Supply Chain Visibility Example #5: Benefiting from GPS

The web 2.0 catchphrase “mashup” refers to the ability to integrate heterogeneous data sources into a single, consistent, view for greater total value to the user. Mashups, both useful and dubious, are making their way into supply chain visibility toolsets in 2011. Today’s visibility example looks at a very simple and productive mashup between order data and GPS location devices riding with delivery drivers. In the article, we look at the situation and then decompose where its value-add comes from. The example below is fairly short and simple, but very effective. This is an example of “simple solutions to complex problems”.



Last year I worked with a major make-to-order supply chain behind the world’s largest whitegoods company, with annual revenue of somewhere above 10 billion USD. The group I worked with was focused on the China domestic market, particularly tier 2 and 3 communities. For due diligence, I should note that I work for Manhattan Associates, where I met this client. But the example shown here not focused on Manhattan, but on an example of how supply chain visibility is used. The details here were presented by the company in question (who I am leaving anonymous) and myself in Singapore at the Supply Chain Council world summit in November 2010.

The business problem:

The company’s push into tier 3 communities in China has required them to partner with very small retailers. In some situations, the retailer is not capitalized sufficiently to hold inventory and is more like a sales agent (similar to a travelling insurance agents, etc). Like the girl-scouts in the USA, these sales are completed when the company makes an at-home delivery of their product (usually washing machines, refrigerators, or other large appliances).

The business problem with these deliveries is that the public road network is underdeveloped in tier 3 communities in China and product can easily be “lost”. By this I mean that a mistaken address and wrong delivery is practically impossible to correct. The accidental recipient will refuse to return the new, expensive item they received. Therefore, the company has to achieve very high verification of the addresses. Remember, the potential address format is NOT consistent, and that there is no central database to validate them against. Finally, delays and mis-delivery events generate overhead costs in dealing with the customer, the transportation staff, and the loss of brand equity.

The traditional (and wrong) way to resolve the problem would be to form centralized databases on “correct” addresses. Then, orders would be validated against the database. Finally, the address data would drive load and route planning for outbound vehicles. But, of course, this is non-sense in the actual environment. The Chinese government doesn’t keep (or even encourage others to keep) comprehensive address databases. And any attempt to do so would be silly, given the incredible volume of changes occurring in the country. Every year, China adds more highway kilometers than all of France’s road network combined. Whole cities are being relocated, and construction of new buildings (hence addresses) usually accompanies a change in the local road and address format.

The business solution:

Quite simply, the best solution to this problem came from abandoning the street address and switching to the GPS coordinates. Where an address can be wrong or misread, it is practically impossible to have an incorrect GPS coordinate. Of course, the sales’ person could mis-key the GPS coordinates. So, rather than key it in, the sales person carries a GPS-enabled mobile device which is used to register the order. At the time the order is confirmed, the GPS coordinates are loaded into the order database. The next logical step is to do the GPS check automatically, rather than with the driver’s eyes. To accomplish this, the driver also carries a GPS-enabled mobile device. The driver’s software checks its present coordinates against the order’s delivery coordinates. Until the driver is within a specific range, the order cannot be released. I could go on into details, but it doesn’t need to be more complicated than the description above. This is a simple and proven technique to minimize mis-delivery events and their associated costs.

As with all posts, I’m closing this one with a summary of how this example was accomplished and what it means for you today, in your various professional environments.

  1. The company switched to using GPS to validate a delivery address. An address in ambiguous, while a GPS coordinate is not. Most importantly, even when the signals get deflected slightly, the GPS coordinate errors decrease exponentially to the distance from the true coordinate.
  2. The data capture of the delivery coordinates was automated by incorporating GPS sensors into the mobile device where the order is created. This greatly reduces chances of keying errors, and prompts traveling sales staff to confirm delivery accessibility (1st vs. 30th floor, etc).
  3. The decision to release a shipment is being interrupted by the software, which automatically checks the order’s ship-to coordinates and the driver’s current coordinates. When the driver is out of a specific range from the delivery point, they simply cannot capture signatures and therefore cannot accidentally (or intentionally) mis-deliver.
  4. Together, steps 1-3 above generated significant savings and service level increases. Most importantly, the change was a very measurable instance of visibility improving the capture, integration, and utilization of information to interrupt a business decision.

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