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
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.
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.
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.
POI analytics through Big Data techniques allows companies to understand an area of interest and thereby implement strategies, expansion models, and solutions within any business sector.
Information about a specific location or a set of similar locations can help companies make better decisions. Combined with additional contextual parameters such as human mobility, sociology, dynamics of an area, etc., POI data can be used to build meaningful information structures that enable more robust analysis and planning for expansion models.
Location intelligence and footfall analytics can be valuable tools for wholesale distributors to maximize their revenue, optimize their processes and choose the best distribution routes for their pickup and last-mile delivery processes.
The correlation between foot traffic patterns, visitation, sales, and the success of wholesale food distribution companies has been studied and proven, so the development of this type of analysis has become a priority in the process of site selection, supply chain process optimization, and expansion modeling.
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.
Foot traffic and location analytics help wholesale distributors maximize profits, allowing them to reveal where operational inefficiencies are and then implement solutions in problem areas.
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.
Several companies, especially in the retail sector, have realized that they need to challenge traditional last-mile delivery solutions in light of recent advances in technology tools.
With the help of location intelligence that gives access to aerial, satellite imagery and high-definition maps, last-mile delivery processes can evolve to the degree that drivers can avoid traffic and expedite deliveries through predictive alerts, fleet managers can focus on planning delivery routes more efficiently based on fuel costs, travel time, road tolls, etc.
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.