Observing The Industry Traversing the Digital Divide — Its Finaly here!

Earlier this week at Nvidia’s GTC conference, I had a moment of reflection that, for me, brought a lot of threads together. The energy around AI was undeniable—but more importantly, it wasn’t just hype or futuristic vision. It was grounded in real capability, real deployment patterns, and a clear signal of where the industry is heading.

I shared some of my immediate thoughts in a LinkedIn post during the event, but stepping back, what stood out most was this: the conversation has fundamentally shifted. AI is no longer being discussed as an isolated capability or an experimental technology. It is being positioned as a core building block of how systems will be designed, how operations will run, and how value will be created.

For someone like me—who has been writing for years about composability, democratization, and the need for a new operational architecture—this felt less like a surprise and more like a confirmation. The pieces I’ve been describing are starting to come together in a very visible way.

And it reinforced something I’ve been saying for a long time: manufacturing is on the verge of a fundamental shift. Not another incremental improvement cycle, not another wave of disconnected digital initiatives, but a real transformation in how operations are run, improved, and scaled.

For years, that message felt like a warning. A call to prepare. Today, it feels more like an observation.

Because what I saw at GTC—and what I’ve continued to see in conversations across the industry—is that companies have reached the divide and looking at crossed it. The conversations have changed. The posture of leadership has changed. And most importantly, the level of commitment has changed.

I referred to this earlier in my 2025 trends webinar as a watershed moment, and what we are seeing now is exactly that playing out in real time. I would strongly encourage you to watch that discussion, as it frames much of what is now unfolding across the industry:

What’s important is not just that change is happening—but how it is happening!

Vibe Coding and the Realization of Democratization

One of the clearest signals of this shift is how solutions are now being created. I’ve spent a lot of time over the years writing about democratization in manufacturing—the idea that the people closest to the work should be empowered to improve it, and that technology should enable that rather than constrain it. What is emerging now with AI, and what some are starting to call “vibe coding,” is the most complete realization of that idea that I’ve seen in my career.

What makes this different from previous waves of low-code or no-code is not just accessibility, but the collapse of effort between intent and execution. The ability to describe a problem, iterate on a solution, and see something functional emerge in minutes fundamentally changes the dynamic of how operations evolve. It brings solution creation directly into the operational context, where engineers, operators, and subject matter experts can shape systems in a much more immediate and iterative way. We are now seeing a world where:

• A process engineer can describe a problem and generate a working application
• An operator can help shape a workflow in real time
• A team can iterate on solutions at a pace that was previously unimaginable

This is not incremental improvement. This is a step change in how value is created and something I have consistently pointed to in my writing on composability and frontline operations platforms.

The shift from centrally developed, rigid applications to adaptable, user-driven solutions that reflect the reality of the shop floor.

But what is becoming clear now is that AI is not just enabling this shift—it is accelerating it to a point where it is unavoidable and, I feel, it's removing the mindset barrier. The discussions about technical capabilities, or features and functions are quickly fading away, including the odd ask about monolithic systems and OOTB configurations. They are shifting to be about how quickly solutions it can be built and how effectively it can be applied. That changes expectations at every level of the organization, particularly at the executive level, where the potential for rapid productivity gains becomes much more tangible.

At the same time, this level of democratization introduces a new kind of responsibility. When the ability to create is broadly distributed, the risk of creating the wrong thing—or creating the right thing in the wrong way—also increases. This is where the narrative needs to mature beyond excitement about capability and into a deeper understanding of what it takes to operate in this new model.

Why Platforms Are Now Critical to Operational Integrity

As AI transforms the ability to create solutions, it is tempting to assume that bringing those solutions into operations will follow the same path. This is where manufacturing fundamentally pushes back. The same forces that make “vibe coding” so powerful—the speed, the accessibility, the freedom to create—also introduce a level of variability that operations simply cannot absorb without consequence. In a production environment, the introduction of new technology, solutions, logic, automation, or decision-making is not an isolated act. It becomes part of a tightly coupled system where even small inconsistencies can propagate quickly.

In these environments, the consequences of error are immediate and often irreversible. A mistake cannot be rolled back with a software update, and failures in safety, quality, or compliance can have serious and lasting impact. This reality fundamentally reshapes what trust means for AI. Trust is not about believing that a model is intelligent or statistically accurate, but about whether a system behaves predictably under changing conditions, supports human judgment, and fails safely when uncertainty arises. In operations, trust is earned through repeated, consistent performance in the flow of everyday work.

While AI can generate applications, workflows, and even autonomous behaviors with remarkable speed, manufacturing requires that every one of those elements operates within clearly understood and controlled boundaries. One misstep—whether it’s an incorrect parameter, an unexpected interaction, or an opaque decision—can create cascading effects. Quality can be compromised, performance can degrade, and most critically, safety can be put at risk. In my experience, nothing halts adoption faster in a manufacturing organization than a single visible failure that undermines confidence in the system.

You cannot afford uncontrolled experimentation in a live production environment. This is why I’ve consistently emphasized the importance of a platform-based approach—not as a technology preference, but as an operational necessity. A true operational platform provides:

• Governance over what is created and deployed
• Context so that solutions are aligned with real processes
• Control to ensure consistency, traceability, and compliance
• Resilience so that failures are contained and managed
• Connectivity so that decision and action are based on a holistic understanding
• Content that is industry specific and ready to increase quality and resilience

Accountability in this environment is unavoidable. When AI influences how equipment is configured, how deviations are handled, or whether a product is released, responsibility does not shift to the algorithm. Humans remain accountable for outcomes, which makes human-in-the-loop not just a design preference, but a requirement. If an AI system makes a mistake, and they certainly do, trust erodes quickly—and once that trust is lost, it is very difficult to regain. This is even more pronounced in regulated industries, where expectations around data integrity, traceability, and explainability are explicit, and systems must be understandable not only to technologists, but to operators, engineers, quality professionals, and regulators.

