The correct answer is option D, Delphi.

Explanation:

Delphi is a forecasting technique that involves obtaining predictions from a panel of experts. It is commonly used for long-range forecasting or forecasting in situations where historical data is limited or unavailable.

Here are the reasons why Delphi is not suited for making forecasts for planning production schedules in the short-range:

1. Time-consuming process: Delphi involves multiple rounds of questionnaires and feedback from experts. This iterative process can be time-consuming, making it impractical for short-range forecasting where quick decisions and actions are required.

2. Lack of historical data: Delphi relies on expert opinions rather than historical data. In short-range forecasting, historical data is often readily available and can provide valuable insights into past trends and patterns. Without historical data, Delphi may not be as accurate or reliable in short-range production planning.

3. Limited focus on quantitative factors: Delphi primarily focuses on obtaining qualitative judgments and opinions from experts. While qualitative factors can be useful for long-range strategic planning, short-range production scheduling requires more emphasis on quantitative factors such as demand patterns, inventory levels, and production capacity. Delphi may not provide the level of quantitative analysis required for short-range forecasting.

4. Potential biases and subjectivity: Delphi involves subjective judgments and opinions from experts, which can introduce biases and uncertainties into the forecasting process. In short-range production planning, where accuracy and precision are crucial, it is important to minimize biases and rely on objective data-driven forecasting techniques.

In contrast, the other forecasting techniques mentioned - moving average, exponential moving average, and regression analysis - are well-suited for short-range forecasting and production scheduling. These techniques rely on historical data, quantitative analysis, and statistical models to make accurate forecasts based on past trends and patterns. They are more objective, efficient, and suitable for short-term decision making in production planning.