# The application of linear programming to management accounting

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1 The application of linear programming to management accounting Solutions to Chapter 26 questions Question (a) M F Contribution per unit Litres of material P required 8 10 Contribution per litre of material P Ranking 1 2 Production/sales (units) a (b) (c) Note a litres of P less (1000 8) for M = litres for F giving a total production of 2325 units ( litres/10) M F Total ( 000) ( 000) ( 000) Sales Variable costs: Material P Material Q Direct labour Overhead Contribution Fixed costs ( ) Profit Maximize Z = 96M + 110F (product contributions) subject to: 8M + 10F (material P constraint) 10M + 5F (material Q constraint) 4M + 5F (direct labour constraint) M (maximum demand for M) F (maximum demand for F) The above constraints are plotted on the graph shown in Figure Q26.16 as follows: Material P; Line from M = , F = 0 to F = 3125, M = 0 Material Q; Line from M = 2000, F = 0 to F = 4000, M = 0 Direct labour; Line from M = 4375, F = 0 to F = 3500, M = 0 Sales demand of M; Line from M = 1000 Sales demand of F; Line from F = 3000 The optimal solution occurs where the lines in Figure intersect for material P and Q constraints. The point can be determined from the graph or mathematically as follows: 8M + 10F = (material P constraint) 10M + 5F = (material Q constraint) multiplying the first equation by 1 and the second equation by 2: 202 THE APPLICATION OF LINEAR PROGRAMMING TO MANAGEMENT ACCOUNTING

2 Product M (units) Product F 3000 Material P 2000 Labour Material Q 1000 Product M FEASIBLE REGION Optimal solution Figure Q26.16 Z = 96M + 110F Product F (units) (d) 8M + 10F = M + 10F = subtracting 12M = 8750 M = Substituting for M in the first equation: 8( ) + 10F = F = ( ) Contribution: (729 units of M at 96) (2542 units of F at 110) Less fixed costs Profit THE APPLICATION OF LINEAR PROGRAMMING TO MANAGEMENT ACCOUNTING 203

3 Moving from the solution in (c) where the lines intersect as a result of obtaining an additional litre of material Q gives the following revised equations: 8M + 10F = (material P constraint) 10M + 5F = (material Q constraint) The values of M and F when the above equations are solved are and Therefore, M is increased by units and F is reduced by units giving an additional total contribution of [ ) ( )] per additional litre of Q. Therefore the shadow price of Q is per litre. (e) See Chapter 26 for an explanation of shadow prices. (f) Other factors to be taken into account include the impact of failing to meet the demand for product M, the need to examine methods of removing the constraints by sourcing different markets for the materials and the possibility of sub-contracting to meet the unfulfilled demand. Question (a) Let X number of units of XL produced each week Y number of units of YM produced each week Z total contribution The linear programming model is: Maximize Z 40X 30Y (product contributions) subject to 4X 4Y 120 (materials constraint) 4X 2Y 100 (labour constraint) X 2Y 50 (plating constraint) X, Y 0 The above constraints are plotted on Figure Q The optimum output is at point C on the graph, indicating that 20 units of XL and 10 units of YM should be produced. The optimum output can be determined exactly by solving the simultaneous equations for the constraints that intersect at point C: 50 4X 4Y 120 4X 2Y 100 Subtracting 2Y 20 2Y 10 4X Y (direct labour) 40 Weekly production of YM F 30 4X Y (materials) A 20 B Z = 40X Y X+ 2 Y 50 (plating) + 30 = C Figure Q E D Weekly production of XL 204 THE APPLICATION OF LINEAR PROGRAMMING TO MANAGEMENT ACCOUNTING

