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.
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
Foot Traffic analytics can identify the characteristics of transit stops and routes, helping to determine and improve the overall coverage of any transit system.
When it comes to public transport, foot traffic analytics can be of great help to authorities, who are faced with various challenges such as road congestion and the diversity of modes of transport, among others. This poses difficulties for public transport managers and operators when it comes to planning.
The secret to site selection in such a competitive market is based on the ability to analyze the right data and be able to understand and interpret the site selection strategies of nearby locations to stay ahead of their expansion plans and gain a competitive advantage.
Where? This is the fundamental question that guides any site selection decision. Tools such as location intelligence and footfall analytics enable the aggregation, analysis and visualization of spatial data and bring significant advantages to the site selection process.
Predictive maintenance based on big data and geospatial data seeks to define the best time to perform work on an asset, so that maintenance frequency is as low as possible and reliability is as high as possible without unnecessary costs.
Maintenance teams are beginning to embrace the use of big data and predictive modeling to improve performance, which helps establish maintenance programs that reduce downtime and save maintenance costs, while extending the life of their equipment, reducing unnecessary tasks and optimizing spare parts inventory.
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.
Retailers need to use predictive modeling and combine it with business, customer, and market data in order to forecast sales or new businesses profitability.
Predictive modelingis a highly effective way to identify a suitable area for expansion strategies, as it has become a vital tool for narrowing down hundreds of potential sites or even a single site. When a potential site is identified, it’s combined with additional layers of data to determine if it meets the model’s requirements, sales performance targets, and other critical factors for any business requirements. Read the full article here
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?
Location analytics is taking its place as a key tool for identifying what consumers want and need, regardless of their wealth or demographic status. COVID-19 has led to completely unexpected behavioral changes.
How can location intelligence help in the recovery of any type of business?
COVID-19 restrictions have caused many owners to halt expansion plans, limit their operational capacity or even close their doors for good. As the world begins the transition to a post-pandemic society, retailers face unprecedented levels of uncertainty.
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.
Micro-mobility analytics improve retailers’ expansion strategies by accurately identifying consumer demographics, understanding customer behavior, and understanding how their competitors are performing.
Micro-mobility is a methodology that combines geospatial data and foot-traffic analytics to solve several problems and improve site selection strategies by helping to understand how people move around specific brick n’ mortar locations, allowing companies to analyze movement patterns around specific locations, such as retail stores, to extract meaningful information.
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.
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