Prescriptive Analytics is the advanced analytics technique used for optimized decision-making with the help of a recommended action. In today’s times, industries are going global and must cater to the demands of customers worldwide. With the global reach of business comes huge data to be analyzed for demand forecasting, efficient planning, and management. It can be very helpful in data analysis and answering complex planning questions. Read on to learn more about its benefits and application in detail.
What is Prescriptive Analytics?
It is data-driven analytics that answers the question “what can be done to optimize business 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 helpful for an organization in maintaining optimal inventory levels so that there are minimum stocks without the problem of frequent stock-outs. Using the right prescriptive 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.
How Prescriptive Analytics Works?
An approach for prescriptive analytics in the supply chain will include running a computer simulation of a given situation multiple times. Each time the simulation is run, some variables are tweaked to get the possible outcomes of different scenarios for comparison. These outcomes are further used for better decision-making during the business planning process.
As artificial intelligence and machine learning are evolving, data analytics models are capable of processing massive data and automatically adjusting as they receive new data inputs. Several optimization techniques such as integral programming, linear programming, and non-linear programming, are applied to a model as prescriptive analysis methods. This model represents decisions to be made, constraints limiting the decisions, and the objective to compare the outcomes.
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.
1. 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 decisions are based on past data.
2. 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.
3. Enhanced Efficiency of Business Processes
It helps you make decisions faster, leading to increased efficiency of business processes. You can save much time as you will have processed data and analytics at your disposal when it is time to take decisions.
1. Requires Valid Input and Huge Data
The tools used for analytics can use huge chunks of data 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 data sources are reliable, then the analytics may not be reliable as well.
2. Not Much Reliable for Long-Term Planning
Another shortcoming of prescriptive analytics is that it can be more accurate and reliable for short-term planning as the timeline is extended, the unpredictability also increases.
What are the Examples of Prescriptive Analytics?
Here are some examples of industrial applications of prescriptive analytics.
Supply Chain Network Design
Supply chain network design is a task that involves strategic planning of the supply chain focusing on two things: the supply chain’s footprint and the product flow. The supply chain 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 process, it increases the complexity of the process and hence these decisions are made after the support from prescriptive analytics solutions.
Integrated Business Planning
The integrated business planning process seeks to align different departments of a business to work towards a single goal. Now as there are different departments involved in the planning process, there are multiple variables involved and the planning process 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.
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 data analysis and model developed based on that 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.
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 data may not be as effective in decision-making.
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 tools are evolving, and it takes a high level of expertise and technical skills to work on these tools and build an optimization model.
Even as analytics tools are becoming more approachable and business-friendly, there is still a gap between the requirement and adoption of analytics tools. 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 tools across the industry.
Prescriptive Analytics Tools
Gone are the days when data analysis on Excel spreadsheets used to be effective. Now, customer behavior is changing every business is trying to leverage the technology and get customers to choose their products by analyzing the data they get from sales and marketing platforms. Analyzing such a big amount of data requires extremely efficient machine learning and AI tools that can help build optimization models for demand forecasting, supply chain planning, and management. Excel spreadsheets are not just enough for data analytics. Modern-day businesses need more agile, accurate, and adaptive prescriptive analytics tools for better planning and decision-making.
Leveraging Prescriptive Analytics at Your Organization And How We Help?
Business planning is a complex process, and you require huge data analysis for effective and accurate planning of processes. Be it retail, healthcare, banking, or education, raw data without any actionable planning is of no use.
3SC offers comprehensive data analytics solutions for businesses that go beyond the prediction and description of possible scenarios but also recommend the best action. Our prescriptive analytics tools analyze huge data using statistical and algorithmic models to provide fast recommendations for the correct course of action to get desired outcomes.
What Does Prescriptive Analytics Mean?
It is an analytical process where huge chunks of data are analyzed to build an optimization model of business processes and the right course of action is recommended for required outcomes.
Why Is Prescriptive Analytics So Important for Businesses?
Once the business planners analyze the data and predict a set of outcomes, the importance of prescriptive analytics increases as it presents the right actions which are beneficial for the businesses in the long run.
What are Prescriptive Analytics Techniques?
The optimization techniques that are used include linear programming, non-linear programming, and integer programming.
What do we use Prescriptive Analytics for?
It is used for predicting an optimization model for business practices and finding the most optimum course of action that will help reach the common business goal of a company.
What Kind of Problems can be solved by Prescriptive Analytics?
It can be used for functions such as supply chain network designing, Sales & Operations Planning, Sales & Operation Execution, Inventory Optimization, and Warehouse Optimization.
Which Companies use Prescriptive Analytics?
Companies working in logistics, healthcare, retail, manufacturing, banking, education, and many other sectors where there is a requirement for huge data analytics and predicting future outcomes, use prescriptive data analysis.