The importance of analysis and analytical skills to the manager making decisions in business - Essay Example

? Managing Decisions The importance of analysis and analytical skills to the manager making decisions in business Table of Contents 0.Introduction 3 1.0.Project planning 3 2.0.Financial modelling 5 2.1.Cash flow analysis 5 2.2.Sensitivity analysis 6 3.0.Diagrammatic Representation 8 3.1.Influence diagram 8 3.2.Decision graphs 9 4.0.Conclusion 10 References 12 1.0. Introduction The importance of analysis and analytical skills to the manager making decisions in a business cannot be refuted. This is more so in today’s rapidly changing business environment and the availability of “too much” information due to the convergence of information technology, media and telecommunications. This report looks at some of the techniques available for managers to aid them in making the ever increasing complex decisions that they encounter on a day to day basis. The report begins with looking at project planning and analysing its importance to the implementation of any project. The report then moves on to address financial modelling and diagrammatic representation approaches. The report concludes with an appreciation of the fact that none of these techniques can work optimally on their own since they give different sets of information to the manager. 1.0. Project planning As is in life or any other important event in our lives, before embarking on a project planning is the first thing that managers have to do. As the cliche goes, failure to plan is planning to fail. Planning involves a series of decisions that may range from operational to strategic. Project planning is a subset of operational planning whose core aim is to come up with a document referred to as a project plan (EAHC, 2011). Within the project plan, managers and other stakeholders who were involved in the planning process define the objectives of the project, its scope, how the project will be conducted, the time allocated, cost estimates and roles and responsibilities of all the personnel who will be involved. Project planning involves four main steps that can be subdivided into additional steps depending on the task at hand. These steps are: establishing project goals, setting project deliverables, agreeing on project schedule and ensuring that there are support plans in place. The advantages of project planning are derived from each of these steps. In step one, stakeholders are identified, there needs outlined and project goals are set prioritizing the stakeholder needs. The advantage of this is that the outcomes are SMART goals which imply that it will be easy to detect whether the project is advancing, stagnant or regressing. Step two specifies the items to be delivered, how they are to be delivered and when they should be delivered. This advantage of this stage is that it sets up the key parameters that can be optimised using software tools to design the most effective and efficient project schedule. With a plethora of project management software currently in the market managers can easily identify critical path. Step two and three enable managers to balance the “tetrad trade-off” of: product scope, quality grade, time-to-produce and cost-to-complete. The final step of setting up support plans involves establishing risk management plans, communications plans and HR plans. The advantages here are numerous such as: having contingencies in place in case of anything, ensuring information goes to the right people during project implementation and using HR allocation to aid in establishing the project budget. There are also disadvantages to project planning such as: (1) at times it may take too long to come up with a plan – as rival companies catch up; (2) at times the planning committee may get lose sight of the purpose of the planning as they get bogged down in unnecessary detailing; and (3) it may lower implementation flexibility – which is dangerous in a rapidly changing environment – since personnel will may feel restricted by the options availed in the plan. 2.0. Financial modelling Investopedia (2011) defines financial modelling as the process by which an analyst performs calculations to develop a financial representation of different aspects of a given company so as to guide him/ her in decision making. There are numerous techniques that analysts use in financial modelling. However, in this paper we shall focus on only the following two types: cash flow analysis and sensitivity analysis. 2.1. Cash flow analysis Cash flow analysis is a financial modelling technique for analysing a company’s operating, investment and financing activities (Hillstrom, 2012). The objective of cash flow analysis is to enable managers / decision makers to timely identify either the company’s cash flow problems or cash flow opportunities. The primary documents used for this analysis are the company’s cash flow statements. Firms reporting in conformity with International Financial Reporting Standards (IFRS) are required to report a cash flow statement that is a summary of their operating, investment and financing cash flows (Palepu et al., 2007). Operating cash flows includes cash from daily business activities for example cash sales and payment to vendors. Investment cash flows includes activities such as sale or purchase of assets for the company. Financing cash flows involves activities such