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Getting the most out of prescriptive analytics

Prescriptive Analytics

Business leaders incorporate big data into their strategic planning and understand their business’s future. Though large data sets cannot give you exact winning lottery numbers,

they can be used to identify problems and understand why they happen. Businesses can then formulate prescriptions to address business issues with the help of data-backed factors.

How does Prescriptive Analytics work?

Probability and likelihood are determined by predictive analytics, while actions are prescribed by prescriptive analytics. Analyzing descriptive and predictive analytics data to construct scenarios and determine the most likely outcomes is called prescriptive analytics.

A marketing team leader might want to know how many dollars to put into Google Ads in order to compete with other marketing channels, but predictive analytics can reveal how well that channel performs against other channels and prescriptive analytics can reveal the dollar amounts they should invest.

To analyze business constraints, analysts will use a variety of what-if scenarios. Prescriptive analytics is a method of analyzing data to predict the outcome of actions that will boost a company’s profits. Simulators and optimization techniques are used in the analysis to answer the question, “What is the next step?”. β€œIn order to generate different scenarios, analysts use a number of assumptions to simulate the future.

In combination with optimization techniques, scenario analysis can be used by businesses to come up with solutions to business problems. Using the results and data from descriptive and predictive analytics, prescriptive analytics provides recommendations based on possible actions to take.

Prescriptive analytics uses data from a business and the business rules to predict outcomes. A business might gather data internally through its operations or externally through social media and other platforms. The concept of business rules describes standards, boundaries, and constraints within an organization. You can get a better idea of how prescriptive analytics works by examining this model, which was retrieved.

Prescriptive analytics uses the following mathematical models:

  • Natural Language Processing
  • Statistics
  • Artificial Intelligence
  • Operational Research

There are variations within each of these disciplines that may complicate prescriptive analytics. Optimization, simulation, and analysis of options are common Operations Research techniques.

  • Prescriptive analytics has been simplified in this guide.
  • Approaches to prescriptive analytics

There are two different ways to approach prescriptive analytics;

Heuristics-based automated decision-making

  • Optimization-based decision-making
  • Optimization-Based Decision Making

Optimization has been applied to logistic planning for decades. Optimization enables better decision-making analytics by enabling the modeling of larger, industry-scale problems and providing support for scenario-based analysis.

Currently, optimized models provide more accurate information than predictive models as they take into account financials, value chains, and business constraints. In addition to ensuring internal consistency, optimization models can identify outcomes that may not be feasible.

Analysts are able to easily analyze pro forma financial statements, contribution margins, and activity-based costing using the optimization models. Users can make the most ideal business decisions with the help of these analyses. One such statement analyzes t-shirt prices and profit margins with different amounts as a basis for comparison.

Optimization can help businesses solve problems with over twenty constraints, trade-offs, and objectives. In order to achieve the majority of a company’s objectives, analysts can use prescriptive analytics to filter the factors to find out the best route to take.

Optimization is a process of maximizing or minimizing objective functions by using complex mathematical algorithms, considering business realities as well.

Heuristics-Based Decision Making

A rule-based approach to decision-making can also be referred to as that. Automatic decision-making is based on rules that have already been defined. Analysts use their business knowledge and top business practices to make these rules, rather than math.

It does not offer answers beyond predetermined rules, unlike optimization. Analysts provide prescriptions using simple algorithms and statistics. In addition to supporting decision-making, prescriptive analytics has a transformational impact. Prescriptive analytics gives you suggestions rather than probabilities.

Using prescriptive analytics to solve complex problems

The world’s most complex problems, such as scheduling, staffing, and routing, have been solved using prescriptive analytics for years. Business leaders did not deal with such problems, but rather data scientists. Rather than being an IT or Data Science tool,

prescriptive analytics is currently a tool within a business unit. A predictive analytics tool is now available to business leaders. Prescriptive analytics have become more widely used in business operations because of many factors:

  • Better and more diverse data sources
  • Prescriptive analytics technologies that do not require a data scientist
  • Day-to-day problems can be optimized with advanced mathematical techniques
  • A majority of medium-sized and large organizations use prescriptive analytics – it has become a “must-have” rather than a “good addition.”

When leadership makes the most feasible decisions, Return on Investment can be as high as 20 times. Prescriptive analytics offers better insights and business model improvement than most other forms of analytics, depending on the approach taken by business leaders and the type of problem addressed.

However, prescriptive analytics provides data-driven recommendations and suggestions using data from all forms of analytics. A prescriptive analytics program can provide business leaders with a variety of benefits.

Creating solid plans with more confidence

Plans are now more confident among business leaders. Business leaders are more likely to feel confident about optimization-based plans since they are by definition feasible. However, one must take into account the nature of the problem and how well the rules are set to determine whether heuristic-based plans are feasible.

Optimized decision-making has a greater chance of delivering positive results for a business since it takes into account the operation and cash flow of the business.

Prescriptive analytics helps companies understand what actions they need to take in order to achieve their goals. Managers who present plans confidently gain respect and can then implement further changes in the company.

Performance Improvement

Prescriptive analytics is able to provide business leaders with real-time insights that will result in improved operations and profitability. Prescriptive analytics can help streamline operations formerly dependent on intuition or unreliable tools like Excel.

