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Business Plan Elaboration & Feasibility Study

Busines Plan Elaboration

Executive Summary

Business concept, goals, and key success factors

Summary of feasibility findings (for the feasibility study)

Market Analysis
– Industry overview
– Target market definition and segmentation
– Market size, trends, and growth potential
– Customer needs and behavior
– Competitive analysis (SWOT, Porter’s 5 Forces)

Product or Service —
– Description
– Features, benefits, and value proposition
– Unique selling points (USP)
Intellectual property, R&D (if relevant)

Marketing & Sales Strategy
– Marketing positioning and branding
– Pricing strategy
– Sales channels (direct, online, partnerships)
– Promotion and communication plans

Operations & Management Plan
– Business location and facilities
– Operational workflow and processes
– Supply chain and logistics
– Organizational structure
– Key management roles and responsibilities

Financial Analysis
– Start-up costs and capital requirements
– Revenue model
– Projected income statement, cash flow, and balance sheet
– Break-even analysis
– Funding sources and financial strategy

Feasibility Study

Technical Feasibility
– Technology requirements
– Production process capability
– Legal/regulatory compliance

Economic Feasibility
– Cost-benefit analysis
– ROI (Return on Investment) estimation
– Sensitivity analysis (scenarios: best/worst case)

Operational Feasibility
– Availability of resources (human, material, infrastructure)
– Scalability and capacity assessment

Legal Feasibility
Legal structure and constraints
– Permits, licenses, zoning, and regulations

Schedule Feasibility
– Estimated timeline to launch
– Key milestones and deadlines

Valuation

Core Valuation Methods
– Discounted Cash Flow (DCF) Analysis
– Comparable Company Analysis (Comps)
– Precedent Transactions – Analysis (M&A Comps)
– Asset-based Valuation
– Residual Income Valuation
– Dividend Discount Model (DDM)

Advanced Valuation Topics
– Valuation of Startups and Early-Stage Companies
– Valuation of Distressed Companies
– Valuation in Emerging Markets (Country Risk Premium, FX risk)
– Real Options Valuation
– Private Company Valuation (Illiquidity, control premiums, discounts)
– Venture Capital Valuation (Berkus Method, Scorecard, VC method)

Valuation Adjustments & Key Concepts
– Cost of Capital (WACC, Cost of Equity, Cost of Debt)
– Terminal Value Calculation (Perpetuity vs. Exit Multiple)
– Sensitivity Analysis & – Scenario Analysis in Valuation
– Control Premium and Minority Discounts
– Synergies Valuation in Mergers & Acquisitions
– Intangible Asset Valuation (Patents, Brands, Goodwill)

Valuation continued

Sector-Specific Valuation
– Valuation of Financial Institutions (Banks, Insurance Companies)
– Valuation of Real Estate Assets
– Valuation of Infrastructure Projects (PPP, concessions)
– Valuation of Technology and IP-heavy Businesses
– Energy & Natural Resources Valuation (Commodities, Oil & Gas reserves)

Regulatory, Legal & Reporting Aspects
– Fair Value Measurement (IFRS 13, ASC 820)
– Purchase Price Allocation (PPA) and Impairment Testing
– Valuation for Tax Purposes (Transfer Pricing, Estate Planning)
– Litigation Support and Expert Valuation Reports

Trends & Emerging Topics
Valuation of ESG and Sustainability Factors
– Valuation in the Context of AI and Digital Assets
– Blockchain and Cryptocurrency Valuation
– Valuation of Data as an Asset
– Intangible and Human Capital Valuation

Risk Analysis Using @Risk

Risk Analysis using @Risk

Defining the Problem and Objectives
– Clarify the decision or outcome to be analyzed
– Set objectives for the risk analysis (e.g., estimating project costs, financial forecasts)

Model Development in Excel
Build a base model representing the system, project, or process in Excel
– Define relationships between input variables and outputs
– Identifying Uncertain Inputs (Risk Variables)

Select key uncertain parameters (e.g., costs, demand, time durations)
– Assign appropriate probability distributions (e.g., normal, triangular, uniform)
– Assigning Probability Distributions (@RISK Functions)
– Use @RISK functions to insert probability distributions into model inputs
– Justify and validate choice of distributions (based on historical data, expert judgment)

Running Monte Carlo Simulation
– Define number of iterations (typically thousands)
– Let @RISK sample random values from distributions and recalculate outputs repeatedly

Analyzing Simulation Results
– Interpret outputs such as mean, standard deviation, percentiles, and probability of outcomes
– Review @RISK-generated graphs (histograms, cumulative distributions, tornado charts)

Risk Analysis using @Risk 2

Sensitivity Analysis
– Use @RISK tools (tornado diagrams, spider plots) to identify key drivers of uncertainty
– Determine which variables have the greatest influence on outcomes

Scenario Analysis
– Evaluate how different assumptions or scenarios affect risk exposure
– Test “what-if” cases using @RISK’s scenario functions

Risk Quantification and Decision Support
– Calculate metrics like Value at Risk (VaR), probability of failure, or expected shortfall
– Support decision-making by comparing risk-adjusted outcomes of alternatives

Documentation and Communication of Results
– Report findings clearly using @RISK output reports and charts
– Communicate implications to stakeholders and recommend risk management actions

Model Validation and Review
– Ensure model accuracy, logic, and appropriateness of input assumptions
– Update model and inputs as new information becomes available

Optimization Using @Risk

Risk Analysis using @RISKOptimizer

Defining the Optimization Objective
– Specify the goal (e.g., maximize profit, minimize cost, optimize resource allocation)
– Define objective function within the Excel model

Model Development with Decision Variables
– Identify controllable decision variables (e.g., prices, production levels, investment amounts)
– Set variable constraints (bounds, integer/binary requirements)

Identifying Uncertainties (Risk Variables)
– Define uncertain input parameters using probability distributions (same as in risk analysis)
– Incorporate @RISK distributions into the Excel model

Combining Simulation and Optimization
– Use RISKOptimizer to run simulations where decision variables are adjusted automatically
– Optimize the objective function under uncertainty (stochastic optimization)

Defining Constraints and Requirements
– Implement constraints in the model (e.g., budget limits, capacity restrictions, regulatory limits)
– Use @RISK constraint functions to ensure feasible solutions

Selecting Optimization Methods (Algorithms)
– Choose appropriate algorithm (Genetic Algorithm, OptQuest, or other heuristics provided by RISKOptimizer)
– Configure settings (population size, stopping rules, iterations)

Risk Analysis using @RISKOptimizer

Running Simulation Optimization
– Execute multiple simulations to search for optimal solution under uncertainty
– RISKOptimizer evaluates thousands of scenarios to balance risk and return

Analyzing Optimization Results
– Review optimal set of decision variables and their associated performance metrics
– Analyze trade-offs between risk and objective outcomes (e.g., mean vs. variance)

Sensitivity and Scenario Analysis
– Explore how sensitive optimal solutions are to changes in assumptions
– Test robustness of optimal solution under different scenarios

Validation of the Model and Results
– Confirm accuracy of model logic, constraints, and assumptions
– Verify that the optimized solution is practical and implementable

Documentation and Communication of Findings
– Prepare reports and visualizations of optimal strategies and risk profiles
– Communicate recommended decisions with quantified risk-return trade-offs

Implementation and Monitoring
– Apply optimal decision variables in real operations or projects
– Monitor outcomes and update model if necessary as conditions evolve