Forecasting
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- Forecasting involves making predictions about future market conditions and demand. It is an important part of business planning but forecasts will always be imperfect. - Market size refers to the number of potential buyers and sellers in a market. Understanding market size is important for launching new products or services. Qualitative and quantitative models can be used to forecast market size. - Qualitative models include expert opinion methods like the Delphi method and jury of executive opinion. Quantitative time series models analyze historical demand patterns using techniques like moving averages, exponential smoothing, and regression analysis. These techniques help minimize forecast errors.
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Forecasting
This document discusses various forecasting methods including qualitative methods like panel consensus and quantitative methods like time series analysis. It explains moving averages, weighted moving averages, and exponential smoothing for time series forecasting. Moving averages are simple to calculate but do not respond well to trends while exponential smoothing accounts for trends using smoothing constants. Linear regression can also be used to explore relationships between dependent and independent variables for forecasting. Overall the key points are that forecasting predicts future demand based on past data, different quantitative methods are suited to different situations, and accuracy depends on how well past patterns predict the future.
Forecasting
This document discusses various forecasting methods including:
- Calculating forecasts using moving averages, weighted moving averages, and exponential smoothing
- Choosing the appropriate forecasting model based on data availability, time horizon, required accuracy, and resources
- Comparing forecast accuracy using metrics like forecast error which measure the difference between actual and forecasted values
Forecasting
This document discusses various forecasting techniques. It covers qualitative and quantitative methods as well as different time horizons for forecasting. Specific quantitative techniques discussed include moving averages, exponential smoothing, regression analysis, and double exponential smoothing. Moving averages and exponential smoothing are described as methods for forecasting stationary time series. Exponential smoothing provides a weighted average of past observations with more weight given to recent observations. Double exponential smoothing accounts for trends by smoothing changes in the intercept and slope over time.
Class notes forecasting
Forecasting involves predicting future events and is commonly used to estimate future demand for products and services. There are different time horizons for forecasts, including short-range (up to 1 year), medium-range (3 months to 3 years), and long-range (3 years or more). Forecasting methods can be quantitative, using mathematical techniques and historical data, or qualitative, using intuition and expert opinions. The objectives of demand forecasting include production planning, financial planning, and long-term strategic planning.
Chapter 15; Short-Term Scheduling
The document discusses short-term scheduling in various contexts such as airlines and manufacturing. It provides examples of how factors like weather can disrupt airline schedules and how investment in technology can help recovery. Forward and backward scheduling are introduced as two common approaches, with forward scheduling beginning as requirements are known and backward scheduling beginning with final due dates. The goals of short-term scheduling are outlined as minimizing completion time and work-in-process inventory while maximizing utilization and minimizing customer wait times. Key factors that affect scheduling are identified as volume, operations nature, and job complexity.
forecasting methods
- Forecasting helps reduce risk and uncertainty in decision making by predicting future outcomes.
- There are three main types of forecasting methods: qualitative, extrapolative/time series, and causal/explanatory.
- Time series forecasting uses historical data patterns to predict future values, accounting for trends, seasonality, cycles, and randomness. Common time series forecasting techniques include moving averages, weighted moving averages, and exponential smoothing.
Forecasting Methods
Forecasting is essential for business operations and involves estimating future events and trends. There are two main types of forecasting: quantitative and qualitative. Quantitative forecasting uses historical data and mathematical models, while qualitative forecasting relies on expert opinions. Common quantitative forecasting methods include moving averages, exponential smoothing, and time series models. Moving averages calculate the average demand over a set time period to smooth out fluctuations. Exponential smoothing places more emphasis on recent data by applying weighting factors. Qualitative methods include jury of executive opinion, Delphi method, and consumer surveys. Forecasting allows businesses to better plan operations and prepare for the future.
Forecasting
The document discusses various quantitative and qualitative forecasting methods. It begins by introducing forecasting and explaining its importance for business planning. It then outlines different types of forecasts based on time horizon (short, medium, long-range). The document proceeds to describe several quantitative time series forecasting models including naive, moving average (simple, weighted), and exponential smoothing (simple, with trend and seasonality components). It also briefly discusses qualitative methods such as executive judgment, sales force composite, market research, and the Delphi method. Examples are provided to illustrate exponential smoothing calculations.
