With Process-Based Artificial Intelligence, profitability does not have to be sacrificed in order for a manufacturing business to adopt a new sustainable modus operandi.
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Cost, Cash, and Service. Known simply as The Supply Chain Triangle, this model demonstrates in very clear terms 3 important elements of a successful manufacturing business.
These 3 elements need to be in balance for a manufacturing company to maintain profitability.
Even for leading manufacturers, this is no simple task; but in recent years, maintaining this balance has become even more challenging.
A 4th element has entered the manufacturing industry as another factor that needs to sit in harmony with Cost, Cash and Service: Sustainability.
The age of sustainable manufacturing is here
Sustainability is not just a buzzword. In today’s manufacturing sector, sustainability plays a central role, influencing operational and business decisions.
Sustainability has become a crucial component in the management of manufacturing businesses all over the world for a number of reasons, including:
- New laws and regulations regarding emissions and materials sourcing
- Increase in consumer demand for environmentally friendly products
- Economic factors stemming from costly materials and energy resources
For different industries, sustainability can mean different things. Usually, manufacturers focus primarily on CO2 emissions reduction – with many aiming to go carbon-neutral in the next few decades (cement manufacturers, for example, have a target of zero emissions by 2050). Sustainability can also be about waste (particularly in the food manufacturing industry)- or sourcing specific types of ingredients and raw materials (in various other industries).
Whatever the case, this new paradigm in the manufacturing sector demands new strategies so that manufacturers can make the transition to a greener operation, but without hurting the bottom line.
Does sustainability hurt Cost, Cash and Service?
A common misconception is that “sustainability hurts profits”.
Sustainability does require that changes be made, some of which may carry upfront costs as well as a certain amount of disruption. However, the basis of sustainability is longevity through smarter management with regards to fuels, energy efficiency, waste and resources management. And this basis goes hand-in-hand with common business goals such as the reduction of process inefficiencies, and increased profitability.
In fact, the root causes harming sustainability are often the same as those that are leading to process inefficiencies.
Related: Automated Root Cause Analysis – preventing production losses in the age of smart manufacturing
So, as a manufacturer becomes more sustainable, it is likely to reach an even more stable Cost, Cash, and Service dynamic.
The challenge of reaching sustainability
For many manufacturers, understanding the benefits of sustainability is the easy part. The real challenge is making the move towards a sustainable operation, and incorporating this new 4th element into the existing Cost, Cash and Service paradigm.
With the emergence of industrial AI in the manufacturing industry, it has become clear that one of the technology’s major advantages is its ability to enable manufacturers to optimize multiple, conflicting objectives at the same time. This makes AI the perfect tool for both introducing sustainable practices into existing operations, as well as for maintaining a continued balance between Cost, Cash and Service.
How AI helps manufacturers on their journey to sustainability
When a manufacturer begins the process towards improved sustainability, changes to production are inevitable. Some of these changes are hardly noticeable, while others might require planning and added monitoring. A new optimal production state needs to be reached that meets the Cost, Cash, and Service requirements.
Industrial AI can be extremely helpful with this transition phase. Since we are changing a number of variables in our production process, calculating how these changes will play out can be very complex.
A word on Process-Based Artificial Intelligence
Adding Sustainability to the Cost-Cash-Service Triangle will at first result in what seems like many conflicting objectives. Even powerful standard AI algorithms will have difficulty finding ways to resolve these conflicts. This is due mainly to the fact that they are not built to focus on the complexities within the process, like dynamic traceability, raw material variances, external influencing factors like weather, and so on.
Process-Based Artificial Intelligence is designed to be able to handle the myriad interrelationships of a process across all its stages. It does this through the combination of process expertise embedded in its algorithms and continuous multivariate analysis.
Removing the risk of disruption
Process-based AI allows us to accurately model what production will look like under sustainable conditions, and work within those conditions until we find a new optimal processing state.
With process-based AI, we can start implementing the settings and parameters that meet our new sustainability requirements, before we need to make any changes to production. This is critical since it allows for a wide range of tests without the risk of any harm to production targets.
A process-based AI solution allows the manufacturer to have a detailed map of the sustainability transition, with target settings that need to be reached along the way. Once the destination is known, a detailed map outlining the steps on a timeline can be created with ease.
AI Bridges Sustainability and Profitability
With the help of process-based AI, introducing Sustainability to the Cost, Cash and Service Triangle does not have to result in a conflict of interests.
Process-based AI is the perfect tool for complex balancing acts such as this, helping manufacturers optimize multiple, conflicting objectives simultaneously.
With sustainability goals to reach, manufacturers significantly cut down on waste and emissions by identifying the root cause of inefficiencies, as the AI provides clear directions towards improved performance.
With process-based AI, manufacturers can achieve sustainability without compromise to the bottom line.
By implementing this technology, we can check all the boxes: balancing Cost, Cash, Service and Sustainability, while actually improving profitability despite production changes.
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