Decision Analytics Research Articles

The use of data analytics for management decision–making in retail

The use of data analytics for management decision–making in retail

Data science and decision analytics

Data science and decision analytics

Multi-criteria group decision analytics for sustainable supplier relationship management in a focal manufacturing company

In the context of sustainable development, while the benefits of incorporating environmental, social, and governance (ESG) considerations into supplier management have been widely discussed in recent years, a significant gap in the literature remains. Specifically, there is a lack of a standardized decision analytics framework for ESG-oriented supplier relationship management (SRM) processes in the manufacturing sector. To address this gap, this study introduces a multi-criteria supplier analytics framework (MCSAF) towards sustainable SRM. Through referring traditional supplier evaluation criteria as well as emerging ESG criteria, a novel decision hierarchy for supplier segmentation is developed. The proposed system utilizes the Bayesian best-worst method and the Canopy-k-means clustering algorithm to formulate different supplier segments for strategic development. The proposed MCSAF has been successfully implemented in a German solar water pump manufacturer. The results show that the five highest-ranked criteria are ‘Quality’, ‘Impact and importance on product quality and business growth’, ‘Environmental policy/management’, ‘Customer and product responsibility’, and ‘Risk and crisis management’. 52 suppliers are categorized into five segments, leading to the formulation of ESG-enabled supplier development strategies are subsequently formulated. The successful implementation of the MCSAF showcases its effectiveness in advancing ESG practices in sustainable supplier management.

An intelligent decision support framework for nursing home resource planning with enhanced heterogeneous service demand modeling

Demand-based nursing home resource planning is of great importance to ensure adequate resources (e.g., beds and staffs) available to provide care services with desired quality, yet challenging. The challenge mainly lies in modeling heterogeneous demand of nursing home residents, reflected by various individual characteristics, diverse dwelling duration with multiple competing discharge dispositions, and diverse daily service need. Existing studies often assumed a homogeneous population of patients and neglected the complexity of demand heterogeneity and uncertainty, leading to biased demand estimation and misguided decisions. The objective of this work is to improve nursing home resource planning decisions in response to the complex demand heterogeneity and uncertainty. To address the challenges, we propose a novel knowledge-guided and data-driven decision support framework. This is the first work of integrating domain knowledge with predictive and decision analytics to enhance modeling fidelity and decision performance for nursing home resource planning. Specifically, to effectively capture different aspects of heterogeneous demand, we develop a novel knowledge-guided demand modeling module with predictive models, including a length-of-stay model with competing risk for duration analysis, a tree-based system for learning daily service need variations, and a demand simulator for capturing uncertainty of fluctuating demand. Moreover, to determine optimal capacity and staffing decisions under demand heterogeneity and uncertainty, we develop a demand-based decision-making module with effective optimization models and solution algorithms, ensuring satisfactory quality of care at reduced costs. Furthermore, to demonstrate the improved prediction and decision performances of the proposed framework, we provide a proof-of-the-concept case study using real data from our industrial collaborator and investigate how demand heterogeneity and uncertainty will impact resource planning decisions. The proposed framework also demonstrates its appealing adaptability under changing resident census compositions.

How AI and ML can help reduce energy usage and carbon emissions in buildings

Commercial buildings play a significant role in global energy consumption and carbon emissions, making their sustainability imperative. This paper explores how artificial intelligence (AI) and machine learning (ML) technologies offer innovative solutions to reduce energy usage and carbon footprint in buildings. By leveraging AI and ML algorithms, building automation systems can optimise energy consumption, enhance occupant comfort and predict future energy demands. The integration of AI and ML into existing building controls requires managing and integrating diverse data sources, followed by cloud-based processing for advanced analytics. The benefits include increased energy efficiency, reduced utility costs and improved occupant experience. Readers will gain insights into: 1) understanding AI and ML’s role in optimising energy usage and reducing carbon emissions in buildings; 2) integrating AI and ML into building automation systems for predictive energy management; 3) overcoming challenges in data integration and cybersecurity for effective implementation; 4) leveraging AI-driven analytics for real-time monitoring and decision making; and 5) achieving sustainability goals and enhancing building resilience through AI-powered solutions. By implementing AI and ML technologies, building owners and operators can not only improve energy efficiency but also strengthen their competitive edge, attract environmentally conscious tenants and contribute to global sustainability efforts.

