Prescriptive Analytics Overview

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Questions and Answers

What is commonly referred to as constraints in optimization problems?

  • Physical laws or company policies (correct)
  • Data coefficients used in decision-making
  • Various optimization algorithms
  • Common guidelines for data input

Which aspect of decision-making requires more granularity with shorter planning periods?

  • Daily operational costs
  • Data to be input as coefficients (correct)
  • Overall sales projections
  • Long-term investment strategies

In optimization, how should an objective be stated to achieve optimal results?

  • To maximize or minimize a specific metric (correct)
  • To increase operational costs
  • To allocate maximum resources
  • To minimize future sales

Which problem is NOT traditionally addressed by optimization techniques?

<p>Direct marketing strategies (D)</p> Signup and view all the answers

What influences the design of optimization algorithms regarding precision and time?

<p>User-defined specifications (B)</p> Signup and view all the answers

Which of the following is a cross-functional application of optimization?

<p>Commodity trading strategies (A)</p> Signup and view all the answers

When determining the cost of raw materials in optimization, which factor is essential?

<p>The price per ton of raw material (A)</p> Signup and view all the answers

Which of the following scenarios requires the use of optimization algorithms?

<p>Solving complex business functions (A)</p> Signup and view all the answers

Under what circumstances is heuristics preferred over optimization?

<p>When the same decision must be made repeatedly (D)</p> Signup and view all the answers

Which of the following is a characteristic of heuristics?

<p>They use problem-dependent rules (C)</p> Signup and view all the answers

In which situation might optimization be a better choice than heuristics?

<p>When a definitive and precise outcome is required (D)</p> Signup and view all the answers

What analogy is used to describe the approach of heuristics?

<p>Driving in an unfamiliar city with vague directions (D)</p> Signup and view all the answers

What kind of techniques do heuristics utilize?

<p>Highly specialized techniques for specific problems (A)</p> Signup and view all the answers

Which tool is commonly mentioned as useful for making business decisions through heuristics?

<p>Excel (A)</p> Signup and view all the answers

Which feature of Excel is specifically designed to help define rules for decision-making?

<p>IF statements and lookups (D)</p> Signup and view all the answers

What is a limitation of using heuristics compared to optimization?

<p>Heuristics can fail to provide the best answer (A)</p> Signup and view all the answers

In which of the following situations is optimization unnecessary and rules of thumb would suffice?

<p>Offering promotional deals based on customer prior purchases (B)</p> Signup and view all the answers

What is a primary disadvantage of using rules of thumb in decision-making?

<p>They can lead to limited benefits for holistic decision-making (D)</p> Signup and view all the answers

Which of the following is NOT a pro of using optimization for decision-making?

<p>Yields personally tailored solutions for every problem (B)</p> Signup and view all the answers

Which component is NOT part of an optimization problem?

<p>Randomized scenarios (D)</p> Signup and view all the answers

What is one of the main reasons why rules of thumb are often easier to implement than optimization techniques?

<p>They are typically embedded in software features (B)</p> Signup and view all the answers

Which of the following describes a common limitation of rules of thumb?

<p>They can create infeasible plans in tactical decision-making (D)</p> Signup and view all the answers

Which statement about optimization techniques is true?

<p>Optimization depends on mathematical modeling and exact algorithms (B)</p> Signup and view all the answers

Which of the following is a key function of decision variables in optimization?

<p>They define the business question to be answered (D)</p> Signup and view all the answers

Flashcards

Optimization Problem Size

Complex problems involving hundreds of thousands to millions of individual decisions.

Input Data (Coefficients)

Data used in optimization problems, which can be costs, prices, Bill of Materials (BOMs), or yields.

Constraints/Bounds

Business realities or restrictions, such as physical limits or company policies.

Optimization Objective

A metric (like profit or cost) to either maximize or minimize in an optimization problem.

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Optimization Algorithm

A method that searches for the best (optimal) solution from various options.

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Traditional Optimization Applications

Applications in areas like transportation planning, equipment replacement, staff assignment, blending of aviation fuels

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Modern Optimization Applications

Cross-functional applications, examples customer profitability, asset investment, product mix, treatment path, workforce planning, commodity trading

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Granular Data

Detailed data used for shorter planning periods.

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Optimization vs. Rules of Thumb

An optimization approach uses mathematical models and algorithms to find the absolute best solution to a problem. A rule of thumb is a simpler, usually pre-defined, method that often produces a 'good enough' solution, but not necessarily the absolute best.

