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  November 9th, 2015 | Written by

How Data Drives Logistics

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  • Understanding how clients think, what they order and why informs fundamental business decisions.
  • If you know how your customers behave then you can make informed predictions on future demand.
  • With wearable technology companies can track staff stress and make the necessary adjustments.
  • Collecting data with no purpose is like trying to deliver a shipment with no destination.

Logistics has always been rooted in data. In fact, the very word can be traced back to the Greek logistiki, meaning financial organization.

Data science is a relatively new discipline, borne out of a convergence of computer science with statistics. The field thrives on data, which is what makes the data-saturated logistics sector so attractive to a data scientist.

Today’s logistics organizations sit on an extensive amount of data, from routes to transport methods, products to vehicle conditions. Yet, to truly benefit from all this data, they need to start using it properly.

A data scientist can look at all your disparate data sets and find insights which can help you make better business decisions.

Combining everything from the performance of your vehicles, the demand for your services, where you deliver and the wellbeing of your staff, with external information such as local traffic and weather data, you can build an incredibly detailed idea of what is happening in your supply chain. Data science works as your eyes on the ground—no matter where in the world that may be. You may be based in the United States, but you’ll know exactly how your shipments in China are doing, right down to the local, environmental, cultural and political issues that could affect shipments.

Being able to understand how your clients think, what they order and why, as well as how this changes in response to seasonal and economic changes informs your most fundamental business decisions. This knowledge helps you plan for the future: if you know how your customers behave then you can make informed predictions on future demand. Further overlaying this information with a country’s demographics, plus local planning and infrastructure policy, can tell you the optimum location to build any new depots.

Sensors in machinery can provide real-time data on performance and condition. You’ll be able to see how efficiently your equipment is working and plan the best time to service your vehicles, or have prior warning before they break down.

This can also be extended to your staff. Wearable technology like Fitbits are now looking towards businesses as a target market. By analyzing the data collected by this technology, you can track how tired your staff are or how stressed, and make the necessary adjustments to improve their productivity and morale.

Yet, like most worthwhile ventures, when you first bring data science into your organization there will be some necessary hard work. Data is growing rapidly, and many organisations think that quantity equals quality. Simply collecting data without any true aim or purpose is like delivering a shipment with no destination.

The data you collect needs to be useable, which means data needs to be in the right format to be analysed. Now a data scientist will do this for you, however, this process is sped up drastically if you ensure that the data you are using is relevant to your specific aim.

Of equal importance is the storage of data. With a slight aside towards data privacy, if you do collect personal information – like your employees’ health data – you’ll need to ensure that this data is secure and have a clearly defined policy specifying who has access to it.

Data science works globally. Different departments, in different offices and different countries may store their data separately. To get the best results these silos of information will have to be combined. Your business development department, for instance, may have invaluable information for your operations team.

Related to this is the way you use data science. Data science works most effectively when you increase your outlook from solving just one business problem to solving them all, and uncovering others which you didn’t even know existed.

Having a basic understanding of how data science works, also helps you think of new ways to use it that may not have been previously considered. It may go without saying, but make sure that you communicate with your data science team to know what they are doing, and how they are doing it.

Data is now being created at an exponential rate and with it the profile of data science rises. In the next century, logistics will be driven by data. The organisations which put the leg-work in now, will find they are on a far better footing when others in the industry try to catch up. The future industry leaders will be the ones who let data science take the wheel.


Mike Weston, CEO of data science consultancy Profusion, discusses how data science and logistics are a perfect match.