Prioritization of Interconnected Processes

Deciding which business processes to improve is a challenge for all organizations. The literature on business process management (BPM) offers several approaches that support process prioritization. As many approaches share the individual process as unit of analysis, they determine the processes’ need for improvement mostly based on performance indicators, but neglect how processes are interconnected. So far, the interconnections of processes are only captured for descriptive purposes in process model repositories or business process architectures (BPAs). Prioritizing processes without catering for their interconnectedness, however, biases prioritization decisions and causes a misallocation of corporate funds. What is missing are process prioritization approaches that consider the processes’ individual need for improvement and their interconnectedness. To address this research problem, the authors propose the ProcessPageRank (PPR) as their main contribution. The PPR prioritizes processes of a given BPA by ranking them according to their network-adjusted need for improvement. The PPR builds on knowledge from process performance management, BPAs, and network analysis – particularly the Google PageRank. As for evaluation, the authors validated the PPR’s design specification against empirically validated and theory-backed design propositions. They also instantiated the PPR’s design specification as a software prototype and applied the prototype to a real-world BPA

Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction

Predictive process monitoring aims at forecasting the behavior, performance, and outcomes of business processes at runtime. It helps identify problems before they occur and re-allocate resources before they are wasted. Although deep learning (DL) has yielded breakthroughs, most existing approaches build on classical machine learning (ML) techniques, particularly when it comes to outcome-oriented predictive process monitoring. This circumstance reflects a lack of understanding about which event log properties facilitate the use of DL techniques. To address this gap, the authors compared the performance of DL (i.e., simple feedforward deep neural networks and long short term memory networks) and ML techniques (i.e., random forests and support vector machines) based on five publicly available event logs. It could be observed that DL generally outperforms classical ML techniques. Moreover, three specific propositions could be inferred from further observations: First, the outperformance of DL techniques is particularly strong for logs with a high variant-to-instance ratio (i.e., many non-standard cases). Second, DL techniques perform more stably in case of imbalanced target variables, especially for logs with a high event-to-activity ratio (i.e., many loops in the control flow). Third, logs with a high activity-to-instance payload ratio (i.e., input data is predominantly generated at runtime) call for the application of long short term memory networks. Due to the purposive sampling of event logs and techniques, these findings also hold for logs outside this study

ProcessPageRank - A Network-based Approach to Process Prioritization Decisions

Deciding which business processes to improve first is a challenge most corporate decision-makers face. The literature offers many approaches, techniques, and tools that support such process prioritization decisions. Despite the broad knowledge about measuring the performance of individual processes and determining related need for improvement, the interconnectedness of processes has not been considered in process prioritization decisions yet. So far, the interconnectedness of business processes is captured for descriptive purposes only, for example in business process architectures. This drawback systematically biases process prioritization decisions. As a first step to address this gap, we propose the ProcessPageRank (PPR), an algorithm based on the Google PageRank that ranks processes according to their network-adjusted need for improvement. The PPR is grounded in the literature related to process improvement, process performance measurement, and network analysis. For demonstration purposes, we created a software prototype and applied the PPR to five process network archetypes to illustrate how the interconnectedness of business processes affects process prioritization decisions

A Simulation-Based Approach to Understanding the Wisdom of Crowds Phenomenon in Aggregating Expert Judgment

Research has shown that aggregation of independent expert judgments significantly improves the quality of forecasts as compared to individual expert forecasts. This “wisdom of crowds” (WOC) has sparked substantial interest. However, previous studies on strengths and weaknesses of aggregation algorithms have been restricted by limited empirical data and analytical complexity. Based on a comprehensive analysis of existing knowledge on WOC and aggregation algorithms, this paper describes the design and implementation of a static stochastic simulation model to emulate WOC scenarios with a wide range of parameters. The model has been thoroughly evaluated: the assumptions are validated against propositions derived from literature, and the model has a computational representation. The applicability of the model is demonstrated by investigating aggregation algorithm behavior on a detailed level, by assessing aggregation algorithm performance, and by exploring previously undiscovered suppositions on WOC. The simulation model helps expand the understanding of WOC, where previous research was restricted. Additionally, it gives directions for developing aggregation algorithms and contributes to a general understanding of the WOC phenomenon

Decision Flexibility vs. Information Accuracy in Energy-intensive Businesses

Demand-side management and demand response are integral building blocks for environmental sus-tainability. Exchange-based power pricing serves as an economic mechanism to set incentives to shift demand to periods where prices are low. Low power prices also serve as an indicator for green(er) power, since high feed-ins from variable renewable sources push the electricity price downward. For businesses, minimizing electricity costs thus not only contributes to economic but also environmental sustainability. Hence, especially energy-intensive businesses can become greener and more competitive by integrating volatile electricity prices into their production planning activities. In this paper, we demonstrate that the length of the planning horizons is key to achieve more sustainable outcomes due to the trade-off between decision flexibility and information accuracy. Decision flexibility – i.e. the ca-pability to shift processes – increases with longer planning horizons. Information accuracy – i.e. price accuracy – increases with shorter planning horizons. Information Systems (IS) can help to balance this trade-off. We follow a data-driven approach and derive both actual and predicted electricity spot prices from historic electricity intraday market data in Germany. We find that decision flexibility and infor-mation accuracy affect the planning horizon as conceived. First results indicate that more sustainable outcomes are achieved with longer planning horizons

Differential utilization of ketone bodies by neurons and glioma cell lines: a rationale for ketogenic diet as experimental glioma therapy

