It’s no secret that downtime is manufacturing’s biggest nemesis. From the C-suite to the supply chain to the plant floor, everyone is working hard to keep lines up and running.

Yet out in cyberspace, a Thanos-like threat lurks, ready to “snap” production if certain demands are unmet. These snaps can come in many forms, including ransomware, backdoors, and spear phishing. According to IBM’s ​​X-Force Threat Intelligence Index, manufacturing was the most attacked industry in 2023. 

The risks to manufacturing are significant. In addition to losing money from downtime and extortion, businesses risk losing face and becoming the latest media headline (see Toyota’s nightmare.) Worst of all, hacking into an Industrial Control System (ICS) can put workers in harm’s way by physically damaging the machinery or processes that people must then go in and fix.

So, when you’re considering rolling out AI-driven machine health, what cybersecurity measures should you consider? 

1.  To integrate, or not integrate? That is the question.

Most machine health vendors integrate their solutions with existing customer data sources in order to provide value. The complex IT architecture requirements of integrating into an ICS can take a significant amount of time to work through. And as we all know, time is money. 

However, not all machine health solutions require this integration. Some solutions are secure by design and operate outside of a manufacturer’s ICS. Using isolated architecture, components within the software are encapsulated, operating independently of each other. Benefits of this approach include reliability and scalability – important factors when you’re deploying machine health across hundreds of machines and need to see quick ROI.

2: Adherence to rigorous standards

Commonly known as the ISO, the International Organization for Standardization is comprised of experts across the globe who have set the standards for keeping sensitive information secure.

There is a laundry list of benefits that come with ISO 27001 and ISO 9001 compliance. In a nutshell, when a solution adheres to these standards, customers can be confident in their vendor’s:

• Risk management
• Resilience to cyber attack/ preparation for new threats
• Consistency/reliability in processes and outputs
• Operational excellence
• Constant improvement of quality, efficiency, and effectiveness

Similarly, data and privacy regulations have been enacted in various geographies, most notably the European Union’s GDPR and the State of California’s CCPA. Vendors who adhere to these regulations demonstrate strict data handling practices designed to protect their customers’ personal privacy.

3. Encryption

Encryption is a fundamental best practice – it protects sensitive data from unauthorized parties. In addition to meeting the ISO standards listed above, encryption:

• reduces the risk of data breaches during data migration
• makes it easier to detect attempted breaches by recognizing unauthorized modifications or tampering

The best security in the world is built on a foundation of basic security. Make sure your machine health vendor is encrypting data during transfer/transmission and at REST in storage devices.

4. Multi-tenant platform with data segregation per data classification

Machine health solutions serving multiple customers must maintain the security and privacy of each customer’s data. A multi-tenant platform ensures a customer’s data is stored and processed in its own isolated space. Data segregation ensures each customer’s data remains separate from and inaccessible to other customers served on the platform. Further segregation happens according to how the data is classified and may include storing certain classes of data in separate, encrypted databases with even tighter access controls.

The result? Sensitive customer data is processed and stored in the cloud, protected due to its isolation and classification level, providing even more security against cyber threats.

5. Penetration testing

Simulations are a safe way to test out different scenarios. Proactive vendors conduct penetration testing, which simulates a real-world attack on their systems. Through this exercise, potential weaknesses are uncovered as the fictional attacker tries to exploit the system. 

The results of penetration testing are used to highlight vulnerabilities, assess the impact of a potential breach, and prioritize fixes to protect against a true security threat.

Cybersecurity – An Ongoing Practice

Managing cyber risk is never a one-and-done task. Once you settle on a machine health vendor, both you and the vendor must be eternally vigilant in protecting your data and systems. Unfortunately, no matter how brilliant new technology is, there will always be bad actors looking to exploit it for their own gain. That’s no reason to fear AI-powered solutions for the plant floor, but it is a reminder to develop the discipline and ask the questions that will keep you one step ahead of cybercrime.

The post Secure By Design: A 5-Point Cybersecurity Checklist for Machine Health appeared first on Augury.

‘The State of Production Health 2024′ report is filled with up-to-the-minute industry insights. However, what most struck Augury CEO Saar Yoskovitz were the many indications of manufacturers’ increased trust in AI to help address their challenges. “AI’s impact on the workforce and user buy-in is improving, enabling manufacturers to look to larger, persistent opportunities such as supply chain issues and capacity.”

Good News Against Manufacturing’s Universal Challenges

“The State of Production Health 2024” builds on the baseline-defining premier report from 2023. Hence, it not only defines the current state of affairs but also charts any ongoing shifts. And on one level, there’s much to celebrate: the trust in AI solutions is growing.

The survey suggests AI-powered tools have helped alleviate workforce stresses – after ‘upskilling the workforce’ was the main reason for leveraging AI in 2023 (25%). Now, it would seem manufacturers are ready to set their sights on AI solving other problems. For example, supply chain issues rose to the top in 2024, up 11% from a fourth-place ranking last year, in terms of being the “primary factor that could limit your ability to meet production targets/business objectives over the next 18 months”. 

The report is based on 705 qualified responses from decision-makers working at $100M+ companies with at least five manufacturing sites. These companies cover various industries, including building materials, cement, chemicals, consumer packaged goods, food and beverage, pharmaceuticals, metals and mining, oil and gas, paper and packaging, and wood products.  

“I’m lovingly calling the market now ‘Covid junior,’ because in a lot of ways we’re right back to where we were during the pandemic.”

Facing Down a Strained Supply Chain – Again 

“With workforce upskilling initiatives returning value, manufacturers are shifting their spend toward supply chain and capacity concerns,” the report states. “With over 90% of the manufacturers agreeing that supply chain disruptions are expected to become more frequent over the next 12 months.” 

This fear is easy to explain. As a director of a logistics company in the article ‘It’s All Happening Again.’ The Supply Chain Is Under Strain’ eloquently says: “I’m lovingly calling the market now ‘Covid junior,’ because in a lot of ways we’re right back to where we were during the pandemic.” 

Except now, instead of battling a virus, we’re facing a perfect geopolitical storm. And: “If the supply chain disturbances of the pandemic proved anything, it was this: Trouble in any one place tends to ripple out widely,” the article states. 

“The consequences of the pandemic were difficult enough to grasp, with great miscalculations over the impacts on demand for factory goods. But everyone understood that pandemics end eventually. The Houthi strikes and the effects on the Suez Canal, on the other hand, involve enormous geopolitical variables that make forecasting difficult. […] There is no clear solution in sight.” 

In other words, we need to make every production moment count. 

“Nearly all the respondents either strongly agree, agree, or somewhat agree that AI and advanced technologies will help create new jobs in the manufacturing industry (97%), and they are personally optimistic about the future of the manufacturing industry (96%).”

Success – Especially Measurable Success – Builds Trust

So while supply chain worries distract manufacturers, the Production Health report also reflects a certain positivity – driven by an increased trust in AI’s ability to help on various levels. 

“Despite acknowledging the industry’s manifold challenges and obstacles to production, optimism for the future is evident. Nearly all the respondents either strongly agree, agree, or somewhat agree that AI and advanced technologies will help create new jobs in the manufacturing industry (97%), and they are personally optimistic about the future of the manufacturing industry (96%).”

And the faith in AI seems to be aligned with how it’s become easier to measure an AI project’s success. As the report states: “The significant growth in respondents reporting the ability to quantify value from AI investments in various areas indicates that manufacturers are getting better at using AI, and thus increasingly realizing a return on investment.” 

