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Big Data has been a hot topic for quite some time now in many industries. But how does it relate to your supply chain, and more specifically the transportation portion? Ultimately, if you are looking to reduce your freight spend and avoid potential supply chain issues while still growing your business, you need data and with the right data, anything is possible.
In this article, we will touch on what big data is and how you can analyze it to decrease freight costs and increase supply chain efficiency.
It is important to understand the difference between lots of data and big data. Conventional technologies have been providing analytics on large-scale data from traditional sources for years, such as warehousing and distribution systems.
However, big data takes this one step further, allowing companies to include non-traditional data such as internet websites, TMS software, and POS systems. This means an even larger volume of data is collected to be analyzed.
In addition to volume, this type of data differentiates itself by offering a wide variety of information sets. We now have the capabilities to collect new kinds of data, not just structured data, but the information contained in text, images, videos and other formats.
We aren’t just looking at numbers anymore, now we’re looking at social media likes, tweets, product/service reviews, online comments, blog activity, and more. All of this information can tell the seller what their customers are thinking, what they want, and where and when they want it.
The third major difference of this form of data is speed. Traditionally companies were content with analyzing or reviewing their data daily, weekly or even monthly. Today that is not the case. If a company wants to better prepare for warehouse inventory, choose the best carrier or prevent late deliveries, they need to have real-time data at their fingertips.
While all of this advanced data can seem intimidating, the first step to leveraging data is figuring out what problems you want to be solved, or areas of your supply chain that need improvements, and then locating that data.
Once you have identified the areas you wish to improve upon, it is time to collect all the necessary data. Figure out what your collection points will be; whether they are a manual entry, automated collection points, or a combination of both (recommended).
Once you have some solid data analyzed, follow the recommendations accordingly and make sure to continuously monitor their performance.
Now that we know exactly what big data is, let’s get into how it’s going to improve your transportation supply chain, and ultimately save you money.
Predictive analytics is essentially forecasting based upon data. These sets of data bring together information from multiple sources to make predictions and real-time decisions.
With transportation analytics, you can optimize transportation routes, including backhaul, optimize your shipment schedules, and reduce delivery and pickup errors. Solid data can also help predict your demand and replenishment planning. Imagine if you could have your replacement inventory already moving to its next location before the current inventory is exhausted.
This data is going to do more than just give you historical information such as "how many times did I ship from A to B in June last year", but rather data that will show you how to ship them faster and cheaper.
Most of your data that relates to transportation will revolve around your TMS, which tracks and analyzes your freight performance. Your TMS is a huge source of data regarding your transportation analytics. The five most important metrics to ensure you’re keeping track of are on-time pickups, mode selection, outbound and inbound costs as a percentage of purchases and sales, and average time in transit.
What’s important to remember with supply chain data is that it’s used to help your company achieve its organizational goals and objectives. Whether your main priority is to reduce costs or improve efficiency, or increase sales, big data will help management make knowledgeable decisions.
To summarize, big data is most effective when you set goals, understand the metrics you are measuring, and then take timely and pivotal action on the data provided. Reach out to your 3PL to see how they can assist you in your big data goals and implementation.
For more information on how Redwood can assist you, request your free consultation today!