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Chapter

Chapter 9

Decision-Making − Relevant Information

9.1 Introduction 119 9.2 Relevant Costs 120 9.3 Limiting Factors 124 9.4 Multiple Limiting Factors 126 9.5 Uncertainty 130 9.6 Attitude to Risk 133 9.7 Qualitative Factors 134 9.8 Summary 134

Learning Objectives

After completing the study of this unit you should be able to:

• describe the nature of the decision-making process

• determine what information is relevant for decision-making

• analyse the relevant costs and revenues for each decision

• account for the problems of limiting factors

• account for uncertainty and risk in the decision-making process.

9.1 Introduction

A variety of decisions made by management involve the analysis of alternative courses of action (Chapter 8). It is important that, in addition to understanding cost behaviour patterns, the information available to managers focuses on the costs and revenues which are relevant to the particular decision being made. Different costs exist that can be used for many different purposes and no single cost can be relevant in all decision-making.

9.1.1 Decision-making

Decision-making for managers is choosing between available alternatives to help achieve some pre-determined objectives that have been set for the department or organisation. See worked example 9.1.

Worked example 9.1

As operations manager for your local clothing manufacturing company, consider a few decisions that you might have to make to achieve your objective of profit maximisation.

Solution

Obviously there is a wide variety of answers that could be offered here, but it is likely that the following are some of the potential decisions that may need to be considered.

• Should we use labour or invest in machinery?

• Should we subcontract part of our operation?

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• Should we purchase our raw materials locally or buy cheaply from overseas?

• Should we introduce labour incentive schemes?

• Should we accept additional special orders?

The process of decision-making can be broken down into a number of different stages, although you should be aware that in practice there is considerable overlap between them.

1. Define your objectives

Managers need to be very clear about the objectives set. Therefore, it is important that the objectives are specific and can be quantified, for example reduce shop floor wastage, maximise product contribution, minimise labour cost, and so on.

2. Consider alternative actions to meet objectives

There are likely to be a number of ways of achieving your objectives and each of the possible options should be considered. For example, shop floor wastage may be reduced either by additional training for the operators or by investing in new technology which eliminates human error.

3. Data collection and option evaluation

At this stage, all the relevant information (particularly the costs and revenues of each alternative) should be gathered and quantified, for example, training costs, new wastage rates, capital expenditure, learning curves.

4. Selection of best option

Having compared the alternatives, you should select the particular course of action which best satisfies the specified objectives.

Of course, decision-making is not quite as clear cut as suggested here. There is always competition throughout an organisation for the limited resources available, there are in- house politics and personal pressures to be overcome, and there are risks and uncertainties associated with all decision-making.

9.2 Relevant Costs

Although profits are often seen as the key indicator of a company’s performance, it is cash flow that gives a more accurate picture of the impact of any potential decision − therefore only cash flow items are included as relevant costs.

Decisions are made for the future benefit of the company and therefore only future costs are relevant to decision-making. Past expenditures (sunk costs) or costs which have already been committed (committed costs) are therefore not relevant to decision-making.

Costs which will be incurred regardless of whether or not a particular option is chosen should be excluded from the decision-making process because management will be interested only in the additional costs (incremental costs) involved with selecting an option.

Finally, costs that are relevant take into account their impact on the whole enterprise. There- fore, benefits foregone (opportunity costs) by choosing one alternative instead of another will have to be considered in the decision-making process. Let’s consider each of these types in more detail.

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9.2.1 Sunk Costs

A sunk cost is the historical cost associated with purchasing an asset or acquiring a resource. Regardless of the circumstances that exist, these past expenditures cannot be recovered. Eventually these assets or resources will no longer be adequate to perform the tasks for which they were originally purchased, due perhaps to obsolescence or below-par performance. Decisions will then need to be made as to whether or not to keep or replace the asset. Sunk costs should not be relevant costs in such decisions. See worked example 9.2.

