E-commerce

Demand Forecasting Summary Generation

Business Problem

E-commerce companies generate large volumes of demand forecasts but lack an automated, human-readable way to interpret and communicate the insights. Business users like category managers, merchandisers, and executives spend hours manually reviewing reports to identify key demand drivers, anomalies, and risks. This delays decision-making and reduces agility.

Solution Overview

Use Generative AI (LLMs) to automatically summarize demand forecasting outputs into natural-language narratives. The system reads structured forecast data, identifies trends, and generates business-ready summaries highlighting growth areas, stock risks, and actionable recommendations.

Workflow

1Collect forecast data by SKU, category, region, and time horizon.
2Ingest contextual data (weather, marketing events, holidays, pricing changes).
3Use a forecasting model (Prophet, ARIMA, or LSTM) to predict demand.
4Feed results into a Large Language Model via prompt templates or APIs.
5Generate human-readable summaries and distribute via dashboards, chatbots, or email.

Technical Architecture

data ingestion

Databricks Auto Loader or Delta Live Tables to process sales and forecast data.

forecasting models

ARIMA, Prophet, LSTM, or AutoML pipelines managed with MLflow.

contextual data

Weather APIs, marketing calendars, pricing data, search trends.

llm integration

OpenAI GPT-4, Azure OpenAI, or local Llama model using LangChain or RAG pattern.

delivery channels

Power BI dashboards, Slack bots, or automated email digests.

Example Prompt & Output

Example Prompt

Summarize weekly demand forecast highlighting top growth categories, anomalies, and suggested actions.

Example Output

Demand for winter jackets in the Northeast is expected to rise by 18% next week due to early cold fronts. Recommend advancing inventory replenishment by 3 days.

Sneakers show +25% YoY growth, while formal shoes are down 10%. Suggest shifting ad spend toward athleisure segments.

Business Impact

speed

Reduces weekly reporting time from hours to seconds.

clarity

Transforms numeric data into actionable narratives for non-technical teams.

scalability

Can summarize thousands of SKUs, stores, or geographies automatically.

decision quality

Enhances business agility by highlighting root causes and recommended actions.

Challenges & Mitigations

Code Example

import openai
import pandas as pd

forecast_df = spark.read.table('sales_forecast_summary').toPandas()
prompt = f'''You are a retail analytics assistant. Summarize key trends in this weekly demand forecast:
{forecast_df.head(10).to_markdown()}
Highlight growth categories, risks, and actions.'''

response = openai.ChatCompletion.create(
    model='gpt-4-turbo',
    messages=[{'role': 'system', 'content': prompt}]
)

print(response['choices'][0]['message']['content'])

Future Extensions

Automated weekly email summaries to category heads and planners.
Voice-based summaries (e.g., Alexa or Google Assistant integration).
Scenario simulation – e.g., 'What if marketing spend decreases by 10%?'
Integration into Databricks Lakehouse dashboards for near real-time insights.

Interested in Implementing This Solution?

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