Lecture Principle of inventory and material management - Lecture 17

Lecture 17 - Forecasting. When you complete this chapter you should be able to : Understand the three time horizons and which models apply for each use; explain when to use each of the four qualitative models; apply the naive, moving average, exponential smoothing, and trend methods.

Forecasting

Books

• Introduction to Materials Management, Sixth Edition, J. R. Tony Arnold, P.E., CFPIM, CIRM, Fleming  College, Emeritus, Stephen N. Chapman, Ph.D., CFPIM, North Carolina State University, Lloyd M.  Clive, P.E., CFPIM, Fleming College

• Operations Management for Competitive Advantage, 11th Edition, by Chase, Jacobs, and Aquilano, 2005,  N.Y.: McGraw­Hill/Irwin.

• Operations Management, 11/E, Jay Heizer, Texas Lutheran University, Barry Render, Graduate School of 

When you complete this chapter you should be able to :

þ Understand the three time horizons and

which models apply for each use

þ Explain when to use each of the four

qualitative models

þ Apply the naive, moving average,

exponential smoothing, and trend methods

þ 20% of customers come from outside the USA

þ Economic model includes gross domestic 

product, cross­exchange rates, arrivals into the USA

þ A staff of 35 analysts and 70 field people survey 

1 million park guests, employees, and travel 

professionals each year

þ Inputs to the forecasting model include airline 

specials, Federal Reserve policies, Wall Street trends, vacation/holiday schedules for 3,000 

Forecasting Time Horizons

þ Medium/long range forecasts deal with more 

comprehensive issues and support management decisions regarding planning and  products, plants and processes

þ Short­term forecasting usually employs different methodologies than longer­term forecasting

þ Short­term forecasts tend to be more accurate than longer­term forecasts

Strengthen niche

Poor time to change image, price, or quality

Competitive costs become critical Defend market position

Cost control critical

Introduction Growth Maturity Decline

CD-ROMs

3 1/2”

Floppy disks

iPods

Competitive product  improvements and  options

Increase capacity Shift toward product  focus

Enhance distribution

Standardization Less rapid product  changes – more  minor changes Optimum capacity Increasing stability 

of process Long production  runs

Product  improvement and  cost cutting

Little product  differentiation Cost 

minimization Overcapacity in  the industry Prune line to  eliminate items  not returning  good margin Reduce capacity

Figure 2.5

þ Economic forecasts

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

þ Human Resources – Hiring, training, laying

off workers

þ Capacity – Capacity shortages can result in

undependable delivery, loss of

customers, loss of market share

þ Supply Chain Management – Good

supplier relations and price advantages

þ Forecasts are seldom perfect

þ Most techniques assume an underlying

stability in the system

þ Product family and aggregated

forecasts are more accurate than

individual product forecasts

and little data exist

þ New products

þ New technology

e.g., forecasting sales on Internet

Qualitative Methods

historical data exist

televisions

Quantitative Methods

þ Involves small group of high-level experts

Decision Makers

(Evaluate responses and make decisions)

Respondents (People who can make valuable judgments)

plans

they actually do are often different

Associative Model

Seasonal

Cyclical

Random Time Series Components

Seasonal peaks

Trend component

Actual demand

Random variation

Figure 4.1

period is the same as

demand in most recent period

February sales will be 68

efficient

Advantages of Moving Averages

  weights

(13 + 16 + 19)/3 = 16 (16 + 19 + 23)/3 = 19 1/3

Moving Average Example

10 12 13

( 10 + 12 + 13 )/3 = 11 2/3

∑ (weight for period n)

x (demand in period n)

∑ weights

[(3 x 16) + (2 x 13) + (12)]/6 = 141/3

[(3 x 19) + (2 x 16) + (13)]/6 = 17 [(3 x 23) + (2 x 19) + (16)]/6 =

Weighted Moving Average

10 12

13

[(3 x 13 ) + (2 x 12 ) + ( 10 )]/6 = 121/6

Weighted moving average

New forecast = Last period’s forecast

+ α (Last period’s actual demand – Last period’s forecast)

Exponential smoothing averages the current smoothed 

estimate with the most recent data point, thus giving 

least weight to the oldest data.  Choosing a “good” value  for   is critical.

New forecast = ( )(latest demand) + 

(1­  )(previous forecast)

Predicted demand = 142 Ford MustangsActual demand = 153

Smoothing constant α = .20

Predicted demand = 142 Ford MustangsActual demand = 153

Smoothing constant α = .20

New forecast = 142 + 2(153 – 142)

Predicted demand = 142 Ford MustangsActual demand = 153

Smoothing constant α = .20

New forecast = 142 + 2(153 – 142)

= 142 + 2.2

= 144.2 ≈ 144 cars

Weight Assigned to

Most 2nd Most 3rd Most 4th Most 5th Most Recent Recent Recent Recent Recent Smoothing Period Period Period Period Period Constant ( α ) α (1 - α ) α (1 - α )2 α (1 - α )3 α (1 - α )4

α = 1 1 09 081 073 066

α = 5 5 25 125 063 031

þ Chose high values of

when underlying average

is likely to change

þ Choose low values of

when underlying average

is stable

The objective is to obtain the most 

accurate forecast no matter the technique

We generally do this by selecting the model that gives us the lowest forecast error

Forecast error = Actual demand - Forecast value

= At - Ft

End of Lecture 17