Worked example 9.2

On the first day of the year, Alan Watkins purchases a Tomy tractor for £200,000. The tractor is expected to have a useful life of 10 years and no salvage value. One week later, Watkins sees an advert in the paper for a Trusty tractor for £180,000. This tractor also has an estimated life of 10 years and no salvage value, but is guaranteed to perform as well as the Tomy tractor and has a much better fuel usage. The Trusty tractor will save £60,000 per year (Tomy £100,000, Trusty £40,000) in fuel costs over the Tomy tractor. Watkins discovers that he could sell his week-old Tomy tractor for £150,000. He has two options:

a. use the Tomy tractor

b. sell the Tomy tractor and buy the Trusty tractor.

Required

Advise Alan Watkins on the preferred option.

Solution

The relevant costs and revenues that Watkins should consider when making his asset replacement decision are as follows.

Option 1: Use the Tomy tractor

Fuel costs over the life of the tractor (10 years × £100,000) 1,000,000

Option 2: Use the Trusty tractor

Cost of Trusty tractor 180,000

Resale value of Tomy tractor 150,000

Net outlay for new tractor 30,000

Fuel costs over the life of the tractor (10 years × £40,000) 400,000

Total cost of Trusty tractor 430,000

Incremental benefit from purchasing Trusty tractor 570,000

Note how the £200,000 purchase price of the Tomy tractor did not affect the decision- making process. This amount was ‘gone forever’ when Watkins bought the tractor. It is common to think initially that Watkins should not buy the new tractor because he will incur a £50,000 net loss (200,000−150,000) on an asset that he has only had for a week. However, consideration of the relevant facts highlights that Watkins will save enough in fuel costs in three years to pay for the new tractor even if he gets nothing back from the sale of the Tomy tractor. (£180,000 purchase price divided by £60,000 fuel cost savings per annum.)

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Watkins had to resign himself to accept the past as a fact and make his new choice based on the relevant information:

• the cost of the Trusty tractor

• the sale price of the Tomy tractor

• the annual fuel savings of the new purchase.

(Note that the time value of money has not been considered at this stage but features in Chapter 11.)

9.2.2 Committed Costs

It is often the case with short-term decision-making that a lot of costs are either committed or perhaps fixed within a specified period of activity. A committed cost is where a previous managerial decision has committed the organisation to spending funds even though payment has not yet been made, for example a three-year agreement with a supplier to purchase a specific quantity of materials at an agreed contract price. After year one, even if an alternative, cheaper material of improved quality becomes available in the marketplace, the company has a pre-determined commitment to continue to source its raw materials from the original supplier.

The avoidance of this committed cost will arise only if another major decision is taken either to amend or reverse the earlier commitment and this would usually involve the company incurring some penalty payments for breach of contract.

Therefore, the planning and control of a committed cost can take place only at the point in time just before the commitment is made. It is, therefore, essential that committed costs, particularly longer-term ones, are properly evaluated before any decision is made to incur them, thus ensuring the optimum use of the company’s resources. Once committed, the costs cannot be relevant for future decision-making.

9.2.3 Incremental Costs

Incremental costs and revenues are the additional costs and revenues that are incurred by following a chosen course of action, for example producing an additional weekly batch of output. If the additional batch was not produced then the incremental costs would be avoidable, unlike sunk costs which have already been incurred and cannot be avoided whatever future courses of action are decided upon.

The use of incremental costs (rather than calculating the total costs of each of a number of possible propositions), allows us to ignore costs which are unavoidable regardless of the decision and enables us to focus more simply on the additional benefits of selecting one specific course of action. Incremental costing is a particularly well-used method in the ‘make or buy’ decision-making situation. See worked example 9.3.

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Worked example 9.3

Dumpy Desks Ltd presently incur the following unit manufacturing costs for their desk deluxe model.