This is precisely why a platform approach is not optional—it is foundational. A manufacturing-focused platform creates the controlled, governed environment where AI can actually operate within the strict realities of production. It is what ensures that solutions are not only created quickly, but behave predictably, meet quality standards, respect safety constraints, and remain compliant over time. Without that structure, the same capabilities that make AI so powerful will introduce unacceptable risk. In manufacturing, you cannot compromise on errors, defects, or safety—and you don’t get multiple chances to get it right. A purpose-built platform is what makes it possible to harness the benefits of AI and “vibe coding” without violating the core requirements of the operation. With a platform, you enable what I often describe as controlled democratization—the ability to innovate broadly, but within a structure that protects the integrity of the operation. Without it, scale is not just difficult—it’s dangerous.

Why Domain Expertise Still Defines Success

The final and perhaps most critical element in all of this is the role of domain expertise—something that is increasingly being underestimated in the current enthusiasm around AI. There is a flawed narrative that AI can compensate for gaps in knowledge or experience, that it can generate solutions independent of deep understanding. But as I have explored in other posts, particularly when experimenting with AI as a creative partner, the technology is only as effective as the context and intent that guide it. In manufacturing, this distinction is not subtle—it is fundamental.

With the incredible democratization AI brings to creating solutions accelerates, this constraint does not disappear—it shifts. It becomes even more important and critical to define the right problem and to judge whether a solution will actually work within the realities of the operation. Manufacturing processes are complex, tightly interconnected, constraints by physical realities, driven by well defined methods, and governed regulatory requirements. Understanding how cause and effect play out in that environment is not something that can be inferred generically; it is built through experience, engineering discipline, and operational knowledge. AI can amplify that expertise, but it cannot replace it—and without it, the risk of creating solutions that fail in practice increases significantly.

In the hands of those with deep expertise, AI accelerates learning, experimentation, and scale. This becomes even more critical as we move toward more autonomous systems, where agents are expected to act within operations. Their effectiveness depends not just on data, but on the depth of understanding embedded in how they are designed—grounded in the experience of those who know how the system behaves, especially when things don’t go as planned.

The Take-Away

What we are seeing right now is the convergence of three defining forces: 

1. The democratization of solution creation through AI.
2. The need for structured platforms to govern and control that creation.
3. The enduring importance of domain expertise to ensure it all works in the reality of manufacturing operations. 

This convergence is not theoretical—it is actively reshaping how companies think about, design, and run their operations.

Crossing the digital divide was never just about connecting systems or digitizing processes. It was about enabling a fundamentally different way of operating—one where the creation, deployment, and continuous improvement of solutions are embedded directly into the fabric of the operation. What we are now beginning to see is what that actually looks like in practice, and it is both powerful and unforgiving.

As with any significant shift in manufacturing, success will not come from simply adopting the latest technology. It will come from understanding how to integrate these capabilities into the operational reality—balancing speed with control, innovation with discipline, and democratization with accountability. The companies that get this right will not just move faster—they will operate differently, and ultimately, outperform.

Composability, Governance, and the Future of Agentic AI in Manufacturing

We’ve all heard the stories: “Somebody created a solution in just a few hours with no-code or vibe coding…”.

It’s exciting, right? Engineers solving problems in record time, building digital tools with nothing more than intuition, creativity, and a bit of AI support. This is the promise of democratization—Agentic AI empowering everyone to innovate and improve.

And in many contexts, that speed is a superpower. But in manufacturing, the story is more complicated. Operations are inherently complex: machines, materials, people, processes, schedules, quality checks, and compliance requirements all interact in a dynamic web that is nonlinear, a complex adaptive system. In such environments, even small changes can cascade unpredictably, amplifying into disruptions far greater than their cause—an inherent feature of systems where interdependencies drive emergent outcomes.

This is where the risk lies. If an unguided agent makes the wrong decision in such an environment, things can go wrong very quickly—and often in ways that are difficult to anticipate or trace. The result might be downtime, compromised product quality, production delays and at worst safety issues. But the bigger danger is cultural: when something fails spectacularly, it doesn’t just cause operational damage—it can scare the organization away from using the technology at all.

Instead of unlocking incredible productivity, one misstep can trigger a mindset of “we don’t dare do this again…”. That’s not just a lost opportunity; it’s a setback that can stall digital transformation for years.

Digital Maturity Gaps

With the incredible pace of innovation of digital technologies and specifically Agentic AI, we have to acknowledge a hard truth: most manufacturers are still not fully ready to capitalize on this technology. The foundations are often shaky, and if we introduce solutions built by and with agents into this environment without addressing the gaps, the risks multiply.

Some of the most common issues include:

• Fragmented data that prevents end-to-end visibility.
• Disconnected IT and OT systems that block seamless collaboration.
• Cultural skepticism and lack of digital maturity that slow adoption and erode trust.

These are not new problems. I discuss them in detail in this blog.  Our traditional models both in technical architecture and deployment of operational solutions for manufacturing were never designed for the agile, composable, and connected environments we’re striving to build today. In addition we can't ignore that in any given manufacturing facility there are machines and systems of varying ages and legacy status.

Consider a Scenario

An enthusiastic engineer uses vibe coding to create an agent that dispatches materials to keep machines and operators as busy as possible. At first, everything looks good, machines are running at full capacity, operators always have work, and parts move quickly into assembly.

But no one realized the agent doesn’t properly recognize WIP limits or downstream capacity. It keeps dispatching jobs even when assembly stations and testers can’t keep up. The result: piles of excess material stack up between stations, overflowing into walkways and creating unsafe conditions for operators.

In a matter of hours, what was meant to boost productivity causes overproduction, flow disruption, and a serious safety hazard. The line is forced to stop because the system created too much of it, in the wrong place, at the wrong time. 

This isn’t hypothetical. It’s the kind of risk that emerges when Agentic AI is unleashed without governance. And unlike traditional tools, agents can act autonomously and at scale, amplifying errors at a speed we may not be able to react to.

