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With this solution, you can make informed decisions to streamline processes, deliver revenue growth and reduce costs across the organization.\r\n\r\n<span style=\"font-weight: bold;\">Benefits</span>\r\nEvolve the way you do business.\r\nGain insights into cost accumulation in a way that management, marketing and operations personnel – not just finance experts – can understand and act on. Tie financial results and KPIs to strategy by measuring profitability at the division, department, channel and individual product and customer levels.\r\nManage shared service cost allocations.\r\nGet a more accurate reflection of true costs and profitability by managing how shared service costs and IT chargebacks get allocated to products, customers, channels and services. Create more accurate budgets and workforce plans.\r\nImprove processes by identifying activities that don’t add value.\r\nIdentify cost bottlenecks, rework and non-value-added activity. Use what-if analysis to model and simulate how changes in driver volumes affect costs, profit and resource requirements.\r\nUnderstand the effects of cost and process variations on performance.\r\nHigh-performance in-memory modeling quickly identifies issues and provides insight into weak links in the supply chain, underutilized capacity and opportunities for cross sell and up sell. Complex models that used to take hours or overnight to update can now be run in a fraction of the time.\r\n\r\n<span style=\"font-weight: bold;\">Features</span>\r\nCost and profitability measurement\r\nModeling\r\nAnalysis and reporting\r\nData integration","shortDescription":"SAS Cost and Profitability Management (the new name for SAS Activity-Based Management) is an analytic application that determines cost and profitability by modeling business processes as cost flows among accounts. Typical accounts include resource, activity, and cost-object accounts. Cost flows between accounts are modeled by various kinds of drivers including evenly-assigned, percentage, and calculated drivers. With this solution, organizations can make informed decisions that streamline processes, deliver revenue growth and reduce costs across the organization.","type":null,"isRoiCalculatorAvaliable":false,"isConfiguratorAvaliable":false,"bonus":100,"usingCount":11,"sellingCount":17,"discontinued":0,"rebateForPoc":0,"rebate":0,"seo":{"title":"SAS Cost and Profitability Management","keywords":"cost, that, profitability, costs, Cost, processes, sell, more","description":"SAS Cost and Profitability Management is an analytic application that models business processes to accurately determine process, product and customer cost and profitability. 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This gives organizations the ability to perform advanced statistical models such as ‘what-if’ calculations, as well as future-proof various aspects of their operations.\r\nThe term is an umbrella for several sub-fields of analytics that work together in their predictive capabilities.\r\nThe major areas that make up advanced analytics are predictive data analytics, big data, and data mining. The process of advanced analytics includes all three areas at various times.\r\n<span style=\"font-weight: bold;\">Data mining</span> is a key aspect of advanced analytics, providing the raw data that will be used by both big data and predictive analytics. <span style=\"font-weight: bold;\">Big data analytics</span> are useful in finding existing insights and creating connections between data points and sets, as well as cleaning data.\r\nFinally, <span style=\"font-weight: bold;\">predictive analytics</span> can use these clean sets and existing insights to extrapolate and make predictions and projections about future activity, trends, and consumer behaviors.\r\nAdvanced analytics also include newer technologies such as machine learning and artificial intelligence, semantic analysis, visualizations, and even neural networks. Taken together, they help advanced analytics software create an accurate enough canvas to make reliable predictions and generate actionable BI insights on a deeper level.","materialsDescription":"<h1 class=\"align-center\">A list of tips on how to manage the process of building an advanced analytics program</h1>\r\n<ul><li>Start with a proof-of-concept project to demonstrate the potential business value of analytics applications.</li><li>Take training seriously. New data management and analytics skills likely will be needed, especially if big data platforms and tools like SAS advanced analytics tools are involved.</li><li>Develop processes to ensure that business units are ready to act on analytical findings so the work of data scientists and other analysts doesn't go to waste.</li><li>Monitor and assess advanced and predictive analytics software on a regular basis to make sure the data being analyzed is still relevant and the analytical models being run against it are still valid.</li></ul>\r\n<h1 class=\"align-center\">Advanced analytics tools</h1>\r\nThere are a variety of advanced analytics tools to choose from that offer different advantages based on the use case. They generally break down into two categories: open source and proprietary.\r\nOpen source tools have become a go-to option for many data scientists doing machine learning and prescriptive analytics. They include programming languages, as well as computing environments, including Hadoop and Spark. Users typically say they like open source advanced analytics tools because they are generally inexpensive to operate, offer strong functionality and are backed by a user community that continually innovates the tools.\r\nOn the proprietary side, vendors including Microsoft, IBM and the SAS Institute all offer advanced analytics tools. Most require a deep technical background and understanding of mathematical techniques.\r\nIn recent years, however, a crop of self-service analytics tools has matured to make functionality more accessible to business users. Tableau, in particular, has become a common tool. 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This gives organizations the ability to perform advanced statistical models such as ‘what-if’ calculations, as well as future-proof various aspects of their operations.\r\nThe term is an umbrella for several sub-fields of analytics that work together in their predictive capabilities.\r\nThe major areas that make up advanced analytics are predictive data analytics, big data, and data mining. 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Taken together, they help advanced analytics software create an accurate enough canvas to make reliable predictions and generate actionable BI insights on a deeper level.","materialsDescription":"<h1 class=\"align-center\">A list of tips on how to manage the process of building an advanced analytics program</h1>\r\n<ul><li>Start with a proof-of-concept project to demonstrate the potential business value of analytics applications.</li><li>Take training seriously. New data management and analytics skills likely will be needed, especially if big data platforms and tools like SAS advanced analytics tools are involved.</li><li>Develop processes to ensure that business units are ready to act on analytical findings so the work of data scientists and other analysts doesn't go to waste.</li><li>Monitor and assess advanced and predictive analytics software on a regular basis to make sure the data being analyzed is still relevant and the analytical models being run against it are still valid.</li></ul>\r\n<h1 class=\"align-center\">Advanced analytics tools</h1>\r\nThere are a variety of advanced analytics tools to choose from that offer different advantages based on the use case. They generally break down into two categories: open source and proprietary.\r\nOpen source tools have become a go-to option for many data scientists doing machine learning and prescriptive analytics. 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