Predictive Analytics

What is Operational Predictive Analytics in manufacturing?

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Operational Predictive Analytics is a model based mathematical systems approach to proactively make the "best possible" manufacturing operational decisions. It is a way to develop and assess scenario options to shape the future/make the business operations more effective. It models the environment of the future.

What does Operational Predictive Analytics do?

Operational Predictive Analytics helps identify the optimal solution for better performance. It considers the manufacturing organization's current capabilities, limitations, business objectives, processes, customer needs and other parameters in a holistic approach. The question is not "what will the future be" but how will the system perform under different configurations and possible scenarios? It helps answer questions such as:

  • What is the cost of manufacturing each product?
  • What happens if demand decreases by 40% or doubles?
  • What happens if the cost of raw materials escalate?
  • What is the impact of consolidating multiple facilities?
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How does Operational Predictive Analytics do what it does?


Operational Predictive Analytics is an AI technique that uses operational data to generate the models. The model integrates the physical process of manufacturing with the business processes of creating value. Each model has a purpose such as operations analysis and how it impacts the other parts of business. It considers all aspects of the manufacturing and business processes to optimize the system for better performance. It presents scenario options by exploring reconfiguration ideas, equipment investments, adding or moving people, etc.

What is unique about Operational Predictive Analytics?

Operational Predictive Analytics is model-based AI technology that unifies operations control with advanced analysis and decision making. This technology embeds multi-dimensional business models that use definitions of assets, resources, products, and policies combined with operations data. These functional models dynamically run along with actual production or in virtual environments to evaluate various options. This approach narrows the data collection requirement only to the interactions between operations and resources and does not require all transactional information. The model is a complex 17-dimensional hypercube at a macro level, and it grows as needed.

This model is highly non-linear and not solvable in real-time using traditional analytical approaches. Specialized geometric algorithms are needed to gain meaningful insights from the dynamic model.  The solution helps resolve operational problems for production planning & execution, costing, pricing, and resource requirements. The geometric approach makes the model configurable without any coding; users can change parameters on the fly. It creates real-time intelligence by evaluating multiple scenarios to take predictive actions. The well-informed business decisions drive higher profits and growth.

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What kind of operational execution challenges does Operational Predictive Analytics help?

  1. Product demand and mix changes.
  2. Process and technology changes.
  3. Balance resource limitations.
  4. Ensure financial gains from improvement programs.
  5. React to events on the floor and changes to the schedule - machine breakdowns, shortage of materials or labor, falling behind schedule or quality standards, increased scrap.
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