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Reasons for demand forecasting: 

Reasons for demand forecasting As an input to Aggregate production Planning or MRP In decision making for facility capacity Planning and for capital budgeting In order to perform adequate staffing to support production requirements To develop, administrative plans and policies internal to an organization (personnel or budget) To develop policies that apply to people who are not part of the organization.

Demand Management: 

Demand Management A B(4) C(2) D(2) E(1) D(3) F(2) Dependent Demand: Raw Materials, Component parts, Sub-assemblies, etc. Independent Demand: Finished Goods

Independent Demand: What a firm can do to manage it?: 

Independent Demand: What a firm can do to manage it? Can take an active role to influence demand Can take a passive role and simply respond to demand Short term : 3 months Medium term : 3 months to 2 years Long term : greater than two years

Forecasting requirements in operations: 

Forecasting requirements in operations Types of Decision Short term planning Long term Planning Specific demand Aggregate demand Strategies & facilities Forecasting horizon in years

Factors - Demand: 

Factors - Demand External factors : changes in interest rate , government regulations, budgetary allocations, rate of unemployment, etc. Internal Factors: Decisions about the product and service, price, after sales service, advertising and promotions and publicity, packaging design or incentives, etc


parameters Forecast should be able to help create a model . The resulting model should tell if we have met our goals with respect to the following four parameters: Goals : What do we need to achieve to become successful? Measures : What parameters will we use to know if we are successful ? Targets : What quantitative value will we use to determine success of the measure? Initiatives : What will we do to meet our goals ?

Types of Forecasting: 

Types of Forecasting

Forecast control : 

Forecast control Correlation Analysis and Coefficient determination Decomposition of a time series Seasonal Index and Seasonal adjustment Trend effect in exponential smoothening Using standard computer programs

Components of Demand: 

Components of Demand Horizontal - Average demand for a period of time Trend Seasonal element Cyclical elements- Cycles are normally of long duration. Random variation Autocorrelation

Indicators : 

Indicators Turning point : Is a point at which demand will change. Leading indicators : are factors those precede the peaks and troughs of a business cycle. Lagging indicators : are those factors that follow after the turning points.

Choosing a forecasting model: 

Choosing a forecasting model Time horizon to forecast Data availability Accuracy required Size of forecasting budget availability of qualified personnel. Firm’s degree of flexibility

Criteria to selecting appropriate forecasting method: 

Criteria to selecting appropriate forecasting method Forecasting method Amt of historical data Data pattern Forecast horizon Simple moving average 6 to 12 months, weekly data are often used Data should be stationary (i.e. no trend or seasonality) Short to medium Weighted moving average and simple exponential smoothing 5 to 10 observations needed to start Data should be stationary Short Exponential smoothing with trend 5 to 10 observations needed to start Stationary and trend Short Linear regression 10 to 20 observations, for seasonality at least 5 observations per season. Stationary, trend and seasonality Short to medium

Finding Components of Demand: 

Finding Components of Demand 1 2 3 4 x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x Year Sales Seasonal variation Linear Trend

Analysis of Environmental Forces: 

Analysis of Environmental Forces Three goals : Forecasting Modeling Characterization

Simple Moving Average Formula: 

Simple Moving Average Formula The simple moving average model assumes an average is a good estimator of future behavior The formula for the simple moving average is: F t = Forecast for the coming period N = Number of periods to be averaged A t-1 = Actual occurrence in the past period for up to “n” periods

Simple Moving Average Problem (1): 

Simple Moving Average Problem (1) Find the 3-Week and 6-Week moving average.

PowerPoint Presentation: 

F 4 =(650+678+720)/3 =682.67 F 7 =(650+678+720 +785+859+920)/6 =768.67 Calculating the moving averages gives us : The McGraw-Hill Companies, Inc., 2004 18

PowerPoint Presentation: 

Plotting the moving averages and comparing them shows how the lines smooth out to reveal the overall upward trend in this example Note how the 3-Week is smoother than the Demand, and 6-Week is even smoother

Simple Moving Average Problem (2) Data: 

Simple Moving Average Problem (2) Data Question: What is the 3 week moving average forecast for this data? Assume you only have 3 weeks and 5 weeks of actual demand data for the respective forecasts

Simple Moving Average Problem (2) Solution: 

Simple Moving Average Problem (2) Solution F 4 =(820+775+680)/3 =758.33 F 6 =(820+775+680 +655+620)/5 =710.00

Weighted Moving Average Formula: 

Weighted Moving Average Formula While the moving average formula implies an equal weight being placed on each value that is being averaged, the weighted moving average permits an unequal weighting on prior time periods w t = weight given to time period “t” occurrence (weights must add to one) The formula for the moving average is:

Weighted Moving Average Problem (1) Data: 

Weighted Moving Average Problem (1) Data Weights: t-1 .5 t-2 .3 t-3 .2 Question: Given the weekly demand and weights, what is the forecast for the 4 th period or Week 4? Note that the weights place more emphasis on the most recent data, that is time period “t-1”

Weighted Moving Average Problem (1) Solution: 

Weighted Moving Average Problem (1) Solution F 4 = 0.5(720)+0.3(678)+0.2(650)=693.4

Weighted Moving Average Problem (2) Data : 

Weighted Moving Average Problem (2) Data Weights: t-1 .7 t-2 .2 t-3 .1 Question: Given the weekly demand information and weights, what is the weighted moving average forecast of the 5 th period or week?

Weighted Moving Average Problem (2) Solution: 

Weighted Moving Average Problem (2) Solution F 5 = (0.1)(755)+(0.2)(680)+(0.7)(655)= 672

Problem 3: 

Problem 3 A department store may find that ina four-month period, the best forecast is derived by using 40% of the actual sales for the most recent month, 30% of two months ago, 20% of three months ago, and 10% of four months ago. If actual sales experience was Month 1 Month 2 Month 3 Month 4 Month 5 100 90 105 95 ?

