Client Background

  • Client Name: A leading IT consulting firm in the USA
  • Industry Type: E-commerce & Supply Chain
  • Products & Services: Online retail & fulfillment operations
  • The client operates in a fast-paced e-commerce environment managing inventory across multiple fulfillment channels including Shipmonk, Amazon, and WooCommerce.

The Problem

The client faced major challenges in:

  • Inventory stockouts and overstocking
  • Lack of centralized data across platforms
  • No predictive system for demand forecasting
  • Manual tracking leading to inefficiencies

Our Solution

We developed an automated inventory forecasting system using Knime and Tableau that:

  • Integrated data from multiple sources
  • Calculated major KPIS
  • Built Dashboard
  • Standardized and cleaned datasets
  • Predicted demand using forecasting logic
  • Generated automated alerts for stock risks

Solution Architecture

  • Data ingestion from Shipmonk, Amazon, WooCommerce using Selenium in a Python node
  • ETL pipeline using KNIME
  • Data transformation and KPI computation
  • Python scripting for forecasting logic
  • Alert system for stockout prediction
  • Tableau Dashboard

Deliverables

  • Automated ETL Knime  workflow
  • Tableau dashboard
  • Alert system for low inventory

Tech Stack

  • Framework used
     KNIME Analytics Platform
  • Language/techniques used
     Python, ETL pipelines, Data preprocessing
  • Models used
     Time-series forecasting (basic predictive models)
  • Skills used
     Data analysis, forecasting, automation, backend logic
  • Databases used
     CSV-based structured datasets

What are the technical Challenges Faced during Project Execution

  • Inconsistent data formats across sources
  • Missing and duplicate data
  • Real-time alert generation

How the Technical Challenges were Solved

  • Implemented data standardization pipelines
  • Applied cleaning and validation rules
  • Built custom Python scripts for KPI logic
  • Automated workflows to reduce manual errors

Business Impact

  • Reduced stockouts significantly
  • Improved inventory planning accuracy
  • Saved manual effort and time
  • Enabled data-driven decision making

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