Identifying Drivers of Inefficiency in Business Processes: A DEA and Data Mining Perspective

2010 ◽  
pp. 120-132 ◽  
Author(s):  
Anne Dohmen ◽  
Jürgen Moormann
Keyword(s):  
Data Mining ◽  
Business Processes

Business Intelligence and Data Mining

2013 ◽  
pp. 57-70
Author(s):  
Zsolt T. Kardkovács
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Business Processes ◽  
Support Systems ◽  
Decision Makers ◽  
Key Factors ◽  
Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

Data Mining and Knowledge Discovery

2011 ◽  
pp. 35-47 ◽  
Author(s):  
Andi Baritchi
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Business Processes ◽  
Knowledge Discovery In Databases ◽  
Business World ◽  
Useful Knowledge ◽  
Customer Support ◽  
Customer Base ◽  
Sales And Marketing ◽  
Customer Profiling

In today’s business world, the use of computers for everyday business processes and data recording has become virtually ubiquitous. With the advent of this electronic age comes one priceless by-product — data. As more and more executives are discovering each day, companies can harness data to gain valuable insights into their customer base. Data mining is the process used to take these immense streams of data and reduce them to useful knowledge. Data mining has limitless applications, including sales and marketing, customer support, knowledge-base development, not to mention fraud detection for virtually any field, etc. “Data mining,” a bit of a misnomer, refers to mining the data to find the gems hidden inside the data, and as such it is the most often-used reference to this process. It is important to note, however, that data mining is only one part of the Knowledge Discovery in Databases process, albeit it is the workhorse. In this chapter, we provide a concise description of the Knowledge Discovery process, from domain analysis and data selection, to data preprocessing and transformation, to the data mining itself, and finally the interpretation and evaluation of the results as applied to the domain. We describe the different flavors of data mining, including association rules, classification and prediction, clustering and outlier analysis, customer profiling, and how each of these can be used in practice to improve a business’ understanding of its customers. We introduce the reader to some of today’s hot data mining resources, and then for those that are interested, at the end of the chapter we provide a concise technical overview of how each data-mining technology works.

A Conceptual Framework for Data Mining and Knowledge Management

2011 ◽  
pp. 1-15 ◽  
Author(s):  
Shamsul I. Chowdhury
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Knowledge Base ◽  
Business Processes ◽  
Data Warehousing ◽  
Case Based Reasoning ◽  
Integrated Framework ◽  
Large Databases ◽  
Main Components ◽  
Mining Tools

Over the last decade data warehousing and data mining tools have evolved from research into a unique and popular applications, ranging from data warehousing and data mining for decision support to business intelligence and other kind of applications. The chapter presents and discusses data warehousing methodologies along with the main components of data mining tools and technologies and how they all could be integrated together for knowledge management in a broader sense. Knowledge management refers to the set of processes developed in an organization to create, extract, transfer, store and apply knowledge. The chapter also focuses on how data mining tools and technologies could be used in extracting knowledge from large databases or data warehouses. Knowledge management increases the ability of an organization to learn from its environment and to incorporate knowledge into the business processes by adapting to new tools and technologies. Knowledge management is also about the reusability of the knowledge that is being extracted and stored in the knowledge base. One way to improve the reusability is to use this knowledge base as front-ends to case-based reasoning (CBR) applications. The chapter further focuses on the reusability issues of knowledge management and presents an integrated framework for knowledge management by combining data mining (DM) tools and technologies with CBR methodologies. The purpose of the integrated framework is to discover, validate, retain, reuse and share knowledge in an organization with its internal users as well as its external users. The framework is independent of application domain and would be suitable for uses in areas, such as data mining and knowledge management in e-government.

Big data analysis on the business process and management for the store layout and bundling sales

2019 ◽  
Vol 25 (7) ◽  
pp. 1783-1801 ◽  
Author(s):  
Shu-hsien Liao ◽  
Yi-Shan Tasi
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Business Process ◽  
Process Management ◽  
Business Processes ◽  
Analysis Data ◽  
Database System ◽  
Big Data Analysis ◽  
Content Type

Purpose In the retailing industry, database is the time and place where a retail transaction is completed. E-business processes are increasingly adopting databases that can obtain in-depth customers and sales knowledge with the big data analysis. The specific big data analysis on a database system allows a retailer designing and implementing business process management (BPM) to maximize profits, minimize costs and satisfy customers on a business model. Thus, the research of big data analysis on the BPM in the retailing is a critical issue. The paper aims to discuss this issue. Design/methodology/approach This paper develops a database, ER model, and uses cluster analysis, C&R tree and the a priori algorithm as approaches to illustrate big data analysis/data mining results for generating business intelligence and process management, which then obtain customer knowledge from the case firm’s database system. Findings Big data analysis/data mining results such as customer profiles, product/brand display classifications and product/brand sales associations can be used to propose alternatives to the case firm for store layout and bundling sales business process and management development. Originality/value This research paper is an example to develop the BPM of database model and big data/data mining based on insights from big data analysis applications for store layout and bundling sales in the retailing industry.

