models and swot analysis of mrcmpu -...
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CHAPTER - VI
MODELS AND SWOT ANALYSIS OF MRCMPU
INTRODUCTION
Functions of logistics are customer service, demand forecasting,
planning, inventory management, logistics communication, material handling,
order processing, packing, parts, service supports, procurement, reverse
logistics, transportation, ware-housing and storage. As far as dairy industry is
concerned, forecasting demand, procurement of raw milk, processing of raw
milk and transportation of processed milk are the important components of
this system.1
As the fourth secondary objective of this study was to assess
operational efficiency of MRCMPU and recommend measures to enhance
efficiency of operations, this chapter takes up these things. The chapter
focuses on developing various models such as forecasting model, processing
model, inventory model, location planning (Figure 6.1) and SWOT Analysis
of MRCMPU.
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Figure: 6.1: Conceptual Model of Operational Efficiency
FORECASTING OF SALES
Logistical forecasting requires the selection of appropriate
mathematical or statistical techniques to generate periodic forecasts. There are
many different forecasting techniques available and it is important to select the
most appropriate technique. Moving average method and least square method
are commonly used to forecast demand etc. Another more complicated
technique is known as exponential smoothing. Forecasting methods such as
exponential smoothing give a much faster response to any change in demand
trends than other methods. It is a refined version of moving average.
Exponential smoothing bases the estimate of future demands on the weighted
average of the previous demand and forecast levels. The new forecast is a
function of the old forecast incremented by some fraction of the differential
between the old forecast and actual sales realised. The increment of
adjustment is called ‘alpha factor’. The basic format of the model is.2
47
Efficiency
Forecasting of Sales
Location Planning Inventory Control
Enhancement in Production and Efficient Capacity Utilisation
Ft = alpha (Dt - 1) + (1 - alpha) Ft - 1
Ft = forecasted demand for a time period t
Ft -1 = forecast for time period t - 1
Dt - 1 = actual demand for time period t - 1
Alpha = alpha factor or smoothing constant (0 < alpha < 1.0)
When alpha factor is one, forecasted demand is equal to actual demand.
This is because alpha -1 (1-1) = 0. Hence (1 - alpha) Ft -1 becomes also zero.
In such a situation alpha (Dt - 1) i.e., 1 x actual demand remains in the
formula. But alpha factor less than one means actual demand and the
forecasted demand for a time period ‘t’ are different. Hence the major decision
when using exponential smoothing is the determination of the alpha factor. A
very low value of alpha reduces the forecast to almost a simple moving
average, whereas large alpha factors make the forecast very sensitive to
change and therefore highly reactive.
In Table 6.1, daily average sales of milk of Kannur dairy for the period
1991-92 to 2005-06 are presented in the second column. Estimated sales of
milk for various years are calculated under least square method and taken in
the third column. Forecasted milk sales (When alpha = 0.9 & 0.1) for a time
period ‘t’ under exponential smoothing are taken in the fourth column.
Forecasted milk sales (when alpha = 0.9) for the period 1992-93under
exponential smoothing is calculated as follows.
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Ft = alpha (Dt - 1) + (1 - alpha) Ft - 1
Alpha = 0.9
Dt -1 = 28.4 (Actual sales 1991-92)
1- alpha = 0.1
Ft -1 = 29.6 (Estimated sales 1991-92)
Ft = (0.9 × 28.4) + (0.1 × 29.6)
= 25.5 + 3 =28.5
In the next period 1993-94 Ft-1 = 28.5 (last years forecast when alpha =
0.9). In this way various years forecasted sales (when alpha = 0.9 & 0.1) are
calculated. Instead of taking estimated sales under least square, any method
can be adopted. Hence the estimation of demand in the initial period and
deciding the alpha factor depend upon personal judgment, experience,
observation, etc.
In the figure 6.2, Trend of actual milk sales, forecasted milk sales under
exponential smoothing (when alpha = 0.9 & 0.1) and forecasted milk sales
under least square method of Kannur Dairy is shown. In this chart forecasted
milk sales under exponential smoothing have faster response to the changes in
actual milk sales rather than forecasted milk sales under least square method.
Moreover, forecasted milk sales figure under exponential smoothing (when
Alpha = 0.9) is very sensitive to the changes in actual milk sales. Whereas
forecasted milk sales under exponential smoothing (when alpha = 0.1) has a
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slower response to the changes in actual milk sales. Hence this model for
forecasting demand is suggested to delineate the reliability of the forecasting
techniques. The task of the forecaster is to judge the alpha factor which is
based on experience, business environment etc. When the forecasted demand
of milk and milk products is correct, necessary production planning can be
done on the basis of accurate forecasting.
Table 6.1: Forecasted Milk Sales of Kannur Dairy (in ’000 LPD)
Year Actual SalesEstimated Sales (Least Square)
Forecasted Sales (when
alpha = 0.9
Forecasted Sales (when
alpha = 0.1)
1991-92 28.4 29.6 0.0 0.0
1992-93 32.3 34.9 28.5 29.4
1993-94 39.6 40.3 32.0 29.8
1994-95 45.3 45.7 39.1 35.4
1995-96 55.3 51.1 44.8 40.8
1996-97 59.9 56.4 54.3 46.6
1997-98 64.3 61.8 59.0 51.9
1998-99 68.1 67.2 63.5 57.2
1999-2000 71.6 72.6 67.5 62.4
2000-01 76.8 78.0 71.2 67.6
2001-02 79.4 83.3 76.4 73.0
2002-03 84.1 88.7 79.3 78.1
2003-04 95.6 94.1 84.0 83.4
2004-05 101.1 99.5 94.9 89.4
50
2005-06 106.2 104.9 100.4 94.8
Source: Compiled from Annual Reports of MRCMPU
51
Figure 6.2: Actual and Forecasted Milk Sales – Kannur Dairy
0
20
40
60
80
100
120
1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-2k 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06
Years
Va
lue
s
Actual Sales Estimated Sales (Least Square) Forecasted Sales (w hen alpha = 0.9 Forecasted Sales (w hen alpha = 0.1)
52
PROCESSING MODEL
Forrester (1961)3 describes one interesting effect of a fragmented
supply chain. Imagine a retailer who notices that the demand for a product
rises by certain units in a day, week, or month. When it is time to place the
next order, the retailer assumes that demand is rising, and orders extra units to
make sure he has enough. Hence the local wholesaler sees demand rise by
additional units, so he orders an extra unit to meet the growth. Again the
regional wholesaler sees demand rise by extra units, so he also orders extra
units, to the manufacturer. As this movement travels through the supply chain,
a relatively small change in final demand is amplified into a major variation
for early suppliers.4
Assumptions: This model is based on the following assumptions.
1. The average daily sales of various milk and milk products are taken as
retailers buy (i.e., retailers order) for ascertaining the estimated
production (Dairy makes) of different varieties of milk and milk
products.
2. Cent percent daily sales of milk and milk products are kept as closing stock
of a day to meet the demand of the next day.
3. The main varieties of milk such as Toned, STD, and White Milk are
considered. The other varieties of milk such as Milma Smart,
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Homogeneous Toned Milk, etc are included in the average daily sales of
Toned Milk.
4. Only the main milk products such as ghee, curd and sambharam are
considered. The other value added milk products such as ice cream, peda
etc are not included.
5. The initial period 1990-91 is taken as base year in the case of Toned Milk
and the period 1992-93 in the case of STD milk and so on. The
assumption is that there are no differences in retailers buy dairy demand,
opening stock, closing stock and dairy makes in the initial period.
6. All estimates are done on average basis and hence seasonal variations in
the production of milk and milk products are not taken into account.
Estimated Production of Milk and Milk Products
The average daily sales of milk and milk products are taken as retailers
buy which is equivalent to the demand of dairy for milk and milk products.
Dairy assumes that demand is rising; hence it keeps the same units of demand
as closing stock in order to meet the existing demand in future. The closing
stock of a day will be the opening stock of the next day. When the demand,
opening stock and closing stock are considered the average production (Dairy
makes) can be ascertained with the following formula.
Dairy makes = Demand + closing stock - opening stock or
Dairy makes = Demand + Changes in stock
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As per Table 6.2, the average daily sales of Toned Milk of Kannur
dairy for the period 1990-91 to 2005-06 are taken as retailers buy that which
are equivalent to the demand of dairy. As an average daily sale of a particular
year is taken as base, the retailer notices the rise in demand in the average
daily sale of next year and so on. During the initial period 1990-91, on an
average, retailers buy 19,500 litres of Toned Milk per day which is equivalent
to the demand of dairy. As the retailer notices the rise in demand in the next
period, dairy keeps the same units of demand as opening and closing stock i.e.
no change in stock in the initial period. The average estimated production of
Toned Milk of Kannur dairy during the period 1990-91 is calculated by the
formula as:-
Dairy makes = Demand + Closing stock - Opening stock
i.e., Dairy Makes = 19,500 LPD + 19,500 LPD - 19,500 LPD = 19,500 LPD
During the next year 1991-92, the average demand of Toned Milk is
28,400 LPD which is equivalent to the demand of dairy. The closing stock of
19,500 LPD of the last year is the opening stock of the year 1991-92. The
closing stock of 1991-92 is equivalent to the demand of 28,400 LPD, and
hence the average daily production of Toned Milk during the period 1991-92
is 37,300 LPD (Table 6.2). In this way the daily average production of Toned
Milk for the subsequent periods is ascertained. The same method is adopted to
ascertain the daily average production of different varieties of milk such as
55
STD milk, white milk, and milk products such as ghee, curd and, sambharam
of Kannur, Kozhikode and Palakkad dairies.
