Transcript: FORECASTING Qualitative Judgmental Opinion Quantitative Time Series Analysis Associative Forecasts (Linear regression) The forecast horizon must cover the time necessary to implement possible changes. The degree of accuracy should be stated. The forecast should be reliable and it should work consistently. The forecast should be expressed in meaningful units. The forecast should be in writing. The forecast should be simply to understand and easy to use. You can pick forecasting models based on: 1. Time horizon to forecast 2. Data availability 3. Accuracy required 4. Size of forecasting budget 5. Availability of qualified personnel Steps in the Forecasting Process Forecast is the statement about the future. Forecasting is a process of predicting the future events based on historical data. Forecasting is basically done to remove uncertainties, enabling managers to develop better plans for the future. THANK YOU !!! Sabin Shrestha (MBA 2nd Semester) FORECASTING Choosing a forecasting technique Error - difference between actual value and predicted value Mean Absolute Deviation (MAD) Mean Squared Error (MSE) Tracking Signal Forecast Accuracy Elements of Good Forecast Step 1: Determine purpose of forecast Step 2: Establish a time horizon Step 3: Select a forecasting technique Step 4: Gather and analyze appropriate data Step 5: Prepare the forecast Step 6: Monitor the forecast Introduction to Forecasting Approaches to forecasting
Transcript: Forecasting A planning tool that helps management in its attempts to cope with the uncertainty of the future, relying mainly on data from the past and present and analysis of trends. Forecasting starts with certain assumptions based on the management's experience, knowledge, and judgment. These estimates are projected into the coming months or years using one or more techniques What is Forecasting? Double Exponential Moving Average Exponential Smoothing Exponential Smoothing Moving Average Naive Naive Method Forecasting Models Regression Equation
Transcript: 3.3 The Strategic Importance of Forecesting Good Forecasts are of critical importance in all aspect of a business. The forecast is the only estimate of demand. 2. DELPHI METHOD Associative Models- variables or factors that might influence the quantity being forecast. 3.5.2. OVERVIEW OF QUANTITATIVE METHOD 1. JURY OR EXECUTIVE 3.6.4 Exponential Smoothing -sophisticated weighed moving average forecasting method that involves very little record keeping of past data New forecast = Last period’s forecast + a (last period’s actual demand – last periods forecast) Time Series – predict on the assumption that the future is a function of the past. V. SUMMARY Qualitative approaches employ judgment, experience intuition and a host of other factors that are difficult to quantity, quantitative forecasting use historical data and casual or associative, relations to project future demand. I. INTRODUCTION II. OBJECTIVES III. MAIN CONTENT III.1 What is Forecasting III.1.1 Forecasting Time Horizons III.1.2 The Influence of Product Life Cycle III.2 TYPES OF FORECASTS III.3 THE STRATEGIC INFLUENCE OF FORECASTING III.3.1 Human Resources III.3.2 Capacity III.3.3 Supply Chain Management III.4 SEVEN STEPS IN THE FORECASTING SYSTEM III.5 FORECASTING APPROACH III.5.1 Overview of Qualitative Method III.5.2 Overview of Quantitative Method III.6 TIME SERIES FORECASTING III.6.1 Decomposition of a Time Series III.6.2 Naïve Approach III.6.3 Moving Averages III.6.4 Exponential Smoothing III.6.5 Measure Forecast Error III.7 ASSOCIATIVE FORECASTING METHODS: Regression and Correlation Analysis III.7.1 Using Regression Analysis to Forecast IV. Conclusion V. Summary 3.6.5 MEASURING FORECAST ERROR Forecast Error = Actual Demand – Forecast Value = AT-FV Five Methods: Two Categories: 1. Naïve Approach Time Series 2. Moving Average Time Series 3. Exponential Smoothing Associative Model 4. Trend Projection Associative Model 5. Linear Regression Associative Model III. What is Forecasting? high level experts/managers, combined with statistical models are pooled to arrive at a group of demand 3.6.1 Decomposition of Time Series -analyzing time series means breaking down data into components and then projecting them forward. 3.3.1 Capacity When it is inadequate, the resulting shortages can mean undependable delivery, loss of customer and loss of market share. This is exactly what happens to Nabisco when it underestimated the huge demand for its new low fat Snackwell Devils Food Cookies. Even with production lines working overtime, Nabisco could not keep up with demand, and it lost customers. When excess capacity is built, on the other hand, costs can skyrocket. 