Statistics for Management: A Detailed Analysis of Tesco PLC Operations

This report provides a detailed analysis of statistics in management, using Tesco PLC as a case study. It begins with an introduction to statistical management, highlighting its importance in decision-making and forecasting. The report is divided into three main tasks. Task A discusses statistics, data gathering, and the types of data used by businesses, including Tesco. Task B focuses on data collection and analysis, using descriptive and inferential statistics to compare two investment projects. The analysis includes calculating measures of central tendency, variability, and conducting a t-test to determine if there is a significant difference between the projects. Task C examines Tesco's marketing campaigns for three products, analyzing the number of campaigns conducted at different levels of success. The report concludes by emphasizing the crucial role of business statistics in achieving organizational success through informed decision-making and strategic planning. The report uses various statistical methodologies to provide valuable insights into Tesco's operations and the effectiveness of its strategies.

STATISTICS FOR
MANAGEMENT

Table of Contents
INTRODUCTION...........................................................................................................................1
TASK A...........................................................................................................................................1
TASK C...........................................................................................................................................6
CONCLUSION................................................................................................................................7
REFERENCES................................................................................................................................8

INTRODUCTION
Statistical Management has gained immense recognition in the recent years. One of the
main reason behind this phenomenon is the ability of statistical techniques to facilitate the
process of decision-making in a given organisation (Barrett and et.al., 2012). This report aims to
provide a detailed account on Statistical Management. For this purpose, the report is divided into
three parts viz. Task A, B and C wherein Task A discusses about Statistics and its characteristics
along with various of data gathering. Whereas Task B and C give an insight on data collection
and analysis using tools such as Descriptive and Inferential Statistics. In order to achieve these
tasks successfully, Tesco PLC has been taken which is a global corporation that deals in the
retail sector mainly focussing on the groceries and supermarket chains. It was founded in 1919
by Jack Cohen and is headquartered in Hackney, London, UK.
TASK A
Statistics in Management can be defined as a body of methods and techniques which
enable informed decisions on the part of the manager in the face of uncertainty. One of the key
characteristics of this discipline is to forecast accurately regarding the future sales or other profit-
making elements. This helps in the removal of uncertainty which is largely faced by various
business managers of an organisation such as Tesco on a day-to-day basis. In order to meet
business objectives, Business Statistical tools and techniques Tesco's manager is able to forecast
different scenarios which helps in evaluation of alternatives, thus, enabling one to undertake only
those strategies which give maximum returns on their implementation.
The sources and types of data as well as information businesses can access include both
qualitative and quantitative data which may be available from a variety of published sources that
are both offline and online (Berenson and et.al., 2012). These published sources may be in the
form of articles, journals, magazines or websites which have important information which is,
more importantly, authentic in nature. As a result, one can easily rely on such data without
giving much thought about whether or not it will be relevant to the organization or the business
objectives one intends to achieve. One may acquire information through this data using different
techniques such as Descriptive, Inferential Statistics and Measures of Association. Here,
descriptive statistics include measures of central tendency such as Mean, Mode and Median.
Whereas the Inferential Statistics are concerned with drawing conclusions in relation to a
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particular hypothesis using tools such as T-Test and ANOVA among others. Lastly, measures of
association include use of scatter plots, forecasting, correlation and regression analysis
(Groebner and et.al., 2013). The application of business data, thus, helps in generating relevant
information from a vast pool of data as well as enhances the manner in which Tesco's manager
takes various types of business decisions.
Employing statistical methods, such as Predictive Analysis, can help in meeting business
objectives and achieving competitive advantage has become quite invaluable in the present-day
scenario. Predictive Analysis is one which facilitates forecasting or prediction of important
variables that are important to the achievement of business objectives. Essentially, this analysis
helps in improvement of Customer Relationship Management (CRM), Price Optimization
Systems and Detection of Fraud among others. Application of methodologies like variability,
probability, normal distribution in order to draw important inferences from the study can help in
the improvement of these areas by enhancing business planning practices within the
organization.
