By Vinit Verma
In our last blog, we described how to build an integrated supply chain with the Boomi AtomSphere Platform. Now we’ll examine how an integrated supply chain provides a reliable flow of relevant, accurate data to power analytics that deliver actionable insights. The kind of analytics that allow organizations to optimize and fine tune their supply chains for competitive advantage.
An optimized supply chain keeps customers happy while ensuring profitability for a business — whether manufacturer or retailer. It speeds time to market, reduces inventory costs, and improves customer satisfaction.
But just how do you get from an integrated supply chain to the data and analytics that drive optimization? It starts with data engineering.
Data Engineering With the Boomi and Snowflake Platforms
Although an integrated supply chain produces an enormous amount of data, it doesn’t provide it in a form that can be easily analyzed. It comes from many sources, in many formats. Data engineering is the process of turning that raw data into data that can be used by an analytics platform.
The process includes data curation, which is the act of creating, organizing and maintaining data sets; data cleansing, which identifies and fixes incorrect, incomplete, and duplicate data in a data set; and transformation, which converts data from one format to another, from the format of a source system into the required format of a destination system.
The Snowflake Platform has powerful data engineering capabilities that offer simplicity, performance, automation, and the flexibility to handle many data formats such as JSON and XML but not EDI data. The Boomi platform’s data integration features include a certified Snowflake connector and the ability to transform EDI data into a format Snowflake can understand. Together, Boomi and Snowflake are a data engineering powerhouse.
Data Lakes and Data Warehouses Are Needed for Supply Chain Analytics
A data lake is a repository of data stored in its original, raw format — often object blobs (binary large objects) or other types of files. With a data lake, you don’t need to know what you want the data for, or what format it should be in for analysis. That can all be determined during the data engineering process. A data lake is designed for storage and exploration. It’s often been called a “data dump,” but it’s a very useful and valuable one.
The Snowflake Data Cloud gives data engineers the flexibility to create a data lake in Snowflake or access and query a data lake outside Snowflake, such as a repository in Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform. For example, if your applications are hosted in AWS, it makes sense to create your data lake in Amazon Simple Storage Service (Amazon S3) and query it from Snowflake.
Snowflake data engineers can curate the data wherever it resides and create data warehouses that, in the case of a supply chain, might include purchase orders, suppliers, or invoices. Data engineering takes the chaos of a data lake and turns a subset of its data into something meaningful — a data warehouse that can be analyzed with business intelligence applications like Power BI and analytics tools like Tableau or Qlik. From the results of that analysis come dashboards, reports, and key metrics.
Supply Chain Data Analytics Establish Key Metrics That Drive Operational Insights
There are dozens of key performance indicators (KPIs) that can measure supply chain performance and management. Big-picture metrics can tell you whether your supply chain is running efficiently or needs some fine tuning. One of these metrics is cash-to-cash cycle time. It tells you the length of time between when you pay suppliers for materials and when customers pay for the final finished product. Of course, you want the cycle time to be as short as possible.
Another common supply chain KPI is tied to top suppliers. If your company works with hundreds or thousands of suppliers, it’s very helpful to know what parts or services a particular supplier provides and how fast and reliable they are. From your data lake, you can create a data warehouse that organizes and ranks that information. So, let’s say your usual top supplier for a particular part is suffering a raw material inventory shortage, with your top supplier data set you can quickly find a substitute.
The same kind of data analytics can be applied to seasonal demand for products by creating machine learning (ML) that crunches historical data to identify seasonal fluctuations in product demand.
Here are several more important supply chain KPIs:
- Customer order cycle time
- Supply chain cycle time
- Service rate
- Perfect order index
- In-full delivery
- Gross margin return on investment
Advanced Analytics With Boomi and Snowflake
Snowflake has a feature called Snowpark, which allows developers to build powerful and efficient pipelines, ML workflows, and data applications, while gaining the performance, ease of use, governance, and security offered inside Snowflake’s Data Cloud. With Snowpark, developers can connect their ML or AI models directly to Snowflake, taking advantage of the platform’s huge data volumes and boosting the performance and effectiveness of data queries. With an analytics data warehouse, you can also set alerts to warn stakeholders when the supply chain is not functioning as it should around specific KPIs, such as customer order cycle time.
One of our clients in medical supplies is very good at using supply chain data to boost and refine the performance of their supply chain. It uses Snowflake as its data cloud and the Boomi platform for application and data integration. Boomi collects data from multiple sources and pushes it to a data lake in Snowflake, where Snowflake data engineers create data warehouses for analytics. Using business intelligence and data science tools, analysts for the company create dashboards with flexible views of the data.
The company created order summaries with a countdown timer to send alerts when the process exceeded a predetermined threshold.
Build a Resilient Supply Chain With Boomi and Snowflake
As we’ve shown over the course of this blog series, supply chains aren’t a single entity but actually a complex configuration of linked networks that generate enormous data between manufacturers and suppliers. Jade has developed a solution leveraging Boomi’s cloud-native integration platform as a service (iPaaS) and the Snowflake Data Cloud platform. This solution can help organizations use their data for supply chain integration, giving them the power to continually monitor and improve supply chain performance while increasing customer loyalty.
Learn more about Jade’s supply chain optimization and analytics solution with Boomi and Snowflake.