5 The relevant cost of resources used in producing ZN consists of the acquisition cost plus the shadow price (opportunity cost). The relevant cost calculation is: ( ) Material A [5 kg at ( 10 5)] 75 Labour [5 hours at ( 8 5)] 65 Plating (1 hour at 12) 12 Other variable costs Question The selling price is less than the relevant cost. Therefore product ZN is not a profitable addition to the product range. (e) The shadow price of labour is 5 per hour. Therefore the company should be prepared to pay up to 5 in excess of the current rate of 8 in order to remove the constraint. An overtime payment involves an extra 4 per hour, and therefore overtime working is worthwhile. Increasing direct labour hours will result in the labour constraint shifting to the right. However, when the labour constraint line reaches point E, further increases in labour will not enable output to be expanded (this is because other constraints will be binding). The new optimal product mix will be at point E, with an output of 30 units of XL and zero of YM. This product mix requires 120 hours (30 4 hrs). Therefore 120 labour hours will be worked each week. Note that profit will increase by 20 [20 ( 5 4)]. (f) The limitations are as follows: (i) It is assumed that the objective function and the constraints are linear functions of the two variables. In practice, stepped fixed costs might exist or resources might not be used at a constant rate throughout the entire output range. Selling prices might have to be reduced to increase sales volume. (ii) Constraints are unlikely to be completely fixed and as precise as implied in the mathematical model. Some constraints can be removed at an additional cost. (iii) The output of the model is dependent on the accuracy of the estimates used. In practice, it is difficult to segregate costs accurately into their fixed and variable elements. (iv) Divisibility of output is not realistic in practice (fractions of products cannot be produced). This problem can be overcome by the use of integer programming. (v) The graphical approach requires that only two variables (products) be considered. If several products compete for scarce resources, it will be necessary to use the Simplex method. (vi) Qualitative factors are not considered. For example, if overtime is paid, the optimum solution is to produce zero of product YM. This will result in the demand from regular customers for YM (who might also buy XM) not being met. This harmful effect on customer goodwill is not reflected in the model. (a) The calculation of the contributions for each product is: X1 X2 X3 ( ) ( ) ( ) Selling price Materials a (51) (45) (54) Manufacturing costs b (11) (11) (11) Contribution Notes a The material cost per tonne for each product is: 206 THE APPLICATION OF LINEAR PROGRAMMING TO MANAGEMENT ACCOUNTING

6 X1 ( ) (0.1 60) ( ) (0.6 10) 51 X2 ( ) (0.2 60) ( ) (0.6 10) 45 X3 ( ) (0.1 60) ( ) (0.6 10) 54 b It is assumed that manufacturing costs do not include any fixed costs. The initial linear programming model is as follows: Maximize Z 21X1 25X2 16X3 subject to 0.1X1 0.1X2 0.2X (nitrate) 0.1X1 0.2X2 0.1X (phosphate) 0.2X1 0.1X2 0.1X (potash) X1, X2, X3 0 (b) The slack variables are introduced to represent the amount of each of the scarce resources unused at the point of optimality. This enables the constraints to be expressed in equalities. The initial Simplex tableau is: X1 X2 X3 X4 (nitrate) X5 (phosphate) X6 (potash) Z (contribution) (c) The starting point for the first iteration is to select the product with the highest contribution (that is, X2), but production of X2 is limited because of the input constraints. Nitrate (X4) limits us to a maximum production of tonnes (1200/0.1), X5 to a maximum production of tonnes (2000/0.2) and X6 to a maximum production of tonnes (2200/0.1). We are therefore restricted to a maximum production of tonnes of product X2 because of the X5 constraint. The procedure which we should follow is to rearrange the equation that results in the constraint (that is, X5) in terms of the product we have chosen to make (that is, X2). Therefore the X5 equation is re-expressed in terms of X2, and X5 will be replaced in the second iteration by X2. (Refer to Choosing the product in Chapter 26 if you are unsure of this procedure.) Thus X2 is the entering variable and X5 is the leaving variable. (d) Following the procedure outlined in Chapter 26, the final tableau given in the question can be reproduced as follow: Quantity X3 X4 X5 X X X Z In Chapter 26 the approach adopted was to formulate the first tableau with positive contribution signs and negative signs for the slack variable equations. The optimal solution occurs when the signs in the contribution row are all negative. The opposite procedure has been applied with the tableau presented in the question. Therefore the signs have been reversed in the above tableau to ensure it is in the same format as that presented in Chapter 26. Note that when an entry of 1 is shown in a row or column for a particular product or slack variable then the entry does not appear in the above tableau. For example, X1 has an entry of 1 for the X1 row and X1 column. These cancel out and the entry is not made in the above tableau. Similarly, an entry of 1 is omitted in respect of X2 and X6. The optimum solution is to produce 4000 tonnes of X1, 8000 tonnes of X2 and zero X3 each month. This gives a monthly contribution of , uses all the nitrate (X4) and phosphate (X5), but leaves 600 tonnes of potash (X6) unused. THE APPLICATION OF LINEAR PROGRAMMING TO MANAGEMENT ACCOUNTING 207

