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TRANSFORMER DEMAND ANALYSIS PROJECT

/in /by

A-Cat Corporation

Jessica Sand

Southern New Hampshire University

                                                                      QSO-510

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.

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

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.

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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