Demand Management & Forecasting Flashcards

What is the primary purpose of demand forecasting in inventory management?

Answer: To determine future inventory requirements

Demand forecasting is a critical process in inventory management that involves estimating future customer demand for products or services. Its primary purpose is to provide insights into how much inventory will be needed at specific times, allowing businesses to plan production, purchasing, and storage efficiently. Accurate forecasts help prevent stockouts and overstocking, optimizing inventory levels and reducing costs.

Which method is considered a qualitative forecasting technique?

Answer: Delphi method

Qualitative forecasting techniques rely on expert judgment, intuition, and subjective assessments rather than historical data and mathematical models. The Delphi method is a structured communication technique that uses a panel of experts to make predictions, often through multiple rounds of questionnaires and feedback, to arrive at a consensus forecast. This contrasts with quantitative methods like moving average or exponential smoothing, which use numerical data.

What does a high forecast error typically indicate?

Answer: Forecast needs adjustment or improvement

A high forecast error indicates a significant discrepancy between the predicted demand and the actual demand. This suggests that the current forecasting method or its parameters are not accurately capturing market trends or underlying demand patterns. Consequently, the forecast model needs to be reviewed, adjusted, or potentially replaced to improve its accuracy and reliability for future planning.

Which component is NOT typically part of demand?

Answer: Stability

Demand typically comprises several components, including trend (long-term increase or decrease), seasonality (regular patterns tied to time of year), cyclicality (longer-term, irregular patterns related to economic cycles), and randomness (unpredictable fluctuations). Stability, while a desirable characteristic for demand, is not a *component* of demand itself; rather, it describes the absence of significant variation in these components.

What is the benefit of collaborative forecasting?

Answer: Improves forecast accuracy and consensus

Collaborative forecasting integrates input from various departments like sales, marketing, and production. This diverse perspective helps to reduce individual biases and incorporate more comprehensive market intelligence. The result is a more accurate and widely accepted forecast, fostering consensus across the organization.

What does MAPE stand for in forecasting?

Answer: Mean Absolute Percentage Error

MAPE stands for Mean Absolute Percentage Error, a common metric used in forecasting. It measures the accuracy of a forecast by calculating the average of the absolute percentage errors. This provides a clear, percentage-based indication of forecast deviation from actual values, making it easy to interpret and compare.

What role does demand planning software play?

Answer: Supports data analysis and model-based forecasting

Demand planning software is a powerful tool designed to analyze vast amounts of historical data and apply various statistical models. It supports the generation of forecasts by identifying trends, seasonality, and other patterns. While it enhances analytical capabilities, human judgment remains crucial for interpreting results and incorporating qualitative factors.

What does a forecast bias of zero indicate?

Answer: No consistent over- or under-forecasting

Forecast bias measures the consistent tendency of a forecast to either overestimate or underestimate actual demand. A bias of zero indicates that, over a period, the forecast errors average out, meaning there's no systematic over- or under-forecasting. This suggests the forecast is unbiased, even if individual predictions may still have errors.

Which of the following is a time series forecasting model?

Answer: Moving average

A time series forecasting model uses historical data points, ordered sequentially in time, to predict future values. The moving average method calculates the average of a fixed number of past observations to smooth out fluctuations and identify trends. This makes it a classic example of a time series technique, distinct from causal models or qualitative methods.