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2013 | Buch

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Business Intelligence and Performance Management

Theory, Systems and Industrial Applications

herausgegeben von: Peter Rausch, Alaa F. Sheta, Aladdin Ayesh

Verlag: Springer London

Buchreihe : Advanced Information and Knowledge Processing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

During the 21st century business environments have become more complex and dynamic than ever before. Companies operate in a world of change influenced by globalisation, volatile markets, legal changes and technical progress. As a result, they have to handle growing volumes of data and therefore require fast storage, reliable data access, intelligent retrieval of information and automated decision-making mechanisms, all provided at the highest level of service quality. Successful enterprises are aware of these challenges and efficiently respond to the dynamic environment in which their business operates.

Business Intelligence (BI) and Performance Management (PM) offer solutions to these challenges and provide techniques to enable effective business change. The important aspects of both topics are discussed within this state-of-the-art volume. It covers the strategic support, business applications, methodologies and technologies from the field, and explores the benefits, issues and challenges of each. Issues are analysed from many different perspectives, ranging from strategic management to data technologies, and the different subjects are complimented and illustrated by numerous examples of industrial applications. Contributions are authored by leading academics and practitioners representing various universities, research centres and companies worldwide. Their experience covers multiple disciplines and industries, including finance, construction, logistics, and public services, amongst others.

Business Intelligence and Performance Management is a valuable source of reference for graduates approaching MSc or PhD programs and for professionals in industry researching in the fields of BI and PM for industrial application.

Inhaltsverzeichnis

Frontmatter

Introduction

Frontmatter

Chapter 1. Business Intelligence and Performance Management: Introduction

Abstract

Globalisation, volatile markets, legal changes and technical progress have an immense impact on business environments in most industries. More and more IT is deployed to manage the complexity. As a result, companies and organisations have to handle growing volumes of data which have become a valuable asset. The ability to benefit from this asset is increasingly essential for business success. Therefore, fast storage, reliable data access, intelligent information retrieval, and new decision-making mechanisms are required. Business Intelligence (BI) and Performance Management (PM) offer solutions to these challenges. Before important aspects of both topics are analysed from different points of view, this chapter gives an introduction to concepts and terms of BI and PM.

Hans-Georg Kemper, Peter Rausch, Henning Baars

BI/PM in Business Analytics, Strategy and Management

Frontmatter

Chapter 2. An Integrated Business Intelligence Framework

Closing the Gap Between IT Support for Management and for Production

Abstract

Information Technology (IT) support in the manufacturing sector has reached a watershed with digital components beginning to permeate all products and processes. The classical divide between “technical” IT and “business” IT begins to blend more and more. Data from design, manufacturing, product use, service, and support is made available across the complete product lifecycle and supply chain. This goes hand in hand with the diffusion of sensor and identification technology and the availability of relevant information streams on the customer side—leading to unprecedented amounts of data. The challenge is to purposefully apply emerging BI concepts for a comprehensive decision support that integrates product and shop floor design phases, the steering and design of operational industrial processes, as well as big and unstructured data sources. This chapter brings those pieces together in order to derive an integrated framework for management and decision support in the manufacturing sector.

Hans-Georg Kemper, Henning Baars, Heiner Lasi

Chapter 3. Linking the Operational, Tactical and Strategic Levels by Means of CPM: An Example in the Construction Industry

Abstract

During the last decade, much progress has been made in the field of CPM. However, there are still some issues to master. Many companies have implemented only isolated CPM bricks, and controlling systems which supply real-time information are still missing. To exploit the whole potential of CPM, it is necessary to integrate the CPM components and to link the operational, tactical and strategic levels. In this chapter, an integration grid is used to address all interconnections between the different components systematically and to fill the gaps between the operational, tactical and strategic levels. The grid is applied to a new generation of CPM systems for the construction industry. The CPM approach presented is based on data of a satellite-supported, machine control & guidance system. This data is combined with data from other sources to enable intelligent analyses. Benefits and open issues are discussed. Finally, possibilities for further developments of the presented approach are mentioned.

