How did a Pharmaceutical manufacturing company solve its supply chain issues using data analytics?

A pharmaceutical manufacturing company based in India produced pharma products, especially for skin care, such as face wash, soap, etc. They had a complex structure because they manufactured different products.
But, within the factory, they faced many supply chain issues.

What was the problem?

The main concern was that they were not able to deliver the products on time to their clients, and they were unaware of what caused the delay in the manufacturing process.
For example, if a cosmetic brand orders 10,000 face creams from the manufacturing company and the company promises delivery in 45 to 60 days, the deadlines must be met without any delay.
But before they start the manufacturing process, there are some crucial steps to be taken. To prepare the cream, you will need around 15 to 20 ingredients. First, the company must procure all the ingredients. The face cream sample takes 1 or 2 days to be prepared. Once it is done, the cream must undergo dermatological tests to ensure it is safe on the skin. Before bulk manufacturing, these tests have to be done.
So this manufacturing process entails various steps, and even if there is a glitch in one step, the overall process gets affected and delayed. Every process is interconnected, and manufacturing can only begin once all the sub-processes are in place.
The company wanted to analyze if there was a delay in a particular process. For example, assume that obtaining ingredients is taking longer than expected. The company must be notified of the same.
Thus, they can plan, adjust the deadline, and optimize other processes, or inform the client about the delay by prior estimation.

How has Fornax solved the issue using data analytics?

At Fornax, we built a fishbone analysis to capture the areas where delays could happen in the manufacturing process. This analysis helped us visually diagram a program's or a condition's root causes, thus allowing us to diagnose the problem instead of focusing on the symptoms.
We were able to identify and break down delays at multiple levels and get a clear picture of all delays at all stages, such as quality, procurement, design, manufacturing, etc.
Based on the analysis, we found out that procurement was causing the biggest delay after converting the sales order.
We also created multiple tables to understand the internal processes and determine what was causing the delay.

Tools used:

We decided on the analytics architecture to solve this issue and used a set of tools.
We used CData, an ETL tool that involves a quick three-step data management process to help extract and transform data from several sources before loading it into a centralized repository.
To manage the entire business cycle, we used Odoo, an open-source ERP and CRM software, and the DBT tool for data transformation.
Then, we used LightDash for analytics and data visualization, and lastly, we used BigQuery for the data warehouse. This tool was used to run complex analytical queries on large data sets and gather more precise data to identify the issues.
So, we built a robust data architecture using this set of tools. We analyzed and tracked data to better understand the root cause of a particular delay.
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