More Success, Less Hurdles 

The increase in speedy ROI perhaps explains the 300% increase in respondents citing no roadblocks when adopting AI tools to solve production challenges. “Six percent of 2023 respondents said there were no roadblocks to AI adoption, whereas in 2024, that figure grew to 19%.”

But perhaps the most heartening is the apparent drop in the number of AI projects ending in “pilot purgatory”. “Half of respondents (50%) said that between 11% to 25% of AI projects reached scale at their sites, and four in 10 (40%) said between 26% to 50% of AI projects reached scale.” These numbers starkly contrast to those of the World Economic Forum, which shows that over 70% of companies investing in Industry 4.0 technologies fail to move beyond the pilot phase of development.

It seems Industry 4.0 is finally coming of age.  

“As manufacturers look toward the future – and specifically toward improving production health – those who have yet to embrace IoT connectivity and AI solutions need to do so immediately.”

Onward and Upward

As the report states, “Crucially, optimism in the value of AI is a key uptick and very promising for the future state of manufacturing. AI continues to offer promises to create a much more empowered workforce, unlock efficiencies, create new collaboration methods, and change how manufacturers plan for and measure success across the organization – including machines, processes, upskilling, waste reduction, operations, and more.”

But yes, there’s still much to be done. 

“As manufacturers look toward the future – and specifically toward improving production health – those who have yet to embrace IoT connectivity and AI solutions need to do so immediately. While those who have already found success should expand those investments to better visualize and act on the data that connects machines, processes, and operations. This is foundational to the ability to meet production objectives while overcoming what they say are today’s key challenges, such as capacity constraints, supply chain issues, workforce concerns, and equipment reliability and efficiency.”

Let’s get to work.

Read the full report: ‘The State Of Production Health 2024’.

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Augury’s recent ‘Beyond the Line’ event showcased how the company’s tagline ‘Predicting A Better Future’ is now a self-fulfilling prophecy, writes James Newman, Augury’s Head of Product and Portfolio Marketing. “We are building what manufacturers need to create a better future. And with four core elements coming together, manufacturers can enter that future today.” 

Self-Fulfilling Prophecy

We unveiled our new Production Health blueprint during our recent ‘Beyond the Line’ event – showing how manufacturers are at a watershed moment in optimizing their production goals. In short, Augury, buoyed by its tagline “Predicting A Better Future,” is already building what manufacturers need to make this better future happen. 

Yes, it remains a work in progress – as any journey of continual improvement should be – but it’s already one offering value to our customers. Four core elements are now coming together that will transform our industry as manufacturers gain more control over achieving their goals.

1)    Machine Health 2.0
2)   Process Navigator
3)   Fusion Diagnostics
4)   Ecosystem

1) Machine Health 2.0 (The Ever-Evolving Foundation)

The blueprint builds on our long-established foundation of Machine Health. Augury has built its business and reputation on making unplanned downtime a thing of the past for our clients. While Machine Health is the foundation for everything that follows, it continues to become broader and deeper. For instance, we can now speak confidently about Machine Health 2.0 as we continue to make advances in infrastructure, improved IoT, and next-generation AI.  

2) Process Navigator (Looping In Clear Prescriptive Insights For Operations)

With precise and real-time actionable insights into the state of your machines, the next logical step is to have the same clarity and control over your Process Health. Our Process Navigator offers dynamic real-time simulation you can use to optimize your operations – and then change when needed. 

During the ‘Beyond the Line’ event, a comparison was made to the history of navigation. Too many manufacturers still rely on “paper maps” to negotiate their processes. But now we’ve gone beyond paper maps and even static GPS routes. We have achieved a Google Maps level of navigation for both machines and the processes: the ability to provide turn-by-turn instructions that adapt to “traffic conditions” and can be calibrated for one’s particular needs – be it speed, energy efficiency, or both. Indeed, you are now able to adjust your production process for multiple objectives.

The next step is supervised self-driving processes. (Tune in next year.) 

It allows us to know how processes are affecting machines and how machines are affecting processes – and not in retrospect, but in real-time.

3) Fusion Diagnostics (Breaking Down The Wall Between Maintenance & Operations)

It may sound fancy, but Fusion Diagnostics does what it says. It’s the connective tissue that allows the tearing down of the wall between maintenance and operations – call it a sledgehammer or a wrecking ball. Call it whatever you want. But it’s the unification we always needed. It allows us to know how processes are affecting machines and how machines are affecting processes – and not in retrospect, but in real-time.

With Machine Health, we already had turn-by-turn instructions on what to do about your machines. With the launch of Process Navigator for Process Health, you now have turn-by-turn instructions on what to do about your processes. And now, Fusion Diagnostics brings machines and processes together – which is entirely different from the two silos they live in today. We are bringing in two vast and complex data sets that AI can use to turn new and previously unseen connections into actionable insights with turn-by-turn instructions that impact the entire organization collectively and not in isolation.

Fusion Diagnostics reconnects all the teams that should have been connected from the beginning – giving them the insights that matter to both: optimizing production.

Do you want to manage your energy use? Go ahead. Do you want to manage waste to meet your sustainability goals? Go for it. 

Thanks to real-time operator-driven actions that respond to changing needs, every shift can deliver its best work. After all, your best route today might not be your best route tomorrow.   

“Unlike in Middle Earth, there isn’t, and there will never be, a single app or platform to rule them all. It’s only by the combined efforts that we will complete this vision of creating a better future.”

4) Ecosystem (No Solution Is An Island)

To attain that better future, you need to realize you can’t do it alone. No one can afford to be an island anymore. And now, with large partners in the ecosystem, we can offer customers more expanded capabilities much sooner than we dreamed of – whether that’s better synching capabilities with Baker Hughes, simplified technology stacks with Schneider Electric, or changing how you manage your assets with IFS Ultimo. 

And there’s so much more potential and partners we are – and will be – tapping into. Whether it’s automating parts-as-a-service or keeping your assets adequately lubricated, the sky is the limit. And it’s impossible to build all these possibilities ourselves. And certainly, it does not best serve our customers. Yes, we will stay focused on building the central capability set that drives value – see above – but meanwhile, we can also leverage entirely new ways of working – thanks to being part of a larger ecosystem.

To put it in another way (and with apologies to JRR Tolkien fans): Unlike in Middle Earth, there isn’t, and there will never be, a single app or platform to rule them all. It’s only by the combined efforts that we will complete this vision of creating a better future. 

A diverse and robust ecosystem will also allow your technologies to be as connected as your teams need to be. Looping in additional data sources will inspire yet more innovation through new techniques and AI capabilities. 

This is why the blueprint for full Production Health will never be finished. This is a house that will only keep growing.   

Conclusion: Upward and Onward

There will always be people who say, “Wouldn’t that be nice?” We can now say, “It’s already nice.” We are making that prophecy of a better future come true: not in three or ten years or Sunday, but in real-time. Now. The building blocks are now in place.

This doesn’t mean we’re done. There are lots of things to come. But for the first time, the vision is not just a wish but a reality.

Learn how to break efficiency barriers with an ever-evolving Production Health blueprint.
Or cut to the chase by reading the ‘Your Production Health Blueprint‘ one-pager.

