What Big Data & Little Data Mean To You in the Freight & Logistics Process

What Big Data & Little Data Mean To You in the Freight & Logistics Process:Big Data in Logistics

Possibly the most important business technology issue of the moment is known as “Big Data”, and its ability to transform an organization by allowing employees at all levels of the organization to make better decisions. Simply defined, Big Data is the compilation of such a large set of data points that cannot be defined or analyzed using existing “low tech” tools. For shippers this essentially means that an Excel spreadsheet of shipments in process just isn’t enough anymore to determine how well your logistics process is moving. In a recent paper written by a large logistics consulting firm, it is stated that the sustained success of Internet powerhouses such as Amazon, Google, Facebook, and eBay provides evidence of a fourth production factor in today’s hyper-connected world. Besides resources, labor, and capital, there’s no doubt that the information feeding Big Data and the use of such data has become an essential element of competitive differentiation.

In our July 24, 2014 blog post we addressed the importance of supply chain metrics, and this is precisely what lies at the heart of Big Data. Metrics are established based on past data generated from transactions or shipments and from this data companies can determine how well their supply chain or logistics process is performing. For example, a simple metric like “On Time Delivery” is calculated by measuring the time it takes an order to depart a shipper’s facility and arrive at the customer’s location. The decision about whether the performance is good is based on previous shipments in most cases.

While Big Data is thought to be a senior management issue, the fact is that the data points being studied at the highest levels of an organization originate from the day-to-day operations of the business. Let’s take a look at an example of how Big Data collection begins in the daily workflow of logistics personnel and how they can use it to improve their performance and hence their business.

Wasted Space – a client of ours, one of the country’s largest foods business, had state of the art distribution centers around the country. They needed such infrastructure to support their massive supermarket and big box store retail business. As a result, their international operations were something of an afterthought. Shipping personnel were simply taking cases of product, shrink wrapping them onto a skid and declaring them ready for export.

As we mentioned in our post on dimensional weight, shippers need to be aware not only of the weight of their product but also the dimensions of the cargo being tendered for air transport. As a result, the shipper was tendering cargo of 45 – 100 kgs on skids that had a volume weight of 275 kgs, effectively doubling or tripling the shipment charges.

By doing a simple analysis of the disparity between gross weight and volume weight (Big Data points) we were able to explain to the shipper that the cost of over-packing their material into cardboard boxes was well worth the time and savings in shipping charges. Within a matter of weeks the customer began to realize a reduction of air freight costs in excess of 50%. The Big Data analysis here entailed nothing more than looking at the discrepancy in weights and coming up with an alternative. Logistics managers can perform this sort of analysis in collaboration with their freight forwarders any day and without high level/hi tech solutions being deployed.

There is no doubt that Big Data gets very sophisticated and has the power to really revolutionize a supply chain. It can increase effectiveness exponentially, however, the fact remains that the data often originates at the warehouse level and can be a part of the daily process of logistics professionals at all levels of the organization.

Clearly the time is at hand to tap the potential of Big Data to improve operational efficiency and customer experience, and create useful new business models. It is time for a shift of mindset, a clear strategy and application of the right data analysis techniques. Those companies that do early will enjoy a disproportionate advantage over their competitors.

Container Info & Spec SheetScreen Shot 2014-05-22 at 1.00.30 PM

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