For this purpose, a manager can undertake either deductive or inductive approach. A
Deductive Approach involves development of a hypothesis and may generally include an
analysis of quantitative data that has been gathered by an organization in relation to a particular
business process. Its main purpose is to seek the truthfulness behind the hypothesis arguments.
Thus, hypothesis testing forms a critical part of this approach. On the other hand, Inductive
approach aims to generate altogether a new theory from the data collected and analyzed which is
generally qualitative in nature. Both the approaches are important in the improvement of
competencies as well as from business intelligence perspective. Since the main purpose of
Business Intelligence (BI) is to ensure timely decisions are made on the part of business
managers, it is important to implement correct approach as the inferences drawn from such
approaches will help in the overall increment in an organization's competencies (Keller, 2015).
Hence, one can affirm that the statistical management is invaluable for global
organisations today. Since such businesses need to compete not only with their domestic
competitors but also internationally, it is important to utilize the power of Business intelligence
and Inferential tactics among others so as to gain valuable insights on a worldwide scale.
Through the employment of different approaches that are suitable for a diverse set of situations,
business entities such as Tesco are not only able to capitalise on the financial as well as non-
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financial information by choosing the best alternative from different scenarios that are generated
through such statistical tools.
TASK B
The following information relates to the quarterly returns on two unrelated investment
projects. The sample extracted is based on a time frame of twenty quarters over the previous
five-year period:
Quarter Project A Project B
1 10154 10991
2 12716 9410
3 12041 10663
4 11949 11498
5 11765 11943
6 12230 11050
7 10393 11756
8 12895 9147
9 9822 10997
10 10370 9950
11 10940 11752
12 11901 8719
13 11166 9029
14 12872 11554
15 9140 8800
16 11635 11833
17 12516 11428
18 12926 8811
19 10101 12122
20 12676 12263
Based on the above table, a series of information has been ascertained which helps in
gaining valuable insights and enables comparison between the two unrelated investment project
on common grounds. This information has been further analysed using following statistical
method:
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Descriptive Statistics:
This is a summary statistic which is employed by managers all around the world to
quantitatively explain information, specifically related to coefficients, collected by an
organisation. These can be further segregated into Measurement of Central Tendency and
Variability (Newbold, Carlson and Thorne, B., 2013). Applying this Data Analysis method, one
can get information similar to that mentioned in the previous table of this section. However,
while the excel formula used earlier are manually input in the cells by the user using specific
functions, the Descriptive Statistics is a much more automated state of affair even though both
have almost same figures in both cases. Using this statistical method, a summary statistic has
been generated for Investment Project A and B respectively. These results have been depicted in
the following tables:
Investment Project A:
Column1
Mean 11510.4
Standard Error 262.1357061162
Median 11833
Mode -
Standard Deviation 1172.3065164116
Sample Variance 1374302.56842105
Kurtosis -0.9796678854
Skewness -0.489258971
Range 3786
Minimum 9140
Maximum 12926
Sum 230208
Count 20
This table shows the summary statistic for Project A wherein the Average (mean) return
on investment gained over the course of five years is £11,510.4 whereas the median (middle
value) return amounts to £11,833 among all the quarterly returns extracted for previous twenty
quarters. The maximum amount of returns given by this project is £12,926 whereas the minimum
return given by this investment project is that of £9,140.
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Investment Project B:
Column1
Mean 10685.8
Standard Error 282.0871142041
Median 11023.5
Mode -
Standard Deviation 1261.5319258743
Sample Variance 1591462.79999999
Kurtosis -1.4095136488
Skewness -0.496469131
Range 3544
Minimum 8719
Maximum 12263
Sum 213716
Count 20
This table shows the summary statistic for Project B wherein the Mean return on
investment gained over the course of five years is £10,685.8 whereas the median return amounts
to £11,023 among all the quarterly returns extracted for previous years. The maximum amount of
returns given by this project is £12,263 whereas the minimum return given by this investment
project is that of £8,719.
