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Prescriptive Analytics: Unleash the Optimization Tool
Technology Category
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Visualization
Applicable Industries
- Buildings
- Equipment & Machinery
Applicable Functions
- Procurement
The Challenge
Philip Mannering, an analyst at ClarusOne Sourcing Service, was faced with the challenge of optimizing the production of chocolate bars to maximize profit. The problem involved determining the number of chocolate bars to produce, given the cost of each bar and the constraints of the available resources. The challenge was further complicated by the introduction of a second type of chocolate bar, which added another variable to the equation. The goal was to find the optimal solution that would yield the highest profit, taking into account the constraints of the available resources. This required a shift from descriptive and predictive analytics to prescriptive analytics, which not only predicts what will happen but also prescribes the best course of action.
About The Customer
Philip Mannering is an analyst at ClarusOne Sourcing Service. He has been using Alteryx for two years and has become an Alteryx Designer Expert Certified. He also won the Grand Prix at Inspire Nashville. Mannering is an expert in using prescriptive analytics and the Optimization Tool to solve complex business problems. He has demonstrated his expertise in this use case, showing how to solve a problem with one or two variables and how to find the best solution to pressing business questions.
The Solution
Mannering used Alteryx's Optimization Tool to solve the problem. He started with a simple example involving one decision variable - the number of chocolate bars to produce. He defined the objective function (the profit to be maximized) and the constraints (the available resources). He then plotted the constraints on a graph to find the optimal solution. The Optimization Tool provided the output, indicating the number of chocolate bars to produce to maximize profit. Mannering then expanded the problem to include two decision variables - the number of two different types of chocolate bars to produce. He used the 'variables in rows' tool configuration to support the structure of the input data. He plotted the constraints on a graph and found the optimal solution that yielded the highest profit. The Optimization Tool provided the output, indicating the number of each type of chocolate bar to produce.
Operational Impact
Quantitative Benefit
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