Illustrative application of Accounting Standard (AS) 2

 

·   Introduction :

Accounting Standard (AS) 2 “Valuation of Inventories” require valuation of stocks on the basis of absorption costing method. This means that the value of closing stock would include all variable and a fair proportion of fixed manufacturing overheads attributable to the stock.

 

·   Scope :

This article illustrates how the provisions of Accounting Standard can be put into practice. The article does not go into detail of all the provisions of the Standard. For definitions of the terms, one may refer to the Standard. The article has been prepared form the point of view of entity which is required to comply with the Standard. However, every attempt has been made to make this article comprehensive by commenting on the theoretical aspects of different statistical methods.Please note that except where specific references are made, the article is based on the personal opinion of the author. There may be alternatives which may be better than that indicated in this article.

 

·   Basis of Valuation:

Accounting Standard (AS) 2 require valuation of stocks at the lower of cost or net realizable value. Cost includes those costs/expenses incurred in bringing the inventories to their present location and condition. The valuation may be done on the following basis:

 

Raw Materials: Price as shown in invoice including duties and taxes (other than those subsequently recoverable by the enterprise from the taxing authorities such as CENVAT, Sales-tax set-off), freight inwards and other expenses directly attributable to the acquisition. Trade discounts, rebates, duty drawbacks and other expenditure and other similar items are deducted in determining the costs of purchase.

 

Work-in-process: Cost of purchase as calculated above and all variable manufacturing overheads and a fair proportion of fixed manufacturing overheads as estimated on the basis of Normal Production Capacity applicable to the percentage of completion of production of Finished Goods.

 

Example showing Apportionment of Fixed Manufacturing overheads:

Normal Production Capacity : 10,000 units

Total Annual Fixed Manufacturing Overheads : Rs.1,00,000/-

Fixed Manufacturing Overhead Absorption Rate = Rs.1,00,000 / 10,000 units = Rs.10 per unit

Total goods in process at the end of the year : 100 units

Percentage of completion of finished goods : 75%

Therefore, fixed manufacturing overheads attributable to the stock in process will be: Rs.10 * 100 units * 75% = Rs.750

 

Finished Goods: Cost of purchase, all variable manufacturing overheads and a fair proportion of fixed manufacturing overheads determined on the basis of normal production capacity and provision for excise duty applicable to the closing stock of goods manufactured.

 

Normal Production Capacity: The objective of finding out Normal Production Capacity is to arrive at a measure of central value that describes the production characteristics of the sample taken for the purpose. Normal Production Capacity may be arrived on the basis of any of the following methods:

 

1.  Simple Average

2.  Weighted Average

3.  Median

4.  Mode

5.  Least squares method of fitting in a trend line

 

Simple average: Simple average may be calculated if no particular rising or decreasing trend can be judged in the production.

 

Example:

Year	Production (in units)
2000	10000
1999	9000
1998	9500
1997	8500
1996	9700

 

Thus, Normal Production Capacity would be (10000+9000+9500+8500+9700) / 5 = 9340 units

 

Merits:

1. It is the simplest average to understand and compute.
2. It is affected by the value of every item in the series
3. The result of simple average does not have any bias in the sense that everyone calculating it will get the same result.
4. It is a calculated value, not based on position in the series.

 

Demerits:

1.  Since the value of mean depends upon each and every item in the series, extreme items, that is, very small and very large items, unduly affect the value of the average. For example, if the production data for last 5 years are 10000, 9000, 9500,1500,9700 then the average production would be 10000+9000+9500+1500+9700=39700/5=7940. Thus, one single item, that is, 1500, has reduced the average production considerably. The smaller the sample, the greater would be sample average error.

2.  The mean is normally a good measure in case the distribution is reasonably normal.

 

Weighted average: One of the main limitations of the arithmetic mean is that it gives equal importance to all the items. However, there are cases, where the relative importance of the different items is not the same. For this weighted average is calculated. Weighted average method may be applied when there is a definite increasing trend, higher weights being assigned to recent years.

 

Example:

 

Year	Units Produced	Weights	Weighted Production
2000	10000	5	50000
1999	9655	4	38260
1998	9505	3	28515
1997	9500	2	19000
1996	9250	1	9260
Total	47920	15	145035

 

Weighted Average Normal Production = 145035 /15 = 9669 units

 

 

Relationship between Simple and Weighted Mean:

1.  Normal Production arrived at by simple mean shall be equal to weighted arithmetic mean if the weights are equal.

2.  Normal Production arrived at by simple mean shall be less than weighted average, if and only if, greater weights are assigned to greater values. This can be seen from the examples stated above.

 

Demerit: An important problem that arises while using weighted mean is regarding selection of weights. Weights are normally arbitrary. The use of arbitrary weights may lead to some error.

 

Median: The median refers to the central value in the distribution. One-half of the items in the distribution have a value the size of the median value or smaller and one-half have a value the size of the median or larger.

