Ever since humans started to trade, goods have had to be moved from one place to another. In the modern world, this is still the case but the process has become ever more complex and demanding. Logistics is the planning, implementation, and management of moving goods from the point of origin to the point of consumption and it requires a great deal of precision and timing. Clients and consumers expect to receive orders within a given timeframe and logistics service providers must optimize the process in order to maximize efficiency and cut costs.
The ‘Real-Time’ Revolution
Global logistics providers live and die on their reputation for making deliveries accurately and on time. Worldwide Express, for example, make a point of using data analysis techniques to combat previously unpredictable disruptions to delivery schedules, such as sudden changes in weather conditions. The company’s founder, David Kiger, has also blogged about the importance of big data in the modern, online focused business environment. Data collection in logistics nowadays is relatively straightforward, and industry leaders are making strides in improving the ways they analyze this data to provide useful insights.
The modern logistics sector utilizes a lot of different technological solutions and many of these are data-driven. Telematics and GPS technology, for example, can be used to interpret data in order to optimize routes in real-time. This means that a delivery route is not set in stone based on map measurements alone, but can dynamically respond to constantly changing conditions such as roadworks and closures, accidents, congestion, and weather. This requires data management systems that can collate correlated streams of real-time events and re-route vehicles on the go. Delivery goods can also be tracked every step of the way, from collection to delivery. Depending on the system used, this tracking information can be made available to clients and customers in real-time or at certain progress points such as arrival and departure from a local depot.
Gathering and interpreting information by using mobile technology systems is becoming increasingly important and ubiquitous in the drive to improve performance. High-tech hardware, such as sensors, Smart chips, and microscopic computers are installed on planes, in trucks, ships, containers, and pallets. The devices are supported by software to improve workflow and automate processes. By using this technology a more unified supply chain can be maintained.
Building Efficiency into the Supply Chain
Big Data brings with it challenges for transportation and logistics companies related to the optimization of internal operations through supply chain networks. In addition, such networks are comparable with internetworking. This is defined as computer networks or segments being connected via the internet through routing technology. To work well, information, data, infrastructure, and policy governance needs to be linked closely together for maximum effectiveness. When everything is working seamlessly, time is saved, efficiency is increased and the bottom line inevitably benefits.
Some couriers and logistics companies now use local crowd-sourced deliverers for the final stage of the journey, which usually involves delivery to the end customer. These ‘last mile’ deliverers could be students, taxi drivers or anyone else and are often people who would be making that journey anyway. Crowd-based delivery does have some drawbacks but it can cut down costs, especially in rural or sparsely populated areas. A real-time data stream is required to organize this approach by matching potential carriers with deliveries based on their current location, destination, and availability.
At the operational level, transport hubs, depots, warehouses and distribution centers must also be managed, ensuring there is capacity for the vehicles entering and leaving, managing the timing of any transfers and shifts for the relevant personnel.
Beyond improving the physical act of making the delivery itself, big data can be utilized at a strategic level to plan ahead in areas such as capacity. Maintaining more capacity than is required can reduce profitability, which can be particularly important for logistics businesses operating on a tight margin. Too little capacity, on the other hand, could lead to missed opportunities and assignments, projects, and partnerships being passed up on. Big data techniques can support future network planning by analyzing historical data pertaining to capacity and associated factors. Algorithms can be developed to incorporate information such as seasonal effects, industry-specific and regional growth forecasts. Like many other industries, the logistics sector can also harvest data to learn about customer satisfaction and expectations.
Finding a Competitive Edge
Perhaps the biggest challenge the industry faces in relation to Big Data is not how to collect the data but how best to make use of it. Computer systems need to be capable of handling and optimizing the usefulness of very large data sets coming to usable and useful conclusions. The president of Transworld Data, of Olympia, Washington, Mary Shacklett, stated:
That drive to be competitive by utilizing Big Data is easier to satisfy due to the development of sophisticated tools that can extract meaning from huge volumes of data. Technologies such as in-memory computing can speed everything up by supplying up to the minute information about all elements of a logistics business.
The modern logistics industry will increasingly rely on high-tech solutions, thriving on precision timing and data that is accurate, informative and accessible 24-hours a day.
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