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.
Retail store expansion strategies are one of the most fundamental issues for growing retailers. Opening a new store can be a game changer if you get the location right, or your new store could be doomed to failure if the location doesn’t attract enough customers.
In addition to geographical factors, such as transportation accessibility and real estate prices, demographic factors and mobility patterns in the areas of interest play a key role in decision making. These data on population, purchasing power and consumption habits are what generate an optimal expansion strategy.
Predictive location, and foot traffic analytics reveal valuable information that helps retailers to choose the right location when setting up geographic expansion strategies.
How are predictive analytics models used to determine the optimal location for a new facility?
Predictive analytics is a type of statistical analysis that uses data mining, statistical modeling and machine learning to extrapolate trends from historical facts and current events and is often used for risk assessment and decision making.
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
To ensure the success of branch expansion strategies for site selection plans, it’s necessary to compare the business model with the the potential market needs and build the corresponding strategy, location and foot traffic analytics are the best tool to do so.
Foot traffic data gives retailers a competitive advantage in site selection by helping to visualize how local market dynamics have changed over a long period of time, uncovering new opportunities and insights in real time that would not necessarily have been evident through more traditional or one-dimensional data sources.
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.
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.
With advertising analytics based on big data techniques, marketers can quickly determine the effectiveness of each channel and thus adjust their strategies, enabling them to run hyper-targeted campaigns, choosing the right ad content for the appropriate ad networks.
What is it?
This type of analytics refers to the use of data and technology tools that help companies and marketers effectively monitor their marketing efforts to ensure that campaigns are targeted to the right audience and use the right channels for effective communication.
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