Master Data Research Articles

Data Profiling Model for Assessing the Quality Traits of Master Data Management

Enterprise Resource Planning (ERP) and Business Intelligence (BI) system demand progressive rules for maintaining the valuable information about customers, products, suppliers and vendors as data captured through different sources may not be of high quality due to human errors, in many cases. The problem encounters when this information is accessible across multiple systems, within same organization. Providing adequacy to this scattered data is a top agenda for any organization as maintaining the data is complicated, as having high quality data. Master Data Management (MDM) provides a solution to these problems by maintaining “a single reference of truth” with authoritative source of master data (Customer, products, employees etc). Master Data Management (MDM) is a highlighted concern now a day as valid data is the demand for strategic, tactical and operational steering of every organization. The lane to MDM initiates with the quality of data which demands for discovery of master data, profiling and analysis. As inadequacy of data may leads to adverse effects such as wrong decision, loss of time, bad results and unnecessary risk. Thus there is a need to deal with master data and quality of this specific data in a successful and efficient manner. For ensuring this purpose, an approach is proposed in this paper. The research focuses on development of a Model for Data Profiling to assess the level of Quality Traits for Master Data Management. Results are shown by executing the defined steps on TALEND tool over collected dataset. Thus, level of quality traits processes directly correlates with an organization’s ability to make the proper decisions and better outcomes.

APLIKASI UJIAN ONLINE BERBASIS WEB DI SMA BHAKTI NUSANTARA PALEMBANG

Palembang Bhakti Nusantara High School is one of the High Schools (SMA) that has used information technology in certain activities. Palembang's Bhakti Nusantara High School utilizes technology to facilitate teaching and learning by applying the Online Examination. At present, the exam still conducts tests using paper so that there is a need for further development to help and improve the effectiveness and efficiency of performance carried out in accordance with the business processes that run at the Bhakti Nusantara Palembang High School. This Online Examination is used to facilitate the management of master data needed to support the examination activities and the management of exam master data values. The software development method used is the web engineering method. This Online Examination implementation uses PHP, HTML, CSS, Javascript and MySQL Database Management System (DBMS).

Concepts to Improve the Quality of Production Plans using Machine Learning

<p class="Abstract">There are always deviations between production planning and subsequent execution. Furthermore, it was found that the reliability of the production plans and thus the planning quality (PQ) can drop down to 25% in the first three days after plan creation. These deviations are caused by uncertainties, e.g. inaccurate or insufficient planning data such as data quality and availability, inappropriate planning and control systems or unforeseeable events. Production planners therefore use buffers in the form of inventories or extended transitional periods to create possibilities for implementing corrective measures in production control. Buffers, however, lead to increased coordination and control effort and to negative effects, e.g. on inventory, throughput time and capacity utilization. Potentials for more accurate planning remains largely unexploited. The objective of this paper is to investigate the possibilities to increase planning quality. Within a case study the authors demonstrate how machine learning can be used to predict cycle times. Furthermore, the increased accuracy compared to the current method is shown. Based on this, two approaches are presented, focusing on the reduction of gaps between master data and predicted data used during the production planning process. Moreover, further research needs are identified.</p>

Using robotic process automation (RPA) to enhance item master data maintenance process

Using robotic process automation (RPA) to enhance item master data maintenance process

Integration Manager Technology in ERP Application System (Case Study: PT. Argo Pantes, tbk)

Many companies are already using ERP application systems but still do not utilize information technology as an interest in helping to achieve the goals of the company's vision. The process of inputting the ERP (Enterprise Resource Planning) application data that prioritizes data integration, the accuracy of the data from the master data to the daily transaction data is then recorded into a financial statement information for the related. From this research, the results achieved are the ERP application system in inputting data integration processes, master data and transaction data are able to validate the level of data accuracy with the help of the INTEGRATION MANAGER Technology which functions as the initial data matrix with ready-to-report data in an ERP application system, As for the TOWS Matrix Analysis external environment (threats and opportunities), and internal environment (weaknesses and strengths) in summarizing the conclusions reached are to help the accuracy of the data in the ERP application of PT. Argo Pantes, Tbk is more structured, more efficient, reduces input errors, input data is correct, accompanied by uptodate managerial decisions

EBSD orientation analysis based on experimental Kikuchi reference patterns

Orientation determination does not necessarily require complete knowledge of the local atomic arrangement in a crystalline phase. We present a method for microstructural phase discrimination and orientation analysis of phases for which there is only limited crystallographic information available. In this method, experimental Kikuchi diffraction patterns are utilized to generate a self-consistent master reference for use in the technique of Electron Backscatter Diffraction (EBSD). The experimentally derived master data serves as an application-specific reference in EBSD pattern matching approaches. As application examples, we map the locally varying orientations in samples of icosahedral quasicrystals observed in a Ti40Zr40Ni20 alloy, and we analyse AlNiCo decagonal quasicrystals.