Table 6.2: Estimated Production of Toned Milk – Kannur dairy (’000 LPD)
1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97
Retailers Buy 19.5 28.4 24.5 25.8 29.2 37.5 49.2
Dairy Demand 19.5 28.4 24.5 25.8 29.2 37.5 49.2
O. Stock 19.5 19.5 28.4 24.5 25.8 29.2 37.5
CI. Stock 19.5 28.4 24.5 25.8 29.2 37.5 49.2
Makes 19.5 37.3 20.6 27.1 32.6 45.8 60.9
1997-98 1998-99 1999-2k 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06
54.0 58.4 62.0 65.4 68.0 72.2 82.4 83.9 88.2
54.0 58.4 62.0 65.4 68.0 72.2 82.4 83.9 88.2
49.2 54.0 58.4 62.0 65.4 68.0 72.2 82.4 83.9
54.0 58.4 62.0 65.4 68.0 72.2 82.4 83.9 88.2
58.8 62.8 65.6 68.8 70.6 76.4 92.6 85.4 92.5
Source: Annual Records of MRCMPU
Estimated Processing of Milk
The standard requirement of fat in cow milk is 4.2 percent and SNF is
8.5 percent. But the cow milk in Kerala, especially the milk collected from
various APCOS by MRCMPU has only 3.5 percent Fat and 8.3 Percent SNF.
Moreover dairy makes different varieties of milk with varied Fat and SNF. For
example, dairy makes Toned Milk (Fat 3.0 percent and SNF 8.5 Percent),
Standardised milk - STD - (Fat 4.5 Percent and SNF 9.0 Percent). Double
Toned Milk - white - (Fat 2.0 percent and SNF 9.0 Percent). Hence total
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quantity of milk required for processing the above varieties of milk is
ascertained. Estimated average production of various varieties of milk for the
period 1990-91 to 2005-06 of Kannur dairy mentioned in the previous
paragraph is taken first. Then the actual milk required for processing Toned,
STD and White Milk is ascertained by considering the required Fat and SNF
contents.
As per Table 6.3, the actual quantity of milk required for processing
Toned, STD and White Milk of Kannur dairy for the period 1990-91 to 2005-
06 is ascertained. Actual quantity of milk required for processing is calculated
by using the following formula.∗
Actual milk required for processing = Estimated average production of
milk × fats required / fats in cow milk.
In this study, Kannur dairy makes 19,500 LPD of Toned Milk during
the period 1990-91. The actual quantity of milk required for processing of
Toned Milk by using the above formula
= 9,500 × 3.0 / 3.5
In a similar way, the actual quantity of milk required for processing
STD and white milk is ascertained by taking the required fats. Kannur dairy
started the production of STD milk during the period 1992-93 and the
production of White milk during the period 2000-01. The total quantity of
milk required for processing Toned, STD and White Milk for the period 1990-
Source: Quality Control Department, Mother Dairy, Ambattur, Tamil Nadu.
57
91 to 2005-06 is ascertained and compared with actual procurement of milk in
order to ascertain the shortage or surplus of milk. In the same way, the
shortage or surplus milk of Kozhikode and Palakkad dairies is also
ascertained.
Table 6.3: Processing of Milk – Kannur Dairy (‘000 LPD)
1990-91 1991-92 1992-93 1993-94 1994-95 1995-96
Dairy - Makes
Toned 19.5 37.3 20.6 27.1 32.6 45.8
STD 0.0 0.0 7.8 19.8 18.4 19.5
White 0.0 0.0 0.0 0.0 0.0 0.0
Total 19.5 37.3 28.4 46.9 51.0 65.3
Actual milk required for processing
(Makes × 3 / 3.5) Toned 16.7 32.0 17.7 23.2 27.9 39.3
(Makes × 4.5 / 3.5) STD 0.0 0.0 10.0 25.5 23.7 25.1
(Makes × 2 / 3.5) White 0.0 0.0 0.0 0.0 0.0 0.0
Total 16.7 32.0 27.7 48.7 51.6 64.3
Total qty of SNF required
(Makes × 8.5 / 90) Toned 1.8 3.5 1.9 2.6 3.1 4.3
(Makes × 9.0 / 90) STD 0.0 0.0 0.8 2.0 1.8 2.0
(Makes × 9.0 / 90) White 0.0 0.0 0.0 0.0 0.0 0.0
Total 1.8 3.5 2.7 4.5 4.9 6.3
Total qty of SNF available (Processing
× 8.3 / 90)
Toned 1.5 2.9 1.6 2.1 2.6 3.6
STD 0.0 0.0 0.9 2.3 2.2 2.3
White 0.0 0.0 0.0 0.0 0.0 0.0
Total 1.5 2.9 2.6 4.5 4.8 5.9
Extra SMP required at 90% soluble (T. SNF required - T. SNF available) -’000 kgs.
Toned 0.3 0.6 0.3 0.4 0.5 0.7
STD 0.0 0.0 - 0.1 - 0.4 - 0.3 - 0.4
White 0.0 0.0 0.0 0.0 0.0 0.0
Total 0.3 0.6 0.2 0.0 0.2 0.3
58
Qty of milk to be processed to meet extra SMP (qty × 90 / 8.3)
3.3 6.2 1.9 0.5 1.7 3.7
Qty of water required (Makes - Milk for processing) - Lakh litres
Toned 2.8 5.3 2.9 3.9 4.7 6.5
STD* 0.0 0.0 0.0 0.0 0.0 0.0
White 0.0 0.0 0.0 0.0 0.0 0.0
Total 2.8 5.3 2.9 3.9 4.7 6.5
Continued
1996-97 1997-98 1998-99 1999-2k 2000-01 2001-02
Dairy - Makes
Toned 60.9 58.8 62.8 65.6 68.8 70.6
STD 3.6 9.9 9.1 9.5 10.4 12.8
White 0.0 0.0 0.0 0.0 1.4 0.0
Total 64.5 68.7 71.9 75.1 80.6 83.4
Actual milk required for processing
(Makes × 3 / 3.5) Toned 52.2 50.4 53.8 56.2 59.0 60.5
(Makes × 4.5 / 3.5) STD 4.6 12.7 11.7 12.2 13.4 16.5
(Makes × 2 / 3.5) White 0.0 0.0 0.0 0.0 0.8 0.0
Total 56.8 63.1 65.5 68.4 73.1 77.0
Total qty of SNF required
(Makes × 8.5 / 90) Toned 5.8 5.6 5.9 6.2 6.5 6.7
(Makes × 9.0 / 90) STD 0.4 1.0 0.9 1.0 1.0 1.3
(Makes × 9.0 / 90) White 0.0 0.0 0.0 0.0 0.1 0.0
Total 6.1 6.5 6.8 7.1 7.7 7.9
Total qty of SNF available (Processing × 8.3 / 90)
Toned 4.8 4.6 5.0 5.2 5.4 5.6
STD 0.4 1.2 1.1 1.1 1.2 1.5
White 0.0 0.0 0.0 0.0 0.1 0.0
Total 5.2 5.8 6.0 6.3 6.7 7.1
Extra SMP required at 90% soluble (T. SNF required - T. SNF available) -’000 kgs.
Toned 0.9 0.9 1.0 1.0 1.1 1.1
STD -0.1 -0.2 - 0.2 -0.2 -0.2 -0.2
White 0.0 0.0 0.0 0.0 0.1 0.0
Total 0.9 0.7 0.8 0.8 0.9 0.8
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Qty of milk to be processed to meet extra SMP (qty × 90/8.3)
9.4 7.8 8.7 9.0 10.1 9.2
Qty of water required (Makes - Milk for processing) - Lakh litres
Toned 8.7 8.4 9.0 9.4 9.8 10. 1
STD* 0.0 0.0 0.0 0.0 0.0 0.0
White 0.0 0.0 0.0 0.0 0.6 0.0
Total 8.7 8.4 9.0 9.4 10.4 10.1
Continued
2002-03 2003-04 2004-05 2005-06
Dairy - Makes
Toned 76.4 92.6 85.4 92.5
STD 12.4 13.5 12.3 13.1
White 0.0 1.0 8.9 5.7
Total 88.8 107.1 106.6 111.3
Actual milk required for processing
(Makes × 3 / 3.5) Toned 65.5 79.4 73.2 79.3
(Makes × 4.5 / 3.5) STD 15.9 17.4 15.8 16.8
(Makes × 2 / 3.5) White 0.0 0.6 5.1 3.3
Total 81.4 97.3 94.1 99.4
Total qty of SNF required
(Makes × 8.5 / 90) Toned 7.2 8.7 8.1 8.7
(Makes × 9.0 / 90) STD 1.2 1.4 1.2 1.3
(Makes × 9.0 / 90) White 0.0 0.1 0.9 0.6
Total 8.5 10.2 10.2 10.6
Total qty of SNF available (Processing × 8.3 / 90)
Toned 6.0 7.3 6.8 7.3
STD 1.5 1.6 1.5 1.6
White 0.0 0.1 0.5 0.3
Total 7.5 9.0 8.7 9.2
Extra SMP required at 90% soluble(T. SNF required - T. SNF available) -’000 kgs.
Toned 1.2 1.4 1.3 1.4
STD -0.2 -0.3 -0.2 -0.2
White 0.0 0.0 0.4 0.3
Total 0.9 1.2 1.5 1.5
60
Qty of milk to be processed to meet extra SMP (qty × 90/8.3)
10.3 13.3 16.3 15.7
Qty of water required (Makes - Milk for processing) - Lakh litres
Toned 10.9 13.2 12.2 13.2
STD* 0.0 0.0 0.0 0.0
White 0.0 0.4 3.8 2.4
Total 10.9 13.7 16.0 15.7
Estimated Quantity of Milk Required for Making Curd
As Kannur dairy started production of curd during the period 1994-95,
the average estimated production of curd from 1994-95 to 2005-06 is
ascertained. As per Table 6.4, the average estimated production of curd of
Kannur dairy during the period 1994-95 is 700 LPD. As one litre of curd is
equivalent to 1 litre of skimmed milk, the total quantity of skimmed milk
required during this period is 700 LPD. Skimmed milk is produced after
extracting whole fats from the milk. Hence one litre of skimmed milk is
equivalent to 1-3.5% fat = 96.5 percent. Total quantity of milk required to
produce 700 LPD of curd is equivalent to 700/96.5 percent = 725.4 LPD. In
the similar way estimated quantity of milk required for processing curd of
three dairies for the period 1994-95 to 2005-06 is ascertained.
