Predictive vs Prescriptive Analytics – How do they differ?
Descriptive Garuda Pancasila, diagnostic analytics, predictive analytics, and prescriptive analytics are the four types of data analytics that businesses use today. These categories each play an important role in the decision-making process. While diagnostic analytics seeks the reasons behind an event, descriptive analytics only recounts the facts of what happened. But predictive vs prescriptive analytics, how do they differ?
The field of predictive analytics examines what is to come and makes predictions about what might occur in the near future. Prescriptive analytics is what comes next, which makes recommendations on what specific steps to take in order to obtain the best possible outcomes. This article expands upon our guide, 'What is prescriptive analytics?', by contrasting predictive vs prescriptive analytic approaches.
Mapping the past and diagnosing causes
Descriptive analytics transforms transaction logs, operational metrics and web-traffic records into visualisations and summary statistics, establishing exactly what has occurred [1]. Teams typically run SQL queries or use business-intelligence platforms to calculate totals, averages and growth rates. Diagnostic analytics then applies hypothesis testing, correlation analysis and root-cause techniques to explain variances from expected trends.
For instance, if there's an unexpected drop in active users, it can be because of a new software update or some external market event. Then, you can take the right steps to fix it [2].
Predictive forecasting techniques
Businesses use many different types of data analytics tools to help them go from insight to foresight. When dealing with time-series data, they may use ARIMA (Autoregressive Integrated Moving Average) to simulate trend and seasonality [3]. Also, researchers may employ LSTM (Long Short-Term Memory) networks to capture intricate temporal connections [4].
Furthermore, techniques for regression or classification are used in order to make predicitve analytics projections regarding continuous measurements. Assigning category outcomes, such as churn risk, or projecting income are examples of this [5]. Effective feature engineering, incorporating variables like economic indicators, weather patterns or social-media sentiment, often unearths hidden correlations that sharpen forecasts.
In practice, inventory managers use ARIMA models to set the appropriate levels of safety stock. This approach can cut stock-out incidents by up to 20%, according to McKinsey research [6]. While this is going on, marketing teams can schedule campaigns with the use of the LSTM insights. These campaigns can improve engagement rates by 15%.
Finance departments rely on regression models to stress test budgets under varying economic scenarios, which helps with more accurate budgeting.
Finance case study: Upstart’s AI lending platform
Upstart Holdings, Inc. partners with banks to deploy machine-learning credit models based on thousands of data points.
Loan loss rates have been greatly lowered using their AI-driven predictive analytics underwriting approach. While keeping approval rates the same, it has reduced loan losses by about 75% when compared to traditional approaches that just use FICO [7]. Upstart further reports that 74% of loan applications receive an instant decision. As a result, the origination cycle is cut down by several days, and the level of customer satisfaction is increased.
Prescriptive strategies for action
Building on forecast accuracy, prescriptive analytics integrates optimisation and simulation to recommend specific decisions under real-world constraints. Techniques include linear and non-linear programming. These methods define goals, such as minimising delivery time, and impose constraints like vehicle capacity or regulatory limits.
In order to measure uncertainty and risk, the Monte Carlo simulation cycles through thousands of possible scenarios. Continuous integration of streaming data traffic feeds, sensor networks or social media sentiment ensures recommendations adapt in real time.
In manufacturing, optimisation solvers allocate production volumes across plants to meet demand. At the same time, they respect labour and material constraints. Healthcare administrators simulate patient flows and staffing levels to reduce process times. Meanwhile, logistics operators dynamically reroute fleets to improve on-time delivery rates.
Healthcare case study: Massachusetts Medical Centre
A private nonprofit medical centre in Massachusetts, which has two campuses, applied a queue-based Monte Carlo simulation [2]. This was done to assess the effects of adding a specialised "fast-track" unit for patients with minimal levels of acute care needs. The group mimicked service times and patient arrivals. The researchers showed that adding one additional nurse to the fast track reduced median wait times by 35.8 ± 2.2%.
Also, as a result of this change, the main emergency department's resource use was optimised without an increase in staffing hours. There was a 10% increase in patient satisfaction levels after the adoption.
Logistics case study: Amazon’s SCOT team
Amazon’s Supply Chain Optimisation Technologies (SCOT) group combines optimisation, simulation and predictive vs prescriptive analytics to manage fulfilment across its network. Delivery windows as short as two hours are made possible in certain urban locations by algorithms driven by SCOT. This is a major reduction from the typical six-to-eight-hour window.
Also, these algorithms help reduce excess inventory by around 10% through dynamic stock-level adjustments. These improvements underpin Amazon Prime’s rapid-delivery promise and drive higher customer loyalty.
Distinctions between forecasting and recommendations
Predictive vs prescriptive analytics
Forecasting is the process of identifying what is likely to occur in the future. In order to achieve this, it examines both historical and present data using predictive analytics. On the other hand, prescriptive analytics tackles the question of what actions ought to be addressed. The possible actions are evaluated in respect to the business objectives and the practical limitations.
The majority of the time, predictive analytic models are dependent on structured datasets. In contrast, prescriptive analytics are also able to include unstructured data such as text logs or streams from the internet of things (IoT). It is because of this that they are able to better reflect the complexity of the real world.
Implementing prescriptive analytics calls for the creation of a robust infrastructure. This infrastructure should include optimisation solvers and governance processes that are focused on ensuring compliance with business standards. Prescriptive outputs can prompt changes to operations in near-real time. Meanwhile, forecasting helps to shape strategy over the medium to long term.
Roadmap for implementation: A practical checklist
If you want to move from having an insight to taking action, follow these steps:
- Audit and prepare data. Validate data sources and clean records to ensure reliable descriptive analytics and diagnostic reporting.
- Develop and validate forecast models. It is advised that you employ prediction approaches such as regression, ARIMA, and LSTM. These methods should be tested against hold-out data, and features should be refined in an iterative manner.
- Pilot prescriptive solutions. Begin with small-scale optimisation projects, such as inventory allocation or staff scheduling, to manage risk.
- Scale Infrastructure. Invest in data warehouses, streaming platforms and solver libraries to support enterprise-wide deployment.
- Establish governance. Make sure analytics are in line with ethical and regulatory standards by documenting methods and implementing oversight.
By following this roadmap, your business can transition from reactive reporting to proactive. This shift will lead to optimised decision-making that delivers measurable improvements.
Conclusion
Companies can learn from their successes and failures and use that knowledge to influence what happens next. Prescriptive advice, predictive forecasts, diagnostic insight, and descriptive analysis all work together to achieve this.
EU Automation plays a pivotal role in ensuring that your supply chain operates at its full potential. Our goal is to support your journey towards smarter decision-making to enhance operational efficiency and reduce downtime.
For more insights and solutions, visit EU Automation's knowledge hub.
Sources:
[1] https://technologyadvice.com/blog/information-technology/descriptive-analytics/
[2] https://doi.org/10.1155/2017/6536523
[3] https://www.statsmodels.org/stable/generated/statsmodels.tsa.arima.model.ARIMA.html
[4] https://keras.io/api/layers/recurrent_layers/lstm/
[5] https://www.geeksforgeeks.org/ml-classification-vs-regression/
[7] https://cloud.google.com/dataproc/docs/tutorials/monte-carlo-methods-with-hadoop-spark
[8] https://www.gartner.com/en/information-technology/glossary/prescriptive-analytics
