Midterm #1

Question 1

Demand Planning

Answer 1

key building block from which all supply chain planning activities are derived from

Question 2

Demand Planning Approach

Answer 2

1)review accuracy, understand past data
2)develop statstical forecast
3) add market intelligence
4) agree in consensus mtg

Question 3

Fundamentals of forecasting

Answer 3

1) forecast wrong
2) simple methods»> complex
3) a correct forecast does not = correct forecast method
4) don’t use data regularly trust it less when forecasting
5) all trends will end
6) forecast is bias
7) tech does not equal better forecasting

Question 4

level

Answer 4

average rate of sales

Question 5

trend

Answer 5

the growth or decline of product

Question 6

seasonality

Answer 6

repeating patterns in demand

Question 7

events

Answer 7

one-time activties that influence demand

Question 8

noise

Answer 8

randomness in the infromation

Question 9

cumulative error

Answer 9

sum of forecast error between each actual demand and forecasted value

Question 10

average error

Answer 10

cummulative error/n

Question 11

Mean Absolute Deviation (MAD)

Answer 11

average of absolute deviation between each actual demand and forecasted value

Question 12

Mean Absolute Deviation (MAD)

Answer 12

average of absolute deviation between each actual demand and forecasted value

Question 13

MPE

Answer 13

(actual - forecast /actual*100)/n

Question 14

MAPE (mean aboslute % error)

Answer 14

average of all the absolute % errors

Question 15

forecasting bias

Answer 15

tendency to over or under forecast consistently over time

Question 16

time series forecasting

Answer 16

set of data obtained by making observations at equally spaced points in time

Question 17

Naïve Model

Answer 17

used when no trends or seasonality are present assumes most recent demand is the best estimate of the next value of demand.

-last period model (Ft=Dt-1) *fluctuation not random
- arithmetic mean (average of Demand) *fluctuations that are random

Question 18

smoothing

Answer 18

for a stationary time series with no trend we need to “smooth out” random fluctuations

Question 19

moving average

Answer 19

is the average of the (n weeks) in demand before forecasted value so if it says 3-week moving average you are forecasting 4th value but using 3 previous values

Question 20

Moving average- choice of N

Answer 20

n is chosen large enough to include enough observations to smooth out random fluctuations but small enough not to give weight to irrelevant past info, smaller n will track shifts in the level of a time series faster

large n- past info, smoothing
small n- recent data

Question 21

weighted moving average

Answer 21

If the most recent periods are weighted too lightly, the forecasted values will assume that actual changes in demand are random.
0.4, 0.3, 0.2, 0.1

Question 22

ABC system

Answer 22

classifies inventory based on the revenue contribution of an item highest -least value

Question 23

80/20 rule

Answer 23

20% of skus make 80% of sales (A iteams)
80% of skis make 20% of sales (B and c Iteams)
b iteams monthly, c quarterly reviews

Question 24

exponential smoothing

Answer 24

weights all past values of demand where the weights decrease geometrically with increasing age of data
Ft+1 = αDt + (1- α)Ft

Question 25

Choice of α

Answer 25

Select α between 0 and 1
large Choice of α give more weight to recent values; greater sensitivty to variation
0.1 < α < 0.2 or 0.3
*on excel data-data anaysis- damping facor = 1-α

Question 26

EXPO SMOOTING TREND FACTOR

Answer 26

T(t+1) = β(F (t+1) – Ft) + (1 – β) Tt
β = smoothing constant for trend; 0.0 ≤ β ≤ 1.0

Question 27

Adjusted Expo

Answer 27

AF(t+1)= F(t+1)+T(t+1)

Question 28

seasonal factor

Answer 28

period demand/ year average

Question 29

deseasonalize data

Answer 29

sales/ seasonal index

Question 30

seasonal index

Answer 30

average of seasonal factors

Question 31

trend values

Answer 31

T=a+bx

Question 32

single regression

Answer 32

based on a sample of n data points for two variable X and Y we from a model to predict the dependent variable y as a function of one independent variable x

Question 33

correlation r

Answer 33

the measure of the strength of the relationship between y and x
-1 ≤ r ≤ 1

Question 34

coefficeint of determination r^2

Answer 34

measure the proportion of the total variation from the mean explained by the regression model 0 ≤ r^2 ≤ 1

Question 35

assumption based forecasting

Answer 35

when forecasting new product with no past data