Data analytics is rapidly evolving and if you haven’t realized it yet, then let’s just declare it now, that this has the potential to be a real game-changer in the quest for improving the global logistics processes.
The global markets have become more demanding than ever before. The pandemic has caused a lot of operational disruptions that most companies were not planned for. This has also resulted in changes in consumer buying patterns and the increase in demand for faster deliveries as they consumers can longer could go out and instantly buy something from the local stores, while at the same time it has put a lot of strain on the already challenging logistics networks. The global same-day delivery market is predicted to reach $20.36 billion by 2027. So, these challenges are not ready to go away anytime soon.
Data analytics can help in deriving insights from large volumes of logistics data through various analytical methods and producing meaningful and actionable knowledge, helping logistics business leaders identify shipment patterns, analyse cost drivers, predict customers’ buying behaviours, provide on-time deliveries by optimizing shipping routes and order consolidations, reduce the risks of inventory errors and more. Decision-making within the logistics domain has been transformed due to the use of data analytics nowadays. The importance of data in logistics decision making is two-fold. First, data helps businesses improve customer relationships by identifying patterns and predicting their buying behaviour. Second, through a series of analytical methods it can be used to identify the process gaps, optimize processes, predict outcomes and create a more efficient logistics setup.
With the emergence of machine learning and statistical techniques, organizations can find patterns in the data, train automated softwares and use them to gain visibility into customer behaviour, shipment trends, find possible solutions to predicted challenges of the future and more. This will play a key role in helping logistics business leaders in their quest for define the best practice standards for logistics processes and deliver better customer experiences.
With the help of analytics, logistics firms can create more intelligent warehouses; by using machine learning techniques they can identify product patterns, track products through products life cycle, optimize storage capacity planning processes, and plan for future requirements such as warehouse expansions. By monitoring real-time data feeds about their inventory levels and market demands they can create more accurate forecasts and make better decisions regarding their shipment planning processes.
In future, logistics firms have the potential to adopt new technologies such as 3D printing to reduce supply chain delays and costs associated with warehousing products until they are purchased by customers. This practice helps organizations create an inventory tailored specifically for buyers who order specific items instead of pre-packaged products shipped from warehouses. With the help of the emerging technology like 3D printing, logistics companies can also explore innovative ways to reduce product packaging expenses.
Optimizing shipping and enhancing customer experiences:
Data analytics models can derive insights from large volumes of data collected from various sources such as messages from carriers, satellite imagery, customer preferences, weather forecasts, and more. Powered by machine learning and statistical techniques, these analytical models can help find patterns in the data and use them to gain visibility into customer behaviour, shipment trends, and more. This will play a key role in helping logistics business leaders identify their shipment patterns and create better customer experiences.
The use of analytics will go a long way in helping logistics companies solve several problems such as the complexity caused by the rise of issues in cross-border deliveries, increasing demand for faster delivery services, rising customer expectations, and more. By gaining deeper insights into their customers’ demands they can provide them with personalized shipping solutions that will match their needs for upcoming shipments.
The ability of analytics to derive insights from large sets of data collected from diverse sources is one of the most important factors that helps logistics companies track and trace their shipments accurately. Analytics, along with IoT devices and cloud technology, can help reduce delivery delays and improve package visibility by assisting in tracking and tracing of the deliveries. This will play a major role in reducing organizations’ loss due to mishandling packages, other damages during transit and avoid delivery non-compliance issues.
Reducing overall logistics costs:
Data analytics and the tools that come with it offer much more than just visibility and insights into logistics costs and challenges. By harnessing its power, companies can also improve their decision-making capabilities and therefore better manage complex supply chains to reduce operational costs.
When used for making decisions related to overall logistics segment, it helps the organization to better utilize its assets, bringing down inventory costs. Furthermore, with data analytics, businesses can identify opportunities for operational improvement by looking at the long-term trends in data which are otherwise difficult to see when looking at each segment individually. For example, warehouse operational analytics data like picking accuracy, labour productivity, storage space and warehouse performance monitored in real time can help increase efficiency for storage space usage, process exception detection, theft protection, and overall warehouse labour efficiency. Similarly using software that analyses past, current and future shipment delivery data, can help companies find areas where they can save money. For example, the analysis of shipping routes will allow them to determine which modes of transportation will be most cost-effective moving forward for particular kind of shipments or areas. By combining this with the real-time location data of shipments, they can minimize costs by using the most efficient routes.
It can help businesses get better insights into their cost structures, logistics providers and transportation modes by comparing data across different criteria. Analytical tools enable organizations to plan their future business requirements in a much more efficient way, by providing them with an estimate of how many warehouses they will need, as well as the space and staff requirements associated with those warehouses.
Data analytics is the rapidly evolving branch of information management that has the potential to improve logistics processes for organizations multi-fold. Global trade continue to increase exponentially over the next decade with an expected average annual growth rate of 3%. This means more goods being shipped around the globe than ever before! With this unprecedented amount of goods moving across borders, and unexpected challenges like the global covid19 pandemic coming your way, it’s critical for all companies involved in international trade - manufacturers, distributors and retailers alike-to find ways they can use their existing resources smarter.
If you are looking for boosting your logistics processes with the power of data analytics soon, we will love to get you connected with one of our experts and let’s together visualize your smarter logistical framework today.