61
Table 6.4: Estimated Quantity of Milk Required for Making Curd - Dairywise (’000 LPD)
Year Kannur Kozhikode Palakkad
Est
imat
edP
rodu
ctio
n
Qty
of
milk
to b
e pr
oces
sed
Est
imat
edP
rodu
ctio
n
Qty
of
milk
to b
e pr
oces
sed
Est
imat
edPr
oduc
tion
Qty
of
milk
to b
e pr
oces
sed
1994-95 0.7 0.7 0.0 0.0 0.0 0.0
1995-96 4.2 4.4 1.1 1.1 0.4 0.4
1996-97 4.0 4.2 2.9 3.0 2.2 2.3
1997-98 3.9 4.0 3.6 3.7 1.9 2.0
1998-99 5.2 5.4 4.4 4.6 2.8 2.9
1999-2000 4.5 4.6 5.2 5.4 2.8 2.9
2000-01 5.1 5.3 6.2 6.4 3.5 3.6
2001-02 6.0 6.2 7.3 7.6 3.0 3.1
2002-03 7.0 7.3 9.1 9.4 4.0 4.1
2003-04 8.6 8.9 9.1 9.4 4.1 4.2
2004-05 8.8 9.1 10.4 10.8 5.0 5.1
2005-06 9.5 9.8 11.2 11.6 5.2 5.3
Source: Annual Records of MRCMPU
Estimated Quantity of Milk Required for Making Sambharam
As sambharam production is started from 1995-96, the average
estimated production of sambharam of dairies is ascertained. On an average,
the estimated production of Sambharam, of Kannur dairy during the period
1995 - 96 is 400 LPD. One litre of sambharam is equivalent to one half litre of
62
skimmed milk plus one half litre of water. Hence the total quantity of
skimmed milk to produce 400 litres of sambharam during this period is 400
litres / 2 = 200 LPD. The total quantity of milk required to produce 200 litres
of skimmed milk (in the case of sambharam) = 200/96.5 = 207.3 LPD (Table
6.5). In this way the estimated quantity of milk required for processing
sambharam of three dairies for the period from 1995-96 to 2005-06 is
ascertained.
Table 6.5: Estimated quantity of Milk Required forMaking Sambharam - Dairywise (’000 LPD)
Year Kannur Kozhikode Palakkad
Est
imat
edP
rodu
ctio
n
Qty
of
mil
k to
be
proc
esse
d
Est
imat
edPr
oduc
tion
Qty
of
milk
to b
e pr
oces
sed
Est
imat
edP
rodu
ctio
n
Qty
of
mil
k to
be
proc
esse
d
1995-96 0.4 0.2 0.2 0.1 0.4 0.2
1996-97 0.8 0.4 1.0 0.5 1.2 0.6
1997-98 0.8 0.4 1.0 0.5 1.0 0.5
1998-99 1.1 0.6 1.6 0.8 1.1 0.6
1999-2000 0.7 0.4 0.2 0.1 0.2 0.1
2000-01 1.0 0.5 0.7 0.4 1.0 0.5
2001-02 0.9 0.5 0.5 0.3 0.6 0.3
2002-03 0.7 0.4 1.0 0.5 0.7 0.4
2003-04 1.0 0.5 0.6 0.3 0.9 0.5
63
2004-05 0.9 0.5 0.7 0.4 0.6 0.3
2005-06 1.5 0.8 0.9 0.5 0.9 0.5
Source: Compiled from Annual Reports of MRCMPU
Surplus/Shortage of Milk at Dairies
As per Table 6.6, the total quantity of milk (estimated) required for
processing milk, curd and sambharam of Kannur dairy during the period 1990
-91 is 16,700 LPD whereas its actual procurement of milk is 11,900 LPD
(average). Hence the shortage of milk during this period is 4,800 LPD. In the
next year, the shortage of milk went up to 11,100 LPD. But, during the period
1992-93 and 1993-94, it has a surplus milk of 8,600 LPD and 700 LPD
respectively. From the period 1994- 95 to 1998-99 it has shortage of milk. In
the year 1999-2000 it has a surplus milk of 4,200 LPD. But during the period
2000-01 and 2001-02 it has again shortage of 400 LPD and 100 LPD
respectively. In the next year it has surplus milk of 4,100 LPD. In the year
2003-04, the dairy has a shortage of milk 3,500 LPD. But in the last two years
it has a surplus milk of 14,900 LPD and 31,000 LPD respectively (Table 6.7).
Table 6.6: Estimated quantity of Milk Required for Making Milk and Milk Products- Kannur Dairy (’000 LPD)
Year Toned STD White Curd Sambharam Total
1990-91 16.7 0.0 0.0 0.0 0.0 16.7
1991-92 32.0 0.0 0.0 0.0 0.0 32.0
1992-93 17.7 10.0 0.0 0.0 0.0 27.7
1993-94 23.2 25.5 0.0 0.0 0.0 48.7
1994-95 27.9 23.7 0.0 0.7 0.0 52.3
64
Year Toned STD White Curd Sambharam Total
1995-96 39.3 25.1 0.0 4.4 0.2 68.9
1996-97 52.2 4.6 0.0 4.2 0.4 61.4
1997-98 50.4 12.7 0.0 4.0 0.4 67.6
1998-99 53.8 11.7 0.0 5.4 0.6 71.5
1999-2000 56.2 12.2 0.0 4.6 0.4 73.4
2000-01 59.0 13.4 0.8 5.3 0.5 79.0
2001-02 60.5 16.5 0.0 6.2 0.5 83.7
2002-03 65.5 15.9 0.0 7.3 0.4 89.1
2003-04 79.4 17.4 0.6 8.9 0.5 106.8
2004-05 73.2 15.8 5.1 9.1 0.5 103.7
2005-06 79.3 16.8 3.3 9.8 0.8 110.0
Source: Compiled from Annual Reports of MRCMPU
Table 6.7: Surplus/Shortage of Milk - Kannur Dairy (’000 LPD)
YearTotal Milk required
Actual procurement
Surplus of milk
Shortage of milk
1990-91 16.7 11.9 0.0 4.8
1991-92 32.0 20.9 0.0 11.1
1992-93 27.7 36.3 8.6 0.0
1993-94 48.7 49.4 0.7 0.0
1994-95 52.3 51.4 0.0 0.9
1995-96 68.9 52.0 0.0 16.9
1996-97 61.4 48.0 0.0 13.4
1997-98 67.6 57.5 0.0 10.1
1998-99 71.5 66.0 0.0 5.5
1999-2000 73.4 77.6 4.2 0.0
2000-01 79.0 78.6 0.0 0.4
2001-02 83.7 83.6 0.0 0.1
2002-03 89.1 93.2 4.1 0.0
65
2003-04 106.8 103.3 0.0 3.5
2004-05 103.7 118.6 14.9 0.0
2005-06 110.0 141.0 31.0 0.0
Source: Compiled from Annual Reports of MRCMPUIn the case of Kozhikode dairy, it has shortage of milk in all the years,
whereas Palakkad dairy has surplus of milk in all the years (Table 6.8).
Table 6.8: Surplus / Shortage of Milk - Kannur, Kozhikodeand Palakkad Dairies (’000 LPD)
YearKannur Kozhikode
Net ShortagePalakkad
Surplus Shortage Shortage Surplus
1990-91 0.0 4.8 3.5 8.3 6.4
1991-92 0.0 11.1 15.6 26.7 4.1
1992-93 8.6 0.0 10.6 2.0 11.6
1993-94 0.7 0.0 29.4 28.7 13.7
1994-95 0.0 0.9 34.4 35.3 30.2
1995-96 0.0 16.9 41.2 58.1 28.5
1996-97 0.0 13.4 50.7 64.1 36.3
1997-98 0.0 10.1 41.3 51.4 52.6
1998-99 0.0 5.5 35.7 41.2 35.5
1999-2000 4.2 0.0 14.8 10.6 47.8
2000-01 0.0 0.4 7.3 6.9 48.5
2001-02 0.0 0.1 25.2 25.1 56.4
2002-03 4.1 0.0 39.9 35.8 48.4
2003-04 0.0 3.5 39.9 43.4 49.2
2004-05 14.9 0.0 37.2 12.3 53.4
2005-06 31.0 0.0 34.6 3.6 55.3
Source: Compiled from Annual Reports of MRCMPU
Net surplus / Net shortage of Milk of MRCMPU
66
As far as Kannur and Kozhikode dairies are considered, MRCMPU has
clear shortage of milk in all the years (Table 6.8). When the surplus milk of
Palakkad dairy is taken into account, MRCMPU has surplus milk in majority
years (Table 6.9). It means surplus milk of Palakkad dairy is the problem of
MRCMPU. As mentioned in Chapter III, the highest procurement of milk is
recorded from Palakkad district, but it can sell milk in less quantity through
Palakkad dairy. Moreover, Palakkad dairy makes only a single variety of milk
called Toned milk. Hence MRCMPU has to concentrate on the processing of
other varieties of milk in Palakkad dairy.
