F5 – Chapter 3: Decision-making techniques

Decision-making techniques

Key Highlights on Chapter 3

Scenario Analysis:

• Evaluates different scenarios by considering various combinations of uncertain variables.
• Helps in understanding potential outcomes and preparing contingency plans.
• Useful in volatile or uncertain environments for informed decision-making and risk mitigation.

Sensitivity Analysis:

• Analyzes the impact of changes in key variables on decision outcomes.
• Helps in understanding the sensitivity of decisions to different factors.
• Facilitates better risk management and decision-making under uncertainty.

Break-Even Analysis:

• Determines the level of output needed to cover total costs and achieve zero profit.
• Helps in assessing the minimum activity required for financial viability.
• Guides decisions on pricing, production levels, and resource allocation.

Linear Programming:

• Identifies the optimal solution to a complex problem subject to constraints.
• Useful in production planning, resource allocation, and inventory management.
• Optimizes resource utilization and achieves organizational goals efficiently.

Weighted Scoring Model:

• Systematically evaluates alternatives against specific criteria.
• Assigns weights to criteria based on importance.
• Facilitates transparent decision-making by considering multiple factors and preferences.

Decision Trees:

• Represents and analyzes sequential decision-making processes and potential outcomes.
• Helps visualize complex decision scenarios and assess risks.
• Facilitates structured decision-making and risk assessment.

Investment Appraisal:

• Evaluates alternatives based on financial performance measures like ROI and NPV.
• Assists in assessing the financial viability and attractiveness of investments.
• Prioritizes projects aligned with strategic objectives and high returns.

Decision Matrices:

• Evaluates alternatives based on specific criteria or attributes.
• Quantifies qualitative and quantitative information for objective decision-making.
• Helps in comparing alternatives and selecting the most favorable option.

Risk Analysis:

• Assesses potential risks and uncertainties associated with decision alternatives.
• Helps in understanding the likelihood and impact of risks on decision outcomes.
• Guides risk management strategies and informed decision-making.

Pareto Analysis:

– Prioritizes and focuses efforts on addressing significant issues or opportunities.

– Identifies vital few factors or problems accounting for the majority of outcomes.

– Efficiently allocates resources and achieves maximum impact.

Cost-Benefit Analysis:

– Evaluates alternatives based on their benefits and costs.

– Quantifies expected benefits and costs to determine net impact.

– Selects alternatives maximizing benefits relative to costs for rational decision-making.

Delphi Technique:

– Achieves consensus among experts on a particular issue or problem.

– Involves multiple rounds of structured communication and feedback.

– Reduces biases and promotes informed decisions based on expert knowledge.

Goal Programming:

– Evaluates alternatives based on their ability to achieve specific objectives or goals.

– Balances competing objectives and constraints for optimal solutions.

– Supports strategic decision-making and performance optimization.

Fishbone Diagram:

– Visualizes and analyzes the root causes of a problem or issue.

– Systematically explores and categorizes potential causes.

– Facilitates structured problem-solving and collaborative decision-making.

Six Thinking Hats:

– Encourages creative thinking and explores different perspectives.

– Represents various modes of thinking through metaphorical hats.

– Stimulates innovation and generates solutions to complex problems.

Grid Analysis:

– Evaluates and compares alternatives based on specific criteria.

– Structured method for systematic assessment and selection of alternatives.

– Considers diverse factors and preferences for optimal decision-making.

Analytic Hierarchy Process (AHP):

– Evaluates alternatives based on multiple criteria and their relative importance.

– Hierarchical structuring and pairwise comparison for priority determination.

– Supports systematic analysis and informed decision-making reflecting diverse perspectives.

1. Relevant Cost Analysis

A) Concept of Relevant Costing:

Relevant costing focuses on future costs that differ between alternative courses of action. It excludes past costs (sunk costs) and any costs that remain unchanged regardless of the decision made. The key principle is to consider only those costs that are relevant to the specific decision under evaluation.

Illustration: A company is considering discontinuing a product line. Relevant costs include variable costs like direct materials and labor, and any avoidable fixed costs associated with the discontinued line. Irrelevant costs include fixed costs that will continue regardless of the decision, such as rent or depreciation on machinery used for other products.

B) Identifying and Calculating Relevant Costs:

Relevant costs can be identified by understanding the behavior of costs under different scenarios. Variable costs change in proportion to the level of activity (production volume), while fixed costs remain constant within a relevant range of activity.

