Modern supply chains attempt to predict and constantly react to consumer demand by optimizing supply sources, product mixes, and distribution channels. And—while analytics can be an amazing differentiator giving leaders key insight into their operations when used to analyze and understand the entire supply chain—legacy analytics have been restricted to understanding data at specific and siloed parts of the supply chain. They simply aren’t designed to provide a holistic, cross-platform understanding of the entire supply chain.
Here’s why.
- The large number and diverse nature of stakeholders and contributors. There are a lot of different participants located at different stages of the supply chain who use many separate systems or applications, and all of them attempt to source, store, validate, share, and understand their own data.
- The lack of real-time data. Supply chain leaders and business analysts either can’t access or have a very hard time accessing up-to-date data reconciled across various systems, such as inventory and order management.
- It’s hard—or even impossible—to quickly and accurately distill supply chain data into relevant, meaningful insights that can help guide business decisions. Even if business users can gather the data they need from various systems or modules, they’re still challenged by how to properly integrate that data, and they can’t build the reports and dashboards they need without involving data architects.
- Traditional analytics are too rigid. Supply chain leaders and analysts need to be empowered to easily investigate issues by dynamically altering existing and creating new queries and insights on their own—capabilities unavailable in most analytics solutions.
With legacy approaches to supply chain analytics, tech resources and analytics power users have to undertake a long, complicated data modeling process to complete even the most basic analysis. It looks something like this:
- Extract Transform Load (ETL) data from all systems of record, such as enterprise resource planning (ERP) and scheduling systems like Oracle E-Business System (EBS) or Oracle Advanced Supply Chain Planning (ASCP);
- Create a data warehouse or purpose-built data mart structures (to answer specific types of questions);
- Map that data into an enterprise business intelligence (BI) environment;
- Finally, grant access to users; and
- Start the process all over again when new questions, new data, or any other changes arise.
As you can imagine, this process is a LOT of work. And even then, these systems tell only part of the supply chain story, can’t meet the self-service analytic needs of non-technical business users, and can’t provide end-to-end visibility across the supply chain.
Instead, to gain a unified and accurate view of their entire supply chain, what manufacturers need is a revolutionary, modern supply chain analytics platform that:
- seamlessly works across multiple modules and systems;
- expedites the analysis process while empowering line-of-business users with self-service, exploratory discovery across all facets of data;
- eliminates IT’s ongoing involvement in the analysis process; and
- allows for easy expansion and customization as the business demands.
That’s where Incorta comes in.
Find out how Incorta dramatically shortens—by 10-100 times—both the incremental and overall analytics processes for modern supply chains, including the one at Shutterfly, Inc. Download the “Data Modeling Sabotages Supply Chain Analytics” ebook.