Big Data Analytics for Financial Decision Making in Malaysia

Purpose: To aim of the study was to analyze the big data analytics for financial decision making in Malaysia. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Big Data Analytics (BDA) significantly enhances financial decision-making in Malaysia by extracting valuable insights from large datasets. It helps improve risk management, customer segmentation, and operational efficiency for financial institutions. BDA enables predictive analysis of market behavior, supports personalized financial services, and enhances fraud detection capabilities. Despite its benefits, challenges like data privacy issues and the need for skilled data professionals persist. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) & network externalities theory may be used to anchor future studies on big data analytics for financial decision making in Malaysia. Prioritize investments in scalable and secure data infrastructure to support the deployment and integration of BDA technologies across financial institutions. This includes upgrading data storage systems, enhancing data processing capabilities, and implementing robust data integration frameworks. Develop and enforce clear regulatory frameworks that govern the ethical use of BDA in financial decision-making. Regulatory guidelines should address data privacy, security standards, transparency in algorithmic processes, and consumer rights protection.

Improving Transparency of Decision Models Through the Application of Decision Analytic Models with Omitted Objects Displayed (DAMWOOD).

The coronavirus disease 2019 (COVID-19) pandemic has increased public awareness of the influence of epidemiological and economic decision models on public policy decisions. Alongside this is an increased scrutiny on the development, analysis, reporting and utilisation of decision models for public policy making. Therefore, it is important that model developers can clearly explain and justify to all stakeholders what is included and excluded from a model developed to support decision-making, to both improve transparency and trust in decision-making. Our aim is to provide tools for improving communication between modellers and decision-makers, leading to improved transparency in decision-making. To do so, we extend the recently described directed acyclic graphs with omitted objects displayed (DAGWOOD) approach from Haber et al. (Ann Epidemiol 68:64-71, 2022) to decision analytic models, giving the decision analytic models with omitted objects displayed (DAMWOOD) approach. DAMWOOD is a framework for the identification of objects omitted from a decision model, as well as for consideration of the effects of omissions on model outcomes. Objects omitted from a decision model are classed as either an exclusion (known and unknown confounders), misdirection (alternative model pathways) or structure (e.g. model type, methods for estimating relationships between objects). DAMWOOD requires model developers to use explicit statements and provide illustration of included and omitted objects, supporting communication with model users and stakeholders, allowing them to provide input and feedback to modellers about which objects to include or omit in a model. In developing DAMWOOD, we considered two challenges we encountered in modelling for pandemic policy response. First, the scope of the decision problem is not always made sufficiently explicit by decision-makers, requiring modellers to intuit which policy options should be considered, and/or which outcomes should be considered in their evaluation. Second, there is rarely sufficient transparency to ensure stakeholders can see what is included in models and why. This limits stakeholders' ability to advocate to decision-makers for the prioritisation of specific outcomes and challenge the model results. To illustrate the application of DAMWOOD, we apply it to a previously published COVID-19 vaccine allocation optimisation model. The DAMWOOD diagrams illustrate the ways in which it is possible to improve the communication of model assumptions. The diagrams make explicit which outcomes are omitted and provide information on the expected impact of the omissions on model results. We discuss the usefulness of DAMWOOD for framing the decision problem, communicating the model structure and results and engaging with those making and affected by the decisions the model is developed to inform.

Individual Analytics Mindset and Decision Quality: The Mediating Role of Effort and Persistence

Individual Analytics Mindset and Decision Quality: The Mediating Role of Effort and Persistence

Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN

This research study demonstrates an efficient scheme for early detection of cardiorespiratory complications in pandemics by Utilizing Wearable Electrocardiogram (ECG) sensors for pattern generation and Convolution Neural Networks (CNN) for decision analytics. In health-related outbreaks, timely and early diagnosis of such complications is conclusive in reducing mortality rates and alleviating the burden on healthcare facilities. Existing methods rely on clinical assessments, medical history reviews, and hospital-based monitoring, which are valuable but have limitations in terms of accessibility, scalability, and timeliness, particularly during pandemics. The proposed scheme commences by deploying wearable ECG sensors on the patient’s body. These sensors collect data by continuously monitoring the cardiac activity and respiratory patterns of the patient. The collected raw data is then transmitted securely in a wireless manner to a centralized server and stored in a database. Subsequently, the stored data is assessed using a preprocessing process which extracts relevant and important features like heart rate variability and respiratory rate. The preprocessed data is then used as input into the CNN model for the classification of normal and abnormal cardiorespiratory patterns. To achieve high accuracy in abnormality detection the CNN model is trained on labeled data with optimized parameters. The performance of the proposed scheme is evaluated and gauged using different scenarios, which shows a robust performance in detecting abnormal cardiorespiratory patterns with a sensitivity of 95% and specificity of 92%. Prominent observations, which highlight the potential for early interventions include subtle changes in heart rate variability and preceding respiratory distress. These findings show the significance of wearable ECG technology in improving pandemic management strategies and informing public health policies, which enhances preparedness and resilience in the face of emerging health threats.

A type-2 neutrosophic entropy-based group decision analytics model for sustainable aquaculture engineering

Fish cages are crucial for the establishment of sustainable fish farms. The selection of fish cages is an essential subject to be examined for ensuring sustainability. This research introduces an advanced decision support model for the selection of fish cage types used in fish farming in reservoirs (R-FFs). The decision support model is based on multi-criteria decision-making (MCDM) approach and utilizes type-2 neutrosophic numbers (T2NNs). The importance levels of criteria are determined using the T2NN-Entropy method, and the ranking of fish cage types is achieved through the alternative ranking order method accounting for two-step normalization (AROMAN). The developed model is referred to as the T2NN-Entropy-AROMAN hybrid method and relies on expert opinions. Two advanced aggregation operators based on Yager t-norm and t-conorm operations, named T2NN Yager weighted arithmetic mean and T2NN Yager weighted geometric mean, are developed for aggregating T2NNs. The validity of these two aggregation operators is demonstrated. A real-life case study is developed to confirm the applicability of the T2NN-Entropy-AROMAN hybrid method. This case study focuses on the selection of fish cage types for the Artvin-Borçka R–FF in Artvin province, Turkey. The research results indicate that floating cages are the best alternative type for sustainable fish farming. Sensitivity analysis scenarios confirm the robustness of the research model and findings. The research findings, coupled with the developed model, offer valuable insights and guidance to decision-makers, researchers, and practitioners within the sector.

Managing Human Resource in the Digital Economy: Balancing Challenges and Opportunities

Digital technologies enable the adoption of more efficient working models, such as process automation, predictive analytics for better decision making, and the use of digital platforms for employee development and performance management. This study aims to investigate employee perceptions and experiences of digital transformation in the context of challenges and opportunities at the company PT Charoen Pokphand Indonesia (CP) Food. The research method used is descriptive qualitative with a data collection approach through questionnaires and observations. The questionnaire was designed to collect employee responses on various aspects of digital transformation in terms of opportunities and challenges. Using a purposive sampling approach, which is a sampling method in which the researcher deliberately selects certain participants or sample units based on certain characteristics or criteria that are considered relevant to the research objectives. The results showed that employees have a positive view of digital transformation at the company PT Charoen Pokphand Indonesia (CP) Food. Employees see it as an opportunity for career development and professional growth, and feel supported in developing their digital skills. A work culture that supports collaboration, innovation, and creativity is also considered important in creating a productive and dynamic work environment. The integration of digital technology is considered successful by employees, indicating that the company has successfully implemented technology in daily work processes well. This research provides valuable insights into how digital transformation affects employee perceptions and experiences in the work environment. The implications of these findings can be used by companies to design more effective human resource management strategies to face the challenges and opportunities in the evolving digital economy.

