Running Head: A-CAT CORPORATION 1 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 A-CAT CORPORATION 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. A-CAT CORPORATION ANOVA Source of SS df MS Variation Between 214772.2 2 107386.1 Groups Within Groups 515773 33 Total 730545.2 F P-value F crit 6.870739 0.003202 3.284918 15629.48 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 ACat Corporation. 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. 2006 779 802 818 888 898 2007 845 739 871 927 1133 2008 857 881 937 1159 1072 2-Month MA 2009 838.5 869.0 909.0 1048.0 1155.5 A-CAT CORPORATION 902 916 708 695 708 716 784 1124 1056 889 857 772 751 820 1246 1198 922 798 879 945 990 1159.0 1222.0 1060.0 860.0 838.5 912.0 967.5 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. A-CAT CORPORATION QSO 510 Final Project Case Addendum Vice-president Arun Mittra speculates: We have always estimated how many transformers will be needed to meet demand. The usual method is to look at the sales figures of the last two to three months and also the sales figures of the last two years in the same month. Next make a guess as to how many transformers will be needed. Either we have too many transformers in stock, or there are times when there are not enough to meet our normal production levels. It is a classic case of both understocking and overstocking. Ratnaparkhi, operations head, has been given two charges by Mittra. First, to develop an analysis of the data and present a report with recommendations. Second, \"to come up with a report that even a lower grade clerk in stores should be able to fathom and follow.\" In an effort to develop a report that is understood by all, Ratnaparkhi decides to provide incremental amounts of information to his operations manager, who is assigned the task of developing the complete analyses. A-Cat Corporation is committed to the pursuit of a robust statistical process control (quality control) program to monitor the quality of its transformers. Ratnaparkhi, aware that the construction of quality control charts depends on means and ranges, provides the following descriptive statistics for 2006 (from Exhibit 1). 2006 Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count 801.1667 24.18766 793 708 83.78851 7020.515 -1.62662 0.122258 221 695 916 9614 12 The operations manager is assigned the task of developing descriptive statistics for the remaining years, 2007-2010, that are to be submitted to the quality control department. A-Cat's president asks Mittra, his vice-president of operations, to provide the sales department with an estimate of the mean number of transformers that are required to produce voltage regulators. Mittra, recalling the product data from 2006, which was the last year he supervised the production line, speculates that the mean number of transformers that are needed is less than 745 transformers. His analysis reveals the following: t = 2.32 p = .9798 This suggests that the mean number of transformers needed is not less than 745 but at least 745 transformers. Given that Mittra uses older (2006) data, his operations manager knows that he substantially underestimates current transformers requirements. She believes that the mean number of transformers required exceeds 1000 transformers and decides to test this using the most recent (2010) data. Initially, the operations manager possessed only data for years 2006 to 2008. However, she strongly believes that the mean number of transformers needed to produce voltage regulators has increased over the three-year period. She performs a one-way analysis of variance (ANOVA) analysis that follows: 2006 779 802 818 888 898 902 916 708 695 708 716 784 2007 845 739 871 927 1133 1124 1056 889 857 772 751 820 2008 857 881 937 1159 1072 1246 1198 922 798 879 945 990 Anova: Single Factor SUMMARY Groups 2006 2007 2008 Count Sum Average Variance 12 9614 801.1667 7020.515 12 10784 898.6667 18750.06 12 11884 990.3333 21117.88 ANOVA Source of Variation Between Groups Within Groups SS 214772.2 515773 Total 730545.2 df MS F P-value F crit 2 107386.1 6.870739 0.003202 3.284918 33 15629.48 35 The results (F = 6.871 and p = 0.003202) suggest that indeed the mean number of transformers has changed over the period 2006-2008. Mittra has now provided her with the remaining two years of data (2009 and 2010) and would like to know if the mean number of transformers required has changed over the period 2006-2010. Finally, the operations manager is tasked with developing a model for forecasting transformer requirements based on sales of refrigerators. The table below summarizes sales of refrigerators and transformer requirements by quarter for the period 2006-2010, which are extracted from Exhibits 2 and 1 respectively. Sales of Refrigerators 3832 5032 3947 3291 4007 5903 4274 3692 4826 6492 4765 4972 5411 7678 5774 6007 6290 8332 6107 6792 Transformer Requirements 2399 2688 2319 2208 2455 3184 2802 2343 2675 3477 2918 2814 2874 3774 3247 3107 2776 3571 3354 3513