as borrowing of funds or issuing of stock. Cash flow analysis expedites decision making by providing a basis for making judgments with regards to a company’s financial condition, profitability and financial management in general (Hillstrom, 2012). This is accomplished by managers mainly through assessment of three things: (1) whether the company’s operations are generating cash flows before investing in operating working capital; (2) what are the company’s free cash flow after making long-term investments; and (3) how the company is financing itself, and whether its financing patterns are too risky (Palepu et al., 2007). One of the advantages of using the cash flow analysis is that managers can investigate the company’s performance in the past or even project into the future by making modifications to the cash flow statements to accommodate for any anticipated future changes in key variables such interest rates, prices and so on. Another advantage is that conducting regular cash flow analysis enables managers to develop proactive cash flow strategies and therefore avoid embarrassing surprises such as issuing bouncing cheques to suppliers. A third advantage is that a company that understands its cash position through the analysis of its cash flow statements is better able to judge its present and future cash needs thus it can minimize on interest payments on loans. Cash flow analysis also has its disadvantages. Firstly, they rely on the accuracy of the figures collected. Any incorrect entry would lead to misleading output / analysis. Secondly, the cash flow analysis does not account to variable change thus it ignores some costs such as damage costs. In practice it is best to use cash flow analysis complement to ratio analysis so as to get a more wholesome picture on the operating, investment and financing activities of a company. 2.2. Sensitivity analysis According to Saltelli (n.d.) sensitivity analysis is the study of how the uncertainty in the output of a model (numerical or otherwise) can be apportioned to different sources of uncertainty in the model input. In financial modelling, it is common practise to make certain assumptions when performing model calculations. Most of these assumptions are often stated so as to fully represent the context of the derived financial model. For example in an activity such as budgeting, management may make assumptions such as increase in material costs by 5% or increase in sales by 10% etc. based on information from market research, industry experience, macroeconomic indicators and so on. This means that if market conditions change the basis of these assumptions then the budgeted projections will also have to change. Sensitivity analysis can therefore be defined as the process of varying the individual assumptions within a financial model so as to measure and understand there different impacts on the same models. Sensitivity analysis provides answers to the question of how the target / dependent variables are affected by one or many of the independent variables. A good sensitivity analysis should therefore conduct analyses over the full range of plausible values of key parameters and their interactions, to assess how impacts change in response to changes in key parameters (Saltelli, n.d.). In financial modelling this is easily done through the drawing up of a sensitivity table to test different business scenarios (Fish, 2011). The, probably, more obvious advantage of conducting sensitivity analysis is that it highlights the estimates that the decision advice is most sensitive to. This enables management to take extra effort in ensuring that those particular estimates are as accurate as can be. Another advantage is that sensitivity analyses give management more information to evaluate whether the results of a particular financial model are acceptable or not. On the converse, Lumby and Jones (2007) stated that sensitivity analyses have two major disadvantages. Firstly, they are designed to only look at effects of changing single variables at a time. In reality, most phenomena, financial or otherwise, are affected by two or more variables simultaneously. Sensitivity analyses lack the ability to reflect this reality. Secondly, sensitivity analyses do not inform management on how to evaluate or make use of the sensitivity data. Management therefore face the risk of easily misinterpreting the sensitivity data. 3.0. Diagrammatic Representation In business, not all decision makers are trained in or are interested in learning the numerous formal descriptions and mathematical expressions used to communicate decision problems. Diagrams provide visual representation that is easily understood by both computers and people with different degrees of technical proficiency. In this section we shall look at two methods that rely on diagrammatic representation, which are used by managers in tackling decision analysis problems. These two techniques shall be the influence diagram and the decision trees / graph. 