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1. Prescriptive Analytics
Lesson 8

2. Introduction
Prescriptive Analytics in Excel refers to the use of
advanced analytical techniques to recommend
actions that can optimize decision-making
processes.

3. Key Principles of Prescriptive Analytics:
1. Optimization:
 Prescriptive analytics seeks to optimize decision-making by identifying the best possible
actions to achieve specific goals.
 It involves the use of mathematical models and algorithms to explore various scenarios
and recommend the most efficient and effective course of action.
2. Incorporating Constraints:
 The optimization process considers constraints, limitations, and business rules to ensure
that recommended actions are feasible and align with practical considerations.
 Constraints could include budgetary constraints, resource limitations, or regulatory
requirements.
3. Decision Variables:
 Decision variables are the elements that the optimization model can control or change
to achieve the desired outcome.
 Prescriptive analytics defines and manipulates these variables to find the optimal
solution.

4. Key Principles of Prescriptive Analytics:
4. Scenario Analysis:
 Prescriptive analytics often involves scenario analysis to assess the impact of
different choices on outcomes.
 Users can evaluate multiple scenarios to understand the trade-offs and risks
associated with various decisions.
5. Continuous Feedback Loop:
 Prescriptive analytics is not a one-time process; it involves a continuous feedback
loop where decisions are made, outcomes are observed, and the model is refined
based on real-world results.

5. How Prescriptive Analytics Works in Excel:
 Solver Tool:
 Excel's Solver is a key tool for implementing prescriptive analytics. It is an
add-in that performs optimization by adjusting selected cells according to
specified constraints and goals.
 Users can define decision variables, set constraints, and specify the
objective function, and Solver will find the optimal solution.
 What-If Analysis:
 Excel provides What-If Analysis tools like Goal Seek, Scenario Manager, and
Data Tables that allow users to explore different scenarios and assess the
impact of changes on outcomes.
 This functionality is crucial for understanding how variations in input values
affect the recommended actions.

6. How Prescriptive Analytics Works in Excel:
 Mathematical Formulas and Functions:
 Excel supports the implementation of mathematical formulas and functions
that can be used to model and solve optimization problems.
 Users can create complex models using Excel functions and expressions to
represent decision variables, constraints, and objectives.

7. Goal Seek, Scenario Manager, and Data in excel applying
prescriptive analytics.
 Goal Seek, Scenario Manager, and Data Tables are powerful
features in Excel that play a crucial role in applying prescriptive
analytics.
 These tools allow users to perform what-if analysis, simulate
different scenarios, and optimize decision-making processes.
 Let's discuss each of these tools in the context of prescriptive
analytics:
1. Goal Seek
2. Scenario Manager
3. Data Tables

8. Goal Seek:
 Functionality:
 Goal Seek is an Excel tool that allows users to find the input value
needed to achieve a specific goal or outcome.
 Application in Prescriptive Analytics:
 Example: In a financial model, you might use Goal Seek to
determine the required sales volume to achieve a targeted profit
margin.
 Prescriptive Aspect: It helps in optimizing decisions by identifying
the input values necessary to meet predefined objectives.

9. Scenario Manager
 Functionality:
 Scenario Manager enables users to create and analyze different
scenarios by changing multiple input values and storing them for
comparison.
 Application in Prescriptive Analytics:
 Example: In a project schedule, you can use Scenario Manager to
evaluate the impact of variations in resource allocation on project
completion time.
 Prescriptive Aspect: It aids in decision-making by allowing users to
explore and compare various scenarios, facilitating optimal
decision selection.

10. Data Tables
 Functionality:
 Data Tables provide a way to perform sensitivity analysis by
systematically changing multiple input values and observing the
resulting impact on calculated values.
 Application in Prescriptive Analytics:
 Example: You can use Data Tables to assess how changes in
interest rates and production costs affect the net present value
(NPV) of an investment project.
 Prescriptive Aspect: It helps in understanding the sensitivity of
outcomes to changes in input variables, supporting better decision-
making under different conditions.

11. Steps for Applying Prescriptive Analytics:
1. Define the Problem:
 Clearly articulate the problem, the decision to optimize, and the business
constraints.
2. Set Up the Spreadsheet:
 Create a well-organized spreadsheet with cells for decision variables,
constraints, and the objective function.
3. Activate Solver:
 Enable the Solver add-in in Excel.
4. Define Objective Function and Constraints:
 Identify the objective function and constraints in your spreadsheet.

12. Steps for Applying Prescriptive Analytics:
5. Configure Solver Parameters:
 Open Solver (Data -> Solver).
 Set the objective cell, decision variable cells, and constraints.
6. Run Solver:
 Click Solve, and Solver will use optimization algorithms to find the optimal
solution.
7. Review Results:
 Examine the Solver results to understand the optimal values for decision
variables and the achieved objective.

13. Application of Optimizations
to Business Problems

14. Steps to Apply Optimization in Excel:
1. Define the Problem:
 Clearly articulate the problem, identify decision variables, and define the objective
function and constraints.
 Example: Optimize production schedules to maximize profit within the constraints of
available resources.
2. Set Up the Excel Spreadsheet:
 Create a structured Excel spreadsheet with cells for decision variables, the
objective function, and constraints. Use labels and formulas to organize the
information.
 Example: Create columns for product quantities, a cell for the profit function, and
rows for capacity constraints.
3. Activate Solver Add-In:
 Enable the Solver add-in in Excel if it's not already activated.
 Steps: File -> Options -> Add-Ins -> Manage Excel Add-ins -> Solver Add-in.

