Advances in Economics and Business 1(2): 89-102, 2013 http://www.hrpub.org DOI: 10.13189/aeb.2013.010205 A Fuzzy Statistical Expert System for Cash Flow Analysis and Management under Uncertainty Ali Asghar Anvary Rostamy 1,* , Vahid Baghaei 2 , Farideh Bakhshi Takanlou 3 , Amin Anvary Rostamy 4 1 Professor, Tarbiat Modares University (TMU), Chair Department of Accounting, Tehran, Iran 2 Master of Accounting, Tarbiat Modarres University (TMU), Tehran, Iran 3 Accounting Student, University of Applied Sciences & Technology Tehran, Iran 4 Department of Software Engineering, Amirkabir University of Technology, Tehran, Iran *Corresponding Author: anvary@modares.ac.ir Copyright © 2013 Horizon Research Publishing All rights reserved. Abstract We live in the world of ambiguity and uncertainty. In such a fuzzy world, using tools that are close to natural language and have the ability to conclude like human mind, and even deal with more data and complex relations are important. This paper provides a fuzzy statistical expert system for cash flow management, one of the most important issues in business. It helps managers in managing their organizational cash resources. For this purpose, first input and output variables and their membership functions have been defined. Then, we formed rules using fuzzy inference system to infer ending cash balance from a set of combination of 25 separate rules. Finally, linguistic levels converted to certain numbers by centered method (defuzzification) to help managers to see the effects of changes in the levels of inputs on ending cash balances. defuzzification represents the relationship between the variables with numerical values. The required data for practical illustration of our mode is gathered from Cement Companies listed on Tehran Stock Exchange, which their financial reports are prepared based on Iranian Accounting Standards. The authors believe that the proposed system helps managers to analyze the effects of changes in input variables on ending cash balances. Keywords Decision Making, Fuzzy-Statistical Expert System (FSES), Cash Flows Statement, Tehran Stock Exchange 1. Introduction The importance of cash flows for business units is of no doubt. Cash is the ideal in the business world, which business cycle starts with and ends to it. There is no need to talk about the importance and effectiveness of cash and its features. So, if it is possible to provide a situation that analysis and control this essential business resource is better off, then the business unit may achieve its goals more conveniently. Statement of cash flows in financial reports is one of the main data resources of cash. It illustrates how it was generated and spent by firms. For instance, it shows how much cash was generated by operational activates and how much of it was used for loans repayments, dividends and reinvestments. Released information related to cash inflow and outflow in a given period helps stakeholders to have a proper evaluation about risk, insolvency, financial flexibility and profit quality of the firm. Moreover, cash availability provides opportunity for economic benefits. To have a proper evaluation of business threats and opportunities, along with precise assessment of management stewardship function, it is required to have a good understanding of nature of a business; including its way of cash generation and usage (Iranian Accounting Standards, 2007). Forecast future using past experiences, desirability that evermore been undeniable for anyone, but the future is always accompanied by uncertainty. As Yuan (2009) states “when market fluctuations cannot be predicted with certainty, managers have to make decisions under condition of uncertainty. Under these conditions, decisions to make or not are often based on managers’ human intuitions, common sense and experience, rather than on the availability of clear, concise and accurate data”. In this condition to help for decision making matter, statistic science can used to representation the phenomenon occurrence probability that helps to decide the issues in different fields. Although the literature provides some probabilistic and stochastic models for analyzing uncertainties, but because of some reasons such as their complexity and/or expensiveness especially for SMEs, some managers do not use them in practice (Yuan, 2009). Furthermore, as Maloo (1991) states, prevalent stochastic and simulation models are restrictive in application because they are based on some unrealistic assumptions. Therefore, managers need a practical and simplified method that could minimize these complexities and that requires minimal resources in solving problems under uncertainty condition.