Master Data-Supply Chain Management, the Key Lever for Collaborative and Compliant Partnerships in Big Data Era

2021 ◽  
pp. 72-101
Author(s):  
Samia Chehbi Gamoura ◽  
Manisha Malhotra
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Information Systems ◽  
Data Governance ◽  
Traditional Management ◽  
Logistics Networks ◽  
Master Data ◽  
Hands On ◽  
Master Data Management

With the advent of big data in supply chain information systems (SCIS), data compliance and consistency are becoming vital. Today, SC stakeholders need to pay more attention to data governance, which requires changing traditional management methods. These can be achieved by mastering a single repository through what is usually named master data management (MDM). However, accomplishing this objective is particularly challenging in the complex logistics networks of supply chains (SC). The volatile nature of the logistics flows that increase exponentially because of the facilitation of exchanges' interoperability in the information systems. In this chapter, the authors propose an MDM-based framework for the supply chain information systems as an enabler for strong collaboration and compliance. For proof of concept, a case study of a French hypermarket is examined through benchmarking scenarios. The outcomes of the case validate our approach as a hands-on solution when applied correctly. Finally, the chapter discusses the key findings and the limitations of our framework.

scholarly journals The mediating effect of big data analysis on the process orientation and information system software to improve supply chain process in Saudi Arabian industrial organizations

2021 ◽  
pp. 135-142 ◽  
Author(s):  
Ahmed Faek Elgendy
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Information Systems ◽  
Data Analysis ◽  
Saudi Arabian ◽  
Big Data Analysis ◽  
Chain Process ◽  
Process Orientation ◽  
Industrial Organizations ◽  
Supply Chain Process

This study aims to investigate the nature of the relationship between Big Data Analysis as a mediator in Process Orientation (PO) and Information Systems Programming (ISP) to supply chains processes in Saudi Arabian industrial organizations. A stratified random sample of 357 managers and employees working in 37 industrial companies in Saudi Arabia was tested. The study relied on the descriptive and analytical research methodology. The results indicated that there is a significant indirect effect of Big Data Analysis (Planning, Procuring, Manufacturing, Delivering) as the mediator on Process Orientation and Information Systems Programming (ISP) and (PO) to improve supply chain process as well as organizational effectiveness. The researcher made a number of recommendations for the Saudi Arabian manufacturing firms to develop analytical capabilities in managers in order to utilize big data analysis as a tool to increase efficiency and effectiveness in the organizational system. A wide spread awareness program about the benefits to adopt big data analysis and management information systems may be adopted to ensure an efficient supply chain system.

A structural equation model for big data adoption in the healthcare supply chain

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dindayal Agrawal ◽  
Jitender Madaan
Keyword(s):  
Factor Analysis ◽  
Big Data ◽  
Supply Chain ◽  
Latent Variables ◽  
Structural Equation ◽  
Equation Model ◽  
Equation Modeling ◽  
Data Governance ◽  
Social Perspective ◽  
Content Type

PurposeThe purpose of this study is to examine the barriers to the implementation of big data (BD) in the healthcare supply chain (HSC).Design/methodology/approachFirst, the barriers concerning BD adoption in the HSC were found by conducting a detailed literature survey and with the expert's opinion. Then the exploratory factor analysis (EFA) was employed to categorize the barriers. The obtained results are verified using the confirmatory factor analysis (CFA). Structural equation modeling (SEM) analysis gives the path diagram representing the interrelationship between latent variables and observed variables.FindingsThe segregation of 13 barriers into three categories, namely “data governance perspective,” “technological and expertise perspective,” and “organizational and social perspective,” is performed using EFA. Three hypotheses are tested, and all are accepted. It can be concluded that the “data governance perspective” is positively related to “technological and expertise perspective” and “organizational and social perspective” factors. Also, the “technological and expertise perspective” is positively related to “organizational and social perspective.”Research limitations/implicationsIn literature, very few studies have been performed on finding the barriers to BD adoption in the HSC. The systematic methodology and statistical verification applied in this study empowers the healthcare organizations and policymakers in further decision-making.Originality/valueThis paper is first of its kind to adopt an approach to classify barriers to BD implementation in the HSC into three distinct perspectives.

Building High Quality Big Data-Based Applications in Supply Chains

2018 ◽  
pp. 1-24 ◽  
Author(s):  
Kamalendu Pal
Keyword(s):  
Quality Assurance ◽  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Quality ◽  
Software Systems ◽  
Data Governance ◽  
Chain Management ◽  
Software Applications ◽  
Retail Business

Global retail business has become diverse and latest Information Technology (IT) advancements have created new possibilities for the management of the deluge of data generated by world-wide business operations of its supply chain. In this business, external data from social media and supplier networks provide a huge influx to augment existing data. This is combined with data from sensors and intelligent machines, commonly known as Internet of Things (IoT) data. This data, originating from the global retail supply chain, is simply known as Big Data - because of its enormous volume, the velocity with which it arrives in the global retail business environment, its veracity to quality related issues, and values it generates for the global supply chain. Many retail products manufacturing companies are trying to find ways to enhance their quality of operational performance while reducing business support costs. They do this primarily by improving defect tracking and better forecasting. These manufacturing and operational improvements along with a favorable customer experience remain crucil to thriving in global competition. In recent years, Big Data and its associated technologies are attracting huge research interest with academics, industry practitioners, and government agencies. Big Data-based software applications are widely used within retail supply chain management - in recommendation, prediction, and decision support systems. The spectacular growth of these software systems has enormous potential for improving the daily performance of retail product and service companies. However, there are increasingly data quality problems resulting in erroneous tesing costs in retail Supply Chain Management (SCM). The heavy investment made in Big Data-based software applications puts increasing pressure on management to justify the quality assurance in these software systems. This chapter discusses about data quality and the dimensions of data quality for Big Data applications. It also examines some of the challenges presented by managing the quality and governance of Big Data, and how those can be balanced with the need of delivery usable Big Data-based software systems. Finally, the chapter highlights the importance of data governance; and it also includes some of the Big Data managerial practice related issues and their justifications for achieving application software quality assurance.

