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Apr 3, 2024

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1 Summary Report The management's primary focus is on monitoring the progress of production operations. With Elizabeth Burke now overseeing these operations, it falls upon me as the chief analyst to scrutinize the available data. This data, stored in an Excel file, is outlined in Table 1, detailing the worksheet, data source, type of measure, and its level. Data sources are classified as either internal (from within the company) or external (sourced externally). Measures within each worksheet are categorized as categorical, ordinal, interval, or ratio. Table 1. The summary of characteristics of the metrics used in each worksheet. Worksheet Data source Measure Level of measure Dealer Satisfaction, End-User Satisfaction External Sample size Ordinal Survey scale Ordinal Year Categorical 2014 Customer Survey External Region Categorical Quality Ordinal Ese of Use Ordinal Price Ratio Service Ordinal Complaints External Month Ordinal World, NA, SA, Eur, Pac, China Interval Mower Unit Sale, Tractor Unit Sales, Internal Mower Unit Sale, Tractor Unit Sales Interval Industry Mower Total Sales, Industry Tractor Total Sales External Industry Mower Total Sales, Industry Tractor Total Sales Interval Unit production cost Internal Tractor, Mower Ratio Operating & Interest Expenses Internal Administrative, Depreciation, Interest Ratio On-Time Delivery Internal Number of deliveries, Number on time Interval Generated Percentage Ratio Defects After Delivery Internal Month Categorical 2010, 2011, 2012, 2013, 2014 Interval Time to Pay Suppliers Internal Month Categorical Working Days Ratio Response Time Internal Response time to customer service calls Ratio Employee Satisfaction Internal Quarter Design & Production, Manager, Total Ratio Sample Size Ordinal Engines Internal Sample Interval Production time Ratio Transmission cost Internal Current, Process A, Ratio

2 Process B Blade weight Internal Sample Interval Weight Ratio Mower Test Internal Observation (1-30) Categorical Employee Retention Internal YearsPLE, YrsEducation, College GPA, Age Ratio Gender, College Grad, Local Categorical Shipping Cost External Plant, Customer Categorical Mower, Tractor Ratio Fixed Costs Internal Current plants, Proposed Locations Categorical Additional Capacity, Maximum Capacity Interval Cost Ratio Purchase Survey Internal Delivery speed, Price level, Price flexibility, Manufacturer image, Overall service, Sales force image, product quality, satisfaction level Ordinal Usage level Interval Size of firm, purchasing Structure, Industry, Ordinal Buying Type Ordinal Table 2 . The aggregate count of responses is examined for each level of dealer satisfaction across all regions and years. Dealer satisfaction consistently registers above 4 across all regions, indicating a generally high level of satisfaction. Instances of satisfaction levels falling below 2 are exceedingly rare among dealers. Dealer Satisfaction Survey Scale 0 1 2 3 4 5 Sample Size North America 5 6 16 63 154 141 385 South America 1 2 4 15 57 111 190 Europe 0 0 5 13 68 29 115 Pacific Rim 0 0 4 8 20 6 38 China 0 0 2 10 10 2 24 Table 3. Total number of responses for each level of end-User satisfaction across all regions for all years. The End-User satisfaction is higher above 4 for all regions. It is very rare to find an End-User with a satisfaction less than one. End-User Satisfaction

