The smart factory: Industry 4.0 use cases technology enablers
Industry 4.0, or the fourth industrial revolution, mostly refers to the shift to a new kind of smart factories and manufacturing end-to-end processes.
They leverage a slew of new technologies and solutions built around better connectivity…
- From sensors to ERPs
- From supplier networks to end customers
And around smarter…
- Upstream processes (supply chain, forecasting, warehousing, etc.)
- Core processes (manufacturing effectiveness and efficiency)
- Downstream processes (distribution, inventory management)
Contrary to a simple automation process where specific operator-led tasks are replaced or augmented with robotic arms, industrial controllers, or CNC machines, the smart factory is more of an ongoing long-haul maturity journey.
There are many processes that will benefit from this manufacturing in the 21st century sea change. Manufacturers that want to benefit from those advances need to carefully pick which processes to evolve, improve, or totally transform, based on their unique conditions.
To help our customers with this daunting task, Neal Analytics has put together a simplified but exhaustive view of the processes involved. The illustration below depicts what those use cases are, a few pointers to help manufacturers get started on this years-long transformation journey.
Let’s review those use cases quickly. For those of you wanting to dig more into a particular topic, we’ve provided additional resources for each section.
Advanced Machine Learning-based forecasting leverages Big Data to reduce raw material and finished goods inventories and improve customer service levels.
Those models will combine internal and external data sources to better discover and leverage the drivers that influence sales and therefore manufacturing needs.
Learn more about Advanced Forecasting techniques.
Combine and visualize supply chain, plant, and distribution data to find actionable insights, uncover trends, and better oversee your manufacturing with tools such as Microsoft Power BI.
Production Yield Optimization with Project Bonsai
When training data is limited, reinforcement learning-trained AI agents can be leveraged to improve process controls efficiency. Microsoft Project Bonsai is an integrated deep reinforcement learning (DRL) toolchain to help bring AI solutions real life manufacturing project.
Learn more about deep reinforcement learning for manufacturing.
To train an AI agent using DRL, a process simulator is required.
Simulations and Digital Twins
At the simplest conceptual level, a Digital Twin is a simulator that regularly gets process data to realign its state. They are digital representations of a real-life system and can be used for many use cases, from DRL training to what-if analysis, operator training, and more.
There are multiple techniques available for building simulators. The most popular ones are:
- Physics-based, such as the one used in this plastic extruder demo,
- Simulation platform-based, such as Anylogic’s or Simulink’s
- AI simulators, that focus on simulating the behavior rather than intrinsic elements of a system.
Regardless of the chosen approach, the first step for any digital twin / simulation project will be to build a solid cloud, data, and IoT/Edge instrumentation infrastructure
Learn more about simulation approaches in this article.
Vision and instrumentation
Smart connected sensors are a foundational element of the smart factory. Using vision AI at the edge or other advanced process instrumentation approaches, manufacturers can monitor and control production flow in real-time. This, in turn, will help reduce waste and improve product quality consistency.
Learn more about Vision AI.
Edge and IoT
Besides vision, many other types of connected sensors and actuators are required to enable the smart factory.
Edge devices bring computation and data storage closer to where data is generated and enable better data control, reduced costs, faster insights and actions, and help implement true close loop manufacturing.
Learn more about IoT and Edge computing.
Predictive and prescriptive maintenance
We all learned as children that an ounce of prevention was worth a pound of cure thanks to our founding father, Benjamin Franklin. Many countries around the world have sayings with different words but the same meaning. There is a reason for that: it is fundamentally true for most human endeavors.
In manufacturing, this translates – among other things – by proactively ensuring that a manufacturing line will not stop in an unplanned fashion. This can be achieved by ensuring the equipment is properly maintained and worn-out parts are changed before it’s too late.
In a nutshell, this is why preventive and predictive maintenance is one of the key Industry 4.0 use cases for the smart factory. Learn more about predictive maintenance here.
The last main element to enable the smart factory of the 21st century is the smart supply chain. Both upstream and downstream, an unreliable, overburdened, or unoptimized supply chain can have devastating impacts on manufacturing planning, efficiency, and eventually the company bottom line.
Fortunately, new techniques using deep reinforcement learning trained AI agent can help. Learn more about this new approach to supply chain optimization here.