Runn
Runn is a real-time resource management platform with integrated time tracking and powerful forecasting capabilities.
Intuitively plan projects and schedule resources with allocations, project phases, milestones, and time off. Flick between monthly, quarterly and half-yearly views to plan for the short and long term. Get a dynamic bird’s-eye view of your entire organization to manage capacity, workload and availability changes as you create your plans.
Runn makes resource management dynamic and visual from a single, shared view. Drill into different roles, teams and tags to compare trends and understand which groups are overbooked. Plan out tentative projects to see how plans might change if work gets confirmed.
Track projects, view forecasts, and get relevant metrics within Runn. Get insights like utilization, project variance, and overall financial performance. Use Runn’s built-in timesheets to monitor project progress.
Runn integrates with Harvest, WorkflowMax, and Clockify. With the API, build your own integrations to connect Runn with your favorite tools.
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Martus
Martus Solutions delivers a powerful budgeting, reporting, and forecasting platform tailored for nonprofits and small to mid-sized businesses (SMBs). Eliminate the hassle of spreadsheets and simplify your financial processes with Martus. Our intuitive tools offer collaborative budgeting and comprehensive personnel planning, enabling your organization to make data-driven decisions with confidence.
Martus empowers teams to work smarter, with real-time data access, customizable reporting, and automated workflows designed to streamline your budgeting and forecasting efforts. Gain critical insights into your organization’s financial health, allowing for strategic growth and mission alignment.
With rapid implementation, seamless ERP integration, and an approachable user interface, Martus delivers both efficiency and value. Hundreds of organizations have already discovered how Martus transforms financial management—bringing transparency, accountability, and accuracy to your financial operations, all at an affordable cost.
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Nixtla
Nixtla is a cutting-edge platform designed for time-series forecasting and anomaly detection, centered on its innovative model, TimeGPT, which is recognized as the first generative AI foundation model tailored for time-series information. This model has been trained on an extensive dataset comprising over 100 billion data points across various sectors, including retail, energy, finance, IoT, healthcare, weather, and web traffic, enabling it to make precise zero-shot predictions for numerous applications. Users can effortlessly generate forecasts or identify anomalies in their data with just a few lines of code through the provided Python SDK, even when dealing with irregular or sparse time series, and without the need to construct or train models from the ground up. TimeGPT also boasts advanced capabilities such as accommodating external factors (like events and pricing), enabling simultaneous forecasting of multiple time series, employing custom loss functions, conducting cross-validation, providing prediction intervals, and allowing fine-tuning on specific datasets. This versatility makes Nixtla an invaluable tool for professionals seeking to enhance their time-series analysis and forecasting accuracy.
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Amazon Forecast
Amazon Forecast is an entirely managed service that employs machine learning techniques to provide exceptionally precise predictions. In the contemporary business landscape, organizations utilize a range of tools, from basic spreadsheets to intricate financial planning applications, in their quest to accurately project future outcomes such as product demand, resource allocation, and overall financial results. These forecasting tools generate predictions by analyzing historical data known as time series data. For instance, they might estimate future demand for raincoats based solely on past sales figures, operating under the premise that future performance will mirror historical trends. However, this methodology can falter when tasked with managing extensive datasets that exhibit irregular patterns. Moreover, it often struggles to seamlessly integrate evolving data streams—like pricing, discounts, web traffic, and workforce numbers—with pertinent independent variables, such as product specifications and retail locations. As a result, businesses seeking reliable forecasts may find themselves facing significant challenges in adapting to the complexities of their data.
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