N 2 AQ Z A P O N P H ML C A N P I N DU S 2 G 7 H B A KG A E F 7A L E VE RAGEO O P B 2 KN A P L 5 M V 5 4O S 7 O5 3 A X Approximately 90% of all data worldwide has been generated in the last two years.
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ig Data is a phenomenon that affects every part, process and person as business leaders strive to become successful in their industry. Nowadays, a new world of opportunities and challenges is open to them, and any area that offers great “dares� and vast prospects for supply chain management. So what are the challenges and opportunities of Big Data technology for futures industries?
The Industrious Issue
First of all, industries with Big Data can benefit from improved customer approachability and inventory reduction that will decrease costs and enhance agility. New Big Data tools transform the design of supply chains, whilst industries may face significant challenges with the process itself. The possibility of greater data accessibility is also a precondition for the decrease of (time-related) searching and processing the data. This new Big Data attribute is widely known as velocity and it is often linked with integration and quality improvement. Another characteristic of Big Data velocity is the lifecycle of products, which is increasingly shorter for industries such as automotive and fashion as a result of increased customer demand and greater pressure for individualisation. The supply chain management strategy follows the product life cycle as it adapts dynamically. As the life cycle shortens, the stages change their velocity and become faster. This results in the need for constant adaptation of the supply chain to keep pace with a situation of uncertainty. There is more pressure on companies to meet the demand for their product with adequate supply chains because the opportunity to gain competitive advantage is shortening. All these facts contribute to the pressure on the supply chain.
As consumers’ expectations have grown, the customisation of products has increased rapidly in the last decade. Manufacturers, regardless of industry, are trying to respond to the demand, this in turn increases product variety. For instance, in the United States in just 20 years (1970-1990), the number of shoe styles rose from 5 to almost 300. This process affects the complexity of logistics and the use of forecasting techniques which as a result are now becoming more challenging and less accurate. As the diversity of product rises, costs and lead times rise simultaneously. However, with more data variety, comes greater uncertainty, which in turn affects the supply chain by making it less efficient and unresponsive. Almost all industries now have the opportunity to deal with semi-structured and unstructured data, which comes in complex formats and is difficult to process and analyse.
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