Read Part 1 of Beyond The Line: ‘The Manufacturer’s Nightmare’
Read Part 2 of Beyond The Line: ‘A New Bag Of Tricks’
Read Part 3 of Beyond The Line: ‘Bringing The Manufacturer’s Dream To Life‘.

The post The Better Future We Predicted Starts Right Now appeared first on Augury.

A sustainability report using CRANE’s model, which estimates future emissions reductions, shows how companies such as Augury allow manufacturers to immediately tackle environmental issues while also streamlining production. “The report is saying nothing exotic: by running your assets more efficiently, you emit less. It’s a double win,” says Augury’s James Newman.

This article was originally published on September 19, 2022.

Better Production For Plant and Planet

“It’s always good to plan ahead – but not if it paralyzes you in taking action now,” says James Newman, Augury’s Head of Product and Portfolio Marketing. He’s feeling bullish in response to a recent report that highlights how manufacturers can start today – not one day – in cutting their emissions. 

It’s as simple as making your machines run more efficiently. “It’s really not rocket science. Augury’s AI-driven insights into Process Health and Machine Health work to improve production for both your plant and the planet.” 

“It’s really not rocket science. Augury’s AI-driven insights into Process Health and Machine Health work to improve production for both your plant and the planet.”

“Manufacturing represents around 24% of global carbon emissions,” according to the ‘2022 Eclipse Carbon Optimization (ECO) Report – Quantifying Sustainability for Technologies Enabling the Industrial Evolution’. “The overwhelming majority of the carbon intensity comes from energy and waste. Eclipse Ventures’ portfolio companies focus on enabling increased efficiency through higher yield, more productive assets, and ultimately, far lower energy intensity per unit of production. Some of the companies addressing the carbon intensity of the physical world include Bright Machines, Augury,  and Arc.”

Industrial Evolution

The Eclipse Carbon Optimization (ECO) Framework uses the open-source CRANE’s model, which estimates future emissions reduction “much like an investor would evaluate an investment’s future revenue and profit potential.”

Developed by climate nonprofit Prime Coalition and in partnerships with ESG advisory firm Rho Impact, Eclipse Ventures applied this methodology to 11 companies in its portfolio to better understand their carbon reduction potential.

“With our ECO Framework, we are able to dynamically assess a technology’s economic value potential and its environmental value potential, both now and 10 to 20 years into the future,” the report states. “Our goal is to reveal the true promise and impact of that technology in order to build and increase investor and public confidence in companies transforming physical industries.”

“It’s great they are now able to make these kinds of assessments,” says James. “It clearly measures the before and after states of Augury’s solutions so you can see the tangible impact it has on your energy consumption, which by extension is directly tied to your energy emissions. It’s an amazing tool – and will only become more accurate moving forward.”

“We can double a manufacturer’s impact by doing what they should be doing anyway: making their assets run better than they were running before.”

Cutting Emissions. Now. And Without Fuss.

The report estimates what Augury brings to the table in terms of cutting emissions: “Due to higher uptime, more efficient energy use, and higher yield, Augury cuts emissions by ~12%, resulting in 3 MMtCO2 reduced annually by 2040.”

To put it into more concrete terms: saving 3 million metric tons of CO2 annually is the equivalent of taking around 750,000 gas-powered cars off the road every year.

“But what I think will get people most excited is how this doesn’t involve anything exotic,” notes James. “Most sustainability programs involve buying new assets, switching over to alternative fuels or some sort of offsetting – and there’s nothing wrong with any of these efforts. But we can double a manufacturer’s impact by doing what they should be doing anyway: making their assets run better than they were running before.”

Impact: Not Additive But Multiplicative

“And this is really just the tip of the iceberg,” says James. “I see a lot more good news in what the report doesn’t cover. Not only are they using the lowest possible numbers to manage expectations, but the report is also very USA-centric. I’d like to know the numbers on a world level – especially when it comes to the global manufacturers who are our current target verticals.”

As the report makes clear, the CRANE analysis also did not factor in the energy and utilities sector, where Augury is now also active, but only included “traditional” manufacturing rotating equipment based on the company’s existing verticals. “Further, we do not assume any emissions reductions from the elimination of product waste during manufacturing downtime,” the report states. “We will make adjustments based on Augury’s progress in the next report.” 

“Indeed, you’ll see a huge jump in our performance in the next report,” says James. “Especially since it will also include the impact of our acquisition of Process Health leader Seebo. As we leverage our ability to combine what we do on a machine level and on a process level, the impact will not be additive but multiplicative.”

 “It has become even more evident that our physical industries bear the greatest responsibility for fulfilling the needs of people worldwide.”

Time is Ticking

Eclipse obviously takes seriously its mandate to invest in technologies that can drive sustainability today, and not just in some unknown future.

“In recent years, it has become even more evident that our physical industries bear the greatest responsibility for fulfilling the needs of people worldwide,” the report states. “Yet, the aging systems and processes upon which physical industries were built have brought forth dire consequences – our environment is at a critical juncture, and if we do not act now, the well-being of our society and planet will be jeopardized.”

To learn more about how Augury is meeting the challenge in our race against the clock, reach out! Meanwhile, you can also read more about sustainability and ESG here.

The post How Manufacturers Can Go After Sustainability Goals appeared first on Augury.

As a lead-up to Augury’s Beyond The Line event on June 18th 2024, Augury Co-Founder and CEO Saar Yoskovitz presents a three-part series on how new technologies are aligned to help manufacturing meet its many different, and often conflicting, goals. In parts 1 and 2, Saar summarized the ongoing challenges and emerging solutions. Now, he outlines the required capabilities manufacturers need to flourish in this new era of data-centric manufacturing.

Let’s do a quick recap! We started with ‘The Manufacturer’s Nightmare’, in which we presented a dreaming golfer who realizes his old bag of golf clubs can no longer make him a winner in today’s modern world. And yes, that golfer represents today’s manufacturers who wonder how they can deliver the massive leaps in efficiency, sustainability, quality, and flexibility needed to meet the demands of their customers, investors, and employees. 

In part two, we showcased ‘A New Bag Of Tricks’ – those macro trends in technology and the workforce that are now creating tremendous opportunities for manufacturers. Indeed, a new set of clubs exists that will help turn many of the rosiest promises of the past years into practical solutions – and do things manufacturers previously only dreamed of.

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But how do manufacturers apply those trends to bring this dream to life? How do they create a blueprint for themselves and their strategic vendors to ensure they have the digital infrastructures, new work processes, and highly skilled teams they need to win?

We will now address these questions. 

Capturing The Magic: Think Capabilities, Not Products

Step one: you need to realize that no one can predict all the answers. A strategy for manufacturing crafted even a few years ago, for example, would not have embraced the emerging power of generative AI platforms such as ChatGPT – much less, Chat GPT 4.0. It would have underestimated the advances being made by smaller startups and emerging leaders in applying Machine Learning, AI, and data engineering to basic but crucial challenges such as energy management, asset reliability, and process optimization. 

“Step one: you need to realize that no one can predict all the answers.”

The key is to think about general capabilities, not specific products. And, here are the five capabilities that must be built into the emerging digital ‘tech stacks’ of successful manufacturers:

Five Required Capabilities for Digital Success 

1)    Infrastructure: Reliable Sensing And Computing – From Cloud To Edge

The modern foundation for AI-driven manufacturing will need to be cloud-based. Only by aggregating massive volumes of data can AI models evolve quickly enough and become trustworthy enough to be used confidently in manufacturing. The Cloud is ideal for this task. 

