Applied modelling and computing in social science
Table Of Contents
- About the author(s)/editor(s)
- About the book
- This eBook can be cited
- Table of Contents
- An outline for business process modelling using spreadsheets
- Manufacturing processes optimisation in a furniture factory
- Researching Industrial Symbiosis: Challenges and Dilemmas
- Game-based Learning and Social Media API in Higher Education
- Negatively Biased Media in Slovenia
- Measuring security culture of users of online banking
- Modelling Synergies between Online and Offline Media
- An open framework biometric system optimisation
- A framework for qualitative evaluation of air pollution levels
- The overview of recent findings in diagnostics of mental disorders
- Improving biological models with experts knowledge and literature
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Explaining and predicting social phenomena is always a challenging task.
In this book, we present a wide range of successful applications of modelling and computing in the social sciences. Several authors contributed papers with recent research results. Business process simulation and optimisation, modelling security and weakly defined organisations, optimisation of synergies between different media in marketing, positive and negative sentiment extraction from web media and its analysis over time and media, and medical data-mining are all examples where advanced modelling and computing contributed new knowledge to the social sciences.
Therefore, this monograph demonstrates the power of modelling and computing on one side and the necessity to combine qualitative and quantitative methods to get a complete picture of given social aspect on the other side.
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Faculty of Information Studies
Sevno 13, 8000 Novo mesto, Slovenia
An outline for business process modelling using spreadsheets
Abstract: Process simulation is the first step towards the redesign of business process models. However, the leading process simulation tools are usually expensive and often unavailable for common users. On the other hand, almost every computer user has experienced the usage of spreadsheets at least once. Their vast usage is due to its simplicity and to its availability in companies. This paper presents an approach for the usage of spreadsheets for process simulation. We argue that for modelling of simple business processes, the usage of spreadsheets provides just as reliable results as any other expensive simulation tool.
Keywords: business process modelling, spreadsheets
Business process simulation (BSP) has an essential role in the process of improvement of business process management (BPM) in the public and private sector. BPS is a tool which companies use to get feedback regarding their performances under different conditions and thus enables the redesign of the business process. Regardless of the fact that BPS is acknowledged as relevant and highly applicable, the use of simulation is limited in reality (Nakatumba, Rozinat and Russell 2008). We have managed to observe two main reasons.
The first reason is that there are more than 100 available tools for BPS, each one with different specifics; however, there is a lack of guidance on how to choose a particular tool for simulating a specific task. The second reason is that most of these tools are either too expensive to buy for small companies and start-ups or require a great knowledge for their set up. This paper provides an alternative to the expensive simulation tools by providing an outline for using spreadsheets for simulation of business processes. The reason that we have chosen spreadsheets is because of their vast usage today. They are easy to install and do not require high programming experience to use them. ← 11 | 12 →
2 Business Process Modelling and Simulations
In general, a process consists of interconnected activities that require certain resources for their implementation. Thus, to understand the real processes, one has to observe their behaviour and consequently the behaviour of activities and process resources. The observation is usually performed in three steps. Firstly, the process is modelled using some well-known technique. Next, the model is validated in order to find out how close the model reflects the real process. Then the model is analysed by using “what-if” questions to test the options of interest and the functional possibilities functioning of the process. Finally, the model is used for simulation of different scenarios in order to observe the behaviour of the process. The simulations are based on different scenarios using a set of data and assumptions about the process activities and resources (Banks et al. 2001).
Business processes (BP) are modelled with the aim of analysing their current states within the organisation, as well as improving them through the execution of potential ‘‘what-if” simulation scenarios (Saven, Olhager 2002). The use of scenario-based what-if analyses enables the design team to test various alternatives and choose the best one (Laguna, Marklund 2005). BP models are simulated with discrete-event simulation (DES) tools. When using DES, the state variable changes only at a discrete set of points in time. When using DES for BPM, the following basic elements have to be considered:
• State: A collection of variables that contain all the information necessary to describe the system at any time;
• Entity: Any object or component of the system which requires explicit representation in the model (i.e. a server, a customer, a machine);
• Attribute: The properties of a given entity (i.e. the priority of a waiting customer, the routing of a job through a job shop);
• Activity: A duration of time of specified length (i.e. a service time or arrival time), which is known when it begins (though it may be defined in terms of a statistical distribution);
• Delay: A duration of time of unspecified indefinite length, which is not known until it ends (i.e. a customer’s delay in a last-in, first-out waiting line which, when it begins, depends on future arrivals);
- ISBN (PDF)
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- Publication date
- 2015 (May)
- state of the art software technologies big data sentiment analysis decision support systems
- Frankfurt am Main, Berlin, Bern, Bruxelles, New York, Oxford, Wien, 2015. 128 pp., 16 tables, 26 graphs