Decision Making – How to Dismantle an Atomic Bomb?
by Rabia Mahboob | Published on 1/18/2005
“Our lives are ruled by impermanence. The challenge is how to create something of enduring value within the context of our impermanence” - says Daisaku Ikeda (A Buddhist Monk)
The same thoughts serve as the main impulsion behind every development of thought for value addition and the same is the case with decision making tools presented by management accounting; and at the same time, the same force pushes forward many more tools going to emerge day by day in this subject. The scarcity of resources all over the world and, in turn, also in business environment makes it necessary to develop tools for effective and efficient utilization of economic resources.
The everyday business environment in the state of continuing flux has resulted in the new problems of doing things rights instead of doing right things and the role of management accounting decision-making tools in this respect can hardly be questioned. This changing business environment ranges from uncertainty to probability, and also from single critical factor to multiple critical factors. The management accounting techniques have covered a long evolutionary process to emulate the arithmetical, mathematical and even statistical techniques to cater for this wide scenario.
Anybody can cut prices, but it takes brains to produce better articles. - P D Armour
Neoclassical theory, having rationality as a premise, is at the heart of management accounting. The decision-making, being a continuous process, may be about short horizon (operational decisions) and it may well relate to long horizon (strategic decisions). Different management tools are of interest both as compliments or substitutes to coup different scenarios.
Capital budgeting decisions are of paramount importance in financial decision-making. In the first place, such decisions affect the profitability of a firm. They have a bearing on the competitive position of the enterprise. This is mainly because of the fact that they relate to fixed assets that involve a current outlay or a series of outlays of cash resources in return for an anticipated flow of future benefits. It, therefore, includes addition, disposition, modification and replacement of fixed assets. The fixed assets represent, in a sense, the true earning assets of the firm (Khan & Jain 1998).
Secondly, a capital budgeting decision has its effects over a long time span and inevitably affects the company’s future cost structure.
Thirdly, these decisions, once made, are not be easily reversible without much financial loss to the firm. It is because there may be no market for second hand plant and equipment and their conversion to other uses may not be financially feasible.
Finally, capital investment involves costs and the majority of the firms have scarce capital resources. This underlines the need for thoughtful, wise and correct investment decisions would not only result in losses but also prevents the firm from earning profits from other investments which could not be undertaken for want of funds.
The rationale underlying the capital budgeting decisions is efficiency. A firm must replace obsolete plant and machinery and acquire fixed assets for current and new products and making strategic investment decisions. Acceptance of a strategic investment involve significant change in company’s expected profits and in the risks to which these profits are subjected (Biermen, H. and S. Smidt 1974)
Capital budgeting refers to total process of generating, evaluating, selecting and following up on capital expenditure alternatives. The firm allocates or budgets financial resources to new investment proposals. In practice firms normally are confronted with three types of capital budgeting decisions namely:
- Accept-Reject Decisions.
- Mutually Exclusive Project Decisions.
- Capital Rationing Decisions.
However, the capital rationing problems are generally resolved by using linear programming techniques in operational management. The graphical approach is much popular amongst the managers as a solution. However, pursuant to its limitation of tackling more than two variables at a time, the simplex method approach is normally used in real life problems where the decisions are to be taken in the given scenario of more than two limiting factors.
Linear Programming techniques is also of much help in such decisions as it helps to:
- Calculate the relevant costs
- Take decisions about selling sales mix
- Determine maximum payment for additional scarce resource
- Control costs; and
- Make decisions regarding capital budgeting
Normally all the organizations use this technique to evaluate their investment decisions in different circumstances to increase their profitability (Amjad Bhatti, 2002)
Capital expenditure decisions are of considerable significance to the firm as the future success and growth of the firm depend heavily on them. But unfortunately they are not easy to take. Responsible factors are as follows:
1- Risk of uncertainty about future inflows including:
a) Customer preferences
b) The actions of competitors
c) Technological developments
d) Changes in political & economic environment
2- It is often impossible to calculate in strictly quantitative terms all benefits or the cost relating to a particular investment decision.
3- Problems of comparison between cost incurred and benefits received in different time periods due to time value of money.
This last problem can be solved by NPV analysis, which is considered below.
CVP analysis can be implemented where the marginal and fixed cost data is readily available from the accounts. The use of absorption costing technique instead of marginal costing on account of legal requirements for reporting is a great hurdle for the easy application of CVP analysis. However, the management uses, the statistical and mathematical rules like regression analysis and high-low method to bifurcate the cost into fixed and variable elements. The use of scatter diagram and the application of lest square method in this respect serves the purpose.
However, the problem arises due to the alternative uses of pure statistical technique of regression analysis and its substitute “scatter diagram method” to measure the fixed cost intercept through the graph. However, where the regression analysis tools are used as an initial decision grounds, the use of either the line of best fit or the computerized diagram of scatter graph are equally acceptable.