Managing Complexity with Composability: Governance, Framework & Platform

Given this reality—where digital maturity is uneven, legacy systems abound, and the dynamic nature of manufacturing operations—the arrival of Agentic AI presents both an enormous opportunity and a serious risk. Agents are coming at us fast, and their power lies in democratization and speed. But those same qualities, in an environment as sensitive as manufacturing, can amplify problems just as quickly as they solve them.

This paradigm shift cannot be left to chance. To harness Agentic AI safely and effectively, we need to approach it in an organized, managed, and focused way. That requires: governance, framework, and platform all working together and grounded in composability.

• Governance gives us the rules, guidance, and guardrails to ensure reliability, safety, quality, and compliance are never compromised. Governance is not just about control; it actively helps organizations overcome barriers like fragmented data, siloed systems, and cultural skepticism by providing structure, discipline, and confidence. Done right, it helps in the transformation of the manufacturing environment from both a technical readiness (data integrity, system integration) and an organizational readiness (culture, mindset, trust).

• Framework provides the structure to make governance actionable. In A Composable Agentic Framework for Frontline Operations, I laid out how agents can be defined by type, given clear goals, and aligned through the artefact model. The framework ensures that agents are not just scattered tools but part of a purposeful, multi-agent system that reflects and supports real operations.

• Platform is the enabling layer. An Agentic manufacturing operations platform purpose-built for frontline operations makes it possible to apply governance and framework seamlessly—across both authoring (building digital solutions) and execution (running the production line). Unlike generic vibe coding environments, such a platform is designed specifically for process engineers and operations teams. It embeds an understanding of how manufacturing works—constraints, variability, compliance, and safety—and provides tools to build solutions quickly while remaining aligned with operational best practices. Most importantly, it enables the creation and deployment of operational agents at scale that are are integral parts of the production system itself.

And underlying all of this is composability. Composability ensures that agents and solutions don’t exist in isolation, but as modular, human-centric, bottom-up elements of a system that adapts as needs evolve. Digital transformation success depends on treating composability not as an IT concept, but as an operational paradigm. In fact, composability was conceived for multi-agent systems (MAS)—and Agentic AI now makes that vision practical.

Final Thoughts

I think that we all agree that Agentic AI despite all the hype is not a fad—it’s here, its real and it’s already reshaping how work gets done. But to harness it safely and effectively, we need to recognize the two distinct scenarios where agents play a role:

1. In building and engineering: where agents help create digital content, processes, and solutions. Here, governance ensures what gets built is valid, safe, and aligned with operational goals.

2. In live operations: where agents support and even run production activities, interacting with machines, data, and humans in real time. Here, governance ensures reliability, compliance, and resilience in execution.

Both scenarios are powerful—they are needed but both also carry risks if unmanaged. And in manufacturing, where operations are complex and dynamic, small missteps can cascade into detrimental consequences.

That is why governance, framework, and platform are critical. Governance provides the rules and guardrails; the framework gives agents structure, goals, and alignment through the artefact model; and the platform operationalizes it all, giving process engineers and frontline teams the environment to deploy agents as integral parts of the production system, and all of this at scale.

And beneath it all is composability. As I’ve argued before, composability was conceived for multi-agent systems—modular, autonomous, and collaborative components working toward shared goals. Agentic AI now makes that vision practical on the shop floor.

The challenge ahead is not whether manufacturers will adopt Agentic AI, but whether they will do so in a way that balances speed with safety, democratization with discipline, and autonomy with alignment. Done right, it promises resilience, reliability, and the kind of productivity gains that digital transformation has always aspired to deliver.

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Digital Maturity Embracing the Paradigm Shift with Composability

In my last post, The 5 Pillars of Composability, I broke down how composable systems have to be bottom-up, agile, democratized, human centric and compliant to enable a resilient digital  manufacturing environment. However, these pillars don't standalone and you may have noticed that the graphic drew the pillars within a structure, i.e. a house. Yeah a bit cliche but its a simple way to drive the point - the foundations is connectivity and data integrity while the roof is digital maturity. Without connectivity to reliable data and a high level of digital maturity, the benefits of composability can be diminished. 

• Data integrity ensures that the digital solutions operate on accurate, consistent, and trustworthy data, preventing breakdowns in decision-making or system performance. High-quality, accurate data is essential for making informed, evidence-based decisions
• Digital maturity enables organizations to effectively adopt composable architectures, ensuring they have the technical capabilities, culture, and processes in place to take full advantage of modular solutions. 

Together, data integrity and digital maturity complete the story of composability by ensuring that organizations can both build and sustain these flexible, adaptive systems in a reliable and future-proof manner. In this post I want to dive deeper into these concepts as they are foundational concept that propels us forward in the digital paradigm shift to reshape manufacturing operations.

Digital Maturity, Connectivity & Data Integrity complete the composability model

What is Digital Maturity?

Digital maturity represents an organization’s capacity to leverage digital tools and processes effectively based on their strategy with the objectives of significant increases in productivity. It's not a simple matter of capabilities related to adoption or implementation new technologies but rather about integrating them strategically to align with long-term goals. As companies mature digitally, they move beyond basic digital adoption to foster seamless connectivity across systems, data transparency, an empowered workforce and with that comes order of magnitude productivity improvements - the ultimate goal for transformation.

A digitally mature organization is one where digital tools support real-time decision-making, democratized technology access, and predictive insights - aligning perfectly with the benefits of composable principles. This is also what the Pharma 4.0 operational model prescribes, that manufacturers need to do more than automate - they need to integrate everything from operations to compliance in a way that’s seamless, agile, and deeply data-driven.

What is Connectivity & Data Integrity?

Its not news that data must be accurate, accessible, and trustworthy across all systems for true digital maturity. It must be connected, collected, contextualized and stored to ensure that data collected from production lines, suppliers, and product design all feed into a single, reliable source, creating actionable insights and reducing costly errors. Yet surprisingly it still is very much a challenge in many solutions that I encounter. Mostly in legacy situation, implementation of monolithic system, but also if not considered appropriately in newer digital technologies.

