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MIT's Hi-Viz Supply Chain Project Aims to Automatically Map Supply Chain Risk

MIT's High-Viz Supply Chain Project is developing a way for companies to automatically map and analyze supply chain risk. Bruce Arntzen, executive director of the Supply Chain Management Program at MIT, explains the methodology underlying this project, progress to date and barriers that still exist.

MIT’s Hi-Viz Supply Chain Project merges two capabilities: automatically drawing a flow diagram and map of a company’s supply chain using data stored in corporate databases and superimposing on that map displays of alerts and other critical information gleaned from outside sources. This visual would immediately allow companies to assess and manage supply chain risk, says Arntzen.

“We did a survey a few years ago of more than 2,000 supply chain managers around the world where we asked them what supply chain risk they worried most about” says Arntzen. “The biggest risk worry, far and away, was a supply failure.”

This worry has been highlighted since then by such disasters as the earthquake and tsunami in Japan.  “After that event most manufacturing companies established a war room for making phone calls to suppliers and trying to figure out how great the impact would be on their operations,” says Arntzen.

This wouldn’t be necessary if companies could quickly and efficiently create a picture of their supply chain that showed them where their parts and raw materials come from, how much of the company’s revenue is connected to these parts and materials and where the most vulnerable links are. “Companies didn’t have this information in 2000 and they don’t have it today. That’s the need we are trying to address with the Hi-Viz Project.”

The method, Arntzen explains, is to develop software that can create these visuals using information about suppliers, bills of materials and parts extracted from corporate databases, and couple that with key risk indicators.

The concept of drawing pictures of the supply chain has been tried using manual processes, but the automatic feature is new. “When you try to do something no one has ever done before, you find out why it hasn’t been done,” Arntzen says. “In this case, the reason is that those big corporate databases and IT systems were never designed to draw pictures and they don’t have all the right information.” One glaring data gap is in the location of suppliers’ plants, he says, noting that most systems only have the address of suppliers' headquarters – the place they send the check. “These systems don’t even have a line for typing in the factory location,” he says. “When a disruption or disaster occurs, you don’t care where the headquarters is, but you care very much where your raw material is made.”

Also missing is the name of alternate suppliers and information concerning how long it would take to get another supplier up and running, Arntzen says. Both of these pieces of information are things that the purchasing organization easily could capture but they have never been asked to do so and, as mentioned previously, there is no place to enter it, says Arntzen. “Trying to get the IT department to add a data field and put definitions and rules around that and then trying to get purchasing to capture factory locations and make guesstimates of how long it would take to replace a supplier takes a tremendous amount of effort and cooperation,” he says. “We have made a lot of progress on the basic structure of a solution, but we are still lagging on getting this information. Technology can race ahead, but people’s attitudes and beliefs and the way they’ve been doing it for 50 years – overcoming that is slowest part of the process.”

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