Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains. Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin. Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed. Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Item Response Models to measure Corporate Social Responsibility

Corporate Social Responsibility (CSR) is a multidimensional con- cept that involves several aspects, ranging from Environment, to Social and Governance. Companies aiming to comply with CSR standards have to face challenges that vary from one aspect to the other and from one industry to the other. Latent variable models may be use- fully employed to provide a unidimensional measure of the grade of compliance of a firm with CSR standards that is both understand- able and theoretically solid. A methodology based on Item Response Theory has been implemented on the multidimensional sustainability rating as expressed by KLD dataset from 1991 to 2007. Results sug- gest that companies in the industry Oil & Gas together with firms in Industrials, Basic Materials and Telecommunications have a higher difficulty to meet the CSR standards. Criteria based on Human rights, Environment, Community and Product quality have a large capacity to select the best performing firms, as they are very discriminant, while Governance does not exhibit similar behavior. A stock selection based on the ranking of the firms according to the proposed CSR measure supports the hypothesis of a positive relationship between CSR and financial performanc

Strategic business management : from planning to performance

https://egrove.olemiss.edu/aicpa_guides/2682/thumbnail.jp

Using predictive analytics for money mule detection on a cryptocurrency exchange

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementCryptocurrencies and blockchain, the novel technology that became widespread with Bitcoin implementation in 2009, offer many applications. While the new technology facilitates fast and pseudo-anonymous transactions, it also leaves room to be exploited for illicit activities, such as money laundering. This dissertation focuses on developing a predictive analytics model with supervised machine learning algorithms for classifying money mule fraud instances. Money mules are an instrument for the layering stage of money laundering and are used by criminals to hide the origins of their wealth. As confirmed cases were a rare event, the algorithms used were optimised for imbalanced data in addition to trying resampling techniques for under and oversampling the dataset. The algorithms used were logistic regression, decision trees and ensemble methods – random forest and two types of gradient boosting: XGBoost and LightGBM. The most promising results were achieved with the random forest algorithm, as it reached the best result metrics values aligned with the given business objective. However, the study concluded that the business objective could not be fully realised with the developed model, as it falsely predicts a percentage of the events, which could cause constraints on the business. Therefore, the selected company can use the results and models as a reference tool and a base for further data analyses

Application of critical controls for fatality prevention in mining operations

In this study, a new risk management approach was applied to mitigate fatal incidents through the utilization of critical controls. The aim of this study was to create a scalable, minimally invasive proof-of-concept for AngloGold Ashanti that can successfully be implemented at any of the company’s mining operations. The system was designed by adhering to organizational requirements, and ensuring that it is suitable to any mining environment. The designed Critical Control Management System was subsequently implemented at Sunrise Dam, one of AngloGold Ashanti’s Australian mining operations. To ensure that critical controls were also assessed at the operational level, a workplace inspection process was modified to generate control data. All sources of data subsequently were fed into a Business Intelligence environment enabling insight into critical control performance to all company stakeholders. Doing so informs decision-making on safety priorities company-wide, based on real-time data generated on the operational level. Two case studies were performed to assess two of the most significant hazards at Sunrise Dam. The studies showed that the effectiveness of reactive controls changes irrespective of their compliance and performance. Furthermore, the influence of human factors within risk management remains difficult to quantify. Finally, it demonstrates the potential for integration of incident data into the Critical Control Management System, thus creating both leading and lagging indicators for safety performance. The conclusion of this study is that an effective and scalable Critical Control Management System can be successfully implemented in a mining operation if the right conditions are generated. The approach of integration in existing processes demonstrates that companies can achieve greater control over fatality prevention without the need for an additional safety management system. On this basis, it is recommended that other operations are supported in creating an environment suitable for adaptation before Critical Control Management is implemented

A Data Quality Multidimensional Model for Social Media Analysis

Social media platforms have become a new source of useful information for companies. Ensuring the business value of social media first requires an analysis of the quality of the relevant data and then the development of practical business intelligence solutions. This paper aims at building high-quality datasets for social business intelligence (SoBI). The proposed method offers an integrated and dynamic approach to identify the relevant quality metrics for each analysis domain. This method employs a novel multidimensional data model for the construction of cubes with impact measures for various quality metrics. In this model, quality metrics and indicators are organized in two main axes. The first one concerns the kind of facts to be extracted, namely: posts, users, and topics. The second axis refers to the quality perspectives to be assessed, namely: credibility, reputation, usefulness, and completeness. Additionally, quality cubes include a user-role dimension so that quality metrics can be evaluated in terms of the user business roles. To demonstrate the usefulness of this approach, the authors have applied their method to two separate domains: automotive business and natural disasters management. Results show that the trade-off between quantity and quality for social media data is focused on a small percentage of relevant users. Thus, data filtering can be easily performed by simply ranking the posts according to the quality metrics identified with the proposed method. As far as the authors know, this is the first approach that integrates both the extraction of analytical facts and the assessment of social media data quality in the same framework.Funding for open access charge: CRUE-Universitat Jaume