Chart 3 presents less optimistic scenarios for options 1 and 4, namely worst cases and most likely cases. These scenarios are four of the possible outcomes that would emerge if events turned out to be different from managers’ projections.
On one hand, the worst cases suppose the utter failure of the initiatives, i.e. all the fixed costs should be borne without retrieving any increase in sales or any reduction in variable costs. For options 1 and 4, this could be due respectively to a disastrous marketing campaign and to a severe machinery breakdown. On the other hand, the most likely cases assume that increases in fixed costs are unvaried from the initial estimates, and variable costs per unit are still reduced by £3 for option 4. As regards sales volumes, if the company is facing difficulties due to erratic sales, perhaps an overconfident budget could be the reason. Therefore, most likely cases suppose that sales volumes are halved compared to the best case valuations. Looking at the net results of the most likely cases, it can be seen that option 1 produces a negative result, whereas option 4 is still positive. However, in the worst cases option 4 produces a much heavier loss than option 1, due to a larger increase in fixed costs which is not balanced by a relative rise in the contribution margin.
The findings show that option 4 would be the most appropriate solution, because of the high risk of loss of option 1. However, a risk-inclined manager would think that implementing two options at the same time could maximize profits. Chart 4 clarifies the concept: a combination of option 1 and 4 would trigger an increase in sales of 45.33%, if the initiatives were both successful. This large increase, matched with the higher contribution per unit of option 4, would produce a profit of $72,080. Chart 4 summarizes the variations of nine pre-formulated combinations of scenarios and the resultant profit margins.
The operating incomes of these scenarios give a reasonable degree of safety: the only loss cases occur when it is assumed the worst scenario for option 4, i.e. when it is speculated an automation process producing zero benefits. Looking at the chart, it can be said that option 4 is the drawing power of profits, thanks to its larger scale. Option 1 boosts profit in case of favourable conditions but, compared to the single implementation of option 4, losses are exacerbated when scenarios are particularly adverse. Deciding to implement option 4 or to combine the two options depends on whether managers tend to see the bright or the bleak side of the situation: it is a matter of risk inclination.
Again, the assumptions of the model make the difference: on one hand, it is assumed that an effective advertising (option 1) combined with quicker deliveries (option 4) could reach a 45.33% increase in sales. This is a massive rise, possible only if an overwhelming competitive advantage is achieved. Clearly, the initiatives should be thoroughly successful to reach this level and, therefore, the optimistic connotation of this scenario must be taken in account. On the other hand, worst cases present a pessimistic feature: indeed, it is unlikely that a significant investment in advertising and automation does not even lead to the slightest increase in value added and contribution. Hence, it is worth mentioning that if the sales variations of some scenarios were calculated more moderately the outcomes would be totally different.
It could also be possible to consider a combination of options 2 and 4: although option 2 taken individually causes a loss, Chart 6 shows that it would rocket profits applied in a combination. However, in circumstances of financial crunch, the 73.33% increase in fixed costs matched to the 10% price shrinkage is perhaps a too aggressive strategy, sustainable for a one-off campaign but probably not over the long run. The combination of options 1 and 4 is a more moderate approach and will be given prominence in the analysis.
In order to clarify the previous findings, a simple risk analysis is presented for the single implementation and for the combination of options 1 and 4. In absence of further information even probability is assumed for each event.
The high risk of loss of option 1 is reflected in a negative expected value, while option 4 presents a positive expected value and a higher break-even probability. The combination of options 1 and 4 shows a negative expected value: as a matter of fact, the combined implementation would be able to produce greater profits in case of favourable prospects, but also extensive losses in hostile conditions. Given that every event is weighted with the same probability, the value ends to be slightly negative. In addition, the expected value is lower than the one for option 4, and therefore this alternative would be riskier under these assumptions. In the light of these considerations, option 4 would look like the best alternative in terms of risk control.
