Operations Management | Forecasting in Operations | Lesson 05 | DSC 2370 | Kuppi | usj | скачать в хорошем качестве

Operations Management | Forecasting in Operations | Lesson 05 | DSC 2370 | Kuppi | usj |

Chapter 05: Forecasting in Operations DSC 2370 – Operations Management Department of Decision Sciences, Faculty of Management Studies & Commerce, University of Sri Jayewardenepura Course Content Overview: This chapter explores the critical importance of forecasting in operations management 📊. Focuses on both qualitative and quantitative forecasting methods, supported by real-world examples. Students will learn how to evaluate forecasting accuracy and apply a combination of forecasting techniques for optimal results 🔍. Key Topics Covered: Demand Management 📦: Ensures companies meet customer demand while minimizing the risk of overstocking or stockouts. Example: Amazon 📦 utilizes sophisticated forecasting techniques to optimize product deliveries and manage warehouse space efficiently. Forecasting Methods 📊: Qualitative Methods: Subjective approaches based on expert opinions, especially useful when data is unavailable or unreliable. Quantitative Methods: Data-driven techniques that analyze historical data to forecast future demand. Example: Apple 🍏 uses quantitative forecasting for iPhone demand predictions, while Coca-Cola 🥤 employs market research for consumer trend forecasting. Qualitative Forecasting Methods 🤔: Applied when historical data is not available or when subjective insights are necessary. Example: Tesla 🚗 uses the Delphi method to forecast self-driving car adoption. Example: Coca-Cola 🥤 collects consumer surveys to anticipate future product demand. Quantitative Forecasting Methods 📈: Time Series Analysis 📅: Utilizes historical data patterns to predict future trends. Regression Analysis 📉: Examines the relationships between variables to forecast demand. Example: Walmart 🛒 applies time series analysis to predict holiday season demand. Example: Netflix 🎬 uses regression analysis to forecast the success of new shows based on past viewing trends. Measurement of Forecast Errors 🔄: Forecast errors are critical for refining future predictions. Common measurement tools include MAD (Mean Absolute Deviation) and MSE (Mean Squared Error). Example: Amazon 📦 employs error analysis to fine-tune inventory strategies, reducing both overstocking and stockouts. Causal Method 🔗: Forecasting based on the relationship between variables. Example: McDonald’s 🍔 uses weather data 🌞 to predict sales of ice cream and cold drinks, which in turn influences the demand for other menu items. Simulation 🎲: Utilizes Monte Carlo simulations to model uncertainty and forecast a range of possible outcomes. Example: Airlines ✈️ rely on simulations to estimate passenger demand, accounting for factors such as weather, ticket pricing, and global events (e.g., COVID-19 🦠). Using Multiple Techniques 🔀: Combining different forecasting methods leads to greater accuracy in predictions. Example: Toyota 🚗 integrates both quantitative and qualitative methods to forecast car demand in various markets, ensuring smooth production schedules. Learning Outcomes: By the end of this chapter, students will be able to: Apply qualitative and quantitative forecasting techniques 📊 in real-world business contexts. Measure and refine forecast accuracy using MAD, MSE, and RMSE 📈. Utilize causal forecasting 🔗 to identify and predict demand based on key variable relationships. Implement simulation techniques 🎲 to plan for uncertainty in operations. Integrate multiple forecasting methods to improve overall accuracy, as demonstrated by Walmart 🛒 and Toyota 🚗. Hashtags: #OperationsManagement #Forecasting #DemandManagement #QuantitativeForecasting #QualitativeForecasting #CausalMethod #Simulation #BusinessForecasting #ForecastingTechniques #DecisionSciences #DSC2370 #UniversityOfSriJayewardenepura #BusinessStudies #OperationsResearch #ForecastError #AcademicHelp #ManagementStudies #JpuraAcademicHelp #kuppi #kuppisession #universityofsrijayewardenepura #education #businessmanagement #sinhala #exam #operationsmanagement #jpura #usj #jpuraacademichelp #academichelp #academic #charith #charithdhanesh