Enhance Efficiency of Your Business with Prescriptive Analytics

Businesses often find themselves in a fix where they must chart out the best way out of a problem statement. In the bid to go global and be the market leader, today's industries have to put an enormous focus on a broad customer base. With the worldwide reach of enterprise comes tremendous information to be analyzed for demand forecasting, efficient planning, and management. This is where the edge of data analytics finds itself in the mix, answering complex questions. Read on to learn more about its benefits and application in detail as we cover Prescriptive Analysis.

What is Prescriptive Analytics?

It is data-driven analytics that answers the question, “What can be done to optimize operations?” This method explores the possible outcomes of an action and its impact on the supply chain and other departments of a business and comes up with recommended steps that can help reduce costs and maximize profits. It consists of graph analysis, complex event processing, recommendation engines, machine learning, heuristics, simulations, and neural networks.

Predictive analytics is also known to helping organization in maintaining optimal inventory levels so that there are minimum stocks without the problem of frequent stock-outs. Using the right analytics techniques and tools allows the management to know the right demands by region and location. To implement Prescriptive analytics in an organization, the administration needs to consider many factors to ensure the best outcomes. In general, a decision related to business processes with more than 20 variables can be an excellent opportunity to implement optimization analytics.

What are Prescriptive Analytics Techniques and Methods?

While prescriptive analytics has its roots in the context of the outcome set by the organization, the roadmap to the objective has various techniques that can be used to prepare the best course of action for the end goals.

Here are some of the crucial methods that are covered in titular subject.

• Optimization: A more mathematical approach to the information set available to the enterprise, the Optimization technique involves linear programming, integer programming, and expansive multivariate methods used by the data scientist. This method comprehends all possible variables and constraints that pertain to a company’s performance in the mathematical way of analysis.
• Simulation: This method runs all the what-if scenarios to help prescriptive analysis come to a more astute conclusion. As the word suggests, this technique creates virtual models of possible real-world situations to test varied decisions and outcomes. This allows stakeholders to see the potential consequences before settling on the final solution. Some prominent simulation techniques are Monte Carlo simulations and discrete event simulation.
• Decision Tree/Analysis: A schematic approach to a problem statement, Decision Tree, or Analysis branches out variables, constraints, and outcomes, helping the analyst break down the risks and rewards of each possible course of action ahead. This method is also considered an easy-to-follow format amongst its counterparts.
• Game Theory: Game Theory is defined as a framework where competitors are put in a social situation, and their responsive actions are recorded for analysis to chart out who took the best way ahead. A term finding its roots in the economic background, this model allows interaction amongst multiple decision-makers in a modeled situation, accounting for every move that benefits or goes against. Some examples of the Game Theory model are Nash equilibrium and backward induction.
• Artificial intelligence and Machine Learning: With AI’s prowess in identifying patterns from vast sets of information, the said platform indeed stands to benefit management with relevant insight aiding a company’s workflow. Furnishing real-time influx for building a constructive roadmap ahead, AI and ML techniques is an extensive, time-sensitive process.

How Prescriptive Analytics Works?

Like any other analytics process, prescriptive analytics also works on relevant information. It starts with Data and its subsets, found in the formatted structure of images, videos, language models, and structured info under defined parameters. Post that, the defined Analytics techniques (as mentioned above) work their way into identifying patterns by running exhaustive learning cycles and re-learning patterns, which subsequently give way to specific recommendations and automated decisions.

What are the advantages and disadvantages of Prescriptive Analytics?

When used correctly, prescriptive analysis can be incredibly beneficial for a business, but it is wrong to say that it has no shortcomings.

Let’s have a look at its pros and cons.

• Informed Data-Driven Decisions: One of the primary advantages of prescriptive analytics techniques is that it improves the decision-making process. Unlike in older times, when decisions were made based on instinct and intuition, now they are based on relevant data.
• Reduced Risks: It analyses a huge amount of data and determines the probability of outcomes. This helps understand the likelihood of failure or success before deciding.  This enables you to avoid any pitfalls and revenue loss while also helping you spot new opportunities.
• Enhanced Efficiency of Business Processes: Leading to increased efficiency of business flow, you can save time as you will have processed data and analytics at your disposal when it is time to take decisions.