Illustration:

A company is considering accepting a special order for 100 units of a product at a price below the normal selling price. The additional costs for producing the special order would be:

• Direct materials: $10 per unit (variable cost)
• Direct labor: $5 per unit (variable cost)
• Additional setup costs: $200 (one-time, not relevant)
• Depreciation on factory equipment: $100 per month (fixed cost, irrelevant)

The relevant costs for the special order are the variable costs, totaling $15 per unit ($10 + $5). The fixed costs and one-time setup costs are not relevant for this decision.

C) Opportunity Cost:

Opportunity cost is the potential benefit that is given up by choosing one alternative over another. It represents the lost value of the forgone option.

Illustration: A company can use its limited production capacity to produce either product A or product B. Product A generates a profit of $20 per unit, while product B generates a profit of $30 per unit. If the company decides to produce product A, the opportunity cost is $10 per unit ($30 – $20).

2. Cost Volume Profit Analysis (CVP)

A) Nature of CVP Analysis:

CVP analysis is a technique that studies the relationship between costs, volume (sales), and profit. It helps managers understand how changes in volume affect costs and profits.

Illustration:

A company has the following cost structure:

• Variable cost per unit: $5
• Fixed costs: $10,000 per month
• Selling price per unit: $10

Using CVP analysis, the company can determine:

• Break-even point (BEP): The sales volume at which the total cost equals total revenue, resulting in zero profit.
• Contribution margin: The difference between the selling price per unit and the variable cost per unit.
• Margin of safety: The excess of sales revenue over the BEP, indicating a buffer zone before incurring losses.

B) Break-even point (BEP) and Margin of Safety:

BEP (units) = Fixed costs / Contribution margin per unit Margin of safety (units) = Sales volume – BEP

Illustration:

Using the information above, the BEP is:

BEP (units) = $10,000 / ($10 – $5) = 2,000 units

If the company sells 2,500 units, the margin of safety is:

Margin of safety (units) = 2,500 units – 2,000 units = 500 units

C) Contribution to Sales Ratio:

Contribution to sales ratio (%) = Contribution margin per unit / Selling price per unit * 100%

This ratio indicates the percentage of each sales dollar that contributes to covering fixed costs and generating profit.

D) Target Profit or Revenue:

Target sales = Fixed costs + Target profit / Contribution margin per unit

Illustration:

If the company has a target profit of $5,000, the target sales are:

Target sales = $10,000 + $5,000 / ($10 – $5) = $15,000 / $5 = 3,000 units

E) Interpreting CVP Charts:

CVP charts visually represent the relationship between cost, volume, and profit. They can show the BEP, margin of safety, and the impact of changes in volume, price, or costs on profit.

3. Limiting Factors

A) Identifying Limiting Factors and Selecting Techniques:

A limiting factor is a resource that restricts the organization’s ability to achieve its desired output level. Identifying the limiting factor is crucial for making informed decisions about resource allocation and production planning.

Techniques for identifying limiting factors:

• Ratio analysis: Comparing the ratio of available resources to required resources for different activities can reveal the bottleneck.
• Contribution margin per unit: Focusing on activities that generate the highest contribution per unit of the limiting factor can optimize resource utilization.

B) Optimal Production Plan with a Single Limiting Factor:

Once the limiting factor is identified, linear programming or simultaneous equations can be used to determine the optimal production plan that maximizes profit or minimizes cost within the constraint of the limiting factor.

Illustration:

A company has 100 hours of labor available per week and can produce two products: Product X and Product Y. Each unit of X requires 2 hours of labor, while each unit of Y requires 1 hour. Product X generates a profit of $5 per unit, and product Y generates a profit of $3 per unit.

The limiting factor is labor. Using linear programming or simultaneous equations, we can determine the optimal production plan that maximizes profit within the 100-hour constraint.

C) Multiple Scarce Resource Problems:

Linear programming can solve problems with multiple scarce resources by defining objective functions and constraints for each resource. Simultaneous equations can also be used in simpler cases.

D) Shadow Prices (Dual Prices):

Shadow prices represent the marginal value of an additional unit of a scarce resource. They indicate the maximum amount the company is willing to pay for one more unit of the resource without reducing profit.

Implications:

• Shadow prices can help prioritize investments in acquiring additional resources of the limiting factor.
• They can be used to evaluate the potential impact of changes in resource availability on profit.

