Just as a doctor cannot offer his patients a suitable treatment based solely on medical records and previous diagnoses, many different factors must also be taken into account in the production environment. Forecasts and, more importantly, optimization simulations play an important role here. Just like in medicine, a diagnosis alone doesn’t help – a treatment based on probabilities is the key to success. This is where Prescriptive Analytics comes in.
Classical reporting presents what has happened and the current status. Decisions are then to be made based on this information, e.g. for optimizations or next steps. In order to go from a pure diagnosis based on historical data to an effective recommendation for action, treatment is needed, just like in medicine. Findings can be derived from the analyzed results. This procedure is called Predictive Analytics.
Today, however, this approach is usually no longer sufficient. Conflicts predicted by Predictive Analytics can usually be solved with a wide range of options for action, which the user usually does not have a complete overview of. The Prescriptive Analytics approach goes one step further.
By simulating various scenarios and weighting them with probabilities, the software suggests the best possible recommendation for action and can automatically initiate optimal measures in the event of conflicts. This prescriptive analysis approach is a crucial part of business intelligence solutions in the corporate environment.
The goal of Prescriptive Analytics is to show the right decision options in order to achieve maximum benefits or, optionally, to minimize risks. To improve the quality of predictions and make the right decisions, Prescriptive Analytics continuously processes and analyzes internal and external data from various sources. Data that have limited availability or are out of date and factors that have not been taken into account can lead to inaccurate or incorrect recommendations for action.
The desire for data-based and, more importantly, action-oriented recommendations with “what if” scenarios, for example for profit maximization, is an inherent part of the work of managers in particular. And this is true regardless of the industry.
Analytical solutions are becoming increasingly important in the healthcare sector. For example, referral coordination and patient management can be supported by multi-resource planning for outpatient and inpatient measures as well as patient and service unit-related alarm lists. When preparing decisions on investments, personnel, working time, or process changes in clinics and hospitals, simulations of the effects of these possible changes on the processes in the respective institution are crucial and can literally save lives.
Prescriptive Analytics helps in various scenarios, especially in the production environment. When planning and optimizing the supply chain, for example, full automation of supply chain processes that can be adapted in real time, such as supplies or material procurement, can be used. Prescriptive Analytics thus continuously optimizes the entire supply chain and production planning and ensures that completion and delivery dates are met.
For a long time, prescriptive analysis tools resembled a black box with limited insight into the inner workings of the data and the reasons for its results and instructions for action. Solution providers have increasingly focused on improving the transparency of their solutions so that greater transparency is possible for the user. For example, what-if analysis functions to help users become more familiar with the validity of the solutions – or to take further steps in other areas not associated with Prescriptive Analytics software.
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Head of Sales Germanedge