Connectivity in manufacturing is all about creating a seamless flow of data across systems, devices, and people. Imagine every machine, sensor, and workstation talking to each other and feeding data into a single network that anyone can access in real time. When systems are connected, it’s like moving from an isolated set of puzzle pieces to seeing the whole picture. Connectivity enables manufacturers to understand what’s happening on the production floor instantly, respond to issues faster, and improve coordination across departments. For example, in a highly connected factory, when a machine experiences a slowdown, that data can flow directly to maintenance teams and operators, letting them address the issue right away.

But connectivity is only as useful as the quality of data being shared, which brings us to data integrity. Data integrity is about making sure that information is accurate, reliable, and complete across its entire lifecycle. It’s not just about having data; it’s about having good data you can actually trust. In the Pharma 4.0 model, where data integrity is critical, maintaining high-quality data is a must, especially for meeting strict regulatory standards. This means putting practices in place to ensure that data isn’t duplicated, corrupted, or altered improperly, so everyone—from operators to auditors—can make decisions with confidence.

Together, connectivity and data integrity are the backbone of any digitally mature operation. They enable real-time visibility, reliable decision-making, and the flexibility to adapt to change. Without them, even the best technology can fall flat. So, as manufacturers embrace digital maturity and composability, focusing on solid connectivity and data integrity will be crucial for a smooth, resilient operation.

The journey from Technology Adoption to Strategic Transformation

Many manufacturers today are adopting digital tools, but there's a significant difference between early digitalization and achieving digital maturity. A mature digital approach emphasizes:

1. Strategic Data Utilization: Digital maturity involves a shift from collecting data in isolated pockets to having unified, actionable insights. For manufacturers, this means no longer relying on static, siloed data but leveraging real-time insights that span from the shop floor to the boardroom. Yes, this in a way nothing new and really dates to Industry 3.0 concepts - however with new digital tools this has become and achievable reality.

2. IIoT & Interoperability: Digitally mature systems don’t merely integrate; they interoperate, embodying the composable principle of Bottom Up where IIoT components are autonomous and collaborative. Composable architectures are inherently emergent in both design and control - the manufacturing solution is required to evolve with minimal friction.

3. Human-Centric Technology: In a departure from an automation focus, the current paradigm shift places people at the center of the digital equation. Technology becomes an enabler for employees, from line operators to managers, allowing them to respond dynamically to changes and resolve issues swiftly.

4. Resilient and Adaptive Workflows: A composable manufacturing ecosystem relies on digitally mature workflows that can adapt to disruptions, whether due to supply chain variances or unexpected equipment breakdowns. A digitally mature manufacturer leverages their digital capabilities to enable resilience, be predictive and adaptive.

The digital transformation journey towards order of magnitude productivity improvements

The path to digital maturity requires a tailored, strategic approach that elevates an organization from a technological upgrade to a business transformation—one that enables agility, resilience, and sustainable growth. The first step in this journey is to assess and align digital initiatives with overarching business goals. Defining what a mature digital state means for each organization—whether it's minimizing downtime, improving product traceability, or streamlining supply chain management—is critical. Aligning digital initiatives with operational excellence or lean initiatives by implementing data-driven approaches to cut down production waste and achieve near-real-time optimization are critical. Drive value by prioritizing areas where digital maturity will have the most impact on operational outcomes.

A characteristic of digitally maturity is how well your organization is equipped to handle the ever-evolving challenges and capitalize on new opportunities. Embracing composability allows your organization to not only keep pace with the current demands but to thrive in the future - thrive with the accelerated pace of digital innovation. Digital transformation should be more that mere adoption of new technology - it is embedding it deeply in your operational fabric, enabling sustainable growth and resilience in the face of change.

Are we seeing the return of the Custom MES, or is it something else? (Re-Posted)

The role of MES in the Industry 4.0 reality is a topic of debate and a source of critical misinformation. In general MES belongs to the era of automation and computer integrated manufacturing, i.e. Industry 3.0. MES or MoM came about in the 1990s to solve the challenges of coordinating and executing work on shop floors with the advent of computers. It really is a relic of the previous industrial era.

The challenge of coordinating and executing work on shop floors however has not changed. Manufacturing is increasing in complexity and the need to adapt to changing business requirements is accelerating. At the same time the advancement in computer technologies have ushered a new digital era that we now call Industry 4.0. How do we solve the shop floor operational challenge with these new technologies?

Are these new digital technologies making it attractive and maybe even necessary to develop custom manufacturing systems solutions, or custom MESs? For years we have advocated that companies focus on their core business competencies and leave the software development to expert best in class software companies. There are many horror stories of companies that are stuck supporting custom built software that is running their critical operations, why build and spend millions maintaining in house developed systems?

Yet the need to tailored solutions for shop floor operations is still valid and in fact even more critical as the rate of change to the business environment coupled with operational complexity increases. It seems like a case of history repeating itself since that is how MESs started in the 80s, however technologies have vastly evolved since. With the digital technologies that are now available we are going well past the simple ability to customize. We are taking a very different approach that is operations and human centric. It allows us to rapidly create tailored solutions that increase productivity by supporting frontline operators for each specific operation and activity.

This allows companies to once again opt to develop custom solutions to solve their manufacturing systems needs. Yet this time around they do not look like MES of the past, they are not custom solutions that are unique and require high cost and effort to manage and maintain. In the digital era solutions are built rapidly by the people that are closest to the operations, they are tailored to the frontline operator and help increase their productivity and all of this in a Cloud-Edge infrastructure that allows easy management and governance. There are a number of forces at play that are causing this to happen.

Emerging digital technologies, specifically no-code cloud based SaaS platforms provide user friendly ways to build tailored digital content with very quick ramp-up times. I.e. you don’t need software development skills. There is a real business need to get the promised productivity gains from these digital technologies. In other words organizations have allocated money and people to get things moving in their digital transformation.

