Prescriptive Analytics

Prescriptive analytics is the logical next step after descriptive and predictive analytics. Instead of merely looking at the past, prescriptive analytics provides concrete suggestions for action. For you, this means less guesswork and more clarity in complex decisions—whether in production, logistics, or pricing.

It becomes particularly exciting when traditional decision-making processes come to a standstill and many factors play a role at the same time.

Prescriptive analytics—also known as optimization or action recommendation analytics—is a method of data analysis that translates forecasts into concrete recommendations. It combines descriptive and predictive analytics with optimization models and simulations, i.e., tools that playfully calculate scenarios and make direct suggestions.

Methods from the field of operations research are at work behind the scenes—processes such as linear programming or simulations that make complex scenarios calculable. AI processes such as reinforcement learning are also increasingly being used, which learn from historical and current data and independently improve strategies.

• Railway companies: Instead of simply predicting when trains might be delayed, prescriptive analytics calculates the optimal rerouting or scheduling to minimize disruptions.
• Production: Maintenance is planned more intelligently. The system recommends specific times for replacing parts so that machines are not unnecessarily idle.
• Retail: Demand forecasts are used not only to predict when a product will sell out, but prescriptive analytics also suggests the best reorder quantity and the ideal supplier.
• Finance: Instead of just flagging suspicious transactions, the system suggests specific actions that can be taken, such as account suspension, additional authentication, or further investigation.
• Marketing: Here, prescriptive analytics not only predicts when an email will be opened, but also provides clear recommendations on how campaigns should be timed and content individually tailored to increase conversions.
• Logistics: When disruptions occur in the supply chain, prescriptive analytics calculates alternative routes and warehouse transfers to ensure that goods still reach customers on time.
• Product development: Different design variants of a product can be simulated. The system recommends which variant has the greatest market potential based on costs, demand, and production time.

The biggest advantage: you no longer have to make decisions in the dark, but based on calculated scenarios. This allows you to choose the most economically sensible path from a wide range of options. Especially in dynamic markets, this is a clear advantage over gut feelings or rigid rules.

• Well-founded decisions in complex scenarios
  Decisions are made with the aid of simulations – for example, regarding the optimal production volume or the best storage strategy.
• Optimization of operational processes
  Resources are used in a more targeted manner and processes are designed to be more efficient.
• Transparency and comparability of options
  Different decision-making paths can be simulated and compared in terms of costs, time, and goal achievement.

In practice, this means that you can plan your production volumes more effectively without having to put up with overstocked warehouses. Investments can be prioritized based on data, and maintenance can be carried out as needed. Risks and opportunities become measurable—even for teams without a background in statistics.

The challenges of prescriptive analytics lie primarily in data quality, complexity, and corporate culture. Clean data, methodological expertise, and a shared understanding of objectives are essential. Many companies have problems precisely defining the relevant decision-making issues. Added to this is the mathematical complexity, which requires specialized knowledge.

Technology is rarely the problem—much more often, it is a lack of willingness to question established decision-making processes. Prescriptive analytics disrupts existing routines and challenges companies to rethink their decision-making processes. This is uncomfortable, but it also presents an opportunity to break out of old habits.

Prescriptive analytics is used in many industries to make data-driven and more efficient decisions. This approach can offer added value wherever there are multiple possible solutions and decisions have far-reaching consequences.

• Logistics & Production: Simulation of various production scenarios with regard to delivery times, storage capacities, and demand. Result: lower storage costs and better use of resources.
• Automotive industry: Optimized robot paths in production save cycle times and reduce scrap.
• Infrastructure & Transport: Dynamic control of train routes ensures better utilization and fewer delays.
• Finance: Models assist with capital allocation and fraud detection.
• Product development: Companies can run through various design options and choose the most promising one based on data.
• Marketing & Email: Recommendations on the best times to send emails or the right combination of offers increase conversion rates.
• Algorithmic recommendations: Smart systems suggest the right combination of products or services—personalized and context-specific.
• Investment decisions: Companies can run through different scenarios, such as when choosing a location or allocating a budget, and decide on the option with the best cost-benefit ratio.

For prescriptive analytics to be effective, the technical, organizational, and cultural conditions must be right. In addition to a stable database, there must be a willingness to approach decisions in a consistently data-driven manner. The following are important:

• Data quality & availability: Prescriptive analytics cannot function without clean, structured, and consistent data. Time series and event data, which can originate from ERP, CRM, or IoT systems, are important. Standardization, maintenance, and good interfaces are essential. A modern data platform or centralized data management provides the necessary foundation.
• Understanding decision-making processes: Prescriptive analytics does not replace thinking—it supports it. You need to clearly define which parameters influence the decision, which restrictions apply, and which target variables should be optimized. The more specific the question, the better the model.
• Organizational maturity & collaboration: It is not sufficient to rely solely on data scientists. Specialist departments contribute their requirements, data scientists develop the models, and IT provides the technical basis. An agile, iterative approach with an open error culture ensures that models improve step by step.
• Technological infrastructure: Simulation and optimization tools, integration frameworks, and visualization platforms are required that are scalable and compatible with existing IT systems.
• Strategic stance & willingness to change: Prescriptive analytics changes decision-making logic. Instead of relying solely on experience, data-based models come into play. This requires courage, trust, and a clear desire for cultural change. Clear rules, continuous learning, and support during the change process help to firmly anchor this innovation.

A prescriptive analytics project is not purely a technical issue, but rather an interdisciplinary process. Each step brings tangible benefits:

1. Clarifying objectives – Workshops help identify the truly relevant issues. This allows you to avoid detours and focus on what matters.
2. Data evaluation – We work together to check which data can be used. This saves time and ensures that the models are built on a solid foundation.
3. Prediction models – These provide reliable input variables for the simulation and improve the quality of subsequent recommendations.
4. Modeling decision spaces – All influencing factors and restrictions are mapped. This makes options comparable.
5. Simulation & optimization – Various alternatives are played through. This allows opportunities and risks to be weighed up objectively.
6. Validate and visualize results – Findings are presented in a comprehensible manner and made available for management decisions.
7. Integration – Optional integration into operational systems ensures that recommendations do not end up in the trash, but deliver real added value in everyday life.

Artificial intelligence makes prescriptive analytics even smarter.

• Reinforcement learning: Systems learn from experience and continuously improve their strategies—for example, in pricing, machine control, or fleet planning.
• Machine learning: ML ensures better input variables, e.g., through more accurate demand forecasts or pattern recognition in customer behavior.
• Managing uncertainty: AI helps to assess risks realistically – for example, in the case of fluctuating delivery times in the supply chain.

It is important to note that results must remain traceable (explainable AI) and be integrated into existing processes. Only then can AI-supported models deliver real business benefits and avoid ending up as proof of concept in a drawer.

We offer many years of experience in data analysis, both in setting up modern platforms and in modeling optimization processes. This enables us to create a basis on which companies can develop simulation-supported decisions.

Our consulting services include:

• Goal and problem structuring: What decision actually needs to be made?
• Data evaluation: What data is available, and what needs to be added?
• Designing decision-making models: Creating realistic simulations and optimizations.
• Deriving recommendations for action: Prepare results in such a way that they can be used in everyday business.

We don't just provide you with a finished product, but rather a consulting approach that combines technology, business, and culture. This results in exactly what your company needs—no more, no less.

Prescriptive analytics is about deriving smart decisions from data. Those who take this step will benefit in the long term from greater efficiency, lower costs, and faster decisions. MaibornWolff supports you in this process—with experience, methodological expertise, and genuine enthusiasm for solutions that work.