Business Analytics Assignment Guide On Prescriptive Analytics for Decision Making.pdf

Business Analytics Assignment Guide On Prescriptive Analytics for Decision Making.pdf

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  Business Analytics AssignmentGuide On Prescriptive Analytics for Decision Making www.tutorhelpdesk.com
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  What is Prescriptive analytics? Prescriptiveanalytics is a branch of data analytics focused on providing actionable recommendations based on data. It goes beyond descriptive analytics (which tells you what happened) and predictive analytics (which forecasts what might happen) by suggesting specific actions to achieve desired outcomes. www.tutorhelpdesk.com
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  Key Components ofPrescriptive Analytics Data Collection: Gathering relevant historical and real-time data from various sources. Data Analysis: Using statistical and machine learning models to analyze the data and identify patterns and trends. Optimization Algorithms: Employing algorithms that can process the data to generate optimal decisions. These may include linear programming, genetic algorithms, and simulation models. Simulation: Running simulations to evaluate the outcomes of different scenarios and strategies. Recommendation Generation: Producing specific, actionable recommendations based on the analysis and optimization results. www.tutorhelpdesk.com
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  Real-World Examples: HowCompanies Use Prescriptive Analytics Netflix Use: Content recommendations. Benefit: Increases user engagement and satisfaction. Coca-Cola Use: Personalized marketing. Benefit: Enhances customer loyalty and boosts sales. Amazon Use: Inventory management. Benefit: Ensures product availability and competitive pricing. Airlines Use: Dynamic pricing. Benefit: Maximizes revenue and occupancy rates. Healthcare Providers Use: Personalized treatment plans. Benefit: Improves treatment effectiveness and resource allocation. www.tutorhelpdesk.com
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  Practical Example: WorkforceScheduling Optimization Step 1: Data Collection: Collect data on historical foot traffic patterns, average transaction times, and employee availability. # Sample data for shifts and employee availability shifts <- c(“Morning”, “Afternoon”, “Evening”) availability <- c(8, 6, 5) # Number of available employees for each shift demand <- c(10, 15, 12) # Estimated customer demand for each shift www.tutorhelpdesk.com
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  Step 2: Modelling:Define the objective function and constraints for the linear programming model. # Coefficients of the objective function (total labor cost) f.obj <- c(10, 15, 12) # Assuming $10/hr labor cost # Constraints matrix (demand must be met and employee availability) f.con <- matrix(c(1, 0, 0, 0, 1, 0, 0, 0, 1, -1, 0, 0, 0, -1, 0, 0, 0, -1), nrow=6, byrow=TRUE) # Right-hand side of the constraints (customer demand and employee availability) f.rhs <- c(10, 15, 12, 8, 6, 5) # Directions of the constraints (greater than or equal to for demand and availability) f.dir <- c(“>=”, “>=”, “>=”, “>=”, “>=”, “>=”) # Solving the linear programming model solution <- lp(“min”, f.obj, f.con, f.dir, f.rhs) # Display the optimal workforce scheduling solution$solution names(solution$solution) <- shifts print(solution$solution) www.tutorhelpdesk.com
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  Business Analytics Assignment HelpServices Learning advance topics like prescriptive analysis and solving complex questions based on it can be overwhelming sometimes. Our website provides a comprehensive business analytics assignment help, specifically designed for business statistics and MBA students in the USA and the UK. Whether you need assistance with tools like R, Python, SAS, JMP, Minitab, SPSS, or Excel, our expert guidance is here to help you excel. www.tutorhelpdesk.com
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  Conclusion • Don’t letprescriptive analytics intimidate you. • Contact us today to harness the power of data-driven decision-making. www.tutorhelpdesk.com
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  Thank You www.tutorhelpdesk.com [email protected] www.tutorhelpdesk.com