Sales and inventory forecasting project experience

Turn historical business data into your next plan.

Combine sales, price, promotions, holidays, and lead time into backtested models for sales targets, purchasing, and inventory decisions.

Contact：Study ExchangeEmail：StudyExchange@163.com

Field: retail, ecommerce, manufacturing, wholesale, and chains

Forecasting goes beyond model training

Forecasts must be accurate and actionable.

Evaluation criteria should reflect actual costs: stockouts, overstock, and forecast errors create different losses.

Data and definition assessment

Standardize SKU, store, order, return, and inventory definitions, and assess sales censoring from stockouts and anomalies in historical data.

Hierarchical demand forecasting

Model at business, region, store, category, or SKU level while reconciling forecasts across hierarchies.

Price and promotion effects

Incorporate exogenous factors such as prices, promotions, holidays, channels, and weather to distinguish organic trends from campaign lift.

Backtesting and model comparison

Backtest in true chronological order and compare seasonal baselines, statistical models, and machine-learning models while preventing future-data leakage.

Inventory strategy integration

Combine lead times, safety stock, and service levels to turn demand forecasts into replenishment or purchasing recommendations.

Monitoring and continuous updates

Monitor forecast error and data drift, identifying new products, stockouts, and unexpected campaigns that require human intervention.

Competition project overview

Diamond price prediction project

This competition is a structured-data regression task that predicts prices from diamond attributes. It demonstrates data cleaning, categorical and continuous feature processing, regression model selection, error evaluation, and tuning—skills transferable to sales and inventory forecasting.

TaskPrice regression

Predict continuous prices from product attributes.

Competition rank2 / 10

Shown from the public Kaggle leaderboard.

Transferable capabilityTabular modeling

Feature engineering, cross-validation, regression, and error optimization.

Project source codePending

The implemented technical approach and metrics will be updated when the source code is received.

Currently verified solution framework

From data definitions to a backtestable model

Until the source code is available, this page describes a sound framework for structured regression projects without claiming unverified model details.

01Data assessment

Assess price distributions, missing data, duplicates, outliers, and the business meaning of categorical and continuous fields.

02Feature engineering

Handle skewed targets, create combined features from dimensions, weight, and grades, and build a pipeline that prevents future-information leakage.

03Model comparison

Use linear or tree models as baselines and compare candidates through cross-validation instead of relying on a single split.

04Error analysis

Break down error by price range, product category, and high-value samples, then align evaluation and optimization with business losses.

Key project insightDiamond price prediction demonstrates structured-regression capability. Sales and inventory forecasting additionally involve temporal order, stockout censoring, promotional interventions, and hierarchical consistency, so a standard regression model cannot simply be reused unchanged.

Source code for the diamond-price prediction project has not yet been provided. Once available, the actual features, models, validation metrics, and error analysis will be documented without substituting speculation for the implemented approach.

Example use cases

Different prediction targets require different evaluation methods.

Start with one category, a group of stores, or a period of historical data to verify whether forecasting improves current decisions.

Retail / E-commerce

Weekly SKU sales and replenishment forecasting

Forecast demand by SKU and warehouse for the coming weeks, accounting for promotions, holidays, stockouts, and long-tail products.

Output: demand forecasts, error analysis, replenishment recommendations, and anomaly list

Manufacturing

Material demand and safety stock

Estimate material demand from orders, BOMs, production cycles, and supplier lead times while balancing shortage and overstock costs.

Output: material forecasts, safety-stock ranges, and risk alerts

Sales management

Regional sales targets and rolling forecasts

Produce rolling forecasts by region, channel, or sales team, identify target gaps, and determine whether changes come from volume, price, or mix.

Output: rolling forecasts, gap analysis, and priority regions

How the project was done · Project experience

Forecasting projects must connect inventory costs to business actions.

What matters is not a column of forecasts, but which planning cycle they serve, who decides from them, and the cost of over- and under-forecasting. The project follows the real decision flow.

Materials prepared in the project Data-definition and feasibility report Time-series backtesting and model comparison Forecast results, errors, and anomaly list Runnable code and integration guide

01
Business goals and cost definition
Define the forecast hierarchy and horizon, quantify stockout, overstock, transfer, and expedited-purchasing costs, and select metrics with genuine business meaning.
02
Historical data and baseline backtesting
Review sales, inventory, pricing, promotion, and lead-time data, using current rules or seasonal baselines for comparison.
03
Focused pilot
Begin with rolling backtests for one category, region, or warehouse and compare the model against current planning on real historical data.
04
Integrate and monitor continuously
Provide replenishment or planning recommendations, retain human override mechanisms, and continuously monitor errors, drift, and unexpected events.

Public Kaggle competition record

2 / 10

Diamond Price Prediction

Diamond price competition practice, showing structured cleaning, features, regression, and error work. Rank follows the public leaderboard.

View leaderboard