OPMAN Forecasting PDF

1. Definition of Forecasting 2. Process Improvement: Anticipating areas Forecasting is the art and science of predicting for improvement to enhance efficiency and future events, typically by analyzing historical reduce defects. data and projecting tr...

1. Definition of Forecasting 2. Process Improvement: Anticipating areas Forecasting is the art and science of predicting for improvement to enhance efficiency and future events, typically by analyzing historical reduce defects. data and projecting trends. It involves estimating 3. Customer Satisfaction: Forecasting uncertain future occurrences such as demand, customer needs to design and deliver costs, or quality issues. better products and services. 4. Supplier Quality Management: Key Definitions from Experts: Forecasting quality requirements to select  Heizer and Render (2011): "Forecasting and manage reliable suppliers. is the art and science of predicting future 5. Continuous Improvement: Identifying events, often by projecting historical data trends and areas for improvement, into the future using a mathematical supporting ongoing enhancements in model." quality.  Kumar and Suresh (2009): "Forecasts are estimates of the occurrence, timing, or 4. Basic Laws of Forecasting magnitude of uncertain future events." 1. Forecasts are almost always wrong:  Russell and Taylor (2011): "A forecast is Perfect predictions are impossible due to a prediction of what will occur in the the complexity of influencing factors. future." 2. Short-term forecasts are more accurate: Near-term predictions are typically more 2. Applications in Operations Management reliable. Forecasting plays a crucial role in operations 3. Group forecasts are more accurate: management by helping organizations plan Predicting groups of products or services effectively and manage their resources efficiently. tends to yield better accuracy. Key areas include: 4. Forecasts are not a substitute for 1. Demand Forecasting: Predicting future calculated values: Use forecasts when customer demand for products or services more precise methods are not feasible. to manage inventory, production, and resource allocation. 5. Types of Forecasting: Qualitative vs. 2. Capacity Planning: Estimating future Quantitative production needs to ensure sufficient resources are available. Qualitative Forecasting 3. Supply Chain Management: Planning - Relies on subjective judgment rather than procurement, logistics, and distribution to numerical data. Suitable when historical maintain a smooth flow of materials. data is unavailable, for long-term 4. Workforce Planning: Estimating future predictions, or when dealing with new staffing requirements for effective hiring markets. and training. 5. Cost Management: Forecasting future Advantages of Qualitative Forecasting: expenses for budgeting and expense 1. Predicts Consumer Behavior: Better control. understanding of sales patterns due to human motivation. 3. Role in Total Quality Management (TQM) 2. Allows for Interpretation: Flexibility for Forecasting contributes significantly to quality researchers to interpret data and generate improvement and customer satisfaction by innovative ideas. aligning product and service quality with 3. Supplements Incomplete Data: Useful customer expectations. Key aspects include: when dealing with missing or unavailable 1. Quality Control: Using historical data to data. predict potential quality issues and 4. Offers Comprehensive Understanding: implement preventive measures. Provides insights into the reasons behind trends, not just numbers.  5. Accessibility: Cost-effective and simple to 2. Difficulty in Interpretation perform. - Purely numerical data can be hard to interpret without context. Disadvantages of Qualitative Forecasting: 3. Assumptions 1. Personal Bias: Prone to subjective - Assumes that past trends will influence, which can affect objectivity. continue, which may not always be 2. Higher Risk of Error: Human judgment true. can lead to inaccuracies, especially if based on untested theories. 7. Demand Forecasting 3. Vulnerability to Unpredictable Events: Demand forecasting estimates the future need for Susceptible to sudden changes in goods and services based on economic conditions. economic or political factors that can It includes several key components: affect reliability.  Average/Stationary Demand: Assumes 6. Quantitative Forecasting no long-term growth. Quantitative forecasting involves making  Trend: Tracks overall movement predictions using numerical data and (upward/downward) with types like mathematical models based on historical patterns. Linear, S-Curve, Asymptomatic, and It is particularly useful for short to intermediate- Exponential trends. range decision-making when past data is  Seasonal Variation: Predictable available, assuming that similar patterns will fluctuations based on seasons. continue in the future. It is typically evaluated  Cyclical Movements: Irregular changes based on accuracy measures. due to factors like elections or economic shifts. Advantages of Quantitative Forecasting  Random Movements: Unpredictable 1. Accuracy changes caused by unforeseen events. - Data-driven forecasts tend to be more accurate than opinion-based predictions. 8. Quantitative Techniques in Forecasting - Reduces bias with numerical evidence. 1. Time Series Forecasting 2. Objectivity - Analyzes historical data to predict - Uses historical data to minimize future outcomes. personal bias. - Key Components: Trend, 3. Consistency Seasonality, Cyclic Patterns, - Reliable and repeatable over time. Irregular Variations. 