A CAT Company [Data Analysis & Processes]

A CAT Company – Data Analysis and Processes
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Company Overview
The A-CAT Company is a medium scale electrical appliance company. Based in India, the company specializes in design, development, and distribution of domestic electrical appliances. The company, as a result of the global expansion, has owned and managed two different manufacturing units located in a location known as Gondia based in India. The company has remained in operation since 1986 and as a result, it has managed to employ a total of 40 permanent employees. The most functional units and departments A-CAT include; design, manufacturing, purchasing department, and sales departments. Given the ever-changing manufacturing landscape, A-CAT operations manager deals with the dynamic work processes. This task requires the integration of data analysis tools and techniques to address fundamental shifts in work processes and to also make an accurate forecast or prediction of the future.
Statistical Tools and Data Analysis
The statistical analysis system [SAS] is a tool for quantitative data analysis mostly used by global enterprises to generate annual reports, business planning, graphics, forecasting, project management and quality improvement. As an appropriate tool for both the intermediate and the advanced businesses, the statistical tool has the ability to manipulate data and identify common trends in terms of demand and consumption (Crossman, 2017). What makes the SAS tool particularly ideal for the A-CAT company is its ability to process extremely large data sets.

For example, a flagship product by A-CAT was the voltage regulator device of 500 kilowatts. The device was regulated, branded, and sold as the model VR-500. The device was mostly used in domestic settings as a protector for appliances like refrigerators and television sets. It is important to note that instead of competing with large-scale operators in India, the company sought to concentrate on small and emerging markets. This strategy led to the pioneering of 100 other different types of electrical devices for domestic use. These devices include; electric chargers, FM Radio Kits, Electronic Ballasts, and signal boosters for TV and radios. The use of the SAS tool is to enable A-CAT map all of its devices in entirety in terms of the type of the device, capacity, voltage, and use. The Statistical Analysis System will further enable the company understands trends in consumption of particular devices. More clearly, by feeding in the codes of different electric and electronic devices, it is possible to derive numerically relevant figures and information that reflect key findings of the organization’s performance and productivity (Crossman, 2017).
Hypothesis
Ho – The null hypothesis is that the SAS data analysis tool will NOT enable A-CAT to realize the actual sales made at a particular period for specific electronic and electrical devices. This is because the SAS may not necessarily reflect the key variables in the A-CAT core operational processes in terms of manufacturing time per unit, the cost per unit, and possibly the turn-around time for each device or appliance.
H1 – The positive hypothesis is that the Statistical Analysis System will analyze the various model numbers for specific electric devices. This means the operations manager will streamline processes with the knowledge of the total number of devices and possible variations in the manufacturing units. Further, the SAS tool will scale processes through the accurate classification of devices and tools as par consumer or industry requirements. This proper and well-arranged classification is altogether pertinent to understanding consumer preferences, most sought-after devices, and those devices that require regular updates and improvements to keep pace with rapid shifts and changing consumer preferences.
Results of the Analysis
The data analysis attached to a separate excel file depicts the transformer requirements during the period of 2006 – 2010. The above figure of transformer requirements is directly derived from the sale of voltage regulators whereby the transformer requirement figures is also derived from the former [voltage regulators]. The major assumption made is that the rise in the demand for voltage regulators is vis a vis the rise in refrigerator sales during the same period. In 2006, the transformer requirements shot from a low 779 in January to 916 in July. In 2007, the demand for transformers to protect refrigerators and to regulate temperature control rose from 739in February to 1133 in May and 1124 in June. The two months had the highest sales consecutively in the entire year. In 2008, there was a steady rise from 857 transformers in January to a high of 1256 in June. However, this figure fell to 798 in September and 879 in October in the same year. In 2009, the figure skyrocketed from 917 transformer requirements in January to 1356 in June and fell to 998 in December. In 2010, the A-CAT company recorded the highest sales from 1256 transformer units in June to a low of 887 transformer units in January. The overall results indicate that the A-CAT transformer sales are inconsistent. However, a common trend is that the firm has a low sale at the start of the year. However, as the year progresses, there is an increased demand which leads to skyrocketing sales. Above happens especially in mid-year when the sales are at their peak and demand is at its highest. The above findings imply that the A-CAT should invest heavily in the development of refrigerator transformers. This investment should be increased as the year progresses to ensure the company keep pace with the rising demand of refrigerators in the course of any fiscal year.
Forecasting Recommendations from Descriptive Statistics
The descriptive statistics show a rise in the sales figures of refrigerators during the first two quarters of 2006. However, this demand for refrigerators continues to dwindle in the last two quarters; the third and the fourth quarters. The above rise and fall trend is noted in the transformer requirements and voltage regulators in the first two quarters and the remaining two quarters respectively. A key recommendation will be to invest heavily in marketing-led innovation to boost sales in the last two quarters.