But while models might be built and trained in the Cloud, real-time insights needed for autonomous production optimization will also require work to be done much closer to the manufacturing process. In other words, computing power needs to extend from the Cloud all the way to the Edge – namely, the assets themselves and even the sensors that are collecting and analyzing the vast amounts of data already generated by modern manufacturing systems. 

AI processing at the Edge not only reduces cost but also increases speed. This way, changes to processes or spotting risks to assets or people can be done in minutes and executed in real-time on the plant floor – and not discovered days later through some centralized data science exercise. 

“But while models might be built and trained in the Cloud, real-time insights needed for autonomous production optimization will also require work to be done much closer to the manufacturing process.”

Integrations must be ubiquitous. This is not just about moving data from one place to another, but also integrating disparate tools to take advantage of raw data and insights generated by other systems in the manufacturer’s overall technology stack. 

2)   Insights: Agility in Data Collection and Sharing

Manufacturing systems will throw off ever more data as sensing becomes more ubiquitous and affordable. This will feed more powerful and accurate AI systems and give the people who use them deeper, more effective insights into the tactics needed to optimize manufacturing. 

But as data volumes grow and analysis becomes more real-time, data collection itself will need to become more responsive. Rather than fixed sensing at regular time intervals, sensing regimes must be more dynamic. For example, you might want to increase or vary your data collection when indications suggest deeper analysis is required or when you want to respond to certain behaviors of machines or processes. To do that, AI engines have to create models at the speed of production – which means in a matter of hours and not months.

Like the ‘gain’ function on a radar, which can be turned up or down to achieve greater accuracy or reduce unnecessary signals, sensing depth and power should be variable to match the depth of insight to the task at hand. This will help control costs, reduce bandwidth requirements, and create new opportunities for analysis.  

“The added context provided by a more diverse array of data types will mean faster and more confident decision-making by people and the digital systems that support them.”

3)  Coverage: Everywhere And Beyond

A) Multidimensionality Of Insights And Data Sources

Just as with a self-driving car finding its way through a combination of data sources and sensors (GPS inputs, on-board cameras, LiDAR systems, etc), the AI-driven processes that will help manufacturers navigate complex process changes and asset management strategies will benefit from more and more sources and types of data. The added context provided by a more diverse array of data types will mean faster and more confident decision-making by people and the digital systems that support them. 

This scenario is already emerging in areas such as Machine Health, where classic data inputs such as vibration or temperature are now being augmented with external processes and factors, such as input pressure for water or materials, environmental conditions like humidity, and even the specific SKU being run at any given time.  

All of this can help get better insights into machine performance and reliability. Hence, manufacturers should ensure they’re architecting strategies to embrace and demand these integrated data collection and analysis approaches. Here, it will be particularly important to break down insights and contexts into four functional levels:

Forensic: Data and context that help determine after an event what caused it to occur
Predictive: Data and insight that can identify with high accuracy when an event will occur
Prescriptive: Data and insight that can predict an event and provide the best means of intervention to prevent negative outcomes and maximize positive ones
Proactive: Data and insight that’s trusted enough to also autonomously give commands or adjust systems or processes to prevent negative outcomes and maximize positive ones. 

B) Broad Instrumentation And Coverage

The manufacturing process is an incredibly complex and integrated effort involving tens or hundreds of mechanical components, raw materials with constantly varying characteristics, and changing goals (cost optimization, throughput, quality, etc). 

Data will also need to come from everywhere to make insight-driven manufacturing a full success. Every machine type and all key subprocesses will need to be instrumented and understood, and those data sources will need to be brought together into a holistic view of the end-to-end process.  

“Leading manufacturers will harness that power to be able to adjust processes in real-time, within individual shifts, and continually tune the mix and behaviors of machines, materials, and even work processes to get the desired outcome every time.”

C) Holistic Approaches To Assets, Processes, And People

Manufacturers will need to be able to see all the elements of a process through a digital lens. Basically, this involves taking the ‘digital twin’ concept to a whole new level. 

Capturing and understanding the interrelationships between the hundreds or thousands of variables that exist in any manufacturing operation will be essential for optimizing overall production. Long the ‘holy grail’ of manufacturing, that end-to-end visibility can become a reality today due to the intersection of technology trends outlined above. Leading manufacturers will harness that power to be able to adjust processes in real-time, within individual shifts, and continually tune the mix and behaviors of machines, materials, and even work processes to get the desired outcome every time. 

Domains of work that are isolated today (such as asset management not being connected to process optimization, or operating parameters not being able to incorporate changes in weather or raw materials compositions, etc) will come together to enable higher-order decision-making by truly cross-functional teams. 

“Manufacturers must have a culture and rewards system that attracts and retains a new generation of digitally native workers while providing the training and opportunities that keep their current expertise in-house and motivated.”

4)   Engagement: Digital-Ready Workers and Culture

While all these technological advances create the opportunity for a new era in manufacturing effectiveness, in the end, it will, as always, come down to the people who use them. Manufacturers must have a culture and rewards system that attracts and retains a new generation of digitally native workers while providing the training and opportunities that keep their current expertise in-house and motivated. 

While worker, product, and environmental safety will remain paramount, the speed of innovation will become more critical. Manufacturers will need to understand where they can take greater risks and where they can even afford to ‘fail fast’ to try out new approaches and tools. 

The decentralized decision-making prevalent at many manufacturers will need to evolve. The benefits of data-driven manufacturing will be amplified by scale. With more data flowing in and insights flowing out, learning will be accelerated – along with the flow of benefits for all the processes and plants across a portfolio.

And as the individual manufacturer tweaks its in-house capabilities, the external world must also lend a hand…  

“The winning manufacturers in this new era will not be the ones who try to work in islands, but those who see the benefits of connectivity and data sharing.”

5) Ecosystem: Long Live Not Living On An Island

Data sharing across supply chains, with OEMs and other partners (and even aggregated data sharing, in anonymous ways, between competitors) will deliver even greater benefits due to the rapid improvement it can drive in AI models and algorithms.

The winning manufacturers in this new era will not be the ones who try to work in islands, but those who see the benefits of connectivity and data sharing. Speed of innovation and the courage to move quickly will outweigh the traditional benefits of secrecy and proprietary thinking.

The Lean Dream Coming True

And of course, all these capabilities interact and build on each other. With data finally breaking down silos between work functions, the full power of concepts in lean manufacturing, TPM, or similar models will finally be able to be realized. 

Just as IT operations and application security eventually fused with software development to shorten cycle times, reliability, process engineering, and other functions can come together in one integrated approach to optimizing manufacturing. 

So, let’s revisit that golfer’s nightmare in the first post… He’s standing at the first tee and realizing all their once trusty clubs were either gone or useless. However, with this new set of capabilities in place, his nightmare can turn into the dream scenario where the bag is full of a new set of clubs – designed with AI precision, crafted of the latest materials, and proven in the hands of the world’s best experts. Now, that’s the manufacturing world we can build for the future. 

In other words, as with golf, we can now enter the zone.

Read Part 1 of Beyond The Line: ‘The Manufacturer’s Nightmare’
Read Part 2 of Beyond The Line: ‘A New Bag Of Tricks’

Ready for the next era of efficiency?

• Register for our event

The post Beyond The Line (3): Bringing The Manufacturer’s Dream To Life appeared first on Augury.

