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Relevant Information for Decision Making, Statistics Problem Example

Pages: 2

Words: 657

Statistics problem

Wal-Mart is public Multinational Corporation in America that operates chains of large discount departmental stores, (Wal-Mart Annual Report, 1994, p. 5). Relevant cost is defined by CIMA as ‘the costs appropriate to a specific management decision’ any kind of study involving relevant costs and benefits help make better decision. Any corporate decision at Wal-Mart must be evaluated on cost-benefit criteria. In order to qualify for relevancy, two criteria must be met by cost:

  • They must affect the future and
  • They must differ among alternatives, (Midega et al, 2010).

Relevant Costs

Relevant costs are categorized into:

  • Differential Cost: this is the difference in cost items which are under two or more decision alternatives.
  • Incremental Or Marginal Cost: Incremental or the marginal cost refers to the cost allied to the production of extra unit.
  • Opportunity Cost before implementing a particular project, organization must consider

Irrelevant Costs

These are Sunk costs or past costs that cannot be changed with any future decision.

Repair or buy decision

Consider the case of the company truck that was involved I an accident, the company had the option of either repairing the company truck for $24,000, or selling the truck the truck based on “as is, where is” for $10,000 and buying a new one for sale at $32,000. In the preceding discussion, the logics are to focus on the incremental items that differ between the two alternatives: In one sense, Wal-Mart was lucky. This is because the insurance proceeds were more than enough to put Wall mart back in operation. Each of the potential decision led to a negative result (Midega et al, 2010). Nonetheless, decisions must be made.

In this example, the irrelevant cost is:

  • The truck’s original cost of $60,000 is a sunk cost, and is therefore irrelevant to the decision process.
  • The actual degree to which the truck is depreciated is also irrelevant in this case.
  • The financial statement “gain” as was reported on a sale is equally irrelevant.
  • The $30,000 that was received from the insurance company remains the same whether the said truck is sold or repaired; this is because it does not vary between the two alternatives as such this is not necessary to factor it into the decision process (Midega et al, 2010).

In this example, what matters is fact that the truck can be repaired for $24,000, or the truck can be sold for $10,000 and a comparable one purchased for $32,000. The implication is that it is advisable to sell the damaged truck and buy a new one on sale (Midega et al, 2010).

Analysis for Sale of Truck     Analysis for Repair of Truck  
Particulars Amount   Particulars Amount
Cost of damaged truck $ 60,000   Cost of damaged truck $ 60,000
Accumulated depreciation on damaged truck 24,000   Accumulated depreciation on damaged truck 24,000
Net book value of damaged truck $ 36,000   Net book value of damaged truck $ 36,000
Less: Insurance recovery 30,000   Less: Insurance recovery 30,000
Resulting reduced basis $ 6,000   Resulting reduced basis $ 6,000
Sales price of damaged truck $ 10,000   Plus: Money to repair truck

 

24,000
Less: Reduced basis (from above) 6,000

 

  Resulting basis $ 30,000
Gain on sale of truck $ 4,000

 

  Gain on sale of truck  
Future depreciation (purchase price/new truck) $ 32,000   Future depreciation (resulting basis) $ 30,000
 

Lifetime income effect:

    Lifetime income effect:

 

 
Gain on sale of truck $ 4,000

 

  Gain on sale of truck $ –

 

Future depreciation  (32,000)

 

  Future depreciation (30,000)

 

Net impact on income $ (28,000)

 

  Net impact on income

 

$ (30,000)

 

Cash flow impacts:     Cash flow impacts:  
Insurance recovery $ 30,000   Insurance recovery $ 30,000

 

Sales price of damaged truck 10,000

 

  Sales price of damaged truck
Purchase price of truck (32,000)   Repair costs (24,000)

 

Net impact on cash $ 8,000   Net impact on cash $ 6,000

References

Midega et al, (2011). Principles of accounting; Analytics for Managerial Decision Making Mc-   Milan Kenya, chapter 24.

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