Peter Rausch, Michael Stumpf

Chapter 4. Adaptive Business Intelligence: The Integration of Data Mining and Systems Engineering into an Advanced Decision Support as an Integral Part of the Business Strategy

Abstract

IT-based decision support is in the heart of business intelligence. It should be based on a successful integration of data analysis techniques and certain system engineering (like system dynamics) concepts. This contribution introduces in the large realm of IT-based decision support and its meaning for a modern business strategy. Central is the relationship to Business Intelligence with its own characteristics and requirements. The relevant data mining techniques are summarized and characterized by its special role within traditional business intelligence approaches.

As an holistic approach this chapter tends to combine a classical data-centric approach with a modern system-engineering concept (“system of systems”-thinking). As a result, this new approach leads to an advanced concept of Adaptive Business Intelligence. It will be characterized and described by several successful examples.

Zafer-Korcan Görgülü, Stefan Pickl

Chapter 5. How to Introduce KPIs and Scorecards in IT Management

Abstract

IT management is based on continuously updated facts and uses scorecards and KPIs. However, the real life in IT organizations is different. On one hand data assets are wasted in traditional reports which are ignored by IT management, on the other hand scorecard introductions fail despite of powerful technologies. This chapter starts with a description of how management and scorecards are linked up against the background of control cycles and the principal agent model. It continues with the discussion of processes to build, use and continually improve scorecards. Finally it shows that the establishment of a scorecard based IT management will be successful only if appropriate processes are implemented, management behaviour is changed towards a culture of measurement and the usage of KPIs is driven by top management representatives.

Martin Kütz

BI/PM Applications to Business Development

Frontmatter

Chapter 6. Identifying Suspicious Activities in Company Networks Through Data Mining and Visualization

Abstract

Company data are a precious asset which need to be truly authentic and must not be disclosed to unauthorized parties. In this contribution, we report on ongoing work that aims at supporting human IT security experts by pinpointing significant alerts that really need closer inspection. We developed an experimental tool environment to support the analysis of IT infrastructure data with data mining methods. In particular, various clustering algorithms are used to differentiate normal behavior from activities that call for intervention through IT security experts. Before being subjected to clustering, data can be pre-processed in various ways. In particular, categorical values can be cleverly mapped to numerical values while preserving the semantics of the data as far as possible. Resulting clusters can be subjected to visual inspection using techniques such as parallel coordinates or pixel-based techniques, e.g. circle segments or recursive patterns.

Preliminary results indicate that clustering is well suited to structure monitoring data appropriately. Also, fairly large data volumes can be clustered effectively and efficiently. Currently, the main focus is on more elaborate visualization and classification techniques.

Dieter Landes, Florian Otto, Sven Schumann, Frank Schlottke

Chapter 7. Exploring the Differences Between the Cross Industry Process for Data Mining and the National Intelligence Model Using a Self Organising Map Case study

Abstract

All Police Analysts in the UK, and many Forces in Europe and the USA, use the National Intelligence Model as a means to provide relevant, timely and actionable intelligence. In order to produce the required documentation analysts have to mine a variety of in-house data systems but do not receive any formal data mining training. The Cross Industry Standard Process for Data Mining is a database agnostic data mining methodology which is logical and easy to follow. By using a self-organising map to suggest offenders who may be responsible for sets of house burglary, this study explores the difference between both processes and suggests that they could be used to complement each other in real Police work.

Richard Adderley

Chapter 8. Business Planning and Support by IT-Systems

Abstract

Business planning is one of the basic tasks of corporate management. Although a detailed presentation of all different business aspects is beyond the scope of this chapter some important characteristics of business planning will be presented. The main focus will be on support by IT-systems, starting by identifying different areas like modeling and manual planning. Then different system categories used for planning purposes will be compared. Important examples are spreadsheets and OLAP based systems. Last but not least some fundamental concepts within planning systems will be discussed, like handling of hierarchies within dimensions and modeled calculations. For this purpose, some implementations will be outlined using software of SAP, Microsoft and the open source solution Palo.

Klaus Freyburger

Chapter 9. Planning Purchase Decisions with Advanced Neural Networks

Abstract

In this chapter we investigate a typical situation of a corporate treasurer: on an ongoing basis some kind of transaction is performed. This may be a regular monthly investment in equities for a pension plan, or a fixed income placement. It might be a foreign exchange transaction to pay monthly costs in another currency. Or it could be the monthly supply of some commodity, like fuel or metal.

