Can food manufacturers turn the raw material crisis to their advantage?

The current global political crisis is already having a direct impact on the food manufacturing sector, with the prices of many raw ingredients reaching new industry highs.

Commonly known as “the bread basket of the world”, Russia and Ukraine together supply the world with around a third of its wheat. The recent conflict in the region has resulted in wheat reaching a record high price increase of over 50%.

Besides wheat, Russia and Ukraine are also major suppliers of corn, sunflower oil, and barley; while Russia is also a significant source of potash - the vital ingredient in fertilizer.

Food manufacturers are now in a tough position, having to deal with drastic price increases of all of these commodities, on top of the existing energy crisis.

So while the seriousness of these price increases is clear, is there any room for optimism?

Let’s break down the problem and explore how food manufacturers can turn this crisis into an opportunity.

The 2 fundamental challenges of the raw material crisis for food manufacturers

Due to the current situation, the agricultural sector in the affected regions is completely unpredictable at the moment. Since farming requires strict scheduling, this disruption will continue to affect the supply chain well beyond the conclusion of the conflict.

In manufacturing terms, we can narrow this challenge down to 2 main problems:

  1. Raw material scarcity. A shortage in supply of raw materials means that manufacturers will have to begin working with different suppliers. This means different types of corn, for example, with different consistencies, quality, etc. This will raise the variance of the raw materials used in production, affecting production and throughput.
  2. Steep rise in costs. While the market has absorbed recent increases in prices, there is a limit, and manufacturers should be aware of consumer behavior with regards to pricing changes. If manufacturers continue to raise prices due to their incurred cost increases, consumers won’t hesitate to switch brands, while general consumer spending will drop, greatly disrupting the economy.

How Artificial Intelligence can solve these challenges

With these 2 major challenges outlined, we can begin to focus on what we can do to mitigate the effects on production, and explore how to improve performance despite these trying conditions.

To deal with raw material scarcity, manufacturers will have to source their wheat, corn, sunflower oil, barley and other raw substances from suppliers they haven’t dealt with in the past. This means greater raw material variance. To make matters worse, they might have to combine multiple types of wheat, or switch between suppliers.

The solution to this is to improve flexibility to suit this new dynamic state of production conditions. In other words, manufacturers will have to improve their ability to adjust their production processes as needed in a timely manner, to cope with the dynamic, unpredictable reality, and to meet demand while keeping waste and quality issues to a minimum.

To deal with rising production costs, manufacturers will have to make significant improvements in overall efficiency in order to cut costs. From the preparation of raw materials to quality control, energy efficiency and everything in between, production should be analyzed in great detail and no stone should be left unturned in seeking out new areas for optimization.

Becoming more flexible to suit sudden condition changes, and seeking out new areas for optimization are both excellent objectives - but don’t they conflict? After all, food manufacturing processes are dynamic, and optimizing for one objective (e.g. reducing costs to offset high raw material prices) can often harm other key objectives (e.g. maintaining or increasing quality and yield, or reducing waste).

One tool that has already demonstrated major value in efficiency improvement and cost reduction in the food manufacturing industry is artificial intelligence. Many leading food manufacturers are already harnessing the power of AI in production, and this will put them in an advantageous position to deal with the current crisis.

AI enables the kind of continuous, multivariate analysis that is needed to optimize multiple, conflicting objectives simultaneously. Food manufacturers can maintain or even improve their existing performance in areas like quality, waste and yield - while also optimizing other areas of their process to drive down costs and remain competitive.

But is Industrial AI relevant if you don’t have data?

While the benefits of AI in manufacturing are increasingly clear, many food manufacturers feel that they “don’t have the data” to implement AI. Fortunately, this is a misconception.

Related content: How Barilla went from zero data, to reducing waste 37% with AI
(in just 4 months!)

The assumption is that in order to implement AI in production, a food manufacturer will already need to have a robust data infrastructure in place throughout the entire organization. So when considering AI adoption, many companies envision a daunting, long-term plan that seems highly demanding on resources.

While data is of course needed for any AI system to work, it’s not necessary to collect huge amounts of data from every aspect of an operation’s activity.