The new generation of workforce comes from the digital age (i.e. digital natives) and are used to in simple words, just download an app for that. They are confronted with what they see as antiquated software systems that are not really user friendly and their reaction is to find another app. The new digital technologies aimed at the manufacturing operations space are in general human centered – they aim to solve (and support) what we as people do, whereas traditional MES is developed to automate a process.

Modern cloud and edge technologies provide a rich playing field for integration and capture of digital data to help bridge the digital divide. I.e. start capitalizing on productivity improvements while not having to decommission existing traditional systems.    

The transformational forces at play are unstoppable at this point. The high skill and expertise level required to implement and maintain the current IT/OT owned systems are becoming a thing of the past. The new tailored manufacturing solutions can be built at a unprecedented speeds by people that are closer to the actual manufacturing process. We will not have unique and specific monolithic systems for each department or business function. The future digital factory will be supported by a network of digital components, apps, edge devices and tools will have been composed in a iterative and agile process, ie bottom-up. Solutions will emerge and mature over time based on continuous improvement rather than a top-down design and development process. With that traditional hierarchical thinking and approaches such as ISA-95/88 will become less relevant unless they are adjusted to fit the new digital reality.

Are we then seeing a case of history repeating itself? Will we see a resurgence of home grown MES solutions that we will in a few years pay dearly to maintain or replace? The answer is yes but not what you might expect. There will be some level of custom software being built but at the same time what we will se are tailored solutions that do not carry the burden of the custom system of the past. Modern digital technologies such as no-code platforms are democratizing the manufacturing systems landscape. They are transforming manufacturing systems software development to a process of composing digital content for the shop floor. They are more of an engineering and operations toolset rather than an IT system. Again we might use the term MES but these new solution will really not look like anything that resembles current MESs, they will consist of digital content that support human operations and digitize all activities and process in the plant. They provide unprecedented levels of detail in the form digital data that is easy to use, analyze and interpret. They provide the foundation for digital maturity toward the predictive and adaptable states in Industry 4.0. Companies adopting these platforms will be able to accelerate their maturity and their digital transformation.

This is a reposting of an article in Engineers Outlook and is based on a previous post on this blog The return of custom built manufacturing software.

Fresh Seeds in a Plowed Field – Leadership Again….

Last night I had time to reflect on my experiences and learning from a 2 days leadership training delivered by Mark Hannum. I really enjoyed it especially all the stories from real world examples such as Allan Mulally's first few days at Ford. It also re-emphasized some of the things that I have known about leadership that I wanted to bring up again as well as my favorite leadership guru – Dr Deming. What makes Deming so intriguing is the fact that his statements and teaching is timeless as I describe in another post.

I wanted to point at this specific video about Deming - the real reason I am writing this long post. But there are also some other interesting observation that were discussed during the 2 days with some interesting information sources behind them such as why Management by Objective can be destructive, why do we do it and why there are no heroes. And of course one of my favorite leadership post "Death to the Performance Review" which is tightly coupled with lessons on how not to be a Bosshole.

There is always something new to learn and I guess that much of it was realization or maybe more confirmation that it really is all simple and logical once you wrap you head around. Or maybe its more unwrapping your head from the spreadsheet-centered engineering perspective on life. Mark described the leadership cycles that are core to leadership development, a concept that really hit home. It made me realize that my first leadership cycle was when I was 16 during an outdoor leadership course leading a group of my puberty infested peers on a survival backpacking trip around Mt Kenya (yes that’s the one in Africa).

Mark mentioned that this reflection is very important and with a bit of help from United (no entertainment on the plane) and low battery life I got a chance to do that. As the lights of the Central Valley appeared, I thought it would be a good idea to put some of what I felt in words, hence this blog post. I felt that during the 2 days there was some novel new understanding in our management group and with that maybe some openness or acceptance of these new concepts. It’s as if Mark has plowed the fields, opened up the ground and made it breathe. It is now ready for new seeds. Sorry, but I am a farmer at heart – I can’t help myself with these analogies.

A Brief Look at MES Products From a Historical Perspective

I am working with a number of Life Science manufacturing companies that have taken a strategic approach for their manufacturing systems landscape. There is a lot of buzz on this topic in the industry, which makes it that much more interesting but with some challenges. I am generally fond of using a historical perspective and so I decided to do the same for the MES software products in the life science industry. This perspective is just mine and I am sure there are many more that can be given by my peers in the industry – a subtle hint.

So let’s start in the 1980’s, the decade that gave us CIM and a growing awareness about the role that computers play in manufacturing operations. The focus at that time was how computer systems, aka software, can be used to increase efficiencies and manage complexity. In fact computer technology was gaining so much momentum that it was considered a major element in revolutionizing the manufacturing landscape in parallel to the advent of the Lean movement.

This gave birth to quite a few Manufacturing Execution System (MES) product companies in the 1980’s. The 90’s then followed by a massive development and spread of information technology, which is now at the core of everything we do today, not only in manufacturing. Manufacturing operations are becoming so dependent on information that these systems have to be considered at a strategic level. Initially this strategic focus was given to expensive business systems such as ERP however it is becoming evident that other systems, specifically MES, sometime have more impact on the bottom line and should be considered equally strategic.

MES software products evolved in different industries and their roots manifest themselves in both the functionality and the corresponding  MES vendor’s organization. Companies and products that currently serve the Life Science industry generally have their roots in the semi-conductor and electronics industry, and understandably also the Life Science industry itself.

In the industries outside Pharma and Bio-Pharma, MES was introduced to deal with the inherent high level of automation and complexity of the high volume manufacturing process where lowering cost and increasing production throughput were crucial. It was virtually impossible to manually manage the wealth and complexity of information and MES provided a solution. The  MES products were centered on a discrete workflow model that allowed rich modeling capabilities while at the same time allowing customizations. In fact early  MESs were merely toolboxes with a workflow engine, rich data modeling capabilities and tools to custom build user interfaces and business logic.