4. May Impress External Stakeholders 2. Naive Forecasting - Provides objective proof of - Assumes the next period will be success, aiding in investor and the same as the last. customer relations. - Advantages: Simple and requires 5. Identifies Patterns minimal data. - Detects trends in sales, demand, - Disadvantages: Ignores trends and and expenses for better planning. seasonality. 6. Utilizes Technology 3. Moving Average - Software and tools streamline data - Smooths data by averaging past analysis, enabling faster and more observations. complex forecasting. - Advantages: Reduces random fluctuations. Disadvantages of Quantitative Forecasting - Disadvantages: Lags behind actual 1. Data Availability trends. - Relies heavily on historical data, 4. Exponential Smoothing which may be incomplete or - Weighs recent data more heavily unreliable. than older data.  - Advantages: More responsive to 1. Delphi Technique changes. - What It Is: A panel of experts - Disadvantages: Requires selection independently provides forecasts in of a smoothing constant. several rounds. Feedback is shared 5. Trend Projection among participants until a consensus is - Uses historical data trends for reached. future prediction. - Application: Useful in long-term - Methods: Graphical, Least predictions or when launching Squares, Box-Jenkins, Machine products in emerging markets. Learning. - Example: Forecasting the rise of - Benefits: Accuracy, adaptability, electric car demand over the next and cost efficiency. decade by consulting experts in 6. Seasonal Indexing automotive, environmental science, - Adjusts forecasts based on seasonal and technology. patterns. - Advantages: Prepares for 2. Market Survey predictable seasonal changes. - What It Is: Involves gathering direct - Disadvantages: Requires input from potential customers through extensive historical data. surveys, interviews, or focus groups to 7. Revenue Run Rate predict market trends. - Projects future revenue based on - Application: Essential for product current performance. launches and understanding consumer - Advantages: Simple calculation. preferences. - Disadvantages: Not suitable for - Example: A smartphone company fluctuating or seasonal markets. conducts surveys to gauge preferred 8. Historical Growth Rate features, helping forecast potential - Compares past and current values sales. to estimate future growth. - Advantages: Tracks long-term 3. Judgmental Forecasting growth trends. - What It Is: Relies on the intuition and - Disadvantages: May not account experience of industry experts to for external factors. predict future trends. 9. Linear Regression - Application: Commonly used in fields - Analyzes relationships between like fashion or technology, where variables to predict outcomes. trends evolve rapidly. - Advantages: Models complex - Example: Fashion designers predict relationships. the popularity of bright colors and - Disadvantages: Sensitive to data oversized clothing based on past quality and outliers. observations and industry knowledge. 9. Qualitative Forecasting Techniques 4. Scenario Writing Qualitative techniques rely on **subjective - What It Is: Involves creating detailed judgment** and insights rather than strictly scenarios based on potential future numerical data, making them ideal for situations developments, which help in preparing where hard data is scarce or non-existent. These for various possibilities. techniques are often crucial in uncertain - Application: Beneficial for industries environments, where expert opinions or consumer with high uncertainty or when behavior can guide future decisions. Here are the planning for multiple outcomes. four key methods: - Example: A tourism company creates scenarios to plan for potential changes in international travel regulations over five years.  - Example: Estimating product 10. Qualitative Forecasting Approaches demand based on rising population These approaches focus on expert judgment and or changes in disposable income. subjective analysis, making them particularly valuable when dealing with new or untested 2. Time Series Forecasting markets. - What It Is: Relies on analyzing historical data to identify patterns 1. Market Research and make predictions. - Methods: Includes customer - Application: Suitable for surveys and focus groups to gather industries with consistent and direct consumer insights. reliable historical data. - Use: Helps refine products based - Example: Forecasting sales based on user feedback and guides on previous monthly or annual predictions about future behavior. data. 2. Executive Judgment - Use: Draws on the experience of company leaders for high-level strategic decisions, especially when historical data is absent. - Example: Executives make educated guesses on the potential success of a new product based on market intuition. 3. Historical Analogy - What It Is: Involves comparing a new product with an existing, successful one that shares similar characteristics. - Application: Useful for predicting potential market behavior and consumer reception. 11. Quantitative Forecasting Approaches In contrast to qualitative techniques, quantitative methods rely on **historical data and mathematical models**. These methods are effective when trends are stable, and past data is abundant. Here are two primary quantitative approaches: 1. Causal Forecasting - Methods: Uses variables like population, income levels, and the price of a product to predict sales. - Application: Effective when identifying cause-and-effect relationships that impact future trends.