Information technology has impacted every sector of society today since the 1990s, and the world of industry is no stranger to it. As it was increasingly important to process higher levels of information, there was also a greater need to adopt new systems for statistical calculations, especially in analytical terms. After all, the large number of logistics processes that make up the supply chain make it necessary to constantly measure which faults are occurring, which merchandise is being damaged or lost, which are the best distribution routes and at what times, etc.
Before Big Data, applying statistical methods required a great deal of work, largely manual, and dependent on human minds. In addition to the fact that it involved a high cost for the company since in many cases it was preferred to hire other companies for developing the statistical work via outsourcing, this work was often inaccurate, defective, and outdated due to the limitations of the applied methods. Then came the Big Data, its new metrics, and analytics to change it all.
Thanks to the advanced developments in the field of computer science, artificial intelligence, cybernetics and information technologies, since the boom of computing in the second half of the twentieth century people have been able to store quantities of data which were previously impossible to collect and much less to analyze properly in order to transform it into information and subsequent knowledge.
Nowadays, the processor of a single server, connected to a complex system of sensors and monitors, can capture all the movements produced by the human, mechanical and electronic activity that takes place during all the logistic processes that compose the supply chain.
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An intelligent machine can trace, observe and measure all interactions since the very production of the raw materials. The packing process, the amount of energy consumed during the production of, let’s say, a batch of beers, the time taken by all inventories during the last year, the amount of damage caused by improper adjustment of the freezer temperature during the cold chain operation, etc. All this, and more can be watched, stored and processed by automatic systems that work 24/7 in a production plant, and not only there since the success of Big Data can reach even the final consumer.
In the future it will be possible, for example, to know who bought your products and services (individually considered,) under what circumstances, and which were his or her motivations to buy them. Then, this great information monster can build more direct and efficient bridges between the production of your products and services and the marketing strategies (digital marketing, especially,) applied for the expansion of your market.
All this means a great competitive advantage compared to other companies because the use of larger amounts of data allows, in the long run, to produce significant improvements in the efficiency with which resources are managed, especially one that is impossible to recover: Time.
However, the main differential factor between traditional statistical methods and Big Data is not simply the cost reduction involved in using sensors and monitors, rather than human minds. The determining factor, technically speaking, is the great gap between the two systems when it comes to contextual intelligence.
Contextual intelligence is the set of diagnostics applied in dynamic contexts, such as the supply chain, performed in this case by high-capacity processors, to extract valuable information. This intelligence, which can undeniably be human, evaluates both the relevant events that happened in the past (for example, an accident in your warehouse a couple of years ago,) the contextual variables that affect the present moment (a hurricane in the Caribbean, which completely alters air, land and sea traffic on the East Coast of the United States, for instance,) as well as the probabilities of future events, analyzed in the form of predictions made by the patterns observed during X amount of time.
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The models generated from the implementation of this revolutionary technology not only allow to improve the risk controls in the production plants (and others) for reducing accidents but, thanks to the geo-analytics based on Big Data it is possible to optimize the delivery of your products more precisely and with fewer delays.
In addition, the use of Big Data is a possibility to comply with green laws regarding the prohibitions of negatively impact the environment that all companies have. Thanks to constant monitoring of the entire supply chain, it is possible to know if the gasoline used by trucks and ships is below the permitted levels of sulfur. Thanks to this, you can detect particles of mercury or cyanide in the fish stored in your coolers. You can even measure the decibels produced by your machines, and know if your production processes generate excessive noise pollution.
Every day will be easier and less expensive to draw statistics about everything that happens in your organization. Big Data is here to stay. More than a trend, it is a paradigm shift in the industrial world.
* Featured Image courtesy of KamiPhuc at Flickr.com