In today’s episode, we will discuss the approach many Manufacturing customers are taking to digitize their business despite the fact there are no potential interactions with customers in real-time to learn from. Click on Subscribe if you want to be informed of new episodes.
In previous episodes, we mentioned that Digital Acceleration is about improving your capability to respond to market needs and business priorities in real-time.
The best possible approach is to interact with customers and partners in real-time. Still, there are specific industry segments where interacting with customers in real-time is not the first option, but rather improving operations and optimizing profits via effective asset management, properly recycling waste, and reviewing and adapting processes to unforeseen events.
This is typical for Manufacturing companies, which are Capital Intensive and focus on streamlining operations. They rely on distribution partners to interact with end customers.
Even though the Customer is at the center of everything we do, it is not always possible to build a digital transformation journey where the Customer will interact in real-time with us.
Despite that, everything we do to improve the quality of our operations and products will return in Customer Satisfaction, Customer Loyalty, and win-win relationships with Customers (and Partners).
Today’s episode’s goal is to share a view of what Digital Transformation means for a typical manufacturing organization.
Digitalization challenges for Manufacturing Customers
Imagine we were going to build a new manufacturing site today. What would be the typical approach?
The typical approach consists of three dimensions: Industry 3.0 (we will discuss Industry 4.0 shortly).
The design stage involves designing the whole site using digital-born assets, agile manufacturing processes, and effective governance.
The operation stage consists of planning and scheduling a business or operations plan, executing the plan, and optimizing processes.
Maintain stage consists of using available technology to identify potential issues with assets in advance and to deliver corrective actions proactively to maximize productivity, end-product quality, and profitability.
In a perfect scenario, everything is designed with digital components. Still, the reality is that many “Capital Intensive” businesses have a portfolio of assets comprised of a mix of legacy manufacturing infrastructure initially built between 1970 and 2020.
Think of Mining, Chemicals, Pharmaceutical, Polymers, CPG, Oil & Gas (Upstream, Midstream and LNG, and Downstream), Power Generation, Transmission and Distribution, Food and Beverage, Pulp & Paper.
Many have a mix of manufacturing sites with analog and digital sensors and actuators. For the most part, they have been able to adapt to legacy technology, but looking forward, they will continue digitalizing their processes to meet the demands of digitalization.
To give you an idea of the age of these sites, the largest refinery in operation in Houston, TX, was founded in 1919, began operations in 1920, was expanded with Chemical Plant in 1940, and has had significant upgrades in 1979 and 2018.
Part of building a Digital Transformation journey for such a facility starts by covering the gaps between its existing infrastructure and what is required to become fully digital.
Let’s talk about what the gap consists of in each one of them
Operational Efficiency in Manufacturing, maturity grid.
Maturity Level 1: Design – Digitizing Assets (Upgrading all analog sensors and actuators to a digital equivalent in case the sensor is not capable to be mapped in the Digital Twin Model)
For these customers, the right action is to digitize all these sensors and actuators and enrich assets with additional sensors and actuators as needed to optimize processes.
This is a perfect sweet spot for Industrial Internet of Things (IioT) artifacts.
A couple of benefits of doing this:
- The organization will be capable of building a Digital Twin Model (episode 11)
- The organization will be able to collect operational information on all assets. This will be very useful in the following Digitalization stages.
Hold on, Jose, there are many organizations that already capture all process variables with physical sensors, it is not clear to me why to change something that works.
Yes, that is true. In addition to that, some organizations have security policies against transmitting data using wi-fi.
My point is, that there are a number of opportunities where having a digital sensor makes a better alternative than having three or four analog sensors, based on reliability, cost, ease of maintenance, and control functionalities. In addition, there are certain scenarios where having a mix of IoT, edge computing, and 5G or satellite connectivity allows new ways of monitoring assets and optimizing their operations.
Quick example: Remote operations of oil wells, capturing data from Sucker Rod Pumps (Temperature, Pressure, Torque, Production) using IoT devices, sending data to a remote server in-site using edge, and communicating information via Satellite to a central site.
Maturity Level 2: Operate – Optimizing Process (Operating business using digital information from sensors).
Once your infrastructure is upgraded and capable to be mapped in a Digital Twin Model, the next step is to upgrade operational applications so you can communicate with all assets using the upgraded version of sensors and actuators.
This is important because the Digital Twin Model must include ways to interact with each digital counterpart. Sometimes it requires an SW version upgrade and a process model review and update, while others require selecting and implementing a new operational application suite.
The benefits of reaching this level of maturity are pretty compelling:
- Capable of modifying production portfolio based on market demands. Imagine a pandemic hits and a pharmaceutical company has a new vaccine or medication. They need to jump-start producing this medication or increase production by 1000x without affecting the production of other medicines. Now you can explore flexible production schemes compared to manufacturing these medications in third-party sites for global distribution.
- Capable of modeling and monitoring all assets and adjusting production to optimize productivity and asset performance.
- Building a Digital Twin model is critical to making a strategic decision based on what-if scenarios.
- Utilize this information to identify failures reducing downtime.
- Build a new process based on adaptive manufacturing.
Typically, this approach is called Asset Optimization Solutions.
Maturity Level 3: Maintain – Business Optimization.
Imagine that your plant and the Digital Twin are perfectly in sync. Imagine that you have a manufacturing platform (OT, Operational Technologies) capable of doing flexible manufacturing changes. Imagine that you have access to all asset’s operational logs. Imagine that you have a good understanding of all asset performance models and reasons for failure.
What is next?
By incorporating Machine Learning and Artificial Intelligence to review all those IioT and digital assets logs and build a predictive maintenance program to reduce the downtown to zero.
What will you look for in these logs?
- Asset offline,
- Asset in regular Operation,
- Asset with known failure,
- Asset with initial degradation of service,
- Unexpected operational behavior subject to analysis.
This analysis allows the OT Platform to predict (not to react) potential service degradation and inform the organization of the need for human intervention before impacting financial metrics, operational metrics, or physical loss of assets and human life.
Some solution providers called this stage AIOT, and others called it AIOps.
We have previous success stories with IT and Datacenters; the challenge here is to model all assets as a digital entity, eliminate analog sensors, and update the OT platform to build a digital process.
Ok, but recently we read about a new term, Industry 4.0; any comment on that?
Maturity Level 4: Self-Optimizing Plant
My personal view of Industry 4.0 is to look for a new frontier, manufacturing sites capable of self-operating, self-learning, and self-adapting to internal and external changes. These sites are aware and enable to respond to internal and external factors and sustainable.
This is achievable: all information needed is available:
- Digital Twin Model Available via Assets upgrades (as needed)
- Digitized Asset Performance models
- Flexible Manufacturing Processes
- Artificial Intelligence and Machine Learning to evaluate what-if scenarios
- Assets capable of acting/responding (sensors, actuators) based on policies defined by the AI model.
There are a couple of Solutions Providers with mature solutions.
We can talk about that in future episodes.
Good enough?
What are your thoughts on the subjects raised in this edition of the Digital Acceleration Newsletter?
Share them in the comments below, and if you have ideas about other topics you’d like to see covered in this newsletter, feel free to add those suggestions.