7 The opportunity costs of the scarce resources are: Nitrate (X4) Phosphate (X5) 170 per tonne 40 per tonne If we can obtain an additional tonne of nitrate then output of X1 should be increased by 20 tonnes and output of X2 should be reduced by 10 tonnes. Note that we reverse the signs when additional resources are obtained. The effect of this substitution process on each of the resources and contribution is as follows: Nitrate (X4) Phosphate (X5) Potash (X6) Contribution (tonnes) (tonnes) (tonnes) ( ) Increase X1 by 20 tonnes 2(20 0.1) Reduce X2 by 10 tonnes 1(10 0.1) Net effect The net effect agrees with the X4 column in the final tableau. That is, the substitution process will use up exactly the one additional tonne of nitrate, 3 tonnes of unused resources of potash and increase contribution by 170. To sell one unit of X3, we obtain the resources by reducing the output of X1 by 3 tonnes and increasing the output of X2 by 1 tonne. (Note the signs are not reversed, because we are not obtaining additional scarce resources.) The effect of this substitution process is to reduce contribution by 22 for each tonne of X3 produced. The calculation is as follows: Increase X3 by 1 tonne 16 contribution Increase X2 by 1 tonne 25 contribution Reduce X1 by 3 tonnes 63 Loss of contribution 22 (e) (i) Using the substitution process outlined in (d), the new values if 100 extra tonnes of nitrate are obtained will be: X (20 100) 6000 X (10 100) 7000 X6 600 (3 100) 300 Contribution ( ) Hence the new optimal solution is to make 6000 tonnes of X1 and 7000 tonnes of X2 per month, and this output will yield a contribution of (ii) Using the substitution process outlined in (d), the new values if 200 tonnes per month of X3 are supplied will be: X (3 200) 3400 X (1 200) 8200 X6 600 ( ) 680 X3 0 (1 200) 200 Contribution ( ) Hence the new optimal solution is to produce 3400 tonnes of X1, 8200 tonnes of X2 and 200 tonnes of X3, and this output will yield a contribution of (Note that the signs in the final tableau are only reversed when additional scarce resources are obtained.) 208 THE APPLICATION OF LINEAR PROGRAMMING TO MANAGEMENT ACCOUNTING

8 (a) The company should invest in companies A, C and D since they yield positive NPVs. The company should be indifferent about investing in B since it yields a zero NPV. (b) (i) A B C D (\$000) (\$000) (\$000) (\$000) Present value Investment at time NPV per \$1 invested at time Ranking The company should invest \$ in C and the balance of the funds (\$ ) in A. (b) (ii) Other factors that should be considered are: 1 The risk of the cash flows of each company. A single cost of capital has been applied to all investments, but the riskier the investment the higher should be the cost of capital. 2 Are the cash flows of the companies highly correlated? If the cash flows for two of the companies are not highly correlated, the total risk of the two investments will be lower than the sum of the individual investments (see Portfolio analysis in Chapter 12 for an explanation). 3 The experience of the staff in the individual companies and the extent to which they are dependent on a small number of key staff. Where the success of a company is dependent on a small number of key staff, obtaining the returns on the investment will be at risk should the staff leave. 4 The speed of the payback period if the cash flows can be used to reinvest in new projects. This is mainly applicable to when funds in future periods are restricted. (c) Let X1 = Proportion of funds invested in company A Let X2 = Proportion of funds invested in company B Let X3 = Proportion of funds invested in company C Let X4 = Proportion of funds invested in company D Question Maximize 60X1 + 0X2 + 77X3 + 80X4 Subject to: 500X X X3 + 80X4 + S1 = 700 (period 0 constraint) 75X1 + 30X X X4 + S2 = (period 1 constraint) 40X1 + 20X2 + 30X3 + 50X4 + S3 = (period 2 constraint) 0 X j 1 (j = 1, 3) Note 1 The constraints are expressed in present values, but it is assumed that constraints applying will be expressed in period 1 and period 2 cash flows so period 1 = \$80 (1.12) and period 2 = \$35 (1.12) 2. The objective function specifies that NPV is to be maximized subject to the investment constraints for the three periods. The terms S1, S2, etc. represent the slack variables in the form of any unused funds for each period. The final term in the model indicates that any project (signified by X j ) cannot be undertaken more than once, but allows for a project to be partially undertaken. For a more detailed explanation of the meaning of the terms in a capital budgeting model you should refer to Appendix (d) The benefits from using linear programming in this situation are: 1 Complex investment problems and constraints can be modelled and solved using computer programs. THE APPLICATION OF LINEAR PROGRAMMING TO MANAGEMENT ACCOUNTING 209

