KARISHMA NAGESHWARAN
Photo by Bernd 📷 Dittrich on Unsplash
DataCo Supply Chain Performance Dashboard
Problem Statement
For this project, I chose the DataCo Global Supply Chain dataset from Kaggle to conduct a supply chain performance analysis. My objective was to address several key challenges, such as delivery performance and its effects on the sales. I designed this case study and utilized Tableau to develop the dashboard to provide an overview of supply chain performance, with a detailed view of late delivery risk across various dimensions.​
Objective
The goal of this analysis was to help enhance supply chain operations and optimize resource allocation by identifying inefficiencies and highlighting areas for improvement in shipping and delivery logistics. Through this dashboard, I aimed to present actionable insights into how DataCo Global can address these logistical challenges, improve delivery performance, and ultimately boost profitability.
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Data Preparation
I processed a dataset of 53 columns, selecting 41 relevant columns for analysis. I addressed missing values, removed irrelevant columns, and renamed fields for clarity (e.g., "Type" to "Payment Type"). I also extracted date components (date, month, year) to enable time-based analysis. After preprocessing the data in Python, I ingested it into a MySQL database for efficient analysis and integration with Tableau for supply chain and delivery performance visualization.
The Python code used for the data cleaning can be found here. ​
Lofi Design
Overview Dashboard
For the Overview Dashboard, I aimed to provide a high-level summary of the supply chain’s performance across key areas such as shipping, delivery, regional efficiency, and profitability. This dashboard was designed to enable stakeholders to quickly identify strong performance areas and potential bottlenecks without needing to delve into granular details.
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The objective of this dashboard is to offer a data-driven approach to monitoring and optimizing the supply chain by highlighting critical issues in shipping, delivery, and order processing. The insights allow stakeholders to improve shipment times, reduce late delivery risks, and maximize profit margins, which directly contribute to enhanced customer satisfaction and operational efficiency.
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Key Questions Addressed:
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How is the supply chain performing in terms of shipping and delivery times?
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Which regions and shipping modes are excelling or encountering difficulties?
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What is the overall profitability, and which areas need optimization?
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Story Flow:
I structured the dashboard to flow logically, starting with the regional and financial performance followed by the shipping performance. This progression allows stakeholders to assess the overall health of the supply chain in an intuitive manner.
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Delivery Performance by Region
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In the Delivery Performance by Region visualization, I displayed the percentage of total orders for each region, categorized by delivery status—late, shipped on time, advance shipping or canceled. This visualization helps stakeholders quickly identify which regions are facing higher rates of late deliveries or cancellations.
By analyzing this, I provided insights into how different regions are performing, enabling stakeholders to pinpoint areas that require improvement or regions that are consistently meeting performance expectations. This breakdown offers a clear view of regional delivery efficiency and helps prioritize interventions where needed.
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Profit Margin by Region
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In the Profit Margin by Region visualization, I focused on displaying profit margins across various regions. This allows stakeholders to quickly see which regions are contributing the most or least to overall profitability.
By highlighting these profitability discrepancies, the visualization helps in identifying areas where resources could be optimized or reallocated to improve financial performance. This insight is crucial for making informed decisions on where to focus efforts for greater operational and financial efficiency across regions.
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Top 10 Products by Shipping Time Variance
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In the Top 10 Products by Shipping Time Variance visualization, I highlighted the products based on their shipping efficiency, calculated by comparing actual shipping days to scheduled days. A positive variance indicates faster-than-scheduled shipping, while a negative variance means delayed shipping.
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For the top 10 products, all have negative shipping variances, meaning they are being shipped later than scheduled. The delays range from -1 to -19 days, with an average delay of -8.8 days. This highlights a significant challenge in meeting scheduled shipping times, even for the most "efficient" products.
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The reference line indicates the overall average shipping efficiency:
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Above the Line: Products with smaller delays (closer to -1 day) are performing better, though still delayed.
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Below the Line: Products with larger delays (closer to -19 days) are underperforming and require immediate attention to improve their shipping performance.
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These findings underscore the need for operational improvements to reduce delays and meet customer expectations.
