Building Better Supply Chain Data
Part 1: Do We Really Know What We're Talking About When We Talk About Data?
13 February, 2020 // “Data is the new oil,” The Economist proclaimed in a widely quoted 2017 article. In an information economy and a digital age, the companies that have the best data and the most sophisticated tools for parsing it will win.
Since that story appeared, there has been lots of discussion about information-driven companies like Google, Facebook and Amazon. But the metaphor works just as powerfully for supply chain. Getting the right goods to all the right places at exactly the right time—or not—amounts to billions of dollars in cost savings or waste. And, the difference is in the data.
So, against that urgent backdrop, forgive us for asking an impertinent question: Do you really know what data is?
The dictionary defines it simply as a piece of information, but that assumes all information is equal. Our experience suggests that there are many important distinctions to be made both in the types and quality of data. Much like the 50 words in the Inuit language language for snow, we need a vocabulary to show how various pieces of information should be valued and correlated.
Think for a minute about just one example of supply chain information:
A shipment is marked as “in transit” by one of your carriers. That may have been true on the day the status was entered if the shipper physically scanned the box as it was loaded onto a vessel. It’s somewhat less reliable if several pallets were loaded but not all of them individually scanned. Or, maybe that status got the facts right but you didn’t get visibility to the data for 48 hours. So, it was already outdated when it first appeared.
Data is even less trustworthy when its creation is triggered by an event in an ERP system. Say, the factory finished production and switched your order’s flag from “in production” to “in transit.” Is the product actually moving, or is it still sitting at the dock? Maybe it’s at destination and stuck in customs.
Unless you know how the data has been gathered and updated, it’s hard to know what “in transit” actually means. If your big data analysis makes the wrong assumption, your finely oiled operations may end up more like a gooey, toxic mess.
In the next issue of Morgan’s Supply Chain News, we’ll look at some of the finer characteristics of various data types—from systems-created data to transactional scans and even condition monitoring and tracking from Internet of Things (IoT) devices. It turns out each of these sources creates its own set of limitations and dependencies.
We’ll also try to broaden your definition of data quality. Bad data isn’t the only problem. Supply chains also suffer from data quality issues created by delays, lack of information sync between suppliers, missing data pieces or “hidden” data that’s inaccessible.
Last, we’ll consider how a supply chain information platform can accommodate all these variables to create a big-picture mosaic of information that includes traditional ERP feeds, carrier and IoT updates, even offline transactions.
The architect Frank Lloyd Wright once observed that we first design our spaces, and then those spaces define our lives. If you’re interested in remodeling your supply chain information environment, join us for the rest of this series—or contact us to discuss your supply chain challenges.
Heard On The Dock
“The world's most valuable resource is no longer oil, but data.”
-- The Economist, May 2017
While You Were Shipping…
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A Tale of Two Coronavirus Supply Chains. (Wall Street Journal; subscription required.) In 2019, Samsung ratcheted down its reliance on China manufacturing and moved production to Vietnam, India and other companies. Apple didn't. Their choices now put the two smart phone leaders on very different paths as they react to the supply chain effects of coronavirus.
Modex + Morgan. Headed to this year’s Modex show March 9-12 in Atlanta? Let’s meet up at the Morgan booth #3980. Or, drop us an email to set up an appointment. We’d love to learn more about your supply chain transformation challenges.