Lab Development for Delivering Information Systems Courses Online at Small Campuses

2012 ◽  
pp. 246-266
Author(s):  
Li Chao
Keyword(s):  
Information Systems ◽  
Network Management ◽  
Quality Teaching ◽  
Computer Lab ◽  
Know How ◽  
Limited Budget ◽  
Hands On ◽  
Software And Hardware

In this case study, you will encounter some of the issues of lab development for delivering lab-based information systems courses online. Many small campuses have very limited budget or no budget at all for the computer lab specifically designed for information systems majors. Sometimes, even with new computers purchased, very few people know how to set them up for lab-based information systems (IS) courses. What are the software and hardware requirements for getting the lab online? How much will it cost? Where can you find resources for the lab development? To ensure quality teaching on IS-related topics, you have to deal with these issues. This case study will discuss how to create a lab that allows students to get hands-on practice for courses such as network management or database processing online with a shoestring budget.

Urban Master Data Management

2017 ◽  
pp. 441-465
Author(s):  
Adolphe Ayissi Eteme ◽  
Justin Moskolai Ngossaha
Keyword(s):  
Information Technology ◽  
Information Systems ◽  
Data Management ◽  
Data Repository ◽  
Distributed Data ◽  
Data Systems ◽  
Policy Makers ◽  
Master Data ◽  
Master Data Management

The use of information technology in council management has resulted in the generation of a large amount of data through various autonomous urban bodies. The relevant bodies barely or never reuse such locally-generated data. This may be due particularly to managers', policy makers' and users' lack of awareness of existing information. The Platform for the Integration and Interoperability of the Yaounde Urban Information Systems (YUSIIP) project seeks to reduce this deficit by establishing a federated operational platform of heterogeneous and distributed data systems based on a distributed data repository. The position developed in this paper is that Master Data Management (MDM) will contribute to achieving this objective in a context marked by the dispersion and duplication of data and diversity of information systems.

scholarly journals Big Data Integration Solutions in Organizations: A Domain-Specific Analysis

2021 ◽  
Author(s):  
Sreekantha Desai Karanam ◽  
Rajani Sudhir Kamath ◽  
Raja Vittal Rao Kulkarni ◽  
Bantwal Hebbal Sinakatte Karthik Pai
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Data Sources ◽  
Holistic View ◽  
Implementation Challenges ◽  
Intelligent Decision Making ◽  
Master Data ◽  
Complex Process ◽  
Master Data Management ◽  
Diverse Data

Big Data Integration (BDI) process integrates the big data arising from many diverse data sources, data formats presents a unified, valuable, customized, holistic view of data. BDI process is essential to build confidence, facilitate high-quality insights and trends for intelligent decision making in organizations. Integration of big data is a very complex process with many challenges. The data sources for BDI are traditional data warehouses, social networks, Internet of Things (IoT) and online transactions. BDI solutions are deployed on Master Data Management (MDM) systems to support collecting, aggregating and delivering reliable information across the organization. This chapter has conducted an exhaustive review of BDI literature and classified BDI applications based on their domain. The methods, applications, advantages and disadvantage of the research in each paper are tabulated. Taxonomy of concepts, table of acronyms and the organization of the chapter are presented. The number of papers reviewed industry-wise is depicted as a pie chart. A comparative analysis of curated survey papers with specific parameters to discover the research gaps were also tabulated. The research issues, implementation challenges and future trends are highlighted. A case study of BDI solutions implemented in various organizations was also discussed. This chapter concludes with a holistic view of BDI concepts and solutions implemented in organizations.

scholarly journals Social Media Big Data Analytics for Demand Forecasting

2020 ◽  
Vol 28 (1) ◽  
pp. 103-120 ◽  
Author(s):  
Rehan Iftikhar ◽  
Mohammad Saud Khan
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply Chain ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Predictive Modelling ◽  
Product Demand ◽  
Emotion Model ◽  
Improving Accuracy

Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.

Using Big Data Analytics to Forecast Trade Volumes in Global Supply Chain Management

2019 ◽  
pp. 70-99
Author(s):  
Murat Ozemre ◽  
Ozgur Kabadurmus
Keyword(s):  
Big Data ◽  
International Trade ◽  
Supply Chain ◽  
Data Analytics ◽  
Open Data ◽  
Big Data Analytics ◽  
Global Supply Chain ◽  
Factors Affecting ◽  
Export Volume

As the supply chains become more global, the operations (such as procurement, production, warehousing, sales, and forecasting) must be managed with consideration of the global factors. International trade is one of these factors affecting the global supply chain operations. Estimating the future trade volumes of certain products for specific markets can help companies to adjust their own global supply chain operations and strategies. However, in today's competitive and complex global supply chain environments, making accurate forecasts has become significantly difficult. In this chapter, the authors present a novel big data analytics methodology to accurately forecast international trade volumes between countries for specific products. The methodology uses various open data sources and employs random forest and artificial neural networks. To demonstrate the effectiveness of their proposed methodology, the authors present a case study of forecasting the export volume of refrigerators and freezers from Turkey to United Kingdom. The results showed that the proposed methodology provides effective forecasts.

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