Increased Agility with Predictive Supply Chain Analytics for a Large Automotive Manufacturer
Our client was collecting large amounts of operational, contractual, and financial data. They required a data environment that would allow for easy consumption and use to leverage the data for predictive analytics and demand forecasting.
time with the quick identification of purchase order changes
agility through increased communication between departments and increased speed to adjust forecasting and planning
customer satisfaction with more efficient and accurate order fulfillment
Challenge Story
Our client was using a proprietary environment – an electronic data interchange (EDI) – that was consolidating large quantities of data. The information housed in the EDI tracked and stored real-time exchanges for over 13 million transactions each month containing operational, contractual, and financial data between 7,000 active partners and 300 divisions.
The client wanted to leverage this valuable information to assist in determining the prioritization of orders and develop predictive analytics to help with forecasting the demand for parts. They recognized the need to access and leverage this data through a modern data analytics platform, resulting in increased business agility and efficiency, and better decision-making.
Solution Story
Adastra, a long-standing partner, has worked with the client on several analytical and data engineering projects. We have a deep understanding of their data, systems, data environments, and organizational priorities. This project was a perfect opportunity for us to showcase our data estate modernization capabilities leveraging Microsoft's Azure ecosystem. Working closely with key business stakeholders, Adastra identified key analytical use cases to build out the solution.
A common use case that can cause significant issues downstream is purchase order (PO) changes. At our client's organization, these changes needed to be tracked manually and took a significant amount of time to identify, analyze, and take appropriate business action on. To solve this, Adastra leveraged the data contained in the client's EDI repository, which was already normalized and deemed of high quality due to the source of the information.
The Adastra team extracted the data straight from EDI and loaded it into an Azure data lake. The data was then pushed into Synapse via Azure Data Factory pipelines and integrated into an enterprise data model through SQL engines. Once in the data model, the data could then be leveraged for analysis and visualization. Adastra set the client up with Microsoft's Power BI to visualize key data and alerts and Power Automate and Power Apps were used to help with the identification of changing values and data. Additionally, the client is now able to leverage advanced search capabilities to easily locate and identify specific records.
For the identification of changing purchase order information, Adastra created a dashboard that provided a quick overview of any changes that occurred to POs within a specific timeframe. The dashboard also indicates what specific elements of the PO changed and allows for a quick drill down to isolate, analyze, and take the appropriate action as it pertains to the changes of the PO, enabling the client to become more agile.
Benefits Story
Moving the EDI data into Azure for analysis resulted in the successful implementation of the PO analysis use case. The analytical environment allowed for quick analysis of changes to POs which have significant impacts across the organization from pricing to forecasting, to planning and order fulfillment.
Adastra's solution resulted in:
- A modern data environment for simple implementation of future use cases
- The ability to create accurate inventories of customer orders, behaviours, financial records and more
- The enablement of predictive analytics and demand forecasting, improving planning, supply chain and order fulfillment processes
