Forecasting

Question 1

Forecast and examples of it being used for

Answer 1

Prediction of future events used for planning purposes etc product, labour, demand

Question 2

Planning

Answer 2

Making management decisions on how to deploy resources to respond to demand forecast

Question 3

Forecasts are based on (4)

Answer 3

multiple types of data, mathematical models, expert opinion, historical data

Question 4

Forecast is used for (2)

Answer 4

process etc bottlenecks and supply chain management

Question 5

Time/Demand Series

Answer 5

Repeated observations of demand for a product/service in their order of appearance

Question 6

Horizontal

Answer 6

Fluctuation of data around a constant mean

Question 7

Trend

Answer 7

Systematic increase or decrease in the mean over time

Question 8

Seasonal

Answer 8

Repeatable pattern of increase or decrease in demand, depending on time, day, week, month, season

Question 9

Cyclical and what is it caused by

Answer 9

Less predictable gradual increase/decrease in demand over longer periods of time (years, decades)

• life cycle of product or economic recession/inflation

Question 10

Random

Answer 10

Un-forecastable variation in demand (lots of variability)

Question 11

Outliers

Answer 11

fluctuations in data that do not reflect or resemble overall pattern

Question 12

Manage Demand (5)

Answer 12

Complementary Service, Promotional Pricing, Prescheduled Appointments, Revenue Managing, Backlogs/Backorders/Stockouts

Question 13

Complementary Service

Answer 13

same resources, different demand cycles (Assiniboine Park)

Question 14

Promotional Pricing

Answer 14

increase demand, shift to new period (clear excess stock and attract buyers)

Question 15

Prescheduled Appointments

Answer 15

level demand based on capacity (balance how much you can accept)

Question 16

Revenue Management

Answer 16

adjust prices in real life time based on demand

Question 17

Backlogs

Answer 17

accumulate orders for future delivery, decrease service level and risk of losing customers

Question 18

backorders

Answer 18

orders that cannot be filled when demanded but filled later

Question 19

Stockouts

Answer 19

customer goes else where as order cannot be fulfilled

Question 20

Key forecasting decisions (3)

Answer 20

What are inputs
What are you predicting
What technique should you use

Question 21

Forecast Inputs (6)

Answer 21

History of Past Demand, Notes Explaining Past Demand, Past Forecasts, consumer research, planned promotions, Inputs from Partners

Question 22

CPFR

Answer 22

Collaborative Planning, Forecasting & Replenishment

Question 23

CPFR what does it require and do?

Answer 23

collaboration with suppliers, independent forecasts generated & compared, adjusted until consensus (everyone has same prediction)

Question 24

What are you trying to predict (2)
what is aggregation?
What is best way to predict revenue?

Answer 24

1.individual/family products 2.Units of measurements
2. cluster of similar products/services so company can make better forecasts
3. find units forecast then multiply by price

Question 25

What techniques used (3)

Answer 25

Judgment, Causal, Time-Series Analysis

Question 26

Judgement (opinions and subjectives -> quantitative) (4)

Answer 26

sales force estimates executive opinion market research Delphi -> consensus of group but group remains anonymous

Question 27

Qualitative Benefits

Answer 27

subjective, variety of information, does not require numerical data

Question 28

Qualitative Downside

Answer 28

results biased or conflicting

Question 29

Quantitative Benefits

Answer 29

objective, volume of information, do not rely on individuals

Question 30

Quantitative Downside

Answer 30

data not available, models too simplistic

Question 31

Time Series Method

Answer 31

predictions based on historical data, dependent variable. Past can predict future

Question 32

Naive Method appropriate for and what pattern sensitive to?

Answer 32

Forecast for next period equals demand for most recent observed short-term forecasts and horizontal trend sensitive to random variation

Question 33

Simple Moving Average and etc smooths out

Answer 33

Forecast for next period equals average demand for n most recent periods etc 2-period moving means average of 2 previous weeks random variation

Question 34

Weighted Moving Average weights given but most recent has

Answer 34

forecast for next period equals average demand for n most recent periods and each observation of demand has its own weight. most weights

Question 35

Exponential Smoothing 3 data points required? and more weight to?

Answer 35

weighted moving average assigning differing levels of weight to recent demand compared to older historical data last period forecast, demand and smoothing parameter Exponential smoothing factor to previous demand, (1-a) applied to forecast

Question 36

Forecast Error

Answer 36

Observed demand - forecast

Question 37

Cumulative Sum if Forecast Errors and evaluates

Answer 37

Assesses total errors in forecasts over time, presence and detection of bias

Question 38

If forecast is consistently lower than demand then If forecast is consistently higher than demand then

Answer 38

CFE is highly positive CFE is highly negative

Question 39

Mean Squared Error

Answer 39

on average how close forecast is to demand, magnify large errors

Question 40

Mean Absolute Deviation

Answer 40

magnitude of error, does not reveal directional bias

Question 41

Mean Absolute Percentage Error

Answer 41

study magnitude of error relative to demand