As a lead-up to Augury’s Beyond The Line event on June 18th, 2024, Augury Co-Founder and CEO Saar Yoskovitz presents a three-part series on how new technologies are aligned to help manufacturing meet its many different and often conflicting goals. After summarizing the ongoing challenges in part one, Saar now presents the macro-trends opening up new possibilities – to make old promises a reality.

Innovation Can Be A Waiting Game

There’s a saying attributed to Bill Gates: “Most people overestimate what they can achieve in a year and underestimate what they can achieve in ten years.” That truism also applies to changes in our technologies and practices: we generally overestimate how quickly and effectively new technologies and trends will impact us in the short term while underestimating their long-term transformational impact. 

For instance, the 1939 World’s Fair predicted flying cars and robot helpers for our homes, and in the 1950s, Dick Tracy comics showed off his two-way wrist radio. All those things have come true to varying degrees – albeit decades after the most optimistic prognostications. In fact, it was only in 2023, the FAA approved the first flying car prototype.

What keeps those things from coming true faster oftentimes isn’t the core idea or invention; it’s the supporting technologies and conditions needed to make them practical. Making small radios was not a problem in the 1950s, but small, powerful batteries were. And while the basic requirements of a flying car were already known for decades, we lacked the compact engines, GPS software, and robotics needed to reduce the risk brought on by non-expert operators.

Read part 1 of this series:
‘Beyond The Line (1): The Manufacturer’s Nightmare’.

Enabling Tech Is Now Catching Up

For decades, manufacturing has had real-time data-driven predictions and insights, as well as automation – innovations that could transform our machines and our relationships with these machines. However, the practical application of that idea has lagged well behind the predictions of how soon this would all play out. The reason, again, was the lack of supporting technologies and conditions. 

However, the developments have been rapid, and the supporting tech is now available to open the door to a massive change in manufacturing. We can now look forward to huge gains in productivity, quality, sustainability, and efficiency. So, what’s changed that allows us to finally turn a decades-long promise into a reality?

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Five Macro Trends Driving The New Era Of Manufacturing  

1)    Cheap Processing Power – And Lots Of It

You need massive processing power close to where the work is being done to get the near-real-time insights that can lead to fast, effective responses to process deviations, reliability risks, or quality escapes. Processing power has made those leaps, and today, cheap ubiquitous processing at the edge of our computing networks – and therefore at the center of our manufacturing processes – is now a reality. 

2)    Trustworthy Data – And Lots Of It

Manufacturing environments have become increasingly chatty over the years. Many assets already come with some level of sensing and data collection capability. For others, it’s more about choosing from an increasing array of third-party sensing solutions, which are often tied to specific use cases (quality, reliability, compliance). 

Either way, or in combination, the overall result is that manufacturers now have vast quantities of data about their processes, assets, and operations. However, to apply all this data you need evolved AI capable of applying powerful analytics… Which leads us to:  

3)    A Massive Leap In AI Effectiveness

While Generative AI systems are getting all the news today, more purpose-built, AI-driven solutions have impacted parts of manufacturing for several years now in areas such as reliability and maintenance. 

What’s made these more purpose-built solutions successful is oftentimes they are end-to-end solutions that solve the entire problem, from data collection to analysis and insight. More general-purpose uses of AI, however, have been less successful. But today, the constraints that have limited AI effectiveness – namely, lack of sufficient processing power where it’s needed and lack of sufficient trustworthy data to train AI models – are falling away. 

The result is that layers of AI-driven solutions, some more purpose-built around quality, reliability, and efficiency, and some more generative and interactive built on platforms such as OpenAI, can be effective in terms of their ability to be trusted copilots or ‘assistants’ to more complex manufacturing problems (such as end-to-end process improvement). 

4)    Greater Connectivity Between Systems

Data is great to have, but data trapped in silos is just an added storage cost. Happily, these siloes are starting to break down thanks to Increased connectivity between manufacturing systems, increased use of cloud infrastructure, and emerging sets of APIs to enable manufacturing systems.

The ability of these systems to “talk” to each other is key to powering the emerging AI-enabled decision-making tools and enabling real-time, autonomous optimization of everything from spare parts management to the manufacturing processes themselves.  

5)    A Workforce Eager To Embrace New Technologies.

New tools and technologies are only as good as the rate at which they are adopted and fully utilized. Fortunately, the workforce entering manufacturing today will not only adopt these tools but demand them. 

These digital natives will be fully comfortable with AI-assisted decision-making and high levels of autonomy in the tools they use. They will accelerate the rate of adoption and the resulting transformation of a wide range of manufacturing processes. 

Companies used to capital investment cycles that span decades will have to learn to iterate at the speed of new technology, evolving their strategies and execution in months and quarters, not years and decades.

Right Place, Right Time

These macro trends around workforce and technology are intersecting just when it’s needed most. As many of the past strategies for increasing manufacturing efficiency become worn out, the new strategies and tactics enabled by AI, data, interconnectivity, and edge computing will step into the breach and spur another wave of productivity in manufacturing – one that’s efficient, sustainable, and more responsive to customer demand. 

But gaining the benefits of these trends doesn’t come without some effort. Manufacturers must develop clear strategies for their technology ‘stack’, ensuring they lay the foundation to grow, iterate, and scale. This will require different thinking on their part. Companies used to capital investment cycles that span decades will have to learn to iterate at the speed of new technology, evolving their strategies and execution in months and quarters, not years and decades.  

They’ll also need a clear blueprint of what they must build for themselves and what they should expect from their technology partners.

Now For The Real Work

Aspiring manufacturing leaders will also need to think of strategies that consider the insights from a strong insight-first strategy as crucial assets on the same level as pumps, rollers, or ovens. They’ll also need a clear blueprint of what they must build for themselves and what they should expect from their technology partners. So please tune in next time as we explore how to best do this on a practical level…

Read Part 1 of Beyond The Line: ‘The Manufacturer’s Nightmare’
Read Part 3 of Beyond The Line: ‘Bringing The Manufacturer’s Dream To Life‘

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Augury CEO Saar Yoskovitz is a committed techno-optimist. A recent webinar with business consulting firm Frost & Sullivan reflects his sunny outlook on how tech can help manufacturing solve its most pressing problems. “It doesn’t matter where you are on that road; the tools are already there to drive you forward,” says Saar.

 

‘The AI-driven Factory: How Production Health Will Shape the Future of Manufacturing’ features our own VP of Strategy, Artem Kroupenev, and Sebastián Trolli, the Research Manager and Global Head of Industrial Automation at Frost & Sullivan.

As a deep-dive fireside chat, it does a wonderful job summarizing current manufacturing realities and outlines how the industry will transform over the next five to ten years – with early adopters having already paved the way.  

Our Age Of Complexity And Innovation

Sebastián ably described the ongoing challenges facing manufacturers today as defined by two words: ‘complexity’ and ‘innovation’. As he formulated it: “On one hand, there’s the urgent need to adapt to evolving market demands and stringent environmental regulations. On the other hand, there’s the challenge of leveraging technology to make these adaptations possible and profitable.”

A flurry of issues works to stir up the complexity: the skilled labor shortage, continued post-COVID supply chain disruptions, nagging inflation, shaky geopolitics affecting trade, etcetera. It’s a long and, unfortunately, long-familiar list.