Background: Even in the presence of oxygen, malignant cells often highly depend on glycolysis for energy generation, a phenomenon known as the Warburg effect. One strategy targeting this metabolic phenotype is glucose restriction by administration of a high-fat, low-carbohydrate (ketogenic) diet. Under these conditions, ketone bodies are generated serving as an important energy source at least for non-transformed cells. Methods: To investigate whether a ketogenic diet might selectively impair energy metabolism in tumor cells, we characterized in vitro effects of the principle ketone body 3-hydroxybutyrate in rat hippocampal neurons and five glioma cell lines. In vivo, a non-calorie-restricted ketogenic diet was examined in an orthotopic xenograft glioma mouse model. Results: The ketone body metabolizing enzymes 3-hydroxybutyrate dehydrogenase 1 and 2 (BDH1 and 2), 3-oxoacid-CoA transferase 1 (OXCT1) and acetyl-CoA acetyltransferase 1 (ACAT1) were expressed at the mRNA and protein level in all glioma cell lines. However, no activation of the hypoxia-inducible factor-1alpha (HIF-1alpha) pathway was observed in glioma cells, consistent with the absence of substantial 3-hydroxybutyrate metabolism and subsequent accumulation of succinate. Further, 3-hydroxybutyrate rescued hippocampal neurons from glucose withdrawal-induced cell death but did not protect glioma cell lines. In hypoxia, mRNA expression of OXCT1, ACAT1, BDH1 and 2 was downregulated. In vivo, the ketogenic diet led to a robust increase of blood 3-hydroxybutyrate, but did not alter blood glucose levels or improve survival. Conclusion: In summary, glioma cells are incapable of compensating for glucose restriction by metabolizing ketone bodies in vitro, suggesting a potential disadvantage of tumor cells compared to normal cells under a carbohydrate-restricted ketogenic diet. Further investigations are necessary to identify co-treatment modalities, e.g. glycolysis inhibitors or antiangiogenic agents that efficiently target non-oxidative pathways

Cerebrospinal fluid proteomics define the natural history of autosomal dominant Alzheimer’s disease

Alzheimer’s disease (AD) pathology develops many years before the onset of cognitive symptoms. Two pathological processes—aggregation of the amyloid- (A ) peptide into plaques and the microtubule protein tau into neurofibrillary tangles (NFTs)—are hallmarks of the disease. However, other pathological brain processes are thought to be key disease mediators of A plaque and NFT pathology. How these additional pathologies evolve over the course of the disease is currently unknown. Here we show that proteomic measurements in autosomal dominant AD cerebrospinal fluid (CSF) linked to brain protein coexpression can be used to characterize the evolution of AD pathology over a timescale spanning six decades. SMOC1 and SPON1 proteins associated with A plaques were elevated in AD CSF nearly 30 years before the onset of symptoms, followed by changes in synaptic proteins, metabolic proteins, axonal proteins, inflammatory proteins and finally decreases in neurosecretory proteins. The proteome discriminated mutation carriers from noncarriers before symptom onset as well or better than A and tau measures. Our results highlight the multifaceted landscape of AD pathophysiology and its temporal evolution. Such knowledge will be critical for developing precision therapeutic interventions and biomarkers for AD beyond those associated with A and tau

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Business Process Management in the Digital Age: Advancements in Data, Networks, and Opportunities

Business Process Management (BPM) is the art and science of managing distributed work, involving various activities, resources, and actors. The increasing prevalence of digital technologies, known as digitalization, affects individuals, organizations, and society as a whole. Business processes themselves, as well as BPM as a management discipline, are also heavily affected by digitalization, specifically in six overarching topics, namely data, networks, opportunities, humans, context, and change. In order to shed light on the ways in which digitalization affects the BPM domain, this doctoral thesis contributes to the latter three overarching topics: data, networks, and opportunities. The overarching topic of data refers to attempts to capitalize on the increasing availability of data, leading to evidence-driven analytical methods and data-intensive business processes. In the context of digitalization, significant advancements in the field of machine learning led to promising new approaches for analysing structured and unstructured data. However, these advancements remain largely unexploited in the BPM domain. Therefore, research paper #1 focuses on the potential impact of deep learning on process outcome prediction. The paper reports on a structured comparison of a deep learning classifier and a classical machine learning classifier, based on five different event logs. The results show substantial potential for deep learning in process outcome prediction. Research papers #2 and #3 focus on the analysis of unstructured data, exploring the potential for cognitive computing in BPM. In doing so, research paper #2 develops a framework for structuring Cognitive BPM use cases. Based on these results, research paper #3 proposes a Cognitive BPM reference architecture. The overarching topic of networks refers to a view of processes as parts of interconnected net-works instead of single units of analysis. Research papers #4 and #5 highlight the need to take the interconnectedness of processes into account when prioritizing processes for improvement. Building on literature related to process improvement, process performance measurement, and network analysis, the research papers propose an approach for ranking processes according to their network-adjusted need for improvement, taking process interconnectedness into account. The overarching topic of opportunities highlights the need for an opportunity-centric mindset in the context of BPM. This is necessary in order to identify the potential of emerging digital technologies, new regulations, and demographic shifts for the BPM domain. Fostering the fusion of the digital and the physical worlds, the Internet of Things (IoT) is regarded as one of the most disruptive emerging digital technologies, yet offers great potential to the BPM domain, e.g., for higher automation, more accurate data collection, reduced errors, and overall efficiency gains. To enable the tapping of this potential, research paper #6 develops design principles which foster the success of IoT ecosystems. Research paper #7 takes an economic view, shedding light on the assessment of the customer value of IoT-solutions from an industrial company’s perspective