Case Studies of Artificial Intelligence, Business Intelligence, Analytics Technologies for Industry Platforms
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ItemThe Responsible Innovation Framework: A Framework for Integrating Trust and Delight into Technology Innovation( 2021-01-05)Although systematic biases in our intelligent systems and lack of privacy, equity, and ethical and trust considerations have entered AI and technology debate, we are still lacking a common practice-based framework for innovation that puts social well-being if not ahead at least on par with growing profits. And it has come at a cost that includes public trust. This paper introduces The Responsible Innovation Framework. This framework is intended for product and technology practitioners rather than relegating the responsibility only to compliance officers, risk assessors, privacy advocates, or ethicists. The paper 1) makes a case for using a common framework starting from ideation and vision stage 2) describes the “essential” components of the framework: stakeholders, value sets, and influencers 3) provides examples of how value sets could be leveraged in a flexible and iterative way for AI or Non-AI technology, and 4) lays out the need for additional work and case studies. The goal of the framework is to bring social considerations an essential part of technology decision making.
ItemTechnology as Actors in Service Systems( 2021-01-05)Service systems are defined as dynamic configurations of resources (people, organizations, technology and shared information), interconnected internally and externally by value propositions with other service systems. Resources are constantly evolving, as are the capabilities and roles of resources in service systems. Cognitive technologies incorporate rapidly advancing artificial intelligence (AI) capabilities. Therefore, their roles are on a trajectory of increasing agency and self-directed interactions with other resources and service systems. With this in mind, a framework for service systems in which AI- based cognitive assistants (CAs) become responsible actors is the current research challenge. Because AI- based CAs have already started to play different roles in service systems. One contribution of this research is to clarify that service system entities are responsible actors, and address the question: Under what conditions does a technology such as a Cognitive Assistant (CA) become a responsible actor?
ItemCloud-based ML Technologies for Visual Inspection: A Case Study in Manufacturing( 2021-01-05)In recent years, cloud-based Machine Learning services have received much attention for promising fast and cost-effective deployment. At the same time, manufacturing companies are beginning to evaluate and implement these new technologies in their production processes. This paper adopts the design science research approach to demonstrate the use of cloud-based Machine Learning services to implement a visual inspection system in the manufacturing industry. As a result, our developed IT artifact can correctly classify all of the given parts in a dataset consisting of 363 images, outperforming the current manual inspection. Thereby, it addresses the various challenges faced by the industry when introducing cloud-based Machine Learning technologies, evaluating return on investment (ROI), and how this can facilitate further digital transformation in production.
ItemBusiness Intelligence in the Database Marketing – A Case Study of a German Insurance Company( 2021-01-05)We perform a case study in order to analyze the BI practices of the database marketing departments of insurance companies and hone in on the challenges they face. More specifically, we take a look at the database marketing department of one of the biggest German insurance companies by executing three guided expert interviews and enhancing the information gathered through a document analysis. Among our findings regarding the department’s BI process are that the department collects customer data solely from internal sources and that the collected data is mainly analyzed through SQL queries. The department’s greatest challenges involve including internal and external data sources that are currently not used in their analyses, gathering and understanding all the data that the company already has — since it is stored in different database tables and in heterogenous formats across the company — and measuring the success of their BI activities.
ItemA Real-world Case Study of Process and Data Driven Predictive Analytics for Manufacturing Workflows( 2021-01-05)We present a novel application of business process modelling and simulation of manufacturing workflows. Using formal methods, we produce correct-by-construction executable models that can be simulated in an interleaved way. The simulation draws advanced analytics from live IoT monitoring as well as an ERP system to provide predictive business intelligence. We describe our process and resource modelling efforts in the context of a collaborative project with two manufacturing partners. We evaluate our results based on the improvement of the scheduling accuracy for real production flows.