Business Analytics Assignment Guide On Prescriptive Analytics for Decision Making

Prescriptive analytics is a advanced form of business analytics that moves beyond descriptive and predictive analytics to prescribe what course of steps you should take or make a strategic roadmap to get the desired results. Prescriptive analytics is critical for MBA students learning statistics and business analytics to make the right and optimal decisions that reflect on the firm’s performance. In this guide, you will gain an understanding of the fundamental concepts of prescriptive analytics, examine case studies, learn how to code in R for doing prescriptive analysis, and receive recommendations on where to find additional learning material. Prescriptive analytics entails finding the appropriate course of action based on the analysis of data using mathematical models and algorithms. To be more specific, it contributes to finding and implementing the best solutions, eliminating or minimizing the misuse of resources, optimize processes and making optimal decisions in an organization.

Prescriptive analytics uses machine learning and data to guide users on what to do in response to the predicted event or trend, while predictive analytics looks into the future and aims to predict future events or trends. We anticipate the global Prescriptive Analytics market to grow at a CAGR of 19.64% during the forecast period, reaching a value of USD 27.77 million by 2031. Prescriptive analytics can be considered the next development in business intelligence.

Key Components of Prescriptive Analytics

Data Collection: It is noteworthy that data collection is the first process in prescriptive analytics. This process involves gathering information from various sources to generate ideas. Internal sources, such as historical sales figures or customer information, or external sources, such as market trends or popular topics on social media platforms, may be used by the organization.
Modeling: This includes the use of mathematical models to help predict several outcomes in any given situation. Thus, by developing these models, analysts will be in a position to determine how various variables play out their roles. For instance, analysts might use an example to estimate the impact of pricing strategy adjustments on total sales. These simulations assist in identifying the results associated with different strategies, as well as the potential risks and opportunities.
Optimization: This involves arriving at a decision out of two or more choices that are conceivable and practicable. The optimization techniques ensure the identification of the right actions that can produce the desired outcomes. For instance, in a larger production environment, optimality may involve identifying the most efficient method of managing resources, typically time, to achieve maximum results while minimizing expenses. Therefore, the goal is to determine the maximum potential outcome from all possible measures.
Implementation: This is the final stage of transforming knowledge into action. This is the practical part of making recommendations once we implement prescriptive analytics.

Real-World Examples: How Companies Use Prescriptive Analytics

Amazon:

Amazon, a company that fully utilizes prescriptive analytics, is one of the best examples. It enhances the vast supply chain by accurately pinpointing the strategic centers for the warehouses, controlling the amounts of stock held, and designing efficient routes for delivery. Meeting customer needs and ensuring cost-effective operations will improve the quality-of-service delivery.

Netflix:

Netflix implements prescriptive analytics using recommendation algorithms. Hence, examining the streaming and watching behavior and patterns of the users, Netflix is in a position to suggest what kind of programs can captivate the viewers’ interest and retain them as permanent clients. Such approaches ensure the retention of users or customers by instilling a sense of specific value in them.

Procter & Gamble:

Procter & Gamble optimizes their marketing resource management by developing a model to allocate their budget across various channels and campaigns. This way, P&G can understand how they are spending their money by comparing the consumer’s behavior and the overall efficiency of the campaign, allowing them to adjust their spending to maximize the results they get from their advertising efforts.

Practical Example: Workforce Scheduling Optimization

Imagine you manage a retail store and need to schedule your employees’ shifts to minimize labour costs while ensuring adequate coverage during peak hours. We will use linear programming to determine the optimal staffing levels for each shift.

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

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)

This output indicates that the optimal scheduling solution recommends scheduling 10 employees for the Morning shift, 15 employees for the Afternoon shift, and 12 employees for the Evening shift. These numbers represent the optimal allocation of workforce resources to meet customer demand while minimizing labour costs.

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Textbooks for Reference:

“Business Analytics: Data Analysis & Decision Making” by Albright & Winston
Business Analytics by Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann

Jeremy Posted on 12-Jun-2024 16:50:00