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Business Administration QUESTION #9600
Question 1
A supply chain manager implements an AI-driven demand forecasting system that integrates point-of-sale data, social media sentiment, and weather patterns. Compared to traditional time-series forecasting, which specific forecasting characteristic is most improved, and what new risk is introduced?
  • Accuracy of long-range forecasts is most improved; new risk is data privacy regulation compliance
  • Responsiveness to causal drivers of demand (beyond historical patterns) is most improved, enabling detection of demand shifts before they appear in sales data; new risk is model opacity ('black box' problem) where managers cannot explain or audit forecast logic, reducing trust and creating accountability gaps✔️
  • Speed of forecast generation is most improved; new risk is over-reliance on technology reducing human judgment
  • Forecast bias is most improved through algorithmic objectivity; new risk is that AI systems amplify existing data biases rather than correcting them
Correct Answer Explanation
AI/ML forecasting improves causal intelligence — it captures leading indicators (social sentiment, weather) that precede demand changes, addressing the fundamental weakness of pure time-series models that only extrapolate past patterns. The critical new risk is model opacity: complex neural networks cannot explain their predictions in ways humans can verify or challenge, creating accountability failures when forecasts are wrong and reducing managerial confidence in overriding automated recommendations — the 'automation bias' and 'black box' risks.