Most supply chain managers have limited visibility into which of their first-tier suppliers have risks and exposures arising from second and third-tier suppliers. Essentially, they do not know who supplies their Tier 1 suppliers.
Location analytics can identify unknown hidden participants or nodes in supply chains, thus helping to minimize and better control the risks of disruption.
Understanding how a product gets into the hands of customers requires a broad and comprehensive view across the list of all the companies involved in the distribution process, from the factories to the last distributor to the final customer.
Location analytics allows businesses to map their entire supply chain, in order to identify all components that are part of the logistic processes.
Big Data brings together data from different applications, infrastructures, third-party sources and emerging technologies such as location analytics to improve decision making in the strategic, tactical and operational processes that make up supply chain management.
This tool reshapes the supply chain by providing useful and actionable data that can help improve the efficiency of individual companies and the ecosystems in which they operate,helping to synchronize supply chain planning and execution by improving real-time visibility into these processes and their impact on customers and the bottom line. Read the complete article here
Big Data is transforming the way leaders manage supply chains across all touch points, from manufacturing and provisioning to logistics and customer service.
What is Big Data applied to supply chain?
The application of Big Data for supply chain sustainability is the application of high-level intelligence derived from an organization’s data analytics of its operational processes, from procurement and processing to inventory management, distribution, etc., providing a basis for automation efforts and continuous improvement of logistics operations. Read the complete article here
Leveraging current and historical data on location movements allows urban planners to understand current challenges and build smart, flexible and efficient cities.
As more cities begin to implement smart city planning based on data science, location intelligence insights help shape policies that will benefit neighborhoods and the people who live in them.
Delivery companies leverage location intelligence to have better market capture and maximize customer experience.
More and more businesses are getting into the product delivery business. This quest, in turn, has led them to need locationintelligence, as it allows them to measure and control various factors critical to the success of their business, or their processes, including real-time traffic updates, delivery address location, routes, among many other things.
By incorporating location intelligence into urban planning, it becomes possible to develop infrastructure adapted to the needs of citizens, enhancing living conditions in any given city. In addition, spatial data helps to optimize costs and prioritize government administration projects.
What does location intelligence provide to urban planning?
The use of geospatial data provides deep insight into the logistical, legal, and commercial relationships between corporations and facilities of different companies all over the world.
Location intelligence and foot traffic analytics have revolutionized the way in which businesses generate competitive advantages within the various business sectors, being able to infer the behavior and relationships of companies has become a reality thanks to this type of technological technique.
There are several ways to bring location intelligence into the supply chain. If you want to improve delivery times and increase throughput, it is vital to identify and solve the root causes of delays.
Most supply chain delays do not occur when goods are in motion between suppliers and locations. Instead, delays often occur in handoffs between organizations and suppliers.
Matching demand and supply is the basis of the business model of any company whose operations depend on micro-mobility, since for every unit of demand that is not satisfied, an order is lost, leading to loss of profits and customer loyalty.
All companies that rely on micro-mobility can better manage their assets by improving their algorithms with location intelligence and foot traffic analytics, identifying demand peaks or drops beyond the average value in order to foresee or solve any kind of unexpected problem and generate solutions based on Big Data. Mobile tracking helps to know what is happening over any terrain and teaches how to be proactive about it.
Heat maps are used by any business sector to identify foot traffic and vehicular mobility patterns in an area or point of interest, as their visualization presents multiple pieces of data in a way that makes immediate sense.
Heat maps can be used to identify foot traffic patterns from a country-level scale to a more detailed level such as the infrastructure of a store or building.
Location intelligence through techniques based on Big Data collects spatial data in order to improve the decisions made in logistics centers, allowing the use of location and its related data points, creating solutions and optimizing distribution routes.
This new technological tool finds its immediate application in space-dependent businesses, such as delivery and logistics companies. The data collected through infrastructure sensors, cameras and traffic mapping not only allows them to determine the best locations for their businesses, warehouses and centers, but also allows them to know why certain locations have a direct impact on the success or failure of a business.
COVID-19 and climate change have directly impacted the supply chains of the sectors and industries that generate the most economic output.
Unfortunately, fiction has become reality, and a global pandemic coupled with sudden climate changes have increased these problems worldwide, also due to unforeseen events in logistics routes and the exponential increase in online shopping, forcing industries to increase the load of transportation, vehicles, staff and resources in general.
Through data analytics it's possible to improve vehicle performance, reduce costs, improve processes, establish strategies, optimize routes and times, and foresee and identify problems, among others.
Transportation analytics takes a variety of data ecosystems, helping industry leaders to use advanced analytical techniques such as machine learning, Big Data and geospatial data to optimize business strategies in the sector.
In Guatemala, according to the air transport union, the project of the new cargo airport to be developed in the Port of San Jose, Escuintla, is unfeasible in operational and commercial terms.
español
2030
