Business Intelligence and Analytics in Small and Medium Enterprises
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Business Intelligence and Analytics in Small and Medium Enterprises

Pedro Novo Melo, Carolina Machado, Pedro Novo Melo, Carolina Machado

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  1. 153 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Business Intelligence and Analytics in Small and Medium Enterprises

Pedro Novo Melo, Carolina Machado, Pedro Novo Melo, Carolina Machado

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About This Book

Technological developments in recent years have been tremendous. This evolution is visible in companies through technological equipment, computerized procedures, and management practices associated with technologies. One of the management practices that is visible is related to business intelligence and analytics (BI&A). Concepts such as data warehousing, key performance indicators (KPIs), data mining, and dashboards are changing the business arena.

This book aims to promote research related to these new trends that open up a new field of research in the small and medium enterprises (SMEs) area.


  • Focuses on the more recent research findings occurring in the fields of BI&A

  • Conveys how companies in the developed world are facing today's technological challenges

  • Shares knowledge and insights on an international scale

  • Provides different options and strategies to manage competitive organizations

  • Addresses several dimensions of BI&A in favor of SMEs

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CRC Press
Process Mining – Prerequisites and Their Applicability for Small and Medium-sized Enterprises
Alexander Zeisler
Salzburg University of Applied Sciences, Puch/Salzburg, Austria
Christopher Bernhard
Salzburg University of Applied Sciences, Puch/Salzburg, Austria
Julian Marius Müller
Salzburg University of Applied Sciences, Puch/Salzburg, Austria
  • 1.1Introduction
  • 1.2What is Process Mining?
  • 1.3Prerequisites for Successful Process Mining
    • 1.3.1Organizational Prerequisites
    • 1.3.2Process-Related Prerequisites
    • 1.3.3IT-Related Prerequisites
    • 1.3.4Data-Related Prerequisites
    • 1.3.5Employee-Related Prerequisites
    • 1.3.6Legal Requirements
    • 1.3.7Means and Resources
  • 1.4Process Mining in SME – Two Case Studies
    • 1.4.1Is Process Mining a Suitable Technology for SMEs?
    • 1.4.2Are the Seven Identified Prerequisites for Process Mining Being Fulfilled in the Respective SME?
  • 1.5Concluding Remarks
  • Notes
  • References


Rapid technical progress, the increasing capability to generate and store data, and the growing fusion of the physical and the digital world are fostering the use of new technologies (Accorsi et al., 2012). Process Mining is a trend in business process management which has finally arrived in industrial application. The most powerful driver for process mining is the collaboration between process mining providers and vendors of enterprise applications such as SAP, Oracle, or Salesforce. Large companies like Siemens or Vodafone could already realize significant benefits by applying process mining tools in business process management (Kerremans, 2018). The reduction of throughput times, cutting costs, or increased satisfaction among customers are examples how an organization can benefit from process mining in order to remain competitive or to gain further competitive advantages.
It is evident that small- and medium-sized enterprises (SMEs)1 show a considerable backlog when approaching the digital transformation compared to large enterprises. Only a small proportion of SMEs is already prepared to foster the full potentials, whereas the majority of SMEs is beginning with initial test applications (Müller, 2019; Müller et al., 2018). The digitization of the value chain, a key aspect in the context of a digitized industrial value chain, is also described as the concept of Industry 4.0 (Müller et al., 2018). It is further the fundamental basis for process mining. In the course of an INTERREG-funded project,2 companies from the federal state of Bavaria (Germany) and the federal state of Salzburg (Austria) were being asked to participate in a digital readiness check. This readiness check is focusing on digitization of the value chain, and the result reaffirms the outcome of the above-mentioned study “Digitization in SMEs 2018”. The level of digitization of the value chain is low; SMEs self-evaluate the digitization of their value chain with a value of 2.1 on average on a scale ranging from 0 to 6 points. Another distinguishing feature between SMEs and large enterprises is the assumption that large enterprises usually have implemented professional business process management and are performing work on the basis of explicit and formal processes (Müller et al., 2018; Müller and Voigt, 2018). SMEs, on the other hand, are expected to largely perform the activities on an implicit basis and professional business process management is not necessarily established (Burattin, 2015).
Based on these characteristics of SMEs described in extant literature, two core questions are of interest: The first question is about the prerequisites for process mining and when an organization is ready for this technology. The second question deals with the applicability of process mining for SMEs. It has to be evaluated if process mining is only suitable for large companies, or if similar benefits can be realized in SMEs – under given circumstances that a low level of digitization has to be expected.
To find an answer to these questions, two steps are conducted: After explaining the basic functionalities of process mining, prerequisites for successful process mining are presented by combining both qualitative and quantitative research methods. These findings are then tested in case studies with two SMEs, both suppliers for the automotive industry and both situated in the Austrian-German border region. The case studies have been carried out in cooperation between the respective SME and the researchers at Salzburg University of Applied Sciences.


In short, process mining “[…] uses business process events for process visualization and analytics” (Yli-Pietilä and Kauppinen, 2016).
A process can be defined as a “sequence of activities performed in a specific order to achieve a specific goal” (Munoz-Gama, 2016). End-to-end processes – like order-to-cash, manufacturing processes, or service processes – are integral part of industries and professional business process management is crucial for companies who want to be competitive in an ever faster and complex economic environment. Process mining is a relatively young research discipline and is bridging the gap between process science and data science, aiming to discover, monitor, and improve real processes by using data from event logs (Van der Aalst, 2016). Today, business processes are being performed with support of IT systems to varying degrees and thus, process mining is possible due to the simple fact that data already exists (Rozinat and Günther, 2014). Information about business processes is being extracted from enterprise transaction systems and hence, information about real-life processes can be generated on the basis of data-driven facts (Davenport and Spanyi, 2019). Process mining offers an innovative approach to analyze the performance of a process. Commonly used manual tools – like spreadsheets in Excel, dashboards, or Power Point slides – are being replaced by dynamic tools. Process mining tools are visually reconstructing the actual flow of business processes, which helps to create a common understanding and process transparency among an organization. As a result, process analysis can be performed much more quickly and efficiently compared to the manual approach (Rozinat and Günther, 2014).


Process mining is a tool to professionalize business process management and needs to be embedded in an adequate professional environment. An organization needs to be prepared accordingly if it wants to implement a process mining tool. The fundamental requirements and prerequisites for successful process mining have been elaborated in the scope of an empirical analysis. First, seven guided interviews have been conducted with experts from three relevant areas: experts from the academic environment, business process experts from the industry, and providers of process mining tools. The outcome of these expert interviews laid the foundation for a quantitative survey that has been carried out in a second step among companies in the German-speaking area. Seventy-nine valid responses have been received from the quantitative survey and as seen in Figure 1.1, both – large enterprises (66%) but also SMEs (34%) – provided responses.
The results have been consolidated in seven prerequisites that are recommended being fulfilled before implementing a process mining tool. These prerequisites are of a general character and can be applied for all types of enterprises.

1.3.1Organizational Prerequisites

Implementing a new technology – like process mining – requires full support from the management team. That does not necessarily implicate that the management team is responsible for the implementation itself, but an adequate environment in the enterprise needs to be created. The management team is accountable to define setup in the organization (e.g., create an own department in Data and Quality Management). A project manager needs to be appointed and all involved employees need to be empowered according to their tasks. Furthermore, the management team has to assure that the implementation of the new technology is understood, accepted, and being supported from all involved functions in the organization. Companies should further truthfully inform employees about the benefits and the expected outcome of process mining. Otherwise, there is a risk that employees might block and put up resistance against process mining, especially if they feel being systematical...

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