Why IIoT is Transformative - its not the technology!

OK so I am not going to bore you with "what is IoT and IIOT", a topic that has been talked, explained and debated at length and for some time now. What I do want to discuss is how and why IIoT is a  fundamental concept of the new industrial paradigm (aka Industry 4.0, smart manufacturing, etc.). We tend to think and talk about IIoT form the perspective of the technology that is used to implement it, ie connectivity, sensors, software, protocols, etc. While IIoT undoubtedly involves cutting-edge technology, it's crucial to understand that it transcends mere technological advancement.

The premise of IIoT is that it can seamlessly connect the physical world to the digital realm to generate a continuous stream of data that empowers us to make data-driven decisions, optimize operations, and create an entirely new reality for the frontline operators. However there is something much deeper, and more novel in IIoT. It enables us to structure and restructure our manufacturing operations in a way that we have never imagined, ultimately providing agility and unseen before productivity improvements.

It starts with understanding the underlying concepts of what makes a device, app, sensor, machine, etc. IIoT? In other words what makes a "Thing", the T in IIoT. The thing has to be a node in a network - meaning that network dynamics are a core ingredient in IIoT. Nodes can be everything from a sensor to a machine, to an digitally enabled operator. And here comes the most important aspect, in order to be a node in this network the thing has to be both autonomous and collaborative.

Autonomous means that it has the ability to operate independently of a system or other nodes in the network. It has a set of rules that governs and control its operation, and it may also have adaptive decision-making. For example algorithms that allow it to analyze data in real-time and make decisions based on predefined rules. This could involve adjusting control settings on machines, triggering maintenance alerts, or even taking corrective actions within its capabilities. Another example are Apps (or digital content) that provide for uniquely integrated frontline operator experience. They provide a specific and unique operational environment in which the operator is connected with the equipment and can operate autonomously as a unit. They instrument the activity and operational processes by capturing granular data in the context of the operation. 

It's important to note that the degree of autonomy in IIoT devices depends on various factors, including their intended purpose, processing power, and communication capabilities. Some devices may operate within pre-defined parameters, while others can learn and adapt over time. Regardless of the level, IIoT autonomy brings significant benefits like faster response times, improved efficiency, and a uniquely integrated human experience or more precisely "augmentation". 

Collaborative means nodes can communicate with each other to achieve complex tasks. This could involve sharing data, coordinating actions, or dynamically adjusting to changing conditions as a collective unit. They interact and cooperate with each other, unlocking a new level of operational efficiency and intelligence. The key in the collaboration is working towards a common objective, be it optimizing production processes, enhancing safety, or predicting equipment failures. With some central oversight the nodes also possess the ability to analyze data and make autonomous decisions based on their local contexts. 

In the context of the frontline operation apps play a crucial role in a coordinated system to execute operational and business processes. They serve as the interface between the human operator and the IIoT network. They provide unique local business logic and rules they provide coordinated execution of production logistics and valuable business processes. They include process routing and material flow logic that is built into each app for its specific scope of operation. They augment operator activity with the execution logic that is seamless for the frontline operator.  By coordinating with other Apps using shared information they allow operators to make decisions and share information, including coordination with business systems to drive production logistics and quality information.

AI generated illustration of a Holon (aka Dynamic Network)

Its interesting to note that in order for IIoT to be autonomous and collaborative they don't necessarily have to be intelligent. The autonomy and collaborative characteristic can be easily achieved by a set of simple rules and logic. In the case of augmentation of frontline operators the intelligence naturally comes from the human. In most manufacturing scenarios the IIoT network will be a hybrid making for a much more powerful solution - one that is more adaptive and performant. The nodes in the solution adjust their roles, refine their responses, and even contribute to collective knowledge over time, leading to continuous improvement and optimization. 

IIoT device and app collaboration is not just a technological marvel, it's a paradigm shift in industrial operations. An IIoT solution as a system can exhibits intelligent adaptive behavior!  That is because there are two interesting and intertwined phenomena at play here, one is emergence and the other called "Collective Intelligence". Collective Intelligence arises from the combined capabilities and interactions of individual nodes, ultimately exceeding the sum of their parts. It's like a team where each member brings their unique strengths and insights, but through collaboration, they achieve more than any individual could on their own. 

Augmented Lean describes how human operators play a part in the IIoT network.

IIoT's significance lies not just in its technological prowess, but in its ability to transform industries from the inside out. It empowers data-driven decision making, optimizes operations, fosters innovation, and creates new business opportunities. IIoT is not just a technological fad; it's the driving force behind the current industrial revolution, shaping the future of how we manufacture, produce, and interact with the world around us. IIoT with augmentation, ie integrating the frontline operator into the network is even more powerful and probably also the easiest way to achieve collective intelligence. In fact even a fully manual manufacturing operations, one without or little technology exhibits these dynamics. Think how quickly you can transform and elevate performance in your operation by introducing augmentation with apps. You can transform your operation into an IIoT network nearly overnight, with unheard of performance gains - try it, it works!

