QSO 510 Milestone Two Guidelines and Rubric The final project for this course is the creation of a statistical analysis report. Operations management professionals are often relied upon to make decisions regarding operational processes. Those who utilize a data-driven, structured approach have a clear advantage over those offering decisions based solely on intuition. You will be provided with a scenario often encountered by an operations manager. Your task is to review the “A-Cat Corp.: Forecasting” scenario, the addendum, and the accompanying data in the case scenario and addendum. In Module Seven, you will submit your selection of statistical tools and data analysis, which are critical elements III and IV. You will submit a 3- to 4-page paper and a spreadsheet that provides justification for the appropriate statistical tools needed to analyze the company’s data, a hypothesis, the results of your analysis, any inferences from your hypothesis test, and a forecasting model that addresses the company’s problem. Specifically, the following critical elements must be addressed: III. Identify statistical tools and methods to collect data: A. Identify the appropriate family of statistical tools that you will use to perform your analysis. What are your statistical assumptions concerning the data that led you to selecting this family of tools? In other words, why did you select this family of tools for statistical analysis? B. Determine the category of the provided data in the given case study. Be sure to justify why the data fits into this category type. What is the relationship between the type of data and the tools? C. From the identified family of statistical tools, select the most appropriate tool(s) for analyzing the data provided in the given case study. D. Justify why you chose this tool to analyze the data. Be sure to include how this tool will help predict the use of the data in driving decisions. E. Describe the quantitative method that will best inform data-driven decisions. Be sure to include how this method will point out the relationships between the data. How will this method allow for the most reliable data? IV. Analyze data to determine the appropriate decision for the identified problem: A. Outline the process needed to utilize your statistical analysis to reach a decision regarding the given problem. B. Explain how following this process leads to valid, data-driven decisions. In other words, why is following your outlined process important? C. After analyzing the data sets in the case study, describe the reliability of the results. Be sure to include how you know whether the results are reliable. D. Illustrate a data-driven decision that addresses the given problem. How does your decision address the given problem? How will it result in operational improvement? Guidelines for Submission: Your paper must be submitted as a 3- to 4-page Microsoft Word document and attached spreadsheet with double spacing, 12-point Times New Roman font, one-inch margins, and at least six sources cited in APA format. Rubric Critical Elements Exemplary Proficient Needs Improvement Not Evident Value Statistical Tools and Meets “Proficient” criteria and Methods: Family of identification demonstrates Statistical Tools nuanced understanding of statistical tools (100%) Statistical Tools and Methods: Category of Provided Data Statistical Tools and Methods: Most Appropriate Tool Statistical Tools and Methods: Justify Tool Statistical Tools and Methods: Quantitative Method Analyze Data: Process Identifies the appropriate family of statistical tools used to perform statistical analysis, including statistical assumptions (90%) Identifies a statistical family of tools used to perform statistical analysis but either the tools are not the most appropriate to use or discussion lacks statistical assumptions (70%) Meets “Proficient” criteria and Determines the category of Determines the category of demonstrates insight into the the provided data, including the provided data but relationship of the category of justification to support claims category is either inaccurate data and statistical tools (90%) or discussion lacks justification (100%) to support claims (70%) Selects the most appropriate Selects a statistical tool but statistical tool used to analyze selection is not the most the data (100%) appropriate given the data (70%) Meets “Proficient” criteria and Justifies why the tool chosen Justifies why the tool chosen justification demonstrates is the most appropriate for is the most appropriate for the insight into the relationship analysis of this data (90%) analysis but justification is between statistical tools and either illogical or cursory type of data (100%) (70%) Meets “Proficient” criteria and Describes the quantitative Describes the quantitative description demonstrates method that will best inform method but either the insight into the relationship the decision, including how method selected will not between the quantitative this method will point out the result in the most reliable data method and data relationships relationships between the or discussion lacks how the (100%) data (90%) method will point out the relationships between the data (70%) Meets “Proficient” criteria and Outlines the process needed Outlines the process needed offers great detail for each to utilize the statistical to utilize the statistical identified step (100%) analysis (90%) analysis but steps are either inappropriate or overgeneralized (70%) Does not determine a family of statistical tools (0%) 7 Does not determine a category