eChapters

Introduction

External environmental analysis is a critical process that organizations undertake to assess and understand the various factors and forces that exist outside their immediate operational boundaries.

It involves examining the external environment in which a business operates to identify opportunities, threats, and potential influences that may impact its performance and strategic decisions.

External environmental analysis is the systematic evaluation of factors outside an organization's control that can significantly impact its operations, strategies, and overall success.

Purpose The aim of conducting an external environmental analysis is to acquire a deeper understanding of the opportunities and threats that exist within the external environment. By comprehending these elements, organizations can make informed choices and craft efficacious strategies to adjust and prosper in an ever-evolving business environment.

Factors to Analyze: Highlight the main factors that organizations typically analyze during external environmental analysis. These encompass the Political, Economic, Social, Technological, Environmental, and Legal (PESTEL) factors.. Additionally, industry-specific factors such as competition, customer behavior, and market trends should also be considered.

Tools and Techniques: Briefly mention some common tools and techniques used in external environmental analysis. These methodologies may involve SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis, the application of Porter's Five Forces framework, scenario planning, trend analysis, and market research.

Introduction to Competitive Analysis

Competitive analysis is a crucial component of strategic planning and business decision-making. It involves assessing and understanding the competitive landscape in which a company operates. Here are some key points to consider when introducing competitive analysis:

Competitive analysis is the process of evaluating and analyzing the strengths and weaknesses of current and potential competitors in a specific market or industry.

Purpose: The primary purpose of competitive analysis is to gain insights into competitors' strategies, capabilities, and market positioning. It assists businesses in pinpointing opportunities, mitigating risks, and formulating effective strategies to surpass their competitors.

Information sources: Competitive analysis relies on gathering information from various sources. These include publicly available data, industry reports, competitor websites, customer reviews, social media, and market research.

Introduction to Internal Environment Analysis

Internal environment analysis is a critical component of strategic management that focuses on evaluating an organization's internal factors and resources. It involves assessing the internal strengths and weaknesses to gain insights into the organization's capabilities and competitive advantage. Here are some key points to highlight when discussing the introduction to internal environment analysis:

Internal environment analysis refers to the process of examining and evaluating an organization's internal factors, including its resources, capabilities, and core competencies. It helps identify strengths and weaknesses that can influence the organization's performance and competitiveness.

Purpose: The primary purpose of internal environment analysis is to understand the organization's internal dynamics and uncover key areas where it has a competitive advantage or faces challenges. It enables organizations to identify their core competencies and allocate resources effectively to achieve strategic goals.

Introduction

It refers to action which are taken with an aim to provide value to customers developing a competitive advantage by encashing upon the strength areas. In case of business Organisation with multiple business, where each business is considered as Strategic Business Unit (SBU).

• It address the following issues

 • Satisfying the Customer needs

 • Achieving an edge over its competitors

 • Avoidance of competitive disadvantage

Michal Porter Generic Competitive Strategy aim to develop position in long run to outperform the competitors. The three strategies includes:

• Cost Leadership: Providing quality product at low cost

• Differentiation: Providing unique goods and services

• Focus: Cost and differentiation focus

Introduction

It includes different levels of competitive strategy and how generic competitive strategies can be formulated. The organisation need to understand how to tackle the competitors and what specific decisions to be taken while formulating a competitive strategy. Each type of industry has to plan its own competitive strategy either to grow or to come out in bad situation. Michal Porter described generic competitive strategies to cope with five competitive forces. These strategies help organisation in different situation to develop appropriate competitive strategy that can be implemented effectively.

Competitive strategies are the methods and approaches used by companies to gain a competitive edge in the market and surpass their rivals. These strategies involve making deliberate choices about how to position the company, allocate resources, and respond to market conditions. Here are some key points to consider when introducing competitive strategies.

Introduction to Corporate Level Strategy

Corporate level strategy pertains to the choices and initiatives undertaken by a company's senior management to define the overarching direction and breadth of the organization. It entails making decisions regarding which industries or markets to engage in and how to distribute resources among various business units.

Scope of Decision-Making: Corporate level strategy concentrates on the entirety of the organization rather than individual business units or functional areas. It involves strategic decisions related to diversification, vertical integration, mergers and acquisitions, strategic alliances & resource allocation.