In the Life Science industry the main driver for introducing  MES was compliance or the electronic batch record and therefore the first such systems provided a “paper-on-glass” solution. The idea was to simply digitize the paper batch records, kind of like the old “overhead projectors”. These systems had simple modeling capabilities and did not allow for much customization. In many cases these “paper-on-glass” systems were supplemented with business logic built as customizations in the automation system. They were commonly implemented in pharmaceutical plants, where the focus on compliance meant low tolerance for customizations and a minimum of change after system were commissioned. This resulted in  MES functionality that was split between the heavily customized automation applications and a “canned” paper-on-glass system to deal with batch records. The Weigh and Dispense feature of these systems was used mostly for traditional pre-weigh activities where the materials are weighed and staged before the process.

In the 2000’s a consolidation started in which some of the independent  MES from vendors where acquired by the major automation vendors and positioned into the life sciences industry. This introduced the rich modeling capabilities that grew out of the semi-conductor and electronics industry to the  Life Sciences  industry accustomed to “paper-on-glass” systems. This leaves us today with a wide choice of  MES that are rapidly gaining maturity and sophistication in the form of advanced functionality and interoperability. I think that this maturity is an important factor and plays nicely into the strategic nature of most Manufacturing Systems initiatives that I have been involved in. There is still a long road ahead but I have not been so optimistic about the Manufacturing System domain, in a long time. It certainly looks like there are some very interesting and also challenging years ahead as we work to execute on these strategic initiatives.

A Picture of MBO Misuse

I was reading “Organizational Sabotage - The Malpractice of Management By Objective” on the Deming Files and I am once again I am dumbfounded by how it is continuously being practiced or misused. It is a topic that I have written about before on the L2L blog and here. Although I liked the article it seemed lacking some real world examples. Needless to say I have experienced these continuously over my career and sometimes when reading these types of articles I just feel like shouting the proverbial “Hello”!

Anyways since I prefer visual interpretation I thought about trying to come up with a simple graphic to convey Deming’s and Drucker’s real message about MBO. I also thought I would add some description in the form of pairs of antonyms to emphasize the visual. Here is what I came up with:

Short-term / Long-Term

Disarrayed / Aligned 

Untidy / Tidy 

Conflicting / Collaborative

Kind of simplistic and vague – I know, yet open to interpretation? The notion is that if you set a short term goal it drives a specific behavior that is not always what you want, and probably not if it is a business strategy. Drucker wrote that “Objectives are the fundamental strategy of a business. Objectives must be derived from what our business is, what it will be, and what it should be.” Clearly he meant long-term objectives? 

I was with my kids at the beach the other way and they were learning to use a stand up paddle board. The 18 year old instructor gave them a simple tip. Always look at the horizon when paddling it helps keep your balance, look down at your feet and you will fall.

Manufacturing Systems Solution – more than MES

Although I have been silent on the blog for a while I have been pretty busy working with customers and a few other things. I have been fortunate to have an article published in ISPE's Pharmaceutical Engineering titled “Manufacturing Systems Solution – more than MES” that I wanted to share (you can download it here). The article take a historical perspective at finding the right Manufacturing Systems solution, starting with the CIM wheel and on to current best practices such as GAMP and ISA-95. The article is a compilation of lessons learned and experiences from industry about Manufacturing Systems solutions and why these solutions are more than an MES. It highlights what works and what does not when considering justification, selection and design of a Manufacturing System solution with an MES at the core.

Considering the history behind Manufacturing Systems’ designs over the last three decades there should be ample fundament to design such a system. Yet the MES Domain is inherently complex and this complexity means that providing a clear and concise return on investment is challenging, given that MES typically involves a substantial capital investment. The result of this is that MES implementations are commonly surrounded with uncertainty and implementation experiences that typically are described as “painful”.

I will be co-presenting the approach described in the article at the MESA NA Conference with Roland Esquivel and Steve Soscia from Amway. We have been using this approach to craft Amway’s global Manufacturing Systems program, which has been a very interesting assignment.

Applying "Monkeynomics" To Manufacturing Solutions

Once again I am involved with a company that is being steered by the “ERP is all you need” approach. I thought that by now we have come to peace with the fact that different systems provide solution to different problems. That ERP has found its place and made peace with MES and other shop floor systems. Maybe it is just human nature and we cannot stop ourselves from making the same mistakes over again. I was just watching one of the TED talks about "Monkeynomics" (see embedded video below). It seems that we as humans have something called “Loss Aversion”, i.e. we will take risks in order to avoid loss rather than play it safe.  This is an interesting observation that in retrospect explains a few of the odd behaviors that I have seen from companies in the past.



Another phenomenon that I find intriguing is the flawed notion that general economic methods are universally applicable. In other words the perception from people with business (or more precisely financial) background that it applies in all domains and all situations, specifically when applied to Manufacturing Systems solution. This means that everything that is in the past is money already spent and that we have to consider future state with no regard to what we currently have. Never mind the sweat and tears that where shed in putting a solution in, the extra hours, the training, etc. The current solution may not be perfect (but who or what is?) but it works, people are trained, they are using it, the company knows how to maintain it, in fact it adds value!  Yet, from a business perspective, which I equate to “the accounting or CFOs perspective”, we should disregard all of this; it is all water under the bridge. Just imaging the disruptions that will occur when the solution is gutted and a new one put in place, time, money, sweat, and tears – I just do not get it? We are slaves to this economic theory, investment planning only looks to the future and all that we have done in the past is irrelevant – I guess I will go out and get my memory erased - problem solved.

The Difference Between Accountants and Production Managers

This is an excerpt from a white paper that I authored a while back. I was helping a company with selecting an MES where ERP (SAP in this case, before SAP ME) was included in the mix as if it would be able to provide MES functionality with no constraints. It prompted me to write about the differences between MES and ERP.

ERP systems are designed to be very effective accounting systems. MES systems are designed to aid in shop floor management. It is naive and risky to assume that one of these systems can be extended to effectively do the other’s job. Similarly, one would not assign an accountant to be a production manager, or vice versa. Each might be an expert in his own field, yet it takes a completely different set of skills, expertise and knowledge to effectively tackle each task.  