Direct materials 600

Direct labour 200

Variable overheads 80

Fixed overheads 60

Total unit cost 940

Of the £60 fixed overheads, only £20 is actually caused by the production of the deluxe model, and could be avoided if the firm chooses not to produce this range. The remain- ing £40 of fixed overheads are allocated common costs that would continue even if production of the deluxe model was dropped.

A supplier in the office furniture industry has quoted a price of £910 for supplying the deluxe model to Dumpy Desks Ltd.

Required

Should Dumpy Desks buy the deluxe model from outside or use their own resources to manufacture internally?

Solution

Production of the deluxe model requires an outlay of £880 for materials, labour and variable overheads. In addition, £20 of the fixed overheads is considered to be a direct product cost because it specifically relates to the manufacture of the deluxe model. The £40 of fixed overheads are common costs because of general production activity and, because these costs would continue under either alternative, they are not relevant to the decision-making.

The relevant cost for the ‘make’ alternative is £900 − the cost that would be avoided if the product was not made. It is this cost that should be compared wiwth the £910 cost quoted by the supplier under the ‘buy’ alternative. Each amount is respectively the incremental cost of each alternative. It would therefore be cost-effective for Dumpy Desks Ltd to continue to manufacture the deluxe model rather than buy it in from outside because £10 will be saved on each desk produced rather than purchased.

9.2.4 Opportunity Costs

Not all the costs relevant to decision-making will be found in the accounting system. As management’s role in an organisation is to improve the overall company position, it is essential that when analysing relevant costs we consider the alternative uses of our available resources. An opportunity cost is best described as the opportunity foregone because we selected and followed a particular course of action. For example, if in the Dumpy Desks Ltd example above, by purchasing externally rather than manufacturing, the available shop floor space can be rented out to a third party then this would be an opportunity cost saving that would need to be considered in the decision-making process. See worked example 9.4.

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Worked example 9.4

A computer consultancy firm has been asked to do an urgent job for one of its clients, for which a price of £50,000 has been offered. The job requires the following resources.

• Thirty hours of the key operator’s time. She is the only member of staff capable of carrying out the work and is currently paid a rate of £400 per hour. If she were not involved on this special project, she would be working for another client where the charge out rate is £900 per hour.

• The use of five hours of mainframe computer time, which is normally charged out to external users at a rate of £1,000 per hour. The mainframe computer currently runs 24 hours a day for seven days a week.

• Computer stationery and other supplies costing £4,000.

Required

Should the computer consultancy firm accept the additional work?

Solution

The opportunity cost of the job would need to be calculated as follows.

Labour (30 hours @ £900) 27,000

Computer time (5 hours @ £1,000) 5,000

Supplies 4,000

Total opportunity cost 36,000

Note that labour and computer time is charged out at the normal charge-out rate because this is the opportunity foregone if this order is accepted.

With the quoted price of £50,000, the firm can boost its profits by £14,000 if it chooses to accept this special order.

In Chapter 8 we looked at cost behaviour and, having analysed costs into their fixed and variable elements, we used this analysis in a range of cost−volume−profit decision-making scenarios. The use of contribution per product unit was seen as particularly relevant in calculating breakeven points and when determining output required to meet targeted profit figures.

However, our analysis in Chapter 8 did not consider the possibility that there may be a scarcity in one or more of the resources required to meet the specific objectives that had been decided upon. It is important that we now consider such limiting factors.

9.3 Limiting Factors

Frequently in practice, management find themselves unable to effect decisions which maximise product contribution because of limited resources (limiting factors) which place a constraint on the organisation’s ability to concentrate on the product yielding the greatest contribution. The following are some of the typical limiting factors faced by businesses:

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• financial resources

• skilled labour

• manufacturing capacity

• material supply

• sales.

Where a single binding constraint can be identified (see worked example 9.5), then the general objective of maximising contribution can be achieved by selecting the alternative which maximises the contribution per unit of the limiting factor. The following formula would apply.

