Problem-solving & Decision-making

• Problem-solving is a systematic process using numerical data, mathematical models, and statistical analysis to develop solutions for organizational challenges.

Steps in Problem Solving

• Identify and Define the Problem: Acknowledge the problem and define it in measurable terms.
• Analyze the Problem: Break down the problem into smaller components to determine the underlying factors.
• Generate Possible Solutions: Brainstorm potential solutions and seek input from stakeholders.
• Evaluate the Solutions: Assess the practicality and feasibility of proposed solutions.
• Select the Best Solution(s): Choose the optimal solution and develop an action plan.
• Implement the Solution: Execute the action plan according to established timelines.
• Evaluate the Outcome: Use metrics to determine if the solution effectively resolved the problem.
• Decision-making is a cognitive process using numerical data and analytical techniques to select the best course of action.
• Single-criterion Decision-making: Identifying the optimal answer based on a single point of reference.
• Multicriteria Decision-making: Making decisions based on multiple criteria.
• Qualitative Analysis: Relies on the manager’s intuitive feel, subjective judgment, and experiential knowledge.
• Quantitative Analysis: Employs complex mathematical and statistical modeling techniques.
• Model Development: Creating representations of real objects or situations to improve comprehension and decision-making.
• A model is a representation of real-world objects, systems, or processes for analysis, forecasting, and communication.

Types of Costs

• Fixed Costs: Expenses that remain constant regardless of production volume, like rent or salaries.
• Variable Costs: Expenses that fluctuate with the volume of production or sales, like raw materials or commissions.

Types of Models

• Iconic Models: Scaled representations that communicate visual characteristics.
• Analog Model: Represents objects or processes with abstracted or no physical form.
• Mathematical Model: Depicts a principle or system using mathematical concepts and terminologies.
• Constraints are limitations or restrictions that can influence the accomplishment of a goal or objective.
• Objective Function: A mathematical expression that defines the quantity to optimize (maximize or minimize).
• Controllable Inputs: Elements inputted into the model that the decision-maker controls.
• Uncontrollable Inputs: Elements inputted into the model that can influence the objective function and constraints, but are not controlled by the decision-maker.
• Deterministic Model: A model where all uncontrollable inputs are determined and consistent.
• Stochastic Model: A model where any uncontrollable inputs are uncertain and prone to change.
• Optimal Solution: A set of values for the decision variables that yields the best possible outcome for the objective function.
• Feasible: A set of values for the decision variables that adhere to all limitations or restrictions.
• Infeasible: A decision alternative rejected for failing to meet one or more constraints.
• Sales Volume: The quantity of units manufactured and sold.
• Selling Price: The monetary value at which each unit is offered for sale.
• Profit: The amount of money generated after subtracting all costs from total income.
• Break-even Point: The level of sales at which total revenue equals total costs, resulting in no profit.
• Contribution Margin: Profit per unit sold, used to cover fixed costs and generate overall profit.
• Margin of Safety: The difference between the present level of sales and the break-even point.
• Marginal Cost: The rate at which total cost changes as the production amount changes.
• Marginal Revenue: The rate at which total profit changes based on sales volume.