Supply Chain Analysis - ATLIQMART
With Power BI
Aliqmart has observed that their customers are not renewing their contracts. They have a hypothesis that this increase in customer churn is because of delays in delivery and incomplete delivery of orders. In this codebasics challenge project, we analyzed their supply chain data and tracked the manufacturing KPIs to evaluate the performance of the supply chain department.
KEY INSIGHTS:
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None of the KPI targets were met. This might explain why customers are not renewing their contracts. Customer churn data will be required to prove a correlation between the KPIs and customer churn.
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The top 2 customers by order quantity - Lotus Mart & Acclaimed stores - account for 22% of all orders, yet the "on time and in full" deliveries for these customers are amongst the lowest - less than 17%.
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There is little or no correlation between the KPIs being tracked and the product category or the day of the week the order was placed.
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The Latest delivery arrived three days late for over 3000 orders.
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RECOMMENDATIONS:
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Collect Customer churn data to ascertain a correlation between customer churn and the KPIs being tracked.
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Improve delivery time for customers with large orders. Customers with Larger orders (>3k) are likely to see their orders arrive three times later than the average customer.
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Ensure the Latest deliveries don't exceed one day.
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Re-evaluate how delivery timelines are estimated to set realistic delivery timelines.
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An overall improvement on "On Time" and "In Full" KPIs. "In Full" KPI can be addressed by improving QA/QC in the supply chain process to ensure deliveries are complete.
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KPI Summary
None of the KPI targets were met, this might explain why customers are not renewing their contracts. Customer churn data will be required to prove a correlation between the KPIs and customer churn.
Orders are half a day late on average
Orders by Customers
The top 2 customers by order quantity - Lotus Mart & Acclaimed stores - account for 22% of all orders yet the "on time and in full" deliveries for these customers are amongst the lowest - less than 17%. Customers with Larger orders (>3k) are likely to see their orders arrive 3 times later than the average customer.
