Forecasting in Engineering Production and Management PDF

FORECASTING IN ENGINEERING PRODUCTION AND MANAGEMENT BS Manufacturing Engineer | 2024 Bulacan State University  INTRODUCTION Forecasting in engineering production and management involves predicting future needs, demands, and conditions based on historical dat...

FORECASTING IN ENGINEERING PRODUCTION AND MANAGEMENT BS Manufacturing Engineer | 2024 Bulacan State University  INTRODUCTION Forecasting in engineering production and management involves predicting future needs, demands, and conditions based on historical data, trends, and other relevant information. This practice is essential for optimizing production processes, managing resources, and ensuring operational efficiency. Accurate forecasting helps organizations reduce costs, improve customer satisfaction, and make informed strategic decisions.  IMPORTANCE OF FORECASTING IMPROVED ENHANCED STRATEGIC COST RESOURCE INVENTORY REDUCTION PLANNING ALLOCATION MANAGEMENT  TYPES OF FORECASTING METHODS Quantitative forecasting methods rely on historical QUANTITATIVE data and statistical techniques to make predictions. These methods are particularly useful METHODS when large volumes of historical data are available. Qualitative forecasting methods are used when QUALITATIVE historical data is insufficient or unreliable. These methods rely on expert judgment and subjective METHODS analysis.  FOR QUANTITATIVE METHODS: QUANTITATIVE METHODS Time Series Analysis Time series analysis examines historical data points collected at consistent intervals to identify patterns and trends. Common techniques include: 1· Moving Averages: Smooths out short-term fluctuations and highlights longer-term trends. Simple moving averages (SMA) and weighted moving averages (WMA) are commonly used. Simple Moving Average (SMA): Calculates the average of data points over a specified period. Weighted Moving Average (WMA): Assigns different weights to data points, giving more importance to recent observations. QUANTITATIVE METHODS Time Series Analysis 2· Exponential Smoothing: Assigns exponentially decreasing weights to past observations, making it useful for data with trends and seasonality. Single Exponential Smoothing: Focuses on smoothing data without accounting for trends. Double Exponential Smoothing: Accounts for trends in addition to smoothing. Triple Exponential Smoothing: Includes seasonal effects along with trends and smoothing. QUANTITATIVE METHODS Time Series Analysis 3. ARIMA Models: Autoregressive Integrated Moving Average (ARIMA) models combine autoregressive (AR) and moving average (MA) components with differencing to handle non-stationary time series data. QUANTITATIVE METHODS Regression Analysis Regression analysis models the relationship between a dependent variable and one or more independent variables. This method helps in understanding how different factors influence the outcome. Linear Regression: Analyzes the linear relationship between the dependent variable and one or more independent variables. Multiple Regression: Extends linear regression to include multiple independent variables. Polynomial Regression: Fits a polynomial equation to data, capturing non-linear relationships. QUANTITATIVE METHODS Machine Learning Algorithms Machine learning algorithms are advanced techniques for forecasting that can handle complex data patterns and large datasets. Neural Networks: Mimic human brain functions to recognize patterns and make predictions. Support Vector Machines (SVM): Classify data into different categories by finding the optimal hyperplane that separates them. Ensemble Methods: Combine predictions from multiple models to improve accuracy, such as Random Forests and Gradient Boosting Machines.  FOR QUALITATIVE METHODS:  QUALITATIVE METHODS Expert Judgment Experts use their knowledge and experience to make forecasts, especially in situations with limited historical data. This method is useful for understanding complex scenarios or new product introductions Delphi Method The Delphi method involves multiple rounds of surveys with a panel of experts. After each round, a summary of responses is provided, and experts are asked to revise their forecasts based on this feedback. This iterative process aims to achieve a consensus forecast. Market Research Market research gathers insights from potential customers, industry trends, and competitive analysis. Techniques include surveys, focus groups, and industry reports. This method helps in understanding customer preferences and market dynamics.  