Analyzing Supply Chain Automation with the Information Value Chain (2/5)

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2. The Information Value Chain

While we intuitively recognize that information has value, scholars have wrestled with the practical challenge of modeling and measuring that value. Early researchers offered a mathematical theory of communication. (Shannon, 1948) Later authors have proposed a model that is based on the willingness to pay for transportation and telecommunications to obtain information. (Takita, 2017)

These models are useful for assigning a dollar value to some types of information, but they offer little practical guidance for the question that supply chain managers urgently need to answer: “What information work should be automated, and what should be done manually.”

To address this challenge, I have developed a different approach called the Information Value Chain (IVC), which places a value on information based on its complexity, and therefore its relationship to cognitive tasks. To explain the IVC, we start by breaking cognitive tasks into four categories. 

●     Data. Tasks that involve processing lots of numbers, words, or symbols. For example, assigning serial numbers to products.

●     Information. Tasks that involve connecting data and creating relationships. For example, keeping track of where items are located in a distribution center. 

●     Knowledge. Tasks that involve filtering and structuring data in order to make decisions and take action. For example, searching through catalogs to find vendors who meet your requirements.

●     Wisdom. Tasks that involve the use of intuition and judgement. For example, determining the tariff classification for products.

This system is illustrated by subjectively assigning values to the effectiveness, or “utility”, of people and machines along the IVC (Figure 1). A quick review of the graph reveals three important insights.

1. Machines have a higher utility for completing simple tasks (those involving data and information) while people have a higher utility for completing complex tasks (those involving knowledge and wisdom).
2. The intersection between these curves represents the ideal location for the human-machine interface. Elements of a task that fall to the left of this intersection should be automated, while those to the right should be performed manually.
3. As technology advances, the curve representing the effectiveness of machines moves higher. This shifts the human-machine interface to the right, meaning that tasks in the affected complexity range shift into the target range for automation.

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Who is Mr. Supply Chain?

Daniel Stanton is a supply chain industry veteran and the best-selling author of Supply Chain Management For Dummies He is dedicated to empowering professionals through education and technology. His courses on LinkedIn Learning (formerly Lynda.com) have been viewed over 1 million times, and he's a frequent speaker at educational conferences and industry events.

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