Contribution (£) Selected limiting factor (e.g. hours, units, etc.)

= Contribution per limiting factor

Worked example 9.5

Returning to worked example 8.5 of Bee, Cee and Dee, let us assume that the following information is available:

• total machine capacity = 100,000 hours

• processing time per product

Bee = 1.72 hours

Cee = 1.20 hours

Dee = 2.50 hours

• unlimited demand per product

• fixed costs are £280,000

• contribution per unit:

Bee = £20

Cee = £15

Dee = £30

Required

Rank the products in order of contribution per unit of the constraint and establish the most profitable product.

Solution

Bee Cee Dee

Contribution per unit £20 £15 £30

Process hours required 1.72 hours 1.2 hours 2.5 hours

Contribution per process hour £11.63 £12.5 £12

Ranking 3 1 2

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Maximum output

Bee Cee Dee

Maximum unit output 58,139 83,333 40,000 (note 1)

£000s £000s £000s

Total contribution 1,163 1,250 1,200 (note 2)

less: fixed expenditure 280 280 280

Profit 883 970 920

Notes

1. Given by dividing the total machine capacity by the process hours per unit (for example Bee: 100,000 hours/1.72 hours).

2. Given by multiplying the maximum unit output by the contribution per unit (for example Cee: 83,333 units × £15 per unit).

Conclusion

Produce the maximum amount of Cee possible.

Let’s extend the example further by assuming that the maximum demand for Cee was 50,000 units. What’s your most profitable product mix now?

Obviously you want to maximise the sales of Cee first. This requires 60,000 processing hours (50,000 units × 1.20 hours per unit) and leaves you only 40,000 hours (capacity of 100,000 hours less the 60,000 used on Cee) to use on the other products. From your previous ranking, Dee would seem to be the next best product to sell. With 40,000 hours available you could manufacture and sell 16,000 units of Dee (40,000 hours/2.5 hours per unit). This would generate a profit of £950,000 as highlighted below.

Cee Dee Total (£)

Contribution per unit £15 £30

Sales volume (units) 50,000 16,000

Total contribution £750,000 £480,000 1,230,000

less: Fixed expenditure 280,000

Profit 950,000

9.4 Multiple Limiting Factors

Where there is more than one scarce resource, it is no longer possible to use the simple technique of ranking items in order of contribution per limiting factor to determine the profit maximising situation. With a choice between two products, where two or more limiting factors exist, then a graphical approach is usually used to determine the maximum objective. See worked example 9.6.

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Worked example 9.6

Tweety plc makes two models of bird cage. The relevant unit price and cost details are as follows.

Model A Model B

£ £ £ £

Selling price 600 1100

Variable cost 160 600

Fixed cost 240 300

400 900

Profit 200 200

The production data per unit is as follows.

1. Model A requires 5 hours of machine time and 1 kg of materials.

2. Model B requires 3 hours of machine time and 3 kg of materials.

3. The maximum available machine hours per week is 200 hours.

4. The maximum available kg of material per week is 80 kg.

Required

Determine the best production plan for Tweety plc using a graphical approach.

Solution

Stage 1 − define variables

The limiting factors facing Tweety plc are the availability of materials and machine hours. The only things which it can vary are the quantities of each model of bird cage produced. Assuming its objective is to maximise contribution, it needs to know the optimum quantity of each model that it should produce. The variables are therefore as follows.

Let x = the number of model A to be produced Let y = the number of model B to be produced

Stage 2 − establish constraints

The first constraint is the machining hours. Each unit of model A requires 5 hours while each unit of model B requires 3 hours. The total machine hours needed to make x number of model A and y number of model B is 5x + 3y. As the total number of machine hours cannot exceed 200 the constraint is:

5x + 3y≤ 200

On the same basis the constraint for materials is:

1x + 3 ≤ y80

Since it cannot produce negative output, non-negativity constraints are added:

x≥ 0 y≥ 0

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Stage 3 − establish the objective function

The contributions of the two models are as follows:

Model A £600 − £160 = £440 Model B £1100 − £600 = £500

The objective of Tweety plc is to maximise contribution, and therefore the objective function is:

Contribution (C) = 440x + 500y

Stage 4 − preparing the graph

The problem has been reduced to four constraints and one equation. Because the con- straints are all linear expressions, by plotting them on a graph they would all give straight lines and help us solve the problem.