Table 6.9: Net Surplus/Net Shortage of Milk – MRCMPU (’000 LPD)
Year Net surplus Net shortage
1990-91 0.0 1.9
1991-92 0.0 22.6
1992-93 9.6 0.0
1993-94 0.0 15.0
1994-95 0.0 5.1
1995-96 0.0 29.6
1996-97 0.0 27.8
1997-98 1.2 0.0
1998-99 0.0 5.7
1999-2000 37.2 0.0
2000-01 41.6 0.0
2001-02 31.3 0.0
2002-03 12.6 0.0
2003-04 5.8 0.0
2004-05 31.1 0.0
67
2005-06 51.7 0.0
Source: Compiled from Annual Reports of MRCMPU
In this model, only the retailers are considered. The dairy can enhance
its production when wholesalers are kept in the distribution channel. When
wholesalers keep the daily sale of milk and milk products as closing stock to
meet the next days demand, production of milk and milk products can also be
enhanced to meet the additional demand from wholesalers. A small change in
the demand from wholesalers will amplify in the total demand of the dairy. In
order to increase production, capacity of milk and milk products equipment
must be utilised in most efficient way. In other words capacity utilisation of
milk and milk products equipment is ascertained to identify idle time and
bottlenecks.
CAPACITY UTILISATION
The capacity of an operation is its maximum throughput in a specified
time. All operations have some limit in their capacity. Capacity is an important
concept as it defines the maximum flow through the production process in a
given time. The capacity of a production process sets the maximum amount of
product that can be delivered to the warehouse in a given time. Designed
capacity and effective capacity are the two sides of capacity. Designed
capacity is the maximum possible throughput in ideal conditions whereas
effective capacity means the maximum realistic throughput in normal
conditions. Each part of the production process has a different capacity, and
68
the overall capacity is set by the bottlenecks. Capacity of production process
can only be increased by adding more capacity at the bottleneck.
Capacity Utilisation (in %) = Amount of capacity used × 100 / designed capacity
Capacity Utilisation (in hours) = Amount of capacity used /designed capacity
Estimated Production of Milk and Milk Products of Kannur Dairy
Opening stock, sales and closing stock of various varieties of milk and
milk products of Kannur dairy for the period 2000-01 are given in Appendices
6.1 and 6.2. The opening stock is the quantity of stock as on 1st April 2000, the
sales means the equivalent total quantity of sales made during the period 2000-
01, and the closing stock means the quantity of stock as on 31st March 2001.
Production of milk and milk products is equivalent to sales + closing stock-
opening stock i.e. sales + changes in stock (closing stock-opening stock). Milk
processed for different varieties of milk and milk products is calculated as per
the formula explained in the processing model and the total quantity of milk
required to process different varieties of milk and milk products per day is
ascertained as total quantity of milk processed during this period is divided by
365. Similarly the average production of different varieties of milk per day is
ascertained as total production of different varieties of milk is divided by 365.
Capacity Utilisation of Milk Processing Plant
In the Table 6.10, various milk processing machineries such as
pasteurizer, cream separator, chiller, milk filling with their capacity are given.
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Suppose the dairy is running for 8 hours, the designed capacity of pasteurizer
is equal to 80,000 (10000 × 8) litres per day. The average milk required for
processing of Toned Milk (throughput) is 56,100 litres per day. Hence the
utilisation of pasteurizer for processing Toned Milk is 70 percent (56,100 ×
100/80,000). In the same way utilisation of different milk equipment for
processing various varieties of milk is ascertained. Utilisation of pasteurizer
for processing milk is 87.3 percent. Similarly utilisation of cream separator,
chiller and milk filling machines is 71.1 percent, 124.6 percent and 128.0
respectively. In the calculation of the utilisation of milk filling machines, the
average milk production per day is taken as the amount of capacity used. It
can be concluded that bottleneck (limiting capacity) is found in cream
separator first and pasteurizer in second while producing milk. But chiller and
milk filling machines are over utilised.
Table 6.10: Capacity Utilisation of Milk Equipments of Kannur Dairy – 2000-01 (in Percentage)
Equipment & Capacity Toned STD White Total milk
1. Pasteurizer (10,000 litres per hour)
70.0 16.3 10.0 87.3
2. Cream separator (10,000 litres per hour)
70.0 0.0 10.0 71.1
3. Chiller (7,000 litres per hour)
100.0 22.5 13.8 124.6
4. Milk filling (7,500 litres per hour)
109.0 16.7 2.3 128.0
Source: Compiled from the Records of Kannur Dairy
70
The estimated working hours of pasteurizer for producing Toned, STD,
and White milk are 5.6 hours, 1.3 hours and 0.8 hours respectively. Similarly,
the estimated working hours of cream separator for producing Toned Milk and
White Milk are 5.6 hours, and 0.8 hours respectively. As cream is not
separated while processing STD milk, hence the utilisation of cream separator
is not considered in this case. Estimated working hours of chiller for
producing Toned, STD, and Milma White are 8.0 hours, 1.8 hours and 1.1
hours respectively. Estimated working hours of milk filling machines for
filling Toned, STD, and Milma White are 8.7 hours, 1.3 hours and 0.2 hours
respectively. Total working hours of pasteurizer, cream separator, chiller and
milk filling machines for processing Toned, STD, and white milk are 7.0
hours, 5.7 hours, 10.0 hours, and 10.2 hours respectively (Table 6.11). When
the dairy functions for about 8 hours, bottleneck (limiting capacity) while
processing milk is found in the cases of pasteurizer and cream separator,
whereas chiller and milk filling machines require more time.
Table 6.11: Capacity Utilisation of Milk Equipment of Kannur Dairy - 2000 - 01 (in Hours)
Equipment & Capacity Toned STD White Total milk
1. Pasteurizer (10,000 litres per hour)
5.6 1.3 0.8 7.0
2. Cream separator (10,000 litres per hour)
5.6 - 0.8 5.7
3. Chiller (7,000 litres per hour)
8.0 1.8 1.1 10.0
71
4. Milk filling (7,500 litres per hour)
8.7 1.3 0.2 10.2
Source: Compiled from the Records of Kannur Dairy
Capacity Utilisation of Milk Products Equipment
In Table 6.12 various milk products equipment such as cream separator,
chiller, curd making, sambharam making, curd filling and sambharam filling
with their capacity are given. Like utilisation of milk equipment, utilisation of
milk products equipment can also be ascertained. In this case also, the
utilisation of equipment in percentage is calculated on the basis of the
assumption that the dairy runs for eight hours. In the case of curd, utilisation
of cream separator, chiller, curd making machine and curd filling machine is
6.3 percent, 8.8 percent, 12.0 percent, and 12.5 percent respectively. But in the
case of sambharam utilisation of cream separator, chiller, sambharam making
machine, and sambharam filling machine is 3.1 percent, 0.9 percent, 2.1
percent, and 2.5 percent respectively.
As curd and sambharam are processed in the same equipment, their
total utilisation can be considered. Hence combined utilisation of cream
separator, chiller, curd & sambharam making, and curd & sambharam filling is
6.9 percent, 9.8 percent, 14.6 percent, and 14.5 percent respectively. It can be
concluded that about 7.0 percent to 15.0 percent of capacity is utilised while
producing curd and sambharam.
72
Table 6.12: Capacity Utilisation of Milk Products Equipment of Kannur Dairy - 2000-01 (in Percentage)
Equipment & Capacity Curd Sambharam Total
1. Cream separator (10,000 litres per hour)
6.3 0.6 6.9
2. Chiller (7,000 litres per hour)
8.8 0.9 9.8
3. Curd making (5,000 litres per hour)
12.5 0.0 12.5
4. Sambharam making (3,000 litres per hour)
0.0 2.1 2.1
5. Curd filling (5000 litres per hour)
12.0 0.0 12.0
6. Sambaram filling (5000 litres per hour)
0.0 2.5 2.5
Source: Compiled from the Records of Kannur Dairy
Similarly, the utilisation of these equipments (in hours) for processing
various milk products such as curd, and sambharam can also be calculated.
The estimated working hours of cream separator for producing curd and
sambharam are 0.5 hours, and 0.25 hours respectively. Estimated working
hours of chiller for producing curd and sambharam are 0.7 hours, and 0.07
hours respectively. The estimated working hours of curd making machine and
sambharam making machine are 1.0 hour and 0.3 hour respectively. The
estimated working hours of curd filling machines and sambharam filling
machines are 1.0 hour and 0.2 hour respectively. As the same equipment are
used for producing curd and sambharam, the total working hours of cream
73
separator, chiller, curd & sambharam making, and curd & sambharam filling
machines for processing curd and sambharam, are 0.8 hour, 0.8 hour, 1.3
hours, and 1.2 hours respectively (Table 6.13). It means that the above
mentioned equipment work more or less for one hour for producing curd and
sambharam.
Table 6.13: Capacity Utilisation of Milk Products Equipment of Kannur Dairy - 2000-01 (in Hours)
Equipment & Capacity Curd Sambharam Total
1. Cream separator (10,000 litres per hour)
0.5 0.25 0.8
2. Chiller (7,000 litres per hour)
0.7 0.07 0.8
3. Curd making (5,000 litres per hour)
1.0 - 1.0
4. Sambharam making (3,000 litres per hour)
- 0.3 0.3
5. Curd filling (5000 litres per hour)
1.0 - 1.0
6. Sambharam filling (5000 litres per hour)
- 0.2 0.2
Source: Compiled from the Records of Kannur Dairy
Capacity Utilisation of Common Equipment
Cream separator and chiller are commonly used for processing milk
and milk products. Hence its combined utilisation can be ascertained. The
combined utilisation of cream separator is 78.0 percent, whereas the chiller is
134.4 percent (Table 6.14). In this case bottleneck is found in cream separator,
74
but chiller over runs. Similarly, the combined utilisation of cream separator is
6.5 hours, whereas the chiller is 10.8 hours (Table 6.15). When the dairy
works for eight hours, the cream separator is idle for 1.5 hours, but the chiller
requires additional 2.8 hours.