Industry experience and best practices, as well as academic literature, strongly suggest integration of ERP and MES rather than the extension of either. The best approach is to implement a best-of-breed MES that is easily integrated into ERP. The benefits that can be gained are immense. They are in fact what will truly and finally allow organizations to realize some return on the large ERP investment.

I wrote this back in 2004 and the reason I came back to it now, since a customer that I am currently helping is doing it again. Well it seem that history repeats itself, and I can only say here we go again....

Once again ROI from MES - Not Really!

So much has bee written and discussed about ROI for MES. I got involved in the latest discussion on a LinkedIn group. I guess that this question is one of the great phenomena of the MES domain. Unlike other types of systems (ERP, QMS, PLM, etc.) the returns and direct benefits hardly provide enough to justify the investment. To complicate matters even more the actual reasons for employing MES differ not only by industry but also by customer. Para-phrasing Paul Boris from the LinkedIn discussion “it is like explaining to your kids why they should eat their veggies”.

It is no wonder that MES solutions are typically the last ones to be implemented in the typical manufacturing solution landscape. It is simply too hard to provide a clear and concise return or benefit from an MES alone or at least to justify the investment – and MESs do not come cheap. On the other hand it is obviously much easier when there is a specific and typically catastrophic event that needs to be remedied, such as a recall, 483 (FDA warning letter), regulatory compliance, detrimental quality issues, etc.

MES need to be thought of as enablers for operational excellence where the ROI and benefit come from the “Whole” solution and not the system. For example more efficient process, better quality, effective material management, etc. The ROI and/or benefits have to then be attributed to The Whole Solution – focus on the solution rather than the System (i.e. MES).

What is a Manufacturing System - Part I

This is a post that is long overdue. A central discussion topic on this blog is Manufacturing Systems and I have not yet really explained what I mean by a Manufacturing System. I have talked about manufacturing system that are agile and Holonic, so it is about time that I posted a more practical or at least clear description of what I mean.

Most definitions of Manufacturing System are focused on describing a solution, or more precisely the functionality and architecture of a Manufacturing System solution. For example the MESA model presents number of functional categories from a business perspective, where as the ISA-95 (S-95) model provides a solution architecture based on functional decomposition. All these are of course relevant and useful yet it seems that the problem only interesting to academia – try to Google it. It is assumed that we in industry all know what it is – a dangerous proposition to have.

We all agree that the key to a successful deployment of a Manufacturing System is the understanding of the problem that it is designed to solve. This obviously not a novel approach – it is what everybody attempts to do with the system’s requirement or URS. Yet my experience shows that even in the requirement phase many resort to using the existing models, thus reverting to describe the problem with the solution itself. Quite confusing isn’t it?

So here is my take on what a Manufacturing System is, or in other words the Shop Floor Management Problem. I like to describe it as the problem of integrating 3 important flows in a manufacturing organization. The 2 vertical flows provide Product and Logistical information while the horizontal flow is the physical flow of material, equipment and people.

The Shop Floor Management problem is therefore: How to make use of the information provided by the Product and Logistical flow to efficiently and effectively manage the physical flow of Resources and Materials (also known as the production process). Simple isn’t it - that is what a Manufacturing System is designed to do. Try to imagine a seasoned and effective production supervisor or plant manager – the Shop Floor Management problem is very close to his real life job duties.

It is obviously not that simple and there is of course much more detail that is yet to be discussed. I plan to provide some of this in upcoming posts (hence this post is named Part I). Also this is not meant to take away from the importance and complexity of product development, process engineering, operations, and planning. It is a model that is focused on explaining the particulars of managing a shop floor (yes this is my disclaimer).

More detail to come in future posts…

Common question - What MES should I use?

I just returned from the CBI MES conference and found that the main theme or at least that many the attendees were interested in finding the right MES. Yes I know - “it is maybe easier to achieve world peace”. It seems there is no clear answer – no surprise! “It depends”, was a common answer, some said “it is what you make of it”, and I completely agree.

In my many years of studying the “Shop Floor Management” (aka Manufacturing Systems) problem I found that apart from all the technical and functional aspects, the common element that makes an MES implementation and operation successful is user involvement. The users (across all functions) really have to want it! MESs are complex and sometimes cumbersome. They touch many different functions in a manufacturing business, most critically they manage the value stream – or where value is being made. That makes their adoption and acceptance brittle.

In most cases MES products are aligned with specific industries and hence it is pretty straightforward to make an initial short list. But this is not always true, a vendor may be trying to penetrate your specific vertical opening up potential to influence them – and possibly also get a good deal. There are obviously many other factors that can and do play in. “It depends” is always a true statement when talking about fit of a specific MES product to your environment, and therefore it is always important to know what you need.

So how do you determine what you need? Most people will tell you, and correctly so, that you have to use formal (true and tested) models such as the MESA model or the ISA 95 standard. This is the boring but necessary part of the preparatory and selection phases for the MES. I am not saying you should skip the detail; it is by all means necessary. We all know that the devil is in the details, but using these models with no clear focus will not help either. You should use these models to make sure that you have not forgotten or omitted something not to define your problem. That is something only you and your company know, and in most cases it is very specific to your company or business. I prefer to use the 5 whys method to get to the root-cause and then write up as a series of problems – in simple language.

Another important thing to remember is to build on success as Lean practitioners advocate. It works best with a focused Kaizen-like event where a specific need is addressed. When people in the company observe or have taken part in a successful change the value seems transparent and obvious - and you don’t have to even explain or sell it. It is infinitely easier to convince people this way. In fact the only really successful MES projects, at least in my experience, have been when the operators and engineers embraced the system because it addressed a specific problem they had. Yes I know this seems trivial and is also the basis of modern product management, but it is really much more challenging than it seems.

So what have we learned from all of this? As many of the speakers at the conference re-iterated; you need to have a clear focus of the problem. It makes it is easier to build a business case to justify the investment – trivial really. But at the end of the day you have to want it, otherwise it will never happen.

