Designing an AI-enabled Bundling Generator in an Automotive Case Study

Procurement and marketing are the main boundary-spanning functions of an organization. Some studies highlight that procurement is less likely to benefit from artificial intelligence emphasizing its potential in other functions, i.e., in marketing. A case study in the automotive industry of the bundling problem utilizing the design science approach is conducted from the perspective of the buying organization contributing to theory and practice. We rely on information processing theory to create a practical tool that is augmenting the skills of expert buyers through a recommendation engine to make better decisions in a novel way to further save costs. Thereby, we are adding to the literature on spend analysis that has mainly been looking backward using historical data of purchasing orders and invoices to infer saving potentials in the future – our study supplements this approach with forward-looking planning data with inherent challenges of precision and information-richness

Forensic Analysis of Smartphones: The Android Data Extractor Lite (ADEL)

Due to the ubiquitous use of smartphones, these devices become an increasingly important source of digital evidence in forensic investigations. Thus, the recovery of digital traces from smartphones often plays an essential role for the examination and clarification of the facts in a case. Although some tools already exist regarding the examination of smartphone data, there is still a strong demand to develop further methods and tools for forensic extraction and analysis of data that is stored on smartphones. In this paper we describe specifications of smartphones running Android. We further introduce a newly developed tool – called ADEL – that is able to forensically extract and analyze data from SQLite databases on Android devices. Finally, a detailed report containing the results of the examination is created by the tool. The whole process is fully automated and takes account of main forensic principles. Keywords: Android, Smartphones, Mobile devices, Forensics

Conceptual evidence collection and analysis methodology for Android devices

Android devices continue to grow in popularity and capability meaning the need for a forensically sound evidence collection methodology for these devices also increases. This chapter proposes a methodology for evidence collection and analysis for Android devices that is, as far as practical, device agnostic. Android devices may contain a significant amount of evidential data that could be essential to a forensic practitioner in their investigations. However, the retrieval of this data requires that the practitioner understand and utilize techniques to analyze information collected from the device. The major contribution of this research is an in-depth evidence collection and analysis methodology for forensic practitioners.Comment: in Cloud Security Ecosystem (Syngress, an Imprint of Elsevier), 201

Designing an AI-enabled bundling generator in an automotive case study

Procurement and marketing are the main boundary-spanning functions of an organization. Some studies highlight that procurement is less likely to benefit from artificial intelligence emphasizing its potential in other functions, i.e., in marketing. A case study in the automotive industry of the bundling problem utilizing the design science approach is conducted from the perspective of the buying organization contributing to theory and practice. We rely on information processing theory to create a practical tool that is augmenting the skills of expert buyers through a recommendation engine to make better decisions in a novel way to further save costs. Thereby, we are adding to the literature on spend analysis that has mainly been looking backward using historical data of purchasing orders and invoices to infer saving potentials in the future – our study supplements this approach with forward-looking planning data with inherent challenges of precision and information-richness

Drebin: Effective and Explainable Detection of Android Malware in Your Pocket

Malicious applications pose a threat to the security of the Android platform. The growing amount and diversity of these applications render conventional defenses largely ineffective and thus Android smartphones often remain un-protected from novel malware. In this paper, we propose DREBIN, a lightweight method for detection of Android malware that enables identifying malicious applications di-rectly on the smartphone. As the limited resources impede monitoring applications at run-time, DREBIN performs a broad static analysis, gathering as many features of an ap-plication as possible. These features are embedded in a joint vector space, such that typical patterns indicative for malware can be automatically identified and used for ex-plaining the decisions of our method. In an evaluation with 123,453 applications and 5,560 malware samples DREBIN outperforms several related approaches and detects 94% of the malware with few false alarms, where the explana-tions provided for each detection reveal relevant properties of the detected malware. On five popular smartphones, the method requires 10 seconds for an analysis on average, ren-dering it suitable for checking downloaded applications di-rectly on the device.

KI 2021 DC: AI Methods in Procurement

Artificial intelligence is a research area that attempts to design mechanisms allowing machines to develop intelligent behavior. It is a key technology for procurement and its usage is still in its infancy. For instance, the Volkswagen "Procurement Strategy 2025" stresses the potential of artificial intelligence to optimize processes and structures-and this applies to the automotive industry and other procurement organizations worldwide. Yet, only a few have successfully integrated artificial intelligence methods into their operations and across their supply chains but is recently starting to emerge. This constitutes a research opportunity on how artificial intelligence increases its performance. The Ph.D. is set up as external doctoral research supported by Porsche and the Volkswagen AutoUni in cooperation with the University of Mannheim. The research goal is to examine and exploit ideas on how methods of artificial intelligence can be utilized in the procurement function. Procurement is often one of the last functions to be digitized. However, it must keep up in the race against the capabilities of our negotiation partners in the sales organizations of our suppliers worldwide. The early career research consortium has provided young researchers from any subject area within AI with the opportunity to present their ideas and receive feedback at an early stage of at their scientific work. We have invited young researchers to present their research and established connections with new researchers. The submitted abstracts and the presentations are in this upload for which permission to publish has been granted. The Early Career Research Consortium was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2176 ’Understanding Written Artefacts: Material, Interaction and Transmission in Manuscript Cultures’, project no. 390893796

Supplier selection with AI-based TCO models: Cost prediction case study in an automotive OEM

The goal of this research is to understand more clearly the lifecycle costs of supplier selection using methods of artificial intelligence (AI) with a total cost of ownership (TCO) model to reduce uncertainty and make better decisions. AI is a key technology for operations management and its usage is still in its infancy. Few have successfully integrated AI methods into their operations and across their supply chains but are recently starting to emerge. The research is driven by the question of how to reduce uncertainty to provide better information for selecting the right supplier. A case study is conducted at a German automotive manufacturer based on three interlinked data sets. These include: 1. Naïve algorithm models are evaluated as baselines for quality of cost prediction based on supplier selection nomination. 2. Engineering and production changes are analyzed since they often lead to price increases. 3. Cost breakdowns are considered, as they are applicable during several lifecycle phases. For the last 50 years, AACE International and the project management community have made significant contributions to increase the maturity in the practice of project management and control. This continuous commitment applies to remain resilient in the era of data science. This study suggests practical ways to break down uncertainty into a measurable quantity. References are drawn from the Total Cost Management Framework and the applicability is discussed to other settings such as construction, aerospace, defense, and public procurement where considerable related research is conducted. The work confirms previous research that in particular regression trees and Bayesian optimization can reduce the uncertainty inherent in supplier selection more than previously utilized methods

Methods of artificial intelligence in procurement: A conceptual literature review

Artificial intelligence is a key technology for procurement and its usage is still in its infancy. This work builds upon literature reviews on big data analytics in supply chain management (Min, 2010, Waller and Fawcett, 2013, Souza, 2014, Gunasekaran et al., 2017, Nguyen et al., 2017) focusing on artificial intelligence in procurement. 174 relevant publications have been identified based on a keyword search and consecutive snowball search. These are classified along the procurement process in eleven use case clusters and enriched with practical ideas. Their business value and ease of implementation are assessed through interviews to derive a research agenda