Business Analytics Assignment Guide: Leveraging Prescriptive Analytics for Optimal Decision Making

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Business Analytics Assignment Guide On
Prescriptive Analytics for Decision Making
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What is Prescriptive analytics?

• Prescriptive analytics 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.

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Key Components of Prescriptive 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.

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Real-World Examples How Companies 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.

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Practical Example Workforce
Scheduling 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 lt- c(Morning, Afternoon, Evening)
• availability lt- c(8, 6, 5) Number of available
  employees for each shift
• demand lt- c(10, 15, 12) Estimated customer
  demand for each shift

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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 lt- c(10, 15, 12) Assuming 10/hr labor
  cost
• Constraints matrix (demand must be met and
  employee availability)
• f.con lt- matrix(c(1, 0, 0,
•                   0, 1, 0,
•                   0, 0, 1,
•                   -1, 0, 0,
•                   0, -1, 0,
•                   0, 0, -1), nrow6, byrowTRUE)
• Right-hand side of the constraints (customer
  demand and employee availability)
• f.rhs lt- c(10, 15, 12, 8, 6, 5)
• Directions of the constraints (greater than or
  equal to for demand and availability)
• f.dir lt- c(gt, gt, gt, gt, gt, gt)
• Solving the linear programming model
• solution lt- lp(min, f.obj, f.con, f.dir, f.rhs)
• Display the optimal workforce scheduling
• solutionsolution
• names(solutionsolution) lt- shifts

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Business Analytics Assignment Help Services

• Learning advance topics like prescriptive
  analysis and solving complex questions based on
  it can be overwhelming sometimes. Our website
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Conclusion

• Dont let prescriptive analytics intimidate you.
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  data-driven decision-making.

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Thank You

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