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Forecasting
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Forecasting
This document discusses various forecasting methods including:
- Calculating forecasts using moving averages, weighted moving averages, and exponential smoothing
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Class notes forecasting
Forecasting involves predicting future events and is commonly used to estimate future demand for products and services. There are different time horizons for forecasts, including short-range (up to 1 year), medium-range (3 months to 3 years), and long-range (3 years or more). Forecasting methods can be quantitative, using mathematical techniques and historical data, or qualitative, using intuition and expert opinions. The objectives of demand forecasting include production planning, financial planning, and long-term strategic planning.
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The document discusses short-term scheduling in various contexts such as airlines and manufacturing. It provides examples of how factors like weather can disrupt airline schedules and how investment in technology can help recovery. Forward and backward scheduling are introduced as two common approaches, with forward scheduling beginning as requirements are known and backward scheduling beginning with final due dates. The goals of short-term scheduling are outlined as minimizing completion time and work-in-process inventory while maximizing utilization and minimizing customer wait times. Key factors that affect scheduling are identified as volume, operations nature, and job complexity.
forecasting methods
- Forecasting helps reduce risk and uncertainty in decision making by predicting future outcomes.
- There are three main types of forecasting methods: qualitative, extrapolative/time series, and causal/explanatory.
- Time series forecasting uses historical data patterns to predict future values, accounting for trends, seasonality, cycles, and randomness. Common time series forecasting techniques include moving averages, weighted moving averages, and exponential smoothing.
Forecasting Methods
Forecasting is essential for business operations and involves estimating future events and trends. There are two main types of forecasting: quantitative and qualitative. Quantitative forecasting uses historical data and mathematical models, while qualitative forecasting relies on expert opinions. Common quantitative forecasting methods include moving averages, exponential smoothing, and time series models. Moving averages calculate the average demand over a set time period to smooth out fluctuations. Exponential smoothing places more emphasis on recent data by applying weighting factors. Qualitative methods include jury of executive opinion, Delphi method, and consumer surveys. Forecasting allows businesses to better plan operations and prepare for the future.
Forecasting
The document discusses various quantitative and qualitative forecasting methods. It begins by introducing forecasting and explaining its importance for business planning. It then outlines different types of forecasts based on time horizon (short, medium, long-range). The document proceeds to describe several quantitative time series forecasting models including naive, moving average (simple, weighted), and exponential smoothing (simple, with trend and seasonality components). It also briefly discusses qualitative methods such as executive judgment, sales force composite, market research, and the Delphi method. Examples are provided to illustrate exponential smoothing calculations.
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Demand Forecasting
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2. Forecasts can use qualitative methods like expert judgment or quantitative methods like time-series analysis of historical data to predict demand trends, levels, and seasonal variations.
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The document discusses quantitative forecasting methods, including exponential smoothing. Exponential smoothing assigns weights to past observations, with more recent observations receiving greater weight, to generate forecasts. Examples are provided to demonstrate how to calculate forecasts for upcoming time periods using exponential smoothing with different smoothing constants (a). Managers can use exponential smoothing on historical demand data to forecast future demand and support inventory and production planning.
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Forecasting involves making predictions about future events and outcomes. There are two main approaches to forecasting - judgmental and quantitative. Judgmental forecasts rely on intuition and experience, while quantitative forecasts use mathematical techniques and historical data. Common quantitative methods include time series analysis, which analyzes past demand data to forecast future demand, and associative models, which use related predictor variables to forecast the variable of interest. Accuracy decreases as the forecasting time horizon increases, so forecasts are more accurate for the near future than the distant future.
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Chapter 7 demand forecasting in a supply chain
1. Forecasting is essential for supply chain planning and involves forecasting demand using historical data and time-series methods.
2. The components of a demand forecast include the systematic components of level, trend, and seasonality as well as the random error.
3. Common time-series forecasting methods include moving averages, exponential smoothing, and Winter's method which accounts for trend and seasonality.
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Forecasting ppt @ bec doms
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Forecasting.pptx
The document discusses various aspects of forecasting, including:
- Forecasting involves making predictions about future values of variables like demand. Accurate forecasts are important for planning operations, supply chains, staffing needs etc.
- Forecasts can be short-term (hours to months) or long-term (years). Both the expected level and accuracy of forecasts are important.
- Qualitative and quantitative methods can be used. Qualitative methods rely more on subjective judgment while quantitative methods analyze objective historical data.
- Specific qualitative methods discussed include Delphi, surveys, consensus among executives. Quantitative time series models examine trends, seasonality and irregular patterns in past data.
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Forecasting involves making predictions about future events based on historical data. Forecasting errors are measured as the difference between actual and predicted values. This document discusses various methods of measuring forecasting errors over time (calendar errors) and across different products (cross-sectional errors). It defines important error metrics like mean absolute deviation and mean absolute percentage error, and the tracking signal ratio, which are used to evaluate forecast accuracy and consistency over multiple periods. The document also includes an example calculating these metrics based on actual and forecast demand values.