Purpose: The primary goal of corporate level strategy is to create value for shareholders by achieving a sustainable competitive advantage and maximizing the overall performance and profitability of the organization. It involves assessing the external business environment, identifying opportunities and threats, and leveraging internal capabilities to gain a competitive edge.

Diversification: One key aspect of corporate level strategy is deciding whether to pursue a strategy of diversification. Diversification can be either related (expanding into industries or markets related to the company's current business) or unrelated (entering completely new industries). The decision to diversify involves assessing the potential benefits, risks, and synergies associated with entering new markets.

Introduction to strategy implementation involves translating strategic plans into actions and achieving desired outcomes. While many organizations focus on the technical aspects of implementation, it is crucial to consider the behavioral dimensions as well. Behavioral dimensions encompass the attitudes, behaviors, and actions of individuals within an organization that impact the success of strategy implementation. Here are some key points to consider regarding the behavioral dimensions of strategy implementation:

Organizational culture: The culture of an organization plays a significant role in strategy implementation. It comprises shared values, norms, beliefs, and practices that influence employee behavior. An accommodating and flexible culture fosters employee engagement, commitment, and alignment with strategic goals.

Leadership and Communication: Effective leadership is essential for successful strategy implementation. Leaders must articulate the strategy clearly, provide guidance, and motivate employees towards the desired outcomes. Communication channels ought to be open, transparent, and promote a culture of collaboration and feedback.

Employee Involvement and Empowerment: Involving employees in the strategy implementation process increases their commitment and ownership. Empowering employees through delegation of authority and decision-making cultivates a sense of responsibility and accountability, leading to enhanced performance and innovation.

Introduction to Corporate Governance

Corporate governance encompasses the framework of rules, practices, and processes that guide the direction, control, and management of a company. It involves the interactions among various stakeholders, including shareholders, management, the board of directors, employees, customers, suppliers, and the community. Successful corporate governance is vital for a company's long term success and sustainability. It helps establish a framework for responsible decision-making, accountability, and transparency, which enhances investor confidence and protects the interests of stakeholders.

Introduction

The strategic control process is an essential component of strategic management that helps organizations monitor and evaluate their strategic plans and initiatives. It involves a systematic approach to assess the progress performance, and effectiveness of strategic objectives and make necessary adjustments to ensure alignment with organizational goals. Here are some key points on the introduction to the strategic control process. Strategic control refers to the ongoing monitoring, evaluation, and adjustment of an organization's strategic activities to ensure they are in line with the desired outcomes and objectives. It The significance of strategic control lies in several key reasons. helps organizations track their progress, identify deviations from the intended course, and take corrective actions. It also enables managers to assess the effectiveness of strategies, make informed decisions, and enhance organizational performance.

Introduction to Evaluation in Strategic Management Process

Strategic management is the process of formulating and implementing strategies to achieve an organization's goals and objectives. It entails making decisions and taking actions that align the organization with both its external environment and internal capabilities. Evaluation is a crucial component of the strategic management process as it helps assess the effectiveness and efficiency of the strategies implemented. It involves the systematic and objective assessment of various aspects of the organization's performance, including its strategy, resources, operations, and outcomes. The primary purpose of evaluation in strategic management is to determine whether the strategies are yielding the desired results and to identify areas for improvement. It offers valuable insights into the organization's strengths, weaknesses, opportunities, and threats, enabling informed decision-making and adjustments to strategies as necessary.

Introduction to Statistics

The word statistics has been derived from Latin word “status”, the German word “statistik”, the Italian word “statistica” or the French word “statistique”. During early periods, it was used by emperors and kings only for collecting the information about state and other information which was needed about their people, their number, land, status of people, revenue of the state etc. Gradually, it meaning and uses extended and there onwards its nature changed.

Some scientist/ researchers were given definition of statistics as per time and place.

• An ancient date, A.L. Bowley gives some definitions according their use and place. He seems to be inadequate because these do not include all aspects of statistics. As per time, place or requirement, he gave three definitions of statistics:

• Statistics may be called the science of counting

• Statistics may rightly be called the science of average

• Statistics as the science of counting and average

Statistical Series: When observations of any characteristic present in the individual of a group are recorded and arranged one after the other in a systematic order, they form a series, known as statistical series. For example: milk yield of 20 cows, height of the students in a class in a systematic order, etc. These are some important types of series:

• Discrete series: In this series, observations can take up only exact values and not any fractional values, is called discrete series. That means, this type of series has whole number of counting. For example: number of the students in a class, members of a family, number of births in a certain year, etc

• Continuous series: A series which can take up any numerical value (integral/ fractional) within a certain range is called continuous series. For example: milk yield of 20 cows, height, weight, temperature, etc.

