A decision tree is used in supply chain analysis to:

Question

BlackTom AI · Accepted Answer

When evaluating the purpose of a decision tree in supply chain analysis, we examine what decision trees are designed to do and how they handle information.
Option 1: 'Eliminate uncertainty' — This is a common misconception. Decision trees don’t eliminate uncertainty; they structure and analyze outcomes under different scenarios to understand potential risks and payoffs.
Option 2: 'Evaluate alternatives under uncertainty' — This aligns with the core use of decision trees in supply chains: comparing different courses of action while accounting for uncertain future events (such as demand, lead times, or supplier reliability) and their associated probabilities and payoffs. This option correctly captures the decision-support role of the method.
Option 3: 'Forecast demand' — While decision trees may incorporate demand scenarios, their primary function is not forecasting a single demand value but evaluating consequences across scenarios; dedicated forecasting models are typically used for predicting demand.
Option 4: 'Schedule production' — Scheduling is more closely related to operations planning and would use optimization or scheduling algorithms; a decision tree is not primarily a tool for sequencing production runs, though it can inform decisions that affect scheduling.
In summary, the most accurate description is that a decision tree is used to evaluate alternatives under uncertainty, as it systematically compares outcomes across different decision branches under varying conditions. The other options describe related concepts but do not capture the essential purpose of the tool in the context of uncertainty and decision analysis.

A decision tree is used in supply chain analysis to:

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