A Clash of Paradigms - Using a Monolithic Mindset for a Composable Solution

I am involved in a few projects where there is an attempt to implement a composable MES by a traditional IT organization and their methods. In other words using a traditional mindset of a monolithic systems for a composable solution that requires enablement of citizen developers. Surprise - its not working out so well!

The results is a clash between tradition and innovation, which is a recurring theme. Tension is particularly pronounced with the use of the traditional waterfall approach instead of a bottom-up iterative development. While the waterfall model has been a longstanding and reliable methodology, its compatibility with the rapid pace required for composability and new digital technology raises a host of conflicts.

So much has been written and said about implementing traditional monolithic MES systems and their inherent challenges compared to the modern approach of leveraging new digital technologies that advocate for citizen development. Monolithic systems have tightly integrated architectures and demand a comprehensive understanding of complex technologies and extensive coding expertise. Developing, deploying, and maintaining these systems requires specialized skills, making it challenging for non-technical users to actively contribute or engage in the process. On the contrary, the rise of citizen development, facilitated by user-friendly low-code or no-code platforms, empowers individuals with diverse backgrounds to participate in building solution that are tailored for their need. 

This clash understandably results in a number of conflicts. I am listing them here as a cry for help - we need to transform and it feels tradition stands in our way driven by the need to manage the unexpected. Traditions are group efforts to keep the unexpected from happening.

• Rigidity in Requirements: The waterfall model and monolithic systems demands a comprehensive set of requirements upfront, often assuming a level of predictability that digital projects may not inherently possess. In the dynamic world of digital technology, user needs and expectations can evolve rapidly, leading to conflicts when rigid requirements fail to accommodate changes.
• Limited Flexibility: Digital technology thrives on adaptability and iterative development, characteristics that stand in stark contrast to the waterfall approach's rigid structure. The inability to pivot quickly in response to emerging trends or user feedback can result in missed opportunities and stifle rapid value creation diminishing project outcomes.
• Slow Value Creation: The sequential nature of the waterfall model can lead to prolonged development cycles. In the fast-paced digital realm, where time-to-value is critical, these delay means creating solution to requirements that have already changed and missing requirements that were not known.
• Communication Challenges: The waterfall model emphasizes documentation and formalized communication, which may hinder the fluid and collaborative communication required in digital projects. The rapid exchange of ideas, quick decision-making, and constant feedback loops are essential elements impeded by the waterfall methodology.
• Risk Management: Digital projects inherently carry a higher degree of uncertainty and risk that traditionalist need to get used to and embrace. The waterfall approach's linear structure is not good at mitigating unknown risks. It does not adequately address uncertainties and is poor in managing unforeseen challenges during later stages of development.
• Human-Centricity Concerns: The waterfall model's focus on completing one phase before moving to the next results in a final product that does not fully meet the need to support the frontline operator. In the digital space human centric solutions are paramount, this misalignment can be a significant source of conflict.

I have not exactly found the magic bullet resolution in these cases and it is inherent to paradigm changes that the biggest problem lies in transforming people and organizations. Some key elements however are emerging. To bring people over to the new paradigm it is critical to combine elements of both approaches and done right will also allow for greater flexibility and adaptability. Basically ease them in to adoption of incremental and iterative development with the rational that it is less risky. Introducing collaborative goals with cross-functional teams enhances communication and aligns more closely with the collaborative nature of digital technology projects. Breaking the supplier-customer mindset also allows the introduction of citizen development to the project teams with continuous feedback loops. Identify and address issues early, being proactive to find solutions increases the sense of accomplishment, aligns with the rapid pace of digital evolution and allows for timely adjustments.

The bottoms up approach to MVPs, Kenneth Cheung

Waterfall approaches have been a cornerstone of project management and IT has a full set of "baggage" from dealing with monolithic system from the old paradigm. IT needs to get with the time and understand that they are no longer in charge of implementation but rather embrace enablement of citizen development. This will allow their companies to harness the digitally native work force to create value fast. It will ease the ongoing and inevitable IT/OT convergence and shine a light on IT as an organization than can rapidly address evolving business needs in the digital era that can show rapid creation of value.

Hour by Hour Boards in the Digital World

What does an Hour by Hour board look like in a digital world? This Lean visual management tool that is a common and useful method to drive performance in manufacturing. But what do they look like in the digital paradigm? I find that the go-to is to just digitize them, meaning replicate the whiteboard without much thought about what they can be and how we can impact outcomes, ie drive performance increase. 

