Strategic Analytics
Description
Enables organizations and managers to make strategic choices based on robust analytics supported by AI
The new edition of Strategic Analytics introduces AI as the natural transition from management science and analytics, explains how AI will shape the managerial capabilities and the process of strategic planning, and showcases the support AI can provide to the process of strategic planning. The text includes numerous updates to critical subjects including sustainability and the UN Sustainable Development Goals, stakeholder management, soft aspects of organizations (including people, culture, and values), and organizational change driven by analytics and AI.
This book discusses the three key types of analytics-descriptive, predictive, and prescriptive-together with multiple methods and frameworks from the field of strategic management. Multiple international real-life case studies, examples, and business issues for further research illustrate the combinations of analytics, AI and strategic management. Theory review questions and exercises are included at the end of each chapter.
Sample topics discussed in Strategic Analytics include:
- Managerial capabilities for a complex world-politics, economy, society, technology, and environment
- External environments known as exogenous factors (PESTE) and endogenous factors (industry)
- Industry dynamics, industry evolution, competitive advantage, dynamic resource management, organizational design, and performance measurement system
- The life cycle of organizations, from start-ups to maturity for maintaining profitability and growth and finally regeneration
Aimed at both academics and practitioners, Strategic Analytics is an ideal text for postgraduates and advanced undergraduate students of business and management.
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Person
MARTIN KUNC received his PhD from London Business School in Decision Science. He has built a distinguished career in business education and strategic consultancy. Currently, he serves as professor of Management Science at Southampton Business School, University of Southampton and holds visiting professorships at several other leading institutions. He is Editor-in-Chief of the Journal of the Operational Research Society and serves as an associate editor for several other academic journals. He is a member of the Operational Research Society and Arthur Andersen Alumni. Throughout his career, he has led consultancy projects addressing strategic challenges across a wide range of industries, contributing valuable insights and solutions to complex business problems.
Content
Preface to Second Edition xiii
Acknowledgments xv
About the Companion Website xvii
1 Introduction to Strategic Analytics 1
1.1 What Is Analytics? 3
1.2 What Is Management Science? 10
1.3 What Is Information Technology: New Challenges? 20
1.4 What Is AI and How Can It Enhance Business Analytics? 23
1.4.1 Integration of GenAI with BA 26
1.4.2 Implications of AI Adoption in Business Decision- making Processes 29
1.5 What Is Strategic Management? 31
1.5.1 What Are the Characteristics of Strategic Problems? 33
1.5.2 Different Approaches to Strategic Management 36
1.6 Strategy Analytics: Integrating Management Science and AI with Strategic Management 40
References 44
2 Dynamic Managerial Capabilities for a Complex World with AI 49
2.1 Dynamic Managerial Capabilities 50
2.1.1 Task Dimension 53
2.1.2 Cognitive Dimension 54
2.1.3 Behavior Dimension 55
2.1.4 AI Dimension 57
2.2 Integrating Management Science, Analytics, and AI for Strategic Management Practice 61
2.2.1 Managers as Modelers 62
2.2.2 Behavior with and Beyond Models 65
2.2.3 Modeling Systems 66
2.2.4 Big Data Analytics Capabilities 69
2.2.5 AI Capabilities 70
2.3 End of Chapter 73
2.3.1 Revision Questions 74
2.3.2 Case Study: The Future of Strategizing with AI 74
References 77
Further Reading 82
3 External Environment: Political, Economic, Societal, Technological, and Environmental Factors 85
3.1 The PESTE Analysis 87
3.1.1 Limitations of PESTE Analysis 88
3.2 Integrating Management Science, Analytics, and AI for Strategic Management Practice 92