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Raw Material Purchases (Rule of Thumb)

Choosing the cheapest raw material, regardless of quality, is a "rule of thumb" approach to purchasing.

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Capacity Allocation (Rule of Thumb)

Assigning capacity to lines sequentially, rather than optimizing for efficiency, is a 'rule-of-thumb approach'.

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Decision Variables

The specific business questions to answer during an optimization problem, such as quantity to produce or product to order.

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Optimization Problem Parts

An optimization problem needs the decisions to be solved for (decision variables), constraints, an objective function, and a model.

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Optimization Process

Mathematical modeling and specialized algorithms determine the optimal solutions.

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Optimization Problem

Problem needing a best possible solution. The problem is modeled mathematically and solved using special algorithms.

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Constrained Optimization

Optimization process in which solutions are subject to specific limitations.

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Optimization vs. Heuristics

Optimization seeks the absolute best solution, while heuristics use rules to make quick decisions when there are very many decisions to make.

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Heuristics Definition

A set of problem-dependent rules used when many decisions must be made quickly, to solve a particular problem quickly.

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Frequency of Decisions

How often a decision needs to be made, impacting whether optimization or heuristics is a better choice. Many decisions per day favors heuristics.

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Heuristics Application

Heuristics are best when the problem is well-defined (detailed), and decisions are operational (everyday tasks) and repetitive (the same kind of decision).

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Heuristic Process

Heuristics relies on mathematical functions, instructions, or both. to make the decision automatically.

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Heuristic Analogy

Finding a destination without precise maps or GPS; relying on general knowledge and intuition, not the shortest path.

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Optimization Analogy

Using a GPS system for a specific route to get the shortest and fastest path.

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Excel's Role in Decisions

Excel can be used for creating heuristic rules for business decisions using tools like IF statements, lookups, and functions—but does not inherently provide an optimal answer.

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Study Notes

Prescriptive Analytics

  • Prescriptive analytics is the final stage of business analytics
  • It finds and suggests the best decision options for a given situation
  • It involves data collection, information extraction, forecasting, optimization, visualization, and what-if analysis
  • It suggests the best decisions based on predictions and illustrates the implications of each decision option

Difficulty

  • Prescriptive analytics determines "What do we need to do to achieve this?"
  • It uses technology to analyze data and help businesses make better decisions
  • It considers possible situations, scenarios, resources, past performance, and current performance, then suggests a course of action
  • It can be used for immediate and long-term decisions
  • It is the opposite of descriptive analytics, which examines decisions and outcomes after the fact

Key Takeaways

  • Prescriptive analytics figures out what needs to be done to reach a goal
  • It uses machine learning to decide the best course of action, based on computer predictions
  • It uses predictive analytics to determine near-term results
  • It helps organizations decide with facts and probabilities rather than instinct
  • Its effectiveness depends upon the quality of its input data

How Prescriptive Analytics Works

  • It tries to answer "How do we get to this point?"
  • It uses artificial intelligence, such as machine learning, to understand and learn from data
  • It continually adjusts to new data, and adapts during its learning process

Examples of Prescriptive Analytics

  • Assessing wildfire evacuation needs
  • Forecasting article popularity based on reader data
  • Adjusting worker training programs based on progress
  • Optimizing hospital patient outcomes through cost-effectiveness of treatments
  • Adjusting airline ticket prices based on demand, weather, and fuel prices
  • Better serving customers in banking, and maximizing company profits

Prescriptive Analytics in Marketing

  • Critical in the marketing sector to stay ahead of consumer trends and leverage past performance
  • It can develop effective marketing campaigns targeting specific customer demographics
  • Helps define how to engage customers and effectively price and discount products/services

How Prescriptive Analytics Works (continued)

  • Heuristic algorithms do not guarantee the best answer, but offer a quick approach for good answers
  • Exact algorithms guarantee the best answer, but require more time for complex problems

Optimizations

  • Optimization uses mathematical modeling & algorithms to find the best solution
  • It outlines decisions as mathematical equations and models
  • It utilizes algorithms to identify that best solution
  • Decision variables are the questions to be answered
  • Data input (coefficients) are costs, prices, bills of materials or yields

Optimization Considerations

  • It's used for complex problems unsuitable for heuristics
  • Examples include transportation, equipment replacement, assignment problems, blending, etc.

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