There is good news. “Manufacturing operations are benefiting from the deployment of new technologies, which are offering solutions to traditional problems and opening up new avenues for growth and revenue,” says Sebastián. “The massive amount of data generated from operations presents a goldmine for optimization and innovation. However, the challenge lies in effectively harnessing this data to drive actionable insights. Analytics and AI are crucial to unlocking this potential.” 

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From Paper To Satellite

So, how far are we, as an industry, in applying such innovations concretely? Frost & Sullivan’s statistics are sobering. Despite the proven impact of AI and analytics on production optimization, a staggering 90% of manufacturers have not embraced AI at all yet. And of those who do take on digital transformation projects, 75% of these projects fail – succumbing to what’s called ‘pilot purgatory’. 

Artem provided an insightful analogy to put these numbers into perspective and highlight how we are, in fact, beginning a journey of ongoing progression. “Look at transportation over the last 20 years. We began with using paper maps. Then, we had these static GPS routes. And now, in the last 10 years, we’re using real-time turn-by-turn navigation. You can add stops. It adapts to traffic conditions. The basis is now laid for autonomous driving,” says Artem. “And now a similar thing is happening in manufacturing.” 

“Though we’re still at maybe 1% of adoption, across the wider industry, in five or ten years, it will be irresponsible not to implement these new approaches that can optimize the health of your machines and processes. You’ll simply be left behind. In fact, technologies are already out there as reliable products providing tons of value.”

ETA May Be Pushed Back, But We’ll Get There

Certainly, a factory can be compared to a car: they both have inputs, outputs, and complexities. To get the plant from A to B in terms of its optimization and driving product, many moving parts need to be navigated towards a similar goal. And that’s exactly what we’re seeing across manufacturing today. Unfortunately, too many manufacturers are still at the paper map stage. 

However, this is set to change fast, according to Artem. “Though we’re still at maybe 1% of adoption, across the wider industry, in five or ten years, it will be irresponsible not to implement these new approaches that can optimize the health of your machines and processes. You’ll simply be left behind. In fact, technologies are already out there as reliable products providing tons of value.” 

Follow The Leaders

Indeed, many factories are already achieving autonomy across various use cases – such as with Machine Health, where unplanned downtime is essentially a thing of the past. “And it’s important to note that by autonomy, I don’t mean that the factory has no people operating it,” says Artem. “In fact, the opposite is true: the people are empowered to really know how to optimize that factory, to create those double-digit improvements, in terms of efficiency, productivity, sustainability, and so forth.”

And, thanks to these pioneers, there’s already a clear roadmap for overcoming standard hurdles, such as establishing an effective change management program, properly documenting ROI and other wins, setting up a reliable IoT infrastructure, and dealing with legacy IT systems.  

Towards The Eternal ‘Golden Batch’

Meanwhile, these pioneers continue to push ahead. Machine Health and Process Health are both great. But the real goal is to combine these data streams, along with others, to provide actionable insights to optimize for full Production Health. In other words, we will be able to choreograph a dance between arenas that were previously wholly separated.

“There is absolutely an optimal way to run a factory at any given time,” notes Artem. “And most factories have run at their peak at some point in their history when all the stars were aligned. Everything was just perfect for a short period of time: the perfect production shift, producing the perfect product in the most efficient way possible. But then things go downhill. And there are too many variables to understand what went so right – or wrong.”

And now, with AI, we have the perfect tool to juggle countless variables – and do that in real-time so we have time to make adjustments to recreate that ultimate “golden batch.” Plus, you’ll be able to adjust your production process for multiple objectives – for instance, increasing yield and quality while saving energy and time. 

As Sebastián summarized nicely: “I see the future of manufacturing as brighter than ever.

Let’s get to work.

Watch the full fireside chat: The AI-driven Factory: How Production Health Will Shape the Future of Manufacturing’.

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The post Driving Towards A Future Of AI-Driven Factories – With You In The Driver’s Seat appeared first on Augury.

As a lead-up for Augury’s Beyond The Line event on June 18th 2024, Augury Co-Founder and CEO Saar Yoskovitz presents a three-part series on how new technologies are aligned to help manufacturing meet its many different, and often conflicting, goals. For part one, Saar begins by outlining the industry’s many sandtrap-like challenges. But rest assured, he’s just setting us up for a clean shot towards a happy ending

I’m not a professional golfer by any stretch, but I can imagine the kind of nightmare these folks could have. It goes like this: you’re approaching the 18th tee with a chance to win a championship. Your fans are eagerly watching; your sponsors are expecting a great outcome; all your hard work over the years is about to pay off. But you look at your golf bag; all your clubs are bent and broken. You have nothing to hit the ball with – and no way to satisfy your audience and achieve your goals. 

Hard To Win

This is a somewhat weird scenario, perhaps. But that’s how nightmares go. And for manufacturing leaders today, there’s a similar bad dream unfolding. But this one’s real, and not waiting for nightfall to cause stress. 

Manufacturing leaders might not have rabid fans or enthusiastic sponsors behind them, but they do have demanding customers, impatient investors, talented employees, and sharp-eyed regulators – and they are all watching to see how well you perform. Success isn’t measured in low scores or championship wins but in metrics such as repeat purchases, earnings per share, worker retention, and sustainability goals.  

“But like that sleepless golfer, when today’s manufacturing leaders look into their bag, they don’t like what they see. The traditional tools are no longer in the same shape they used to be.”

You Need A Whole New Bag

None of these targets are easy to hit individually – never mind balancing each against each other. For years, those manufacturing leaders have had a strong and varied set of tools to use to achieve success – their bag of clubs, if you will. For instance, they could outsource manufacturing to lower cost countries or pressure suppliers to keep costs under control. They could dial up or down the workforce size or the number of their facilities to match changes in demand. They could adopt new technologies and new work processes to increase efficiency. And more recently, they could enjoy strong pricing power to pass higher costs onto customers without risking loyalty.

But like that sleepless golfer, when today’s manufacturing leaders look into their bag, they don’t like what they see. The traditional tools are no longer in the same shape they used to be. So, while customer demands, shareholder expectations, and regulatory burdens remain or grow, the tool bag looks pretty empty for today’s manufacturers.  

“It’ll sound pretty discouraging but don’t despair.”

Disclaimer: Good News Ahead

I’ll use the remainder of this post to detail how the macro environment has emptied the tool bag for these leaders. It’ll sound pretty discouraging but don’t despair. In follow-up blogs, we will explore how a different set of macro trends are coming together to create new opportunities to transform manufacturing – a new bag of tricks for leaders to draw on to continue to drive their business forward.

We will also talk about the trends themselves, how manufacturers will have to think about putting them to work, and what they should expect or demand from their partners and suppliers, who will be critical for delivering the solutions that turn these new possibilities into real-world answers.

So let’s dig into the nightmare first, but with the optimism that we will all wake up to find there’s actually a brighter day ahead for manufacturing leaders.

“We’ve gone from a ‘just in time’ supply chain optimized for efficiency to a ‘just in case’ approach centered on risk.”

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The Rethinking Of The Global Supply Chain

For decades, leading consulting organizations, economists, and even global geopolitical strategies drove the idea that the key to improving manufacturing efficiency was leveraging the lower costs and high efficiencies of global manufacturing. The idea was that cheaper labor, affordable shipping, and relative global calm would make it possible to produce goods in the least expensive locations and then sell them in the richest markets safely and efficiently. 