To sum up, it can be said that the essential conditions for the company are either a reduction of the variable cost or an increase of the sales volumes. The single implementation of option 1 hardly breaks even and hence can be ruled out. Option 4 is the best initiative, if the assumptions are correct, because it satisfies both of the above conditions. However, a quicker delivery system does not trigger automatically an increase in sales and, hence, an advertising campaign could help to push the initiative. The combination of options would be riskier, assuming even probability, and therefore a risk-averse manager should opt for option 4 taken individually. A risk inclined-manager should instead opt for the combination of options 1 and 4, especially considering the equal break-even probability. However, with an increase of £88,000 in fixed costs (+48.9%), the utter failure of the initiatives is unlikely to constitutes the 33.33% of the total probability and, therefore, the model should be reappraised with more realistic data.
As regards the limitations of the model, they are a consequence of the attempt to simplify the reality: it is assumed a neat separation between fixed and variable costs and a linear, constant behaviour for costs and revenues. Therefore, the only factor able to influence the total costs is a change in the sales volumes. These assumptions limit the analysis within the relevant range of activity and the short run, and preclude the use of elements such as discounts and inventories. In addition, in case of insufficient or inaccurate information, a number of biased assumptions may influence the results of the analysis.
The loss is worse than the one reported in last month (-£4,500).
The assignment text hints at the likely poor objectivity of the estimates (the president “believes”… the sales manager “is convinced”… the marketing managers “think that”…he “assumes”).
If different assumptions should be considered, options 2 and 3 could be reappraised.
If the increase in sales should be only 0.4% lower than predicted, i.e. +11.6%, option 1 would produce a loss.
Data from option 4 best case – margin of safety.
At the moment each alternative is still assumed to be implemented individually.
In the most likely cases it is only assumed a good probability to reach lower sales volumes than in the best cases, due to managers’ confidence in estimating. Conversely, valuations of costs are assumed to be reliable.
In the most likely case option 4 would need a minimum sales increase of 8% to break even (data from option 4 most likely case – 8% sales increase instead of 16.67%).
The sales increase for the combination of options 1 and 4 is calculated adding up the sales increases of the respective scenarios taken from the analysis of the individual implementations.
If net profits of the single implementations are simply added up, the result is lower, due to a lower contribution per unit (60,000+560=60,560) < 72,080.
This matrix can be only used as basis for a more detailed sensitivity analysis, since the variations among the 9 scenarios are very steep. However, it is possible to use the desired pre-formulated scenario as benchmark, and then attempt slight variations of 1 or 2 percentage points, so as to test more realistically the robustness of each hypothesis.
Note that only 3 scenarios out of 9 do produce a loss and the total failure of option 4 is implied in each of them. As regards the combination of the most likely cases of options 1 and 4, a minimum increase of 15% in sales is required to break even (data from option 1 most likely case and option 4 most likely case – 15% sales increase instead of 22.67%).
This is due to the heavy increase in fixed costs, which is not balanced by a relative increase in contribution in the worst cases.
For the scope of this coursework, it has been assumed that adding up sales increases while treating best combinations were more illustrative. Similar rationales were followed in assuming rates for other cases.
Chart 5 shows that, in terms of contribution, the £3 reduction in variable cost per unit is nullified by the £3 price shrinkage. In other words the contribution per unit is unvaried and, therefore, if the 133.33% sales volume increase were not sustained, this initiative would be a failure. A minimum sales increase of 78% is required to break even (data from combination of options 2 and 4 - sales increase 77% instead of 133%). Hence, this combination is assumed to be far too risky. In addition, there may be a further issue about the availability of finance.
Using even probability does not provide additional useful information. However, entering more realistic data can vary considerably the result (a pre-formulated scenario with more realistic probabilities is available in the “scenario manager”).
The expected value calculation is more indicative in case of repeated events. In case of one-off decision, such as this implementation, it constitutes a mere average calculation.
It is shown in the Excel Spreadsheet that the use of more realistic values (although purely speculative) gives opposite results.
The company struggles to break even and therefore needs to enhance the contribution margin. To this end, an increase in price would probably pull down the sales volumes. The best solution is considered to be a more efficient production system, because it would lower the variable costs per unit.
The break-even probability is roughly 67% for option 4 and for the combination.
Fixed costs are almost doubled: this will almost certainly add value to the product and, therefore, sales volumes are unlikely to increase by 0% in the 33.33% of the cases.
Costs are all variable in the long run and beyond the relevant range of activity.