Drug Studies on Rett Syndrome: From Bench to Bedside.

Drug studies on Rett syndrome (RTT) have drastically increased over the past few decades. This review aims to provide master data on bench-to-bedside drug studies involving RTT. A comprehensive literature review was performed by searching in PUBMED, MEDLINE and Google Scholar, international, national and regional clinical trial registries and pharmaceutical companies using the keywords "Rett syndrome treatment and/or drug or compound or molecule". Seventy drugs were investigated in non-clinical (N = 65 animal/cell line-based studies; N = 5 iPSC-based study) and clinical trials (N = 34) for ameliorating the symptoms of RTT. Though there is good progress in both clinical and non-clinical studies, none of these drugs entered phase III/IV for being launched as a therapeutic agent for RTT.

Evaluasi Deteksi Smell Code dan Anti Pattern pada Aplikasi Berbasis Java

This paper presents an evaluation result of smell code and anti pattern detection in java based application development. The main objective to be achieved in this research is to determine the proper way in the detection of smell code and anti pattern in the development of java based software, and to evaluate the impact of using code inspection tools and software metrics to refactoring code in java based software development. Smell code to be detected in this research is Long Parameter List, Large Class, Lazy Class, Feature Envy, Long Method, and Dead Code. Anti pattern that will be detected is The Blob / God Class and Lava Flow. The selection of smell code and anti pattern is based on the definition, characteristics, detection factor, and software metrics. To support the research process is done through the evaluation stage of a case study java based application as a sample for inspection of code for the detection of smell code and anti pattern and calculation software metrics. Case studies of selected applications as sample applications are E-Commerce applications with functional master data management of goods and customers as well as management of sales and payment transactions. The detection of the smell code and anti-pattern on the case study is done in stages so it can be determined whether or not to refact. As well as ensuring the technique of making the program better fit the characteristics and rules of object-oriented programming.

Discovering XML Conditional Dependencies for Data Quality Issues

Extensible Markup Language (XML) is emerging as the primary standard for representing and exchanging data, with more than 60% of the total; XML considered the most dominant document type over the web; nevertheless, their quality is not as expected. XML integrity constraint especially XFD plays an important role in keeping the XML dataset as consistent as possible, but their ability to solve data quality issues is still intangible. The main reason is that old-fashioned data dependencies were basically introduced to maintain the consistency of the schema rather than that of the data. The purpose of this study is to introduce a method for discovering pattern tableaus for XML conditional dependencies to be used for enhancing XML document consistency as a part of data quality improvement phases. The notations of the conditional dependencies as new rules are designed mainly for improving data instance and extended traditional XML dependencies by enforcing pattern tableaus of semantically related constants. Subsequent to this, a set of minimal approximate conditional dependencies (XCFD, XCIND) is discovered and learned from the XML tree using a set of mining algorithms. The discovered patterns can be used as a Master data in order to detect inconsistencies that don’t respect the majority of the dataset.

Towards a software quality certification of master data-based applications

Master data management (MDM) can provide an integrated and unified view of key business entities to offer better support in business processes. Due to the very nature of master data-based applications, it is possible to use data with the highest possible level of quality. MDM can help ensure that some common concerns, like duplicates or inconsistencies, are prevented by sharing a ‘single version of the truth’ throughout the organisation, and, in some cases, allowing collaborative updates to the master data repository. Therefore, assuring the reliability of master data-based applications, would improve the organisation efficiency. This type of application should implement a set of functional requirements covering the basic operation of MDM principles. We propose a solution based on the evaluation and certification of ‘functional suitability’ of MDM applications. As part of our proposal, we inferred a set of functional requirements from parts 100 to 140 of ISO 8000. This set will be used as a reference in the required matching to compute values for each one of the metrics, properties, subcharacteristics and ultimately, functional suitability following a bottom-up procedure. Finally, the paper also describes the application of the evaluation procedure of an existing master data-based application.

Propensities of Amino Acid Pairings in Secondary Structure of Globular Proteins.

