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Quizzes > Quizzes for Business > Business

Demand Forecasting Knowledge Test Quiz

Assess Your Demand Prediction Expertise Now

Difficulty: Moderate
Questions: 20
Learning OutcomesStudy Material
Colorful paper art illustrating a quiz on Demand Forecasting Knowledge Test

Ready to sharpen your forecasting skills? This engaging Demand Forecasting Knowledge Test is designed for supply chain professionals, analysts, and students seeking a robust demand forecasting quiz. By tackling real-world scenarios, participants will deepen their understanding and refine their predictive techniques - feel free to customise any question in our editor. For broader insight, explore related Demand Planning Knowledge Test or Financial Planning and Forecasting Quiz. Don't forget to browse all quizzes for more learning opportunities.

Which of the following best describes demand forecasting?
Recording daily sales figures without projection
Predicting future customer demand based on historical data
Adjusting prices in response to competitor actions
Designing marketing campaigns for new products
Demand forecasting involves using past sales and other data to estimate future demand. It enables businesses to plan inventory and resources effectively.
What is the primary purpose of plotting historical sales data on a time series chart?
To identify patterns like trends and seasonality
To compare supplier lead times
To visualize customer demographics
To calculate profit margins for each period
Time series charts reveal underlying patterns such as trends and seasonal effects in historical sales. Identifying these patterns is essential for selecting appropriate forecasting models.
Which term describes regular short-term fluctuations in sales that recur within a year?
Level shift
Cyclicality
Random noise
Seasonality
Seasonality refers to predictable, periodic fluctuations that occur at regular intervals within a year. These patterns are key to adjusting forecasts for seasonal demand variations.
Which simple forecasting method uses the average of the last few observations to project the next value?
Decomposition
Exponential smoothing
Moving average
Regression analysis
The moving average method smooths data by averaging a fixed number of the most recent observations. It is a straightforward way to filter out short-term fluctuations.
In forecasting software outputs, which metric measures the average absolute percentage error between forecast and actuals?
R-squared
MSE
MAD
MAPE
MAPE (Mean Absolute Percentage Error) expresses forecast error as a percentage of actual values. It is widely used because it is scale-independent and easy to interpret.
A steadily increasing sales trend over several years is an example of which component?
Random component
Seasonal component
Cyclical component
Trend component
A trend component represents a long-term increase or decrease in data. It reflects underlying growth or decline over time.
Which forecasting technique is best suited for data with both trend and seasonality?
Naive forecasting
Simple exponential smoothing
Holt-Winters exponential smoothing
Linear regression without seasonal terms
Holt-Winters exponential smoothing accounts for level, trend, and seasonal components. It adapts to changes and makes accurate forecasts when seasonality is present.
An ARIMA(1,1,1) model includes which operations?
One seasonal lag without differencing
First order moving average only
One autoregressive term, first differencing, one moving average term
Second differencing and two AR terms
ARIMA(p,d,q) denotes p autoregressive terms, d differences, and q moving average terms. ARIMA(1,1,1) thus has one AR term, one differencing step, and one MA term.
Using a naive forecasting method, if sales in period 10 were 500 units, what is the forecast for period 11?
450 units
0 units
500 units
Average of periods 1 - 10
The naive method sets the forecast for the next period equal to the most recent actual observation. It is simple but often serves as a benchmark.
In forecasting software, a Ljung-Box test p-value greater than 0.05 indicates what about residuals?
Residuals are non-stationary
Seasonality has not been captured
Model is overfitting
Residuals are uncorrelated
A Ljung-Box test p-value above 0.05 means there is no significant autocorrelation in residuals. This suggests the model has captured the structure adequately.
If the 3-month moving average for January - March sales of 120, 130, and 140 units is used to forecast April, what is the forecast?
120 units
130 units
150 units
140 units
A 3-month moving average forecast equals the sum of the last three actual values divided by three: (120+130+140)/3=130. This smooths out short-term fluctuations.
Which pattern spans more than one year and is often economic in nature?
Seasonal pattern
Trend pattern
Cyclical pattern
Random pattern
Cyclical patterns reflect longer-term economic or business cycles that extend beyond a year. They differ from regular seasonal patterns.
Which error metric squares the forecast errors before averaging?
Mean Absolute Percentage Error (MAPE)
Mean Absolute Deviation (MAD)
Tracking Signal
Mean Squared Error (MSE)
MSE calculates the average of squared forecast errors. It penalizes larger errors more heavily than absolute metrics.
When comparing two time series models, which criterion penalizes model complexity to avoid overfitting?
Mean Absolute Error (MAE)
Seasonal Index
Akaike Information Criterion (AIC)
Cumulative Sum of Forecast Errors
AIC balances model fit and complexity by including a penalty term for the number of parameters. Lower AIC indicates a better model choice.
In ARIMA(p,d,q), what does the 'd' parameter represent?
The number of non-seasonal differences applied
The dimension of the data set
The degree of the polynomial trend
The number of seasonal periods
The 'd' in ARIMA(p,d,q) specifies how many times the series is differenced to achieve stationarity. Differencing removes trends or seasonality.
If a seasonal index for July is 1.2 and the deseasonalized forecast is 100 units, what is the actual forecast for July?
220 units
83 units
100 units
120 units
The actual forecast is obtained by multiplying the deseasonalized forecast by the seasonal index: 100×1.2=120 units. This reintroduces seasonal effects.
Which approach in scenario analysis uses probability distributions to model demand variability?
Break-even analysis
Monte Carlo simulation
PERT analysis
Linear programming
Monte Carlo simulation repeatedly samples from probability distributions to generate a range of possible outcomes. It quantifies variability and risk in demand forecasts.
What is a key advantage of the Yeo-Johnson transformation in forecasting data?
It guarantees a normal distribution
It requires no parameter tuning
It only works on seasonal data
It can handle zero and negative values
The Yeo-Johnson transformation extends the Box-Cox method to accommodate zero and negative values. It stabilizes variance and improves normality for modeling.
A 95% prediction interval for a forecast means what in demand forecasting?
Demand will increase by 95% over the forecast
There is a 95% chance actual demand will lie within the interval
Forecast will be within 5% of actual demand
95% of past errors fell within the same width
A 95% prediction interval implies that, given the model and data, there is a 95% probability the actual future demand falls inside that range. It quantifies forecast uncertainty.
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Learning Outcomes

