The accelerating economic growth powered by digitization has transformed the business strategies across the niches, and it is more evident with the manufacturing distribution sector. The traditional idea representing vertical integration is already considered outdated in production facilities as an increasing number of businesses are focusing on enhancing the core competencies and strengths of businesses.
The major transformations led by digitization and Internet-driven solutions have made substantial value additions to the supply chain and logistics sector in dealing with their complexities. As customer demands corresponding to faster delivery, precision, and better handling of shipments have taken a sharp upturn; logistics operations now look up to the latest technologies such as Big Data analytics.
How Big Data can boost the efficiency of the logistics solutions and ensure increased adaptability of the logistics solutions to the changing customer demands and dynamic market conditions is something we are going to explain through this post.Â
Understanding the Promise of Big Data Analytics for Logistics
Source:Â Euconsult
A multitude of goods transport companies is now relying heavily on data analytics to make their operations better. For instance, truck companies now use analytics to analyze fuel consumption and ensure optimum fuel efficiency. These truck companies and fleet operators also make use of GPS technologies to minimize the waiting times by tracking the availability of warehouse bays.
Courier and logistics companies are also on the front-footing to use geolocation data of the trucks, cargo vehicles and on-road traffic for efficient delivery routing in real-time. For instance, leading courier company UPS developed On-Road Integrated Optimization and Navigation system (Orion) to track the 55,000 routes in its network.
So, big data analytics can play a major role in real-time analysis of the routes and other factors. Big Data needs a constant supply of a large amount of data from quality sources to deliver effective data-driven insights. Some of the key data sources impacting logistical operations include the following.Â
- Traditional business data from different systems used in the operation
- Road traffic data and weather data are captured by sensors, forecast equipment, and monitors.
- Driving habit patterns, vehicle diagnostics, and location data
- Financial forecasts corresponding to the logistics business
- Data referring to responses from ads
- Web browsing pattern
- Data sources from social media platforms
From the attributes mentioned above, it is quite clear that modern data analytics systems can drive insights from their fed information. Since there is no dearth of quality data sources, Big Data analytics can continue to optimize logistics, supply chain, warehousing, and the doorstep delivery process.
Since the need for data sourcing and data management is becoming more important across the industries than ever before, the professionally managed SaaS tools for business intelligence are getting popular.
High-Speed Last Mile ShippingÂ
Source: Freepik
The last-mile delivery for any supply chain demands the highest efficiency and across most companies consume more than one-fourth of the total cost. The challenges to efficient last-mile delivery are too many. Let’s explain them in detail.
The large logistics delivery trucks can find it hard to park near urban areas. Since they need to park some miles away from the city destination and the package needs to be delivered to the final address covering some distance, this adds to the cost and need for resources. In the case of some items, signing of the customer is a prerequisite requirement, and hence when the customer isn’t home, repeated delivery attempts can consume more time and resources.
The last-mile delivery of items also has to give extra care to prevent damages to the deliverable items. On top of all these challenges, the uncertainty of conditions during the doorstep delivery is always something adding to the worries of any logistics service.
Big data seems to be capable of addressing several of these challenges quite efficiently. These days logistics companies are relying on last-mile data analytics to yield relevant insights for optimizing the process. Since the faster mobile internet is already available to everyone along with GPS powered smartphones, getting access to this last-mile delivery data is no longer difficult for the analytics solutions used by logistics companies. The entire idea behind building an on-demand food delivery app relies heavily on this ability to deliver foods faster based upon customers‘ real-time location data.
Let’s think of an imaginative scenario where advanced and real-time data analytics optimizes the delivery process. A courier delivery truck coming with a GPS sensor arrives with the goods near a city. Now the GPS sensor of the delivery personnel continues sending location data to the company’s data center.
The real-time tracking of the personnel’s delivery time and location allows the logistics company to plan to accommodate the truck in the right warehousing bay and supply goods to the delivery personnel at the right time. Big Data analytics used by the data center of the logistics company can deliver such awesome results in terms of efficiency.Â
Big Data to Optimize Routes and Routing Vehicles in Real-TimeÂ
Big data is being popular for efficient route optimization in several industries that depend on traffic conditions. Data-driven route optimization is transforming logistics and supply chain efficiency in many ways. Just because the logistics and supply chain processes need to transport goods between places at a faster speed, several factors impacting the traffic condition and speed need to be taken into consideration.
By taking various data-driven inputs regarding traffic and other factors into consideration, a data centre of a logistics company can guide the vehicle to take the shortest route possible for delivery. This is possible because of the real-time data inputs sent by the sensors in vehicles, real-time streaming of weather reports and traffic updates sourced from on-road monitors and sensors. By analyzing such multifaceted data corresponding to diverse factors, Big Data analytics can easily produce data-driven insights on the selection of best routes for delivery vehicles in real-time.
Dynamic Demand Forecasting
Another crucial way Big Data can add substantial value to the logistics industry is through the precise and accurate anticipation of expenses and cost factors. There are several different expenses that the logistics companies need to deal with. A precise calculation of the expenses of a logistics business corresponding to its need is crucial to help the business garner profits. This is where smart and dynamic forecasting of demands can play a major role.
It was not long ago when forecasting the volume of logistic shipment was mainly carried out by manually putting together data in enormous spreadsheets. But such an approach is impractical now as today’s logistics companies need to handle huge volumes of data beyond the capability of human tracking and analysis. This is another area where Big Data comes to the rescue. Big data analytics, often in close collaboration with artificial intelligence, can allow today’s logistics companies to track and analyze huge volumes of data in real-time.
Big Data Analytics for Delivery of Perishable Items
Source:Â Kearney
Big data has also emerged as a technology to ensure the delivery of items at their best condition and quality. From vegetables to dairy products to products with lower shelf life need to be delivered on time and they need to be protected from decomposition en route to delivery. The sensors tracking the quality of goods and the signs of their deterioration can help the logistics company to take proactive measures to ensure faster delivery.
The data collected corresponding to the delivery of perishable items across different locations over perfidy of time can help the logistics company with insights on how to manage the delivery and on-route measures for maintaining the optimum condition of the items.
Streamlined Record and Back-Office Management
The data-driven automation led by Big Data technology will benefit goods tracking and customer service and help automate the entire supply chain and the logistical process by streamlining record-keeping and back-office operations. All the paperwork and paper trails can be reduced to digital footprints shared automatically across locations and personnel in real-time.
From monitoring fuel uses to transportation timing to the hiring process to tracking the quality of deliverables, every aspect of the business operation can be streamlined with data-centric processes. The logistics companies and management can easily track their performance and output through key metrics in real-time and plan measures to improve them.
Big Data for Smart Warehousing
How many times have you faced the out of stock notice after ordering something online? In most cases, this happens not because of the exhausted stock but because of the delay in updating stock information. In the case of logistics companies, such delay in updating stock information can only lead to substantial losses.
Big data comes to the rescue here by helping to manage the supplies more efficiently. An array of major e-commerce companies and logistics solution providers such as Amazon, DHL and many others embrace the dynamic stock updating abilities of Big Data-based warehousing solutions.
Conclusion
So, the impact of Big Data on the logistics industry is far-reaching than we could ever imagine. Since digital data is already in the driver’s seat to push automation and business transformation, Big Data remains the future for efficient logistical solutions.Â
