Since the ascent of widespread global positioning system (GPS) use in the early 2000s, organizations have grappled with how to blend GPS with traditional geographical information systems (GIS) that have primarily been used for mapping so they could develop new and relevant applications capable of transforming business and making it more profitable.
While there is a tendency to think that historical big GIS users like construction, civil engineering and surveying are in the vanguard of this new application development, it is actually new users of GIS and GPS that are paving the way to innovation.
Among these new users are the logistics, and the food and beverage industries. Both are making headway into age-old problems that confront them in the present and are also using the technology to help predict the future.
Managing the Present
Logistics companies that ship cold chain (refrigerated) goods, and food and beverage retailers and shippers that must get their goods to market in time to avoid spoilage, have invested millions of dollars into the modernization of transport systems by equipping trucks with onboard sensors that not only GPS track these trucks en route, but that also monitor the temperature and humidity of containers and reefers (refrigerated trailers) that the goods are stored in. If a logistics and supply chain manager receives a sensor alert that the environmentals of a shipment of goods have been compromised, he or she can immediately reroute a truck to another more proximate market, or dispatch emergency help to remedy the situation, which can be pinpointed to a specific GPS location.
The technique was able to assist a major retailer in rerouting a truckload of lettuce to avoid spoilage. It was heading to Atlanta during a severe July heat wave when the truck’s refrigeration began to fail. Sensors emitted warnings and both the trucker and the supply chain manager at headquarters knew exactly where the truck was, thanks to GPS. By combining the intelligence of the company’s GIS system with this GPS location, the company was able to reroute the truckload of lettuce to Washington, D.C., which was more proximate than Atlanta. This shorter distance would enable faster delivery and avert the threat of the lettuce getting spoiled before it reached the market. Information aggregated into the GIS from other sources also confirmed that there was solid demand for the lettuce in Washington, D.C. Shippers and carriers had end to end system visibility of the entire process.
Predicting the Future
Each year, logistics and food and beverage managers are asked to put together the next year’s budget for transportation. In the course of doing this, they look at spreadsheets that tell them what was spent for last year, or even the past few years, if they want to apply a percentage increase for the new budget. In many cases, they also make an assumption that they are going to use their carriers and their shipping lanes in much the same way as they were used before. Unfortunately, using overworked assumptions and spreadsheets isn’t necessarily going to help improve operational and financial performance.
Consequently, many logistics and food and beverage managers are starting to use predictive analytics technologies where they can blend their GIS with other business systems — such as what particular carriers charge for what types of foodstuff loads and what their on-time performances are likely to be — and whether there are alternative ways of shipping that can both save money and improve on-time performance.
Here is an example:
A shipper wants to optimize shipment on-time delivery and cost, so it uses predictive analytics to combine information from its GIS and its other business systems to ask hypothetical “what if” questions like: Can I move some of my shipping volume from premium service priority next-day shipping to standard overnight or second-day shipping to save money and still meet delivery timetables in a particular geographic area? Am I using the least cost carriers on my most active shipping lanes?
“In the past, this was hard to figure out because invoicing and cost data from different logistics providers was expressed in different terms and data formats,” says Don Baptiste, President of Trax Technologies. “There were data errors, and the end to end process of manually reviewing costs, correcting errors, reconciling differences between the information from different suppliers, and then trying to determine where logistics overspend was occurring was so daunting that many companies just gave up.”
Now, cloud-based systems that Trax Technologies and others provide make the process easier, and can incorporate systems like GIS, purchasing, etc., directly into a predictive analytics database.
A Final Work on GPS/GIS Optimization
The work in the logistics and food and beverage industries has been able to tap the advantages of GPS and GIS for exciting and groundbreaking business insights. But the reality is more organizations and industries need to do this.
What if construction companies used predictive what-if modeling to assess the cost of excavation depending upon where a building site is situated on a parcel? What if foresters used GPS and GIS in combination to check locations against timber volume and understory data?
“The best place to begin with predictive analytics is to target a particular area of performance that you wish to improve,” Baptiste says. In this way, both you and your management see immediate returns from technology investment in the business, and it becomes easier to chart a path for improved GPS and GIS utilization.