Statistical Analysis Report: A-Cat Corporation Operations

This report presents a quantitative analysis plan designed to address the overstocking and understocking issues faced by A-Cat Corporation. The analysis emphasizes the need for accurate data collection from both internal and external stakeholders to ensure reliable results. The proposed plan involves a review of transformer requirements, linear regression analysis to determine the relationship between transformers and refrigerators using ANOVA, and time series analysis to forecast supply versus demand. This approach aims to determine the average number of transformers needed, understand the correlation between refrigerator and transformer sales, and forecast future trends to minimize operational costs and maximize profits. The report concludes that this analysis will enable managers to make informed decisions regarding production levels, balance the supply and demand curve, and improve overall operational efficiency and employee productivity. Desklib offers a range of similar reports and study tools for students.

Running head: QUANTITATIVE ANALYSIS
1
Quantitative analysis
Student’s Name
Institutional Affiliation
Professor’s Name
Date

QUANTITATIVE ANALYSIS
2
Quantitative analysis
Recommendation for operational improvements
Summary of an analysis plan
For the A-Cat Corporation to address the problem of overstocking and understocking there is the
necessity for a clear analysis plan that can address the challenges of balancing between the
supply and demand. The practice calls for a sufficient amount of data that would give more
accurate and reliable results that could even be replicated to other problems of operations and
management in different sectors. It has been noted that the insufficient and inaccurate data
collected from some internal and external stakeholders is the primary setback affecting the
validity of the results in the analysis aimed at giving the solution to the improvement of
operations at the firm. This calls for an analysis plan that would cater for accuracy in the delivery
of the answers.
An adequate analysis plan for both the internal and external stakeholders’ demands that both the
transformers and the refrigerators are taken as the variables in the analysis. The first task would
call for review to determine the number of transformers required for balancing out the demand
and supply curve of the product at the organization for their customers. After obtaining the
potential transformers using the available data for the past five years against the sales made, the
second variable, in essence, the number of refrigerators would be brought into question for
analysis. Taking the transformers as the dependent variable while the refrigerators as the
independent variable, a linear regression analysis would reveal the relationship between the two
products from ANOVA as acknowledged by Chatfield (2018). . After obtaining the correlation
from ANOVA, for regression results, a time series analysis would be put in place to give a

QUANTITATIVE ANALYSIS
3
prediction of the supply versus the demand thus addressing the overstocking and understocking
problems in operation management.
How the analysis plan addresses the problem
As stated in the analysis plan, determining the possible average number of transformers is an
essential factor that helps in pointing out the variation in the underestimated supply and the
overestimated demand. Having an average value would help the manager to map the theoretical
assumptions and possible challenges to the real problem based on the past data sample of five
years. Moreover, there is the need for a starting point in solving any managerial problem that
affects both the internal and the external stakeholders and by extension the overall profitability of
an organization. Obtaining the relationship between the refrigerators and the transformers is
critical since the sales of the refrigerators partially determine the sales of the transformers, which
should reflect the production volume transformers as revealed through regression analysis.
Additionally, the correlation between the two variables is elemental that paves the way for
forecasting future trends. In the analysis, the time series method for forecasting would determine
the trends in the stock performance through the sales made that would enable the firm to assess
and evaluate the profits and the returns for both the internal and external stakeholders (Cheng
and Yang, 2018). The decision to deploy this analysis is to target future sales in a manner that
minimizes the operations costs and maximizing the profits through actual production whereby
the supply is inclined to the demand. Such a move would ensure that all the transformers
produced are sold out. From the analytic statistics, the manager would now be able to decide the
time for massive production, medium production, or no production at all thus cutting down the
costs.

QUANTITATIVE ANALYSIS
4
Why the decision is the best option
The given analysis plan would point out the trend in the sales based on the previous data as well
as providing a prediction of the possible deals in the future. Such information obtained from the
analyzed data would give way for efficient planning for the managers, which would minimize
the costs of operation. For instance, if the managers understand that during certain months the
sales are abysmal, they could temporarily lay off some staff so that the remaining can undertake
the production of the few transformers according to the current demand thus cutting off the
production costs. Moreover, the stakeholders, in essence, the employees would work hard to
ensure that maximum sales are made for them to continue working at the firm especially the
marketing and quality control teams whose efforts directly determine the sale volumes to be
made. Therefore, the analysis would bring a balance for the supply and demand curve that would
address the overstocking and understocking problem for the firm as well as increase productivity
for employees due to the improved operations.

QUANTITATIVE ANALYSIS
5
References
Chatfield, C. (2018). Introduction to multivariate analysis. London: Routledge.
Cheng, C. H., & Yang, J. H. (2018). Fuzzy time-series model based on rough set rule induction
for forecasting stock price. Neurocomputing, 302, 33-45.