for the data (0%) 7 Does not select a tool to be used for analysis (0%) 7 Does not justify why a particular tool was chosen (0%) 7 Does not describe the quantitative method (0%) 7 Does not outline the process needed to utilize the statistical analysis (0%) 15 Analyze Data: Valid, Meets “Proficient” criteria and Data-Driven explanation demonstrates a Decisions nuanced understanding of how following a process will lead to a valid decision (100%) Analyze Data: Meets “Proficient” criteria and Reliability of Results description demonstrates keen insight into identifying reliable data (100%) Explains how following the outlined process leads to a valid data-driven decision (90%) Explains how following the outlined process leads to a valid decision but explanation is inappropriate or cursory (70%) Describes the reliability of the Describes the reliability of the results based on data sets, results but description is including a justification to either cursory or lacks support claims (90%) justification to support claims (70%) Analyze Data: Data- Meets “Proficient” criteria and Illustrates a data-driven Illustrates a data-driven Driven Decision illustration demonstrates a decision that addresses the decision that addresses the deep understanding of the problem and operational problem but illustration is interplay between a problem, improvement (90%) either inappropriate or the operation, and operational overgeneralized (70%) improvement (100%) Articulation of Submission is free of errors Submission has no major Submission has major errors Response related to citations, grammar, errors related to citations, related to citations, grammar, spelling, syntax, and grammar, spelling, syntax, or spelling, syntax, or organization and is presented organization (90%) organization that negatively in a professional and easy to impact readability and read format (100%) articulation of main ideas (70%) Does not offer an explanation why following the outlined process leads to a valid decision (0%) 15 Does not describe the reliability of the results (0%) 15 Does not illustrate a decision that addresses the problem (0%) 15 Submission has critical errors related to citations, grammar, spelling, syntax, or organization that prevent understanding of ideas (0%) 5 Earned Total 100% 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 6729 Transformer Requirements 2399 2688 2319 2208 2455 3184 2802 2343 2675 3477 2918 2814 2874 3774 3247 3107 2776 3571 3354 3513 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 W13377 A-CAT CORP.: FORECASTING Jitendra Sharma wrote this case solely to provide material for class discussion. The author does not intend to illustrate either effective or ineffective handling of a managerial situation. The author may have disguised certain names and other identifying information to protect confidentiality. This publication may not be transmitted, photocopied, digitized or otherwise reproduced in any form or by any means without the permission of the copyright holder. Reproduction of this material is not covered under authorization by any reproduction rights organization. To order copies or request permission to reproduce materials, contact Ivey Publishing, Ivey Business School, Western University, London, Ontario, Canada, N6G 0N1; (t) 519.661.3208; (e) cases@ivey.ca; www.iveycases.com. Copyright © 2013, Richard Ivey School of Business Foundation Version: 2013-09-06 Shirish Ratnaparkhi, operations manager at A-CAT Corporation (A-CAT), had to submit a forecasting report concerning the sales of the company’s primary product — voltage stabilizers. Ratnaparkhi, who was handling the manufacturing department, wanted to get to the bottom of the matter before making recommendations to his immediate superior, vice-president Arun Mittra. Ratnaparkhi remarked: “If we have worked out all the factors and know the right figures, then there is no reason that we cannot foresee the future correctly — provided that everything we presume turns out to be how we predicted it to be. Otherwise, even the best of the forecasting techniques will fail.” THE COMPANY 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 manufacturing units had been in operation since 1986. The budget year 2010—2011 showed annual sales of Rs. 9,800,000, and the company employed about 40 employees. The primary functional departments of A-CAT were its purchasing department, design department, manufacturing department and sales and service department. 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. This document is authorized for use only by Matthew Phillips (lokey1000.mp@gmail.com). Copying or posting is an infringement of copyright. Please contact customerservice@harvardbusiness.org or 800-988-0886 for additional copies. Page 2 9B13D016 Instead of competing with the large-scale operations prevalent in a similar type of industry, A-CAT preferred to focus on the rural segment. The company offered nearly 100 different models of various electrical appliances for household use, including TV signal boosters, FM radio kits, electronic ballasts, battery chargers and voltage regulators. The broad range of products catered to the rural population in and around the district and Gondia, which comprised a large population. At the low end of the market were customers who were quite sensitive towards pricing. In the opinion of top level management, there was more scope in this segment of the market. They had been proved right but were worried that this situation was not going to last long and that the downward trend in sales was getting worse. THE