  1. Analyse historical sales patterns for demand trends
  2. Evaluate forecasting techniques to select optimal models
  3. Apply statistical methods to calculate demand projections
  4. Identify seasonality and cyclic patterns accurately
  5. Demonstrate interpretation of forecasting software outputs
  6. Master scenario analysis to manage demand variability

Cheat Sheet

  1. Understand the Importance of Demand Forecasting - Catch a sneak peek into the future by learning how accurate demand forecasting can supercharge your supply chain! It helps reduce waste, lower costs, and keep customers smiling. Guide to Demand Forecasting in Supply Chain
  2. Differentiate Between Qualitative and Quantitative Forecasting Methods - Mixing art and science, qualitative methods lean on expert insights while quantitative methods crunch historical numbers with fancy algorithms. Knowing when to pull the human trump card or rely on data helps you craft rock-solid forecasts. Guide to Demand Forecasting in Supply Chain
  3. Master Time Series Analysis - Time series analysis is like spotting patterns in your favorite song - only this time, it's demand data! You'll identify trends, seasonality, and weird cycles that help predict what customers will crave next. Demand Forecasting Methods
  4. Apply Regression Analysis for Forecasting - Think of regression as the detective tool that uncovers hidden relationships between factors like price, promotion, and demand. By fitting a clever line through your data points, you'll predict future demand like a pro sleuth. Demand Forecasting Methods
  5. Explore Exponential Smoothing Techniques - This technique gives extra love to recent demand spikes and dips, so your forecasts stay fresh and responsive. It's like having a forecasting reflex that instantly adjusts to the latest market buzz. Demand Forecasting Methods
  6. Recognize the Role of Seasonality and Cyclic Behavior - Seasonality and cycles show up like your favorite holiday traditions or weekend routines in your data. By spotting these patterns, you'll avoid nasty surprises and keep your supply chain humming smoothly. Forecasting (Wikipedia)
  7. Evaluate Forecasting Algorithms - Whether it's Auto-ARIMA, ETS, or Prophet, each algorithm has its own superpower. Testing and comparing them on your data ensures you pick the model that nails your forecast with pinpoint accuracy. Forecast Algorithm Types
  8. Understand the Delphi Method - Gather a panel of wise experts, shield their identities, and collect rounds of anonymous predictions until everyone converges on an answer. It's like a secret society for demand forecasting that turns individual hunches into a collective crystal ball. Demand Forecasting Methods
  9. Interpret Forecasting Software Outputs - Learning to read charts, confidence intervals, and error metrics transforms raw software output into actionable insights. With this skill, you'll confidently explain why the numbers say what they do and recommend smart moves. Forecast Algorithm Types
  10. Conduct Scenario Analysis - Put on your strategist hat and play "what if" by creating different demand scenarios - best case, worst case, and everything in between. This way, you'll be ready for surprises and keep your supply chain agile no matter what comes. Guide to Demand Forecasting in Supply Chain
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