Exponential smoothing: 

Exponential smoothing The major drawback of simple and weighted moving average is the need to continually carry large amount of historical data. In Exponential smoothing each increment in the past is decreased by (1- a ). Application – ordering inventory in retail firms, whole sale companies and service agencies. Why Exponential Smoothing is the most used of all forecasting techniques? E Models are surprisingly accurate. Formulating an exponential models is relatively easy. The user can understand how the model works. Little computation is required to use the model. Computer storage requirements are small because of the limited use of historical data. Tests for accuracy as to how well the model is performing are easy to compute.

Exponential Smoothing Model: 

Exponential Smoothing Model Premise: The most recent observations might have the highest predictive value Therefore, we should give more weight to the more recent time periods when forecasting F t = F t-1 + a (A t-1 - F t-1 )

Exponential Smoothing Problem (1) Data: 

Exponential Smoothing Problem (1) Data Question: Given the weekly demand data, what are the exponential smoothing forecasts for periods 2-10 using a =0.10 and a =0.60? Assume F 1 =D 1

Exponential Smoothing Problem (1) Plotting: 

Exponential Smoothing Problem (1) Plotting Note how that the smaller alpha results in a smoother line in this example

Exponential Smoothing Problem (2) Data: 

Exponential Smoothing Problem (2) Data Question: What are the exponential smoothing forecasts for periods 2-5 using a =0.5? Assume F 1 =D 1

Exponential Smoothing Problem : 

Exponential Smoothing Problem

Trend effects in exponential smoothing: 

Trend effects in exponential smoothing Besides the smoothing constant a, the trend equation also uses a smoothing constant delta ( d ). If both alpha and delta are not included, the trend overreacts to errors.FIT – Forecasting including trend. FIT t = F t + T t F t = FIT t-1 + a (A t-1 – F t-1 ) T t = T t-1 + d (F t – FIT t-1 )


problem Assume an initial starting F t of 100 units, a trend of 10 units, an alpha of 0.20, and a delta of 0.30. if actual demand turned out to be 115 rather than the forecast 100, calculate the forecast for the next period.

Forecast errors: 

Forecast errors Bias error – when a consistent mistake is made. Random error – those errors which cannot be explained. Measurement of error : MAD TS – Tracking signal

The MAD Statistic to Determine Forecasting Error: 

The MAD Statistic to Determine Forecasting Error The ideal MAD is zero which would mean there is no forecasting error The larger the MAD, the less the accurate the resulting model

MAD Problem Data: 

MAD Problem Data Month Sales Forecast 1 220 n/a 2 250 255 3 210 205 4 300 320 5 325 315 Question: What is the MAD value given the forecast values in the table below?

MAD Problem Solution: 

MAD Problem Solution Month Sales Forecast Abs Error 1 220 n/a 2 250 255 5 3 210 205 5 4 300 320 20 5 325 315 10 40 Note that by itself, the MAD only lets us know the mean error in a set of forecasts

Tracking Signal Formula: 

Tracking Signal Formula The Tracking Signal or TS is a measure that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand. Depending on the number of MAD’s selected, the TS can be used like a quality control chart indicating when the model is generating too much error in its forecasts. The TS formula is:


problem Month Demand forecast Actual 1 1000 950 2 1000 1070 3 1000 1100 4 1000 960 5 1000 1090 6 1000 1050 Compute MAD, The RSFE and TS from forecast and Actual data

Simple Linear Regression Model: 

Simple Linear Regression Model Y t = a + bx 0 1 2 3 4 5 x (Time) Y The simple linear regression model seeks to fit a line through various data over time Is the linear regression model a Linear regression is used both for time series forecasting and fo causal relationship forecasting.

Simple Linear Regression Formulas for Calculating “a” and “b”: 

Simple Linear Regression Formulas for Calculating “a” and “b”

Simple Linear Regression Problem Data: 

Simple Linear Regression Problem Data Question: Given the data below, what is the simple linear regression model that can be used to predict sales in future weeks?

PowerPoint Presentation: 

Answer: First, using the linear regression formulas, we can compute “a” and “b” 46

PowerPoint Presentation: 

Y t = 143.5 + 6.3x 180 Period 135 140 145 150 155 160 165 170 175 1 2 3 4 5 Sales Sales Forecast The resulting regression model is: Now if we plot the regression generated forecasts against the actual sales we obtain the following chart: 47

Delphi Method: 

Delphi Method l. Choose the experts to participate representing a variety of knowledgeable people in different areas 2. Through a questionnaire (or E-mail), obtain forecasts (and any premises or qualifications for the forecasts) from all participants 3. Summarize the results and redistribute them to the participants along with appropriate new questions 4. Summarize again, refining forecasts and conditions, and again develop new questions 5. Repeat Step 4 as necessary and distribute the final results to all participants

Web-Based Forecasting: CPFR: 

Web-Based Forecasting: CPFR Collaborative Planning, Forecasting, and Replenishment ( CPFR ) a Web-based tool used to coordinate demand forecasting, production and purchase planning, and inventory replenishment between supply chain trading partners. Used to integrate the multi-tier or n -Tier supply chain, including manufacturers, distributors and retailers. CPFR’s objective is to exchange selected internal information to provide for a reliable, longer term future views of demand in the supply chain. CPFR uses a cyclic and iterative approach to derive consensus forecasts.

Web-Based Forecasting: Steps in CPFR: 

Web-Based Forecasting: Steps in CPFR 1. Creation of a front-end partnership agreement 2. Joint business planning 3. Development of demand forecasts 4. Sharing forecasts 5. Inventory replenishment