What’s Big Data in Supply Chain & Logistics? Why Should I Be Looking at This Tech Trend?

Data reflect all the small, seemingly insignificant details of the modern world. From a review of your personal bank account spending habits to larger, more advanced processing capabilities, data evolve and expand with each passing day.

 If you were to look at how closely people interact with their digital selves, you would not be mistaken for comparing humanity to a combination of digital and organic matter. This is not a discussion of fantasy either. It is real, and the volume of data being produced every 48 hours rivals that of all data and information gathered over thousands of years of human history, explains Bernard Marr. Big data in supply chain has become synonymous with better business, improving efficiency in the supply chain, continually improving and innovation. But first, you need to understand a few things about its basics.  
 
When Did Big Data in Supply Chain Become a Game-Changer? 
 
Big data in supply chain is not a new concept. In reality, it reflects the common use of data information for marketing purposes. A simple comparison of big data can be seen by thinking about the factors that go into setting up a child's lemonade stand. 
 
Location, time and cost are the primary influences on how many customers will visit the stand. Now, imagine how collecting information on each of these different possibilities could influence future sales.  
 
For example, does location A offer more benefits than location B. In this example, location A is located outside of shopping center with few restaurants. Location B is located inside of the food court in the same shopping center. 
 
Passersby will be likely to stop in either instance, but knowing what is driving these potential consumers can help the lemonade operator make a profit. For instance, the bitter flavoring of lemonade may be better paired with sweet treats. As a result, sales may be hypothetically higher near a bakery or other confectionery. Over a few hours, the same lemonade stand is moved by parents to different locations in the shopping center. Sales change with each movement, and at the end of the day, the parents and child know the best and worst locations to set up for the next day.  
 
In other words, they have actually used data analysis to realize what does and does not improve profit margins. This is exactly the point behind big data.  
 
Why Is Everyone Interested in Big Data? 
 
Big data in supply chain is almost incredibly affordable. Unlike the computer systems of the mid-19th century, modern data processing is actually more economical than calculating information on paper. In addition, the cost of storing data digitally is cheaper than storing physical copies of information, especially when considering climate control, security and accessibility. Yet big data can exist in several forms, unstructured or multi-structured data, reports Lisa Arthur of Forbes magazine.. These varying forms play a major role in what and who create and use big data.  
 
What and Who Create and Use Big Data? 
 
Data collection units are used by practically everyone in business. The amount of information collected by a single cash register can exceed the farthest imaginations of past civilizations. Unfortunately, this data source lacks value if left unattended and unstructured. As a result, big data may need to be isolated to specific events to ensure data collection does not reflect extraneous factors.  
 
For example, an analysis of fuel efficiency may require information on road conditions, tire pressure and octane ratings. However, competitor reporting may not necessarily impact these factors. In essence, the data collection points and analyses must be useful in determining the end result, which implies a direct need for ongoing isolation and analysis of specific data structures as they relate to one another.  
 
Fortunately, today's computer systems can complete these analyses faster than any human possibly could. Consequently, businesses can take advantage of big data without having the financial resources readily available, especially considering the amount and volume of big data processing options via the cloud.  
 
Where Is Big Data Impacting Business? 
 
Big data is an omnipotent, omnipresent topic in successful business models of modernity. Every enterprise needs to fully understand big data in supply chain in order to maintain even a modest competitive advantage. Unfortunately, businesses that forgo this course will not be able to maintain efficiency at levels necessary to produce the cheapest and most effective products or services. Additionally, big data is going to start causing problems for many businesses very soon.  
 
The technology is there, but the pressures on all industries involved in the digital space are growing. According to IBM, the number of available data scientists needed to create the next generation of big data processing equipment and innovation is simply too small, which is shown in the following graphic: 
 
big data in supply chain
 
While this may not seem like a problem, it means the limitations of today's systems will become more pronounced and evident. Therefore, all businesses must work together to promote big data as a future industry in itself through initiatives in educational institutions and beyond. Ultimately, the current and future generations depend upon the biggest players in big data, such as supply chain and transportation leaders, to further this cause in ensuring the survival and growth of big data for next revolution of business in the logistics industry and beyond.
 
Adam Robinson oversees the overall marketing strategy for Cerasis including website development, social media and content marketing, trade show marketing, email campaigns, and webinar marketing. Mr. Robinson works with the business development department to create messaging that attracts the right decision makers, gaining inbound leads and increasing brand awareness all while shortening sales cycles, the time it takes to gain sales appointments and set proper sales and execution expectations.
 
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