It is worthy to note that Mode in both cases is nil as there is no single value which has
been observed in repetition. Apart from this, the count for both the projects states the number of
variables taken into account while conducting this method. A count of 20 shows that no variable
has been excluded for either of the projects while generating a summary statistic for the given
dataset. Comparing the two projects it can be inferred that Project A has much higher returns in
comparison to Project B both in terms of average as well as minimum and maximum amounts.
Thus, indicating more consistency in terms of rewards rendered to the investors of Project A.
Apart from this the statistical difference between the two projects has also been
undertaken using Inferential Statistical Technique known as t-test with an assumption that the
variability found among the two projects is unequal. These results have been showcased as
under:
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Hypotheses:
H1: Significant difference exists between the two investment projects.
H0: Significant difference between the two investment projects does not exist.
Results and Interpretation:
t-Test: Two-Sample Assuming Unequal Variances
Variable 1 Variable 2
Mean 11510.4 10685.8
Variance 1374302.56842105 1591462.79999999
Observations 20 20
Hypothesized Mean Difference 0
df 38
t Stat 2.1413612208
P(T<=t) one-tail 0.0193596157
t Critical one-tail 1.6859544602
P(T<=t) two-tail 0.0387192314
t Critical two-tail 2.0243941639
As per the above analysis, Variable 1 and 2 relates to Project A and B respectively. It is
clearly evident from the results derived above that there exists a difference in mean between the
two investment projects. Generally, if t-Stat < -t Critical two-tail or t Stat > t Critical two-tail, we
reject the null hypothesis (Rhodes, C., 2015). Since it is the case of latter, that is 2.1413>2.0243,
null hypothesis stands rejected. Thus, one can affirm that alternative hypothesis (H1) is true.
TASK C
Tesco PLC has three products viz. A, B and C whose marketing campaigns have been
tried and tested over the years. Some have been successful while others have failed. Based on
this the following results have been ascertained product-wise as well as different levels of
success for each of the marketing campaigns:
Product A Product B Product C Number of Marketing
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Campaigns (Different
Levels)
No increase in sales 9 3 41 53
Increase in sales of less than
10% 6 1 5 12
Increase in sales of more
than 10% 10 11 14 35
Number of Marketing
Campaigns (Product-Wise) 25 15 60 100
From the above table it can be ascertained that product-wise the number of marketing
campaigns conducted by Tesco are 25,15 and 60 for Products A, B and C respectively. Whereas
for each level, these campaigns are 53, 12 and 35 respectively. It is important to note that the
success of marketing campaign is highly dependent on the product being marketed, especially in
the case of Products A and B who have experienced no increase in sales after 9 and 3 campaigns
respectively. However, Product C has seen no increase in sales even after holding 41 such
promotional activities. Thus, indicating that it is highly independent of the success of the
campaign itself.
CONCLUSION
From the above report it can be clearly inferred that business statistics play a major role
in the success or failure of a commercial organisation. This is due to the fact that a business has a
set of operational needs that are to be fulfilled in order to achieve an enterprise's short-term as
well as long-term goals or objectives. Applying various statistical methodologies such as
Predictive Analysis or Forecasting, Descriptive Statistics, Inferential Statistics and Measures of
Association, a business manager is able to gain valuable insights through elimination of
uncertainty and evaluation of alternatives.
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REFERENCES
Books and Journal
Barrett, K. C. and et.al., 2012. IBM SPSS for introductory statistics: Use and interpretation.
Routledge.
Berenson, M. and et.al., 2012. Basic business statistics: Concepts and applications. Pearson
higher education AU.
Groebner, D. F. and et.al., 2013. Business statistics. Pearson Education UK.
Keller, G., 2015. Statistics for Management and Economics, Abbreviated. Cengage Learning.
Newbold, P., Carlson, W. L. and Thorne, B., 2013. Statistics for business and economics.
Boston, MA: Pearson.
Rhodes, C., 2015. Business statistics. Briefing paper. 6152.
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