The smart approach to AI deployment is to focus on a specific business problem as the ones outlined above, and collect the data that is relevant for that problem. In this way, a defined data infrastructure can be built that can supply information regarding a specific challenge, instead of attempting to capture all the production data. This method drastically simplifies and shortens the process, enabling production lines to literally go from zero data to successful AI adoption in a matter of months.

At Seebo, we refer to this approach as the Lighthouse Strategy, and it is an excellent way for manufacturers to implement AI in a way that allows for an early ROI (read more on that topic here.)

“AI” can only provide value if the algorithms understand your unique production process

While building a data infrastructure is far less difficult than many food manufacturers imagine, there is one additional condition for successful AI adoption - and that relates to the AI technology itself.

Since manufacturing data is uniquely complex, a generic Machine Learning algorithm won’t be able to make sense of the data in-context. Buffers, loops, dynamic traceability, parallel processes and multi-product lines are just some of the “curve balls” that a typical algorithm will miss, thereby providing out-of-context and ultimately inaccurate insights.

Only an algorithm with embedded process expertise, that understands your unique production process, can provide long-term value. The advantage of “process-based AI” of this type is that it has the power and objectivity to reveal hidden causal relationships and suggest direct action that will lead to optimal performance.

An added advantage is that teams on the production floor now have an extremely powerful tool to help them deal with challenging conditions. Process-based AI is not only an analytical tool, it is designed to be understood by humans - to suggest actions and when to take them in order to maintain a high level of performance.

So for example, if production conditions change - say, due to raw material variance - the algorithm can suggest specific parameter changes that can be made to maintain optimal performance.

Food manufacturers can emerge more competitive from the raw material crisis

So on the one hand, it’s clear that manufacturers have no choice but to step up their game starting now.

But this could also be used as a catalyst for innovation that will in fact improve food manufacturer’s competitiveness in the long-term. Specifically through the use of AI, companies can not only weather this storm, but actually thrive, harnessing sustainable energy, and becoming more flexible to deal with disruptions in raw materials supply, waste reduction, and quality in the future.

Artificial intelligence is by no means an instant “just add water” solution, but with a smart, strategic and focused approach to this challenge, food manufacturers don’t need to undertake a lengthy plan to start reaping the benefits of this technology. And there’s no better time to start than right now.


Could the energy crisis spur unprecedented innovation in manufacturing?

The current energy crisis has made it abundantly clear that manufacturers must quickly find a way to be smarter and more efficient with their energy consumption.

With gas and oil prices soaring and wreaking havoc on inflation rates and the global economy - and threatening to continue doing so in the coming weeks, months and even years - manufacturers risk seeing their bottom lines whittled away by both rising costs and falling demand for increasingly expensive products.

Governmental policies, and other external factors that we can’t even predict yet, will continue to affect the cost and availability of energy, as well as other factors that have a direct impact on key manufacturing objectives.

Manufacturers are now in the hot seat and have no choice but to find innovative ways to manage costs and enhance their capabilities when it comes to handling their production objectives in an environment that is far more dynamic and uncertain than what they’ve been used to.

Managing energy costs has always been a challenge for process manufacturers

Rising energy prices in manufacturing

Even when things are fairly stable politically and economically, managing energy efficiency in alignment with other objectives is a complex challenge for process manufacturers.

Particularly in energy-intensive industries such as cement, steel, glass and chemical production, manufacturers rely heavily on dependable energy sources at costs that must be manageable.

By using renewable and alternative fuel sources, and constantly optimizing processes, manufacturers are constantly at work to stay efficient. But the cost of energy can be hard to balance against other objectives such as yield, throughput and in some cases, quality levels.

So what has really changed?

As seen by these recent events, fuel availability and government policies have a huge effect on energy costs, and manufacturers are at the whim of these external factors. This is an added complexity - the volatility of the energy market. Or in other words, the potential of major factors such as fuel costs, government regulations and market demand to change drastically over very short periods of time.

For example, the current energy crisis may force western governments to dial-down their emission reduction targets. On the other hand, it might also accelerate the need for alternative fuels. How will this uncertain, highly fluid environment affect industries like cement, glass and steel, who have spent many years painstakingly adapting their processes and facilities to meet targets and regulations that may radically change or even no longer apply in a few months? How will they react without hurting their bottom line other than hiking prices, which may itself lead to pushback from the market?

