Applying Advanced Analytics and Artificial Intelligence in Industry 4.0

Analítica Avanzada Inteligencia Artificial Industria 4.0

In this article we talk about how applying Advanced Analytics and Artificial Intelligence techniques in Industry 4.0 can maximise the efficiency of production processes.

The manufacturing industry is facing new market, social, environmental and digital transformation challenges. Thanks to technological advances in computing power and Data Science, Industry 4.0 presents a perfect scenario to address them.

By applying Advanced Analytics and Artificial Intelligence in Industry 4.0, companies can:

  • have full visibility of production processes,
  • adapt quickly to changes,
  • identify improvement points along the supply chain,
  • and act on critical resources and processes.

In this way, they are able to know their operations perfectly and act on any change, incident or possible improvement quickly.

Applying Advanced Analytics and Artificial Intelligence in Industry 4.0
Evolution from Industry 1.0 to Industry 4.0

What Advanced Analytics and Artificial Intelligence initiatives can help CIOs improve productive operations?

Industry operational priorities, such as getting to market quickly, responding rapidly to market changes, increasing profitability and ROI, or focusing on continuous process improvement, demand rapid action from CIOs. They must leverage new digital technologies and invest in improving production operations through their powerful capabilities.

Demand Forecasting

Using forecasting models to anticipate future demand is something that many manufacturing companies already do today. Basing resource planning and management on demand forecasts reduces inventories, helps align the flow of sales and operations, and increases customer satisfaction levels.

It provides information on estimated cash flow and production needs, identifies seasonality and market trends, and uses simple forecasting techniques such as time series or more complex causal models to account for multiple factors.

Planning based on future demand

Planning based on future demand allows for optimising the use of critical capacities and resources, and anticipating customer needs. In addition to avoiding shortages of raw materials, product surpluses or delivery failures.

  • Sales and operations planning: Align production, sales and logistics. It allows you to create scenarios based on demand forecasts, production constraints, inventory costs and marketing campaigns, and to compare the KPIs of each scenario with the company’s overall objectives.
  • Integrated production planning: Align production capacity with incoming orders, linking resources at “bottleneck” times and promised delivery dates. Thus, available capacity is used optimally, ensuring the quality of service and order delivery.
  • Production scheduling: Allow the creation of optimal production schedules and sequences for the different machines, and monitor the evolution of operations and orders at all times, automatically assigning raw materials to the different production units. 
Digital Twins

The Digital Twin consists of creating a virtual replica of a product, process or service using real data, and simulating its behaviour in the face of different changes or stimuli, making it possible to analyse its performance and results in order to improve its efficiency.

It is based on data science and applies advanced analytics techniques, Machine Learning, AI, optimisation algorithms, constraint management and the latest demand forecasting methods, to make it a very powerful tool. In this way, it is possible to compare what happened in a given scenario with the predictions, and analyse deviations, updating and improving the models over time.

Learn more about Digital Twins in our article: “Digital Twins, When to Use Them and Why“.

Predictive maintenance

Predictive maintenance consists of the application of different techniques to predict the future failure of a machine component so that the component can be replaced just before it fails. In this way, equipment downtime is minimised, component life is maximised and parts are purchased when needed, eliminating stocks of parts that become obsolete.

Advanced manufacturing companies are already implementing these new technologies to drive growth and profitability in their operations.

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