Unfortunately, this strategy has been put into doubt by a confluence of factors: rising costs in those countries (who knew their workers would want to get paid too?!?), constraints in global transportation networks (remember COVID, the Ever Given, and more recently the Baltimore Port disaster?), and an increasingly nationalistic political climate rife with wars of both the tariff and shooting kind. 

As a result, supply chain resilience has too often come to mean giving up cost efficiency to gain safety and predictability or to placate political policymakers. We’ve gone from a ‘just in time’ supply chain optimized for efficiency to a ‘just in case’ approach centered on risk. To localize those global supply chains, many countries are investing billions of dollars – or even a trillion dollars in the case of the US. And this is a task that will take years to accomplish, assuming the political will remains… 

So, there’s all that…

“But those suppliers rely on the same bag of tricks as every other manufacturer, so they are also finding it harder and harder to find new efficiencies.”

The Squeezing Dry of the Supplier Network

Meanwhile, leading manufacturers have been great at doing all they can to make cost issues their supplier’s problem through massive cost pressure on supplier networks. This has had the benefit of squeezing inefficiencies out of supplier networks, materials costs, and such. 

But those suppliers rely on the same bag of tricks as every other manufacturer, so they are also finding it harder and harder to find new efficiencies. They’re starting to push back on their own customers and forcing those further down the line to look for other avenues to reduce cost or increase efficiency.

The Growing Power of the Worker

Certainly, the workforce is no longer a place to go for cost savings. As with other industries, layoffs and hiring cycles have always been a part of managing costs and capacity in manufacturing. When workers were plentiful and had limited options, this approach was easy to implement. New workers (or furloughed ones) could be brought in relatively quickly – and thus limiting the risk of reducing the workforce during slow times. 

Today, however, manufacturers struggle to fill even their currently available jobs. New plants are slow to come online even with billions in construction incentives because workers can’t be found to fill them. This scarcity of new talent is exacerbated by the ongoing retirement of the current experts, draining institutional knowledge and practical experience. These losses will take decades to replace without new approaches such as skills-based hiring practices, on-the-job training, and actively appealing to younger generations by, for example, more actively addressing their concerns around sustainability. 

Regardless, companies are increasingly unwilling to reduce headcount in technical and manufacturing roles because of the risk they won’t be able to backfill those roles – or replace that experience – down the road. Add to that the ability of workers in this time of scarcity to demand higher wages, everyone is now very aware of how we need to look elsewhere to cut costs. 

The Limits of New Work Processes (Manufacturing Creates Boat, Then Misses Boat)

More advanced companies embraced processes around Total Production Maintenance (TPM) and Lean Manufacturing, hoping that more cross-functional, agile teams could wring new gains out of productivity. While those processes made an impact, limits were reached because of constraints beyond anyone’s control. 

While islands of people could quickly be reorganized into cross-functional teams, the islands of data tied to each part of the process could not be so quickly integrated. Like the blindfolded men and the elephant, even though the teams could speak together and plan together, they were still forced to see the manufacturing process through disparate lenses. As a result, data-driven decision-making was slow at best and impossible at worst.

But while execution did not live up to expectations for manufacturing, the concept around TPM has been proven, ironically, outside of the industry that developed it. For instance, software development and sales and marketing were able to more readily integrate unified goals and the sharing of the same real-time data and insights to fundamentally change how they worked. As terms such as Agile and Empowered Teams became universal, DevOps and then DevSecOps became common practice for software development, and integrated sales and marketing ‘pods’ emerged in business-to-business selling.

And while other industries now have their Salesforce and Atlassian based on inspiration from manufacturing, the industry that originally pioneered this approach still lacks the tools to make this game-changing shift on a large scale. (Until now…)

The Changing In The Power of the Purse

And really, the manufacturing sector obviously needs all the help it can get. As they do battle with a shifting landscape as outlined above to stay in business, they must also still forge ahead in meeting sustainability goals – defined by both government regulators and public pressure – while also making sure they still cater to consumer demand. 

“Manufacturers can no longer look to their customers as the solution to their own rising costs or continued inefficiencies.”

Certainly, in more recent years,the supply and demand curve has been greatly distorted by the COVID-19 pandemic and subsequent government responses. Apart from the absence of goods on shelves, inflation skyrocketed even as consumers saw increased spending power due to COVID-related subsidies and other related government stimulus across many countries. This enabled manufacturers to pass higher costs on to consumers and, in some cases, increase profits in the process. 

Today, as those factors have washed through the global economy, pricing power has become more balanced. Consumers are no longer as willing to accept higher prices, and instead do more comparison shopping and bargain hunting. And in some cases, governments have ridden the consumer frustration wave and threatened penalties for ‘price gouging’.

This means manufacturers can no longer look to their customers as the solution to their own rising costs or continued inefficiencies. Meanwhile, increasing interest rates have pulled the rug from under the manufacturer’s feet by also working to decrease consumer demand.  

And From Here?

So here they are –  the world’s manufacturing leaders – staring at a long drive to the flag, all eyes upon them, but knowing that the tricks and tools that have served them so well for the past years and decades are no longer in their bag. Time is ticking down for them to make their next play. But what’s it going to be?

Read Part 2 of Beyond The Line: ‘A New Bag Of Tricks’
Read Part 3 of Beyond The Line: ‘Bringing The Manufacturer’s Dream To Life‘

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More than two-thirds (69%) of CPG manufacturers said they planned to increase AI investments over the last year (second only to food and beverage manufacturers at 74%). And they have high hopes for those investments: when asked which production goals they believe AI can help them achieve, CPG leaders listed workforce, capacity, and yield/throughput issues.  

Consumer packaged goods (CPG) manufacturing covers a lot of ground–from toothpaste, diapers, and cosmetics to yogurt, chips, and beer. But while diverse in products, CPG organizations all face similar challenges–but some companies are driving deep changes in the industry.

Consider just a few shifts happening in CPG: Barriers to enter the market are lower than ever, consumer tastes are moving toward more personalized and healthy products, and sustainability standards are changing how products are produced up and down the supply chain. All of this is putting strain on  manufacturers to become leaner, cleaner, and more efficient across the board–from improving capacity and uptime to reducing waste and attracting and keeping workers. 

CPG Sees Red Flags with Capacity and Workforce Issues 

Augury’s recent manufacturing-wide survey, The State of Production Health, uncovered fascinating details around those challenges, shedding light on how CPG compares to other industrial sectors and offers insight into how organizations are using technology to overcome their biggest hurdles. 

When asked to name their three biggest manufacturing challenges, CPG respondents answered with a consistency not seen in the other verticals: 

• Workforce constraints/upskilling and capacity constraints tied for first–the top picks among almost half of all respondents; forecasting production and scheduling came in just under those.

By comparison, manufacturers as a whole across all nine surveyed verticals listed the high cost of materials/energy, capacity constraints, and quality/yield/throughput issues as their top three challenges. 

CPG leaders reiterated their workforce concerns elsewhere in the survey:

• When asked to name the primary factor that could limit their ability to meet production targets and business objectives over the next 18 months, staffing constraints topped the list by a mile with nearly 42%–considerably higher than any other vertical in the survey.
• Diving deeper into those staffing challenges, CPG leaders tagged increasing competition and knowledge transfer as their thorniest workforce obstacles; interestingly, lack of technology came in third.  

The findings paint a difficult picture, to be sure, but there is also evidence of positive approaches to these challenges, especially in the industry’s embrace of Artificial Intelligence (AI) technology. Still, they’ll need to better harness its true potential before they can claim victory. 

