SIMULATION

Simulation



INTRODUCTION

Simulation is widely used in accounting practice to analyze risk. It is employed in managerial accounting practice for (1) financial forecasting and budgeting, (2) replacement and maintenance of machinery/equipment, (3) analyzing capital investment, (4) make or buy decisions, (5) planning and controlling the budgetary process, (6) planning and implementing revisions in accounting systems, (7) inventory analysis and control, (8) production planning and control, and (9) strategic enterprise management (O'Leary, 1983; Kramer et al, 2008). It is also heavily used in auditing practice. For example, parallel simulation is used frequently for controls testing and substantive testing (Turney and Watne, 2003). Simulation is widely utilized in accounting education as well to introduce uncertainty modeling, risk analysis, financial fraud detection, unique accounting needs of smaller businesses, and strategic enterprise management to students, and to develop students' critical thinking skills (Blankley et al, 2004; Jabbour and Liu, 2005; Rich and Cascini, 2006; Cascini and Rich, 2007; Ragan et al, 2008; Schiff and Smith, 2008; Togo, 2007 & 2008).

Addressing the functional competencies in the AICPA Core Competency Framework for Entry into the Accounting Profession, AICPA states that individuals entering the accounting profession must be able to build appropriate models and simulations using electronic spreadsheets and other software (AICPA, 2009). Though user-friendly simulation software packages (e.g. Arena, ProcessModel and ProModel) are available, developing a simulation model on an electronic spreadsheet has several advantages: (1) the elimination of the need for learning a programming language, (2) the wide spread availability of and familiarity with spreadsheet software, (3) the availability of many built-in statistical functions for generating random numbers and data summarization, (4) the capability of displaying information in detail and in easy-to-read format, not just the final results, (5) the ease of experiments for different values of the parameters, (6) the capability of developing simulation models without add-ins such as Crystal Ball and @RISK, and (7) a better understanding of the basic concepts behind the simulation models.

RANDOM NUMBER GENERATORS

One of the requirements of almost any simulation model is some facility for generating random numbers. Microsoft Excel provides two uniform random number generators in the form of functions: RAND() and RANDBETWEEN(a,b). However, other theoretical distributions - such as normal, exponential, gamma, and Poisson distributions - are encountered more frequently than is the uniform distribution. In many cases, an empirical distribution is used. The inverse transformation method is a general method for transforming a standard uniform deviate into any other distribution, especially, when the distribution is an empirical one. In this section, we briefly discuss procedures for generating random numbers from empirical and frequently used theoretical distributions based on the inverse transformation method. Our emphasis is on procedures that can be coded in a single statement. The procedures for generating random numbers are well discussed in many simulation books. We refer the interested readers to Random Number Generation and Monte Carlo Methods by James Gentle (Gentle, 2004).

Empirical Distribution

Using the inverse transformation method for a general discrete distribution is essentially a table ...