E) Slack:

Slack refers to the unused capacity of a resource. It indicates the amount by which a resource constraint is not binding.

Implications:

• The existence of slack in a resource constraint suggests that the resource is not fully utilized and there may be opportunities to improve efficiency.
• Analyzing slack across different constraints can help identify areas for resource reallocation or process improvement.

4. Pricing Decisions

A) Factors Influencing Pricing:

Several factors influence pricing decisions, including:

• Cost of production: The minimum price required to cover all costs and generate a profit.
• Demand: The relationship between price and the quantity of goods or services demanded.
• Competition: Pricing strategies of competitors in the market.
• Government regulations: Price controls or regulations imposed by the government.
• Marketing objectives: Pricing strategies used to achieve specific marketing objectives like market share growth or brand positioning.

B) Price Elasticity of Demand:

Price elasticity of demand (PED) measures the sensitivity of demand to changes in price. It is calculated as the percentage change in quantity demanded divided by the percentage change in price.

Illustration:

If the price of a product increases by 10%, and the quantity demanded decreases by 5%, the PED is -0.5. A PED of:

• -1: Demand is elastic (quantity changes more proportionally than price).
• -1 between 0 and -1: Demand is somewhat elastic.
• 0: Demand is perfectly inelastic (quantity remains unchanged despite price changes).
• Positive: Demand is negatively elastic (quantity changes in the opposite direction of price changes).

C) Deriving Demand and Cost Equations:

Demand equation: This equation represents the relationship between price and quantity demanded. Linear (straight line) or more complex equations can be used depending on the market dynamics.

• Total cost function: This equation represents the total costs incurred by the company at different production volumes. It includes both variable and fixed costs.

D) Optimum Selling Price and Quantity:

Profit maximization can be achieved by equating marginal cost (MC) and marginal revenue (MR). MC is the change in total cost due to a one-unit change in production, while MR is the change in total revenue due to a one-unit change in sales.

Illustration:

By solving the equations for MC and MR simultaneously, the company can determine the optimum selling price and quantity that maximizes profit.

E) Evaluating Production and Sales Increase:

Increasing production and sales levels involves considering:

• Incremental costs: The additional costs incurred due to the increase in production.
• Incremental revenues: The additional revenue generated by the increase in sales.
• Other factors: Capacity constraints, product quality, and potential market saturation.

F) Profit Maximization using Demand-based Approach:

Tabular Method:

1. Construct a table that shows different price levels, corresponding demand quantities based on the demand equation, and total revenue for each price level.
2. Calculate the contribution margin per unit for each price level by subtracting variable costs from the selling price.
3. Multiply the contribution margin per unit by the demand quantity at each price level to determine the total contribution margin.
4. Select the price level that results in the highest total contribution margin, which corresponds to the maximum profit.

Algebraic Method:

1. Express total revenue as a function of price (using the demand equation).
2. Derive the expression for total cost (using the total cost function).
3. Define profit as the difference between total revenue and total cost.
4. Differentiate the profit equation with respect to price and set it equal to zero.
5. Solve the equation for price to find the price that maximizes profit.

G) Price Strategies:

Cost-plus pricing: 

Setting the price based on adding a desired markup percentage to the total cost per unit.

• Skimming pricing: 

Setting a high initial price for a new product to capitalize on early demand and then gradually lowering the price over time.

• Penetration pricing: Setting a low initial price to gain market share quickly, often used for new products in competitive markets.
• Complementary product pricing: Setting the price of a product low to encourage the purchase of another, higher-margin product.
• Product-line pricing: Setting prices for a range of related products based on their features, costs, and target market segments.
• Volume discounting: Offering discounts to customers who purchase larger quantities.
• Price discrimination: Charging different prices to different customer groups based on their willingness to pay or other factors (e.g., student discounts).
• Relevant cost pricing: Setting the price based on the relevant costs associated with producing and selling the product, considering the specific decision context (e.g., special orders, discontinued products).

H) Calculating Price from a Given Strategy:

Cost-plus pricing: Price = Total cost per unit + (Markup percentage x Total cost per unit)

Relevant cost pricing: Price = Relevant costs per unit + Target profit margin per unit

5. Make-or-Buy and Other Short-term Decisions

A) Make-or-Buy Decisions:

These decisions involve determining whether to manufacture a component or product internally (make) or purchase it from an external supplier (buy).

