Big data and logistics are a match made in heaven. Many sources have already highlighted the importance of big data in logistics and how it can change the way we view the supply chain and how we generally manage all aspects of the industry. In this moment what we have is the ability to gather large amounts of data, but people are largely confused about what to do with it. It seems like the first challenge of big data when it comes to gathering the actual information is being met with flying colors, but when it comes to exploiting the vast amount of statistics, facts, and numbers, the focus has turned towards finding a way to use it to improve both operational efficiency and customer satisfaction in today’s business models.
One of the most important aspects of this phenomenon, is the need to move away from the innovative mentioning of the word as a simple buzz term being thrown around, and to actually find footing and offer solutions that can be used in real-world scenarios to show what is happening right now and what will most likely happen in the future. Supply chain management is an area in which big data can have a huge influence and to which it can offer a vast amount of benefits. There are great examples of applications being utilized to manage inventory, forecast and transport goods. Digital cameras are even being used in warehouses to monitor stock levels and send alerts about actions needed to make corrections, sometimes the whole process is even automatic from end to end. If this continues to advance, we will shortly have warehouses running themselves without the need for operators.
Supply chain management is an area in which big data can have a huge influence and to which it can offer a vast amount of benefits. There are great examples of applications being utilized to manage inventory, forecast and transport goods. Digital cameras are even being used in warehouses to monitor stock levels and send alerts about actions needed to make corrections, sometimes the whole process is even automatic from end to end. If this continues to advance, we will shortly have warehouses running themselves without the need for operators.
Intelligent stock management systems are just one of the many applications that are not just available right now, but also that truly fulfill a need and come to fill a void in logistic operations. The purpose of these advances is to deliver insights that are predictive, in order to know what is going to happen in the industry, and also prescriptive, to understand what to do with this information and how to properly react to those needs. Another application that is getting an overhaul is the way products are placed on shelves for example. Sensors can monitor and measure how logos and placing are influencing the visibility of products shelved in retail stores.
Planning is the cornerstone of effective supply chain management and that is why forecasting is such an important aspect of this new concept of big data analysis. As the person in charge, you are responsible for making sure that the right resources are at the right place, at the right time. That is why understanding the underlying reasons that command the ebb and flow of supply and demand are so important. Giants of the industry like Amazon, Google, Alibaba, and Ebay are perfect examples how taking advantage of the information gathered in our hyper-connected world will give a company the advantage over their competitors. Information more than ever has become a crucial element of differentiation when it comes to competition.
Why is the use of big data so complex? The answer comes perhaps from the name itself. Information stored online when represented in bits surpassed the number of stars in the universe about ten years ago, and today it continues to expand at a rate that doubles itself every couple of years. This means that there is a lot of data out there to sift through, and differentiating what matters and what can be considered background noise is very difficult to work that requires a fair amount of technology. It is important to also consider the fact that human input is not the only source that continues to feed the seemingly never-ending stream of data coming in. Smartphones, smart cars, smart homes, RFID readers, webcams and sensor networks are continuously adding more and more information into the network in both quantity and diversity of data.
What companies need to understand and decide, is not so much whether or not they will include big data solutions into their business model, but instead what type of value will big data drive? Are the reasons non-financial? Are they related to the top or bottom line value? It is obvious that most big data applications fall under the category of operational efficiency, in which the information is used to make better decisions and to improve upon the existing quality of processes and performance, but it doesn’t mean that there aren’t other factors to keep in mind. Another important dimension in which information becomes an important asset is the enhancement of the customer experience, in which customer loyalty and customer service are both maximize and last but not least, complementing revenue streams by treating data a new product or an added value to existing products that will allow for the organization to modify its business model.
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