You can only use a graph where, as we have here, there are two variables in the problem. One variable is represented by the xaxis and the other by the y axis. Since we don’t want negative outputs the graph should only show zero and positive values of x and y.

a. Plot the first constraint 5x + 3y = 200

If x = 0 then y = 66.66 If y = 0 then x = 40

Any combined value of x and y within the shaded area (known as the feasible area) below would satisfy the constraint. See Figure 9.1.

Figure 9.1 Feasible area with one constraint

b. Plot the second constraint 1x + 3y = 80

If x = 0 then y = 26.66 If y = 0 then x = 80

Where there is a second, or several constraints, then the feasible area of combina- tions of values of x and y must be an area where all the constraints are satisfied. Thus by plotting the second constraint, the feasible area has reduced. See Figure 9.2.

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Figure 9.2 Feasible area with two constraints

c. Determine the optimum product mix.

This could be calculated manually by finding out what contribution each of the possible solutions would give, but would be a very labour intensive operation. Graphically, we can work this out by using ‘contribution lines’. Let us assume that we wish a contribution of £8,800. The possible combinations required to earn a contribution of £8,800 can be given by the objective function 440x+500y = £8,800 and plotted on a straight line (contribution line 1). See Figure 9.3.

If x = 0 then y = 17.6 If y = 0 then x = 20

Figure 9.3 Contribution line 1

Likewise, for a contribution of £13,200 another straight line (contribution line 2) could be drawn (see Figure 9.4).

If x = 0 then y = 26.4 If y = 0 then x = 30

Note that the contribution lines are parallel, with the larger contribution being shown by a line furthest from the origin. Look at Figure 9.5. As we move the line away from the origin, there comes a point where the contribution line would cease to lie in the feasible area and a greater contribution would not be achieved

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Figure 9.4 Contribution line 2

because of the constraints. Our optimum point in this example is, therefore, where the contribution line (contribution line 3) passes through the intersection of the constraint lines, and our optimum mix is approximately 30 of model A and 16 of model B.

Figure 9.5 Contribution line 3

Self-check for constraints

Machine hours: (30 model A × 5 hrs) + (16 model B × 3 hrs) = 198 hours Materials: (30 model A × 1 hr) + (16 model B × 3 hrs) = 78 kg

The solution approximates to the constraints of 200 hours and 80 kg respectively.

Note that in worked example 9.6, the choice was restricted to a mix of two products with two or more limiting factors. Where the choice allows a mix of three or more products, with two or more limiting factors, a more complicated linear programming approach has to be used. This is, however, beyond the scope of this module.

9.5 Uncertainty

So far in this unit, we have stressed that decisions are made for the future benefit of the organisation. If we could predict with 100% accuracy what was going to happen in the future,

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then decision-making would be greatly assisted and targets that we had set ourselves would be achieved. Unfortunately, life is not as simple as this, and the reason that the final outcome rarely turns out as anticipated is that uncertainty exists.

9.5.1 The Decision-Making Model

To introduce uncertainty into the decision-making model, we need to expand slightly the process we looked at earlier in the unit. The starting point is still to define the objective that you hope to achieve, for example minimisation of product cost, and then to consider a list of possible alternative courses of action that will enable the objective to be achieved. At this stage, because of the uncertain environment in which we operate, you need to consider the following:

• All the uncontrollable factors (usually known as events) which may influence the outcome of each possible course of action, for example an increase or no increase in the level of interest rates, growth or no growth in the level of product demand

• The probabilities of these events occurring

• A set of possible outcomes (diagramatically expressed as decision trees) which measure the predicted consequences of the various possible combinations of actions and events

• The attitude of the decision-maker to risk-taking.