3.2.1 Achieving Consistency in PESTE Analysis Using the Analytic Hierarchy Process 92
3.2.2 Understanding the Evolution of PESTE Factors Using Visualization Analytics 105
3.2.3 Scanning PESTE Factors Using AI 106
3.2.4 Developing Scenarios Supported With AI 107
3.2.5 Developing Scenarios Supported With System Dynamics 108
3.3 End of Chapter 109
3.3.1 Revision Questions 110
3.3.2 Case Study: Westmill Co- op and the Rise of Renewable Energy 111
References 113
Further Reading 115
4 Industry Dynamics 117
4.1 Defining the Industry 118
4.2 Porter's Five Forces and Industry Dynamics 119
4.2.1 Bargaining Power of Suppliers 120
4.2.2 Bargaining Power of Buyers 121
4.2.3 Substitutes 122
4.2.4 Threat of New Entrants 122
4.2.5 Intensity of Rivalry 123
4.2.6 Strategic Issues Derived from Five Forces Analysis 125
4.3 Integrating Management Science, Analytics, and AI for Strategic Management Practice 130
4.3.1 Revenue Management 130
4.3.2 Evaluating Competitors' Performance in the Market Using Text Mining 133
4.3.3 Managing Five Forces Through AI- enhanced Methods 139
4.4 End of Chapter 141
4.4.1 Revision Questions 141
4.4.2 Case Study: Strategic Evaluation of Entering in a New Market as a Low- cost Airline Using System Dynamics Modeling 142
4.4.2.1 Describing the Key Strategic Aspects of a Business Using a System Dynamics Model 142
4.4.2.2 The easyJet Case 143
References 148
Further Reading 150
5 Industry Evolution 153
5.1 Dynamic Behavioral Model of Industry Evolution 157
5.1.1 Industries as Feedback Systems 158
5.1.2 A Behavioral Model of Organizations 159
5.1.3 Dynamic Behavioral Model of Industry Evolution 161
5.1.4 Types of Dynamic Behavior and Strategic Implications on the Evolution of Industries 163
5.1.5 Industry Evolution: A Regional Socioeconomic Perspective 166
5.1.6 Industries Evolution: A Power Game 168
5.2 Integrating Management Science, Analytics, and AI for Strategic Management Practice 173
5.2.1 Exploring Industry Evolution Using System Dynamics 174
5.2.1.1 Market Development 174
5.2.2 Understanding How the Levels of Integration/Interaction Between Companies Affect the Evolution of Companies Using NKC Models 181
5.2.2.1 Insights from the Model 183
5.2.3 Uncovering the Evolution of Technology in an Industry Using Text Mining Methods 185
5.2.4 Mapping Industry Evolution Using AI 186
5.3 End of Chapter 187
5.3.1 Revision Questions 188
5.3.2 Case Study: How Smartphones and AI Have Shaped the Camera Industry 189
References 192
Further Reading 194
6 Competitive Advantage: Static Analysis 195
6.1 The Direction of a Company: Vision and Mission 196
6.2 Defining Value and Market Segmentation 196
6.3 Mapping the Activities to Deliver Value 200
6.3.1 Value Chain 200
6.3.2 Activity System Map 202
6.3.3 Business Model Canvas 203
6.4 Type of Business Strategies 205
6.4.1 Cost Advantage 205
6.4.2 Differentiation Advantage 206
6.4.2.1 The Management Response 209
6.4.2.2 The Market Response 210
6.4.3 Blue Ocean Strategy 211
6.4.4 Sustainability as a Strategy 212
6.4.5 AI as Strategy 213
6.5 Integrating Management Science, Analytics, and AI for Strategic Management Practice 213
6.5.1 Uncovering Market Segments Using Analytics Tools: Market Basket Transactions Analysis 214
6.5.2 Uncovering Market Segments Using Analytics Tools: Clustering 219
6.5.3 Defining Market Positioning Using AI 220
6.6 End of Chapter 220
6.6.1 Revision Questions 223
6.6.2 Case Study: Revisiting Porter's Generic Strategies Using System Dynamics 224
6.6.2.1 The Model 226
6.6.2.2 Expanding the Model to Consider Sustainability 231
References 233
Further Reading 235
7 Dynamic Resource Management 237
7.1 Resources and Capabilities 238
7.2 Resource Management 240
7.2.1 Resource Conceptualization 241
7.2.2 Resource Development 246
7.2.3 Business Performance 248
7.3 Sustainable Resource Management 249
7.3.1 Sustainability and Stakeholder Management 250
7.4 Integrating Management Science, Analytics, and AI for Strategic Management Practice 252
7.4.1 Boundary Definition: Using Systems Thinking to Facilitate Sustainable Systems 253
7.4.2 Resource Conceptualization Using Resource Mapping (as a Problem Structuring Method) 256
7.4.3 Resource Development Using Resource Mapping, System Dynamics, and Scenarios 259