All these cases have in common that the treasurer has to choose an appropriate time for the transaction. This is the day on which the price is the most favorable. Ideally, we want to buy at the lowest price within the month, and we also want to invest our money at the highest available interest rate.

This problem is complex, because the underlying financial time series are not moving independently. Rather, they are interconnected. In order to truly understand our time series of choice, we have to model other influences as well: equities, currencies, interest rates, commodities, and so on. To achieve this we present a novel recurrent neural network approach: Historically Consistent Neural Networks (HCNN). HCNNs allow to model dynamics of entire markets using a state space equation: s _t+1=tanh(W⋅s _t). Here, W represents a weight matrix and s _t the state of our dynamic system at time t. This iterative formulation easily produces multi step forecasts for several time points into the future.

We analyze monthly purchasing decisions for a market of 25 financial time series. This market approximates a world market: it includes various asset classes from Europe, the US, and Asia. Our benchmar, an averaging strategy, shows that using HCNNs to forecast an entry point for ongoing investments results in better prices for every time series in the sample.

Hans Georg Zimmermann, Ralph Grothmann, Hans-Jörg von Mettenheim

Methodologies

Frontmatter

Chapter 10. Financial Time Series Processing: A Roadmap of Online and Offline Methods

Abstract

Because financial information is a vital asset for financial and economic organizations, it requires careful management so that those organizations can enhance and facilitate the decision making process. The financial information is usually gathered over time providing a temporal and historical trace of the financial evolution in the form of time series. The organizations can then rely on such histories to understand, uncover, learn and most importantly make appropriate decisions. The present chapter tries to overview the analysis steps of financial time series and the approaches applied therein. Particular focus is given to the classification of such approaches in terms of the processing mode (i.e., online vs. offline).

Daniela Pohl, Abdelhamid Bouchachia

Chapter 11. Data Supply for Planning and Budgeting Processes under Uncertainty by Means of Regression Analyses

Abstract

Planning and Budgeting (P&B) is an important part of Performance Management (PM). The corresponding processes for medium-sized and large organisations are usually very resource-intensive, time consuming and costly. These issues are mainly caused by uncertainty, which is a big challenge for companies. It is shown that available software and tools do not address this challenge in an appropriate way. Before possible issues and solutions are analysed in detail, an overview of different types of uncertainty is given. Afterwards important steps of the P&B process which suffer from uncertainties are outlined. Quite often it is not really clear which parameters have an impact on the planning object and how strong the planning object is influenced by certain parameters. Additionally, forecasts of the most important parameters which anticipate uncertainties are needed at an early stage of the P&B process. To resolve these issues, the application of different types of regression analyses will be explored. Also, ideas for further processing of fuzzy data in the following P&B steps are given. Furthermore, organisational and cultural prerequisites for the successful application of the outlined approaches will be indicated.

Peter Rausch, Birgit Jehle

Chapter 12. Minimizing the Total Cost in Production and Transportation Planning—A Fuzzy Approach

Abstract

In this chapter, we deal with the production and transportation planning of a household appliances manufacturer that has production facilities and central stores for resellers in several sites in Europe. Each store can receive products from all production plants and it is not necessary that all products are produced in all production units. The transport between any two bases is done by trucks. For simplicity we assume, that each truck has the same capacity of M EURO-pallets, and for each product the unit is EURO-pallet. The target of this chapter is to determine a combined production and transport plan that minimize the total sum of the production cost and the transportation cost. For working in a realistic environment we assume that the production capacities in the different plants and the demand in the sales bases are not known exactly but the management can describe the data in form of fuzzy numbers. By using an inter-active algorithm for solving the fuzzy linear programming system we achieve a stable production and a satisfactory supply of the products. Moreover, we demonstrate that this integer programming problem can adequately be solved without using computation-intensive integer programming algorithms. Additionally, in the course of the inter-active solution process the production bottlenecks get clearly visible. A numerical example illustrates the efficiency of the proposed procedure.

