Operations Management 1- Chapter 4 Handouts 1

Operations Management 1 – Chapter 4 – Handouts 1

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OPERATIONS MANAGEMENT 1 – HANDOUTS No 4

Chapter 4 - FORECASTING

1. What is Forecasting? Process of predicting future events Underlying basis of all business decisions

Production Inventory Personnel Facilities

2. Forecasting Time Horizons There are 3 categories of Time Horizons: Short-range forecast

Up to 1 year, generally less than 3 months Purchasing, job scheduling, workforce levels, job assignments, production levels

Medium-range forecast 3 months to 3 years Sales and production planning, budgeting

Long-range forecast 3+ years New product planning, facility location, research and development

How do medium and long-range forecast differ from short-range forecast? They differ by three features:

(1) Intermediate (medium) and long-range forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants, and processes.

(2) Short-term forecasting usually employs different methodologies than longer-term forecasting.

(3) Short-term forecasts tend to be more accurate than longer forecasts because factors that influence demand change every day.

3. Types of Forecasts We have the following types of forecast: Economic forecasts

Address business cycle – inflation rate, money supply, housing starts, etc. Technological forecasts

Predict rate of technological progress Impacts development of new products

Demand forecasts Predict sales of existing products and services



4. Steps in the forecasting system

5. Forecasting Approaches There are two types of forecasting approaches: A. Qualitative Methods Used when situation is vague and little data exist

Forecasting new products, new technology Involves intuition, experience

e.g., forecasting sales on Internet B. Quantitative Methods Used when situation is ‘stable’ and historical data exist

Forecasting existing products, current technology Involves mathematical techniques

e.g., forecasting sales of color televisions 5.1 Overview of Qualitative Methods Qualitative forecasting methods include: Jury of executive opinion

Pool opinions of high-level experts, sometimes augment by statistical models Involves small group of high-level experts and managers Group estimates demand by working together Combines managerial experience with statistical models Relatively quick ‘Group-think’

disadvantage Delphi method

Panel of experts, queried iteratively Iterative group process, continues until consensus is reached 3 types of participants Decision makers Staff Respondents

Sales force composite Estimates from individual salespersons are reviewed for reasonableness, then

aggregated Each salesperson projects his or her sales



Combined at district and national levels Sales reps know customers’ wants Tends to be overly optimistic

Consumer Market Survey

Ask the customer Ask customers about purchasing plans What consumers say, and what they actually do are often different Sometimes difficult to answer

Overview of Quantitative Approaches

Time Series Forecasting Set of evenly spaced numerical data

Obtained by observing response variable at regular time periods Forecast based only on past values, no other variables important

Assumes that factors influencing past and present will continue influence in future Time Series Components



Trend Component Persistent, overall upward or downward pattern Changes due to population, technology, age, culture, etc. Typically several years duration

Seasonal Component Regular pattern of up and down fluctuations Due to weather, customs, etc. Occurs within a single year

Cyclical Component Repeating up and down movements Affected by business cycle, political, and economic factors Multiple years duration Often causal or associative relationships

Random Component Erratic, unsystematic, ‘residual’ fluctuations Due to random variation or unforeseen events Short duration and no repeating

Naive Approach Assumes demand in next

period is the same as demand in most recent period e.g., If January sales were 68, then February sales will be 68

Sometimes cost effective and efficient Can be good starting point

Moving Average Method MA is a series of arithmetic means Used if little or no trend Used often for smoothing

Provides overall impression of data over time



Moving Average Example Donna’s Garden Supply wants a 3-month moving average forecast Approach: Storage shed are shown in the middle column of the table below. The 3-month

moving average is shown on the right.

Weighted Moving Average Used when trend is present

Older data usually less important Weights based on experience and intuition

Weighted Moving Average Example Donna’s Garden Supply (See the first example) wants to forecast storage shed sales by weighting the past 3 months, with more given to recent data to make them more significant. Approach: Assign more weight to recent data as follows:

Forecast this month = 3 x Sales last month + 2 x Sales 2 months ago + 1 x Sales 3 months ago Sum of the weights

Potential Problems With Moving Average Increasing n smoothes the forecast but makes it less sensitive to changes Do not forecast trends well Require extensive historical data



Exponential Smoothing Form of weighted moving average

Weights decline exponentially Most recent data weighted most

Requires smoothing constant (a) Ranges from 0 to 1 Subjectively chosen

Involves little record keeping of past data New forecast = Last period’s forecast + α (Last period’s actual demand – Last period’s forecast) Ft = Ft – 1 + α (At – 1 - Ft – 1) Where Ft = new forecast Ft – 1 = previous forecast a = smoothing (or weighting) constant (0 ≤ a ≤ 1) Exponential Smoothing Example In January, a car dealer predicted February demand for 142 Ford Mustangs. Actual February demand was 153 autos. Using a smoothing constant chosen by management of α = 0.20, the dealer wants to forecast March demand using the exponential smoothing model. Approach: The formula introduced above can be applied Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20 Common Measures of Error