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Actual vs. Scheduled Shipping Time by Shipping Mode
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In the Actual vs. Scheduled Shipping Time by Shipping Mode visualization, I compared the real shipping days against the scheduled days for each shipping mode. This allows stakeholders to quickly spot discrepancies between planned and actual shipping performance, highlighting areas of inefficiency.
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By identifying delays in specific shipping modes, this visual helps pinpoint which methods are underperforming, potentially impacting customer satisfaction and operational efficiency. It also provides insights into whether delays are consistent or seasonal, guiding better resource allocation and optimization of shipping methods.
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Ultimately, this comparison aids in reducing late delivery risks, improving customer satisfaction, and increasing profitability by helping avoid penalties or customer churn.
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On-Time & Late Delivery Over Time
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In the On-Time & Late Delivery Over Time visualization, I tracked the percentage of on-time deliveries versus late deliveries over various time periods. This comparison helps stakeholders monitor overall delivery performance and quickly identify periods where late deliveries spiked in relation to on-time deliveries.
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The goal is to highlight trends and pinpoint specific times when delivery performance dipped, providing insight into potential causes and helping improve future delivery outcomes.
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Detailed Dashboard - Late delivery risk
Late Delivery Risk % vs. Avg Shipping Time % Over Time
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In the Late Delivery Risk % vs. Avg Shipping Time % Over Time visualization, I compared two key metrics:
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Late Delivery Risk (%): The percentage of orders at risk of being delivered late. A higher percentage indicates more late deliveries.
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Average Actual Shipping Time (%): This measures how actual shipping times compare to scheduled times, expressed as a percentage.
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100% means shipping matched the schedule.
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Above 100% indicates delays.
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Below 100% means shipping was completed faster than expected.
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The goal of this comparison is to illustrate the correlation between these two metrics. As Average Shipping Time (%) increases (i.e., orders take longer than scheduled), the Late Delivery Risk (%) tends to rise. Conversely, when shipping times are faster, the risk of late delivery decreases.
I also added a reference line at 119%, indicating that, on average, it takes 19% longer than the scheduled shipping time. This suggests a systemic delay in the shipping process, contributing to higher Late Delivery Risk. When the Average Shipping Time exceeds this line, it points to operational inefficiencies that can lead to more late deliveries.
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Late Delivery Risk Rate by Market, Customer Segment & Shipping Mode
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In the Late Delivery Risk Rate by Market, Customer Segment & Shipping Mode visualization, I analyzed late delivery risks across three key dimensions: market, customer segment, and shipping mode. This helps identify where late deliveries are more common and how they impact overall shipping performance.
By examining these specific dimensions, stakeholders can gain deeper insights into which markets, customer segments, or shipping methods are facing the most challenges. This information is essential for improving delivery efficiency and reducing delays, ultimately enhancing customer satisfaction and operational effectiveness.
Key Metrics & Filters
KPIs:
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Total Sales = SUM(Order Item Total)
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Total Profit = SUM(Order Profit Per Order)
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Profit Margin (%) = (Total Profit / Total Sales) * 100
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On-time delivery rate = SUM([On Time]) / COUNT([Order Id])
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Late Delivery Risk % = SUM([Late delivery risk]) / COUNT([Order Id])​​
Filters:
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Order Region
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Market
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Customer Segment
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Shipping Mode
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Order From Date
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Order To Date
Key Findings
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The delivery performance by region indicates a consistent trend of late deliveries across all regions. However, Western Europe and Central America stand out with the highest rates of late deliveries, at 55.85% and 54.75%, respectively, highlighting significant areas for improvement in these regions.​
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In terms of profitability, Southern Africa shows a promising profit margin of 15.09%, while Eastern Asia, with a profit margin of 11.05%, requires further attention to improve its sales and supply chain efficiency.
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For individual products, the Garmin Forerunner has performed relatively well when examining shipping time variance, which is calculated as the difference between actual and scheduled shipping time. Although this product performs better than others, being closer to the reference line at -1 day, it still faces delays, indicating room for improvement.
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During February 2018, the late delivery rate spiked to 66.67%, while the on-time delivery rate was only 15%, indicating poor delivery performance. This increase in late deliveries could be attributed to factors such as operational bottlenecks or unexpected demand fluctuations during that period.
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