TRANSFORMER DEMAND ANALYSIS PROJECT

A-Cat Corporation

Jessica Sand

Southern New Hampshire University

                                                                      QSO-510

Table of Contents

No.	Name of Topic	Page No.
1	Introduction to the Problem
2	Analysis Plan & Quantitative Factors
3	Problem Statement
4	Strategy to Addresses the Problem
5	Conclusions
6	References

TRANSFORMER DEMAND ANALYSIS PROJECT

Introduction to the Problem

My analysis is focused on A-Cat’s Corporation need to determine the number of transformers needed to satisfy the demand. The firm had previously relied upon archaic methods in projecting how many transformers will be needed to meet customer demand. What they referred to as usual method was to look at the sales figures of the last two to three months and the sales figures of the preceding two years in the same period or the months in questions. They would then guess as to how many transformers will be needed. This case represents a failure in the inventory control and forecasting techniques which can result in big losses as they the management of the company admit it to be characterized by a cycle of   either too many transformers in stock (overstocking) and , when the stock is  not enough to meet our normal production levels and needs (under stocking).

This is the scenario A-Cat Corporation is facing and hence the need for a more accurate forecasting tool to ensure proper stock control and ensure it reduces cost (shortage costs and holding or the carrying costs) and enhances overall profitability. The main stakeholders of A-Cat Corporation mainly originate from within (internal stakeholders) which include A-Cat’s president who is not named and the person who appears to be in charge, Mittra- vice-president of operations. This responsibility has been directly relegated to Ratnaparkhi who is operations head, to develop an analysis of the data and present a report with recommendations which will help satisfy the need of the external stakeholder who are mainly customer.

Analysis Plan & Quantitative Factors

The main quantitative factors that affect the performance of operational processes is mainly the poor approximations and forecast of inventory and demand levels. Inventory and demand levels are very crucial to the performance of any ideal manufacturing firm such as A-Cat Corporation.

The study involves the two variables such as the number of transformers for the year 2007, 2008 and 2009 and the sales of the refrigerator. Both variables have the ratio scale of measurements. We have to find the linear relationship exists between these two variables. We will use the regression analysis for the prediction purpose of the number of transformers. We would consider the dependent variable as the number of transformers and the independent variable as the sales of the refrigerator.

For the variable number of transformers, the average number of transformers for the year 2006 is given as 801.1667 with the variance of 7020.515. The average number of transformers for the year 2007 is given as 898.6667 with the variance of 18750.06 while the average number of transformers for the year 2008 is observed as the 990.3333 with the variance of 21117.88. From this information it is observed that there is significant differences appear in the average number of transformers for the given three years. We have to check these significant differences by using the one way analysis of variance in the next topic.

This is because both deterministic model and stochastic inventory models concur to the fact that the amount of inventory to be ordered or stock emanates from the demand. Indeed the

Anova single factor has facilitated by noting that there is change. i.e The results (F = 6.871 and p = 0.003202) suggest that indeed the mean number of transformers has changed over the period 2006–2008.

ANOVA
Source of Variation	SS	df	MS	F	P-value	F crit
Between Groups	214772.2	2	107386.1	6.870739	0.003202	3.284918
Within Groups	515773	33	15629.48
Total	730545.2	35

For above ANOVA test for checking the significant differences between groups and within groups, we get the test statistic value f as 6.87 with p-value as 0.003 which is very less than the given level of significance or alpha value as 0.05 or 5%. We know as per decision rule we reject the null hypothesis if the p-value is less than the given level of significance or alpha value. Here, p-value is less than the level of significance, so we reject the null hypothesis that there is no any significant difference between the averages of given groups. This means we conclude that there is a significant difference exists between the average values of given groups.

Now, we have to see the regression model for the purpose of estimation of the number of transformers based on the sale of the transformers which is given as below:

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.925948991
R Square	0.857381533
Adjusted R Square	0.849458285
Standard Error	179.467867
Observations	20
ANOVA
	df	SS	MS	F	Significance F
Regression	1	3485332.925	3485333	108.2109	4.84979E-09
Residual	18	579756.8751	32208.72
Total	19	4065089.8
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	1233.499456	167.475485	7.365254	7.79E-07	881.646519	1585.352393
Sales of transformers	0.314901799	0.030271902	10.40245	4.85E-09	0.251302893	0.378500705

For this regression model, we get the correlation coefficient between the number of transformers and sales of transformers as 0.9259 which means there is very strong positive correlation or linear relationship exists between the given two variables. The R square or the coefficient of determination is given as 0.8574 which means about 85.74% of the variation in the dependent variable number of transformers is explained by the independent variable sales of transformers. The p-value for this regression model is given as 0.00 which is less than the given level of significance so we conclude that there is a significant relationship exists between the dependent variable number of transformers and independent variable sales of transformers. The regression line for this model is given as below:

Number of transformer = 1233.50 + 0.3149*Sales of transformer

By using this regression equation, we can estimate the required numbers of transformers by using the given sales of transformer.

