Using Azure Databricks to optimize supply chain and inventory management

A leading multinational company from the oil and gas industry was facing several challenges in optimally running their business. The major challenges included inventory distribution and business operations. Glorious Insights helped them in the optimization of various processes thus resulting in improving the supply chain and inventory distribution. Check out this case study that elaborates on how the use of Azure Databricks helped this multinational giant optimize its supply chain and inventory distribution.

Customer background

The client for this case study is a leading multinational in the oil and gas industry. It carries out its functions across the globe. The company maintains over 3000 different spare parts geographically dispersed locations to enable production. This clearly describes the company's need for a system that can streamline its supply chain and inventory management to ensure the availability of the right products at the right time.

The company needed to collaborate with a solution provider who can understand its needs and provide a solution to the required scale. The company put its faith in Glorious Insights to help them in the development of a solution system to optimize their functioning.

Challenges

As mentioned earlier, the business of the company is spread across the world. To keep its production going, it has to maintain a large number of parts at each location. The aim is to maintain ample stock to avoid shortage and overstocking. The present system and processes face the following challenges.

• Incoherent inventory distribution

The stocking at every location is governed by the vendor's recommendations, previous experience, and gut feeling of the executives. This results in a lack of coordination in the units and a disjointed distribution of inventory.

• Limited data available for decision support

The decision-making process is still old school. Not much emphasis is given in the advanced analysis of historical data for more informed and pragmatic decision making.

• Reduced business pace

The sloppy system often leads to insufficient or excess of stocking of some parts at different locations. This has a massive collective impact on the business.

Solution

The Glorious Insights worked closely with the company to provide a solution to optimize its inventory and supply chain. We developed a system that can keep track of the inventory and maintain the right amount of stock.

The solution system is based on Azure Databricks and machine learning. This system is scalable and reduces the business cost significantly by improving supply chain and inventory operations.

• Functional aspect

The new system exploits Databricks to provide a cloud-native unified platform for data analytics. It offers a variety of functional improvements in the supply chain processes and inventory operations.

• Improved the performance of simulations

Our solution allowed multiple and insightful reviews of the scenarios to estimate the requirements of each unit.

• Interactive workspace

It provides the data science team an interactive workspace. With this feature, the team can work together on the data and models to maintain consistency throughout the units.

• Efficient clustering

Different units can now be segregated into clusters with similar needs. This reduced the cost of operations and boosted operational efficiency.

• Automation of material and data flow pipeline

The automation allows the company to establish a faster and reliable pipeline for data and material flow. Every unit can accurately predict the purchase of the parts and duration of their storage. The company can better recognize the need and placement of its products.

• Technical aspect

We deployed the Databricks environment to enable the company to identify inventory issues and equipment failure. Timely information regarding these factors induces prompt action. We developed a scalable model for predictive analytics that can cover over 3000 types of material and work at over 50 locations. Models for each material could conduct over 10000 stimulation iterations to identify the pattern in issue distribution.

This provides a deeper understanding of the kinds of parts and their quantity needed at each unit. This helped the company to predict and arrange the supply of these parts to ensure cost-effective and uninterrupted operations.

Results

The Databricks based system is focussed on performance improvement. It assisted the data science team for a more accurate inventory analysis at different locations. The team sees a huge reduction in the analysis time required to get the necessary information. The cost of operation is also reduced to a great extent and a positive impact on the business is observed at all 50+ locations.

Customer background

The client for this case study is a leading multinational in the oil and gas industry. It carries out its functions across the globe. The company maintains over 3000 different spare parts geographically dispersed locations to enable production. This clearly describes the company's need for a system that can streamline its supply chain and inventory management to ensure the availability of the right products at the right time.

The company needed to collaborate with a solution provider who can understand its needs and provide a solution to the required scale. The company put its faith in Glorious Insights to help them in the development of a solution system to optimize their functioning.

Challenges

As mentioned earlier, the business of the company is spread across the world. To keep its production going, it has to maintain a large number of parts at each location. The aim is to maintain ample stock to avoid shortage and overstocking. The present system and processes face the following challenges.

• Incoherent inventory distribution

The stocking at every location is governed by the vendor's recommendations, previous experience, and gut feeling of the executives. This results in a lack of coordination in the units and a disjointed distribution of inventory.

• Limited data available for decision support

The decision-making process is still old school. Not much emphasis is given in the advanced analysis of historical data for more informed and pragmatic decision making.

• Reduced business pace

The sloppy system often leads to insufficient or excess of stocking of some parts at different locations. This has a massive collective impact on the business.

Solution

The Glorious Insights worked closely with the company to provide a solution to optimize its inventory and supply chain. We developed a system that can keep track of the inventory and maintain the right amount of stock.

The solution system is based on Azure Databricks and machine learning. This system is scalable and reduces the business cost significantly by improving supply chain and inventory operations.

• Functional aspect

The new system exploits Databricks to provide a cloud-native unified platform for data analytics. It offers a variety of functional improvements in the supply chain processes and inventory operations.

• Improved the performance of simulations

Our solution allowed multiple and insightful reviews of the scenarios to estimate the requirements of each unit.

• Interactive workspace

It provides the data science team an interactive workspace. With this feature, the team can work together on the data and models to maintain consistency throughout the units.

• Efficient clustering

Different units can now be segregated into clusters with similar needs. This reduced the cost of operations and boosted operational efficiency.

• Automation of material and data flow pipeline

The automation allows the company to establish a faster and reliable pipeline for data and material flow. Every unit can accurately predict the purchase of the parts and duration of their storage. The company can better recognize the need and placement of its products.

• Technical aspect

We deployed the Databricks environment to enable the company to identify inventory issues and equipment failure. Timely information regarding these factors induces prompt action. We developed a scalable model for predictive analytics that can cover over 3000 types of material and work at over 50 locations. Models for each material could conduct over 10000 stimulation iterations to identify the pattern in issue distribution.

This provides a deeper understanding of the kinds of parts and their quantity needed at each unit. This helped the company to predict and arrange the supply of these parts to ensure cost-effective and uninterrupted operations.

Results

The Databricks based system is focussed on performance improvement. It assisted the data science team for a more accurate inventory analysis at different locations. The team sees a huge reduction in the analysis time required to get the necessary information. The cost of operation is also reduced to a great extent and a positive impact on the business is observed at all 50+ locations.