This project is part of the Codebasics Resume Project Challenge. In this project I have generated insights to solve a supply chain issue in FMCG domain. The dashboard was made using Microsoft Power BI.
AtliQ Mart is a growing FMCG manufacturer headquartered in Gujarat, India. It is currently operational in three cities Surat, Ahmedabad and Vadodara. They want to expand to other metros/Tier 1 cities in the next 2 years.
AtliQ Mart is currently facing a problem where a few key customers did not extend their annual contracts due to service issues. It is speculated that some of the essential products were either not delivered on time or not delivered in full over a continued period, which could have resulted in bad customer service. Management wants to fix this issue before expanding to other cities and requested their supply chain analytics team to track the ’On time’ and ‘In Full’ delivery service level for all the customers daily basis so that they can respond swiftly to these issues.
The Supply Chain team decided to use a standard approach to measure the service level in which they will measure ‘On-time delivery (OT) %’, ‘In-full delivery (IF) %’, and OnTime in full (OTIF) %’ of the customer orders daily basis against the target service level set for each customer.
Peter Pandey is the data analyst in the supply chain team who joined AtliQ Mart recently. He has been briefed about the the task in the stakeholder business review meeting. Now imagine yourself as Peter Pandey and play the role of the new data analyst who is excited to build this dashboard and perform the following task:
- Create the metrics according to the metrics list.
- Create a dashboard according to the requirements provided by stakeholders in the business review meeting.
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Dataset: the dataset contained 6 csv files: a.dim_customers which conatined details of all customers of Atliq Mart such as customer id, name and city. b.dim_product which contained details of all the products sold by Atliq Mart. c.dim_date this table contains the dates at daily, monthly level and week numbers of the year. d.fact_targets_orders this table contains all target data at the customer level such as OT target %, IF target % and OTIF target %. e.fact_order_lines which contains all information about orders and each item inside the orders. f.fact_orders_aggregate which contains information about OnTime, InFull and OnTime Infull information aggregated at the order level per customer
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Relevant Business Knowledge & explanations
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Peter Pandey’s notes taken during the stakeholder meeting