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 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
Logistics managers need to implement location intelligence in supply chains in order to reduce delays, keep costs down, generate a competitive advantage, and thereby improve the global network of multiple carriers, service providers and physical locations from the constant threat of unexpected problems.
By leveraging location intelligence, decision makers gain deeper insight into market trends, consumer behaviors, foot traffic patterns, manufacturing activity, competitors’ logistics operations and much more.
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.
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.
The current global crisis in supply chains is forcing companies to manage their distribution methods by adopting a proactive approach based on Big Data and advanced analytics.
The supply chain crisis has resulted in restaurant chains and fast food outlets running out of key ingredients (e.g.
Location intelligence and POI characterization through Big Data are increasingly being used to make business decisions in the retail, real estate, logistics, and port sectors, among others.
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.
Unlike historical analytics, predictive supply chain analytics allows you to anticipate and prepare for the future, taking out the conjectures planning processes and improving decision making.
Predictive supply chain analytics use advanced technological tools such as machine learning, geomarketing, data mining that enables organizations to identify hidden patterns, understand market trends, identify demand, establish pricing strategies, achieve a high return on investment, optimize and reduce inventory costs.
Geospatial construction, or "geo-construction", uses data from a wide range of focal points, such as location, population and environment, to influence the design and construction of any infrastructure in order to save, time, money and reduce waste.
GIS (Geographic Information Systems) improve the effectiveness of construction planning and design by integrating location information into a single environment.
Location choice has become a critical point in the success or failure of any industry, as it has a great impact on the company's overall risk, as well as on transportation, logistics, salaries, rents and raw materials costs, among others.
Where to locate industrial facilities is one of the most important strategic decisions that companies must make.
The last mile is the journey of a product from the warehouse shelf to the back of a truck and the customer's door, thus being the final step in the operational process, when the package finally arrives at the consumer's door. In addition to being one of the keys to customer satisfaction, last mile delivery is the most problematic part of the shipping process.
It is one of the logistics areas where Big Data can have a real impact on daily operations, offering the opportunity to optimize internal processes and better control external factors, developing qualitative and quantitative improvements in operations, supply chain areas and logistics processes, bringing significant improvements in last mile deliveries.
More and more companies are turning to predictive analytics to optimize their processes, achieve better business results and increase their market share.
Organizations use internal predictive analytics to forecast trends, understand and predict customer behavior, improve performance and drive strategic decision making.
Predictive models are statistical tools that use machine learning supported by Big Data mining to predict and forecast likely future outcomes with the help of historical and existing data by inputting multiple parameters.
They can be used to predict virtually anything containing existing data, in every sector imaginable, from ratings of any program, a customer's next purchase, credit risks, decision making among others.
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