes, a button, the user can see a comparison between some of which are very complex. Management was the target and actual process and also visualize the seeking better ways of making the business more main cause of delays and additional expenditure. efficient and turned to business process mining If a process model doesn't already exist, the technology. In 2014 the company established a software will try to create one automatically, unit called Process Dash (which stands for Data sometimes using artificial intelligence techniques Analytics, smart handling) to actively support such as machine learning (see section 11-4). If a global process optimization in all Siemens divi. process model is available, the process mining sions. It started collecting and analyzing ERP data software will compare it to the event log to identify to identify bottlenecks in its production, delivery, discrepancies and their possible causes. For pro- and payment processes using Celonis Process cess modeling. Siemens uses a Celonis tool called Mining analysis and visualization software for this Pi Conformance and Machine Learning. The soft- purpose. Celonis partners with SAP, and its soft- ware predicts which customer orders are likely ware runs on the SAP HANA in memory database to arrive late using algorithms that continuously platform learn from Siemens' performance, Process mining software analyzes data in en Siemens started using Celonis analysis and terprise application event logs to determine how visualization tools to learn how quickly it pays business processes are actually working in order to its suppliers. Some suppliers offer discounts for identify bottlenecks and other areas of inefficiency early payment. Siemens was often unable to take so that they can be improved. The technology can advantage of these discounts because it was un analyze millions of transaction records and spot able to pay quickly enough. The company used deviations from normal workflows. A push of a process mining to analyze data from its ERP, button produces a snapshot of an entire business accounting, and payment approval systems to process. Process DAsh used the Celonis software understand why this was happening Siemens to take all the individual data in a large number also used process mining to study inefficiencies of information systems and use them to construct in the way it takes orders from and is paid by its logical models of existing business processes and customers (order-to-cash processes) automatically visualize them. The software docu- Before implementing the Celonis software, ments actual processes in real time, as the sequence Siemens had to manage its business processes of events is taking place, manually. Individual supervisors were responsible When process mining software is used to ana- for specific processes. When things did not go as lyze the transaction logs of an ERP or CRM sys. planned, such as when a machine broke down or a tem, data visualization capabilities in the software parts shipment arrived late, there was no casy way can show users what processes are running at any to determine exactly how these occurrences im- given time. An organization might use process pacted overall operations There was some resistance to process min- mining software to find the cause of unexpected delays in invoice processing by examining the logsing among some long-term Siemens managers of the accounts payable module in its ERP sys who thought they already knew how to handle tem. Users can see at a glance where inefficiencies processes efficiently. Lars Reinkemeyer, head of 1993 of dollars. Siemens AG now has over 2,500 users of Process DAsh worldwide. Siemens global process mining services, was able to promote analytics adoption by identifying in- dividuals who were receptive to process mining and enlisting them to promote the new technology Since Siemens AG implemented process mining, it has been able to identify slowdowns in parts pro- curement, late product deliveries, and billing inef- ficiencies that were costing the company millions Sources Lindsay Clark, Siemens Success Sets the Scene for Growth in Proces Mining." Computer Weekly. April 12, 2018 Juliun Baumann, "Siemens Is the World's Biggest User of Process Mining" and "Success Story: Siemens." www.celonis.com, accessed April 22, 2019: Margaret Rouse, "Process Mining Software,"searchERP.com, June 30, 2017, and Ed Burns, "Siemens Uses Process Mining Software to Improve Manufacturing Visibility." Search Business Analyties.com, December 15, 2016 CASE STUDY QUESTIONS 1. Identify the problem in this case study. What people, organization, and technology factors contributed to the problem? 2. Describe the capabilities of process mining software. Was this an effective solution? Explain your answer. 3. How did process mining change decision making at Siemens? 4. What people, organization, and technology issues need to be addressed when implementing process mining systems