Heinrich J. Rommelfanger

Chapter 13. Design and Automation for Manufacturing Processes: An Intelligent Business Modeling Using Adaptive Neuro-Fuzzy Inference Systems

Abstract

The design and automation of a steel making process is getting more complex as a result of the advances in manufacturing and becoming more demanding in quality requirements. It is essential to have an intelligent business process model which brings consistent and outstanding product quality thus keeping the trust with the business stakeholders. Hence, schemes are highly needed for improving the nonlinear process automation. The empirical mathematical model for steel making process is usually time consuming and may require high processing power. Fuzzy neural approach has recently proved to be very beneficial in the identification of such complex nonlinear systems. In this chapter, we discuss the applicability of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to model the dynamics of the hot rolling industrial process including: roll force, roll torque and slab temperature. The proposed system was developed, tested as well as compared with other existing systems. We have conducted several simulation experiments on real data and the results confirm the effectiveness of the ANFIS based algorithms.

Alaa F. Sheta, Malik Braik, Ertan Öznergiz, Aladdin Ayesh, Mehedi Masud

Chapter 14. How to Measure Efficiency in IT Organizations

Abstract

IT systems are an essential part of firms and other organisations. If management is the brain then IT is the nervous system. As any other part of the organisation’s IT is subject to the economic principle. It has to optimise IT efficiency, which is a major task in IT performance management. But if something has to be improved it has at first to be measured or to be made measurable. This chapter covers the aspect of measuring efficiency in IT organisations and provides an overview of selected methods. After a brief introduction of terms in the field of business administration approaches to measure efficiency are discussed. The focus is set on the aspect of measuring the output of IT by means of utility functions and the Analytical Hierarchy Process (AHP). A new AHP-based method to build scales for measurement is presented. Some fields of application are outlined.

Martin Kütz

Technologies

Frontmatter

Chapter 15. Business Activity Monitoring (BAM)

Abstract

Providing reliable and timely management information is crucial for supporting the agility of organizations. Business Activity Monitoring (BAM) describes a concept and technology that complements periodic ex-post analysis of process execution by permanently identifying particular situations at runtime and reacting to them by setting alerts or triggering actions with no or low latency. Complex Event Processing (CEP) has emerged as a basic technology for an effective BAM environment. In an integrated BAM/CEP architecture enterprise applications and workflow engines can serve both as event producers and consumers, while event processors filter and transform events, find patterns among them and derive new events. The ladder are consumed e.g. by workflow or enterprise resource planning systems causing new processes or dashboard solutions displaying management information as it arises.

Werner Schmidt

Chapter 16. Scaling up Data Mining Techniques to Large Datasets Using Parallel and Distributed Processing

Abstract

Advances in hardware and software technology enable us to collect, store and distribute large quantities of data on a very large scale. Automatically discovering and extracting hidden knowledge in the form of patterns from these large data volumes is known as data mining. Data mining technology is not only a part of business intelligence, but is also used in many other application areas such as research, marketing and financial analytics. For example medical scientists can use patterns extracted from historic patient data in order to determine if a new patient is likely to respond positively to a particular treatment or not; marketing analysts can use extracted patterns from customer data for future advertisement campaigns; finance experts have an interest in patterns that forecast the development of certain stock market shares for investment recommendations. However, extracting knowledge in the form of patterns from massive data volumes imposes a number of computational challenges in terms of processing time, memory, bandwidth and power consumption. These challenges have led to the development of parallel and distributed data analysis approaches and the utilisation of Grid and Cloud computing. This chapter gives an overview of parallel and distributed computing approaches and how they can be used to scale up data mining to large datasets.

Frederic Stahl, Mohamed Medhat Gaber, Max Bramer

From Past to Present to Future

Frontmatter

Chapter 17. Evolution of Business Intelligence

Abstract

From the first simple report to data warehousing to BI tools to today’s evolved state of intelligence, BI continues the evolution. Once BI was for structured data only. Now BI can operate on textual data, and in doing so BI can operate on the full spectrum of data found in the corporation.

W. H. Inmon

Titel: Business Intelligence and Performance Management
herausgegeben von: Peter Rausch
Alaa F. Sheta
Aladdin Ayesh
Verlag: Springer London
Electronic ISBN: 978-1-4471-4866-1
Print ISBN: 978-1-4471-4865-4
DOI: https://doi.org/10.1007/978-1-4471-4866-1