A class of secondary structure prediction algorithms use the information from the statistics of the residue pairs found in secondary structural elements. Because the protein folding process is dominated by backbone hydrogen bonding, an approach based on backbone hydrogen-bonded residue pairings would improve the predicting capabilities of these class algorithms. The reliability of the prediction algorithms depends on the quality of the statistics, therefore, of the data set. In this study, it was aimed to determine the propensities of the backbone hydrogen-bonded residue pairings for secondary structural elements of α-helix and β-sheet in globular proteins using a new and comprehensive data set created from the peptides deposited in Worldwide Protein Data Bank. A master data set including 4882 globular peptide chains with resolution better than 2.5 Å, sequence identity smaller than 25% and length of no shorter than 100 residues were created. Separate data sub sets also were created for helix and sheet structures from master set and each sub set includes 4594 and 4483 chains, respectively. Backbone hydrogen-bonded residue pairings in helices and sheets were detected and the propensities of them were represented as odds ratios (observed/[random or expected]) in matrices. Propensities assigned by this study to the residue pairings in secondary structural elements (as helix, overall strands, parallel strands and antiparallel strands) differ from the previous studies by 19 to 34%. These dissimilarities are important and they would cause further improvements in secondary structure prediction algorithms.

Big Data Velocity Management–From Stream to Warehouse via High Performance Memory Optimized Index Join

Efficient resource optimization is critical to manage the velocity and volume of real-time streaming data in near-real-time data warehousing and business intelligence. This article presents a memory optimisation algorithm for rapidly joining streaming data with persistent master data in order to reduce data latency. Typically during the transformation phase of ETL (Extraction, Transformation, and Loading) a stream of transactional data needs to be joined with master data stored on disk. To implement this process, a semi-stream join operator is commonly used. Most semi-stream join operators cache frequent parts of the master data to improve their performance, this process requires careful distribution of allocated memory among the components of the join operator. This article presents a cache inequality approach to optimise cache size and memory. To test this approach, we present a novel Memory Optimal Index-based Join (MOIJ) algorithm. MOIJ supports many-to-many types of joins and adapts to dynamic streaming data. We also present a cost model for MOIJ and compare the performance with existing algorithms empirically as well as analytically. We envisage the enhanced ability of processing near-real-time streaming data using minimal memory will reduce latency in processing big data and will contribute to the development of high-performance real-time business intelligence systems.

Approaches for the Prediction of Lead Times in an Engineer to Order Environment—A Systematic Review

The interest of manufacturing companies in a sufficient prediction of lead times is continuously increasing - especially in engineer to order environments with typically a large number of individual parts and complex production processes. A multitude of approaches have been proposed in the literature for predicting lead times considering different data and methods or algorithms from operations research (OR) and machine learning (ML). In order to provide guidance at setting up prediction models and developing new approaches, a systematic review of the available approaches for predicting lead times is presented in this paper. Forty-two publications were analyzed and synthetized: Based on a developed framework considering the used data class (e.g. product data or system status), the data origin (master data or real data) and the used method and algorithm from OR and ML, the publications are classified. Based on the classification, a descriptive analysis is performed to identify common approaches in the existing literature as well as implications for further research. One result is, that mostly order data and the status of the production system are used for predicting lead times whereas material data are used seldom. Additionally, ML approaches primarily use artificial neural networks and regression models for predicting lead times, while OR approaches use mainly combinatorial optimization or heuristics. Furthermore, with increasing model complexity the use of real data decreased. Thus, we identified as an implication for further research to set up a complex data model considering material data, which uses real data as data origin.

Machine Learning Methodology for Management of Shipbuilding Master Data

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

Master Data Management Planning: A Case Study of Flight Information System at PT Angkasa Pura I (Persero)

The flight information system used for flight data management at PT Angkasa Pura I (Persero) uses several applications that have different features. This results in inconsistent values that affect data quality due to absence of single master data source. Therefore, this study aims to provide recommendations to PT Angkasa Pura I (Persero) regarding the planning of master data management for flight information system according to its business processes. Master data management planning is carried out using the key user interview method, analyzing the database schema of each application, analyzing IT policy documents, and using the framework of the Data Management Body of Knowledge (DMBOK). The research succeeded in producing the needs of master data management at PT Angkasa Pura I (Persero) using collaborative methods and transaction hub architecture patterns. The results of this master data management plan are expected to help PT Angkasa Pura I (Persero) in managing and managing flight data so that there are no more inconsistent data between applications and can improve data quality.