Issues to consider:

• Cost comparison: Compare the total cost of making the component internally with the price quoted by external suppliers. Consider both variable and fixed costs.
• Quality control: Evaluate the ability to maintain desired quality standards if production is outsourced.
• Capacity utilization: Assess the impact on internal production capacity if the component is made in-house.
• Flexibility and responsiveness: Analyze the ability to respond to changes in demand or product specifications if outsourcing is chosen.

B) & c) Calculating and Comparing Make and Buy Costs:

Make costs: Include direct materials, direct labor, variable overhead costs, and any relevant portion of fixed overhead costs associated with producing the component internally.

• Buy-in costs: Include the purchase price from the supplier, transportation costs, and any additional costs like inspection or quality control.
• Compare the total make and buy costs to determine the most cost-effective option, considering other relevant factors like quality, flexibility, and risk.

D) Relevant Costing for Other Decisions:

• Shut down decisions: Evaluate the relevant costs (e.g., avoidable fixed costs) associated with closing down a department or production line compared to the benefits of continuing operations.
• One-off contracts: Apply relevant costing principles to analyze the marginal cost and revenue associated with accepting a one-time contract at a special price.
• Further processing of joint products: Allocate joint costs to separate products using appropriate costing methods (e.g., sales value method) before making pricing or other decisions.

6. Dealing with Risk and Uncertainty in Decision-making

A) Research Techniques:

Techniques like market research, focus groups, and competitor analysis can help reduce uncertainty by gathering information about customer preferences, market trends, and competitor actions.

B) Simulation, Expected Values, and Sensitivity Analysis:

Simulation: A method that involves creating a computer model to simulate different scenarios and predict the likelihood of various outcomes.

• Expected value: The average of the values of all possible outcomes, weighted by their probabilities.

• Sensitivity analysis: Evaluating how the optimal decision or expected value changes in response to variations in key factors like costs, prices, or demand.

These techniques help quantify the potential risks and rewards associated with different decision options, enabling more informed choices under uncertainty.

C) Applying Expected Values and Sensitivity:

Illustration: A company is considering investing in a new product launch with three possible outcomes:

• Success: Profit of $100,000 with a probability of 40%.
• Normal: Profit of $50,000 with a probability of 30%.
• Failure: Loss of $20,000 with a probability of 30%.

Expected value (EV) = (Outcome 1 x Probability 1) + (Outcome 2 x Probability 2) + (Outcome 3 x Probability 3)

EV = ($100,000 x 0.4) + (50,000×0.3)+(-20,000 x 0.3) EV = $40,000 – $6,000 = $34,000

The expected value of $34,000 indicates the average profit the company can expect from the new product launch, considering the probabilities of each outcome.

Sensitivity analysis can involve:

• Varying the probabilities of each outcome to see how the expected value changes.
• Changing key factors like the cost of investment or the potential profit in each scenario to assess the impact on the decision.

D) Maximax, Maximin, and Minimax Regret:

These are decision-making criteria used under conditions of high uncertainty:

• Maximax: Choose the option with the highest possible payoff (regardless of probability).
• Maximin: Choose the option with the highest minimum payoff (worst-case scenario).
• Minimax regret: Choose the option with the smallest potential regret (difference between the chosen option’s payoff and the highest payoff achievable in each state of nature).

Each criterion has its limitations and should be used in conjunction with other decision-making techniques and risk analysis to make informed choices under uncertainty.

E) Decision Trees:

A decision tree is a diagrammatic representation of a decision-making problem, showing:

• Decision points where different options can be chosen.
• Chance events with associated probabilities.
• Outcomes associated with each combination of decisions and events.

Decision trees help visualize the potential consequences of different choices under uncertainty and can be used to calculate expected values and identify the optimal decision based on chosen criteria.

F) Value of Perfect and Imperfect Information:

Perfect information eliminates all uncertainty, allowing for the optimal decision with certainty.

• Imperfect information reduces some uncertainty but doesn’t eliminate it completely.

The value of perfect information is the maximum amount a decision-maker would be willing to pay to eliminate all uncertainty. It can be calculated by comparing the expected value with perfect information to the expected value with imperfect information.

The value of imperfect information is the maximum amount a decision-maker would be willing to pay for additional information that reduces uncertainty. It can be measured by comparing the expected value with the initial information to the expected value after acquiring the additional information.

By understanding the value of perfect and imperfect information, decision-makers can evaluate the cost-effectiveness of investing in further research or data gathering to reduce uncertainty before making a choice.