# 9-2 Final Project Submission: Statistical Analysis Report

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## Instructions

Submit your statistical analysis report and recommendations to management. It should be a complete, polished artifact containing all of the critical elements of the final product. It should reflect the incorporation of feedback gained throughout the course.

Note that you will need to refer to the scenario in the article "A-Cat Corp.: Forecasting." See the syllabus for information on accessing the article.

For additional details, please refer to the Final Project Guidelines and Rubric document and the Final Project Case Addendumdocument.

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 ANOVA Source of Variation Between Groups Count Sum Average Variance 12 9614 801.1667 7020.515 12 10784 898.6667 18750.06 12 11884 990.3333 21117.88 SS 214772.2 df MS F P-value F crit 2 107386.1 6.870739 0.003202 3.284918 Within Groups Total 515773 730545.2 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 6729 Transformer Requirements 2399 2688 2319 2208 2455 3184 2802 2343 2675 3477 2918 2814 2874 3774 3247 3107 2776 3571 3354 3513

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School: Rice University

Attached.

Statistical analysis report
Name
Institution
Date

1

2

STATISTICAL ANALYSIS REPORT

Introduction
Description
Arun Mitra the A-CAT company vice president, a firm that manufactures voltage
regulators intends to evaluate the number of transformers that the company should produce in
order to meet the market demand. Based on past data, Arun approximated the number of
transformers by assembling sales data. The collected embodies monthly data from 2006 to 2010
on transformer requirements and quarterly sales figures from 2006 to 2010. Notably, the
company had been able to estimate the number of transformers required in the past but recently it
has been faced with problems managing its inventory. In this regard, sometimes the company has
too much inventory whilst other times it has fewer inventories meet the prevailing demand.
Given that the sale revenues for voltage regulators had shown a sluggish growth, the
organization had to efficiently control the inventories for voltage regulators to ensure that it cuts
down on additional costs while at the same time meeting the prevailing demand in the market
(Steger, Dik, & Duffy, 2012). The issues affect a wide range of internal stakeholders in the
operations department, finance department, and the vice president, as well as, external
stakeholders such as the suppliers, distributors, and end users. Company investors will also be
affected by this problem since its persistence is likely to affect their investment return and
shareholder’s value.
Analysis Plan
Quantifiable factors are actions whose effects can be quantified using numerical values or
measurable units. There are various quantifiable factors that may affect the operational
performance of the company processes. These quantifiable factors include the data provided by

STATISTICAL ANALYSIS REPORT

3

the company operational head and how the data to be analyzed is chosen. Notably, the mean
number of transformers required has changed over time since 2006 to changes in consumer
demand and preferences over time. As such, the mode of forecasting used by the company
whereby it only focuses on sales data for about 2-3 months a year for the last 2 years could result
in unreliable forecasts. The data selected to evaluate the number of transformers required is
unrepresentative of the whole operations figures. However, due to volatility in market
movements, technology, and consumer preferences, using very old data could result in valid but
unreliable results which can be misleading. As such, the organization should use recently
available data, most probably, for the last three years. Additionally, the demand for transformers
has been observed to vary significantly each year, as such taking sales figures for two or three
months and last two years can be very inconsistent. In this regard, using this data can result to
estimation errors regarding the required inventory thus affecting the operational process
performance (Steger, Dik, & Duffy, 2012). Another quantifiable factor may be the number of
sales personnel. A smaller number could be ineffective in meeting the...

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Review

Anonymous
Awesome! Exactly what I wanted.

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