Supply-chain-Business-Analysis

Supply chain dashboards play a crucial role in providing insights and visualizing key performance indicators (KPIs) across various stages of the supply chain. The types of supply chain dashboards can vary based on the specific needs and focus areas of a business Analyze and visualize key metrics in the supply chain to optimize efficiency and decision-making.

Table of Contents

• 🎇Project Overview
• 🧵Data
• 🧮KPIS
• 🔎Analysis Techniques
• 💹Results
• 📥Getting Started
  • Prerequisites
  • Installation
• Usage
• Contributing
• License

Dashboard & Report

Project Overview

This data analysis project focuses on exploring and analyzing various supply chain metrics, including inventory levels, lead times, and supplier performance. The goal is to identify areas for improvement and make data-driven decisions to enhance the overall efficiency of the supply chain.

Data

The project uses a dataset containing information about product availability, order quantities, lead times, supplier details, and other relevant supply chain metrics.

KPI`s

Key Performance Indicators (KPIs) for a Demand Planning Dashboard using DAX involves defining measures that capture important metrics related to demand forecasting, actual sales, and forecast accuracy.

• Demand Planning Dashboard using DAX

  • Demand Forecast KPI

      -- Demand_Forecast_KPI = AVERAGE(processed_data[Demand Forecast])
    
      -- Actual Sales KPI
        Actual_Sales_KPI = AVERAGE(processed_data[Actual Sales])
      
      -- Forecast Error KPI
        Forecast_Error_KPI = AVERAGE(processed_data[Forecast Error])
      
      -- Forecast Accuracy KPI
        Forecast_Accuracy_KPI = 1 - DIVIDE(AVERAGE(ABS(processed_data[Forecast Error])), AVERAGE(processed_data[Demand Forecast]))
      
      -- Customer Demand Variability KPI
        Customer_Demand_Variability_KPI = AVERAGE(processed_data[Customer Demand Variability])
      
      -- Sales Growth KPI
        Sales_Growth_KPI = (LASTDATE(processed_data[Date]) - FIRSTDATE(processed_data[Date])) / FIRSTDATE(processed_data[Date])
      
      -- Monthly Sales Growth KPI
        Monthly_Sales_Growth_KPI = (LASTDATE(processed_data[Date]) - FIRSTDATE(processed_data[Date])) / FIRSTDATE(processed_data[Date])
      
      -- Monthly Sales Distribution KPI
        Monthly_Sales_Distribution_KPI = 
          IF(
              SELECTEDVALUE(processed_data[Actual Sales]) < MINX(ALL(processed_data), processed_data[Actual Sales]),
              "Below Average",
              IF(
                  SELECTEDVALUE(processed_data[Actual Sales]) > MAXX(ALL(processed_data), processed_data[Actual Sales]),
                  "Above Average",
                  "Average"
                  )
              )
    

  • Supplier Performance KPI

  • Supplier Performance Dashboard using DAX involves defining key performance indicators (KPIs) related to supplier performance

              -- On-Time Delivery KPI
      On_Time_Delivery_KPI = 
          DIVIDE(
              CALCULATE(COUNTROWS(FILTER(processed_data, processed_data[Actual Delivery Date] <= processed_data[Expected Delivery Date]))),
              COUNTROWS(processed_data),
              0
          )
    
        -- Supplier Lead Time Adherence KPI
        Supplier_Lead_Time_Adherence_KPI = 
                            DIVIDE(
                                CALCULATE(COUNTROWS(FILTER(processed_data, processed_data[Actual Delivery Date] - processed_data[Order Date] <= processed_data[Lead times]))),
                                COUNTROWS(processed_data),
                                0
                            )
          
          -- Defect Rate KPI
          Defect_Rate_KPI = 
              DIVIDE(
                  CALCULATE(COUNTROWS(FILTER(processed_data, processed_data[Defects] > 0))),
                  COUNTROWS(processed_data),
                  0
              )
          
          -- Supplier Performance Score KPI
          Supplier_Performance_Score_KPI = 
              (1 - [On_Time_Delivery_KPI]) * (1 - [Supplier_Lead_Time_Adherence_KPI]) * (1 - [Defect_Rate_KPI])
          
          -- Supplier Cost Efficiency KPI
          Supplier_Cost_Efficiency_KPI = 
              DIVIDE(
                  CALCULATE(SUM(processed_data[Cost])),
                  CALCULATE(SUM(processed_data[Order Quantities])),
                  0
              )
    

Analysis Techniques

Utilizing descriptive statistics, time-series analysis, and supplier performance metrics, this project aims to uncover patterns, trends, and insights within the supply chain data.

Results

Key findings include:

• Identification of peak demand periods.
• Supplier performance trends and areas for improvement.
• Optimization opportunities for inventory levels.

Know more about supply chain

Getting Started

Follow these steps to set up and run the data analysis project locally.

Prerequisites

Make sure you have the following installed:

• Python 3.x
• Jupyter Notebook
• Required Python libraries (NumPy, Pandas, Matplotlib, Seaborn)

Installation

Clone the repository and install the required libraries:

                  ```bash
                  git clone https://github.com/abduallheid/Supply-chain-Business-Analysis.git
                  cd Supply-chain-Business-Analysis
                  pip install -r requirements.txt

Usage

Run the Jupyter Notebook supply_chain_analysis.ipynb to reproduce the analysis and explore the results.

Contributing

Contributions are welcome! Feel free to submit issues or open pull requests for improvements or additional analyses.

License

This project is licensed under the MIT License.