Making the Newsvendor Smart – Order Quantity Optimization with ANNs for a Bakery Chain

Accurate demand forecasting is particularly crucial for products with short shelf life like bakery products. Over- and underestimation of customer demand affects not only profit margins of bakeries but is also responsible for 600,000 metric tons of food waste every year in Germany. To solve this problem, we develop an IT artifact based on artificial neural networks, which is automating the manual order process and capable of reducing costs as well as food waste. To test and evaluate our artifact, we cooperated with an SME bakery chain from Germany. The bakery chain runs 40 points of sale (POS) in southern Germany. After algorithm based reconstructing and cleaning of the censored sales data, we compare two different data-driven newsvendor approaches for this inventory problem. We show that both models are able to significantly improve the forecast quality (cost savings up to 30%) compared to human planners

Adhesion and host cell modulation: critical pathogenicity determinants of Bartonella henselae

Bartonella henselae, the agent of cat scratch disease and the vasculoproliferative disorders bacillary angiomatosis and peliosis hepatis, contains to date two groups of described pathogenicity factors: adhesins and type IV secretion systems. Bartonella adhesin A (BadA), the Trw system and possibly filamentous hemagglutinin act as promiscous or specific adhesins, whereas the virulence locus (Vir)B/VirD4 type IV secretion system modulates a variety of host cell functions. BadA mediates bacterial adherence to endothelial cells and extracellular matrix proteins and triggers the induction of angiogenic gene programming. The VirB/VirD4 type IV secretion system is responsible for, e.g., inhibition of host cell apoptosis, bacterial persistence in erythrocytes, and endothelial sprouting. The Trw-conjugation system of Bartonella spp. mediates host-specific adherence to erythrocytes. Filamentous hemagglutinins represent additional potential pathogenicity factors which are not yet characterized. The exact molecular functions of these pathogenicity factors and their contribution to an orchestral interplay need to be analyzed to understand B. henselae pathogenicity in detail

I DON’T GET IT, BUT IT SEEMS VALID! THE CONNECTION BETWEEN EXPLAINABILITY AND COMPREHENSIBILITY IN (X)AI RESEARCH

In explainable artificial intelligence (XAI), researchers try to alleviate the intransparency of high-performing but incomprehensible machine learning models. This should improve their adoption in practice. While many XAI techniques have been developed, the impact of their possibilities on the user is rarely being investigated. Hence, it is neither apparent whether an XAI-based model is perceived as more explainable than existing alternative machine learning models nor is it known whether the explanations actually increase the user’s comprehension of the problem, and thus, their problem-solving ability. In an empirical study, we asked 165 participants about the perceived explainability of different machine learning models and an XAI augmentation. We further tasked them to answer retention, transfer, and recall questions in three scenarios with different stake. The results reveal high comprehensibility and problem-solving performance of XAI augmentation compared to the tested machine learning models

From Symbolic RPA to Intelligent RPA: Challenges for Developing and Operating Intelligent Software Robots

Robotic process automation (RPA) is a novel technology that automates tasks by interacting with other software through their respective user interfaces. The technology has received substantial business attention because of its potential for rapid automation of process-driven tasks that would otherwise require tedious manual labor. This article explores the dichotomy between the practical reality of symbolic RPA, which requires handcrafting robots using process models and rulesets, and the promise of intelligent RPA, which relies on artificial intelligence technology to implement intelligent robots. Our research is based on a scholarly literature review as well as an interview study to derive and discuss challenges for this transition. We found that issues such as the lack of training data, human bias in data, compliance issues with transfer learning, poor explainability of robot decisions, and job-security-induced fear of AI robots all need to be addressed to enable the transition from symbolic to intelligent RPA

From Symbolic RPA to Intelligent RPA: Challenges for Developing and Operating Intelligent Software Robots

Robotic process automation (RPA) is a novel technology that automates tasks by interacting with other software through their respective user interfaces. The technology has received substantial business attention because of its potential for rapid automation of process-driven tasks that would otherwise require tedious manual labor. This article explores the dichotomy between the practical reality of symbolic RPA, which requires handcrafting robots using process models and rulesets, and the promise of intelligent RPA, which relies on artificial intelligence technology to implement intelligent robots. Our research is based on a scholarly literature review as well as an interview study to derive and discuss challenges for this transition. We found that issues such as the lack of training data, human bias in data, compliance issues with transfer learning, poor explainability of robot decisions, and job-security-induced fear of AI robots all need to be addressed to enable the transition from symbolic to intelligent RPA