How to approach the problem

So how can manufacturers take back control in such a volatile, unpredictable environment? How can they react quickly and adapt their processes to consistently meet their production objectives and maintain profitability and sustainability goals - no matter what external challenges come their way? What manufacturers need is the ability to simultaneously consider and optimize all their objectives, even those that might sometimes seem conflicting. We need to be able to view the manufacturing environment with enough oversight to identify correlations between a vast number of factors, and prioritize, adapt and optimize manufacturing objectives quickly and efficiently.

In this way, we can meet “conflicting” challenges such as increasing throughput and yield, while reducing energy costs and meeting emissions reduction goals - without sacrificing other objectives such as quality.

This calls for a multidimensional objective - a state of production that can meet all the parameters involved in the manufacturing environment.

Success depends on the ability to adapt - fast

Of course, not only do we need an overview that includes all our objectives (multidimensional) we also need this overview to be dynamic - to be flexible enough to handle sudden and drastic changes, particularly in external factors beyond the control of manufacturers: from the weather to energy prices, from emissions regulations to market demand, and more.

The current crisis has shown us that companies that have the ability to adapt quickly to changes - raw material variances, hikes in energy costs, new emissions regulations, surges in demand - will be the ones to emerge as leaders in their markets.

For this reason, the most accurate description of this new goal is the dynamic multidimensional objective.

Process-Based Artificial Intelligence is perfectly suited to address this challenge

The concept of business, technology, the Internet and the network. virtual screen of the future and sees the inscription: Continuous improvement

Process-Based AI can create multidimensional objectives by providing a unified view of the manufacturing operation within its real-time environment - with its constantly shifting priorities and goals.

These dynamic multidimensional objectives can then be used to create Operating Envelopes, which recommend the ideal set points and process values to achieve global optimization, across multiple production objectives (e.g. quality, yield & throughput vs. energy costs, emissions & alternative fuel rates) that would have previously seemed like they were in conflict.

Process-based AI allows manufacturers to adjust these envelopes with ease, as the consequences of production decisions can be clearly ascertained. This also means changes carry a far lower risk, and production losses, energy costs and emissions levels are kept to a minimum.

This system also allows for manufacturers to program envelopes that suit a variety of production configurations, enabling a new level of control over objectives to meet the uncertainty of changes caused by external factors, but with the security of knowing how changes will affect production ahead of time.

Process-Based AI software empowers manufacturing teams

While we’ve focused mainly on the technical aspects of what the current energy crisis means to manufacturers, it’s crucial to remember that manufacturers ultimately rely on their teams on the factory floor to perform at a high level - particularly the process experts and engineers, and operators.

To consistently achieve optimum efficiency, production teams need to know how to act, and when to act, to maintain operating envelopes that are in line with the objective.

This requires real-time alerts that supply teams with timely actionable insights across the entire production process.

Learn more about how dynamic multidimensional objectives can empower your manufacturing teams to overcome the energy crisis:


Cost, cash, service… sustainability? The future of green manufacturing is already here

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.


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

The concept of business, technology, the Internet and the network. virtual screen of the future and sees the inscription: Continuous improvement

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.

WATCH - How energy-intensive manufacturers reduce carbon emissions without sacrificing quality, yield and other key KPIs:


4 things to consider when adopting AI in your manufacturing organization

Choosing the right production line as a first step to AI deployment in manufacturing is critical since it will influence the organization's progression towards successful AI integration. Here’s how to make the right choice…

Leading manufacturers are using AI to solve perennial problems such as unstable or low yield and throughput, quality issues, CO2 emission levels, and waste. In this way, AI helps manufacturers produce better products at a lower cost, and as a result, markets are becoming more competitive.

Because of this, more manufacturers are looking to AI technologies as a way to improve their operations’ performance.

The AI deployment process, however, can seem like a considerable challenge for many manufacturers.

A common question is how can manufacturers deploy AI into their existing operations.

Recap: The Lighthouse Strategy

In a previous blog post, we outlined the proven strategy used by leading manufacturers to successfully deploy AI:

Focus on a specific production line to begin with, and then scale to other lines from there.

Known as “The Lighthouse Strategy”, this approach is effective because it enables manufacturers to build a clear "proof of concept" that can demonstrate value relatively quickly.

Additionally, any failures that do occur are manageable since they are controlled within the boundary of a single line, and can be quickly resolved and learned from.