Only on giving the above some consideration will you be in a position to select a specific course of action which will enable you to achieve your pre-determined objective − see worked example 9.7.

Note the distinction between actions and events. Actions are choices made by management which are under the management’s control, for example the price an organisation should charge for its products. Events, uncontrollable factors affecting the outcome, are occurrences that management cannot control, for example fluctuating foreign exchange rates.

Worked example 9.7

Millie Black plans to sell her new, revolutionary, state of the art guitar at a highly respected and extremely well-attended musician’s trade convention in London, England. Millie buys these guitars from her uncle, who manufactures them at a cost of £2,400, and plans to sell them for £4,000. On approaching the convention organisers she is offered three ways of renting her stall:

• option 1 pay a fixed fee of £40,000.

• option 2 pay a fixed fee of £28,000 plus 5% of her guitar sales revenue.

• option 3 no fixed fee but pay 20% of her guitar sales revenue.

Millie estimates that there is a 0.60 probability that sales will be 40 units and a 0.40 probability that sales will be 70 units. Which of the rental options should Millie choose?

Stage 1 − define objective

Millie’s objective is to maximise her net cash inflow from the convention.

Stage 2 − identify options

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Millie has three possible options:

• pay £40,000 fixed fee

• pay £28,000 plus 5% of revenue

• pay 20% of revenue but no fixed fee.

Stage 3 − identify events

Millie’s only uncontrollable factor is the number of guitars that she can sell.

Stage 4 − assign probabilities

Millie assesses that she has a 60% chance of selling 40 guitars and a 40% chance of selling 70 guitars.

Stage 5 − identify possible outcomes

As you can see in the decision tree presentation of data shown in Figure 9.6, there are six possible net cash flows.

Outcomes

1. 40 guitars × (£4000 − £2400) = £64,000 − fixed fee £40,000 = £24,000

2. 70 guitars × (£4000 − £2400) = £112,000 − fixed fee £40,000 = £72,000

3. 40 guitars × (£3800 − £2400) = £56,000 − fixed fee £28,000 = £28,000

4. 70 guitars × (£3800 − £2400) = £98,000 − fixed fee £28,000 = £70,000

5. 40 guitars × (£3200 − £2400) = £32,000

6. 70 guitars × (£3200 − £2400) = £56,000

Figure 9.6 Decision tree

Expected values

Option 1 (£24,000 0.6 + £72,000 0.4) = £43,200

Option 2 (£28,000 0.6 + £70,000 0.4) = £44,800

Option 3 (£32,000 0.6 + £56,000 0.4) = £41,600

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Based on what has been studied to date in this unit, Millie would most likely choose option 2, which gives the highest net cash flow of £44,800 on an EV basis.

9.6 Attitude to Risk

At this point we need to introduce the final consideration in the decision-making process − the attitude of the decision-maker to risk-taking.

The previous worked example 9.7 assumed that the decision taker was completely indifferent towards risk and that decisions were made solely on the basis of expected values. Obviously, this is not true, as the following example of investment alternatives highlights.

Investment Outcome (£000s) Probability

A 200 loss 0.90

4,800 profit 0.10

B 100 profit 0.50

500 profit 0.50

Both investments have an expected value of £300,000.

Expected value A in £000s = (£4,800 × 0.10) + (£ − 200 × 0.9) = £300 Expected value B in £000s = (£100 × 0.5) + (£500 × 0.5) = £300

Yet investment A has a 90% probability of a £200,000 loss, whereas investment B has two positive outcomes. Most decision-makers would prefer investment B.

