Expected output
The deliverable should include Baseline forecast, Scenario forecasts, Error and confidence interval, Demand drivers, and Review plan.
IntermediateSupply Chain & Fulfillment AI
Demand Forecasting | Comparative Decision Prompt
Demand Forecasting | Comparative Decision Prompt is a copyable AI prompt for ecommerce sellers. Use it to compare candidate options against one consistent decision framework. Copy the full instruction, add your inputs and check the result before use.
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How the Demand Forecasting | Comparative Decision Prompt works
Compare candidate options against one consistent decision framework. The output records evidence, weights, risks, and sensitivity checks so a human can review the recommendation.
Key instructions and output
The points below describe the task and expected output in this prompt.
Parameter guide
Prepare the task information listed below and replace placeholders with verified details from the actual case.
[Option A]
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[Option B]
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[Option C]
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[At least 24 months of sales history]
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[Stockout flags]
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[Promotions and price]
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[Holidays]
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[Lead time and units]
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Copy the complete prompt
The complete source is shown below. Copy it from the top right to use it.
You are a retail demand-planning analyst. Compare [Option A], [Option B], and [Option C] for the objective: forecast SKU demand and explain seasonality, promotions, price, channel, and external drivers. Inputs: [At least 24 months of sales history], [Stockout flags], [Promotions and price], [Holidays], [Lead time and units]. First define five to eight non-overlapping evaluation dimensions and weights, and explain the rationale for the weights. Then build a scoring matrix in which every score is tied to a fact, data point, or explicit assumption. Provide the best choice, conditions under which it is best, irreversible risks, the lowest-cost validation method, and Baseline forecast, Scenario forecasts, Error and confidence interval, Demand drivers, Review plan. Do not invent missing information to force a conclusion. Special requirement: Use a Situation-Task-Constraints-Output structure. Specify units such as pieces, cartons, or pallets, and do not treat stockout-period sales as true demand.Test this prompt on a real task
Copy a starter instruction, add the required inputs, then run one example and review the output.
01Demand Forecasting | Comparative Decision Prompt
The deliverable should include Baseline forecast, Scenario forecasts, Error and confidence interval, Demand drivers, and Review plan. Compare options and make a reviewable demand forecasting decision
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Demand Forecasting | Comparative Decision Prompt
The deliverable should include Baseline forecast, Scenario forecasts, Error and confidence interval, Demand drivers, and Review plan. Compare options and make a reviewable demand forecasting decision
You are a retail demand-planning analyst. Compare [Option A], [Option B], and [Option C] for the objective: forecast SKU demand and explain seasonality, promotions, price, channel, and external drivers. Inputs: [At least 24 months of sales history], [Stockout flags], [Promotions and price], [Holidays], [Lead time and units]. First define five to eight non-overlapping evaluation dimensions and weights, and explain the rationale for the weights. Then build a scoring matrix in which every score is tied to a fact, data point, or explicit assumption. Provide the best choice, conditions under which it is best, irreversible risks, the lowest-cost validation method, and Baseline forecast, Scenario forecasts, Error and confidence interval, Demand drivers, Review plan. Do not invent missing information to force a conclusion. Special requirement: Use a Situation-Task-Constraints-Output structure. Specify units such as pieces, cartons, or pallets, and do not treat stockout-period sales as true demand.Inputs to replace in this prompt
[Option A]- Replace this placeholder with verified, task-specific information before running the prompt.
[Option B]- Replace this placeholder with verified, task-specific information before running the prompt.
[Option C]- Replace this placeholder with verified, task-specific information before running the prompt.
[At least 24 months of sales history]- Replace this placeholder with verified, task-specific information before running the prompt.
[Stockout flags]- Replace this placeholder with verified, task-specific information before running the prompt.
[Promotions and price]- Replace this placeholder with verified, task-specific information before running the prompt.
[Holidays]- Replace this placeholder with verified, task-specific information before running the prompt.
[Lead time and units]- Replace this placeholder with verified, task-specific information before running the prompt.
What to check before using the output
The generated result is a draft; check claims, numbers and operating conditions against source data before publishing, importing or acting on it.
Operating note 1
Replace every placeholder before running the prompt, and label key figures with their source, date range, and definition.
Operating note 2
Use a Situation-Task-Constraints-Output structure. Specify units such as pieces, cartons, or pallets, and do not treat stockout-period sales as true demand.
Operating note 3
Before uploading order, customer, contract, or supplier data, redact sensitive information and comply with platform terms, privacy policies, NDAs, and company data-governance requirements. Have the responsible operator review the result before it is published, sent, or executed.
Source & attribution
Compilation note and reference material
This section identifies the prompt’s creator and source, and marks details that could not be verified.
Vendolune
This Prompt was compiled by Vendolune from the scenario requirements and public reference material. The reference-page author is not credited as this Prompt's author.
Reference materialLucky Orange
This link is reference material and does not establish its page author as the author of this Prompt. The article provides ecommerce analysis prompts across strategy, inventory and merchandising, marketing, and customer experience, emphasizing trend detection, competitive gaps, segmentation, and data-backed opportunities.
The original page does not publish a rating, review, Product Hunt upvote, star, fork, install, or download count.
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