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      • What is Cluster Analysis
      • Data Reduction and Unsupervised Learning
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      • Hierarchical and k-Means Clustering
      • Defining Output Variables and Analyzing the Results
      • Using Historical Data to Model Uncertainty
      • Models with Correlated Uncertain Variables
      • Creating and Interpreting Charts
      • Using Average Values versus Simulation
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      • Formulation and Solution of Binary Optimization Problems
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      • Chance Constraints and Value At Risk
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      • ANOVA – Introduction
      • Example -Inspect Spray and Tooth Growth
      • Logit Model - Binary Outome and Forecastign linear regression
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      • Social media Microscope
      • N-Gram - Frequcy Count and phase mining
      • LDA Topic Modeling
      • Machine-Learned Classification and Semantic Topic Tagging
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      • Understanding The Growth Of Data
      • Evaluating Methods Of Data Access
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      • Introduction
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      • Survey Overview
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      • Causal Data Collection and Summary
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      • Regression Analysis
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      • Results and Predictions
      • Perspective Analytics (Maximize Revenue and Market Structure Competitions)
    • Analytics for Advance Marketing >
      • Visualisation and statistics (Political Advertising,Movie Theater and Data Assembly)
      • Excel Analysis of Motion Picture Industry Data
      • Displaying Conditional Distributions
      • Analyzing Qualitative Variables
      • Steps in Constructing Histograms
      • Common Descriptive Statistics for Quantitative Data
      • Regression-Based Modeling
      • Customer Analytics
      • Illustrating Customer Analytics in Excel
      • Customer Valuation Excel Demonstration
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    • Introduction to Managerial Economics >
      • Basic Techniques
      • The firm: Stakeholders, Objectives and Decision Issues
      • Demand and Revenue Analysis >
        • Demand Estimation and Forecasting
        • Demand Elasticity
        • Demand Concepts and Analysis >
          • Formulation and Solution of Binary Optimization Problems
      • Scope of Managerial Economics
    • Prodution and Cost Analysis >
      • Production Function
      • Estimation of Production and Cost Functions
      • Cost Concepts and Analysis I
      • Cost Concepts and Analysis II
    • Pricing Decisions >
      • Pricing strategies >
        • Adding Optimization to a Spreadsheet Model
      • Market structure and microbes barriers to entry
      • Pricing under pure competition and pure monopoly
      • Pricing under monopolistic and oligopolistic competition
    • Narendra Modi Development Model of Gujarat
  • JBDON Golf
    • Digital Marketing Application of Optimization
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      • IIMC says PepsiCo CEO Indra Nooyi was an average student
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      • The Start-Up of you.
      • BUYING AND MERCHANDISING
      • HUMAN RESOURCE MANAGEMENT
      • Do You Suffer From Decision Fatigue?
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      • Building Your Own Start-up Technology Company, Part 3
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      • Renewable energy is no longer alternative energy
      • What Makes an Exceptional Social Media Manager?
      • The Forgotten Book that Helped Shape the Modern Economy
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      • Marketing Analytics and Customer Satisfaction
      • Mitesh Thakker
      • Tresa Sankoorikal
    • Speed Techniques
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  • Untitled
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    • Cluster analysis using excel and excel miner
    • Chance Constraints and Value At Risk
    • Adding Uncertainty to a Spreadsheet Model
  • Adidas

CORE COMPETENCIES

Introduction

Core competencies are those capabilities that are critical to a business achieving competitive advantage. The starting point for analysing core competencies is recognising that competition between businesses is as much a race for competence mastery as it is for market position and market power. Senior management cannot focus on all activities of a business and the competencies required to undertake them. Therefore, the goal is for management to focus attention on competencies that really affect competitive advantage.

The Work of Hamel and Prahalad
The main ideas about Core Competencies where developed by C K Prahalad and G Hamel through a series of articles in the Harvard Business Review followed by a best-selling book - Competing for the Future. Their central idea is that over time companies may develop key areas of expertise, which are distinctive to that company and critical to the company's long-term growth.

'In the 1990s managers will be judged on their ability to identify, cultivate, and exploit the core competencies that make growth possible - indeed, they'll have to rethink the concept of the corporation itself' C K Prahalad and G Hamel 1990
These areas of expertise may be in any area but are most likely to develop in the critical, central areas of the
company where the most value is added to its products.