Databased prediction and planning of order-specific transition times

An increasing demand for individualized products leads to smaller lot sizes and a shift towards workshop manufacturing. Similarly, customers demand for cost-efficient production, high delivery reliability and short lead times, which are difficult to achieve in a workshop production at once. As a result, transition times, the times between value-adding operations, can account for more than 90 % of lead times, but are often treated as static master data and still estimated manually. In this article, resulting prediction improvements for order-specific transition times based on methods of machine learning are presented and an approach for integration into practice is discussed.

Application of time series data mining for the prediction of transition times in production

Transition times between value-adding operations often account for more than 90 % of lead times in workshop productions and vary greatly from order to order. However in day-to-day business, they are treated as static master data or only roughly estimated. One approach for a more precise prediction of future transition times of production orders is time series data mining. This paper presents a first application approach as basis for improving the quality of production planning. Based on empirical findings, future research issues are derived.

Education Management of Policy Reform pf Extraordinary School Known as (Slb) in Central Java Province

Every citizen has the same rights to obtain the education, including for children with disabilities. However, from the current researchers there are several problems faced with the implementation of Extraordinary School (SLB) education in Central Java Province, which is the limitation of human resources policy, budget and infrastructure facilities. This research aims to observe and conduct analysis of the policy reform of Sekolah Luar Biasa (Extraordinary School) of Education management in Central Java Province and supporting factors and inhibitors of SLB education management policy conducted by the government of Central Java Province. The research method used by researchers is qualitative, with data collection using triangulation techniques. The results showed that the policy reform of SLB Education management in Central Java Province in the field of apparatus resources still need to be addressed because still many who have not been literate with technology. However, it does not affect them in the service given to the community. These recommendations given improving the quality and quantity are human resources apparatus through the technology and training. The addition of personnel, development of infrastructure adapted to disabled, master data collection and budget adjustments. Preparation of Standard Operational Procedure (SOP) sustained to the LAW No 8 in 2016 about the disabled. The creation of governor regulation that is specifically governs the disability. 
 Key words: Policy Reformation, Disability, Standard Operational Procedure, Education Management, Extraordinary School.

Warehouse configuration in omni-channel retailing: a multiple case study

Purpose The purpose of this study is to explore warehouse configuration in omni-channel retailing. Design/methodology/approach A multiple case study is conducted with six large omni-channel retailers from three different sectors. Findings The study shows an increase in the number, variation and frequency of flows passing through omni-channel warehouses. Along with an increased variety of stock keeping units (including singles vs multipacks), there is an increase in the complexity of planning and coordination of order fulfillment. Retailers test a mix of different solutions for storage and picking and partly shift focus to advanced sorting operations. The companies already have or plan to invest in substantial automation systems, which emphasize the importance of capturing and using accurate master data. Research limitations/implications The study highlights the need to understand the interrelations and co-development of configuration elements in omni-channel warehousing. The findings also suggest that a successful transformation requires increased collaboration with upstream and downstream partners. Conceptual models are developed to illustrate strategies and development paths in omni-channel warehousing, and suggestions for future research are summarized in a research agenda. A research limitation is the focus on Swedish retailers in three sectors (fashion, consumer electronics and DIY/construction material). Future studies can include additional sectors, extend the geographical scope and explore cross-regional differences. Practical implications As one of the few deeper case studies on omni-channel warehousing, practitioners will find new configurations described and analyzed here. Along with conceptual models, a synthesis of challenges and potential solutions are presented to support retailers’ practical analysis and decision making. Originality/value This is one of the first multiple case studies that go deeper into omni-channel warehouse configuration, which is of increasing importance to both scholars and practitioners in the field.

Aplikasi Nilai Online, Pembayaran SPP dan Absensi Berbasis Website Di SMP Islam Al-Ikhlas Jakarta Selatan

Lack of publications to deliver online value information makes Al-Ikhlas Islamic Junior High School has a weakness in disseminating information to parents in Al-Ikhlas Islamic Junior High School South of Jakarta in the publicized value for children - her son, because the conventional media used by teachers Al-Ikhlas Islamic Junior High School South of Jakarta only form of notes on ledger Master Data Master.
 Data collection methods used were observation and interviews, with observation and interviews are expected to obtain sufficient data for the manufacturing of value-line application will be made for Al-Ikhlas Islamic Junior High School south of Jakarta. The approach used is object-oriented approach (Object Oriented).
 By designing a web-based online application value in SMP Islam Al-Ikhlas South Jakarta, is expected to improve efficiency and streamline the process of accessing information and maximize the delivery of academic information which will be aimed at students, teachers and parents. With value-based online application he designed the website in Al-Ikhlas Islamic Junior High School South of Jakarta, is expected to also school - other schools will make this application in every school.