 

Example:

 

Year	Production (Units)
2000	10000
1999	9655
1998	9505
1997	9500
1996	9250

 

Median = (N+1)/2 = (5+1)/2 = 3^rd observation = 9505 units

 

However, median is not normally used unless the sample is very large in which there are extreme observations in opposite directions thereby distorting mean. Median reflects proper value only when the upper and lower halves are not largely dispersed. For example, the values in the lower half of a distribution range from say, Rs.10 to Rs.100, while the same number of items in the upper half of the series range from Rs.100 to Rs.6000 with most of them near the higher limit. In such a distribution the median value of Rs.100 will provide little indication of the true nature of the data.

 

Mode: According to Croxton and Cowden, “The mode of a distribution is the value at the point around which the items tend to be most heavily concentrated. It may be regarded as the most typical of a series of values.

 

Mode can be used as a measure of central value for determining normal production when no trend can be discerned, the values fluctuating haphazardly. In such a case, mode will give a value surrounding which most of the values lie thus giving an approximate average normal production.

 

 

Least Squares Method:

 

Year	x	Units Produced (y)	X * x	X * y
2000	2	10000	4	20000
1999	1	9655	1	9655
1998	0	9505	0	0
1997	-1	9500	1	9500
1996	-2	9260	4	18520
		47920	10	57675

 

Now, 47920 / 5 = 9584 and 57675 / 10 = 5768

 

Difference = 9584 – 5768 = 3816

Therefore, normal production for the year 2001 will be 3816 * 3 = 11448 units

 

However, the above arrived figure just complies with the trend and hence, should be adjusted for normal factors.

 

Rationale on allocation of weights to X:

It has been assumed that the trend follows normal distribution. Now, under normal distribution, mean, median and mode are equal and standard deviation is 1. This means that all the observations, both above and below the mean, are equidistant from it. Thus, if we assume mean to be zero, and allocate 1 to first observation above it, the first observation below it has to be –1.

 

Fixed Manufacturing Overheads would also include depreciation on plant and machinery and other fixed assets used for the purpose of production. In case no separate records are maintained showing separately variable and manufacturing overheads, total manufacturing expense incurred excluding raw material consumed may be segregated as follows:

 

Year	Units Produced (x)	Manufacturing Expenses treated Semi-variable(y)	X * x	X * y
1996	200	10000	40000	2000000
1997	300	11500	90000	3450000
1998	400	12650	160000	5060000
1999	450	13000	202500	5850000
2000	500	14500	250000	7250000
Total	18750	61650	742500	23610000
Average	370	12330	148500	4722000

 

Variable Expenses per unit = (472200 –(370 * 12330)) / (148500 – (370 *370))

              = 159900/11600

              =13.7485 equivalent to Rs.14 per unit

 

Therefore, total variable expense would be 370 * 14 = Rs.5180/-

Therefore, estimated fixed manufacturing expenses would be 12330 – 5180 = Rs.7150/-.

 

The above way of segregating fixed and variable overheads is statistical regression analysis method of estimating y on the basis of X. However, in making estimates on the basis of the above method, it is important to remember that the assumption is being made that relationship has not changed since the regression equation was computed.

 

Over and under-absorption: The above suggested treatment of fixed manufacturing overheads on normal production capacity may give rise to following situations:

 

Ø   Production is less than Normal Capacity

Ø   Production is higher than Normal Capacity

 

Situation 1: If production is less than normal capacity, the fixed manufacturing overheads to be absorbed would be number of units produced * overhead absorption rate. Thus, there would arise a difference between the actual overheads incurred and that absorbed. Such difference is not to be considered for the purpose of valuation of closing stock of finished goods.

 

Example:

Normal Capacity: 10000 units

Annual Fixed Manufacturing Overheads: Rs.100000/-

Fixed manufacturing overhead absorption rate per unit: Rs.100000 / 10000 units = Rs.10 per unit

Goods actually produced: 9000 units

Fixed Manufacturing Overheads absorbed = Rs.10 * 9000 units = Rs.90000/-

Under-absorption of fixed manufacturing overheads = Rs.100000 – Rs.90000 = Rs.10000/-.

Closing Stock of Finished Goods: 500 units

 

Therefore, fixed manufacturing overheads that would be considered while valuing closing stock would be 500 units * Rs.10 = Rs.5000/-.

 

Situation 2: If production during the year is more than normal capacity, the fixed manufacturing overhead rate would be reduced so as to satisfy the condition that the goods are valued at the lower of cost or net realizable value.

 

Example:

All data regarding Normal Capacity, Fixed Manufacturing Overheads remain the same as per the above example except that the goods actually produced is 20000 units. The fixed overheads to be absorbed in this case would be Rs.100000/- only and the overhead absorption rate would be computed as follows: Rs.100000 / 20000 units = Rs.5/- per unit. If closing stock consist of 500 units then fixed manufacturing overheads attributable to the closing stock would be 500 * 5 = Rs.2500/-.

 

Implications of FIFO: Stocks are to be valued as per FIFO basis. This has the following implications:

 

Ø   Raw Material Stocks would be those that have been bought recently. Such stocks should correspond to the latest goods receipt notes.

Ø   Work-in-Process and finished goods stock would be valued on the basis of cost incurred for goods produced during the year.

 

The above suggested application would work well in case of single product line and may be adopted when the concern does not have proper records for the purpose. For ensuring a continuing compliance, it is suggested that costs be accumulated on cost center and product line basis.