For example, for a manufacturer of electronic equipment, key areas of expertise could be in the design of the electronic components and circuits. For a ceramics manufacturer, they could be the routines and processes at the heart of the production process. For a software company the key skills may be in the overall simplicity and utility of the program for users or alternatively in the high quality of software code writing they have achieved.

Core Competencies are not seen as being fixed. Core Competencies should change in response to changes in the  company's environment. They are flexible and evolve over time. As a business evolves and adapts to new circumstances and opportunities, so its Core Competencies will have to adapt and change. Identifying Core  Competencies Prahalad and Hamel suggest three factors to help identify core competencies in any business:

What does the Core

Competence Achieve?

Comments / Examples

Provides potential access

to a wide variety of

markets

The key core competencies here are those that enable the creation of new

products and services.

Example: Why has Saga established such a strong leadership in supplying

financial services (e.g. insurance) and holidays to the older generation?

Core Competencies that enable Saga to enter apparently different markets:

- Clear distinctive brand proposition that focuses solely on a closely-defined

customer group

- Leading direct marketing skills - database management; direct-mailing

campaigns; call centre sales conversion

- Skills in customer relationship management

Makes a significant

contribution to the

perceived customer

benefits of the end

product

Core competencies are the skills that enable a business to deliver a fundamental

customer benefit - in other words: what is it that causes customers to choose one

product over another? To identify core competencies in a particular market, ask

questions such as "why is the customer willing to pay more or less for one

product or service than another?" "What is a customer actually paying for?

Example: Why have Tesco been so successful in capturing leadership of the

market for online grocery shopping?

Core competencies that mean customers value the Tesco.com experience so

highly:

- Designing and implementing supply systems that effectively link existing shops

with the Tesco.com web site

- Ability to design and deliver a "customer interface" that personalises online

shopping and makes it more efficient

- Reliable and efficient delivery infrastructure (product picking, distribution,

customer satisfaction handling)

Difficult for competitors

to imitate

A core competence should be "competitively unique": In many industries, most

skills can be considered a prerequisite for participation and do not provide any

9

significant competitor differentiation. To qualify as "core", a competence should

be something that other competitors wish they had within their own business.

Example: Why does Dell have such a strong position in the personal computer

market?

Core competencies that are difficult for the competition to imitate:

- Online customer "bespoken" of each computer built

- Minimisation of working capital in the production process

- High manufacturing and distribution quality - reliable products at competitive

prices

 

