299,135 research outputs found

    Materiality Maps – Process Mining Data Visualization for Financial Audits

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    Financial audits are a safeguard to prevent the distribution of false information which could detrimentally influence stakeholder decisions. The increasing integration of computer technology for the processing of business transactions create new challenges for auditors who have to deal with increasingly large and complex data. Process mining can be used as a novel Big Data analysis technique to support auditors in this context. A challenge for using this type of technique is the representation of analyzed data. Process mining algorithms usually discover large sets of mined process variants. This study introduces a new approach to visualize process mining results specifically for financial audits in an aggregate manner as materiality maps. Such maps provide an overview about the processes identified in an organization and indicate which business processes should be considered for audit purposes. They reduce an auditor’s information overload and help to improve decision making in the audit process

    Penerapan Metode Naive Bayes Untuk Klasifikasi Pelanggan

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    Business location plays an important role in sales. The business location in cities makes the seller easier to distribute activities for people. Distribution activities are closely related to sales activities. If there is a sales transaction, a classification of potential and non-potential customers will be required. One method that can be used for classification is mining data. One of the most frequently used data mining for classification is the Naive Bayes method. The attributes used in the customer classification process are purchase amount, time interval, and location. The result of the classification system is 23 true reactions and 2 false reactions. Based on the results are using the confusion matrix method, it shows that the accuracy value reaches 92%, the precision value reaches 100%, the recall value reaches 91%.Keywords: Trading Business, Customer Classification, Naive Bayes, Confusion Matri

    EVALUASI IMPLEMENTASI MODUL SALES AND DISTRIBUTION (SD) SAP PADA PROSES BISNIS PENJUALAN PRODUK KEPADA PELANGGAN JENIS MODERN TRADE STUDI KASUS : PT. XYZ INDONESIA TBK.

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    PT. XYZ Indonesia TBK (PT. XYZ) merupakan salah satu perusahaan multi nasional dalam industri Fast Moving Consumer Goods (FMCG). Untuk bersaing dalam industri tersebut, PT. XYZ melakukan pendefinisian Standard Operating Procedure (SOP) sebagai acuan melakukan aktivitas, pengukuran performa menggunakan Key Performance Indicator (KPI), dan mengintegrasikan data menggunakan sistem ERP yaitu SAP ECC. Meskipun telah menggunakan SAP ECC namun dalam praktiknya terdapat perbedaan pengeksekusian proses bisnis. Dalam rangka mempertahankan capaian positif, melakukan evaluasi setiap capaian proses menjadi penting. Penelitian ini berfokus pada salah satu proses bisnis paling kompleks yaitu lini bisnis XYZ Retail, merupakan penjualan produk kepada pelanggan jenis Modern Trade (MT). Proses bisnis tersebut memiliki keterkaitan dengan modul Sales and Distribution (SD) dalam SAP ECC. Pengevaluasian dilakukan dengan membandingkan proses bisnis ekspektasi dengan kondisi kekinian pada oprasional menggunakan metode kualitatif dan process mining. Metode kualitatif digunakan untuk memperoleh gambaran proses bisnis ekspektasi. Sedangkan process mining yang berasal dari event log digunakan untuk menghasilkan informasi terkait alur dan waktu pelaksanaan dengan menggunakan tools Disco dan ProM. Pada penelitian ini ditemukan bahwa alur proses bisnis yang teridentifikasi tidak sepenuhnya sesuai dengan alur proses bisnis ekspektasi dan panduan modul SD SAP. Ditemukan 23502 nomor penjualan, dimana 9400 diantaranya tidak sesuai dengan alur ekspektasi. Selain itu dari 37 variant alur yang dihasilkan terdapat 5 jenis variant proses bisnis tidak sesuai dan hanya satu variant yang sesuai dengan proses bisnis ekspektasi. Proses bisnis yang tidak sesuai ekspektasi tersebut mempengaruhi perfoma dari segi waktu, sehingga dihasilkan rentang mencapai 12 hari lebih lama dari proses bisnis yang sesuai ekspektasi. ========================================================= PT. XYZ Indonesia TBK (PT. XYZ) is one of multinational company in Fast Moving Consumer Goods (FMCG) industry. In case of competing in FMCG industry, PT. XYZ defining clearly Standard Operating Procedure (SOP) as a baseline, performance measurement using Key Performance Indicator (KPI), and integrating data using ERP. Despite used SAP ECC but in the practice there is gap between execution and defined business process. To maintain positiveness, evaluation becomes an important thing for every activity. This research focused on one of the most complex business processes in Retail business, selling a product to customer type Modern Trade (MT). That business process has a relation with sales and distribution (SD) module in SAP ECC. The evaluation is used by comparing expected business processes with the current operational using qualitative and process mining. Qualitative methods are used to obtain the picture of the expected business process. While the process mining used to generate related information about flow and time by using tools Disco and ProM. In this study found that the identified flow of the business process, not fully in line with the expected and SAP SD module guide. Totally 23502 sales numbers there are 9400 that are not same with the expected. In addition, there are 37 variants of the business process flow, where 5 of the variants are not same and only one variant that suits with the expected business process. The not same with expected business process flow affects the performance in terms of time, resulting there are gap more than 12 days longer than the expected business process