Among the benefits for businesses are:

  • Improved effectiveness in attaining business objectives Increased efficiency of business operations – a company uses resources more effectively
  • to maximize returns on investments, for instance by optimizing how resources are allocated between different investments
  • Improve the Decision-Making Process

We can spend weeks and even months making difficult decisions. Sometimes businesses hire outside consultants, and this may add to the company’s costs. It is never thought through and given the time that significant decisions receive. Once again, there may not be sufficient time for business leaders to conduct analyses for these weekly decisions.

Through prescriptive analytics, organizations are better able to understand how different functions of a business affect one another and recommend a path that can help them evaluate different what-if scenarios to make faster business decisions.

Investment Risk Reduction

In finance or operations, there are risks. Businesses may not experience these risks. Business leaders use predictive analytics to identify and quantify risks involved in both short- and long-term decision-making processes. Leadership strategies can be developed in this way.

Increase Returns on Existing Investments

In addition to providing information on investments already made in tools like ERP, prescriptive analytics provide insights on how to leverage wholly existing investments in software. A prescriptive analytics solution shows a business how to move forward, so employees can contribute to its success and climb the career ladder.

  • Business planning challenges
  • Prescriptive analytics is able to provide business leaders with solutions to complex problems that other analytical approaches are unable to.
  • A Prescriptive Analytics Workflow
  • The algorithms that are used in Prescriptive analytics are:
  • Heuristic algorithms (rules-based)
  • Exact algorithms

A well-designed heuristic algorithm offers a less time-consuming route to finding feasible solutions than a specific answer but does not provide specific answers. However, a business needs time to develop a solution, especially if the problem is big.

Alternatively, exact algorithms provide precise answers. A precise algorithm is also an optimization. It is only when proven scientific techniques are applied to optimize those specific solutions that can be provided to business issues.

It is not necessary to prove the heuristics of the rule-based approach to be valid. In some cases, you cannot tell if you can provide the most accurate answer using the heuristics approach.

 

It uses heuristics and optimization in its prescriptive analytics. Although it is less common, there can sometimes be two solutions applied simultaneously. A business leader needs to understand which approach is suitable for their business strategy, so he or she knows where to use each.

How to Determine the Best Approach in Prescriptive Analytics

Take into consideration the following factors:

Nature of the Problem: Some problems fit more efficiently into the heuristic category than the optimization category, as we will see later on.

The complexity of the Problem: It can be challenging to solve well-known problems using optimization. A rules-based approach may be more effective in some cases than optimization because it finds an answer faster.

Using a heuristic approach for urgent problems has the advantage of getting you the answer today. An optimization approach might make more sense if you’re willing to wait.

Frequency: If you have to make a decision several times a day, an optimization approach may not be feasible; a heuristic approach, however, can come in handy.

Heuristics and mode of working

Heuristics refer to the rules governing a particular problem. It is possible to apply heuristics when you can define a problem narrowly or if the problem is operational. When hundreds of decisions are to be made every day, these rules apply. As you walk through an unfamiliar neighborhood, look for a building you haven’t yet seen but heard about.

As you begin walking, you follow the directions of the person who sent you. For example, “go east until you see a large water fountain.” In the absence of a map, GPS, or step-by-step directions that provide exact time and distance information, you can rely on your intuition and your knowledge of traffic to find the fountain.

Because you don’t know what awaits you, you can’t take the shortest route. By not knowing the exact location of the building, you might end up walking for ten minutes longer. A building might even be inaccessible without additional details. An example of a heuristic-based problem is presented here.

A business decision can be made using Excel. A hypothesis is made regarding a feasible answer with the help of features like IF functions. The solution appears immediately after entering values. It is impossible to determine whether the answer returned is the best unless you use an optimization approach.

Optimization isn’t always the best approach to solving certain problems or making decisions. Here are some of these scenarios and problems:

Purchase: When a business needs to purchase raw materials at the cheapest possible price.

Allocation: For example, allocate resources to line one, then two, and so on without regard to cost.

Marketing: This is when prices are reduced or promotions offered based on prior purchase.

Demand Fulfillment: Among many rules, the requirement to satisfy the needs of tier 1 customers before those of other customers can be met.

What role does predictive analytics play?

However, questions remain unanswered despite the use of predictive analytics. Analyses of descriptive analytics help identify the channels that generate revenue and predict outcomes, but the analytics themselves do not give you any advice.

What is the best amount of money to spend on marketing channel A in order to benefit most from this channel? In marketing, you might need to understand how much money to invest for the best ROI.

Whenever precise instructions on what to do are required, prescriptive analytics can help. Analysts conduct analysis of process flows, rules, objectives, preferences, constraints, policies, boundaries, best practices, revenues, and costs in businesses.

An analyst uses data and algorithms to chart a course forward. In prescriptive analytics, you specify your mathematical problem, then a set of algorithms finds the most feasible solution. An excellent image illustrates how these two analytical tools work in conjunction with each other.

Summary

Unlike other analytics which provide you with so much data to help you make the right decision, prescriptive analytics will help you make the decision in precisely the right direction. Heuristics-based and optimization-

based prescriptive analytics are respectively used in predictive analytics. In a heuristics-based analytics approach, solutions are prescribed depending on a range of what-if scenarios.

There may be no feasible solutions to these problems. Prescriptive analytics based on optimization find the most feasible solution by using math and algorithms.

A number of tools can be used, but they can be categorized into two groups: modeling platforms and packaged applications. A packaged application can be used immediately with only a few configurations, while a modeling platform enables you to develop apps.

 

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