ISOM 351 Unit 2 CORRECT 100%
Descriptive Analytics - ANSWERThe use of past data to understand business
performance and make informed decisions
Predictive Analytics - ANSWERExtracts information from data and uses it to predict
future trends and identify behavioral patterns
Prescriptive Analytics - ANSWERTechniques that create models indicating the best
decision to make or course of action to take
Components of Time Series Data - ANSWERPredictable Components and Random
Noise
Predictable Components - ANSWERTrends (up or down)
Cycles, move in wave form
- Seasonality (short term)
- Cyclicality (2+ years)
Forecast Error Measures - ANSWERUnexplained variability
Forecast accuracy
Forecast quality
Overforecasting - ANSWERCost of forecast error
Excess inventory/resources
Opportunity cost
Underforecasting - ANSWERCost of forecast error
Lost Sales
Lost market share
Reputation and customer impact
Stable data effect on n - ANSWERNot much effect
Data trending up effect on n - ANSWERHigh n = stable
Leads to actual demand higher than forecast
Bias to underforcast
Forecasting - ANSWERUsed to anticipate market and competitive changes
- Business model disruption and innovation
- Consumer tastes and preferences
Increase the chance that resources are allocated correctly
- Right sized aircraft in the right city and right time to fly
- Enough consultants for client demand
Descriptive Analytics - ANSWERThe use of past data to understand business
performance and make informed decisions
Predictive Analytics - ANSWERExtracts information from data and uses it to predict
future trends and identify behavioral patterns
Prescriptive Analytics - ANSWERTechniques that create models indicating the best
decision to make or course of action to take
Components of Time Series Data - ANSWERPredictable Components and Random
Noise
Predictable Components - ANSWERTrends (up or down)
Cycles, move in wave form
- Seasonality (short term)
- Cyclicality (2+ years)
Forecast Error Measures - ANSWERUnexplained variability
Forecast accuracy
Forecast quality
Overforecasting - ANSWERCost of forecast error
Excess inventory/resources
Opportunity cost
Underforecasting - ANSWERCost of forecast error
Lost Sales
Lost market share
Reputation and customer impact
Stable data effect on n - ANSWERNot much effect
Data trending up effect on n - ANSWERHigh n = stable
Leads to actual demand higher than forecast
Bias to underforcast
Forecasting - ANSWERUsed to anticipate market and competitive changes
- Business model disruption and innovation
- Consumer tastes and preferences
Increase the chance that resources are allocated correctly
- Right sized aircraft in the right city and right time to fly
- Enough consultants for client demand
