SMS Storetraffic
Introducing intelligent, effective, and discreet People Counters and Analytics for the physical world.
Our innovative solution simplifies the process of deploying, capturing, analyzing, and reporting the foot traffic within any given location. Additionally, we offer the option to monitor and report occupancy levels in real-time.
We support a variety of sectors, including Retail, Education, Gaming, Religious Institutions, Corporate Offices, and more, helping them to understand and respond to their visitor trends.
For retailers, we provide a tailored package designed to evaluate traffic performance, encompassing metrics such as conversion rates and service quality. Our seamless integrations facilitate the combination of point-of-sale data with staffing information. Moreover, the Retail Equation simulator allows users to experiment with different scenarios to boost sales and serves as a valuable educational resource to comprehend the interplay between traffic, staffing, conversion rates, and service excellence. By leveraging these insights, businesses can make informed decisions to optimize their operations.
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JOpt.TourOptimizer
When creating software solutions for Logistics Dispatch, you may encounter various challenges, including those related to staff dispatching for mobile services, sales representatives, or other workforce issues; managing truck shipment allocations for daily logistics and transportation needs, which involves scheduling and optimizing routes; addressing concerns in waste management and district planning; and tackling a variety of highly constrained problem sets. If your product lacks an automated optimization engine to address these complexities, JOpt can be an invaluable addition, providing you with the tools to reduce costs, save time, and optimize workforce efficiency, allowing you to focus on your primary business objectives. The JOpt.TourOptimizer is a versatile component designed to tackle Vehicle Routing Problems (VRP), Capacitated Vehicle Routing Problems (CVRP), and Time Windowed Vehicle Routing Problems (VRPTW), making it suitable for any route optimization tasks in logistics and related sectors. Available as either a Java library or a Docker container that incorporates the Spring Framework and Swagger, this solution is tailored to facilitate seamless integration into your existing software ecosystem.
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XLMiner
The XLMiner® Platform has undergone a rebranding to become Analytic Solver® Data Mining, an effective and user-friendly solution for data visualization, forecasting, and mining directly within Excel. This powerful tool empowers users to explore their data comprehensively, allowing for both visualization and transformation while leveraging a mix of classical statistical methods and cutting-edge data mining techniques, such as classification and regression trees, neural networks, and widely used forecasting time series approaches. It is capable of extracting data from virtually any database, including Microsoft’s Power Pivot, which can handle over 100 million rows, while providing robust features to clean, transform, and segment data into training, validation, and testing sets. Its performance and capabilities rival those of high-end enterprise data mining software, which typically carries a price point that is often ten times greater. Furthermore, with the latest enhancements in both features and performance, users also gain additional benefits from Analytic Solver Data Mining, including complimentary access to its cloud-based version and free usage of optimization and simulation tools. Consequently, this platform not only streamlines the efficiency of data analysis but also delivers substantial value for users aiming to harness the power of advanced analytics to make informed decisions. Additionally, the platform's intuitive interface ensures that users of all skill levels can effectively utilize its extensive capabilities.
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python-sql
Python-SQL is a library that streamlines the process of crafting SQL queries in a more Python-friendly way, providing a range of features such as basic selects, where clause selections, and intricate joins involving multiple connections. It supports grouping and naming outputs, organizes results, and allows for the execution of sub-selects across various schemas. The library also facilitates insert operations, whether using default values, specific entries, or even drawing from another query for the insertion process. In addition, it provides capabilities for updates with designated values, constraints, or lists, and enables deletions that rely on conditions or sub-queries. Moreover, it showcases different styles for constructing queries, including limit style, qmark style, and numeric style, to meet the varied preferences of developers. Such extensive functionality ensures that Python-SQL stands out as a robust solution for developers engaged in database management within a Python context, making it a valuable asset for enhancing productivity and efficiency in database interactions.
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