Data Mining Tool Selection

2011 ◽  
pp. 511-518
Author(s):  
Christophe Giraud-Carrier
Keyword(s):  
Data Mining ◽  
Process Model ◽  
Business Processes ◽  
Process Models ◽  
Tool Selection ◽  
Business Decision ◽  
Software Packages ◽  
Or Methodology ◽  
Business Requirement

It is sometimes argued that all one needs to engage in Data Mining (DM) is data and a willingness to “give it a try.” Although this view is attractive from the perspective of enthusiastic DM consultants who wish to expand the use of the technology, it can only serve the purposes of one-shot proofs of concept or preliminary studies. It is not representative of the complex reality of deploying DM within existing business processes. In such contexts, one needs two additional ingredients: a process model or methodology, and supporting tools. Several Data Mining process models have been developed (Fayyad et al, 1996; Brachman & Anand, 1996; Mannila, 1997; Chapman et al, 2000), and although each sheds a slightly different light on the process, their basic tenets and overall structure are essentially the same (Gaul & Saeuberlich, 1999). A recent survey suggests that virtually all practitioners follow some kind of process model when applying DM and that the most widely used methodology is CRISP-DM (KDnuggets Poll, 2002). Here, we focus on the second ingredient, namely, supporting tools. The past few years have seen a proliferation of DM software packages. Whilst this makes DM technology more readily available to non-expert end-users, it also creates a critical decision point in the overall business decision-making process. When considering the application of Data Mining, business users now face the challenge of selecting, from the available plethora of DM software packages, a tool adequate to their needs and expectations. In order to be informed, such a selection requires a standard basis from which to compare and contrast alternatives along relevant, business-focused dimensions, as well as the location of candidate tools within the space outlined by these dimensions. To meet this business requirement, a standard schema for the characterization of Data Mining software tools needs to be designed.

Knowledge Creation

2011 ◽  
pp. 527-538 ◽  
Author(s):  
Nilmini Wickramasinghe
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Organizational Performance ◽  
Knowledge Creation ◽  
Business Processes ◽  
Business Environment ◽  
Sustainable Competitive Advantage ◽  
Continuous Innovation ◽  
Knowledge Spiral ◽  
Business Challenges

Knowledge management (KM) is a newly emerging approach aimed at addressing today’s business challenges to increase efficiency and efficacy of core business processes, while simultaneously incorporating continuous innovation. The need for knowledge management is based on a paradigm shift in the business environment where knowledge is now considered to be central to organizational performance and integral to the attainment of a sustainable competitive advantage (Davenport & Grover, 2001; Drucker, 1993). Knowledge creation is not only a key first step in most knowledge management initiatives, but also has far reaching implications on consequent steps in the KM process, thus making knowledge creation an important focus area within knowledge management. Currently, different theories exist for explaining knowledge creation. These tend to approach the area of knowledge creation from either a people perspective—including Nonaka’s Knowledge Spiral, as well as Spender’s and Blackler’s respective frameworks—or from a technology perspective—namely, the KDD process and data mining.

Data Mining for Supply Chain Management in Complex Networks

2005 ◽  
pp. 708-713
Author(s):  
Mahesh S. Raisinghani ◽  
Manoj K. Singh
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Business Processes ◽  
Supply And Demand ◽  
Organizational Level ◽  
Order Fulfillment ◽  
Chain Management ◽  
Planning And Control ◽  
And Control

Supply chain comprises the flow of products, information, and money. In traditional supply chain management, business processes are disconnected from stock control and, as a result, inventory is the direct output of incomplete information. The focus of contemporary supply chain management is to organize, plan, and implement these flows. First, at the organizational level, products are manufactured, transported, and stored based on the customers’ needs. Second, planning and control of component production, storage, and transport are managed using central supply management and replenished through centralized procurement. Third, the implementation of the supply chain involves the entire cycle from the order-entry process to order fulfillment and delivery. Data mining can create a better match between supply and demand, reducing or sometimes even eliminating the stocks.

Data Mining for Supply Chain Management in Complex Networks

2001 ◽  
pp. 173-193 ◽  
Author(s):  
Manoj K. Singh ◽  
Mahesh S. Raisinghani
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Complex Networks ◽  
Business Processes ◽  
Operating Efficiency ◽  
Chain Management ◽  
Making Sense ◽  
Large Databases ◽  
Very Large Databases

The concept and philosophy behind supply chain management is to integrate and optimize business processes across all partners in the entire production chain. Since these are not simple supply chains but rather complex networks, tuning these complex networks comprising supply chain/s to the needs of the market can be facilitated by data mining. Data mining is a set of techniques used to uncover previously obscure or unknown patterns and relationships in very large databases. It provides better information for achieving competitive advantage, increases operating efficiency, reduces operating costs and provides flexibility in using the data by allowing the users to pull the data they need instead of letting the system push the data. However, making sense of all this data is an enormous technological and logistical challenge. This chapter helps you understand the key concepts of data mining, its methodology and application in the context of supply chain management of complex networks.

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