Table 6.14: Capacity Utilisation of Common Equipment of Milk & Milk Products Kannur Dairy – 2000-01 (in Percentage)
Equipment & Capacity Milk Milk products Total
1. Cream separator (10,000 litres per hour)
71.1 6.9 78.0
2. Chiller (7,000 litres per hour)
124.6 9.8 134.4
Source: Compiled from the Records of Kannur Dairy
Table 6.15: Capacity Utilisation of Common Equipment of Milk & Milk Products Kannur dairy – 2000-01 (in Hours)
Equipment & Capacity Milk Milk products Total
1. Cream separator (10,000 litres per hour)
5.7 0.8 6.5
2. Chiller (7,000 litres per hour)
10.0 0.8 10.8
Source: Compiled from the Records of Kannur Dairy
While enhancing the production, one of the problems from the part of
dairy is the problem of inventory. In this situation, dairy has to concentrate
over regular stock out, performance cycle time, location of depot. Necessary
inventory control techniques must be adopted to ascertain average inventory
and safety stock level to meet the demand from the wholesalers and retailers.
75
In this case combined probability of demand and performance cycle time
should be considered. For this purpose inventory model is developed.
INVENTORY MODEL
Sales forecasting projects unit demand during the inventory
performance cycle. However, demand during replenishment cycle often
exceeds or falls short of what is anticipated. To provide protection against a
stock out when demand exceeds forecast, safety stock is added to base
inventory. Under the conditions of demand uncertainty, the average inventory
is defined as one half of the order quantity plus safety stock. The task of
planning safety stock consists of three steps. First, the likelihood of stock out
must be determined. Second, the demand potential during periods of stock out
must be estimated. Finally, a policy decision is required concerning the design
of stock out protection to introduce into the system.5
Logistical Performance Cycles
The primary unit of analysis for integrated logistics is the performance
cycle. At a basic level, suppliers, the firm, and its customers are linked
together by communications and transportation. The facility locations that
performance cycles link together are referred to as nodes. In addition to nodes
and links, a logistical performance cycle requires inventory. Inventory
committed to a system consists of base stock and safety stock positioned to
protect against variance. It is at the facility nodes that work related to logistics
occurs. Within nodes, inventory is stocked or flows through the node,
76
necessitating a variety of different types of material handling and at least
limited storage. While a degree of handling and in-transit storage takes place
within transportation, such activity is minor in comparison to that typically
performed within a logistical facility, such as a warehouse. The efficiency and
effectiveness of performance cycles are key concerns in logistical
management.6
The amount of safety stock necessary to satisfy a given level of demand
can be determined with the use of statistical technique. In calculating safety
stock levels it is necessary to consider the joint impact of demand and
replenishment cycle (performance cycle) variability. Safety stock requirement
can be determined by using the following formula.7
( ) ( )222 RSSRc σσσ +=
Where:
c = Units of safety stock needed to protect 68.27 percent of all
performance cycle (one standard deviation)
R= Average performance cycle
σ R = Standard deviation of the performance cycle
S= Average daily sales
σ S = Standard deviation of daily sales
Average inventory = (Mean sales + Mean performance cycle time) / 2
77
Units of closing stock to be kept (68.27 percent of all performance
cycle time, i.e., one STD deviation) = Average inventory + safety stock
It means that stock out would occur 31.73 percent of all performance
cycle time.
Units of closing stock to be kept (95.45 percent of all performance
cycle time, i.e. two STD deviation) = Average inventory + (2 × safety stock)
It means that stock out would occur 4.55 percent of all performance
cycle time.
Units of closing stock to be kept (99.73 percent of all performance
cycle time, i.e., three STD deviation) = Average inventory + (3 × safety
stock)
It means that stock out would occur 0.27 percent of all performance
cycle time.
In this analysis performance cycle time is calculated on trial and error
method when the sale is one (Appendix 6.3).
Generally the dairies of MRCMPU distribute milk in the morning and
in the evening. Hence the frequency of distribution of milk in the morning and
in the evening is one. The average daily sale of Toned Milk of Kannur dairy
during the period 1990-91 is 19, 500 LPD. When the daily sales are in 50:50
(percent) i.e., half of the daily sale in the morning and half of the daily sale in
the evening (morning and evening sales of Toned Milk are 9,750 litres each)
78
and, the average performance cycle time is in 4:4 (4 hours in the morning and
4 hours in the evening), to protect 99.73 percent, 95.45 percent and 68.27
percent of performance cycle time, the units of closing stock needed by the
dairy can be calculated by adopting the above mentioned formula.
Table 6.16: Mean and Standard Deviation of Toned Milk Sales of Kannur Dairy
Sales (’000 LPD)Deviation from mean Deviation squared
(d) d2
9.75 0 0
9.75 0 0
S 9.75 Σ d2 0
02
02
=== ∑n
dSσ
Table 6.17: Mean and Standard Deviation of Performance Cycle Time
Performance cycle time (Hours)
Deviation from mean Deviation squared
(d) d2
4 0 0
4 0 0
R 4 Σ d2 0
02
02
=== ∑n
dRσ
( ) ( )222 09.7504Cσ +=
Safety stock = 0
79
19.52
4x9.75
2
RxSInventoryAverage ===
Units of Closing stock needed to protect 68.27 percent of all
performance cycle (1 stand deviation) is equivalent to average inventory +
safety stock.
= 19,500 litres + 0 = 19,500 litres
Units of Closing stock needed to protect 95.45 percent of all
performance cycle (2 stand deviation) is equivalent to average inventory + (2
× safety stock)
= 19,500 litres + (2 × 0)
= 19,500 litres
Units of closing stock needed to protect 99.73 percent of all
performance cycle (3 standard deviation) is equivalent to average inventory +
(3 × safety stock)
= 19,500 litres (3 × 0)
= 19,500 litres
Similarly the units of closing stock needed by the dairy at various levels
of sales at different performance cycle time are calculated.
When the daily sales are in 50:50 (percent) and, the average
performance cycle time is in 3:3 (hours), to protect 99.73 percent, 95.45
percent and 68.27 percent of performance cycle time, the dairy has to keep
80
14,600 litres (approximately 75.0 percent of sales) of Toned Milk as closing
stock (Table 6.18).
When the daily sales are in 50:50 (percent) and, the average
performance cycle time is in 2:2 (hours), to protect 99.73 percent, 95.45
percent and 68.27 percent of performance cycle time, the dairy has to keep
9,750 litres (50.0 percent of sales) of Toned Milk as closing stock (Table
6.18).
Table 6.18 Safety stock, Average Inventory and Closing Stock (in 50:50)
Sales(’000 LPD)
Performance cycle time
(Hours)
Safety stock(’000 LPD)
Average Inventory
(’ 000 LPD)
Closing stock (’ 000 LPD)
1 sigma 2 sigma 3 sigma
9.75: 9.75 4:4 0 19.5 19.5 19.5 19.5
9.75: 9.75 3:3 0 14.6 14.6 14.6 14.6
9.75:9.75 2:2 0 9.75 9.75 9.75 9.75
When the daily sales is in 60:40 (percent) and, the average performance
cycle time is in 2.2:2.2 (hours), to protect 99.73 percent of performance cycle
time, the dairy has to keep 19,400 litres (approximately cent percent of sales)
of Toned Milk as closing stock. To protect 95.45 percent of the performance
cycle time, the dairy has to keep 16,500 litres of Toned Milk (approximately
83.0 percent of sales) as closing stock. Similarly, to protect 68.27 percent of
the performance cycle time, the dairy has to keep 13,600 litres of Toned Milk
(approximately 69.0 percent of sales) as closing stock (Table 6.19). It also
supports that daily sales would be the closing stock to protect 99.73 percent of
81
the performance cycle time, otherwise stock out would be high with a
corresponding decrease in closing stock (Appendix 6.3).
When the daily sales are in 60:40 (percent) and, the average
performance cycle time is in 2.7:2.7 (hours), to protect 99.73 percent of
performance cycle time, the dairy has to keep 22,800 litres, (approximately
119.0 percent of sales) of Toned Milk as closing stock. To protect 95.45
percent of performance cycle time, the dairy has to keep 19,600 litres of
Toned Milk (approximately 102.0 percent of sales) as closing stock. Similarly
to protect 68.27 percent of performance cycle time the dairy has to keep
16,400 litres of Toned Milk (approximately 85.0 percent of sales) as closing
stock (Table 6.19). It supports that the closing stock would be 120.0 percent of
the daily sales to protect 99.73 percent of the performance cycle time,
otherwise stock out would be high with a corresponding decrease in closing
stock (Appendix 6.3).
When the daily sales are in 60:40 (percent) and, the average
performance cycle time is in 3.3:3.3 (hours), to protect 99.73 percent of
performance cycle time, the dairy has to keep 26,700 litres, (approximately
134.0 percent of sales) of Toned Milk as closing stock. To protect 95.45
percent of performance cycle time, the dairy has to keep 23,200 litres of
Toned Milk (approximately 117.0 percent of sales) as closing stock. Similarly
to protect 68.27 percent of performance cycle time the dairy has to keep
19,600 litres of Toned Milk (approximately cent percent of sales) as closing
82
stock (Table 6.19). It supports that closing stock would be 130.0 percent of the
daily sales to protect 99.73 percent of the performance cycle time, otherwise
stock out would be high with a corresponding decrease in closing stock
(Appendix 6.3).