3 Survey Scale 0 1 2 3 4 5 Sample Size North America 3 11 22 80 170 214 500 South America 2 11 27 93 183 184 500 Europe 4 7 18 104 193 174 500 Pacific Rim 4 10 24 82 204 176 500 China 1 6 6 13 89 35 150 Figure 1. The Mower Test comprises 30 observations in each sample, where the number of failures is recorded. Across all samples, there are a total of 54 failures. These results are illustrated in a column chart for easy visualization. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 0 1 2 3 4 5 6 3 4 1 0 1 5 2 1 0 2 2 3 3 1 1 2 2 3 2 4 2 1 1 2 1 0 2 1 0 2 Sample Number of Failures Table 4. Annual sales of mower for the five regions and the worldwide totals. North America has the highest sales followed by Europe and then South America. Year NA SA Europe Pacific China World 2010 $13,575,000 $439,500 $2,340,000 $223,500 $0 $16,578,000 2011 $15,442,000 $549,500 $2,688,000 $306,250 $0 $18,985,750 2012 $16,263,000 $610,200 $2,430,000 $365,400 $0 $19,668,600 2013 $16,946,000 $667,850 $2,380,950 $458,800 $0 $20,453,600 2014 $17,451,500 $735,300 $2,205,900 $494,000 $21,470 $20,908,170 Total $79,677,500 $3,002,350 $12,044,850 $1,847,950 $21,470 $96,594,120 Table 5 . The yearly sales figures for tractors across five regions, along with the global total, are available. North America stands out with the highest sales, trailed by Europe and subsequently South America in the second and third positions, respectively. Year NA SA Europe Pacific China World 2010 $20,985,250 $10,673,000 $26,399,750 $9,896,250 $0 $67,954,250

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4 2011 $27,557,000 $17,533,800 $28,512,400 $11,294,800 $0 $84,898,000 2012 $36,464,400 $26,424,000 $30,240,000 $12,358,800 $1,047,600 $106,534,800 2013 $56,843,100 $34,117,700 $27,413,300 $14,015,600 $3,566,800 $135,956,500 2014 $92,902,400 $41,416,200 $24,928,000 $10,423,400 $5,867,200 $175,537,200 Total $234,752,150 $130,164,700 $137,493,450 $57,988,850 $10,481,600 $570,880,750 Table 6. The company's mower market share across various regions is documented for the years spanning 2010 to 2014. Notably, South America boasts the highest market share for the company's mowers, while China holds the lowest market share among all regions. Year NA SA Europe Pacific China World 2010 10.26% 35.83% 5.54% 10.23% 0.00% 9.33% 2011 10.33% 39.08% 5.47% 10.53% 0.00% 9.36% 2012 10.48% 41.50% 5.57% 10.73% 0.00% 9.67% 2013 10.55% 45.67% 5.67% 10.47% 0.00% 9.84% 2014 10.33% 47.47% 4.87% 10.89% 0.01% 9.51% Average 10.39% 41.91% 5.42% 10.57% 0.00% 9.54% Table 7. The market share of the company for the tractor in each region for the year 2010 to 2014. The company has a large market share in South America followed by Pacific and then North America. Year NA SA Europe Pacific China World 2010 8.09% 26.43% 10.90% 21.52% 0.00% 11.26% 2011 10.83% 27.35% 10.50% 20.87% 0.00% 12.90% 2012 12.99% 28.15% 10.01% 20.50% 4.03% 13.98% 2013 15.76% 29.27% 9.60% 20.26% 4.81% 15.03% 2014 18.65% 29.68% 9.31% 19.80% 5.17% 16.37% Average 13.26% 28.17% 10.06% 20.59% 2.80% 13.91% Conclusion As a vital member of the production management team, my familiarity with the intricacies of our company's production data positions me to grasp it with ease. In the forthcoming weeks, I am committed to delving deeper into this data, enabling me to offer more comprehensive analysis. Through this heightened understanding, my aim is to contribute to a positive trajectory for the company by implementing improvements to our production processes.

5 Reference Evans, J. R. (2013). Business Analytics (2nd ed.). Pearson. ISBN: 9780132950619. Agresti, A. (2010). Analysis of ordinal categorical data (Vol. 656). John Wiley & Sons. Meulman, J. J., Van der Kooij, A. J., & Heiser, W. J. (2004). Principal components analysis with nonlinear optimal scaling transformations for ordinal and nominal data. The Sage handbook of quantitative methodology for the social sciences , 49-72.

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