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1. Demand forecasting forms the basis of supply chain planning as it allows managers to plan production, transportation, and other activities in anticipation of or in response to customer demand.
2. Forecasts can use qualitative methods like expert judgment or quantitative methods like time-series analysis of historical data to predict demand trends, levels, and seasonal variations.
3. The appropriate forecasting method depends on the forecast horizon, with short-term forecasts relying more on time-series analysis, medium-term using both time-series and causal models, and long-term relying more on judgment.
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The document discusses logistics outsourcing and the differences between third-party logistics (3PL) and fourth-party logistics (4PL). It defines 3PL as outsourcing logistics functions like transportation and warehousing to external providers. 3PL providers range from basic to advanced, offering value-added services. 4PL providers go a step further by integrating the supply chain resources, capabilities, and technologies of multiple organizations. While 3PL focuses on individual functions, 4PL manages the entire supply chain for a client.
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The document discusses quantitative forecasting methods, including exponential smoothing. Exponential smoothing assigns weights to past observations, with more recent observations receiving greater weight, to generate forecasts. Examples are provided to demonstrate how to calculate forecasts for upcoming time periods using exponential smoothing with different smoothing constants (a). Managers can use exponential smoothing on historical demand data to forecast future demand and support inventory and production planning.
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The presentation gives Glimpses of meaning of forecasting and analytical ways to know the forecast demand for future
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Demand forecasting is used to predict future demand for products and services. There are short, medium, and long-range forecasts used for production planning, budgeting, and new product development. Qualitative and quantitative methods are used depending on data availability. The aggregate planning process involves determining demand and capacity, developing alternative plans, and selecting the optimal plan to minimize costs while meeting demand and capacity constraints. Accurate demand forecasting is crucial for aggregate planning and supply chain management.
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types of inventories
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functions of inventory
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objective of inventory control
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effective inventory management
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inventory counting systems
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key inventory terms
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economic order quantity models
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assumptions of eoq model
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Forecasting involves making predictions about future events and outcomes. There are two main approaches to forecasting - judgmental and quantitative. Judgmental forecasts rely on intuition and experience, while quantitative forecasts use mathematical techniques and historical data. Common quantitative methods include time series analysis, which analyzes past demand data to forecast future demand, and associative models, which use related predictor variables to forecast the variable of interest. Accuracy decreases as the forecasting time horizon increases, so forecasts are more accurate for the near future than the distant future.
Operations management forecasting
The document summarizes key concepts about forecasting from the 8th edition of the textbook "Operations Management" by William J. Stevenson. It discusses definitions of forecasting, the importance and uses of forecasts in various business functions. Methods of forecasting include qualitative judgmental forecasts, quantitative time series analysis, and associative models using explanatory variables. Specific forecasting techniques covered include naive forecasts, moving averages, exponential smoothing, trend analysis, and regression. The document also addresses evaluating forecast accuracy and controlling forecasts.
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Chapter 7 demand forecasting in a supply chain
1. Forecasting is essential for supply chain planning and involves forecasting demand using historical data and time-series methods.
2. The components of a demand forecast include the systematic components of level, trend, and seasonality as well as the random error.
3. Common time-series forecasting methods include moving averages, exponential smoothing, and Winter's method which accounts for trend and seasonality.
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This document discusses inventory management concepts including independent and dependent demand, types of inventories, functions of inventory, objectives and effective inventory management. It also covers inventory models like the single period model and multiple period models including the fixed order quantity and fixed time period models. Key terms discussed include lead time, holding costs, ordering costs, shortage costs. The ABC classification system for inventory is also explained.
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Forecasting
- 1. Forecasting Market Size Sudarshan Kumar Patel(1320)
- 2. What is a Forecast? ā¢ A guess about what is going to happen in the future. ā¢ An integral part of almost all business enterprise ā¢ Logical and rational, but still a guess. ā¢ Objective is to minimize error (as you will always be wrong!) ā¢ Could be a complicated or simple process
- 3. Market Size ā¢ The number of buyers and sellers in a particular market. This is especially important for companies that wish to launch a new product or service, since small markets are less likely to be able to support a high volume of goods. Large markets could bring in more competition. ā¢ The number of individuals in a certain market who are potential buyers and/or sellers of a product or service. Companies are interested in knowing the market size before launching a new product or service in an area.
