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The Methods of Risk Management in Business


Environmental factors that cause risk

The success of any business is highly dependent on the strategies it employs as well as the environmental conditions in which it runs. The business environment of today is being changed frequently. Changes in technology, market, and political environments increase the uncertainties of managers as they try to make decisions. This usually makes obsolete or fruitless the business practices of the past. Irrespective of the kind or magnitude of business, the situation is similar. Today’s managers encounter external factors that are changing significantly. The manager therefore is required to be familiar with such changes and should be in a position to comprehend them and react to them in an appropriate manner. Any of the changing factors may have a significant influence on the decisions and success of businesses. As a result of the changing environment, decision makers operate under conditions of uncertainty (Daft and Marcic 2009).

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The result of such environmental variations and uncertainty is the dramatic changes in the competitive nature of today’s business. Many newer, and less conventional competition at times international, are entering the markets. This is especially the case with unregulated industries such as trucking, telecommunications, financial, airlines, and travel industries. In most occasions, the competitors introduce new products and services, newer modes of competition as well as more efficient methods of production such as lower costs of labour. As a result, the traditional modes of competition are rendered obsolete. Due to the dramatic change of business environment and uncertainty, numerous techniques have been proposed and used to deal with the uncertainty and manage risks (Slywotsky 2004).

Decision making in businesses

A decision refers to a choice made from available alternatives. Decision making is the process in which people identify problems and the possible opportunities and then try to resolve them. The decision making entails effort both before and after the actual choice is made. For instance, the decision to hire one of the applicants for the position of a junior auditor necessitates the accounting manager to determine if a new junior auditor is indeed required, ascertain the availability of possible job candidates, interview the candidates to obtain the required information, choose one candidate and follow him/her up with the socialization of the new employee into the organization to make certain that the decision is successful. There are two categories of decisions that businesses have to make: programmed and non-programmed decisions (Daft and Marcic 2009).

Programmed decisions entail conditions that have already taken place frequently enough to assist the decision makers to develop and apply appropriate solutions in the future. These decisions are made in reaction to persistent organizational challenges. Examples of a programmed decision include the decision to make additional orders of paper and other office supplies when inventories fall below a specific level or the decisions that concern the types of skills needed to fill certain job vacancies. On the other hand, non-programmed decisions are made as a reaction to exceptional events, are inadequately defined and mostly unstructured, but have significant repercussions for the firm. Majority of the non-programmed decisions entail strategic planning due to the great level of uncertainty involved and the complexity of the decisions. Examples of non-programmed decisions include the decision to construct a factory, the decision to develop a new product or service, the decision to enter into a new geographical market, the decision to move the firm’s headquarters to a different location and the decision to invest in the stock market (Daft and Marcic 2009).

A good example of a non-programmed decision is the decision by ExxonMobil to enter into a consortium to drill for oil in Siberia. This decision created one of the largest foreign investments in Russia and the costs involved are more than $12 billion (Daft and Marcic 2009). The major difference between the programmed and non-programmed decisions is the degree of risk and uncertainty involved. Programmed decisions are less risky because they are made based on events that have already occurred or are occurring presently. On the other hand, non-programmed decisions are highly risky because they are made based on future and predicted events. Nevertheless, the risk involved in non-programmed decisions cannot stop businesses from engaging in them. This is the reason why techniques have been created and proposed to assist businesses minimize the risks involved in decision-making and forecasting. This paper will examine a few of the techniques used to manage risk and uncertainty namely: investment in corporate venture capital, scenario planning, and business process modelling.

Risk and Uncertainty Management Techniques

Investment in corporate venture capital

Companies have often reacted to the necessity of continuous innovation through corporate entrepreneurship strategies for instance the creation of additional businesses within the current business and/or making changes to the current businesses through strategic renewal. However, a common problem facing firms is the decision of how to organize for far-reaching innovation because structures needed to optimize on the existing resources can hinder a company’s capability to react swiftly to innovations that are troublesome to the business. Even though corporate entrepreneurship often entails internal venturing, new projects may prove to be more effective when established outside of the firm. As a result, large firms may reap benefits from expanding their territories by forming partnerships with and making investments in small entrepreneurial ventures (Henley 2007).

Minority investments offer enhanced bargaining power of firms and enhance their coordination with the venture. Vertical strategies are important for the strategic management of the firm because it requires a stable and continuous supply of raw materials and a market for its products. This has been demonstrated by the investment of IBM in Intel whereby the minority investment increased IBM’s bargaining power to better ensure the availability of supplies and to prevent its competitors from gaining control of the small company supplier. This is important in cases where small firms have fundamental competencies that enable them to produce better inputs or to engage in more efficient production than the large firm (Deogun & Scannell 2000). To help establish a market for its goods, Intel has made numerous investments in fledging ventures that buy its goods, a similar action was taken by Compaq Computer before it was bought by HP (Hill & Rothaermel 2003).

