WSC 2009 Final Abstracts

Econometric Simulation of the Income Tax Compliance Process for Small Businesses
George Contos and John Guyton (Internal Revenue Service), Scott Stilmar (IBM), Ardeshir Eftekharzadeh (Internal Revenue Service) and Brian Erard (B. Erard & Associates)

Abstract:
Econometric models can be very useful for estimating the marginal impacts of changes in policy. However, their broader application as a tool for micro-simulation analysis poses a number of challenges and limitations. This paper uses the context of modeling taxpayer compliance burden for small businesses to explore some extensions to standard econometric simulation techniques that provide more robust support of the distribution of the characteristics of interest. Key to the approach is explicitly simulating a random draw from the specified error distribution and a pair of calibration factors reflecting some of the technical limitations of a finite simulation. Further technical considerations regarding the retransformation of the dependent variable in a log-linear regression model are also discussed. Final comments include thoughts on potential refinements and implications for simulating the domain area of interest beyond the current scope of small business taxpayers.

Communicating with Management About the Benefits of Business Process Simulation
Ty Avni and Arnold Greenland (IBM)

Abstract:
While good simulation methodology is a necessary condition for success of simulation projects, such projects cannot even get started unless the modelers can succeed in communicating benefits to management in advance of starting that work. Managers are reluctant to commit resources, especially the precious time of their own staff, unless they have hard evidence that they will see clear improvements to their business performance. This paper will describe the challenges and strategies that can be helpful in communicating this message to managers. We will report on a specific situation in which we used those strategies and succeeded in that communication process.

Simulation in Retail: A Case Study for Process Improvement in the Receiving Area
Marissa A. Vallette, Prajwal Khadgi, Reinaldo Moraga, Ehsan Asoudegi, and Omar Ghrayeb (Northern Illinois University)

Abstract:
Simulation tools allow its users to computationally model real-life systems in order to determine their best future outcome. One real-life system that can benefit from simulation is that of the retail industry. This paper describes how simulation can be an effective tool for this type of industry, especially for process improvement projects. In addition, a small case study is presented to demonstrate the use of simulation for a large retailer which needs to improve its unloading and receiving processes. Among the future ideas for research, this paper shows that less obvious methods for process improvement, such as tracking customer loyalty, can be analyzed using simulation to determine which route a retailer should take in order to please its customers. Other topics on this subject are suggested at the conclusion of this paper.

A Simulation Study of Mutual Influences of Engineering Change Management Process and New Product Development Process
Weilin Li and Young B. Moon (Syracuse University)

Abstract:
This paper presents a simulation model for assessing the mutual impacts of Engineering Change Management (ECM) process and New Product Development (NPD) process on each other. The discrete-event simulation model incorporates ECM into an NPD environment by allowing Engineering Changes (EC) to compete for limited resources with regular NPD activities. The goal is to examine how the relative size and frequency of NPD as well as ECM, NPD process structure (in terms of overlapping and departmental interaction), and the operational policy of resource using priority that one organization employs affect lead time and productivity of both NPD and ECM. Decision-making suggestions considering EC impacts are drawn from an overall enterprise system-level perspective based on the simulation results.

Extending Discrete Event Simulation by Adding an Activity Concept for Business Process Modeling and Simulation
Gerd Wagner, Oana Nicolae, and Jens Werner (Institute of Informatics, Brandenburg University of Technology Cottbus, Germany)

Abstract:
We show how a basic discrete event simulation language can be enabled for business process modeling and simulation by adding an activity construct. While activities are often not considered at all or not treated in a conceptually satisfactory way in the discrete event simulation literature, the great majority of business process modeling languages are based on a concept of activities. However, unlike a simulation language, the predominant business process modeling languages, including UML Activity Diagrams and the Business Process Modeling Notation (BPMN), are not executable. So, the challenge for business process modeling is to define an executable semantics for activities, while the challenge for discrete event simulation is to find a way how to introduce an activity concept on top of the basic discrete event simulation concepts of objects and events. The main idea is defining an activity as a complex event having a start event and an end event.

Comparison of Call Center Models
Luiz Augusto Franzese (PARAGON Consultoria), Marcelo Moretti Fioroni (Paragon Consultoria), Paulo Jose de Freitas Filho (Universidade Federal de Santa Catarina) and Rui Carlos Botter (Universidade de São Paulo)

Abstract:
Call Centers are important channels of communication within the consumer relationship and a point of integration between suppliers and their customers. Correctly sizing the capacity of a given Call Center can bring benefits not only in terms of improved customer service (efficacy), but also in terms of reduced operating costs (efficiency). However, specifying the capacity of a Call Center is not a trivial task, but one that demands a significant knowledge of mathematics, in particular of analytical models. This paper presents the Erlang B, Erlang C and Simulation models followed by a comparison based on a case study, in order to identify the advantages of using simulation.

Demand Curve Prediction Via Bayesian Probability Assignment Over a Functional Space
Michael G. Traverso and Ali Abbas (University of Illinois at Urbana Champaign)

Abstract:
One of the important aspects of energy modeling is the process of demand curve prediction. Existing demand curve prediction methods generally rely on statistical curve fittings which assume a certain functional form such as constant price elasticity. There are a number of disadvantages to this approach. For one, this method makes certain assumptions about the functional form of the price-demand curve that may not be exhibited in practice. In addition, since curve fits rely on only a single function, and not a distribution of functions, they do not capture the uncertainty about price-demand curves. In this work, demand curve prediction is instead treated by assigning a probability measure to the space of all functions that meet the global regularity (non-decreasing conditions). Using this method, a numerical example of Bayesian demand curve prediction is presented.

Estimating Performance of a Business Process Model
Farzad Kamrani and Rassul Ayani (Royal Institute of Technology (KTH)) and Farshad Moradi and Gunnar Holm (Swedish Defence Research Agency (FOI))

Abstract:
In this paper we suggest a model for estimating performance of human organizations and business processes. This model is based on subjective assessment of the capabilities of the available human resources, the importance of these capabilities, and the influence of the peripheral factors on the resources. The model can be used to compare different resource allocation schemes in order to choose the most beneficial one. We suggest an extension to Business Process Modeling Notation (BPMN) by including performance measure of performers and the probability by which an outgoing Sequence Flow from a Gateway is chosen. We also propose an analytical method for estimating the overall performance of BPMN in simple cases and a simulation method, which can be used for more complicated scenarios. To illustrate how these methods work, we apply them to part of a military Operational Planning Process and discuss the results.