This integrated solution factors in your business goals (minimizing cost, maximizing profit, satisfying at least 90% of the demand, etc) and resource constraints with built-in algorithms designed to model based on the variability of predicted results (predicted demands, sales, etc) to give you the best decision options. SPSS Modeler 17 with IBM Decision Optimization can easily help with that translation. If we can predict the hourly expected workload (incoming and discharging volume) and its variability (standard deviation), how should we then schedule the nurses hour by hour so that we can utilize the smallest number of nursing staff while meeting a certain level of hourly demand?īusinesses with advanced analytics capabilities often face this problem of translating what will happen into what action they should take. The incoming patient volume and discharging volume fluctuate hour by hour and day by day. This is where prescriptive analytics come in.įor example, a hospital’s specialty unit has a certain number of nurses. Raw predictive analytics can give you some expectations to work with, but sometimes you’ll need help to know exactly what these results mean and what path you should take because of them. Because of this, you need to make decisions about how to best allocate the resources you have to meet your goals without exceeding your constraints. the spending cannot exceed a certain amount, the number of working stations is predetermined, or the production needs to satisfy a certain level of demand). You also usually have some constraints confining you (e.g. funding, staff, a machine) and need to achieve certain goals using it (e.g. In their daily lives, people often face this kind of scenario: you are given some type of resource (e.g. Products Involved: SPSS Modeler 17, IBM Decision Optimization Through this method of presentation, we’ll give you a practical understanding of what these new features mean from an implementation standpoint and how they can help you further optimize and leverage your predictive analytics infrastructure. This article will illustrate use cases for the new features of SPSS Modeler 17 using different industries as reference points. In fact, our strategies can be broadly applied to a wide range of industries, such as predicting response rate and campaign optimization, predicting patient volume and optimizing staffing scheduling, predicting violent crimes and optimizing the deployment of police forces, fraud detection, and a variety of other scenarios. Ironside’s Customer Analytics practice strives to use predictive modeling to propel businesses forward and upward by delivering information on what will happen in the future or what their customers will do based on certain actions or offers to help decision makers act more quickly and with the highest degree of certainty possible.Ĭustomer Analytics and our practice are not limited to traditional B2C analytics. About Ironside’s Customer Analytics Practice Something worth noting as you begin exploring this most recent round of upgrades is that Analytics Server will be heavily leveraged in a lot of these new features. This article will briefly introduce the enhancements added to Modeler 17 from a practical perspective. There are several important changes in licensing structure and system infrastructure, as well as many innovative new functionality enhancements. IBM SPSS Modeler 17 and Statistics 23 were officially released at the beginning of March 2015.
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