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Minority investments create limits for the investing firm’s asset exposure and risk while at the same time they retain the strategic flexibility that is significant when the venture’s technology or market is in its initial stage or is altering drastically. Two techniques of managing risk and uncertainties that may be valuable in corporate venture capital strategies include a real options approach and diversification. In the real option approach, a firm can make investments in promising ventures using a comparatively limited resource commitment for minimal asset exposure. However, as the firm obtains additional information, the level of uncertainty is minimized and, the larger firm can enhance its investment and even acquire the venture if both parties consent to the arrangement (Beer & Nohria 2000). If the expected benefits of the minority investment are not achieved or the technology of the small firm becomes obsolete, the resultant loss, in form of money and time is negligible. This strategy enables firms to invest in projects that may lack a significant impact in the immediate future but can improve the future opportunities of the firm. A collection of ventures makes the diversification of the firm possible because not everything relies on a sole partner. Large firms can protect their bets by making investments in numerous small firms that offer similar but somewhat diverse innovative technology. This strategy is effectual in extremely uncertain business environments because it can spread the risk until the emergence of a dominant firm, technology or product is realized (Bourgeois, Duhaime & Stimpert 1999).

Scenario planning

In a business environment where changes may be unpredictable and occur suddenly, companies have to be prepared for identifying changes and adapting accordingly (Ahn 2002). Under such an environment companies need strategic planning to be flexible enough to survive. However, formal strategic planning methods have not proved satisfactory (Mintzberg 1994), nor have traditional forecasting methods provided credible answers under changing environments (Bunn & Salo 1993). Correspondingly, strategic planning moved towards a new style in the 1990s (Taylor 1997): less bureaucratic formal planning, more emphasis on implementation and innovation and fewer staff planners but more line managers and teams of employees (Clarke 1997). In particular, the dissatisfaction with formal planning and forecasting techniques has resulted in the development and widespread use of scenario planning (Phelps, Chan & Kapsalis 2001).

Scenario planning was first used in the Second World War. Kahn brought the approach to the business world through his associations with the RAND Corporation and later the Hudson Institute. Throughout the late 1960s scenario planning gained popularity in the corporate world. One of the well-documented applications of scenario planning was at Royal Dutch/Shell Group. Through a framework for constructing a flexible and dynamic world-view, Wack’s (1985) team made significant progress in adapting to a changing business environment. They used scenario planning for broadening the perspective of key decision makers and as an effective communications vehicle for disseminating new thinking throughout the organization. Wack and his colleagues considered the scenario technique central to Shell’s success in the 1970s and 1980s. Other success stories of scenario planning include: Cable & Wireless, the US Electronic Commerce Resource Centre, Electrolux, the UK’s National Health Service, KRONE, Shell and United Distillers (Ringland 1998). The applications were based on the premise of scenario planning: preparing for the future by understanding the driving forces behind major uncertainties rather than holding a simple view of the world.

Scenario planning takes place in a sequence of five stages: opportunity identification, design, testing, introduction and life cycle management (Urban & Hauser 1993). The best market to enter is defined in the opportunity identification step and ideas for market entry are then generated. The ideas are converted into a physical and psychological entity in the design step through engineering, advertising and marketing. Actual products or services and marketing/advertising strategies are evaluated until they are ready for a market test. The product or services are then tested in the testing step along with marketing/advertising strategies. If the final testing is successful, the product or service can be introduced to the market. In this step marketing warning signals have to be monitored and handled to improve the product or service launching process. Once the product or service is successfully introduced life cycle management begins. In this step return on a successful product/service launch is realized and periodic improvements and strategic movements are used for maintaining maximum profitability over the product’s life cycle.

Business process modelling

In the context of information processing, a business process can be defined as a series of transformation steps used to create information from data. For example, in the financial services sector, loan applications are turned into creditworthiness indicators. The business process uses resources such as computer systems and labour to turn inputs into outputs. The business process model is an instantiation of the workflow necessary to turn inputs into outputs (Cemauskas & Tarantino 2009). The governance of business process management is described by Spanyi (2008). Spanyi argues that it is critical to overlay a form of corporate governance that empowers the appropriate organizational framework. It also needs to create rules with a system of measurements and alerts to manage an organization’s end-to-end business processes. A first step in any business process model deployment should be to create a business process management governance framework. Then one can proceed with finding the fastest and cheapest way to get from process A to process B. It should include an enterprise-wide collaboration that goes across functions and locations while enforcing management accountability and compliance to all applicable standards and regulations. Therefore, it makes sense that proposed business process models be reviewed by such stakeholders as the chief compliance officer, chief risk officer, and internal auditors, before going into production (Cemauskas & Tarantino 2009).

Business process management is a successful methodology, in most cases, because it presumes that owners of businesses just have an overall notion of their needs but not how they can attain an optimized process. Business process management also presumes that needs will be altered as process and technologies are implemented and used. When replacing labour-intensive and/or inefficient manual risk management processes with automated controls, workflows and electronic forms, business and IT users will typically recognize additional opportunities to shorten and standardize processes. Business process modelling would typically look to apply a Pareto approach – providing 20% of functionality that offers 80% of the benefits. As business users become more familiar with the new processes, they will become much more likely to propose improvements in functionality. Unlike the rigid waterfall approach, business process management supports a feedback mechanism and variations in the initial plan of a process. This is particularly vital when dealing with the multifaceted and often intricate characteristic of financial risk management.