9 2 The model generates opportunity costs and marginal rates of substitution which can be useful for decision-making and control. 3 The model is flexible and can be applied to where fractional projects can be undertaken or modified to exclude fractional investments. Question (a) Let a, b, c, d, e, f and g represent the proportion of projects A, B, C, D, E, F and G accepted and X represent the amount of money placed on deposit in 2001 in 000. The NPV of 1000 placed on deposit is: 1000(1.08) (in 000) 1.10 Maximize Z 39a 35b 5c 15d 14e 32f 24g 0.018X subject to 80a 70b 55c 60d X DIV0 250 (2001 constraint in 000) 30a 140e 80f 100g DIV b 40c 30d 1.08X (2002 constraint in 000) DIV0 100 DIV DIV0 a, b, c, d, e, f g 0 a, b, c, d, e, f g 1 Note that surplus funds are placed on deposit only for After 2003, capital is available without limit. Consequently, it is assumed at 2002 that it is unnecessary to maintain funds for future periods by placing funds on deposit to yield a negative NPV. It is assumed that capital constraints can be eased by project generated cash flows. (b) The dual or shadow prices indicate the opportunity costs of the budget constraints for 2001 and The dual values indicate the NPV that can be gained from obtaining an additional 1 cash for investment in 2001 and The zero dual value for 2002 indicates that the availability of cash is not a binding constraint in 2002, whereas in 2001 the dual value indicates that 1 additional cash for investment in 2001 will yield an increase in total NPV of The dual values also indicate how much it is worth paying over and above the existing cost of funds. In this question it is worth paying up to 0.25 over and above the existing cost of capital for each 1 invested in In other words, the company should be prepared to pay up to 35% (10% acquisition cost 25% opportunity cost) to raise additional finance in Dual values can also be used to appraise any investments that might be suggested as substitutes for projects A to G. If the company identifies another project with a life of one year and a cash outflow of at t 0 and an inflow of at t 1, the NPV would be 3364 at a cost of capital of 10%. However, acceptance of the project would result in a reduction in NPVs from diverting funds from other projects of ( ). The project should therefore be rejected. Dual values are not constant for an infinite range of resources. They apply only over a certain range. The question indicates that the values apply over a range between and Outside this range, it will be necessary to develop a revised model to ascertain the dual values that would be applicable. If resources continued to be increased, a point would be reached at which all potential projects with positive NPVs would have been accepted. Beyond this point, there would be no binding constraints and capital rationing would cease to exist. (c) Capital rationing can be defined as a situation where there are insufficient funds available to undertake all those projects that yield a positive NPV. The literature distinguishes between hard and soft capital rationing. Hard capital rationing refers to those situations where firms do not have access to investment funds and 210 THE APPLICATION OF LINEAR PROGRAMMING TO MANAGEMENT ACCOUNTING

10 are therefore unable to raise any additional finance at any price. It is most unlikely that firms will be subject to hard capital rationing. Soft capital rationing refers to those situations where capital rationing is internally imposed. For various reasons, top management may pursue a policy of limiting the amount of funds available for investment in any one period. Such policies may apply to firms that restrict their financing of new investments to internal funds. Alternatively, in a large divisionalized company, top management may limit the funds available to divisional managers for investment. Such restrictions on available funds may be for various reasons. For example, a company may impose its own restrictions on borrowing limits or avoid new equity issues because of a fear of outsiders gaining control of the business. Where capital rationing exists, the NPV rule of accepting all positive NPV projects must be modified. Where capital is rationed for a single period, the approach outlined in Chapter 14 (see Capital rationing ) should be applied. Where capital is rationed for more than one period, the optimal investment plan requires the use of mathematical programming (see Chapter 26). The application of the latter is based on a number of underlying assumptions that are unlikely to hold in the real world. For a discussion of these assumptions see the Appendix to Chapter 26. THE APPLICATION OF LINEAR PROGRAMMING TO MANAGEMENT ACCOUNTING 211

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