Stochastic Models for Cash-Flow Management in SME

Stochastic Models for Cash-Flow Management in SME

Rattachut Tangsucheeva (Pennsylvania State University, USA), HyunJong Shin (Pennsylvania State University, USA) and Vittaldas Prabhu (Pennsylvania State University, USA)
Copyright: © 2014 |Pages: 11
DOI: 10.4018/978-1-4666-5202-6.ch206
OnDemand PDF Download:
$30.00
List Price: $37.50

Chapter Preview

Top

Introduction

Financial management is one of the top concerns of many small and medium enterprises (SMEs). Especially, when the enterprise progresses through various life-cycle stages, they face more problematic situations in managing their financial assets (Mcmahon, 2001). Furthermore, SMEs usually incur high interest rate for any types of financial services due to their higher credit risk, and resource constraints they face when they use financial services (Baas & Schrooten, 2006). The need for more careful and effective financial management is of critical importance to researchers and managers of SME’s. Accurate cash-flow forecasting models that are easy to use are becoming important for businesses to manage their finances efficiently. Such models will be especially critical for SMEs when liquidity and credit decrease in the economy. In this chapter, first, we model a business process of a furniture manufacturer SME in a flow chart to visualize the product flow, information flow, and cash flow in order to identify the relationship among them and potential re-engineering opportunities. Additionally, we apply a Markov Chain based cash-flow forecasting technique from management literature proposed by Corcoran who extended the work of Cyert et al. (1962) to predict cash collection of a real furniture manufacturing SME in Pennsylvania (Corcoran, 1978; Cyert, Davidson, & Thompson, 1962). We aim to compare the accuracy of the stochastic model with those of other common practices in industries. This forecasting technique uses exponential smoothing to accounts receivable aging to forecast annual cash-flow within about 2% error, a considerable improvement compared to the industries’ prevailing practice which is about 15%-18% error. The cash flow forecasting model is implemented in Excel to ensure ease of use and data integration. Lastly, we present “what-if” analysis discussing impacts of profit margin, high versus low, on the cash flow.

Key Terms in this Chapter

Business Process: A business process is a set of related tasks in order to produce a good or provide a service for a customer. Typically a business process is visualized with flowchart in order to identify re-engineering opportunities or areas to improvement.

Accounts Receivable Aging: A technique to evaluate the financial health of a company by identifying whether irregularities exist. It shows a company’s accounts receivable according to the length of time the amounts have been outstanding. This report allows a financial manager to identify which receivables require more attention since they have been overdue longer.

Cash Flow Forecast: An estimation of future cash inflows and outflows over a specific timeframe. The estimation includes the dates and the amounts of the cash flows. The management uses the cash flow forecast as an early warning system for a coming crisis. As a result, it can prepare for future cash requirements.

Supply Chain Finance: Supply Chain Finance refers to a set of solutions to finance short term assets or reduce financial burdens of participants in the supply chain by integrating and sharing information.

Exponential Smoothing: A statistical technique to forecast a time series data. This technique commonly applies a heavier weight (smoothing factor) to the most recent data whereas the simple moving average applies a weight equally.

Stochastic Model: A model, which has one or more random variables as input variables, is used for estimating probabilities of potential outcomes.

Markov Chain: Markov chain is a stochastic process or a random process which the probabilities of the next states depend only on the current state or the immediately preceding state. It is used for analyzing the likelihood of the next event which depends on information of the current event.

Complete Chapter List

Search this Book:
Reset