ISSUE 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. A few of the functional department heads were questioning the nature of keeping a big stock of transformers on hand. Since a large number of products manufactured and sold by A-CAT, such as the voltage regulator, had a transformer as one of their principal or primary components, the store managers always tried to keep a good quantity of spares as a buffer for this vital part. Orders for the main product of A-CAT — that is, voltage regulators — came in 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.1 Thus, ordering too many transformers not only exposed the capacity constraints at the supplier’s end but also led to a substantial tie-up of the company’s cash reserves. Moreover, the blocking of capital had a domino effect on the purchase and inventories of other products’ spares and components. Considering the prevailing market scenario and the economic outlook, A-CAT could not afford to fight problems on different fronts. An increased cost of its primary spare component was the last thing A-CAT needed. Although the revenue and profits for the whole organization had steadily grown over the years of its existence, sales of voltage regulators had been quite volatile. They were already showing a sluggish year-on-year growth rate in comparison to their closest competitors. 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. Though transformers were used in other A-CAT products, the company decided to consider only the issue of the number of transformers required for voltage regulators. TVs, refrigerators, washing machines and other white goods used voltage regulators as a guard against potential voltage fluctuations and hence the resulting forecast of voltage regulators had an effect on the 1 Refer to Jitendra Sharma, “Decision Making at A-CAT,” Ivey Case No. 9B11D011, September 19, 2011. This document is authorized for use only by Matthew Phillips (lokey1000.mp@gmail.com). Copying or posting is an infringement of copyright. Please contact customerservice@harvardbusiness.org or 800-988-0886 for additional copies. Page 3 9B13D016 supply chain. It also had an impact on the number of workers called in for shifts, resulting in scheduling issues. THE BRIEF Vice-president Arun Mittra carried out several rounds of discussion at different levels in this regard, but they failed to completely resolve the issue. So he decided to take the matter in his own hands and called for a comprehensive data report of sales figures for the last several years. Mittra deliberated on this for a while and asked his secretary to contact his operations head for an urgent meeting. He said, We have always estimated how many transformers will be needed to meet demand for our final products, especially for voltage regulators. The usual method is to look at the sales figure of the last two to three months and also the sales figure of the last two years in the same month and make a guess as to how many transformers will be needed to sell this many voltage regulators. With this method, we were able to manage quite well until now; however, the general complaint is that either we have too many transformers in stock or there are times when there are not enough to even meet our normal production levels. It’s a classic case of both under-stocking and overstocking. In either case, we are facing troubles at the final product delivery front and also our supplier is raising a lot of objections about our haphazard manner of placing orders. As operations head, Ratnaparkhi was asked by Mittra to have a closer look at the data report and to offer his understanding and analysis of the issue within a fortnight, when the next order for the transformers would have to be placed. The size and timing of the order was the bone of contention between various functional departments. Refer to Exhibits 1 and 2 for the data summary. Mittra also emphasized that Ratnaparkhi’s recommendations were to be very crystal clear and precise and accompanied by justification for the decision taken. He said, “We should be able to plan our transformer purchasing better. I would like you to not only understand, analyze and propose your projections but would also like you to come up with a report that even a lower grade clerk in stores should be able to fathom and follow.” This document is authorized for use only by Matthew Phillips (lokey1000.mp@gmail.com). Copying or posting is an infringement of copyright. Please contact customerservice@harvardbusiness.org or 800-988-0886 for additional copies. Page 4 9B13D016 EXHIBIT 1: TRANSFORMER REQUIREMENTS DURING THE PERIOD (TAKEN FROM THE SALES OF VOLTAGE REGULATORS) 2006 2007 2008 2009 2010 January 779 845 857 917 887 February 802 739 881 956 892 March 818 871 937 1001 997 April 888 927 1159 1142 1118 May 898 1133 1072 1276 1197 June 902 1124 1246 1356 1256 July 916 1056 1198 1288 1202 August 708 889 922 1082 1170 September 695 857 798 877 982 October 708 772 879 1009 1297 November 716 751 945 1100 1163 December 784 820 990 998 1053 Source: Company files. EXHIBIT 2: SALES FIGURES OF REFRIGERATORS DURING THE PERIOD Quarter 2006 2007 2008 2009 2010 I 3832 4007 4826 5411 6290 II 5032 5903 6492 7678 8332 III 3947 4274 4765 5774 6107 IV 3291 3692 4972 6007 6729 Source: Company files. This document is authorized for use only by Matthew Phillips (lokey1000.mp@gmail.com). Copying or posting is an infringement of copyright. Please contact customerservice@harvardbusiness.org or 800-988-0886 for additional copies. 
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