Table 6.19: Safety stock, Average Inventory and Closing Stock (in 60:40)
Sales (’000
LPD)
Performance
cycle time
(Hours)
Safety stock
(’000 LPD)
Average
Inventory
(’ 000 LPD)
Closing stock (’ 000 LPD)
1 sigma 2 sigma 3 sigma
11.7:7.8 2.2:2.2 2.9 10.7 13.6 16.5 19.4
11.7:7.8 2.7: 2.7 3.2 13.2 16.4 19.6 22.8
11.7:7.8 3.3:3.3 3.5 16.1 19.6 23.2 26.7
When the daily sales are in 70:30 (percent) and, the average
performance cycle time is in 1.3.:1.3 (hours), to protect 99.73 percent of
performance cycle time, the dairy has to keep 19,700 litres, (approximately
cent percent of sales) of Toned Milk as closing stock. To protect 95.45 percent
of performance cycle time, the dairy has tokeep15, 200 litres of Toned Milk
(approximately 77.0 percent of sales) as closing stock (Table 6.20). Similarly
to protect 68.27 percent of performance cycle time the dairy has to keep
10,800 litres of Toned Milk (approximately 55.0 percent of sales) as closing
stock. It also supports that daily sales would be the closing stock to protect
99.73 percent of the performance cycle time, otherwise stock out would be
high in corresponding decrease in closing stock (Appendix 6.3).
83
When the daily sales are in 70:30 (percent) and, the average
performance cycle time is in 1.85:1.85 (hours), to protect 99.73 percent of
performance cycle time, the dairy has to keep 24,900 litres, (approximately
124.0 percent of sales) of Toned Milk as closing stock. To protect 95.45
percent of performance cycle time, the dairy has tokeep19, 600 litres of Toned
Milk (approximately cent percent of sales) as closing stock. Similarly to
protect 68.27 percent of performance cycle time the dairy has to keep 14,300
litres of Toned Milk (approximately 72.0 percent of sales) as closing stock
(Table6.20). It supports that 120.0 percent of daily sales would be the closing
stock to protect 99.73 percent of the performance cycle time, otherwise stock
out would be high with a corresponding decrease in closing stock (Appendix
6.3).
When the daily sales are in 70:30 (percent) and, the average
performance cycle time is in 2.7:2.7 (hours), to protect 99.73 percent of
performance cycle time, the dairy has to keep 32,400 litres, (approximately
167.0 percent of sales) of Toned Milk as closing stock. To protect 95.45
percent of performance cycle time, the dairy has to keep 26,000 litres of
Toned Milk (approximately 134.0percent of sales) as closing stock. Similarly
to protect 68.27 percent of performance cycle time the dairy has to keep
19,600 litres of Toned Milk (approximately cent percent of sales) as closing
stock (Table 6.20). It supports that 170.0 percent of daily sales would be the
closing stock to protect 99.73 percent of the performance cycle time,
84
otherwise stock out would be high in corresponding decrease in closing stock
(Appendix 6.3).
Table 6.20: Safety stock, Average Inventory and Closing Stock (in 70:30)
Sales (’000 LPD)
Performance cycle time
(Hours)
Safety stock (’000 LPD)
Average Inventory
(’000 LPD)
Closing stock (’ 000 LPD)
1 sigma 2 sigma 3 sigma
13.7:5.8 1.3 : 1.3 4.5 6.3 10.8 15.2 19.7
13.7:5.8 1.85 : 1.85 5.3 9.0 14.3 19.6 24.9
13.7:5.8 2.7 : 2.7 6.4 13.2 19.6 26.0 32.4
From the above analysis following conclusions are drawn.
1. At all levels of sales, units of closing stock to be kept are directly
related to the performance cycle time. Units of closing stock are
increased when the performance cycle time is increased and vice versa
(Appendix 6.3).
2. Suppose the sales are in 50:50 (percent), units of closing stock can be
reduced by about one-fourth of sales when the performance cycle time is
reduced by one hour. It means every one hour reduction in the
performance cycle time would enable the dairy to reduce the units of
closing stock to one-fourth of its sales (Appendix 6.3).
3. When sales are in 60:40 (percent), and the performance cycle time is in
2.2:2.2, the dairy has to keep about cent percent of sales as closing stock
to protect 99.73 percent of the performance cycle. When sales are in
85
60:40 (percent) and, the average performance cycle time is in 2.7:2.7
(hours), the dairy has to keep 119.0 percent of sales as closing stock to
protect 99.73 percent of performance cycle time. When sales are in 60:40
(percent) and, the average performance cycle time is in 3.3:3.3 (hours),
the dairy has to keep 134.0 percent of sales as closing stock to protect
99.73 percent of performance cycle time (Appendix 6.3).
4. When sales are in 70:30 (percent), and the performance cycle time is
1.3: 1.3, the dairy has to keep about cent percent of sales as closing stock
to protect 99.73 percent of the performance cycle. When sales are in
70:30 (percent) and, the average performance cycle time is in 1.85:1.85
(hours), the dairy has to keep 124.0 percent of sales as closing stock to
protect 99.73 percent of performance cycle time. When sales are in 70:30
(percent) and, the average performance cycle time is in 2.7:2.7 (hours),
the dairy has to keep 167.0 percent of sales as closing stock to protect
99.73 percent of performance cycle time (Appendix 6.3).
5. When the performance cycle time is increased, the volume of safety
stock, average inventory and closing stock would also be increased. In
this case the dairy has to keep the increased volume of closing stock.
Otherwise, the dairy cannot protect stock out at 99.73 percent of
performance cycle.
6. A small increase in the performance cycle time would amplify the
increase in the volume of closing stock.
86
7. When the volume of closing stock is not kept by the dairy in
correspondence with the increase in the performance cycle time, the
stock out would occur earlier than 99.73 percent of the performance cycle
time.
From the above, we can conclude that the change in the performance
cycle time is directly related to the volume of safety stock and closing
inventory. In this case capacity of warehouse is the main problem from the
part of dairy. Frequent stock out must be done to avoid bottlenecks in the
warehouse capacity. Location planning is another problem from the part of
MRCMPU. For this purpose location of logistics centres in the various
distribution routes must be determined.
LOCATION PLANNING
Location decisions are needed wherever an organisation opens new
facilities. If an organisation makes a mistake and opens facilities in poor
location, it cannot simply close down and move to a better place. Working at
the wrong location can give very poor performance, but moving can be
equally difficult. The only solution is to choose the right location in the first
place. The right location does not guarantee success but the wrong location
will certainly guarantee failure. Location decisions are invariably difficult;
hence organisations have to consider many factors. Some of these factors are
operating costs, distance from current locations, demand for the product etc.
Facility location involves a hierarchy of decisions. Alternative areas within
87
this region come next. Then alternative towns and cities within this area are
considered. Finally different sites within a preformed town are considered.
The broad decisions about geographical regions come from the business
strategy. Two standard models, single median problem and covering problems
are taken to locate the logistic centers.
In this model, a particular distribution route of Kannur dairy for the
year 2004-05 called ‘Alakode a.m.’ is taken as sample. This route consists of
thirteen logistics centers (including the dairy). These logistics centers are
given in the Table 6.21 vertically and horizontally. In the second column
minimum distance from the dairy to different logistics centers is given
vertically. Similarly in the third to last columns minimum distance between
two logistics centers are taken vertically. In Table 6.22, units of Toned Milk
distributed in packets per day (morning) to different logistics centers are taken
as weight in the second column. Next the minimum distance of each logistics
centre is multiplied by the corresponding weight of each logistics centre so as
to ascertain weight distance for each logistics centre. These weight distances
of each logistics centre are given vertically in the third to last columns. Then
adding these down the column gives the total weight distance for each
logistics centre. By comparing the total weight distances of each logistics
centre, Pulimparamba has the lowest total cost and is the single median. Hence
Kannur dairy should start looking for a location at this logistics centre so as to
minimise the weight distance in the case of Toned Milk distribution of this
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route in the morning. In a similar way, this model can be used to locate depot
for different routes for other varieties of milk and milk products of various
dairies under MRCMPU.
Table 6.21: Maximum Distance between Each Logistics Centre (Alakode Route a.m.) - Kannur Dairy – 2004 – 05 (in K.M.)
