Supply Chain News: 03/11/20

Building Better Supply Chain Data

Part 2: It Turns Out, Not All Data Types Are Created Equal

11 March, 2020 // In our last newsletter, we introduced the concept that “data” is an inadequate term to describe all the various types of supply chain information. Not all data is the same. Unless we recognize the subtle differences in how events are reported, we can’t fully understand them.

This second part of our series looks in greater depth at some of those data types and differences.How do we classify data so that we can work with it usefully, change data types and sources where needed and correlate information across a supply chain? We propose a set of definitions to refine our crude data, much in the same way that data scientists categorize data types for statistical purposes.

Physical Versus Synthetic Data: Physical data refers to any notifications that result from actual interaction with goods. Scanning a box is physical data. Even though the level of reliability is quite different, a worker who enters damage information into a spreadsheet after inspecting a pallet is reporting physical information. Some types of condition monitors and Internet of Things (IoT) sensor devices yield physical data. Think about a tip-and-tell placard, a GPS tracker attached to a pallet or a light/humidity/temperature sensor.

Synthetic data, by contrast, results from the application of rules, predictions and correlations. Inventory auto-replenishment triggers are synthetic in the absence of a warehouse worker to count how many goods actually remain on the shelf. Many ERP events are synthetic: A delivery notification to the distribution center might trigger those goods to be marked “on hand and available” at the warehouse regardless of whether they’ve actually been put away on the shelf yet. And, forecasts depend on synthesizing other information to make a prediction.

Captive Versus Dynamic Data: While the physical / synthetic pairing helps us assess data at the time that information is created, it doesn’t assure ongoing accuracy. For that, we need to explore two more opposing categories. 

Think of captive data as information snapshots frozen in time. The cells of an offline spreadsheet may or may not have been accurate when they were populated. But, they maintain the same value once they’re entered unless manually updated. Similarly, a status update of “in transit—departed origin” reflects the moment that event occurred. A shock indicator tells you that goods were jolted violently, but it doesn’t say when or if additional upsets occurred subsequent to the first one.

Dynamic data is more like the frames of a movie, showing its subject over time. A GPS tracker pings out a continuous stream of locations. An IoT condition monitor likewise tells you exactly when—and how many times—goods suffered shocks, temperature excursions, humidity, light exposure, etc.

All of this is not to denigrate any particular data type. But it’s critical that supply chain managers understand the unique characteristics and limitations of each. Unfortunately, many supply chain control and visibility systems treat different data types similarly, which can lead to poor decision-making. 

At Morgan, we advocate mapping a supply chain’s data sources against these data types. And, we engineer systems like our own ChronosCloud  that connect disparate data types, enable multi-party analytics and break free of traditional enterprise computing rules and restrictions.

In our next issue, we’ll wrap up this series by looking at data quality issues. Meanwhile, if you’d like some help digitizing your supply chain or transforming transportation efficiency, let us know. We would be happy to learn more about your supply chain challenges.

Heard On The Dock

“Those who rule data will rule the entire world.”

-- Masayoshi Son, Founder and CEO, Softbank

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