How can prescriptive analytics enable sustainability and efficiency in manufacturing?

As mentioned below, is the highest point of maturity in terms of analytics. For example, let's say a manufacturing plant wants to increase efficiency and reduce waste. It is possible to use analytics to analyze data from various sources – like machine performance, supply chain logistics, and worker productivity. We can describe what happened (descriptive analytics), why it happened (diagnostic) and even predict that a particular machine is likely to break down soon (that's predictive analytics). Then, we go a step further – by suggesting a specific action, like scheduling maintenance for that machine at the least disruptive time or adjusting production schedules (that's prescriptive).

Prescriptive analytics is the highest point an organization can reach on the analytics maturity curve. I typically classify it as the fourth highest though with predictive (what will happen), diagnostic (why it happened) and descriptive (what happened) preceding it respectively. Its effectively a mechanism to dictate the best action a user should take.

Prescriptive analytics significantly enhances sustainability and efficiency in manufacturing by offering actionable insights and recommendations based on data analysis. It optimizes resource utilization, leading to reduced waste and better allocation of materials and energy. By facilitating more accurate demand forecasting and enabling on-demand production, it helps in aligning manufacturing output with market needs, reducing overproduction. Furthermore, prescriptive analytics guides manufacturers in complying with environmental regulations and improving workplace safety, contributing to a more sustainable and efficient manufacturing process.

Prescriptive analytics represents the pinnacle of data analytics, following descriptive and predictive analytics, by not only forecasting future outcomes but also providing actionable recommendations on the optimal steps to achieve desired results. It leverages advanced technologies such as machine learning, artificial intelligence, optimization algorithms, and simulation models to process vast amounts of data and identify the most effective strategies. By incorporating real-time data and feedback, prescriptive analytics can dynamically adjust its recommendations, ensuring that decision-makers are equipped with the most current and relevant insights.

In my view, the future of manufacturing demands that production engineers be equipped with advanced analytics knowledge. Specifically, integrating individual machine output data into the ERP system and leveraging this detailed information will be key. By having Power-BI alongside SAP-ERP for this purpose, engineers can conduct thorough analyses of production data. This integration will enable the identification of inefficiencies at a granular level, facilitating targeted corrective actions. Ultimately, by linking this data with market demand, we can better align production efficiency with actual needs.

As mentioned above, prescriptive analytics not only predicts outcomes but also recommends actions. It still will start by analysing past and current data. Then, using algorithms and models, it forecasts different scenarios. And finally, based on this modelling, it will recommend which action is best to take.

Certainly! One real-world example of prescriptive analytics is the optimization of delivery routes used by United Parcel Service (UPS). By leveraging advanced algorithms that consider various factors such as package weight, size, destination, and delivery routes, UPS can determine the most efficient path for package delivery. This not only enhances operational efficiency but also reduces fuel consumption and improves customer satisfaction by ensuring timely deliveries. Prescriptive analytics here plays a pivotal role in streamlining logistics and driving sustainable business practices.

Prescriptive analytics uses machine learning algorithms to analyze large datasets and identify patterns, relationships, and correlations. It then uses these insights to generate recommendations for specific actions or decisions. The system considers multiple factors, such as current performance, future goals, and potential outcomes, to provide a clear direction for future action. By combining descriptive and predictive analytics with prescriptive analytics, organizations can make data-driven decisions that drive business outcomes.

Prescriptive analytics works by analyzing data and leveraging optimization techniques to provide actionable recommendations. Using algorithms, machine learning, and simulations, it creates models to determine the best actions or strategies to achieve desired results. Optimization techniques, like linear programming or decision trees, are employed to find solutions under constraints. This process helps businesses make informed decisions that drive efficiency, improve outcomes, and reduce risks.

The main benefit is that it suggest us how we can do something, like improving OEE in a factory instead of just describing the trends of the KPI or diagnostic why the KPI was lower or higher from last month. Even compared with predictive analytics, prescriptive analytics can be a more powerful tool since it's allowing you not just to predict what will happen in the future but instead "how to make it happen".

Prescriptive analytics enables sustainable manufacturing by optimizing resource allocation and energy use through real-time data analysis. It minimizes waste by predicting material needs and maintenance schedules, reducing downtime, and preventing overproduction. Analytics-driven demand forecasting ensures just-in-time production, lowering inventory costs and emissions. For sustainability, it models the environmental impact of different production scenarios, helping balance profit and eco-responsibility. By simulating alternative processes, manufacturers can enhance efficiency while meeting regulatory standards and improving worker safety, aligning operational goals with long-term sustainability strategies.

