Currently, risk management in management/managerial accounting is treated as deterministic. Although it is well-known that risk estimates are necessarily uncertain or stochastic, until recently the methodology required to handle stochastic risk-based elements appear to be impractical and too mathematical. The ultimate purpose of this paper is to “make the risk concept procedural and analytical” and to argue that accountants should now include stochastic risk management as a standard tool. Drawing on mathematical modelling and statistics, this paper methodically develops risk analysis approach for managerial accounting and shows how it can be used to determine the impact of different types of risk assessment input parameters on the variability of important outcome measures. The purpose is to: (i) point out the theoretical necessity of a stochastic risk framework; (ii) present a stochastic framework for modelling and computing stochastic input variables; and (iii) illustrate how currently available technology has made this stochastic framework easier. The Global Financial Crisis of the last couple of years has re-accentuated the relevance of a concept of risk, and the need for coherence and interrelations between risk theory and areas within management control. Our results show that – evaluated from four simulation scenarios – a company may benefit by developing different optimal risk strategies and then focusing on a few risk performance measures that can be used for decision making.
Management control; risk; CIMA; experimental path; computational thinking; Monte Carlo simulation; structural equation modelling