Case Studies

From Real-Time Supply Chain Management to… “Realer Time”?

Posted on June 13th, 2017 | Todd Goldman

 

Folks have been talking about real-time supply chain management for at least a decade now. And so, with Big Data only recently beginning to make a meaningful impact on how the flow of goods and services is managed, what are we talking about now? Realer time supply chain management?

 

I kid, of course. But Big Data is driving some very big leaps in the continued evolution of supply chain management right now. And some of these very big innovations are being spearheaded by a global food and beverage company that just happens to be a customer of ours.

 

In food retail, even incremental streamlining of inventory results in tremendous cost cutting. We’re talking many millions of dollars. This particular organization wants to whittle six days of inventory to just two days. The cost savings will be monumental.

 

The transformative potential that predictive analytics represents for more accurate forecasting has been duly recognized. But, our customer has a truly grand vision. Instead of relying on just its own data, they are embarking on a journey that would eventually see it working with its supply chain partners on a holistic sharing of data. This means the company will be able to coordinate with everyone from their regional distribution centers and shipping companies to the farmers in the fields harvesting fruits and vegetables. The real-time supply chain depends deeply on visibility after all, and what our customer does to push that visibility from the heart of the organization and the limbs of its operations to the very fingers of each and every one of its key partners.

 

And it will take some doing. To conduct the kind of cross-system analytics that they need to reduce inventory time, the customer needs genuine data integration, so their analyst can tap into a data lake where data is aligned across their supply chain. So “Food item” for the restaurant might be “Cargo item” for the transportation company even though both organizations are talking about the same thing.

 

They need to identify these differences and tag the data in a consistent way so they have a common language. Data tagging across the ecosystem has to be uniform if data analytics across the supply chain is going to work. Because, it’s one thing to share your data. It’s quite another to make sense of it all. Whether it’s data about the raw materials on the truck or what market research knows about current consumer wants and needs, the data must be lined up consistently in order to manage not only this tremendous opportunity in supply chain management (where the customer is focusing its efforts now), but for other upcoming opportunities for menu optimization and more (where the customer wants to go next).

 

Right now, by cutting inventory down from six to two days, the customer has less to order, make, move, store or deliver. Food is fresher. Less money is spent on refrigeration. The money saved can go back to the consumer or right to the bottom line. But just think of the transformation the sharing of Big Data across an entire ecosystem can bring to all kinds of organizations, industries, governments and causes. Data is, remember, power. By putting more hands on the collecting, managing and making sense of data, organizations benefit as part of an overall ecosystem as well as on their own. (After all, one man’s trash could be another man’s data!)

 

And now here’s the product push. This customer is doing this all this with Waterline, which is helping them discover, organize, and surface all the high-quality information scattered across not just their organization, but also the key partners they work with. By allowing them to standardize and centralize their data, they are able to connect the right people to the right data, so more data can be put to better use.

 

Want to learn more? See how we help organizations put their data to work here.