APPLICATIONS IN ENGINEERING PRODUCTION Demand Forecasting Demand forecasting predicts future product demand, which is crucial for planning production schedules and inventory levels. Techniques such as time series analysis and regression can forecast demand based on historical sales data and market conditions. Seasonal Demand: Identifies patterns related to specific times of the year, such as holiday seasons or weather-related changes. Trend Analysis: Detects long-term movements in demand, such as increasing or decreasing trends over several years. Capacity Planning Capacity planning involves determining the production capacity required to meet future demand. Forecasting helps in aligning production capabilities with anticipated needs. Long-Term Capacity Planning: Involves strategic decisions about facility expansion, new equipment investments, and workforce planning. Short-Term Capacity Planning: Focuses on adjusting production schedules, overtime, and temporary labor to meet immediate demand changes. Supply Chain Management Effective supply chain management relies on forecasting to synchronize supply chain activities with anticipated demand. This includes: Inventory Optimization: Ensures that inventory levels are sufficient to meet demand without overstocking. Supplier Coordination: Helps in managing relationships with suppliers and scheduling deliveries based on forecasted needs. Logistics Planning: Coordinates transportation and warehousing activities to align with forecasted demand. Maintenance Scheduling Predictive maintenance uses forecasting to schedule maintenance activities based on equipment usage and failure patterns. Condition-Based Maintenance: Monitors equipment condition in real-time and schedules maintenance when certain thresholds are reached. Predictive Maintenance: Uses data analysis and forecasting to predict when equipment is likely to fail and perform maintenance before failures occur. CHALLENGES IN FORECASTING Data Quality The accuracy of forecasts depends on the quality of data used. Issues such as missing data, inaccuracies, and inconsistencies can undermine forecast reliability. Data Cleaning: Involves identifying and correcting errors in the data. Data Integration: Ensures that data from various sources is combined effectively for analysis. Uncertainty and Variability Forecasting involves inherent uncertainty due to unpredictable factors such as economic shifts, market volatility, and technological advancements. Scenario Analysis: Evaluates different potential outcomes and their impact on forecasts. Sensitivity Analysis: Assesses how changes in key variables affect forecast accuracy. Integration with Other Systems Effective forecasting requires integration with other management systems such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems to ensure data consistency and alignment. System Integration: Connects forecasting tools with ERP and CRM systems for seamless data flow and real-time updates. Data Synchronization: Ensures that data across different systems is consistent and up-to-date. BEST PRACTICE Use Multiple Methods Combining quantitative and qualitative methods can provide a more comprehensive and accurate forecast. For example, using time series analysis for historical data and expert judgment for new products. Continuously Update Forecasts Regularly update forecasts with new data and adjust for changing conditions to maintain accuracy. Implement feedback loops to refine forecasting models. Implement Forecasting Tools Utilize advanced forecasting tools and software that offer capabilities for data analysis, trend identification, and scenario planning. Forecasting Software: Includes tools such as SAP Integrated Business Planning, Oracle Forecasting, and Microsoft Power BI. Analytics Platforms: Tools like Google Analytics and Tableau for visualizing and analyzing data trends. Involve Stakeholders Engage relevant stakeholders in the forecasting process to incorporate diverse perspectives and expertise. This includes cross-functional teams, including sales, production, and supply chain managers. Monitor Performance Continuously monitor the accuracy of forecasts and compare them with actual outcomes. Use performance metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to evaluate forecast accuracy.  CONCLUSION Forecasting is a crucial element of engineering production and management, enabling organizations to anticipate future needs, optimize resources, and improve overall efficiency. By employing a combination of quantitative and qualitative methods, addressing challenges, and following best practices, businesses can achieve more accurate forecasts and make informed decisions that drive success.  THANK YOU Presentation by Cuevas, John Lesley Legaste, Angelo Nhikos Linao, Cedrick S. Manahan, Rhaniel Nicolas, Jomel