DairyKeecherr
yKolathu-
vayalAchan - peedika
S I Mukku
Ozhakram Kuttikkol
Dairy 0 7 8 16 17 18 22
Keecherry 7 0 1 9 10 11 15
Kolathuvayal 8 1 0 8 9 10 14
Achanpeedika
16 9 8 0 1 2 6
S I Mukku 17 10 9 1 0 1 5
Ozhakram 18 11 10 2 1 0 4
Kuttikkol 22 15 14 6 5 4 0
Ezham mile 24 17 16 8 7 6 2
Palayad 26 19 18 10 9 8 4
Puliparamba 27 20 19 11 10 9 5
Mukola 29 22 21 13 12 11 7
Karuvanchal 51 44 43 35 34 33 29
Alakkode 54 47 46 38 37 36 32
Maximum 54 47 46 38 37 36 32
Continued
89
Ezham mile Palayad Puliparamba Mukola Karuvanchal Alakkode
Dairy 24 26 27 29 51 54
Keecherry 17 19 20 22 44 47
Kolathu vayal 16 18 19 21 43 46
Achanpeedika
8 10 11 13 35 38
S I Mukku 7 9 10 12 34 37
Ozhakram 6 8 9 11 33 36
Kuttikkol 2 4 5 7 29 32
Ezham mile 0 2 3 5 27 30
Palayad 2 0 1 3 25 28
Puliparamba 3 1 0 2 24 27
Mukola 5 3 2 0 22 25
Karuvanchal 27 25 24 22 0 3
Alakkode 30 28 27 25 3 0
Maximum 30 28 27 29 51 54
Source: Compiled from the Records of Kannur Dairy
Table 6.22: Total Weight Distances Between Each Logistics Centre (Alakode Route a.m.) – Kannur Dairy - 2004-05 (in Units)
1 2 3 4 5 6 7 8
Weight
Dairy KeecherryKolathuv
-ayalAchanpee
dikaS I-
MukkuOzhakram
Dairy 0 0 0 0 0 0 0
Keecherry 100 700 0 100 900 1000 1100
90
Kolathuvayal 50 400 50 0 400 450 500
Achanpeedika 80 1280 720 640 0 80 160
S I Mukku 80 1360 800 720 80 0 80
Ozhakram 70 1260 770 700 140 70 0
Kuttikkol 25 550 375 350 150 125 100
Ezham mile 100 2400 1700 1600 800 700 600
Palayad 60 1560 1140 1080 600 540 480
Puliparamba 144 3888 2880 2736 1584 1440 1296
Mukola 302 8758 6644 6342 3926 3624 3322
Karuvanchal 80 4080 3520 3440 2800 2720 2640
Alakkode 120 6480 5640 5500 4560 4440 4320
Total 32716 24239 23228 15940 15189 14598
continued
9 10 11 12 13 14 15
Kuttik-kol
Ezham mile
Palayad Puli -paramb
a
Mukola Karuvan-chal
Alakkode
Dairy 0 0 0 0 0 0 0
Keecherry 1500 1700 1900 2000 2200 4400 4700
Kolathuvayal 700 800 900 950 1050 2150 2300
Achanpeedika 480 640 800 880 1040 2800 3040
S I Mukku 400 560 720 800 960 2720 2960
Ozhakram 280 420 560 630 770 2310 2520
Kuttikkol 0 50 100 125 175 725 800
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Ezham mile 200 0 200 300 500 2700 3000
Palayad 240 120 0 60 180 1500 1680
Puliparamba 720 432 144 0 288 3456 3888
Mukola 2114 1510 906 604 0 6644 7550
Karuvanchal 2320 2160 2000 1920 1760 0 240
Alakkode 3840 3600 3360 3240 3000 360 0
Total 12794 11992 11590 11509 11923 29765 32678
Source: Compiled from the Records of Kannur Dairy
However, the standard findings are that the best location is always in
one of the logistical centers. As per Table 6.21, the logistics centre
Pulimparamba gives maximum distance of 27 kilometers. This is an example
for single location. But two or more locations may be identified to give best
customer service. As per Table 6.21 maximum travel distances varies from 0
to 54 kilometers; the average travel distance of 27 kilometers is taken as
maximum journey distance. In this table, the combination of logistics centers
that gives service distance of 27 kilometers or less than 27 kilometers is taken.
Out of these logistics centers, five logistics centers such as the dairy itself,
Ezham mile, Pulimparamba, Karuvanchal and Alakode have given maximum
travel distance (Table 6.23). When Kannur dairy considers to locate two or
more depots of this route, any of the above five logistics centers can be
considered to give best customer service. In this way the location of two or
more logistics centers of other distribution routes of different varieties of milk
and milk products of dairies under MRCMPU can be decided.
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Table 6.23 - Location of Logistics Centre (Alakode Route a.m.)
– Kannur dairy 2004 – 05 (in Km)
1 2 3 4 5 6 7 8
Dairy Keecherry
Kolathu-vayal
Achan - peedika
S I Mukku
Ozhakram Kuttikkol
Dairy 0 7 8 16 17 18 22
Keecherry 7 0 1 9 10 11 15
Kolathuvayal 8 1 0 8 9 10 14
Achanpeedika 16 9 8 0 1 2 6
S I Mukku 17 10 9 1 0 1 5
Ozhakram 18 11 10 2 1 0 4
Kuttikkol 22 15 14 6 5 4 0
Ezham mile 24 17 16 8 7 6 2
Palayad 26 19 18 10 9 8 4
Puliparamba 27 20 19 11 10 9 5
Mukola 22 21 13 12 11 7
Karuvanchal
Alakkode
Maximum time 27 22 21 16 17 18 22
continued
9 10 11 12 13 14
Ezham mile
Palayad Puli paramba
Mukola Karuvanchal Alakkode
Dairy 24 26 27 0
Keecherry 17 19 20 22
Kolathuvayal 16 18 19 21
93
Achanpeedika 8 10 11 13
S I Mukku 7 9 10 12
Ozhakram 6 8 9 11
Kuttikkol 2 4 5 7
Ezham mile 0 2 3 5 27
Palayad 2 0 1 3 25
Puliparamba 3 1 0 2 24 27
Mukola 5 3 2 0 22 25
Karuvanchal 27 25 24 22 0 3
Alakkode 27 25 3 0
Maximum time 27 26 27 25 27 27
Source: Compiled from the Records of Kannur Dairy
Assumptions
The following assumptions are made for this modeling.
1. Operating costs in nearby logistics centre remain same.
2. Transport cost is proportional to the distance moved.
3. Type of vehicles, frequency of journeys, ways of combining
customer orders, order patterns etc are the same.
4. Speed of vehicle from one logistics centre to the nearby logistics
centre is same and hence the time taken to cover one kilometer is same
for all logistics centers.
94
SWOT ANALYSIS
In the following part of this chapter, SWOT Analysis of MRCMPU is
given. An effective strategy can be formulated with the help of SWOT
Analysis. The strengths and weaknesses existing within the firm can be
matched with the opportunities and threats operating in the environment.
Hence the strategy of a firm is to capitalise the opportunities through the use
of strengths and to neutralise the threats by minimising the impact of
weaknesses. The following part of this chapter discusses the strengths, the
weaknesses, opportunities and threats of MRCMPU.
Strengths
1. The average annual growth rates of functional APCOS of MRCMPU
and MCDMU were 12.8 percent and 2.9 percent respectively. As
functional APCOS were the well support to procure milk, MRCMPU
gains operational strength.
2. The average annual growth rates of procurement of milk by MRCMPU
and MCDMU were 15.2 percent and 8.0 percent respectively. It showed
that MRCMPU need not depend upon the milk of other unions or nearby
states. Hence it is an operational strength.
3. The average growth rates of milk sales of MRCMPU and MCDMU
were 11.5 percent and 2.3 percent respectively. On an average,
MRCMPU sold 151 thousand litres of milk per day. On the other hand,
MCDMU sold 89 thousand litres of milk per day. The average annual
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growth rate of ghee sales of MRCMPU was 19.8 percent, whereas it was
7.1 percent in the case of MCDMU. Similarly the average annual growth
rate of curd sales of MRCMPU was 23.1 percent, whereas it is 21.5
percent in the case of MCDMU. MRCMPU could gain marketing
strengths in the case of milk, ghee and curd sales.
4. The average market share of milk of MRCMPU and MCDMU were
81.3 percent and 78.6 percent respectively. Similarly the average market
share of butter milk (sambharam) of MRCMPU and MCDMU were 0.6
percent and 0.3 percent respectively. It showed that MRCMPU gains
marketing strengths in milk and butter milk sales.
5. The average market growth rate of milk (value) of MRCMPU and
MCDMU were 9.1 percent and 7.6 per cent respectively. The average
market growth rate of curd (value) of MRCMPU and MCDMU were
16.0 percent and 15.5 percent respectively. Similarly the average market
growth rate of peda (value) of MRCMPU and MCDMU were 65.9
percent and 28.9 percent respectively. Hence MRCMPU could gain
marketing strengths in the case of milk and peda sales.
6. About 98.0 percent of the retailers were satisfied with the present
delivery time of milk products. MRCMPU could gain marketing strength
in distributing milk products to the retail outlets.
96
7. 75.5 percent of the retailers were of the opinion that Toned Milk was
the fast moving milk. It meant that MRCMPU could gain marketing
strength in the case of Toned milk sales.
8. About 39.5 percent of total sales of Toned Milk, 41.0 percent of total
sales of Milma Rich, 40.8 percent of total sales of Milma Smart, 38.3
percent of total sales of Homogenous Toned Milk and 40.0 percent of
total sales of all varieties of milk per year were made in summer. It
showed that MRCMPU could sell all varieties of milk in large scale in
summer season than in winter and monsoon seasons. Hence it was the
marketing strength of MRCMPU.
9. About 67.5 percent of the retailers in monsoon, 82.5 percent of the
retailers in winter, 67.5 percent of the retailers in summer were of the
opinion that ghee sale was average and above average. When ghee sale in
quantity was considered, about 12.6 kgs. per month could be sold in
monsoon, which increased to 22.0 kgs. in winter and decreased to 14.2
kgs. in summer. MRCMPU could sell ghee in large scale in winter
season than in summer and monsoon seasons. Hence it is the marketing
strength of MRCMPU.
10. About 62.5 percent of the retailers in monsoon, 77.7 percent of the
retailers in winter, 95.9 percent of the retailers in summer were of the
opinion that curd sale was average and above average. When curd sale in
quantity was considered about 25 packets (500 ml) per day could be sold
97
in monsoon. This increased to 36 packets in winter and again increased to
59 packets in summer. 13.3 percent of the retailers in monsoon, 40.5
percent of the retailers in winter, 91.9 percent of the retailers in summer
were of the opinion that sambharam sale was average and above average.
When sambharam sale in quantity was considered, about 9 packets (200
ml) per day could be sold in monsoon. Cent percent of the retailers in
summer were of the opinion that ice cream sale was average and above
average. When ice cream sale in quantity was considered, about 1.7 litres
per week could be sold in winter and this increased to 16.3 litres in
summer. It showed that MRCMPU could sell curd, sambaram and ice
cream in large scale in summer season than in winter and monsoon
seasons. Hence it was the marketing strength of MRCMPU.
11. About 93.8 percent of the retailers in monsoon and summer, and 90.7
percent of the retailers in winter, were of the opinion that peda sale was
average and above average. When peda sale in quantity was considered
in numbers, one hundred and thirty three could be sold per day in
monsoon. This decreased to one hundred and six in winter and increased
tone hundred and thirty-four in summer. It showed that MRCMPU could
sell peda almost in same numbers in all seasons. Hence a steady peda sale
in all seasons was the marketing strength of MRCMPU.
