While the buzz around big data is growing every day, it still remains a new term for supply chain management. Big data in the logistics industry will bring myriad opportunities offering real-world use cases. 2017 is anticipated to become one of the most important years for people associated with the logistics sector. Supply chains have long been leveraging the deluge of data in the form of structured, unstructured and semi-structured datasets by using quantifiable performance indicators, dashboards and statistical facts and figures. But supply chain management is demanding the adoption of analytics from various industries today like never before- including real-time analytics of rapidly growing and disorganized, even unstructured datasets.
There’s no denying that businesses have been comparatively slower in implementing big data analytics in supply chain management against other operations such as manufacturing and marketing. There are numerous factors affecting supply chain management, warehousing and distribution processes, ranging from the condition of containers and vehicles, to weather conditions during transportation. This is why people associated with logistics have long been thinking about how big data analytics could be harnessed to drive improved efficiencies.
Big data, undoubtedly, has the potential to ignite new and improved business models and drive channel distribution opportunities. However, ‘big’ data cannot be a big thing by itself. Companies should align their business projects with iterative and trial runs, while embracing new technologies in order to move forward.
A white paper published by the Journal of Business Logistics in 2013 called for research into the applications of big data analytics. Since then, a number of steps have been taken and numerous concepts associated with ‘big’ data have been embraced enthusiastically by logistics providers across the world.
In order to analyze unstructured datasets in logistics, numerous applications have already been found in forecasting, transportation, and inventory management. Warehouses today use several digital cameras to observe stock levels. The same cameras provide alerts when restocking is needed by monitoring the unstructured datasets.
Forecasting then takes the process to a new level by feeding the data stored in the footage through machine learning algorithms. These algorithms later teach the ‘smart’ stock management systems the ways to predict a possible pending resupply. Software developers in supply chain management are trying to derive valuable insights from ‘what is going to happen’ to ‘what should they do about it?’- Both predictive and prescriptive types of data analytics, therefore, coming into play.
With a good data strategy, logistics companies can do a number of tasks enabling shipment, personnel visibility, and supply chain collaboration. Besides, a strategic data plan can eliminate environmental pollution and traffic congestion and provide the condition of maintenance predictability and supply requirements with precision.
Logistics operators don’t only optimize internal operations. Logistics supply chain networks interoperate with numerous external networks as well. The logistics networks are similar to internetworking-which simply means the connection of two or more computer networks or segments through a common router technology. It’s high time for the logistics industry to introduce a new framework known as ‘Logistics internetworking’ in order to design, build and operate a new segment of networks. Logistics internetworking connects policy governance, data, workflow, and information for interoperability.
Conventional data monitoring methods revolving around sales tracking and points of order and sales data are now being used in addition to the weather, news and events with the aim to derive valuable insights in lesser time.
Big data for some might be in its infancy as of now, but it has already started making a ‘big’ difference in almost every industry it is entering. When it comes to the logistics industry, big data promises to boost the overall logistics business process and create numerous opportunities.