wentorai/strategic-management-guide

Strategic Management Guide

A research-oriented skill for applying strategic management theories and frameworks to academic case studies, organizational analysis, and business research. Covers classical and contemporary strategy frameworks with rigorous analytical methods.

Core Strategic Frameworks

Resource-Based View (RBV)

The RBV framework (Barney, 1991) evaluates competitive advantage through internal resources and capabilities. Apply the VRIO test:

| Criterion | Question | If "No" | |-----------|----------|---------| | Valuable | Does it exploit opportunity or neutralize threat? | Competitive disadvantage | | Rare | Is it controlled by few firms? | Competitive parity | | Inimitable | Is it costly to imitate? | Temporary advantage | | Organized | Is the firm organized to capture value? | Unrealized advantage |

All four "Yes" answers indicate a sustained competitive advantage.

# VRIO analysis scoring framework
def vrio_analysis(resources: list[dict]) -> dict:
    results = {}
    for r in resources:
        name = r['name']
        v, r_score, i, o = r['valuable'], r['rare'], r['inimitable'], r['organized']
        if not v:
            results[name] = 'competitive_disadvantage'
        elif not r_score:
            results[name] = 'competitive_parity'
        elif not i:
            results[name] = 'temporary_advantage'
        elif not o:
            results[name] = 'unrealized_advantage'
        else:
            results[name] = 'sustained_advantage'
    return results

# Example usage
resources = [
    {'name': 'proprietary_algorithm', 'valuable': True, 'rare': True, 'inimitable': True, 'organized': True},
    {'name': 'office_space', 'valuable': True, 'rare': False, 'inimitable': False, 'organized': True},
]
print(vrio_analysis(resources))
# {'proprietary_algorithm': 'sustained_advantage', 'office_space': 'competitive_parity'}

Strategic Planning Process

A structured approach to strategy formulation for research purposes:

Phase 1: Environmental Scanning

• External analysis: PESTEL (Political, Economic, Social, Technological, Environmental, Legal)
• Industry analysis: Porter's Five Forces with quantified metrics
• Stakeholder mapping: Power-interest matrix to prioritize engagement

Phase 2: Strategy Formulation

Apply Ansoff's Growth Matrix to map strategic options:

                Existing Products    New Products
Existing Markets  Market Penetration   Product Development
New Markets       Market Development   Diversification

For academic research, operationalize each quadrant with measurable indicators: market share growth rate, R&D investment ratio, geographic expansion metrics, and portfolio diversification index.

Phase 3: Strategy Evaluation

Use the SAFe criteria (Johnson, Whittington & Scholes):

1. Suitability -- Does the strategy address key strategic issues?
2. Acceptability -- Risk, return, and stakeholder reactions
3. Feasibility -- Resources, capabilities, and timeline

Balanced Scorecard Implementation

Translate strategy into measurable objectives across four perspectives:

financial:
  objectives:
    - "Increase revenue by 15% YoY"
    - "Reduce cost-to-serve by 10%"
  kpis: ["revenue_growth_rate", "operating_margin"]

customer:
  objectives:
    - "Achieve NPS > 50"
    - "Reduce churn below 5%"
  kpis: ["net_promoter_score", "monthly_churn_rate"]

internal_process:
  objectives:
    - "Reduce cycle time by 20%"
    - "Achieve 99.9% uptime"
  kpis: ["avg_cycle_time_days", "system_uptime_pct"]

learning_growth:
  objectives:
    - "Increase training hours per employee"
    - "Launch 3 new capabilities per quarter"
  kpis: ["training_hours_per_fte", "new_capabilities_shipped"]

Quantitative Strategy Tools

Game Theory Applications

Model competitive interactions using payoff matrices:

import numpy as np

# Prisoner's Dilemma payoff matrix
# (Row player payoff, Column player payoff)
# Strategies: Cooperate (0), Defect (1)
payoffs_row = np.array([[3, 0],
                         [5, 1]])
payoffs_col = np.array([[3, 5],
                         [0, 1]])

# Find Nash equilibria
for i in range(2):
    for j in range(2):
        row_best = payoffs_row[i, j] >= max(payoffs_row[:, j])
        col_best = payoffs_col[i, j] >= max(payoffs_col[i, :])
        if row_best and col_best:
            print(f"Nash Equilibrium at ({i}, {j})")

Scenario Planning

Structure scenario analysis with two critical uncertainties on perpendicular axes. Develop four internally consistent narratives, assign probability weights, and compute expected values for key decision variables. This approach enables robust strategy under deep uncertainty.

References

• Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99-120.
• Porter, M. E. (1980). Competitive Strategy. Free Press.
• Kaplan, R. S., & Norton, D. P. (1996). The Balanced Scorecard. Harvard Business Press.