So, how do manufacturers identify which production line to start with?

How to choose the right production line to start AI deployment

For many organizations, their first deployment process will determine the future success of AI implementation in the company.

For this reason, it’s critical for manufacturers to choose the right production line to begin with since a positive result will draw support from teams and justify budget allocation moving forward.

Here are 4 things for manufacturers to consider when choosing the right production line for their first AI deployment:

1. Capacity constraints

Look for a production line that has a high demand from the market. The performance level of this line should have an effect on sales, and therefore plays a significant part in the overall business. Deploying AI to this line will enable you to gauge the business benefits brought on by AI deployment.

2. Losses and inefficiencies at the line

By comparing the level of losses on the lines, we can see how much potential gain we have by optimizing the process with artificial intelligence. A production line with very apparent production losses (throughput, quality, waste, etc.), or high potential for improvement in other key areas (e.g. emissions), is the right choice for AI deployment since there is great opportunity for improvement.

3. Data maturity of the line

What level of data is the production line already producing?

How far along is this production line to being able to provide good data for analysis?

By answering these 2 questions, we can compare how ready each production line is for providing the type of data needed for AI to make significant improvements.

Question: Does this mean the production line producing the most data is the right one to start with?

Answer: Not necessarily. While data maturity is a factor (as seen in point 3. above), choosing a line with capacity problems and significant losses far outweighs the amount of data produced by a line. A data-rich production line that’s performing at peak levels might yield an excellent analysis, but won’t give us the meaningful business benefits we are looking for at this stage.

Related: How Barilla went from no data to 36% waste reduction with AI - in just 4 months

Deployment concerns

Each organization is different, and so are the challenges brought about by taking on a project such as AI deployment. These may range from the performance levels of the production teams, to how the deployment process fits into the current business dynamic. The more we know about each production line, the better we can decide on the best candidate for deployment. Take the time to map out any additional concerns regarding AI deployment to each line.

The first step towards successful AI deployment starts here

Once your production lines have been analyzed according to the criteria in the 4 channels outlined above, it will be much easier to identify which line should be the first in the organization’s AI deployment process.

This will set your company in the right direction towards successful AI deployment and significant business impact early on.

Watch the full video on how to choose the right line to begin your Industrial AI adoption:


The proven strategy for successfully adopting AI in manufacturing

Industrial AI is rapidly defining the future of manufacturing, and manufacturers that adopt it now will gain a competitive edge in the near future. But how do you roll out an advanced new technology across a large or medium-sized manufacturing organization?

With a growing number of manufacturing leaders reporting significant financial benefits due to AI adoption, it seems that the technology is set to define the future of the industry.

Companies adopting artificial intelligence today will benefit from both early wins now, and a competitive edge moving forward.

And while there are many advantages to incorporating AI in manufacturing, the essence of this technology’s game-changing ability is that it allows manufacturers to know 4 key, strategic insights:

1. WHAT is the correct order of priority across your conflicting production objectives?

Each production line typically has multiple objectives, which often conflict with one another, e.g. quality vs. waste, throughput vs. energy costs, quality and throughput vs. emissions reduction, etc. The ultimate goal is to maximize all of them as much as possible; but in order for process experts to do that, they must first understand their respective “weight” vis-à-vis each other. Namely: in the event of a conflict which objective should take precedence of another, and to what degree?

This question is extremely difficult for a human being to answer - particularly since the answer can change depending on a whole range of factors. That’s where Artificial Intelligence can play a critical role.

Armed with this knowledge, process experts can know precisely how to navigate between the conflicting objectives without harming either unnecessarily.

2. HOW much can you improve your current production process?

On any given production line, there is usually room for improvement. This is true even for many highly-efficient lines. The question is: how much potential is there? This is crucial for setting your business objectives/KPIs on that line.

Here, Artificial Intelligence can provide the answers, by mapping the overall efficiency of your production line, and highlighting the most efficient periods of consistent operation - taking into account all your different objectives (yield, quality, emissions reduction, throughput, etc.).

The end goal here is to replicate that highly efficient behavior more often, which takes us to the next objective…

3. WHICH parameters influence your production process?

Once you’ve quantified the untapped potential of your production line, the obvious question is: how do you get there?