3.5 FORECASTING APPROACH FORECASTING 3.3.1 SUPPLY CHAIN MANAGEMENT Good supplier relations and the ensuing price advantage for materials and parts depend on accurate forecasts. IV. CONCLUSION Forecasts are a critical part of the operations managers’ decision. It drives a firm’s production, capacity and scheduling systems and affects the finances, marketing and personal planning. II. OBJECTIVES Products and even services do not sell at a constant level throughout their lives. Most successful products pass through 4 stages: 3.1.1. Forecasting Time Horizons 3 Categories 1. Short Range Forecast – Time span up to 1 year but is generally less than 3 months. Used for: • Planning • Purchasing • Job Scheduling • Work force levels • Job assignments • Production levels 2. Medium Range Forecast (Intermediate Forecast, - 3months to 3 years – It is useful in: • Sales Planning • Production Planning and Budgeting • Cash and Budgeting • Analyzing Various Operating Plans 3. Long Range Forecast (Generally 3 years or more in time span Used in : • Planning for new products • Capital Expenditures • Facility Location/Expansion • Research & Development Describe or explain • Moving average • Exponential smoothing • Seasonality TOPICAL OUTLINE 3.6 TIME SERIES FORECASTING I. INTRODUCTION Moving Averages -uses a number of historical data values to generate a forecast. Moving Average = Demands in previous n periods / N Where N is the number of periods in the moving average for ex. 4,5, or 6 months respectively for a 4, 5, or 6 period moving average. "It is no use saying we are doing our best. You have got to succeed in doing what is necessary..." - Winston Churchill solicits inputs from customers or potential customers regarding future purchasing plans) based on informal conversations with customers 1. Determine the use of the forecast 2. Select the items to be forecasted 3. Determine the time horizon of the forecasts 4. Select the forecasting model 5. Gather the data needed to make the forecast 6. Make the forecast 7. Validate and implement the results. 3.5.1 OVERVIEW OF QUALITATIVE METHOD QUANTITATIVE – uses variety of mathematical models that rely on historical data and (or casual) variables to forecast demand. Naïve Approach -assumes that demand in the next period will be equal to demand in the most recent period. 3.7
Transcript: Climate~ the weather conditions prevailing in an area in general or over a long period. Weather Stratus Clouds~ a large dark low cloud Forecast~ predict or estimate Precipitation~ rain, snow, sleet, or hail that falls to the ground Humidity~ the state or quality of being humid Wind ~ natural movement of the air in the form of a current of air blowing from a particular direction. Forecasting Warm Front~ the boundary of an advancing mass of warm air, in particular the leading edge of the warm sector of a low-pressure system. Weather~ the climate at any given time Name Cold Front~ the boundary of an advancing mass of cold air, in particular the trailing edge of the warm sector of a low-pressure system
Transcript: Cold Front Stationary Front Occluded Front Station Model Climate A line showing weather patterns drawn or shown on a weather map, used to indicate the creation or movement of a weather system. A certain pattern for entering weather symbols, Isobar Forecasting By Julianah Perialas A front (two air masses meeting) bringing in a cold air mass. The boundary between two air masses that meet, but don't remove each other. When warm air is forced above cold air, and they meet, the cold air forces the warm air away from the Earth's surface. Step 4
Transcript: Forecasting Summary Each Tab Final Notes: Do not delete cells where there are formulas Do not delete entire rows Do not do cutting and pasting Note: It is always better to manually delete and retype where a change is needed so as to make sure no formulas get erased Ask us questions, we're here to help! Meet with us at the end to review forecast Keep an eye on the system Pay Forecast Non-Pay
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Transcript: Location: American River Basin Forecast points: 8 (referred to the 8 watersheds supplying the 8 powerhouses) Forecasted variable: runoff from the prediction month to July Available data: historical runoffs by month precipitations data (8 stations) snowpack measurements (19 snow courses) Assumptions The model depends on the month (what are the most predictible variables?) Ex: February Aim: For each point predict the monthly inflow from February to July Precipitation: 2 variables (Jan and Nov-Dec) use of indices and averages... Oct-Jan runoff (at the considered point Snowpack: 19 snow courses 2 indices (based on PCA), 1 used Historical data: October 1975 to September 2007 Principal component analysis Water Supply Forecasting Historical water year analysis Overview Predictor variables Monthly forecast Computation: for each forecast point... Linear regression using the first component Gives the Feb-July runoff 1. Historical water year inflow fitted to a Gamma distribution 2. Exceedance proba (EP) determined via the distribution For each EP: a. Apr-Jul and Oct-Mar runoff found using other fitted Gamma dist. b. Aug-Sept inflow computed by regression using Apr-Jul c. The 3 inflow are combined to adjust the water year inflow Disaggregation snowmelt season and summer: d. Apr, May, June, July inflows from Apr-Jul (4 different regressions) e. August abd Sept from Aug-Sept (2 regressions) Disaggregation of the snow accumulation season: f. March using fit to Gamma distribution g. Oct-Feb by substracting March to Oct-March h. Oct, Nov, Dec, Jan, Feb fronm Oct-Feb inflow (5 regressions) Aim of this analysis: to have an idea of the annual and monthly inflows (with exceedance probabilities) without any information about the current year. Offsets from median values normally distributed errors homoscedastic distribution of the residuals independence of the observations variates normally distributed linear relationship between independent and dependent variates Water year = from October to September
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