A key reason why big data is changing the nature of supply chain management is the opportunity it creates for knowledge sharing. Instead of relying on a linear chain of knowledge, we now have access to 360° information – from sources in every industry and in every geography – in real time. News can break faster on Twitter than on traditional news platforms. What’s more, information that wouldn’t make it onto the mainstream news agenda is available on social media. And all this information has a value to someone.
Indeed, 64% of supply chain executives view big data analytics as a disruptive and important technology that will catalyse long-term change in their organisations, according to SCM’s Chief Supply Chain Officer Report. These executives view big data analytics as more valuable than digital supply chains, the Internet of Things, Cloud computing and 3D printing, among others.
Boston Consulting Group (BCG) considers big data and advanced analytics as “the next frontier of supply chain innovation ”. Through big data and advanced tools like geoanalytics, businesses can optimise their distribution, logistics and production networks. With such huge volumes of data available, they can use this information to reduce inventory, lower costs, enhance customer experiences and increase their agility. What’s more, by identifying patterns and trends, companies can better forecast demand and collaborate with others to provide better services, based on that demand.
One of the ways big data and the Internet of Things are coming into their own is in optimising delivery routes. Driver GPS technology, combined with real-time information feeds and analysis are making logistics management easier. By looking at historical traffic and driving patterns, big data technology can direct drivers to use the best routes and driving techniques to reduce fuel costs and improve their efficiency.
Companies can now combine fast-moving data from customers, machinery, suppliers, environmental factors and contextual factors, such as pricing, competitor activity and geopolitical influences. BCG estimates that companies who are able to predict the future more accurately can reduced their inventory by 20-30% while increasing the ‘fill rate’, which is the effectiveness of inventory to meet demand, by 3-7%.
With such broad analytical capabilities, big data has the potential to analyse companies’ distribution networks and highlight areas to streamline them. One fast moving consumer goods company was able to consolidate its warehouses from over 80 to around 20, according to BCG. By storing inventory in a smaller number of warehouses, which are larger in size, customer demand is pooled and subject to less volatility, which means manufacturers can hold lower levels of inventory.
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