Is anyone listening? Evaluating the impact of decision systems and analytics research

ABSTRACT The decision support systems (DSS) area is an integral part of the Information Systems discipline. DSS’s main objective is to contribute to the common good by facilitating access to relevant information for improved decision-making. IFIP Working Group 8.3 Decision Support was established in 1981 as a community passionate about creating a body of knowledge on how to support better decision-making. The advances in addressing this mission were regularly presented at the bi-annual meetings of the IFIP WG8.3. Our aim in this paper is to analyse the impact of the research of the group through these conference proceedings. We use a ‘test of time’ as a starting point to identify the sample of papers that have been most noted and cited. Further analysis helps to clarify other parameters that define the impact of this research and allow us to propose strategies to inform more impactful research outcomes in the future.

Barriers and Facilitators of Using R for Decision Analytic Modeling in Health Technology Assessment: Focus Group Results.

Decision models for health technology assessment (HTA) are largely submitted to HTA agencies using commercial software, which has known limitations. The use of the open-source programming language R has been suggested because of its efficiency, transparency, reproducibility, and ability to consider complex analyses. However, its use in HTA remains limited. This qualitative study aimed to explore the main reasons for this slow uptake of R in HTA and identify tangible facilitators. We undertook two semi-structured focus group discussions with 24 key stakeholders from government agencies, consultancy, pharmaceutical companies, and academia. Two 1.5-hour discussions reflected on barriers identified in a previous study and highlighted additional barriers. Discussions were recorded and semi-transcribed, and data were organized and summarized into key themes. Human resources constraints were identified as a key barrier, including a lack of training, prioritization and collaboration, and resistance to change. Another key barrier was the lack of acceptance, or clear guidance, around submissions in R by HTA agencies. Participants also highlighted a lack of communication around accepted packages and decision model structures, and between HTA agencies on standard decision modeling structures. There is a need for standardization, which can facilitate decision model sharing, coding homogeneity, and improved country adaptations. The creation of training materials and tailored workshops was identified as a key short-term facilitator. Increased communication and engagement of stakeholders could also facilitate the use of R by identifying needs and opportunities, encouraging HTA agencies to address structural barriers, and increasing incentives to use R.

Editorial Statement—Optimization and Decision Analytics

Editorial Statement—Optimization and Decision Analytics

Hybrid black-box classification for customer churn prediction with segmented interpretability analysis

Customer retention management relies on advanced analytics for decision making. Decision makers in this area require methods that are capable of accurately predicting which customers are likely to churn and that allow to discover drivers of customer churn. As a result, customer churn prediction models are frequently evaluated based on both their predictive performance and their capacity to extract meaningful insights from the models. In this paper, we extend hybrid segmented models for customer churn prediction by incorporating powerful models that can capture non-linearities. To ensure the interpretability of such segmented hybrid models, we introduce a novel model-agnostic approach that extends SHAP. We extensively benchmark the proposed methods on 14 customer churn datasets on their predictive performance. The interpretability aspect of the new model-agnostic approach for interpreting hybrid segmented models is illustrated using a case study. Our contributions to decision making literature are threefold. First, we introduce new hybrid segmented models as powerful tools for decision makers to boost predictive performance. Second, we provide insights in the relative predictive performance by an extensive benchmarking study that compares the new hybrid segmented methods with their base models and existing hybrid models. Third, we propose a model-agnostic tool for segmented hybrid models that provide decision makers with a tool to gain insights for any hybrid segmented model and illustrate it on a case study. Although we focus on customer retention management in this study, this paper is also relevant for decision makers that rely on predictive modeling for other tasks.

Circular Intuitionistic Fuzzy Median Ranking Model with a Novel Scoring Mechanism for Multiple Criteria Decision Analytics

ABSTRACT This study aims to pioneer an innovative circular intuitionistic fuzzy (C-IF) scoring-mediated median ranking model designed for multiple criteria decision analytics. The primary goal is to establish a comprehensive precedence ranking for competing alternatives, effectively addressing the inherent uncertainties present in decision-analytic challenges within the C-IF environment. The core content delves into the creation of an original scoring mechanism tailored to navigate the complexities of C-IF uncertainties. Moreover, the research introduces a specialized C-IF median ranking model for decision analytics, leveraging the foundational concept of the C-IF scoring mechanism. A significant contribution is made through the formulation of a robust implementation procedure, specifically tailored for the seamless operation of the C-IF scoring-mediated median ranking model within the framework of C-IF information. Drawing from the suggested C-IF scoring mechanism, this research introduces novel concepts related to comprehensive C-IF scoring functions and comprehensive disagreement metrics. Subsequently, a comprehensive disagreement matrix is formulated, with its entries quantifying the extent of disagreement in assigning specific ranks to each alternative across all criterion-wise precedence relationships. This paves the way for the development of a new C-IF scoring-mediated median ranking model, offering decision analysts a tool to navigate intricate C-IF information and derive dependable decision-analytic outcomes.