The management as tool in decision-making process has appreciated equate solutions presented by Colin Drury 2003 as follows:
BEP (in absorption costing)= (tmfc- [(fmoh/ud)*up])/(usp-uvc- [fmoh/ud])
BEP =Breakeven Point In Sale Revenue
TMFC =Total Manufacturing Cost
FMOH =Total Manufacturing Cost
UP =Units Produced
UD =Unit Denominator Used To Calculate The Production Overhead Rate
TSP =Total Sale Value
TVC =Total Variable Cost
This is a subjective method of examining the relationship between changes in activity and changes in total sales revenue, expenses and net profit. As a model of these relationships CVP analysis simplifies the real world conditions that a firm can face. Like most models, which are abstractions of reality, CVP analysis is subject to a number of underlying assumptions and limitations, nevertheless, it is a powerful tool for decision making in certain situations.
The objective of CVP analysis is to establish what will happen to the financial results if a specified level of activity or volume fluctuates. This information is vital to management since one of the most important variables influencing total sales revenue, total cost and profits is output or volume (T. Lucey 1996). For this reason, the output is given special attention, since knowledge of this relationship enables management to identify critical output level such as level at which neither a profit nor a loss occurs (i.e. breakeven point).
CVP analysis is based on the relationship between volume and sales revenue, cost and profit in the short run in which output of a firm is restricted to that available from current operating capacity.
CVP analysis becomes more complex and questionable if we extend our application to a long-term time horizon. In long term other factors besides volume are likely to be important e.g. utilization of additional capacity, reduction in selling price, alternative advertising strategies and expansion of product range and mix. The additional variable cannot be easily incorporated into CVP analysis. Hence it is unlikely to be appropriate for long term decisions because other factors that are nor captured by it are unlikely to remain unchanged. CVP analysis is only appropriate if all variables, other than volume, remain unchanged.
The output from CVP model is as good as input. The analysis includes assumptions about sales mix, production efficiency, price levels, total fixed costs, variable costs and selling price per unit. Obviously, estimates regarding these variables are subject to varying degree of uncertainty, which is further coped with using certain techniques like probabilities, expected values, decision trees, portfolio analysis and maximax and maximin criteria.
Sensitivity analysis is one of the approaches for coping with changes in the values of variables (sensitivity is considered below).
The widespread use of spreadsheet packages has enabled management accountants to develop CVP computerized models. Managers can now consider alterative plans by keying the information into a computer that can quickly show changes both numerically and graphically. Thus managers can study various combinations of changes in different variables quickly without waiting for formal reports.
Net Present Value
CVP is best suitable for short-term decisions. There are many limitations to apply it in the long run (Colin Drury 2003). So how would these long run decisions be taken. Following techniques are answer to the above questions.
- ARR (accounting rate of return)
- IRR (internal rate of return)
- ROCE (return on capital employed)
- Pay Back Method
- Discounted Pay Back Method
- NPV (net present value)
Most widely used of all is NPV, which is considered here.
As a decision criterion, this method is used to make a choice between mutually exclusive projects. On the basis of NPV method, various proposals would be ranked in order of the net present values. The project with highest NPV would be assigned the first rank, followed by others in descending orders.
The present value method including NPV variations possesses several methods. The first and probably most significant advantage is that it explicitly recognizes the time value of money (Amjad Bhatti 2002)
Secondly it considers the total benefit arising out of the proposal over its life time, not just the time period in which initial outlay is recovered, as compared to pay back period.
Thirdly, a changing discount rate can be built into the NPV calculations by altering the denominator. This feature becomes important as this rate normally changes because the longer the time span, the lower the value of money thus higher the discount rate.
Fourthly, NPV is particularly useful for the selection of mutually exclusive projects.
Finally this method of asset selection is instrumental in achieving the objective of financial management, which is the maximization of shareholders’ wealth. The rationale behind such contention is the effect on the market prices of shares as a result of acceptance of the proposal having NPV greater than zero.
A serious problem associated with NPV method involves the calculation of required rate of return to discount the cash flows. Rate of return of an organization differs from that of others therefore a single project has different impacts on different organizations.
Sensitivity analysis is a tool to measure the risk surrounding a capital expenditure project and enables an assessment to be made of how responsive the projects NPV is to alterations in the variables that are used to calculate that NPV. An alternative indication is thus provided of sensitive/critical variables and the extent to which those variables may change before the investments result in negative NPV.
The NPV depends on a number of uncertain independent variables e.g.
- Selling price
- Sales volume
- Cost of capital
- Initial cost
- Operating costs
Sensitivity analysis therefore provides an indication of why a project might fail. Management should pay particular attention to any critical variables to assess whether there is strong possibility of events occurring which will lead to negative NPV and also to control those to which NPV is particularly sensitive, once the decision has been taken to accept the project.
Despite all this sensitivity analysis has following limitations:
- Absence of cumulative changes in variables.
- Looking at factors in isolation is unrealistic since they are often interdependent.
- Sensitivity analysis does not examine the probability that any particular variation in costs or revenues might occur.
- Uncontrollable critical success factors.