15. Steps to Apply Optimization in Excel:
4. Define Solver Parameters:
 Open Solver (Data -> Solver).
 Set the objective cell (the cell containing the objective function), decision variable
cells, and constraints.
 Choose the optimization method (e.g., Simplex LP for linear programming
problems).
5. Run Solver:
 Click Solve, and Solver will use optimization algorithms to find the values for
decision variables that maximize or minimize the objective function while satisfying
the constraints.
6. Review Results:
 Examine the Solver results to understand the optimal values for decision variables
and the achieved objective.
 Adjust the model or constraints as needed based on the results.

16. Examples of Optimization in Excel:
1. Production Planning:
 Objective: Maximize profit.
 Decision Variables: Quantity of each product to produce.
 Constraints: Production capacity, resource availability, and budget.
2. Inventory Management:
 Objective: Minimize holding costs.
 Decision Variables: Order quantities for each product.
 Constraints: Storage capacity and demand requirements.
3. Financial Portfolio Optimization:
 Objective: Maximize return on investment.
 Decision Variables: Allocation of funds to different assets.
 Constraints: Budget constraints and risk tolerance.

17. Benefits of Application of Optimizations to Business
Problems in Excel
1. Cost Reduction:
 Benefit: Optimization in Excel helps identify the most cost-effective solutions,
minimizing expenses in areas such as production, logistics, or resource allocation.
 Example: Optimizing the supply chain to reduce transportation costs and
warehousing expenses.
2. Resource Efficiency:
 Benefit: Optimal allocation of resources leads to increased efficiency in operations,
ensuring that resources are utilized effectively to achieve business objectives.
 Example: Optimizing workforce schedules to meet demand without excess labor
costs.
3. Improved Decision-Making:
 Benefit: Optimization models provide data-driven insights that contribute to better
decision-making, helping organizations choose the most strategic courses of action.
 Example: Optimal project portfolio selection based on available resources and
expected returns.

18. Benefits of Application of Optimizations to Business
Problems in Excel
4. Enhanced Productivity:
 Benefit: Optimizing workflows and processes in Excel leads to increased productivity
by streamlining operations and minimizing bottlenecks.
 Example: Optimizing production schedules to maximize output without overloading
machinery or staff.
5. Risk Mitigation:
 Benefit: Optimization models in Excel can account for uncertainties and risks,
providing businesses with robust solutions that consider potential challenges.
 Example: Optimizing inventory levels to account for demand fluctuations and
supplier uncertainties.
6. Strategic Planning:
 Benefit: Optimization supports long-term strategic planning by helping organizations
align their resources and activities with overarching business goals.
 Example: Optimizing marketing budgets to achieve maximum impact on strategic
marketing objectives.

19. Benefits of Application of Optimizations to Business
Problems in Excel
7. Competitive Advantage:
 Benefit: Businesses that effectively apply optimization techniques gain a competitive
advantage by operating more efficiently and adapting to changing market
conditions.
 Example: Optimizing pricing strategies to remain competitive while maximizing profits.
8. Supply Chain Optimization:
 Benefit: Optimizing supply chain processes in Excel improves overall efficiency,
reduces lead times, and enhances responsiveness to market demands.
 Example: Optimizing order quantities and delivery schedules to minimize inventory
holding costs.
9. Financial Performance Improvement:
 Benefit: Optimization contributes to financial performance improvement by
identifying opportunities to increase revenue, reduce costs, and maximize
profitability.
 Example: Optimizing product pricing to maximize profit margins while staying
competitive.

20. Benefits of Application of Optimizations to Business
Problems in Excel
10.Adaptability to Change:
 Benefit: Optimization models in Excel can be easily adjusted to adapt to changes in
market conditions, business strategies, or external factors.
 Example: Quickly adjusting production schedules in response to changes in customer
demand.
11.Ease of Implementation:
 Benefit: Excel's user-friendly interface and optimization tools make it accessible for a
wide range of users, simplifying the implementation of optimization models.
 Example: Business analysts using Excel's Solver add-in to optimize resource allocation.
12.Time Savings:
 Benefit: Optimization in Excel allows for rapid scenario analysis, enabling users to
quickly assess various options and find optimal solutions.
 Example: Swiftly optimizing project schedules to meet tight deadlines with minimal
resource constraints.

21. Video Guide about Prescriptive Analytics
 https://www.youtube.com/watch?v=pmj8c0a7mck
 https://www.youtube.com/watch?v=GMakbBlPMJ8&list=PLNLDEHOJTZSifHv--F
1WF4Xj21Ap_pN18
 https://www.youtube.com/watch?v=1RGt8gDhPjE
 https://www.youtube.com/watch?v=FneGDSAcXKs

22. References:
 Winston, W. L. (2020). Microsoft Excel Data Analysis and Business Modeling
(6th ed.). Microsoft Press.
 Cao, L., Yu, P. S., & Zhang, C. (2021). Data Science: An Introduction. CRC
Press.
 Chen, J. (2023). Practical Business Analytics Using R and Excel. Springer.
 Hill, R., & Hill, T. (2023). Excel for Business Professionals: The Essential Handbook
for Solving Business Problems with Excel. Apress.
 Pohl, E. A. (2021). Business Analytics: Turning Data into Insights. Wiley.
 Alexander, M., & Kusleika, D. (2020). Excel 2019 Power Programming with VBA.
Wiley.
 Buser, S. (2020). Mastering Financial Modeling: A Professional’s Guide to
Building Financial Models in Excel. Wiley.

23. Thank You!!!!
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