AI as Tool, Not a Remedy 

More than two-thirds (69%) of CPG manufacturers said they planned to increase AI investments over the last year (second only to food and beverage manufacturers at 74%). And they have high hopes for those investments: when asked which production goals they believe AI can help them achieve, CPG leaders listed workforce, capacity, and yield/throughput issues. 

Those are smart targets for the industry. CPG manufacturing processes are complex and dynamic, with variables like raw material use, quality concerns, energy consumption, machine health conditions, and other shifting priorities making it incredibly difficult to find the right balance as you try to meet capacity or yield goals. 

Applied to manufacturing process health, purpose-built AI (solutions developed for and directed at specific process challenges) infuses machine learning algorithms with deep process expertise from a production line. This allows AI to go beyond just looking at data to consider all the unique variables of that production line, from dynamic traceability to loops, buffers, raw material variances, parallel processes, multiple SKUs, and other considerations. This, in turn, allows teams to better target and meet goals around capacity, yield, throughput, and other KPIs. 

AI-powered machine health solutions use sensors to monitor pumps, motors, conveyors, and other types of rotating assets for anomalies in vibration, temperature, and other factors. By analyzing the data in real time, the solution provides accurate and actionable insights to maintenance and reliability teams, helping them resolve issues before they cause unplanned downtime and production disruption. 

Importantly for CPG companies, purpose-built AI solutions not only outperform traditional, human-led approaches to manufacturing optimization, but they also improve upon the skills of the humans themselves. Empowered with AI co-pilots, teams are freed from mundane tasks and firefighting, allowing them to act faster and smarter and focus on optimizing production lines. 

It’s precisely this kind of AI investment CPG companies should be pursuing as they look to retain or regain market share and become truly resilient, efficient organizations. 

One leading global CPG company’s experience with AI-driven predictive maintenance serves as a prime use case. By embracing AI and IoT technologies early and quickly, they were able to uncover issues in a critical machine, saving 2.8M pieces of end-product and avoiding 192 hours of machine downtime.”

The manufacturing environment is changing fast, across all verticals, and leveraging purpose-built AI is not just a nice-to-have–it’s an essential component for a competitive company. With more efficient processes and machines, organizational resilience, and an intelligent, well-armed workforce, CPG organizations will not just survive, but thrive, while also impressing consumers with higher quality, fairly priced, and innovative products. 

Read the full “State of Production Health” report here. Then see how Augury’s AI can deliver value to your manufacturing business.

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Manufacturers see efficiency and savings, so why is the industry moving too slowly for its own good, and the good of consumers?

When industrial food supply chains slowed to a crawl or fractured outright during the Covid pandemic, attention turned toward governments–what could be done to get things back on track? And when food prices around the world soared last year, people again asked leaders to intervene. UK Prime Minister Rishi Sunak’s recent Farm to Fork summit, which aimed to address the country’s multi-layered food supply-chain issues, brought attention and some investment to the problems, but was considered either a well meaning yet ineffective first step, or a blatant PR stunt. Mr. Sunak is also in the midst of trying to address inflation by working directly with supermarkets. In the U.S., interest rate hikes and price-gouging investigations provided mixed results, though manufacturing and supply chain investments have been cheered by most as an essential move to help make the industry more competitive through advanced technology. 

While government oversight and investment is crucial to the industry’s safety and its move toward 4.0 maturity, the real solution could be in industrial AI technology, which is beginning to have real, measurable impact on everything from food quality to plant-floor efficiencies, with benefits for both manufacturers and consumers.

The State of AI in Food and Beverage Manufacturing

In data collected for “The State of Production Health 2023” survey, Augury found that confidence in AI’s capabilities is quite high among food and beverage manufacturing professionals:

• 37% believe AI could help them achieve quality, yield, and throughput goals
• 32% say AI could help them optimize asset care
• 26% say AI would assist them in controlling the cost of materials and energy

This confidence in AI technology is translating to actual adoption in the industry, with supply chain optimization, tracking energy consumption, and overall production health being the top three use cases. Yet, it appears that food and beverage players are missing one of the most impactful AI use cases in the industry: AI-driven machine health. Just 9% of manufacturers say they try to improve machine health and reliability using AI tools, far below the average of 28% across other industries. 

This is a surprisingly low number given the success many food and beverage manufacturers experience after deploying machine health solutions. For instance, Augury’s Machine Health helped one of the world’s largest food and beverage manufacturers document less machine downtime and fewer unexpected breakdowns, while also helping them lower spending on replacement parts and avoid the loss of more than one million pounds of product. 

Looking at another finding in the report helps explain why the industry might be hesitant to embrace AI more fully. When asked about their ability to quantify the impact of AI in meeting business objectives, their self-reported scores revealed a disconnect between how AI was being used and how its impact was being measured. 

For which of the following areas are you able to quantify the impact of AI in meeting business objectives?

• Improving production health: 15%
• Reducing loss, wastes, and emissions: 15%
• Maximizing yield and capacity: 14%
• Reducing machine downtime: 12%

So while AI is being used across organizations, including on the plant floor, businesses are still in the dark when it comes to understanding how or if the technology is paying off, either flying blind or lost in a sea of data they can’t act on. Those gaps need to be filled in order for the industry to find true success with AI. 

Still, there are AI bright spots for food and beverage manufacturers. Rising investment is one such area, with nearly 14% of respondents saying their companies planned to invest significantly more in AI in 2023 and 60% saying they plan to invest at least slightly more. 

The survey also revealed encouraging statistics around workforce and AI: 

• 78% say AI, IoT, and Machine Learning will positively impact their workforce upskilling efforts
• 29% say AI and advanced technologies will help create new jobs in the manufacturing industry

These workforce findings will come as a beacon of hope in an industry where 73% of employers face hiring challenges.  

How Food and Beverage Manufacturers Will Advance their AI Journey 

The industry is ticking some boxes when it comes to AI–they’re deploying it where it can help both the business and, ultimately, the consumer, such as for production health, process optimization, and materials and energy efficiency; and they are incorporating the technology with the workforce as part of their upskilling plans. But they are also not getting full ROI from their solutions, which means they are not meeting their true production potential, not lowering costs, and not working fast enough toward Industry 4.0 standards. 

First, each food and beverage manufacturer–and every other industry, of course–should understand AI for what it can do for them specifically, and not view it as a cure-all. That means using it as a purpose-built tool applied to their biggest production challenges–like machine downtime, food quality, or energy tracking. 

Second, manufacturers need to find AI that works with and for the people using it. Most companies using AI to its full potential know that the technology is a co-pilot, a way to give workers more capabilities  while advancing their skills at the same time. 

Lastly, and maybe most importantly, AI solutions should be more than a technology. It should come with end-to-end services, analysts, system integration managers, trainers, and change management assistance to ensure adoption and value-at-scale. 

Advances in AI are changing the manufacturing world, giving companies the information they need to improve their machine reliability, optimize processes, and transform their operations. That means they can save time and money, and those savings can be passed on to shoppers through lower prices. The good news is that the sooner manufacturers start or expand the use of AI solutions, the sooner the benefits will show up for businesses and consumers alike. 

Read the full “State of Production Health” report. Then see how Augury’s AI can deliver value to your manufacturing business.

The post Why Food & Beverage Makers Should Fast-track AI  appeared first on Augury.