If you already know what the potential room for improvement is (see above), Artificial Intelligence can be used to analyze what precise combination of set points and ranges contributed towards those highly-efficient periods. Conversely, AI can also be used to conduct root-cause analysis on those periods of particularly low efficiency, so you can know what tags, process settings and ranges contribute toward inefficiencies and production losses.

4. WHEN to act to prevent inefficiencies

All of these insights can only provide real value if they can be translated into actions on your line by your operators. AI can provide real-time alerts that proactively inform your production teams of inefficiencies on the line, and recommend how to fix them.

Taking the Leap Forward

So the advantages of AI for manufacturers are as clear as day, but how can the technology be implemented without risking your key business objectives?

To many manufacturers, this may seem like a significant undertaking, but it’s encouraging to note:

There’s a smart way to implement AI in manufacturing.

Let’s begin by taking a look at the journey any manufacturer will typically take in deploying Industrial AI…

The 3 Steps towards Smart Loss Reduction

We can break down the journey to AI deployment into 3 fundamental steps:

Step 1: Data Extraction

Data is collected from the production floor across various input channels including raw materials, process data, quality data, etc. Sometimes this stage also includes data on external factors like weather, temperature and so on.

Step 2: Data organization/visualization

The data is organized and analyzed. It is represented visually in order to better communicate findings to the relevant teams.

Step 3: Deployment of advanced analytics and AI

AI is successfully integrated into production, and using the information provided by the previous two steps, begins to provide actionable insights into production losses.

NOTE: This final step is where manufacturers actually begin to see major financial gains, as the AI works to optimize processes that have significant business impact. Until that point, the financial impact of the first two steps is minimal or none.

The proven strategy to successfully implementing AI on a production line

Because of the complexity of manufacturing processes, it’s important to have a clear AI-adoption strategy for introducing the technology, monitoring progress, and scaling up.

The basis of this strategy is that the adoption plan needs to be specifically suited to the production process in question, and that process should be selected with the utmost care.

Customization is key

There is no absolute, blow-by-blow template for implementing AI into a manufacturing process. There are simply too many variables at play in the process. Of course, there are best practices for approaching the task, but understanding the individuality of a production line is key to discovering an optimal route for successful AI implementation.

Introducing the Lighthouse Strategy

Broadly-speaking, there are two common approaches to deploying AI in manufacturing:

The Horizontal Strategy - AI is deployed throughout the organization, one step at a time (see above).

The Lighthouse Strategy - AI is deployed in a single production line, by fully implementing all 3 steps on that line.

There are a number of problems with deploying AI across an organization as per the Horizontal Strategy, layer by layer. This approach is slow and resource-heavy, and it typically will take years to see the benefits. Another disadvantage is that mistakes made in the deployment process are production-wide and carry a higher price.

More fundamentally, since every production line is unique - with different, dynamic challenges and circumstances - each one has its own unique needs. In some cases, they may not have a real need for advanced AI analytics at all (e.g. if the losses are small, or the potential for improvement slim). The “one-size-fits-all” approach inherent to the Horizontal Strategy means that even after full implementation, the solution itself will most likely not adequately address the challenges on at least some of the lines where it is implemented.

By contrast, deploying AI in a single production line as per the Lighthouse Strategy, leads to a more agile process that is quick and resource-light - and the benefits can be seen very early on, even after a few months. Any mistakes made in the process are controlled within that line and can be corrected and learned from quickly, moving forward.

Using the Lighthouse Strategy leads to a more stable deployment process and early wins that justify budget allocation for scaling.

The Formula for Success

In summary, the Lighthouse Strategy allows manufacturers to adopt AI technology to improve process efficiency, while controlling disruption, minimizing risks, and achieving benefits very early on.

Watch the full video to learn more about the Lighthouse Strategy:

 


How "human-led" optimization hurts process manufacturers

Traditional approaches to manufacturing process optimization rely mostly on human-led analysis and decision-making. This limits manufacturers and ultimately harms their bottom line. How does Process-Based Artificial Intelligence tackle this complex problem?

Continuous production presents some of the most complex challenges faced by manufacturers today.

With constant pressure from competitors, stringent emissions laws, and the increasing demand for product quality and quantity, it’s clear why manufacturers need all the help they can get to stay ahead. For these reasons, AI is quickly gaining recognition as a vital tool in helping continuous manufacturing operations evolve.