Determining the Mean Absolute Deviation (MAD) During the past 8 quarters, the Port of Baltimore has unloaded large quantities of grain from ships. The port’s operations manager wants to test the use of exponential smoothing to see how well the technique works in predicting tonnage unloaded. He guesses that the forecast of grain unloaded in the first quarter was 175 tons. Two values of α are to be examined: α = 0.10 and α = 0.50 Approach: Compare the actual data with the data we forecast (using each of the two α values) and then find the absolute deviation and MADs



Solution: The table below shows the detailed calculations for α = 0.10 only

Quarter Actual

Tonnage Unloaded

Forecast with α = 0.10

Forecast with α = 0.50

1 180 175 175 2 168 175.50 = 175.00 + 0.10(180 – 175) 177.50 3 159 174.75 = 175.50 + 0.10(168 – 175.50) 172.75 4 175 173.18 = 174.75 + 0.10(159 – 174.75) 165.88 5 190 173.36 = 173.18 + 0.10(175 – 173.18) 170.44 6 205 175.02 = 173.36 + 0.10(190 – 173.36) 180.22 7 180 178.02 = 175.02 + 0.10(205 – 175.02) 192.61 8 182 178.22 = 178.02 + 0.10(180 – 178.02) 186.30 9 ? 178.59 = 178.22 + 0.10(182 – 178.22) 184.15

Based on the calculations, it is advisable to perform forecasts using α = 0.10, because the MAD of the forecast with α = 0.10 is the smallest.



Practice Problems: Chapter 4, Forecasting

Problem 1:

Auto sales at Carmen’s Chevrolet are shown below. Develop a 3-week moving average.

Week Auto Sales

1 8

2 10

3 9

4 11

5 10

6 13

7 -

Problem 2:

Carmen’s decides to forecast auto sales by weighting the three weeks as follows:

Weights Applied

Period

3 Last week

2 Two weeks ago

1 Three weeks ago

6 Total

Problem 3:

A firm uses simple exponential smoothing with α = 0 1. to forecast demand. The forecast for the week of January 1 was 500 units whereas the actual demand turned out to be 450 units. Calculate the demand forecast for the week of January 8.

Problem 4:

Exponential smoothing is used to forecast automobile battery sales. Two value of α are examined, α = 0 8. and α = 0 5. . Evaluate the accuracy of each smoothing constant. Which is preferable? (Assume the forecast for January was 22 batteries.) Actual sales are given below:

Month January February March April May June

Actual Battery Sales 20 21 15 14 13 16

Forecast 22



ANSWERS:

Problem 1:

Moving average =demand in previous n periods

n∑

Week Auto Sales

Three-Week Moving Average

1 8

2 10

3 9

4 11 (8 + 9 + 10) / 3 = 9

5 10 (10 + 9 + 11) / 3 = 10

6 13 (9 + 11 + 10) / 3 = 10

7 - (11 + 10 + 13) / 3 = 11 1/3

Problem 2:

Weighted moving average =(weight for period n)(demand in period n)

weights∑

∑

Week Auto Sales

Three-Week Moving Average

1 8

2 10

3 9

4 11 [(3*9) + (2*10) + (1*8)] / 6 = 9 1/6

5 10 [(3*11) + (2*9) + (1*10)] / 6 = 10 1/6

6 13 [(3*10) + (2*11) + (1*9)] / 6 = 10 1/6

7 - [(3*13) + (2*10) + (1*11)] / 6 = 11 2/3

Problem 3:

F F A F unitst t 1 t t 1= + − = + − =− − −α( ) . ( )1 500 0 1 450 500 495



Problem 4:

Month Actual Battery Sales

Rounded Forecast with a =0.8

Absolute Deviation with a =0.8

Rounded Forecast with a =0.5

Absolute Deviation with a =0.5

January 20 22 2 22 2

February 21 20 1 21 0

March 15 21 6 21 6

April 14 16 2 18 4

May 13 14 1 16 3

June 16 13 3 14.5 1.5

S = 15 S = 16

2.56 2.95

SE 3.5 3.9

On the basis of this analysis, a smoothing constant of a = 0.8 is preferred to that of a = 0.5 because it has a smaller MAD.

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Operations Management 1- Chapter 4 Handouts 1