The sales figures of transformers is the lowest in the last two to three months of the year; November, October and December. Transformer requirements are also at their lowest in the above months of the year. This means the company may consider using a low-cost, customer-centric approach to boost sales during these periods. As reflected in the addendum, sales figures continue to dwindle as the year comes to a halt. A low-cost approach will enable consumers acquire more refrigerators and hence the company will bolster sales and annual revenues when the market is relatively unstable as reflected in falling sales.

Transformer requirements are at their highest at the end of the second quarter. As seen, May, June and July have the highest transformer requirements and sales figures during this period are further at their peak. This means the company should cut on marketing and advertising costs during this period. The above recommendation will ensure that the firm channels the additional operational costs to end year where the sales and the transformer requirements are at their lowest.

Finally, it is important for internal stakeholders to streamline sourcing and supply efforts to keep pace with changing demand of refrigerators and voltage regulators. The personnel in sales, purchasing, and design should develop better stocking strategies, sourcing and supply chain techniques to address shortages and rises in demand in each and every fiscal year. Hence, a crucial recommendation is to streamline operations and processes, through the collaboration inclusion of internal stakeholders to reduce the costs that result from unsteady sale of voltage regulators.
Demand for transformers-Statistical Analysis
Linear regression line of best fit
2006-1010
There is a linear difference in the demand for transformers between January to June and July to December. The linear have almost a similar gradient but at different levels of demand thus the significant difference
Demand for transformers :first 6 months 2006 2007 2008 2009 2010
Jan 779 845 857 917 887
Feb 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
5087 5639 6152 6648 6347
average demand 847.8333 939.8333 1025.333 1108 1057.833
141.3056 156.6389 170.8889 184.6667 Graph showing the demand for transformers (JAN –JUNE)
Demand (transformers
Time (YEARS JAN- JUNE 2006-2010)
Demand for the second part of the year
Graph showing the demand for transformers (JULY TO DECEMEBER)
Demand
TIM IN YEARS (JULY TO DEC)
T-TEST
Average demand (JAN TO JUNE ) 2006 2007 2008 2009 2010 5087 5639 6152 6648 6347 847.8333 939.8333 1025.333 1108 1057.833 Average demand July to December 4527 5155 5732 6354 6867 754.5 859.1667 955.3333 1059 1144.5 SUM difference 93.33333 80.66667 70 49 -86.6667 206.3333 42573.44
squared differences 8711.111 6507.111 4900 2401 7511.111 30030.33 N= 6 T= (ED)/N / (ED^2 – (ED)^2 / N-1 T= 0.0137 T from tables at 5% level of significance t= 2.0150
t value calculated > t from tables The calculated t value is smaller compared to the t value from the tables’ .Thus; we reject the hypothesis that the pair wise difference is equal between the two samples. The t value from the tables is greater thus there’s is a linear relationship between the Jan to June data and the July to December data but there is a significant difference in the amounts of data, that is, there is a significant difference in the demand for transformers between the two periods from 2006 to 2010. References
Crossman, A. (2017). A Review of Software tools for Quantitative Data Analysis. The Thought Company Official. Retrieved from https://www.thoughtco.com/quantitative-analysis-software-review-3026539