By adopting AI, more and more process engineers are moving from a touch-and-go improvisational mindset to one that is deeply data-driven, focused, and agile, to meet the dynamic nature of continuous process manufacturing.

Despite that, most manufacturers are still conducting their root-cause analysis and process optimization efforts manually. This approach relies heavily on human experience, judgement and intuition - which is inherently limited.

Human-led optimization harms the bottom line of any production process

Of course, process experts aren't working with a paper-and-pen. Most modern-day manufacturers utilize tried-and-tested statistical models and data visualization and analytics tools to conduct root-cause analysis. Some are also using generic machine learning algorithms for more advanced analysis. But when measured over time, these traditional approaches struggle to reach targets when it comes to process-related challenges like yield, waste reduction, energy efficiency, emissions reduction, quality and throughput.

Here’s why…

The true root causes are hidden

Due to the complex nature of process manufacturing, the abundance of complex data also means an abundance of “noise” which clouds efforts to pinpoint the root cause. Manual analysis - even with the help of data visualization and self-serve analytics tools - simply won't cut it. There's just too much data, spread across a vast tangle of interrelated set points, for a human being to constantly consider.

Manufacturing processes are dynamic

Variability of raw materials, humidity and temperature fluctuations, dynamic traceability, weather conditions and a slew of other factors, make it impossible to reach actionable conclusions that have value over time. This is true even when using generic machine learning, as these algorithms don't take the complex, dynamic realities of the production process into account.

Production teams are already under pressure

A human-led approach to process optimization can pile work onto already very busy teams - via hours of manual analysis, discussions and trial and error - often without yielding any benefits to the bottom line.

Human-led optimization doesn't scale

Even if a root cause is determined using manual or human-led analytical methods, the solution cannot be replicated for use with another production line since each has its own set of performance values. That means starting from scratch every single time...

Augmenting your human expertise with Process-Based AI

The shortcomings of the human-led approach are clear: continuous process manufacturing is simply too complex and dynamic for manual calculations to provide actionable solutions.

The motivation behind Process-Based Artificial Intelligence is to help manufacturers solve process inefficiencies by revealing root causes and recommending the right actions.

Just to emphasize: this isn't about replacing your human process experts and manufacturing teams - it's about empowering them to transcend the limitations of what is humanly-possible, and ultimately master their manufacturing processes.

What is Process-Based Artificial Intelligence?

In a nutshell: Process-Based AI takes advanced Machine Learning algorithms, and infuses them with deep process expertise from any given production line. This means that when the "AI" is analyzing your production line, it's not just looking at the data - it's also considering all the unique complexities of that production line, from dynamic traceability to loops, buffers, raw material variances, parallel processes, multiple SKUs and so on.

Naturally that makes for more accurate, real-time analysis. (See here for more details)

Process-Based AI makes it easy for process experts and production teams

Apart from being able to determine the root cause of inefficiencies in even the most complex manufacturing processes, a process-based AI solution should be intuitive and provide straightforward user interfaces and solutions to investigate and prevent production losses.

Process-Based AI constantly adapts to changes in your process

Process-based AI continuously works behind the scenes, instantly adapting to changes in your process to provide insights and recommendations that are always relevant.

Process-Based AI can easily scale across production lines, and across plants

Traditional static algorithmic approaches just don’t scale. In stark contrast, with the correct adoption strategy, process-based AI can be scaled across production lines and even multiple factories without significant disruption.

Process-Based AI frees your people from endless data tasks, and empowers them to focus on processes optimization

“AI” in popular culture often conjures up images of machines replacing people. In reality, for manufacturers at least this could not be further from the truth.

In fact, perhaps the greatest benefit of Process-Based Artificial Intelligence is that it empowers your process experts and production teams to work more effectively and efficiently.

Process-Based AI relieves your people from mundane, never-ending, Sisyphean data crunching and theorizing, and delivers clear, timely insights and recommendations for them to act on.

This will start them on the right foot, and enable them to focus on what they do best: optimizing the production line.

Innovation that pays off, quickly

A growing number of manufacturers are learning that AI is not simply a nice-to-have tool for superficial streamlining, but a powerful game-changing technology.