Prescriptive analytics provides significant advantages to the manufacturing sector, here are some specific use cases and benefits: - Optimizes Production Processes to Identify bottlenecks and inefficiencies - Predictive Maintenance optimizes schedules and minimizes downtime - Reduces investments in MRO spares. - Improved Management of labor, materials, and equipment - Improved Supply Chain Management - Increases Flexibility and Agility - Sustainability and Compliance - Cost Reduction and Profitability Incorporating prescriptive analytics into manufacturing processes empowers organizations to make informed decisions that drive operational excellence, enhance customer satisfaction, and support sustainable growth.

Prescriptive analytics guides business users to the most optimal decision or action that can be taken based upon all relevant variables. Rather than simply display information and let the user infer the best course of action to take (i.e. - descriptive analytics), prescriptive analytics guides users to that decision.

The real payoff of decision optimization lies in the ability to make reliable business decisions that create value. Optimization and simulation techniques can drive huge efficiencies in dynamic manufacturing environments, helping manufacturers make demanding decisions, under different & complex scenarios and uncertainty. Prescriptive analytics can help in a variety of manufacturing areas, such as production scheduling, inventory management, logistics, etc.

Prescriptive analytics can be challenging due to the complexity of the data, the need for accurate predictions, and the requirement for interpretable results. Additionally, prescriptive analytics often requires significant computational resources and may be limited by data quality, missing values, and outliers. Integrating prescriptive analytics with other forms of analytics, such as descriptive and predictive analytics, can also be complex and require careful planning. Furthermore, ensuring that the results are actionable and usable by non-technical stakeholders can be a significant challenge.

In this days and age I think the main challenges are 1) having the correct data to do the analysis and 2) be able to fully interpret the algorithms for people to trust their outputs. For example, if we would want to optimise the electricity usage of a factory it would be great to have "live" and clean data for all the machines in the factory and data for all the products coming out of it. Then we would need enough trust in the algorithms to fully apply the decisions suggested by them - for that, algorithm explainability is key!

To start, poor data quality and incomplete metadata will serve as a hindrance to producing any reliable result set to govern a decision. Transparency and visibility into the model's decision making process is also needed in order for users to "trust but verify" the process itself.

Prescriptive analytics faces several challenges. Data quality and availability are critical, as incomplete or inaccurate data can lead to flawed recommendations. The complexity of models requires specialized skills in data science, optimization, and machine learning, which can be hard to find. High computational power is often needed, leading to increased costs and time. Finally, the complexity of the models may reduce interpretability, making it harder for decision-makers to trust and act on the recommendations.

I would also add that gaining fundamental competencies in the preceding three types of modern analytics (descriptive, diagnostic, and predictive) in the analytics maturity curve is a necessary first step. Its hard to jump to prescriptive analytics without gaining the necessary, foundational skills within the analytics maturity curve first.

To get started with prescriptive analytics, identify a specific business problem or opportunity that can be addressed with data-driven decision-making. Gather relevant data and ensure it is clean, accurate, and complete, and then select a suitable prescriptive analytics tool or platform. Develop a clear understanding of the business requirements and goals, and work with a data scientist or analytics expert to design and implement a prescriptive analytics model. Finally, validate the model and refine it as needed to ensure it is accurate and effective in driving business decisions.

To get started with prescriptive analytics, follow these steps: 1. Define Objectives: Identify specific business problems or areas for optimization. 2. Data Collection: Gather relevant, high-quality data from various sources and ensure it’s clean and structured. 3. Select Tools: Choose suitable analytical tools and software (e.g., Python, R, or specialized platforms). 4. Apply Optimization Techniques: Implement algorithms like linear programming or decision trees to generate actionable recommendations. 5. Test and Validate: Simulate different scenarios to validate models and ensure recommendations align with business goals.

Prescriptive analytics is a powerful tool that can help manufacturers optimize their production planning, scheduling, inventory, and supply chain logistics to meet business needs. Experts recommend using decision optimization technologies to solve complex problems ranging from hundreds to millions of constraints and variables that could never be analyzed manually. Decision optimization technology can make it easier, even for non-tech-savvy users, to consider constraints, trade-offs, and various goals to get better and faster answers to questions in manufacturing.

To embark on prescriptive analytics, organizations should first integrate siloed data, laying a foundation for a holistic view. This integration enables businesses to leverage prescriptive analytics, which not only forecasts future opportunities and risks but also recommends decisions. By evaluating the implications of each option, leaders can make informed choices that ensure operational sustainability. This approach is crucial for sustainable operations, as it aligns decision-making with both efficiency and environmental goals. Start by identifying key data sources across your organization, unify them into a central analytics platform, and apply prescriptive analytics to drive actions that balance profitability with sustainability.