7.4.4 Resource Development Under Uncertainty Using Decision Trees 263
7.4.5 Developing Decision Trees Using Big Data and AI 269
7.4.6 Inferring Business Performance from Management Science Methods 272
7.4.7 Using AI to Predict and Improve Business Performance 272
7.5 End of Chapter 273
7.5.1 Revision Questions 275
7.5.2 Case Study: Majestic Wines 275
References 278
Further Reading 281
8 Organizational Design 283
8.1 Organizational Components 284
8.1.1 Structure 284
8.1.2 Processes 287
8.1.3 The Soft Side of Organizations: Culture and Values 288
8.1.4 Digital Transformation 294
8.2 Integrating Management Science, Analytics, and AI for Strategic Management Practice 296
8.2.1 Network Analysis for Organizational Structure Design 296
8.2.2 Business Process Modeling 301
8.2.3 Improving Manufacturing Productivity Using Analytics 306
8.2.4 How AI Can Address the Soft Side of Organizations 307
8.3 End of Chapter 308
8.3.1 Revision Questions 309
8.3.2 Case Study: Improving Processes in Health Services Using Simulation and Digital Twin 310
References 315
Further Reading 317
9 Performance Measurement System 319
9.1 Measuring Financial Performance 323
9.2 Strategic Controls 327
9.3 Integrated Reporting 328
9.4 Sustainability Reporting 329
9.5 Integrating Management Science, Analytics, and AI for Strategic Management Practice 331
9.5.1 Causal Models to Design Performance Management Systems 331
9.5.2 Implementing the Performance Management System: Analyzing, Reviewing, and Reporting Performance Data - the Role of Analytics 343
9.5.3 Automating Performance Management Systems Using AI 347
9.6 End of Chapter 347
9.6.1 Revision Questions 348
9.6.2 Case Study: The Impact of Performance Measurement Systems Adoption in Business Performance - the Shipping Industry Case 349
References 354
Further Reading 356
10 Start- ups 359
10.1 The Components of a Business Plan for a Start- up 362
10.1.1 Management 362
10.1.1.1 Entrepreneur 362
10.1.1.2 Team 363
10.1.1.3 Risk Management 363
10.1.1.4 Growth Strategy and Business Idea as a Competitive Advantage of the Business 364
10.1.2 Market 366
10.1.3 Product/Service and Business Processes 368
10.1.4 Organization Design and Resources 370
10.2 Financial Management 373
10.2.1 Critical Assumptions and Scenarios/Cases for Cash Flow Calculations 373
10.2.2 Breakeven Analysis 374
10.2.3 Projected Profit and Loss (Income) Statement 376
10.2.4 Key Performance Indicators 379
10.2.5 Sources and Amount of Funding 380
10.3 Sustainable Entrepreneurship 382
10.4 AI- based Start- ups 384
10.5 Integrating Management Science, Analytics, and AI for Strategic Management Practice 386
10.5.1 Monte Carlo Simulation 386
10.5.2 Using AI to Create a Business Plan 391
10.6 End of Chapter 394
10.6.1 Revision Questions 395
10.6.2 Case Study: Designing the Next Boutique Winery 396
References 400
Further Reading 402
11 Maturity 405
11.1 Strategies for Mature Organizations 408
11.1.1 Concentrated Growth, and Market and Product Development 408
11.1.2 Integration 414
11.1.3 Diversification 416
11.1.4 Associations With Other Companies: Joint Venture, Strategic Alliances, and Consortia 421
11.2 Integrating Management Science, Analytics, and AI for Strategic Management Practice 424
11.2.1 Linear Optimization 426
11.2.2 Extensions in Linear Programming 428
11.2.2.1 Integer Programming 428
11.2.2.2 Nonlinear Programming 429
11.2.2.3 Programming Under Uncertainty 429
11.2.3 Making the Integration of Organizations Reality Through Internet of Things and Analytics 430
11.2.4 Linear Programming Using AI 431
11.2.5 Digitalization, AI, and New Product Development 431
11.3 End of Chapter 433
11.3.1 Revision Questions 434
11.3.2 Case Study: Choosing the Right Set of Capabilities- Development Projects to Achieve Multiple Organization Goals 435
References 443
Further Reading 445
12 Regeneration 447
12.1 Strategies for Regenerating Organizations 450
12.1.1 Innovation 450
12.1.2 Turnaround 455
12.1.3 Ambidextrous Strategies 460
12.1.4 Toward a Fully AI- enabled Organization 462
12.2 Integrating Management Science, Analytics, and AI for Strategic Management Practice 467
12.2.1 New Product Development: The Use of Text Analytics 467
12.2.2 Implementing Turnaround Strategies Using Data Envelopment Analysis: Identifying Operational Units for Either Improving or Pruning 470
