Linking business analytics to decision making effectiveness: a path model analysis

While business analytics is being increasingly used to gain data-driven insights to support decision making, little research exists regarding the mechanism through which business analytics can be used to improve decision-making effectiveness (DME) at the organizational level. Drawing on the information processing view and contingency theory, this paper develops a research model linking business analytics to organizational DME. The research model is tested using structural equation modeling based on 740 responses collected from U.K. businesses. The key findings demonstrate that business analytics, through the mediation of a data-driven environment, positively influences information processing capability, which in turn has a positive effect on DME. The findings also demonstrate that the paths from business analytics to DME have no statistical differences between large and medium companies, but some differences between manufacturing and professional service industries. Our findings contribute to the business analytics literature by providing useful insights into business analytics applications and the facilitation of data-driven decision making. They also contribute to manager's knowledge and understanding by demonstrating how business analytics should be implemented to improve DM

SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning

Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better. In this paper, we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can handle both high-dimensional sensory inputs and symbolic planning. The task-level interpretability is enabled by relating symbolic actions to options.This framework features a planner -- controller -- meta-controller architecture, which takes charge of subtask scheduling, data-driven subtask learning, and subtask evaluation, respectively. The three components cross-fertilize each other and eventually converge to an optimal symbolic plan along with the learned subtasks, bringing together the advantages of long-term planning capability with symbolic knowledge and end-to-end reinforcement learning directly from a high-dimensional sensory input. Experimental results validate the interpretability of subtasks, along with improved data efficiency compared with state-of-the-art approaches

Data-Driven Decision Making terkait Penetapan UKT di UIN Sunan Gunung Djati Bandung

This paper discusses and interprets the results of several studies related to data-based decision-making. Data-based decision-making capabilities consist of Data Governance Capability, Data Analytics Capability, Performance Management Capability, Insight Exploitation Capability, and Integration Capability. The discussion is carried out by analyzing the description of four data-based decision-making capabilities related to tuition at UIN SGD Bandung: Data Governance Capability, Data Analytics Capability, Performance Management Capability, and Insight Exploitation Capability. The approach used in this study is qualitative with a case study design. The informants were obtained through a purposive sampling technique which was then tested for the validity of the results of the interviews by triangulating the data. The goal to be achieved is to describe how the process of using data in decision-making is carried out by campus authorities regarding tuition at UIN SGD Bandung so that it can produce a policy output. The findings in this study are that determining the amount of tuition accepted by UIN SGD Bandung students has gone through various processes, including using data as a reference for decision-making. However, a capability still needs to be fully fulfilled, namely, Performance Management Capability.Keywords: big data, data driven. Decision-making, tuitio

Principles for aerospace manufacturing engineering in integrated new product introduction

This article investigates the value-adding practices of Manufacturing Engineering for integrated New Product Introduction. A model representing how current practices align to support lean integration in Manufacturing Engineering has been defined. The results are used to identify a novel set of guiding principles for integrated Manufacturing Engineering. These are as follows: (1) use a data-driven process, (2) build from core capabilities, (3) develop the standard, (4) deliver through responsive processes and (5) align cross-functional and customer requirements. The investigation used a mixed-method approach. This comprises case studies to identify current practice and a survey to understand implementation in a sample of component development projects within a major aerospace manufacturer. The research contribution is an illustration of aerospace Manufacturing Engineering practices for New Product Introduction. The conclusions will be used to indicate new priorities for New Product Introduction and the cross-functional interactions to support flawless and innovative New Product Introduction. The final principles have been validated through a series of consultations with experts in the sponsoring company to ensure that correct and relevant content has been defined

A domain-specific modeling technique for value-driven strategic sourcing

Strategic sourcing recognizes that procurement should support a firm’s effort to achieve its long-term objectives. In particular, procurement needs to be a cross-functional end-to-end process inside the organization that is oriented towards value creation within the company and between the company and its partners in the value chain. The main challenge to the implementation of value-driven strategic sourcing is the lack of instruments that are characterized by analytical rigor and robustness in the identification of strategic sourcing options to achieve strategic goals. Therefore, this research aims to develop a domain-specific modeling technique founded on the Service-Dominant Logic which focuses on the systemic exploration of sourcing alternatives and emphasizes the delivery of value to achieve desired outcomes. This paper reports on a first cycle of Design Science Research which includes the demonstration and the evaluation of the value and utility of the modeling artefacts by means of a case study about IT outsourcing in the healthcare industry

From big data to big performance – exploring the potential of big data for enhancing public organizations’ performance : a systematic literature review

This article examines the possibilities for increasing organizational performance in the public sector using Big Data by conducting a systematic literature review. It includes the results of 36 scientific articles published between January 2012 and July 2019. The results show a tendency to explain the relationship between big data and organizational performance through the Resource-Based View of the Firm or the Dynamic Capabilities View, arguing that perfor-mance improvement in an organization stems from unique capabilities. In addition, the results show that Big Data performance improvement is influenced by better organizational decision making. Finally, it identifies three dimensions that seem to play a role in this process: the human dimension, the organizational dimension, and the data dimension. From these findings, implications for both practice and theory are derived