As more tools and technologies become available, distribution companies are beginning to realise the benefits of big data analytics in logistics and supply chain management and how it can be used as a tool to better their capabilities across every side of their organisations.
What is important to realise is that it is not necessarily the amount of data available that is key, but what you do with it that will have a substantial impact on your business.
What is “Big Data”?
Big data is a term used to describe vast quantities of data available to a business throughout its day-to-day operations. The data collected is typically divided into three main categories; transactional, machine, and social. This data is gathered through various channels available throughout the distribution industry.
How is Big Data in logistics gathered?
The emergence of recent technologies such as IoT (Internet of Things), Machine Learning, and AI (Artificial Intelligence) have enabled companies to accumulate data at an unprecedented rate.
Machine data is drastically increasing the rate at which volumes of big data in logistics can be obtained. With IoT technologies, businesses can attach different IoT sensors to machinery, products, shipments, and much more to gain vast amounts of data on their usage, locations, temperatures, stock levels, etc. This can be particularly useful in understanding how to keep good inventory management and demand forecasting. The capabilities of such technology are ever-expanding, indicating that this form of data will continue to grow exponentially over the coming years. Websites can gather machine data, tracking the usage of websites to follow several types of customer behaviour that can be utilised for many different departments like customer services, marketing and sales.
Transactional big data in logistics comes in the form of day-to-day dealings made throughout a business. This comprises; sales orders, invoices, delivery receipts, and storage records from both an online and offline setting. This type of data alone is meaningless, but careful analysis can supply key insights into customer behaviour to aid in future inventory management and demand forecasting.