12.2.3 Sourcing Change Through AI 474
12.3 End of Chapter 474
12.3.1 Revision Questions 475
12.3.2 Case Study: Managing Strategic Change Successfully: the Role of Benefits Realization Management 476
12.3.3 Case Study: A Success Story of Transformation Using AI 480
References 483
Further Reading 486
Index 489
1
Introduction to Strategic Analytics
OBJECTIVES
- To explain Strategic Analytics
- To introduce the main pillars of the book: the fields of analytics, management science, information technology, statistics, artificial intelligence, and strategic management
- To explain the fields of analytics, management science, information technology, artificial intelligence, and strategic management
LEARNING OUTCOMES AND MANAGERIAL CAPABILITIES DEVELOPED
- Managers can learn to tackle complex problems in strategy through the integration of analytics and artificial intelligence within strategic management processes
- Principles of Analytics: tools, support systems, and methods
- Principles of Artificial intelligence: developments, types, and approaches
- Identification of strategic problems
In today's environment, managers face turbulence and crisis. More information than ever is generated continuously: social media, financial performance management systems, customer relationship management systems, and internal and external reports. There are powerful trends: measuring and quantifying everything and skills in quantitative subjects are widely available. The problem for managers is not only to make sense of abundant quantitative information but also to engage with staff possessing analytical skills and exploit the Artificial Intelligence (AI) capabilities to support their work. Simultaneously managing multiple factors under pressure requires new managerial capabilities. Managers need to develop their strategies using clear strategy processes supported by the increasing availability of data. This situation calls for a different approach tostrategy, such as integration with analytics, as the science of extracting value from data and structuring complex problems, and AI to increase the productivity of the team developing strategies.
Manager's decision processes can fall into a continuum which has on one hand pure analysis, which relies on established processes, and pure synthesis that involves identification of patterns and new ideas (Pidd, 2009). Strategic planning involves a mix between both extremes: analysis to identify problems and synthesis to observe trends and emerging situations before competitors. However, there is not a clear process when combining the two. Sometimes, emerging situations are discovered and then analysis is employed to confirm their impact while transforming them into new emergent strategies. In other circumstances, there is a careful planning process involving extensive data gathering and the construction of complex financial, operational, and other type models to validate the new strategies.
The idea behind "Strategic Analytics" is to answer a simple question: how can quantitative and qualitative information be used to make strategic decisions? Strategic Analytics does not imply turning managers into quantitative analysts or quantitative analysts into expert strategists. Organizations need interdisciplinary teams comprised by members who can talk to each other sharing a common language. Thus, Strategic Analytics works on the basis of providing a reasonable understanding of how a variety of quantitative methods, in conjunction with structured and unstructured data, can be used to help strategic decision-making in any organization. There is also the intent to show the real and practical benefit of Strategic Analytics. Strategic Analytics does not pretend to offer easy solutions to strategic problems but different ways of analyzing and solving strategic problems beyond the traditional qualitative approach to strategic management. Strategic Analytics is also an understanding of the context and processes in which analytics skills can be applied to support strategic management. Strategic Analytics also implies exploiting the AI capabilities to facilitate strategic decisions.