My Presentation at the MESA 2010 Annual Conference

In June I will be presenting at the MESA 2010 North American conference. The conference is going to be held in Dearborn, MI (near Detriot) hosted by Ford at their conference facility. I wanted to share the abstract here on my blog. I will be co-presenting with Kasper Larsen from Novo Nordisk and my colleague from NNE Pharmaplan, Asger Sharp-Johansen

The presentation shares our experiences from the current global MES rollout project at Novo Nordisk. This is an ongoing long term multi faceted project that involves a staggered deployment of a commercial MES software package to all of Novo Nordisk’s manufacturing sites.

Deploying a new manufacturing system is a complex and risky proposition for any company, rolling out such a system to multiple sites on a global scale may be even considered scary. In order to mitigate the risks and manage the complexity of this immense undertaking Novo Nordisk makes the best use of people and technology to evolve a best practice approach that is continually improved upon. This approach includes technical and organizational aspects that cover the complete life cycle of the manufacturing system’s deployment. In addition the “out-of-the-box” feature set of the software obviously did not suffice and navigating this predicament, coined as "Customize or Compromise", is another interesting topic.

Novo Nordisk is a global healthcare company with 87 years of innovation and leadership in diabetes care, haemophilia care, growth hormone therapy, and hormone replacement therapy. It has international production facilities and employs more than 29,300 employees in 76 countries.

What is Intelligence, and why do we need it?

I believe that it is time to get back on topic in this blog, which is Intelligence in manufacturing and manufacturing systems in general. With that in mind I was looking thru my archives and came across something I once wrote as a positioning statement for an intelligence product – I guess it is not hard to figure out what company that was for? So here goes…

In one of my previous posts I tried to bring up the point that we need to consider metrics in the context of what they are needed for and how they are going to be used. I believe that is the best way to understand how to provide the right intelligence in a given scenario. But what is Intelligence? Well that is a very serious subject, but let’s take in the context of manufacturing and process improvement.

Intelligence implies the ability to comprehend; to understand and profit from experience. As such Intelligence is information valued for its timeliness and relevance rather than its detail or accuracy in contrast with "data" which typically refers to precise or particular information, or "fact".

In the context of manufacturing, Intelligence is a fundamental ingredient influencing the system’s level of performance in reaching its objectives. A manufacturing business system (humans included) is a system that learns during its existence. In other words, it learns, for each situation, which response permits it to reach its objectives. It continually acts and by acting reaches its objectives more often than pure chance would indicate. We can observe the following about Intelligence in manufacturing:

• It is an enabler for performance – Performance Management
• We need to act on it – Actionable Intelligence
• We need to learn from it – Continuous Improvement

Intelligent manufacturing is not a smarter way of producing things; it is a human centric approach where humans interact with the process be it automatic or manual, gathering the right information to take intelligence decisions based on actionable information. It is much more than visibility. Just having the information is of course helpful, but it needs to be taken one step farther. It needs to be provided in a way that people can intuitively capitalize on it using their knowledge and understanding to make effective decisions.

Henri Poincare once noted in a related topic that “Science is facts; just as houses are made of stones, so is science made of facts; but a pile of stones is not a house and a collection of facts is not necessarily science.”

What is an effective decision then? It is a decision that has an outcome that drives increased performance and continuous improvement. Intelligence is therefore not solely about metrics, KPIs or the ability to drill down into the data. In order to increase performance we need to quantify what is important. Hence intelligence is about quantifying what is important, or quantifying the unquantifiable.

Why are lay-offs so harmful?

Many thanks to Mark Graban that pointed me to a very interesting News Week article titled “Lay Off the Layoffs - Our over reliance on downsizing is killing workers, the economy - and even the bottom line”, by Dr. Jeffrey Pfeffer. The article speaks to the fact that lay-offs in fact do not save a company money, not in the long run nor in the short term. In fact it may have even more detrimental affects on a company than you may realize. The article states with reference to empirical evidence that

“…contrary to popular belief, companies that announce layoffs do not enjoy higher stock prices than peers—either immediately or over time.”

It is an interesting read for everybody, obviously for them that have been laid-off but also for the people doing the lay offs.

I think that the story behind the story is once again the topic of management and leadership. I strongly believe that in any situation it is always about people. That means that it is the people (managers) and their way of running a company that is the root-cause here. I have said before that you manage processes but you have to lead people. When you just manage people then they become an assets and when hard times are upon us, cutting costs by reducing your capital makes sense – right? Well no – isn’t that obvious - Apparently not?

“In the face of management actions that signal that companies don't value employees, virtually every human-resource consulting firm reports high levels of employee disengagement and distrust of management.”

“Layoffs are more like bloodletting, weakening the entire organism. That's because of the vicious cycle that typically unfolds. A company cuts people. Customer service, innovation, and productivity fall in the face of a smaller and demoralized workforce. The company loses more ground, does more layoffs, and the cycle continues.”

This reminds me of one of Dr. Demmings most commonly used quotes:

"Running a company on visible figures alone is one of the seven deadly diseases of management."

In this case it is financial metrics. It seems that in a recession most management executives are thinking of short term financial metrics rather than the long term health of their company? I am sure that these managers do not intend to do this and that they probably believe that they are doing the right thing. However it is this focus on managing rather than leading that is the problem. Managing the company’s business (The process) is more important than leading the people (organization). The end of the quarter's bottom line is more important than long term viability. Although this seem to be common practice, there is ample proof of companies that thrive by doing differently. Yes, by simply motivating and empowering their people. That is what makes them great companies, and also immensely profitable I may add

We are driven by this need to satisfy investors, whom I may add are also typically about short term gain. It seems that everything revolves around the need to make money now - the typical sales man approach, which is to pick up the closest shiniest pennies rather than to look ahead and possibly see a pot of gold on the horizon. Even if there is not one there at least you looked, and people appreciate that – do not underestimate what that means?