12. About 95.7 percent of the consumers of Milma milk were of the
opinion that Milma Rich was always available in all seasons. But cent
98
percent of the consumers of Milma Smart and Homogenous Toned Milk
were of the opinion that Milma Smart and Homogenous Toned Milk
were always available in all seasons. Availability of Milma Rich Milma
Smart and Homogenous Toned Milk in all seasons was the marketing
strength of MRCMPU.
13. As per the opinion of consumers of Milma milk and milk products, the
mean score of various factors such as price of Milma milk and milk
products, quality of Milma milk and milk products, packing of Milma
milk and milk products, handling of consumers’ complaints, innovation
in marketing, scheduling of delivery time, granting of agencies were
ascertained. The mean score of price of Milma milk and milk products,
and packing of Milma milk products were above the score of 4.0. Hence
these factors were considered as marketing strengths of MRCMPU.
Weaknesses
1. The average share of defunct APCOS on registered APCOS of
MRCMPU was 11.5 percent whereas it was 9.1 percent in the case of
MCDMU. It showed that the share of defunct APCOS on registered
APCOS of MRCMPU was higher than that of MCDMU. It was an
operational weakness of MRCMPU.
2. On an average, the annual growth rate of procurement of milk was
15.2 percent as against 11.5 percent growth of milk sales. This imbalance
between procurement and selling of milk was an operational weakness.
99
3. Milk procurement price of MRCMPU was higher than that of
MCDMU. About 80.0 percent of the selling price of the milk was given
by way of procurement price to the farmers. Hence high procurement
cost is the operational weakness of MRCMPU.
4. The average operating ratio of MRCMPU was 103.2 percent. It
meant that MRCMPU sustained operating loss. It was also an operational
weakness of MRCMPU.
5. Milk sales per agency of MRCMPU showed a steady decrease. It
might be either the overcrowding of agencies or inefficiencies of
agencies to sell milk. This is the marketing weakness of MRCMPU.
6. The average market growth rate of sambaram of MRCMPU was
0.4 percent. In the case of MCDMU it was 13.6 percent. The low market
growth rate of sambaram of MRCMPU was a marketing weakness.
7. The average market share of ghee and curd of MRCMPU were 7.8
percent and 5.4 percent respectively. Whereas the average market share
of ghee and curd of MCDMU were 12.1 percent and 7.4 percent
respectively. The market share of ghee and curd of MRCMPU was low;
hence it was a marketing weakness of MRCMPU.
8. Majority of the retailers (54.0 percent) had not taken up selling milk
and milk products of Milma as their main business. It showed that
majority of retailers was doing other business. This was the marketing
weakness.
100
9. 93.3 percent of the consumers of Milma milk were of the opinion
that Toned Milk was always available in summer, 96.7 percent of the
consumers of Milma milk were of the opinion that Toned Milk was
always available in monsoon and winter. It showed that there was the
possibility of shortage of Toned Milk in summer season. This was the
marketing weakness.
10. About 24.0 percent of the consumers were aware of the quality of
Milma milk through advertisement. Similarly 28.0 percent of the
consumers of the consumers were aware of the price of Milma milk
through advertisement. Low awareness of quality and price of Milma
milk was a marketing weakness.
11. About 32.0 percent of the retailers supported under ordering of
milk. It might result shortage of milk sales. This was a marketing
weakness.
12. In the case of Milma Rich, 48.6 percent of the retailers supported
that this variety of milk was average moving and slow moving. In the
cases of Milma Smart and Homogenous Toned Milk, majority of the
retailers were of the opinion that these varieties of milk were average
moving. Average or slow movement of Milma Rich, Milma Smart and
Homogenous Toned Milk was the marketing weakness.
13. Milma would lose about one fourth of its consumers within fifteen
minutes of delivery time, again one fourth of its consumers within the
101
next fifteen minutes, and one fourth of its consumers in the next thirty
minutes. It showed that Milma would lose more than 78.0 percent of its
customers if Milma did not arrive in time. Loss of buyers due to non
arrival of Milma milk in time was the marketing weakness.
Opportunities
1. 50.0 percent, of the consumers of Milma milk were of the opinion that
fresh milk other than Milma was not available during normal business
hours. The lack of alternative sources of milk other than Milma milk was
the marketing opportunity.
2. About 44.0 percent of the retailers were of the opinion that competition
was very low. But 30.0 percent of the retailers were of the opinion that
competition was moderate and above moderate. Moreover, 68.0 percent
of the retailers were of the opinion that competition had rarely or rather
never affected Milma sales. It showed that the intensity of competition
was not high. The low or moderate competition from other firms was the
marketing opportunity.
3. 89.2 percent of the consumers of Milma milk were of the opinion that
the same grade of milk at lower price was not available other than Milma
milk. Non availability of quality milk at lower price from other source
was the marketing opportunity.
4. 1.7 percent of the respondents used milk of other firms. Similarly 4.1
percent of the respondents used milk products of other firms. The low
102
purchase of milk and milk products of other firms was the marketing
opportunity.
5. 55.8 percent of the consumers and 66.7 percent of the non users were of
the opinion that they purchase Milma milk on special occasions like
marriage etc. This was the marketing opportunity.
6. 82.5 percent of the consumers of Milma milk (for the last five yeas)
were the regular consumers of Milma milk, and it increased to 90.0
percent. Again 3.3 percent of the consumers of Milma milk (for the last
five years) who were the non users of Milma milk became regular
consumer of Milma milk. Similarly, 59.2 percent of the consumers of
Milma milk products (for the last five years) were regular consumers of
Milma milk products, and this had increased to 60.8 percent. The
existing rate of regular consumers of Milma milk and milk products was
most likely to increase in future. Increase in the number of consumers of
Milma milk and milk products was the marketing opportunity.
Threats
1. About 40.0 percent of milk sale was made in summer, which decreased
to 28.0 percent in monsoon, and 32.0 percent in winter. All varieties of
milk sales were high in summer and low in monsoon. Seasonal variation
in the selling of all varieties of milk was the marketing threat.
3. 95.0 percent of the retailers in monsoon, 55.0 percent of the retailers in
winter, 82.5 percent of the retailers in summer were of the opinion ghee
103
sale was on average and below average. The average or below average
ghee sales in summer and monsoon was the marketing threat.
4. Cent percent of the retailers in monsoon, 66.7 percent of the retailers in
winter, and 31.2 percent of the retailers in summer were of the opinion
that curd sale was average and below average. The average or below
average curd sales in monsoon was the marketing threat.
5. Cent percent of the retailers in monsoon, 95.1 percent of the retailers in
winter, 31.1 percent of the retailers in summer were of the opinion that
sambharam sale was average and below average The average or below
average butter milk (sambharam) sales in winter and monsoon was the
marketing threat.
6. Cent percent of the retailers in monsoon and winter were of the opinion
that ice cream sale was average and below average The average or below
average ice cream sales in winter and monsoon was the marketing threat.
7. About 58.0 percent of the retailers had net profit less than Rs. 2500 per
month from Milma business. Low profitability of dealers from Milma
business was the marketing threat.
8. Private firms ensured more commission to the retailers than that given by
Milma. They provided credit facility to the retailers. Almost all retailers
can settle their accounts after the sale of milk and milk products. They
were taking back the unsold milk and milk products and encouraged
104
hotels, restaurants etc to buy their milk by advertising more fat content in
their milk. Hence these were the marketing threats.
9. 86.3 percent of the consumers of Milma milk used milk powder and the
remaining 13.7 percent used condensed milk. About 53.3 percent of the
non users of Milma milk purchased milk substitute for domestic use.
Purchase of milk substitute by consumers and non consumers for
domestic use is the marketing threat.
Generally MRCMPU focuses its strategy over the quality of milk and
milk products. As the consumers of Milma milk place much importance on
hygiene factor, quality based strategy of MRCMPU was effective. But a very
important factor like keeping (shelf) life got the lowest score. It supported that
the quality based strategy was not effective. Mainly, MRCMPU must focus its
strategy on the main factors such as availability, quality, price, and packing of
milk and milk products. From the analysis of consumers' survey, availability
of all varieties of milk products, competitive price of Milma milk and milk
products, and packing of Milma milk products are the strengths of MRCMPU.
But the quality of Milma milk and milk products, packing of Milma milk, are
not considered as strengths of MRCMPU. Moreover, the other factors such as
handling of consumer’s complaints, innovation in marketing, scheduling of
delivery time, granting of agencies are also not considered as strengths of
MRCMPU. In this situation MRCMPU must focus its strategy on these factors
to gain strengths and capitalise on its opportunities.
105
SUMMARY
To enhance the efficiency of MRCMPU, an attempt is made to develop
forecasting model, milk and milk products processing model and capacity
utilisation to suggest ways and means to increase the volume of production; to
suggest an inventory model to ascertain the safety stock level required to
satisfy consumers at all levels; to indicate location planning to fix two or more
locations in a particular route of distribution. The strengths, weaknesses,
opportunities and threats of MRCMPU are identified with the help of SWOT
Analysis in order to evaluate the strategy of MRCMPU. The next chapter is
the concluding chapter which focuses the summary and findings of this study.
106
REFERENCES
107
1 Kulkarni Sunil (2004). “Role of Logistics in Dairy Industry”, Indian Journal of
Marketing, Vol. XXXIV, No. 3, p17.
2 Bowersox (2000). “Logistical Management”, p.234.
3 Forrester J (1961). “Industrial Dynamics”. MIT Press, Boston, M. A.
4 Waters Donald (2004). “Logistics, An Introduction to Supply Chain Management”,
Pal grave Macmillan, New York, pp 40-41.
5 Bowersox Donald J. & David J. Closs, (1996). “Logistical Management: The Integrated
Supply Chain Process”, New York: Mc Graw-Hill, p 267.
6 Ibid., pp 46 - 47
7 Lambert M. Douglas and James R. Stock (1993). “Strategic Logistics Management”,
Irwin/Mc Graw - Hill, INC, p.415.