- 4. Forecasting Models Forecasting Techniques Qualitative Models Time Series Methods Delphi Method Jury of Executive Opinion Sales Force Composite Consumer Market Survey Naive Moving Average Weighted Moving Average Exponential Smoothing Trend Analysis Causal Methods Simple Regression Analysis Multiple Regression Analysis Seasonality Analysis Multiplicative Decomposition
- 5. Qualitative & Quantitative Forecasting Methods QualitativeA.Executive Judgement B. Sales Forse Composite C.Market Research/Survey D.Delphi Method QuantitativeA. Time Series Models a.NaĆÆve b.Moving Average 1.Simple 2.Weighted c.Exponential Smoothing- 1.Level 2.Trend 3. Seasonality B.Regression Models
- 6. Jury of Executive Opinion ļ¾ Involves small group of high-level experts and managers ļ¾ Group estimates demand by working together ļ¾ Combines managerial experience with statistical models ļ¾ Relatively quick ļ¾ āGroup-thinkā
- 7. Sales Force Composite ā¢ Each salesperson projects his or her sales ā¢ Combined at district and national levels ā¢ Sales reps know customersā wants ā¢ Tends to be overly optimistic
- 8. Delphi Method ā¢ The Delphi Method is a group decision process about the likelihood that certain events will occur. ā¢ Today it is also used for environmental, marketing and sales forecasting. ā¢ The Delphi Method uses a panel of experts. ā¢ Expert responses to a series of questionnaires are anonymous. ā¢ Each round of questionnaires results in a median answer. ā¢ The process guides the group towards a consensus.
- 9. Consumer Market Survey ā¢ Ask customers about purchasing plans. ā¢ What consumers say, and what they actually do are often different. ā¢ Sometimes difficult to answer.
- 10. Demand Patterns in Time Series Model ā¢ Time Series: The repeated observations of demand for a service or product in their order of occurrence. ā¢ There are five basic patterns of most time seriesā¢ Horizontal- The fluctuation of data around a constant mean. ā¢ Trend- The systematic increase or decrease in the mean of the series over time. ā¢ Seasonal- A repeatable pattern of increases or decreases in demand, depending on the time of day, week, month, or season. ā¢ Cyclical-The less predictable gradual increases or decreases over longer periods of time (years or decades). ā¢ Random- The unforecastable variation in demand
- 11. Demand Patterns
- 12. Naive Approach ā¢ Demand in next period is the same as demand in most recent period ā¢ Assumes demand in next period is the same as demand in most recent period ā¢ e.g.- If May sales were 48, then June sales will be around 48. ā¢ Sometimes it is effective & cost efficient ā¢ e.g.- when the demand is steady or changes slowly ā¢ when inventory cost is low ā¢ when unmet demand will not lose
- 13. Moving Average Method ā¢ ā¢ ā¢ ā¢ ā¢ MA is a series of arithmetic means Used if little or no trend, seasonal, and cyclical patterns Used often for smoothing Provides overall impression of data over time Equation MA Demand in Previous n Periods n
- 14. Moving Average Example ā¢ S.K. Patel is manager of a museum store that sells historical replicas. You want to forecast sales of item (123) for 2000 using a 3-period moving average. 1995 4 1996 6 1997 5 1998 3 1999 7
- 16. Contā¦
- 17. Contā¦
- 18. Exponential Smoothing Method ā¢ Form of weighted moving average ā¢ Weights decline exponentially ā¢ Most recent data weighted most ā¢ Requires smoothing constant ( ) ā¢ Ranges from 0 to 1 ā¢ Subjectively chosen ā¢ Involves little record keeping of past data
- 19. Exponential Smoothing Equations ļ® Ft = Ft-1 + (At-1 - Ft-1) = At-1 + (1 - ) Ft-1 F = Forecast value ļµ At = Actual value ļµ = Smoothing constant ļµ t ļ® Ft = At - 1 + (1- )At - 2 + (1- )2Ā·At - 3 + (1- )3At - 4 + ... + (1- )t-1Ā·A0 ļµ Use for computing forecast
- 20. Regression Analysis as a Method for Forecasting ā¢ Regression analysis takes advantage of the relationship between two variables. Demand is then forecasted based on the knowledge of this relationship and for the given value of the related variable. ā¢ Ex: Sale of Tires (Y), Sale of Autos (X) are obviously related ā¢ If we analyze the past data of these two variables and establish a relationship between them, we may use that relationship to forecast the sales of tires given the sales of automobiles. ā¢ The simplest form of the relationship is, of course, linear, hence it is referred to as a regression line