Sales Forecasting and Inventory Management for A-CAT Corporation

This report analyzes the sales forecasting and inventory management challenges faced by A-CAT Corporation, a leading electrical appliance producer in India. The company experienced declining sales of its primary product, voltage regulators (VR-500), prompting an investigation into its inventory ...Read More

A-CAT Corporation
A-CAT was one of the leading producers of electrical appliances in India. It competed with
and belonged to the category of medium scale industry, which produced and distributed
domestic electrical appliances to the rural population in and around the Vidarbha region. The
company owned and operated two medium-sized manufacturing units in a sleepy town called
Gondia, in one of the remote districts in Vidarbha, ironically a backward region in the most
progressive state of India, Maharashtra. A-CAT had an alliance partnership with Jupiter Inc.
for the production of cabinets and had a collaborative venture with Global Electricals for
manufacturing TV signal boosters and battery chargers.
A-CAT's primary flagship product was a voltage regulator of 500 kilovolt amps (KVA) that
was branded and sold under the tag of VR-500. These voltage regulators were used for varied
purposes but were most commonly used in households as a protective device for refrigerators
and television sets, so as to protect the latter from the vagaries of load fluctuations and/or
frequent power failures, which were a very common phenomenon in Vidarbha.
Forecasting Process – Significance
During the past few months, the sales of voltage regulators had fallen off. In reaction, A-CAT
recently started deliberating on its policy of purchasing and stocking spares and components
in the system especially with regard to schedule and stock-in-hand inventory. The firm stored
all its spares and components, including the transformers, in its factory store.
Orders for the main product of A-CAT - that is, voltage regulators is throughout the year.
Most of the time, these orders were categorized as "rush orders," and the store managers
knew that the supplier of the transformers required for the product needed at least one week,
if not more' for delivery. On top of this, it was likely that the transformer supplier would raise
prices if uniformity and continuity in placing of orders for transformers was not guaranteed.
Placing orders beyond a certain limit also stretched the system - whereas A-CAT had
previously had access to four suppliers. Now there was only one. Moreover, the blocking of
capital had a domino effect on the purchase and inventories of other products' spares and
components. An increased cost of its primary spare component was the last thing A-CAT
needed. The sales division was supposed to forecast the demand for voltage regulators as a
measure for determining the right amount of transformers to keep in inventory.

January
February
March
April
May
June
July
August
September
October
November
December
0
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TRA NSFORMER R EQUIR EMENTS
2006 2007 2008 2009 2010
Figure 1: Transformer Requirements
Figure 2A and 2B: Sales Figures of Refrigerators
From the Figure 1, we can observe the trend of transformer requirements over the period of
January’06 to December’10. We can see that there is a higher requirements during the month
of April to July and then a dip near month of September followed by a slight increase during
the end of the year. This trend was almost followed in all the year, under consideration, but
there was a overall increase from the past year.
The Figure 2 shows the sales figures of refrigerators during the period of January’06 to
December’10. We can observe in Figure 2A, that for every quarter sales have almost linearly
I II III IV
0
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7000
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9000
SALES FIGUR ES OF
REFRIGER ATOR S
2006 2007 2008 2009 2010
2006 2007 2008 2009 2010
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SALES FIGUR ES OF
REFR IGER ATOR S
I II III IV

increased over the years and from Figure 2B that the highest sales is during the 2nd quarter
and the lowest during 3rd or 4th quarter in every year.
AR (1) Model
Coefficients Standard
Error t Stat P-value
Intercept 3033.47677
4 1166.006047 2.60159609
1
0.01861849
1
Yt-1
0.45604719
5 0.213631253 2.13474006
1
0.04763642
8
Y t =3033.476774+0.45697195 Y t −1+ ε
AR (2) Model
Coefficients Standard
Error t Stat P-value
Intercept 2508.78026
6 1468.879177 1.70795549
8
0.10825057
4
Yt-1
0.39709226
5 0.259119099 1.53247007 0.14622368
1
Yt-2
0.15763474
2 0.252748441 0.62368235
2 0.54220498
Y t =2508.780266+0.397092265 Y t −1 +0.157634742 Y t−2 +ε
I II III IV I II III IV I II III IV I II III IV I II III IV I II III IV
2006 2007 2008 2009 2010 2011
0
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9000
Sales Forecast of Refrigerators
Ft AR(1)
Ft AR(2)
Yt
Sales

From the P-value, we can see that AR (1) model is significant with alpha of 0.05 but AR (2)
is not significant.
Similarly the transformer requirement was forecasted as:
123456789101112123456789101112123456789101112123456789101112123456789101112123456789101112
2006 2007 2008 2009 2010 2011
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Transformer Requirement
Yt
Ft AR(1)
Ft AR(2)
Requirement
AR (1) Model
Coefficients Standard
Error t Stat P-value
Intercept 228.436302
3 81.58283587 2.80005346
5
0.00696305
9
Yt-1
0.77014985
1 0.082511897 9.33380373
3
4.42088E-
13
Y t =81.58283587+0.770149851 Y t−1 +ε
AR (2) Model
Coefficients Standard
Error t Stat P-value
Intercept 276.6648624 86.78857661 3.187802741 0.002365565
Yt-1 0.933888337 0.132173668 7.065615655 2.94491E-09
Yt-2 -0.214087604 0.132093876 -1.62072316 0.110796927
Y t =276.6648624+0.933888337 Y t −1−0.214087604 Y t −2+ ε
MA (4) Model

Where,
Y t =Sales
CMA=Centred Moving Average Series
Dt =De−seasonalized Series
Ft=Forecast
And for the transformer, we got:
I II III IV I II III IV I II III IV I II III IV I II III IV I II III IV
2006 2007 2008 2009 2010 2011
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12000
Sales Forecast of Refrigerators
Sales
CMA(4)
Dt
Ft
Sales

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2006 2007 2008 2009 2010 2011
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Transformer Forecast
Yt
CMA(12)
Ft
Requirements
We can observe that the MA model was better than AR model in predicting the future as it
can capture the trend and seasonality in the historical data.
ARMA Model
ARMA model contains both AR and MA models. Now, trying to capture the AR part in the
MA model:
AR (1)
Coefficients Standard Error t Stat P-value
Intercept -30.7041079 72.69247527 -0.42238358 0.678038958
Et-1 0.017396597 0.26047515 0.066787934 0.947529726
AR (2)
Coefficients Standard
Error t Stat P-value
Intercept -52.7970748 76.70661384 -0.68829886 0.501771474
Et-1 -0.01562309 0.294626833 -0.0530267 0.9584104
Et-2 -0.29593752 0.283373202 -1.04433841 0.312867965
From the P-value, we can see that adding both AR (1) and AR (2) model to the MA model
was found to be insignificant.
The MA model was found to be with the least amount of error. This model can be used for
making the prediction, as done in the excel sheet and shown in the chart above. This is the