Process-Based AI provides essential stabilization to even the most dynamic of processes, leading to significant improvements across the board - from improving quality, throughput, energy efficiency and yield, to reducing CO2 emissions and waste.

See the video below for how Allnex improved quality and yield with Process-Based AI.

If you want to learn more, speak to one of our experts today.


[Podcast] How AI prevents production floor waste for baked goods manufacturers

Can you cut waste while maintaining, or even improving, quality levels?

What is the relationship between waste level, and other key KPIs like quality and yield?

How can baked goods manufacturers identify what Artificial Intelligence technology is right for them?

Find out the answers to these questions in this BakeryandSnacks Chat podast with Seebo co-founder and COO Liran Akavia.

Also: How the challenges presented by the coronavirus pandemic are morphing into exciting opportunities for baked goods manufacturers to become more competitive, profitable, and sustainable.


[Podcast] Is AI the key to BOTH net-zero and greater profitability in cement manufacturing?

  • How are leading cement manufacturers saving millions, boosting profits and cutting carbon emissions with Artificial Intelligence?
  • How can cement manufacturers use AI to meet multiple KPIs that on the surface contradict one another - e.g. quality vs throughput; throughput vs. energy efficiency and emissions; alternative fuel rate vs. overall efficiency?
  • What different AI solutions exist for cement manufacturing - and how can you choose the right one for your business challenges?
  • Can a Machine Learning algorithm be taught to truly understand cement production processes?

In this World Cement Association podcast, Seebo COO and co-Founder Liran Akavia addresses these questions, as well as other pressing topics surrounding the use of Industrial AI to improve efficiency and lower carbon emissions in the cement industry.


Taking advantage of Artificial Intelligence in cement manufacturing

How AI can help cement manufacturers to reduce emissions, whilst maintaining or even improving their production process efficiency and key business KPIs.

Decarbonization has gained increasing importance over the past decade, and continues to occupy the minds of cement manufacturing executives.

From improving energy efficiency to alternative fuels and clinker alternatives, cement manufacturers have a number of options at their disposal. Of course, many of these options are only as viable as local and national governments make them - but still, the race to net-zero is progressing at a global level.

However, there’s a problem.

Cement manufacturers need to produce as much high-quality cement as possible, to meet demand and beat out the competition. Or in fewer words: to be profitable. But if they use less energy, or switch to less ideal fuel or clinker alternatives, won’t they be forced to compromise key KPIs like clinker quality and kiln throughput?

Related: How a cement manufacturer reduced quality, throughput & energy losses - while cutting emissions

This tension between profitability and environmental protection lies at the heart of the issue of decarbonization. Cement manufacturers cannot be expected to cut emissions at the expense of their business. While this might rankle many die-hard environmentalists, it’s simply a reality - for better or for worse.

But is there a way to do both?

The answer is yes. Cement manufacturers can both reduce emissions, and still maintain or even improve their production process efficiency and key business KPIs.

Yes, it is possible to decarbonate and still run a highly efficient plant

At a recent cement industry panel hosted by Seebo, a senior cement manufacturer discussed how some of their factories boast alternative fuel rates of 90% - and still continue to push the limits in terms of process efficiency.

That might sound like a fairytale, but the “secret” is actually quite straightforward. The production losses that harm cement manufacturers’ bottom line - like unstable kiln throughput, clinker quality and energy inefficiency - usually stem from the same process inefficiencies that cause higher emissions levels as well.

So if these process inefficiencies are eliminated, it is possible to reduce quality, throughput and energy losses - and bring down carbon emissions at the same time.

Read the full World Cement article here


[Podcast] How AI drives sustainability in baked goods manufacturing

  • How can Artificial Intelligence be used to forge a more sustainable food production chain - and simultaneously improve the quality and availability of products for consumers?
  • How are some baked goods manufacturers already using Industrial AI to be more flexible and agile, handle a much larger number of SKUs, and consistently meet consumer demand?
  • How can baked goods manufacturers implement AI at their factories, when few if any possess significant data infrastructures?
  • Why are the world’s leading baked goods manufacturers placing AI adoption at the center of their business strategies?
  • Also: why full automation, or “lights out” manufacturing, isn’t the goal of Industrial AI.

Find the answers to these questions and more in this American Bakers Association podcast with Seebo co-founder and COO Liran Akavia.