3.1. Influence diagram According to the Decision Systems Laboratory, University of Pittsburgh (2003) influence diagrams are non-cyclic directed graphs that are used to denote decision problems. Information diagrams incorporate decisions as well as chance events into a single diagram that is easy to understand and can represent the likely utility or value of pursuing a given course of action. Their primary goal is to provide as simple a model as possible of a decision process so that managers can easily comprehend the process involved and alter the model to enable them select the decision alternative that will result in the greatest utility. These diagrams are especially good in displaying the structure of a decision problem in a much more compact manner than the popularly used decision tree (Lumina, 2011). The influence diagram derives its power of representation from its capacity to “serve at the three levels of specification of relation, function, and number, and in both deterministic and probabilistic cases (Howard & Matheson, 2005, p.127)”. This means that with an influence diagram the analysts can display how a single variable depending on several others, he can specify their relationships, and also the numerical value that results from each given decision. Another advantage of the influence diagrams is that it permits different individuals to make the successive degrees of specification. For example, an analyst may know that sales depend in some way on price, but he may leave to other persons to describe that relationship. One big disadvantage of the influence diagram is that it is not always so easy to construct and are highly resource intensive. Another disadvantage is that the precise relationship between decisions and available information is hidden within the nodes when one uses influence diagrams. Thirdly, computations for solutions of influence diagrams are generally difficult such that even a relatively simple influence diagram requires the use of a computer and specialized software (Shahar, n.d.). 3.2. Decision graphs Decision graphs were coined not so long ago, in early1990s, by Jonathan Oliver and Christopher Wallace as a generalisation of the popular decision analysis visual tool, the decision tree (Dowe et al., 1993). This means that just like the decision tree it is made up of three types of nodes: decision, chance and end nodes. However, unlike decision trees the graph can contain join nodes in addition to decision nodes and leaves. For this reason, decision graphs are more efficient in learning rules with a disjunctive nature because they have join nodes (Dowe et al., 1993). Oliveira and Sangiovanni-Vincentelli (1994) defined it as a rooted, directed, acyclic graph where each non-terminal node is labelled with the index of the attribute being tested at that node and each terminal node is labelled with one label. One advantage of decision graphs is the general fact that graphs are more expressive and quicker to learn especially where data involved is less. Another advantage is a result of the join nodes that enable the decision graphs to join paths with related outcomes. This attribute greatly increases the inference ability of a decision graph over that of a decision tree. In fact, decision trees suffer for two main reasons: the replication of sub-trees required to represent some concepts and the rapid fragmentation of the training set data when high “arity” attributes are tested at a node. According to Oliveira and Sangiovanni-Vincentelli (1994) decision graphs were specifically proposed as a way to alleviate these two problems. One major challenge to the use of decision graphs is that the algorithms proposed to date for the construction of these graphs suffer from serious limitations (Oliveira & Sangiovanni-Vincentelli, 1994). 4.0. Conclusion There is a long adage that states that projects do not fail at their end, they fail at their beginning. As has been discussed earlier one of the major disadvantages raised about project planning is that they have a tendency of taking too long and sometimes getting to detailed. However, as the adage above infers this investment in planning in the long run guarantees greater returns. Almost in the same line, the accuracy of financial modelling has often been questioned. The thing to note is that financial models are tools and just like any other tool are highly susceptible to their use. Financial modelling allows managers to evaluate business options and risks against a range of assumptions. In the spirit of basic economics, resources are scarce and as such managers must identify optimal solutions in evaluating their financial returns thus the need for financial modelling. Finally, as was stated under the section on diagrammatic representation not all stakeholders or shareholders have the technical proficiency to comprehend financial models. Managers need other tools to enable them communicate to such constituencies. This naturally leads to the need for use of visual tools to convey complex decisions to all stakeholders. From this we can therefore realise that all these approaches have to work together for effective decision making to take place. 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In: The Twelfth International Conference on Machine Learning. 1994, Morgan Kaufmann, pp. 421-429. Palepu, K.G., Healy, P.M., Bernard, V.L. & Peek, E. (2007). Business Analysis and Valuation: IFRS Edition Text and Cases. London: Thomson Learning. Saltelli, A. (n.d.). Global Sensitivity Analysis: An Introduction. Shahar, Y. (n.d.). Diagnostic Modeling:_ Bayes’ Theorem, Influence Diagrams and Belief Networks. [Online]. Available from: http://www.ise.bgu.ac.il%2Fcourses%2Fdm%2FLectures%2Flecture5.ppt&ei=gyL_ToXJOYmChQe1r4GnDA&usg=AFQjCNHifScSb_61GQG5CsOEeWUB27QE4A&cad=rja.  Read More