Future managers are taught a wide variety of concepts in strategy subjects but they are not taught how to apply them or even to connect them to related problems. Future managers need to develop capabilities to tackle problems that are not structured in a neat way like case studies are. In that sense, each chapter focuses on a case study with limited information that has to be solved applying a combination of theoretical concepts and analytical methods (quantitative and qualitative). The aim of this founding principle is to integrate strategic concepts with analytical tools in a unique set of capabilities (skills) to help future managers to tackle strategic problems using multiple sources of information. The main benefit is that quantitative methods, which are usually seen as a difficult experience for managers, are connected with a hands-on subject like strategy. Therefore, managers can learn capabilities to tackle complex problems in strategy throughthe integration of analytics (quantitative/qualitative methods to extract value from data) and AI within strategic management processes. Therefore, the book will provide a bridge to integrate quantitative methods with their application in strategy adding rigorous methods to solve real issues in strategy.
The rest of the chapter introduces the main pillars of the book: the fields of analytics, management science, information technology, statistics, AI, and strategic management.
1.1 What Is Analytics?
Organizations are competing using analytics because there is an increasing amount of data, people with capabilities to use data, and, in a highly competitive environment, it is more difficult to compete effectively. While organizations can use basic descriptive statistics from any of their existing data, organizations using analytics apply modeling to understand their environments, predict the behavior of key actors, e.g., customers and suppliers, and optimize operations. Organizations can obtain competitive advantage using multiples analytics applications but it requires a new type of organization and management (Davenport, 2006):
A companywide embrace of analytics impels changes in culture, processes, behavior and skills for many employees. And so, like any major transition, it requires leadership from executives at the very top who have a passion for the quantitative approach . . . CEOs leading the analytics charge require both an appreciation of and a familiarity with the subject. A background in statistics isn't necessary, but those leaders must understand the theory behind various quantitative methods so that they recognize those methods' limitations-which factors are weighed and which ones aren't . . . Of course, not all decision should be grounded in analytics . . . For analytics-minded leaders, then, the challenge boils down to knowing when to run with the numbers and when to run with their guts
(Davenport, 2006: pages 102-103).
An analytical perspective is important, when data has become a key strategic asset of organizations in recent years, and analytics creates value by delivering systematic decision support in a well-timed way (Laursen and Thorlund, 2010; Holsapple et al., 2014). Business Analytics (BA), one of the multiple branches in Analytics, comprises three key elements: Information Systems, Human Competencies, and Business Processes (Laursen and Thorlund, 2010). Business analytics reflects the convergence of threedisciplines: statistics, information systems, and management science (Laursen and Thorlund, 2010). While the supporting disciplines are traditional, the innovation lies in their intersections. For example, data mining aims to understand characteristics and patterns among variables in large databases using a variety of statistical analysis, e.g., correlation and regression analysis. Information technology provides data and supports decision support systems, which are sustained by management science tools together with statistical analysis, for the development of analytics. BA is rooted in advances of information technology systems, which involve the acquisition, generation, assimilation, selection, and presentation of data, together with tools, statistics, and management science, to develop the data into knowledge to support decision-making. In a similar definition, Mortenson et al. (2015) suggest analytics is the intersection of basic disciplines: technologies (electrical engineering and computer science), decision-making (psychology and behavioral science), and quantitative methods (mathematics, statistics, and economics), and their applications: information systems, AI, and operational research. Figure 1.1 shows Mortenson et al.'s (2015) representation of the concept of analytics.
Figure 1.1 The analytics field.
Source: Mortenson et al., 2015 / with permission of Elsevier.
In terms of types of analytics, Davenport proposes three types: descriptive, predictive, and prescriptive (Davenport, 2013), which are described below.
- Descriptive analytics. It employs traditional statistical skills to present data collected from internal organizational activities and external data. It is utilized to understand what happened during their past business activities in order to disclose whether the current business objectives have been obtained. Then the next step is to investigate the reasons behind the results by drilling down into more detailed data and explore scientifically, e.g., test and validate or reject hypotheses. It is characterized traditionally as data mining, business...
