Operations 3 473.31 Fall 2015 Bruce Duggan Providence University College.

Operations3

473.31Fall 2015

Bruce DugganProvidence University College

Summary

• Forecasting is fundamental to any planning effort.

• In the short run, a forecast is needed to predict the requirements for materials, products, services, or other resources to respond to changes in demand.

• In the long run, forecasting is required as a basis for strategic changes, such as developing new markets, developing new products, or services, and expanding or creating new facilities.

Learning Objectives

• Understand role of forecasting as a basis for supply chain planning

• Classify:• independent demand• dependent demand

• Understand basic components of independent demand:

• average• trend• seasonal variation• random variation

• Understand common qualitative forecasting techniques

• e.g.: Delphi method

• Know how to make time-series forecasts using

• moving averages • exponential smoothing.

• Know how to measure forecast error

Demand Management

Dependent demand • is the demand for a product or service caused by the demand for other

products or servicesIndependent demand

• is the demand that cannot be derived directly from that of other products

A

B(4)

E(1)D(2)

C(2)

F(2)D(3)

Demand Management

Independent Demand:• finished goods

Dependent Demand:• raw materials • component parts• sub-assemblies• etc.

Types of Forecasts

• qualitative techniques • subjective or judgmental • based on estimates & opinions

• time-series analysis• key idea:

• past demand data can be used to predict future demand

• causal forecasting• key assumption:

• demand is related to some underlying factor or factors in the environment

• simulation models• allow the forecaster to run

through a range of assumptions about the condition of the forecast

Components of Demand

• average demand for a period of time• trend• seasonal variation• cyclical variation• random variation vs. autocorrelation

Components of Demand

Qualitative Techniques in Forecasting

• market research• sales team estimates

o (bottom up)• executive estimate

o (top down)• panel consensus• historical analogy• Delphi method

Delphi Method

1. Choose the experts to participate representing a variety of knowledgeable people in different areas.

2. Through a questionnaire (or e-mail), obtain 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 if necessary and distribute the final results to all participants.

Time Series Analysis

options1. simple moving average2. weighted moving average3. exponential smoothing

which to choose depends on:• time horizon to forecast• data availability• accuracy required• size of forecasting budget• availability of qualified personnel

Time Series Analysis

1. Simple Moving Average

The simple moving average model assumes an average is a good estimator of future behavior.

formula:

F = A + A + A +...+A

ntt-1 t-2 t-3 t-n

Ft = Forecast for the coming periodN = Number of periods to be averagedA t-1 = Actual occurrence in the past period, for up to “n” periods

1. Simple Moving Average Example

1. Simple Moving Average Example

2. Weighted Moving Average

Weighted moving average permits an unequal weighting on prior time periods.

formula: F = w A + w A + w A +...+w At 1 t-1 2 t-2 3 t-3 n t-n

w = 1ii=1

n

Ft = Forecast for the coming periodN = Number of periods to be averagedA t-1 = Actual occurrence in the past period, for up to “n” periodswt = weight given to time period “t” (must total 1)

2. Weighted Moving Average Example

month sales1 1002 903 1054 955 ?

period weightst-4 0.10t-3 0.20t-2 0.30t-1 0.40

F = .40(95) + .30(105) +.20(90) + .10(100) = 97.5

3. Exponential Smoothing

Premise: • The most recent observations might have the highest predictive value.

Conclusion:• Therefore, we should give more weight to the more recent time periods when

forecasting.

3. Exponential Smoothing Formula

Ft = Ft-1 + a(At-1 - Ft-1)

Ft = Forecast for the coming periodFt-1 = Forecast value in 1 past time periodA t-1 = Actual occurrence in the past periodα = Alpha smoothing constant

3. Exponential Smoothing Example

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 F1=D1

LO5

month sales1 8202 7753 6804 6555 7506 8027 7988 6899 775

10 ?

Week Demand 0.1 0.61 820 820.00 820.002 775 820.00 820.003 680 815.50 793.004 655 801.95 725.205 750 787.26 683.086 802 783.53 723.237 798 785.38 770.498 689 786.64 787.009 775 776.88 728.20

10 776.69 756.28

3. Exponential Smoothing Example

• Answer:• The respective alphas

colums denote the forecast values.

Note that you can only forecast one time period into the future.

Measurement of Error

Mean Absolute Deviation (MAD) refers to the average forecast error using absolute values of the error of each past forecast.

• The ideal MAD is zero which would mean there is no forecasting error.• The larger the MAD, the less the accurate the resulting model.

Measurement of Error

Running Sum of Forecast Errors (RSFE) • considers the nature of the error

Tracking Signal• a measure that indicates whether the forecast average is keeping pace with

any genuine upward or downward changes in demand

Measurement of Error

Tracking signal formula:

Learning Objectives Review

1. How does forecasting aid effective supply chain planning?2. Why is forecasting not necessary for dependent demand items?3. What are the four basic components of independent demand?4. What are some qualitative forecasting techniques that can be used when no historical demand data is available?5. What is the inherent assumption for moving average and exponential smoothing forecasts?6. What is the purpose of measuring forecast error?

End of Chapter 3

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