• Requires Valid Input and Huge Data: The tools used for analytics can use huge chunks of relevant info to help you make decisions for your business. The only limitation is that the efficacy of analytics is directly dependent on the correctness of data. If the data is not correct and the sources unreliable, then the analytics may not be worthy as well.
• Not Much Reliable for Long-Term Planning: Another shortcoming of prescriptive analytics is that it can be more accurate and reliable for short-term timeline. As the window increases so does the unpredictability.

What are some of the use cases of Prescriptive Analytics in Supply Chain Industry?

Here are some examples of industrial applications of prescriptive analytics.

• Prescriptive Analysis in Supply Chain Network Design: Supply chain network design is a task that involves strategic planning of the value chain focusing on two things: the operation’s footprint and the product flow. The network is designed according to the capacity of the business facilities, and the required movement of raw materials, unfinished, and finished goods – from the source of raw materials to the industries and the point of consumption. Planners need to consider complex variables in the network design choices they make, which include the cost of labor, the location of the customer, and transportation networks that are available in a particular area. As there is a big number of variables included in the planning, it increases the complexity of the process and hence these decisions are made after the support from prescriptive analytics solutions.
• Prescriptive Analysis in Integrated Business Planning: The IBP process seeks to align different departments of a business to work towards a single goal. Now as there are different departments involved in the brainstorming, there are multiple variables involved and the planning becomes extremely complex. The process starts with a forecast and then results in a sales plan, inventory planning, production planning, and financial planning. To make more efficient decisions, planners can benefit greatly from prescriptive analysis for decision-making.
• Prescriptive Analysis in Sales and Operations Execution: S&OE is gaining importance in the industries now because of demand volatility. Planners need to consider inventory management, production planning, and supply management. In addition to that, customer prioritization is another factor to be considered. With so many things to be taken care of, it can be difficult to bring in improvements. Prescriptive analytics can be very helpful in dissolving this complexity and help in better communication using the data available. Optimization models obtained from the analytics help teams choose the correct action when faced with unexpected errors and get information about the impact of their decisions on products and customers.

What are the Challenges of Prescriptive Analytics?

Optimization of business processes through analytics is not an easy task. There are many constraints that impact the efficiency and effectiveness of the analysis. Prescriptive analytics models present new challenges as the data is analyzed and you dive into machine learning. Here are some of the challenges that planners may face in the industry while working with big data.

• Incomplete Data: The first and foremost challenge is the accuracy of the data. As analytics work on big amounts of data, it is important to ensure that the data being used for model creation is complete, accurate, and coming from reliable sources. A model created from incomplete factors may not be as effective in decision-making.
• Skills: Prescriptive data analysis is a team effort. It requires the whole team to work in the same direction toward a goal to ensure success. As the market conditions change rapidly, the number of people on the team who have domain knowledge is important. Machine learning gadgets are evolving, and it takes a high level of expertise and technical skills to work on these tools and build an optimization model.
• Adoption: Even as analytics tools are becoming more approachable and business-friendly, there is still a gap between the requirement and adoption of analytics platform. Distrust for the metrics that are used for the building optimization model and major dependence on Excel are the reasons for the low outreach of analytics framework across the industry.

Why Prescriptive Analytics is must for organizations?

Gone are the days when data analysis on Excel spreadsheets used to be effective. Customer behavior is changing as every business is trying to leverage technology and get customers to choose their products by analyzing the facts and figures they get from sales and marketing platforms.

Analyzing such a large amount of data requires highly efficient machine learning and AI that can help build optimization models for demand forecasting, supply chain planning, and management. Be it retail, healthcare, banking, or education, modern-day businesses need more agile, accurate, and adaptive prescriptive analytics tools for better groundwork and decision-making.

3SC offers comprehensive analytics solutions for businesses that go beyond predicting and describing possible scenarios by recommending the best action plan. Our prescriptive analytics tools analyze huge amounts of data using statistical and algorithmic models to provide fast recommendations for the correct course of action to get desired outcomes.