How can businesses determine the underlying causes behind supply chain disruptions? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.
Supply chain disruptions have become more and more common due to ramifications stemming from the pandemic and the eCommerce boom. A recent survey Harris Poll conducted on behalf of Sisu revealed that a majority of American consumers (89%) purchased gifts online in the past year, with almost 3 out of 4 (71%) experiencing supply chain issues. And as it relates to supply chain issues, those who did experience these issues were faced with late delivery (64%), order processing delays (49%), or orders missing in transit (31%).
When you’re in the wake of a supply chain issue and dealing with disappointed customers, it is difficult to quickly and accurately identify what exactly is causing the impact. This is a problem that folks at every stage of the supply chain (manufacturing, transporting, order processing, etc.) face.
At Sisu, we know these answers are located within an organization’s data. However, tech-enabled industries and companies spend an immense amount of time putting the infrastructure in place to ensure their data is usable. Tools like Snowflake and Databricks help expedite the data collection and consolidation process, but the real struggle lies in the colossal amount of time data analysts dedicated to combing through the overwhelming amount of data points in order to pinpoint why an order got damaged on its way to the customer.
Data does not necessarily bring organizations to the point of making a decision that spurs a resolution like changing a carrier partner or updating packaging processes. Myriad companies deal with the “last mile problem,” meaning they struggle to deliver the right insights to the right people at the right time in order to make an impactful change. Once a business is able to identify the “why” behind supply chain disruptions, they can then help those on manufacturing floors, running the shipping department, or processing orders to make better decisions for optimization.
Diagnosing symptoms from data is not an easy feat – company decision makers don’t have actionable data in front of them and instead have to wait days or even weeks for their analyst teams to manually sift through the data and surface insights.
This is where a tool like Sisu can be a company’s greatest asset. Sisu’s Decision Intelligence Engine guides decision-making by putting the power of artificial intelligence and machine learning in the hands of data analysts. While legacy reporting and BI workflows are optimized for pre-defined aggregated datasets, which leave big and complex data underutilized, Sisu helps data analysts quickly and comprehensively analyze billions of data points to surface statistically significant, relevant insights – while eliminating unnecessary noise. With Sisu, analysts and decision makers can quickly reveal what matters most regarding their burning questions with fast, comprehensive, and actionable insights.
The Sisu decision intelligence engine helps companies dig up interesting insights within their supply chain data. Customers have been able to track product damage by skew, slice data by size and weight of a product, and compare warehouse, shipping, and receiving vendors, which has helped determine that some are better at handling big orders vs. small ones. With Sisu, customers can also compare packaging solutions for big vs. small items, enabling them to better structure their logistics contracts with vendors by having a greater understanding of which skews should be assigned to which vendors.