STAY FLEXIBLE: A PRESCRIPTIVE PROCESS MONITORING APPROACH FOR ENERGY FLEXIBILITY-ORIENTED PROCESS SCHEDULES

The transition of energy supply from fossil fuels to renewable energy sources poses major challenges for balancing increasingly weather-dependent energy supply and demand. Demand-side energy flexibility, offered particularly by companies, is seen as a promising and necessary approach to address these challenges. Process mining provides significant potential to prevent a deterioration of product quality or process flows due to flexibilization and allows for exploiting monetary benefits associated with flexible process operation. Hence, we follow the design science research paradigm to develop PM4Flex, a prescriptive process monitoring approach, that generates recommendations for pending process flows optimized under fluctuating power prices by implementing established energy flexibility measures. Thereby, we consider company- and process-specific constraints and historic event logs. We demonstrate and evaluate PM4Flex by implementing it as a software prototype and applying it to exemplary data from a heating and air conditioning company, observing considerable cost-savings of 1.42ct per kWh or 7.89%

A Model-Driven Framework for Enabling Flexible and Robust Mobile Data Collection Applications

In the light of the ubiquitous digital transformation, smart mobile technology has become a salient factor for enabling large-scale data collection scenarios. Structured instruments (e.g., questionnaires) are frequently used to collect data in various application domains, like healthcare, psychology, and social sciences. In current practice, instruments are usually distributed and filled out in a paper-based fashion (e.g., paper-and-pencil questionnaires). The widespread use of smart mobile devices, like smartphones or tablets, offers promising perspectives for the controlled collection of accurate data in high quality. The design, implementation and deployment of mobile data collection applications, however, is a challenging endeavor. First, various mobile operating systems need to be properly supported, taking their short release cycles into account. Second, domain-specific peculiarities need to be flexibly aligned with mobile application development. Third, domain-specific usability guidelines need to be obeyed. Altogether, these challenges turn both programming and maintaining of mobile data collection applications into a costly, time-consuming, and error-prone endeavor. The Ph.D. thesis at hand presents an advanced framework that shall enable domain experts to transform paper-based instruments to mobile data collection applications. The latter, in turn, can then be deployed to and executed on heterogeneous smart mobile devices. In particular, the framework shall empower domain experts (i.e., end-users) to flexibly design and create robust mobile data collection applications on their own; i.e., without need to involve IT experts or mobile application developers. As major benefit, the framework enables the development of sophisticated mobile data collection applications by orders of magnitude faster compared to current approaches, and relieves domain experts from manual tasks like, for example, digitizing and analyzing the collected data

A Predictive Approach Enabling Process Execution Recommendations

In enterprises, decision makers need to continuously monitor business processes to guarantee for a high product and service quality. To accomplish this task, process-related data needs to be retrieved from various information systems - periodically or in real-time -- and then be aggregated based on key performance indicators (KPIs). If target values of the defined KPIs are violated (e.g., production takes longer than a predefined threshold), the reasons of these violations need to be identified. In general, such a retrospective analysis of business process data does not always contribute to prevent respective key performance violations. To remedy this drawback, process-aware information systems (PAIS) should enable the automated identification of processes, which are not well performing, and support users in executing these processes through recommendations. For example, it should be indicated, which problems might occur in future when taking the current course of the process instance as well as previous process instances into account. This chapter presents a methodology as well as an architecture for the support of predictive process analyses. In this context, algorithms from machine learning are applied to compare running process instances with historic process data and to identify diverging processes. In particular, the predictive approach will enable enterprises to quickly react to upcoming problems and inefficiencies