    Robust and distributed top-n frequent-pattern mining with SAP BW accelerator

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    Mining for association rules and frequent patterns is a central activity in data mining. However, most existing algorithms are only moderately suitable for real-world scenarios. Most strategies use parameters like minimum support, for which it can be very difficult to define a suitable value for unknown datasets. Since most untrained users are unable or unwilling to set such technical parameters, we address the problem of replacing the minimum-support parameter with top-n strategies. In our paper, we start by extending a top-n implementation of the ECLAT algorithm to improve its performance by using heuristic search strategy optimizations. Also, real-world datasets are often distributed and modern database architectures are switching from expensive SMPs to cheaper shared-nothing blade servers. Thus, most mining queries require distribution handling. Since partitioning can be forced by user-defined semantics, it is often forbidden to transform the data. Therefore, we developed an adaptive top-n frequent-pattern mining algorithm that simplifies the mining process on real distributions by relaxing some requirements on the results. We first combine the PARTITION and the TPUT algorithms to handle distributed top-n frequent-pattern mining. Then, we extend this new algorithm for distributions with real-world data characteristics. For frequent-pattern mining algorithms, equal distributions are important conditions, and tiny partitions can cause performance bottlenecks. Hence, we implemented an approach called MAST that defines a minimum absolute-support threshold. MAST prunes patterns with low chances of reaching the global top-n result set and high computing costs. In total, our approach simplifies the process of frequent-pattern mining for real customer scenarios and data sets. This may make frequent-pattern mining accessible for very new user groups. Finally, we present results of our algorithms when run on the SAP NetWeaver BW Acceleratorwith standard and real business datasets

    Pemodelan dan analisis kerja proses bisnis distribusi produksi dengan algoritma heuristic miner pada departemen production distribution di PT. XYz

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    Saat ini banyak perusahaan menggunakan Sistem Informasi Perusahaan yang bertujuan untuk mengintegrasikan seluruh proses bisnis yang dijalankan. Perkembangan sistem informasi juga mengalami kemajuan yang sangat pesat. Data yang berasal dari sistem informasi dapat digunakan untuk optimasi proses bisnis perusahan. Namun, tidak banyak perusahaan mampu untuk mengeksplorasi data yang diperolehnya menjadi informasi yang bermanfaat. Dalam tugas akhir ini dilakukan penelitian mengenai model proses bisnis pada Departemen Production Distribution Center (PDC) di PT XYZ dengan menggunakan teknik Penggalian proses. Penelitian diarahkan untuk menganalisis kinerja dan mengidentifikasi faktor-faktor yang mempengaruhi kinerja proses bisnis di departemen PDC melalui gambaran model Petri nets yang didapatkan dari hasil ekstraksi catatan kejadian basis data SAP. Teknik Penggalian proses yang dilakukan dalam penelitian ini menggunakan pendekatan algoritma Heuristic Miner.Dengan adanya penelitian ini diharapkan dapat membantu mengevaluasi proses bisnis manajemen pergudangan pada bagian production distribution center di PT XYZ. ======================================================================================================== Today many companies use Enterprise Information System in order to integrate their business processes. The development of Information Systems has also increased very rapidly. Data that derived from information system can be used to optimize Enterprise business process. However, not many companies can utilize the data obtained into valuable information. This final project investigates business process at PDC Department in PT XYZ using process mining. The aim is to analyze and identify factors that affect the performance of business processes in PDC department. This doneby extracting event logs from SAP database and develop the business model using Heuristic Miner algorithm. The result is expected to help evaluate warehouse management at Production Distribution Center department in PT XYZ

    Perspectives On Data-Driven failure diagnosis : With a case study on failure diagnosis at an Payment Service Provider

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    Data-driven failure diagnosis aims to extract relevant information from a dataset in an automatic way. In this paper it is being proposed a data driven model for classifying the transactions of a Payment Service Provider based on relevant shared characteristics that would provide the business users relevant insights about the data analyzed. The proposed solution aims to mimic processes applied in industrial organizations. However, the methods discussed in this paper from these organizations does not directly deal with the human component in information systems. Therefore, the proposed solution aims to offer the relevant error paths to help the business users in their daily tasks while dealing with the human factor in IT systems. The built artifact follow the next set of steps: • Categorization of variables following data mining techniques. • Assignation of importance for variables affecting the transaction process using predictive machine learning method. • Classification of transactions in groups with similar characteristics. The solution developed effectively and consistently classify more than 90% of the faults in the database by grouping them in paths with shared characteristics and with a relevant failure rate. The artifact does not depends in any predefined fault distribution and satisfactorily deal with highly correlated input variables. Therefore, the artifact has a scalable potential if previously, a data mining categorization of variables is performed. Specially, in companies that deals with rigid processes

    CASP-DM: Context Aware Standard Process for Data Mining

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    We propose an extension of the Cross Industry Standard Process for Data Mining (CRISPDM) which addresses specific challenges of machine learning and data mining for context and model reuse handling. This new general context-aware process model is mapped with CRISP-DM reference model proposing some new or enhanced outputs
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