• Array series: When we arranged the observations of a series either in ascending order of their magnitude or descending order of their magnitude, then it becomes an array series.

• Spatial series: When items of a series are given according to space or place or time, then such type of series is known as spatial series. For example: month wise milk yield of a herd of cows, month wise income or expenditure of an industry, population of a country according to states, etc.

Population: The word population or universe in statistics means a set of observations relating to a phenomenon under investigation.

Sample: It is a fraction or a subset of population drawn through a valid statistical procedure so that it can be regarded as representative of the entire population.

Sampling: The statistical procedure of drawing a sample from the population is called Sampling.

Census enquiry or complete enumeration: When each and every unit of the population is investigated for the characteristic under study, known as census enquiry or complete enquiry.

Sample enquiry or sample enumeration: It is a part of the population is investigated for the characteristic under study, known as sample enquiry or sample enumeration.

Necessity of sampling: Since, the process of collecting information from all the elements of a large population may be, in general, expensive, time consuming and difficult. We help to sampling and generalise the properties of the sample to the entire population. The statistical procedures which are used for drawing inferences or conclusions about the population from sample data are covered under Inferential Statistics or Statistical inference. Thus, sampling theory is the basis of statistical inference in which we wish to obtain maximum information about the population with minimum effort and maximum precision.

Diagrammatic Presentation of Data

The quantitative data when classify are converted into averages and if the number of average is large then it is not possible to draw a conclusion from their large numbers of averages. Moreover quantitative data are dull, confusing, boring for many of us. Therefore, a statistical device to make such confusing data more attractive was developed i.e. if such complex data are presented by geometric f igures, pictures, graphs or curves then it will become more attractive and more impressive, is called as diagrammatic representation of data. These diagrams are nothing but the use of geometrical figures to improve the overall presentation and offer visual assistance for the reader.

Types of Diagrams

Generally, diagrams are made for a special series or a time series and they are divided into five different categories:

1) One dimensional diagrams

2) Two dimensional diagrams

3) Three dimensional diagrams

4) Pictograms

5) Cartograms

Need of an average

Through frequency distribution are the condensed form of raw data, but these are not sufficient for making comparison between two or more data. Therefore further condensation of frequency distribution into a single figure is made. This single f igure is always representative of the frequency distribution and such a figure is known as average.

Measure of Central Tendency

According to Croxten and Cowden “An average is a single value within the range of the data that is used to represent all the values in the series”. A statistical measure used for representing the centre of central value of a set of observations is known as “Measure of Central Tendency”. It is also called as “Measure of f irst order”.

Characteristics of an ideal Measure of Central Tendency

An ideal measure of central tendency should possess the following properties:

1) It should be rigidly defined i.e. it has a fixed and finite value

2) It should be easy to understand 3) It should be easy to calculate

4) Its calculation should be based on all the observations

5) It should be capable for further algebraic treatment

6) It should not be affected by extreme values of the observations

7) It should be least affected by fluctuations of sampling

Dispersion

Measure of central tendency tells about the central position of the series. They do not throw like on the formation of series because in some cases, two series may have a same measure of central tendency but differ in their composition. In order to study the formation, composition, structure and scatteredness of the items of the series, measure of second order are necessary. The measure of second order will give idea about the formation of the series. As the representation of a series on the basis of its measure of central tendency is not sufficient but when a series is represented on the basis of its measure of central tendency as well as its measure of dispersion, then it will give the complete adequate representation of the series. Therefore, it is defined as the extent or degree of which data tend to spread around an average is called the dispersion. It is also known as variability or measure of second order.