Business process modelling allows an organization to gain insight, reduce risk, and potentially optimize a process. It provides a framework from which key risk and performance indicators can be identified and utilized to indicate quality process performance when process metrics fall within pre-specified tolerance limits (Wiel et al. 1992). Additionally, business process modelling provides the framework into which process controls for monitoring, adjusting and controlling the output of a process can be added. Classical control theory has been used in manufacturing for years to improve the quality of and reduce the losses associated with the business processes of any firm. Process control can be executed using statistical methods (statistical process control). Statistical process control attempts to answer two main questions: Is the process under control? Does the process meet the intended specifications? (Pruett & Schneider 1993). Statistical process control (SPC) has been employed extensively to monitor and control processes through the use of control charts and focuses on eliminating the root cause of variability. Statistical process control tries to improve the process over the long run. It focuses on the oversight of processes and fault recognition with minimal process adjustments. Statistical process control is most effective when the process outputs are independent and identically distributed and the quality goal is to find departures from this assumption (Michaud 1998).

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Businesses are often exposed to environmental factors which expose them to risk and increase their uncertainties about the future. The worst thing about environmental factors of risk is that businesses lack control over them. Despite this, businesses have a wide range of options that can be used to manage their risk and uncertainties about the future. While the options are numerous, this paper focused only on investment in venture capital, scenario planning, and business process modelling. Risk and uncertainty management techniques enable firms to accurately predict future occurrences and to implement measures that will cushion the firm against incurring losses when the events do indeed occur. In short, risk and uncertainty management techniques make it possible for firms to make decisions that serve the best interest of the firms.

Reference List

Ahn, J 2002, ‘Managing risk in a new telecommunications service development process through a scenario planning approach,’ Journal of Information Technology, vol. 17, pp. 103-118.

Beer, M, & Nohria, N 2000, Breaking the Code of Change, Harvard Business School Press, Boston, MA.

Bourgeois, L, Duhaime, I, & Stimpert, J 1999, Strategic Management: A Managerial Perspective, Dryden Press, Fort Worth, TX.

Bunn, D, & Salo, A 1993, ‘Forecasting with scenarios,’ European Journal of Operational Research, vol. 68, pp. 291-303.

Cemauskas, D & Tarantino, A 2009, ‘Operational risk management with process control and business process modelling,’ The Journal of Operational Risk, vol. 4, iss. 2, pp. 3-17.

Clarke, C 1997, ‘The strategic planning society: the first 30 years,’ Long Range Planning, vol. 30, no. 3, pp. 327-33.

Daft, R, & Marcic, D 2009, Understanding Management, Cengage Learning, Mason, OH.

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Deogun, N & Scannell, K 2000, ‘Microsoft puts muscle in deals,’ Wall Street Journal.

Henley, G 2007, ‘Extending innovation boundaries: corporate venture capital gives large firms a strategic option,’ The Journal of Business Strategy, vol. 28, iss. 5, pp. 36.

Hill, C, & Rothaermel, F 2003, ‘The performance of incumbent firms in the face of radical technological innovation,’ Academy of Management Review, vol. 28, no. 2, pp. 257-74.

Michaud, R 1998, Efficient Asset Management, Harvard Business School Press, Cambridge, MA.

Mintzberg, H 1994, The Rise and Fall of Strategic Planning, Prentice-Hall, Englewood Cliffs, NJ.

Phelps, R, Chan, C, & Kapsalis, S 2001, ‘Does scenario planning affect performance? Two exploratory studies,’ Journal of Business Research, vol. 51, pp. 223-32.

Pruett, J, & Schneider, H 1993, Essentials of SPC in the Process Industries, Instrument Society of America, Research Triangle Park, NC.

Ringland, G 1998, Scenario planning: Managing for the future, John Wiley & Sons, Chichester.

Slywotsky, A 2004, ‘Exploring the strategic risk frontier,’ Strategy & Leadership, vol. 32, no. 6, pp. 11-19.

Spanyi, A 2008, BPM Governance, Web.

Taylor, B 1997, ‘The return of strategic planning – once more with feeling,’ Long Range Planning, vol. 30, no. 3, pp. 334-44.

Urban, G, & Hauser, J 1993, Design and Marketing of New Products, Prentice-Hall, Englewood Cliffs, NJ.

Wack, P 1985, ‘Scenarios: uncharted waters ahead,’ Harvard Business Review, vol. 63, no. 5, pp. 72-9.

Wiel, S, Vander, T, William, T, Faltin, F, & Doganaksoy, N 1992, ‘Algorithmic statistical process control: Concepts and an application,’ Technometrics vol. 34, no. 3, pp. 286-297.

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