If the decision-maker were indifferent to risk (risk neutral), then he or she would be indifferent to both of the alternatives because the expected values were equal. However, a risk seeker who is willing to take a chance might just have plumped for investment A because of the opportunity to gain £4,800,000. Faced with the investment choice above, a risk averter, who reacts with much greater caution in circumstances where uncertainty is present, would unquestionably have selected investment B.

Worked example 9.8

Let’s look again at the worked example on Millie’s dilemma. The decision she will take regarding the method of renting her stall would depend upon her risk preference.

If she was a risk seeker then she might select option 1 because this gives her the possibility of earning £72,000 (although it also has the worst possible outcome).

If she was averse to risk then she might select option 3 because this gives her a minimum outcome of £32,000 (although the best it might generate is only £56,000).

We have reviewed some of the mathematical techniques that can be used to reduce the impact of uncertainty on decision-making. In the final analysis, however, much depends on the psychological outlook of the decision team and its attitude to risk.

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9.7 Qualitative Factors

Up to this point, we have probably given the impression that only items that can be quantified in terms of income or costs influence the decision-making process. In practice, qualitative factors can be just as significant, depending on the circumstances.

There are a variety of factors to take into account depending on the nature of the opportunity being considered. Some examples are listed below.

• Legal constraints

Occasionally, a preferred option might have to be rejected because there are doubts about the legality or otherwise of the planned action.

• Political pressure

Governmental or public pressure might influence a company’s proposed actions where, for example, there were some perceived environmental implications.

• Employees

Often decisions affect the welfare of the employee and a caring company would have regard for their employee’s acceptance of such a decision. Changes in shift patterns, shutting down production units, introduction of incentive schemes and so on will all have a material effect on the employee.

• Customers

Customer loyalty and demand will need to be considered when introducing new product ranges or closing down some existing lines.

• Competitors

The likely reaction of competitors to your selected decision will also need to be con- sidered. If you extend credit terms to customers, will the competition adopt a similar tactic?

• Suppliers

What effect will the closure of a product line have on the supplier of the raw material? Reducing demand, changing stockholding policy or delaying payment might all place great strain on suppliers.

• Availability of cash

Often the best and most profitable ideas involve an initial outlay of cash, for example the purchase of state of the art production plant. If no cash is available, the project will either have to be dropped or some additional funding sought from elsewhere.

• Feasibility

Even after all of the quantitative and many of the qualitative factors mentioned above have been considered, the overall feasibility of the project should be assessed by someone with the necessary technical expertise. If there are any reservations then perhaps the project should be halted at this relatively early stage.

9.8 Summary

In this unit, you have studied the decision-making process, which involves:

• defining the objectives

• considering actions to meet objectives

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• collecting data and evaluating options

• selecting the course of action which best satisfies the objective.

There are a number of different types of cost:

• sunk costs

• committed costs

• incremental costs

• opportunity costs

• fixed and variable costs.

Only future costs are relevant to decision-making.

Often, in practice, the most beneficial decisions cannot be made because limiting factors, such as a lack of key resources, place a constraint on the organisation’s ability to focus purely on the product yielding the highest contribution. Where a single constraint is identified, the alternative is selected which maximises the contribution per unit of limiting factor. Where multiple limiting factors occur, the mathematical approach of linear programming is adopted to determine the profit-maximising situation.

An added problem with the decision-making process is uncertainty and there is a need for you to try and predict the outcome of each possible course of action. Such outcomes are often shown diagrammatically in a decision tree format, which enables you to select the specific course of action which is best suited to your risk preference and which will best enable you to achieve your organisation’s objectives.

Having quantified all the costs and revenues influencing the decision-making process, there are a number of qualitative factors which could just as easily influence your choice of options:

• legal constraints

• employee welfare

• customer loyalty

• competitor action.

Decision-making, therefore, is making a choice between alternatives in pursuit of an objective, having firstly considered all the relevant quantitative and qualitative information affecting each course of action.