The under-estimation of demand will lead to low amount of ordered against the high demand which may arise during the period. In this case the firm will suffer from acute shortage costs as it tries to issue irregular orders to satisfy the extra demand as well as idle capacity especially the warehouse section and the intellectual assets. Over-estimation at the expense of declining demand leads to accumulation of inventory. This will lead to high holding or carrying costs. The fact that the firm is using mean as a central point is unfounded as it is supposed to use the EOQ to determine its optimal inventory it needs to order. The presence of quantifiable errors in mean and the guess work is very costly as aforementioned. The mean has talked of at least 745 transformers but EOQ will accurately provide the optimal level of order.

Problem Statement

The case of A-Cat Corporation presents the need for differential approach to applied demand forecasting and analysis. This requires overall collaboration from downstream vendor to upstream consumer. The demand systems of it has been using  as a manufacturing firm are general, having estimations that  are far  necessarily not realistic neither do they possess reasonable accuracy in the modern flat  world. Our evaluation will consider the comprehension of the differential demand systems and derive the absolute and relative inventory forecasts for A-Cat Corporation.

I address the estimation and projection issues and point out that, unlike most parametric and semi-nonparametric demand forecast systems such as moving average and exponential smoothing. The problem in this revolves around the efficient and accurate method that A-Cat Corporation can to forecast its inventory to ensure it meets transformer demand levels. This will ensure the firm reduce the costs resulting from poor inventory management. The other problem revolves around the relationship between sales of refrigerators which seems to be influencing transformer demand.

Strategy to Addresses the Problem

Due to the strategic importance of Inventory Management in respect to demand levels, there exists an undeniable need for a systematic analysis before deciding accurately on the best and most reasonable method in estimating the demand level to help in creating a good inventory sourcing mechanism. There is need to use  a more  systematic analysis for evaluating components of the company’s inventory  for outsourcing may be useful to practitioners and  analyzing information on current inventory forecasting techniques. The best expertise choice is used to implement the moving average and exponential smoothing in forecasting demand of transformers. In the inventory management model, I will propose the EOQ model to ensure optimal orders to reduce high ordering and carrying costs of inputs.

Illustration; Using 2-Month Moving Average using Microsoft Word, the transformer Demand for 2009 can be extracted as shown.

			2-Month MA
2006	2007	2008	2009
779	845	857	838.5
802	739	881	869.0
818	871	937	909.0
888	927	1159	1048.0
898	1133	1072	1155.5
902	1124	1246	1159.0
916	1056	1198	1222.0
708	889	922	1060.0
695	857	798	860.0
708	772	879	838.5
716	751	945	912.0
784	820	990	967.5

This will ensure A-Cat Corporation is complements a robust statistical process control (quality control) program to monitor the quality of its transformers. Without proper forecast of inventory, the quality is at stake because customers view the presence of inventory as quality service.

Conclusions:

1. For the variable number of transformers, the average number of transformers for the year 2006 is given as 801.1667 with the variance of 7020.515. The average number of transformers for the year 2007 is given as 898.6667 with the variance of 18750.06 while the average number of transformers for the year 2008 is observed as the 990.3333 with the variance of 21117.88.
2. We reject the null hypothesis that there is no any significant difference between the averages of given groups. This means we conclude that there is a significant difference exists between the average values of given groups.
3. The correlation coefficient between the number of transformers and sales of transformers as 0.9259 which means there is very strong positive correlation or linear relationship exists between the given two variables. The R square or the coefficient of determination is given as 0.8574 which means about 85.74% of the variation in the dependent variable number of transformers is explained by the independent variable sales of transformers.
4. We conclude that there is a significant relationship exists between the dependent variable number of transformers and independent variable sales of transformers.

REFERENCES

Agueda E.T.&  Eva R. L., (1996) .Research on tourist demand in Spain: An analysis and
            summary”, The Tourist Review,  51(1),29 – 33

Barnett A.W, &  Serletis A. (2009),. The Differential Approach to Demand Analysis and the
              Rotterdam Model, in Daniel J. Slottje (ed.) Quantifying Consumer Preferences
             (Contributions to Economic Analysis, Volume 288) Emerald Group Publishing Limited,
             61 – 81.

Claveria O, & Datzira J., (2010) .Forecasting tourism demand using consumer expectations”,            
            Tourism Review,  65(1),18 – 36

Udo G., (2000). Using analytic hierarchy process to analyze the information technology
             outsourcing decision. Industrial Management & Data Systems, 100 (9), 421 – 429.

 

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