- In itself it does not provide a decision rule. Managers must lay down parameters defining acceptability.
As an aid to sensitivity analysis described above, the simulation models are widely used in conjunction with sensitivity analysis. Simulation is just to pretend the real thing while working on the imitation. And this happens to save time and money to be spent on real models and thereafter facing the failure. Simulation model incorporates the plotting of graphs to take inferences. These graphs are called the sample path. Such artificial construction and analysis of analysis of sample paths is called simulation. Two types of simulations are normally used as a tool to decision-making and all this depends on the events or circumstances. Where the number of events are finite, the Discrete event simulation model is used and where the system is in the state of change all the time and not when a discrete event happens, the Continuous simulation model is more helpful, although discrete event simulation may help as an approximation.
The simulation is often performed manually however the system can be written in a computer programme or some kind of input into simulator software. The vast amount of simulation software serves the purpose. The choice of software depends on the properties of package, such as support, reactivity to bug notification, interface, etc and properties of the user, such as their needs, their level of expertise, etc. Such software available in the market include:
ACSL, APROS, ARTIFEX, Arena, AutoMod, C++SIM, CSIM, Call$im, FluidFlow, GPSS, Gepasi, JavSim, MJX, MedModel, Mesquite, Multiverse, NETWORK, OPNET Modeler, POSES++, Simulat8, Powersim, QUEST, REAL, SHIFT, SIMPLE++, SIMSCRIPT, SLAM, SMPL, SimBank, SimPlusPlus, TIERRA, Witness, SIMNON, VISSIM, and javasim.
Learning Curve Effect
Learning curve, first observed by Wright in the 1930s in the American aircraft industry and its usage and fruition later confirmed by Crawford in the 1940s, serves as solution to most difficulties arising in predicting future costs when technological changes occur. It is concerned with cumulative production over time and recognizes that it takes less time to assemble a product the more times that product is made by the same labour. The mathematical formula of y = aXb (refer appendix) serves as an important computing technique. Its applicability to labour cost and inapplicability to material and fixed cost is quite realistic. The learning curve is not an infinite process. A time comes when learning effect brings to a standstill and labour cannot advance to additional proficiency.
It is generally applicable to those situations where labour input for an activity is large and the activity is complex. Learning curve are not theoretical abstractions but are based on observations of past events.
Aerospace, Electronics, Shipbuilding, Construction and Defence are the examples of sectors where learning effect has ever been pronounced. In aircraft industry studies indicated that a learning curve effect of 80% was appropriate but in other industries this figure vary between 70% and 90%.
The practical implications of learning curve effect are:
- Pricing Decisions
- Proper Scheduling of Work
- Budget Setting
The landing and losing of the contracts greatly depends on the proper pricing of the contracts. Early experience with a new product could confer an unbeatable lead over competitors and that leading competitor should be able to reduce its selling price for the product which would further increase their value and market share and eventually force some lagging competitors out of the industry (Simmonds 1981). Moreover, this enables them to produce more accurate delivery schedules. All this leads to improved customer relationship and consequently increased future sales.
Meaningless variances are likely to occur without experience curve effect. In certain situations such standards and budgets lead to improper set of targets that can be easily thrashed out, where as with the effect of learning curve, efficiency is more likely to occur.
The learning curve can be used to prepare competitive tenders by utilizing earlier learning for new contracts for the same or similar products. Customers, being aware of the learning effect, expect tenders to visualize this. From 1930s this phenomenon is still relevant for modern business environment.
Network analysis/critical path analysis (CPA), is a useful technique to help with planning and controlling large projects, such as construction projects and the computerization of systems. The main motive behind is that it requires breaking down the project into tasks, arranging them into logical sequence and estimating the duration of each. CPA aims to ensure the progress of the project in the minimum possible time.
It pinpoints the tasks, which are on the critical path, i.e. those parts which, if delayed beyond the allotted time, would delay completion of the project as a whole. CPA can also assist in allocating the resources such as labor and equipment. The duration of the whole project will be fixed by the time taken to complete the largest path through the network. This path is called critical path and the activities on it are called critical activities. These activities on critical path must be started and completed on time; otherwise the total project time will be extended.
Critical path is indicated by drawing thick arrows (or double lines). Listing paths through the networks, in this way, should be easy enough for small networks, but it becomes a long and tedious task for bigger and more complex networks. This is why software packages are used in real life.
Project management software packages offer a much larger variety of techniques than can easily be done by hand. Microsoft projects allow each activity to be assigned to any one of variety of types; “Start as late as possible”; “Finish no earlier than a particular date”; “Finish no later than a particular date”, and so on.
In real life, too, activity times can be shortened by working weekends and overtime, or they may be constrained by non-availability of essential personnel. In other words with any more than a few activities the possibilities are mind-boggling, which is why software is used.
The author Rabia Mahboob is an ACCA affiliate and has worked as a lecturer in Pioneer College, Multan.
Article courtesy of Rabia Mahboob
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