scholarly journals Planning Culture Activation Program Using Predictive Analytics: A Case Study at PT. Telkom Indonesia-TR3

2020 ◽  
Vol 0 (1) ◽  
pp. 1
Author(s):  
Pratami Anggina ◽  
Shandy Asri Achmad ◽  
John Welly
Keyword(s):  
Predictive Analytics ◽  
Planning Culture

scholarly journals Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

Procedia CIRP ◽  
2021 ◽  
Vol 104 ◽  
pp. 641-646
Author(s):  
Peter Burggräf ◽  
Johannes Wagner ◽  
Benjamin Heinbach ◽  
Fabian Steinberg ◽  
Alejandro R. Pérez M. ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Industry 4.0 ◽  
Predictive Analytics ◽  
Lessons Learned

scholarly journals Performance Predictive Analytics for Operations Management of Shared Services

2020 ◽  
Keyword(s):  
Knowledge Sharing ◽  
Operations Management ◽  
Predictive Analytics ◽  
Shared Services ◽  
Partner Organization ◽  
Shared Service ◽  
Individual Partner

Shared services have been widely used in many organizations as an alternative to outsourcing. For shared services, common services are standardized and consolidated across multiple organizations to reduce the operational cost and to increase information and knowledge sharing. Two major advantages of shared services over outsourcing are long-term stable cost-saving and knowledge sharing. One important aspect of successful operations management of shared services is to ensure the quality of services delivered by a shared service provider to each individual partner organization. This paper proposes a performance predictive analytics framework for operations management of shared services. The paper presents a case study to demonstrate the usefulness and effectiveness of this framework.

scholarly journals A Predictive Analytics Model for E-commerce Sales Transactions to Support Decision Making: A Case Study

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Shereen Morsi
Keyword(s):  
Decision Making ◽  
Predictive Analytics ◽  
Strategic Decisions ◽  
Business Decision ◽  
Innovative Capabilities ◽  
Data Volume ◽  
Precautionary Measures ◽  
Support Decision Making

Given the significant growth in electronic commerce, firms are seeking technological innovations and innovative capabilities to deal concurrently with the data’ volume generated and gaining insights from it for better decisions. Although recent studies identify predictive analytics as becoming the keystone of all business decision making and a crucial aspect in firms by it is a possible means for driving strategic decisions. Significant inroads into the interrelationships between capabilities and the execution of a pathway to an analytical capability to many Egyptian e-commerce businesses have yet to be made. Therefore, this paper aims to shed light on the importance and the role of using predictive analytics models in the Egyptian e-commerce firms where these tools became dominant resources for gaining valuable knowledge for better decision making by precautionary measures from prediction rates and different applications that have been applied by global e-commerce firms. The aim of the paper was achieved by building a predictive analytics model for sales forecasting by tackling to one of the e-commerce company in Egypt, and the online transaction dataset has been analyzed. The result obtained from the model has been displayed, and some insights extracted from the prediction model have been explained.

scholarly journals Implementation and Transfer of Predictive Analytics for Smart Maintenance: A Case Study

2020 ◽  
Vol 2 ◽  
Author(s):  
Sebastian von Enzberg ◽  
Athanasios Naskos ◽  
Ifigeneia Metaxa ◽  
Daniel Köchling ◽  
Arno Kühn
Keyword(s):  
Predictive Analytics ◽  
Spare Parts ◽  
Sensor Data ◽  
Industrial Plant ◽  
Industrial Manufacturing ◽  
Model Transfer ◽  
Holistic Integration ◽  
Quantitative Impact ◽  
Maintenance Process

Smart maintenance offers a promising potential to increase efficiency of the maintenance process, leading to a reduction of machine downtime and thus an overall productivity increase in industrial manufacturing. By applying fault detection and prediction algorithms to machine and sensor data, maintenance measures (i.e., planning of human resources, materials and spare parts) can be better planned and thus machine stoppage can be prevented. While many examples of Predictive Maintenance (PdM) have been proven successful and commercial solutions are offered by machine and part manufacturers, wide-spread implementation of Smart Maintenance solutions and processes in industrial production is still not observed. In this work, we present a case study motivated by a typical maintenance activity in an industrial plant. The paper focuses on the crucial aspects of each phase of the PdM implementation and deployment process, toward the holistic integration of the solution within a company. A concept is derived for the model transfer to a different factory. This is illustrated by practical examples from a lighthouse factory within the BOOST 4.0 project. The quantitative impact of the deployed solutions is described. Based on empirical results, best practices are derived in the domain and data understanding, the implementation, integration and model transfer phases.

scholarly journals Dashboard stories: How narratives told by predictive analytics reconfigure roles, risk and sociality in education

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110255
Author(s):  
Juliane Jarke ◽  
Felicitas Macgilchrist
Keyword(s):  
Machine Learning ◽  
Support Systems ◽  
Predictive Analytics ◽  
Learning Support ◽  
Main Argument ◽  
Temporal Dimensions ◽  
Machine Readable ◽  
Do So ◽  
Structural Inequalities

In this paper, we explore how the development and affordances of predictive analytics may impact how teachers and other educational actors think about and teach students and, more broadly, how society understands education. Our particular focus is on the data dashboards of learning support systems which are based on Machine Learning (ML). While previous research has focused on how these systems produce credible knowledge, we explore here how they also produce compelling, persuasive and convincing narratives. Our main argument is that particular kinds of stories are written by predictive analytics and written into their data dashboards. Based on a case study of a leading predictive analytics system, we explore how data dashboards imply causality between the ‘facts’ they are visualising. To do so, we analyse the stories they tell according to their spatial and temporal dimensions, characters and events, sequentiality as well as tellability. In the stories we identify, teachers are managers, students are at greater or lesser risk, and students’ sociality is reduced to machine-readable interactions. Overall, only data marked as individual behaviours becomes relevant to the system, rendering structural inequalities invisible. Reflecting on the implications of these systems, we suggest ways in which the uptake of these systems can interrupt such stories and reshape them in other directions.

A case study on the application of predictive analytics toward forecasting swing door failure

2013 ◽  
Author(s):  
Raj T Vinayaka ◽  
Jinka Parthasarathi ◽  
SubrahmanyaVRK Rao ◽  
Gopinath J Mohan
Keyword(s):  
Predictive Analytics
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