A competence which is central to the business's operations but which is not exceptional in some way should not be considered as a core competence, as it will not differentiate the business from any other similar businesses. For example, a process that uses common computer components and is staffed by people with only basic training cannot be regarded as a core competence. Such a process is highly unlikely to generate a differentiated advantage over rival businesses. However, it is possible to develop such a process into a corecompetence with suitable investment in equipment and training. It follows from the concept of Core Competencies that resources that are standardised or easily available
will not enable a business to achieve a competitive advantage over rivals.
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  • Home
  • Applied Analytics
    • Analytics for Decision Making >
      • What is Cluster Analysis
      • Data Reduction and Unsupervised Learning
      • Preparing Data and Measuring Dissimilarities
      • Hierarchical and k-Means Clustering
      • Defining Output Variables and Analyzing the Results
      • Using Historical Data to Model Uncertainty
      • Models with Correlated Uncertain Variables
      • Creating and Interpreting Charts
      • Using Average Values versus Simulation
      • Optimization and Decision Making
      • Formulating an Optimization Problem
      • Developing a Spreadsheet Model
      • Adding Optimization to a Spreadsheet Model
      • What-if Analysis and the Sensitivity Report
      • Evaluating Scenarios and Visualizing Results to Gain Practical Insights
      • Digital Marketing Application of Optimization
      • Advanced Models for Better Decisions
      • Business Problems with Yes/No Decisions
      • Formulation and Solution of Binary Optimization Problems
      • Metaheuristic Optimization
      • Chance Constraints and Value At Risk
      • Simulation Optimization
    • Analytics for Marketing >
      • Marketing Analytics and Customer Satisfaction
      • Customer Satisfaction
      • Measurements and Scaling Techniques – Introduction
      • Primary Scales of Measurement
      • Comparative Scaling
      • Non-Comparative Scaling
      • Experiment Design: Controlling for Experimental Errors
      • A/B Testing: Introduction
      • A/B Testing: Types of Tests
      • ANOVA – Introduction
      • Example -Inspect Spray and Tooth Growth
      • Logit Model - Binary Outome and Forecastign linear regression
      • Text Summarization
      • Social media Microscope
      • N-Gram - Frequcy Count and phase mining
      • LDA Topic Modeling
      • Machine-Learned Classification and Semantic Topic Tagging
    • Data Engine >
      • Understanding The Growth Of Data
      • Evaluating Methods Of Data Access
      • Communication journey
      • Data Journey
      • Planning for data visualisation
      • Visualisation Component
      • Content Connection and Chart Legitibility
    • Customer Insights >
      • Introduction
      • What is Descriptive Analytics?
      • Survey Overview
      • Net Promoter Score and Self-Reports
      • Survey Design
      • Passive Data Collection
      • Media Planning
      • Data Visualization
      • Causal Data Collection and Summary
      • Asking Predictive Questions
      • Regression Analysis
      • Data Set Predictions
      • Probability Models
      • Results and Predictions
      • Perspective Analytics (Maximize Revenue and Market Structure Competitions)
    • Analytics for Advance Marketing >
      • Visualisation and statistics (Political Advertising,Movie Theater and Data Assembly)
      • Excel Analysis of Motion Picture Industry Data
      • Displaying Conditional Distributions
      • Analyzing Qualitative Variables
      • Steps in Constructing Histograms
      • Common Descriptive Statistics for Quantitative Data
      • Regression-Based Modeling
      • Customer Analytics
      • Illustrating Customer Analytics in Excel
      • Customer Valuation Excel Demonstration
  • Soft Skills
    • Adaptability
    • Confidence
    • Change Management
    • Unlearning and Learning
    • Collaboration and Teamwork
    • Cultural Sensitivity
  • Marketing
  • Finance
  • Economics
    • Introduction to Managerial Economics >
      • Basic Techniques
      • The firm: Stakeholders, Objectives and Decision Issues
      • Demand and Revenue Analysis >
        • Demand Estimation and Forecasting
        • Demand Elasticity
        • Demand Concepts and Analysis >
          • Formulation and Solution of Binary Optimization Problems
      • Scope of Managerial Economics
    • Prodution and Cost Analysis >
      • Production Function
      • Estimation of Production and Cost Functions
      • Cost Concepts and Analysis I
      • Cost Concepts and Analysis II
    • Pricing Decisions >
      • Pricing strategies >
        • Adding Optimization to a Spreadsheet Model
      • Market structure and microbes barriers to entry
      • Pricing under pure competition and pure monopoly
      • Pricing under monopolistic and oligopolistic competition
    • Narendra Modi Development Model of Gujarat
  • JBDON Golf
    • Digital Marketing Application of Optimization
  • Let's Talk
  • MBA Project Sharing
  • About Us
    • Good Read >
      • IIMC says PepsiCo CEO Indra Nooyi was an average student
      • India’s middle class figures in Fortune’s Top Ten list of those who matter
      • The Start-Up of you.
      • BUYING AND MERCHANDISING
      • HUMAN RESOURCE MANAGEMENT
      • Do You Suffer From Decision Fatigue?
      • New Page
      • About social media and web 2.0
      • Building Your Own Start-up Technology Company, Part 1
      • Building Your Own Start-up Technology Company, Part 2
      • Building Your Own Start-up Technology Company, Part 3
      • Building Your Own Start-up Technology Company, Part 4
      • Renewable energy is no longer alternative energy
      • What Makes an Exceptional Social Media Manager?
      • The Forgotten Book that Helped Shape the Modern Economy
      • Home
      • How to Think Creatively
      • A Lighthearted Looks at Project Management and Sports Analogies
      • Why Trust Matters More Than Ever for Brands
  • CET Knowledge Zone
    • Tips From JBIMS Students >
      • Prasad Sawant
      • Chandan Roy
      • Ram
      • Ashmant Tiwari
      • Rajesh Rikame
      • Ami Kothari
      • Ankeet Adani
      • Sonam Jain
      • Marketing Analytics and Customer Satisfaction
      • Mitesh Thakker
      • Tresa Sankoorikal
    • Speed Techniques
    • CET Workshops
  • Untitled
  • New Page
    • Cluster analysis using excel and excel miner
    • Chance Constraints and Value At Risk
    • Adding Uncertainty to a Spreadsheet Model
  • Adidas