Characteristics of an ideal Measure of Dispersion

An ideal measure of dispersion should possess the following properties:

1) It should be rigidly defined i.e. it has a fixed and finite value

2) It should be easy to understand

3) It should be easy to calculate

4) Its calculation should be based on all the observations

5) It should be capable for further algebraic treatment

6) It should not be affected by extreme values of the observations

7) It should be least affected by fluctuations of sampling

Correlation

In a statistical analysis, when two variables are studied simultaneously, then it is called “Bivariate study’. Now, in a bivariate study, if it is found that change in the values of one variable then in sympathy with the change in the values of other variable, then these two variables are said to be associated and the association is termed as correlation. In other words, the term correlation indicates the relationship between two such variables in which with changes in the values of one variable, the values of the other variable also change. In short, the tendency of simultaneous variation between two variables is called correlation.

Types of Correlation

Correlation can be classified by-

• Positive and Negative correlation: The correlation may be classified according to the direction of change in the two variables. In this regard, correlation may be either positive or negative. Positive correlation refers to the movement of variables in the same direction. Negative correlation efers to the movement of variables in the opposite direction.

• Simple, Partial and Multiple correlations: When only two variables are studied it is a problem of simple correlation. When three or more variables are studied simultaneously, it is a problem of multiple correlations. On the other hand, in partial correlation we recognize more than two variables, but consider only two variables to be influencing each other the effect of other influencing variables being kept constant.

• Linear and non-linear correlations: In linear correlation for every unit change in the values of one variable, there is constant change in the value of other variable. The perfect positive and negative correlations are also linear correlations. In non-linear correlation the change in the values of one variable does not have a constant ratio to the change in the other variable.

The theory of probability has its origin in the games of chance related to gambling such as throwing a die, tossing a coin, drawing cards from a pack of cards etc. Jerame Cardon (1501-1576), an Italian mathematician was the first man to write a book on the subject entitled “Book on Games and Chance”, which was published after his death in 1663. The systematic and scientific foundation of the mathematical theory of probability was laid in mid-seventeenth century by two French mathematicians B. Pascal (1623-1662) and Pierre de Fermat (1601 1665).

Important terms and concepts

To understand probability, let us first get acquainted with some important terms and concepts associated with probability.

• Trial: An experiment is any natural or planned activity for which it is not possible to predict the particular result called a trial.

• Events: The possible outcomes of experiments of a trial are called events. For example: Tossing of a coin in an experiment and one toss of the coin is called trial. In tossing of a coin there are only two outcomes or events, i.e. head or tail.

Types of events

Events can be of following different types:

• Exhaustive events

• Equally likely events

• Mutually exclusive events

• Dependent events

• Independent events

• Null or impossible events

• Exhaustive events: It is the total number of all the possible outcomes of an experiment.

Theoretical Probability Distribution

Many random variables associated with statistical experiments have similar properties, and as such can be described by the same probability functions. The distribution obtained from a theoretical listing of outcomes and probabilities, which can be described by a mathematical formula representing same phenomenon of interest is called a theoretical probability distribution. The distributions of discrete and continuous random variables are accordingly called discrete or continuous probability distributions.

Discrete Probability Distribution: It can be defined as a probability distribution of discrete random variables. Such a distribution will represent data that has a f inite countable number of outcomes. If the random variable X is a discrete random variable, the probability function P(X) is called probability mass function and its distribution is called discrete probability distribution. There are two conditions that a discrete probability distribution must satisfy. These are given as follows:

Types of Discrete Probability Distributions

There are several important discrete probability distributions-

• Uniform distribution

• Binomial distribution

• Negative Binomial distribution

• Geometric distribution

• Poisson distribution

• Hyper-Geometric distribution

Time Series

A series of observations recorded over time is known as a time series. In other words, a time series is a set of observations taken at specified times, usually at equal intervals. For example: Annual retail sales, rainfall measurements, heartbeats per minute, etc. Such studies are to be based on the analysis of time series data collected over time. Thus, the analysis of time series plays an important role in an investigation of economic, social, commercial and even biological phenomenon.

Components of time series

In a long time series, generally, there are following four components:

• Secular trend or long term movements

• Seasonal variations

• Cyclic variations • Random or irregular movements

• Secular trend or long term movements: It is that characteristic of a time series which extends consistently throughout the entire period of time under consideration. It shows a long term tendency of an activity to grow or to decline. The term long period of time in a relative phenomenon and cannot be defined exactly. For some cases a period as small as a weak may be fairly long while in other cases, a period as long as two years may not be assumed long.