Business Analytics in Steel Product Fabrication Cluster

Forty micro and small steel products manufacturing enterprises in Salem District of Tamil Nadu, South India faced problems in value addition of the steel products like windows, grill gates, truss work and panel boards manufactured by them. They formed M/s Salem Steel Cluster Pvt Ltd; Salem, a special purpose vehicle, in 2012 by getting funds from the Government of Tamil Nadu and the Government of India through the Tamil Nadu Small Industries Development Corporation under Micro Small Enterprises Cluster Development Programme of Ministry of Micro, Small and Medium Enterprises, Government of India. The objective is to find the physical and financial performance of the Steel Product Fabrication Cluster (SPFC) before and after Cluster Development Approach (CDA) to find the productivity of the cluster by taking independent variables like number of units, employment and production and dependent variable like turnover, and to find the performance of SPFC before and after CDA. To find business analytics models like Diagnostic Analytics, Descriptive Analytics, Inferential Analytics, Predictive Analytics, Prescriptive Analytics and Decision Analytics. The methodology adopted is by collecting primary data like number of units, employment in numbers, production in crores and turnover in crores before and after CDA and analysing using Compound Annual Growth Rate, Descriptive Analysis, Correlation Analysis, Trend Analysis, Regression Analysis, Structural Equation Modelling and T-Test. There is a Difference in Difference between controlled units which have not adopted CDA and experimental units which have adopted CDA, where there is an increase in number of units, employment, profit and turnover. To conclude, there is an increase in number of units, employment, production and turnover after CDA when compared to before CDA, which leads to an increase in productivity thereby Sustainable Development Goals of 1, 4, 5, 8 and 9 are achieved.

The Integration of HR Analytics and Decision Making

Integrating HR Analytics into organizational decision-making processes is crucial for modern businesses looking to improve human resource management practices and drive better decision-making outcomes. This systematic literature review explores the importance of HR Analytics integration, its key determinants, and its impact on organizational performance and decision-making effectiveness. Through an analysis of existing literature, this review identifies factors that influence HR Analytics integration, such as data collection practices, technology availability, managerial skills, and organizational policies. This review also revealed a research gap regarding the optimal use of HR Analytics for strategic decision making, highlighting the need for further research in this area. Overall, these findings underscore the importance of HR Analytics in driving better HR management practices and better organizational decision-making outcomes.

AI-Driven Predictive Analytics for Financial Decision Making and Fraud Detection in Financial Institutions

AI-Driven Predictive Analytics for Financial Decision Making and Fraud Detection in Financial Institutions

Shaping Extended Product Design and Development with Predictive Maintenance Capability for Digital Servitisation

Traditional business models of selling a product as a single item can no longer satisfy the growing service-oriented needs of customers. Service-oriented business models, like servitisation or product-service systems (PSS), have been introduced to respond to this market demand. Predictive maintenance (PdM) strategy is regarded as a key element in the development of such business models because of its effective role in maximising the availability and usability of products. The PdM strategy requires technologies including, sensing, data acquisition, data analytics algorithms and decision support tools. In traditional business models, these technologies are typically developed by third parties when products are already commissioned or in service, which is sub-optimal. It can be argued that PdM technologies developed by original equipment manufacturers (OEMs) can significantly reduce development costs and increase competitiveness. This paper proposes a new framework for guiding OEMs to develop and integrate relevant PdM technologies during the development of new products in support of their new journey in a digital servitisation business model. This encompasses the full life cycle of the product from customers’ needs/values analysis to after-sales service. The model-based system engineering (MBSE) approach was adopted to implement the proposed framework using the selected system modelling language (SysML) tool, namely Capella, and to verify it in an industrial case study.