If we are to really gain order of magnitude productivity increases when introducing digital technology we have to go past that "just digitize the board" mentality. Let's start by considering why its hour by hour (or some standard period). Its to provide a tangible target to aim performance at with a given frequency. Its also because the operators or person who is supposed to update the board does it at that frequency. 

However if we introduce digital tools then we also introduce means to capture the required data at higher frequencies and also varying frequencies. This can be done a simple screen to quickly quantities, to interactive capture of quantities through buttons, input devices and sensors and ultimately through advanced sensory devices such as vision.  With that in mind it becomes a bit trivial to just do it every hour! In addition we can also capture a lot of context about the data. From the obvious such as operators, stations, orders, etc. but also things like environmental data, events, materials used, stock levels, etc. 

Lets take an example of a digital solution that essentially is capturing good and bad quantities of parts produced with some context such as time stamp, operator, station, shift, product and optionally some comments. However unlike the manual boards the operators can enter data at any given frequency and much more frequent than hour by hour. The system can also prompt and even require that he enter data at a given frequency. This is of course, if we only we rely on manual entries, if we supplement with sensory devices we can get much more granular and frequent data.

Now that we have all this data we can of course display hour by hour board with total quantities. Great but let's think about what else we can derive from this data? First of all we can calculate throughput and output rates, for example:

• Throughputs: Good parts per hour, Good parts per shift, etc.
• Output: Total bad parts by day, total good parts by line

And then include simple predictions of performance, for example:

• Predicted good parts by end of shift, predicted total bad parts by line, etc.

But it doesn't end there! With this data you can also visualize trends of performance for example:

• Trend good parts by line by shift. 
• Daily performance trend 

We can also do comparison by a multitude of dimensions for example:

• Bad parts by vs good parts by operator, or by day 
• Good parts by product for different shifts or operators 

We can take actions on deviations and critical scenarios, for example:

• Send a text when Good parts per hour is below 10

With all this digital data there is just so much insight to gain from just a simple capture of quantities. We now have a continuous feed of information and the hour by hour transforms into a wealth of information and importantly insights. This information can be displayed in numerous, dare I say infinite, formats for use by the operator, supervisor, leadership and any function in the operation. 

And it doesn't end there. With enough volume of data we can start applying more advanced analytics (read AI/ML) and gain insights that we did not uncover. Then operationalize these insights by proactively doing something about the performance predictively and intelligently.

Remember we started by simply capturing good and bad quantities of parts produced in a digital form. This took us from just looking at quantities and performance against a target to the ability to look at performance trends, patterns historically, performance in the context of different dimensions. Then to insights based on human intelligence, taking proactive actions, then to predictive analytics with AI/ML ending with deep insights into our operation. This is where and how digital transformation offers order of magnitude productivity increases.  

Let me end with a favorite quote from Dr. Seuss:

"Think left and think right and think low and think high. Oh, the things you can think up if only you try!"

So do "think high", digital is much more than digitizing, re. the hour by hour board in this example. The "things that you can think" once you have digital data is where the value is.

The Decline of Monoliths and the JAM (Just Another MES) Trap

How many times have we heard this? "We are implements XYZ system and the project plan has a go live date in 1.5 years." We also know that immediately after hearing this statement that anybody with even minimal experience will adds 6 months to this date - just to be realistic. This is what we have been accustomed to in the area of traditional manufacturing systems -there is a sense of inevitability and even desperation. 

Good news, in the current era of digital technology this does not need to be so. As I have explained many times before transformative digital technologies time to value is measured in days and weeks and not years. They are implemented in a bottom up iterative manner that is focused on adding value by making frontline operations more productive. 

But what makes this possible and why can't traditional systems do the same? That is because traditional systems are monolithic, they are built on the premise of providing a business function that works the same for all. The same solution that can serve all industries, in all modalities, in all scenarios, with any equipment, and for all operators. They have to be implemented top-down with a lengthy implementation process that maps out all the requirements, scenarios and contingencies upfront. They require the adaptation of existing operational processes to what the system can support and in the way that it supports it. They provide standardized rigid hierarchical structures for representing manufacturing operations with a standard data model in a one-size fits all approach.  Monolithic systems are also designed for maintainability, meaning that they try to optimize to ease the maintenance and management of the solution by a team with specialized skillsets. 

Bottom Line! Monolithic Solutions rob your organization of rapid time to value and exponential productivity increases that is at the core of the digital transformation (Industry 4.0, Smart Factories, etc).  This may not be news to some but the reason I felt it was necessary to discuss this topic is because I see many companies adopting new digital technologies but then go happily down the path of recreating monoliths. A path that will inevitably result in what I call "Just Another X": JAM, JAL, JAW, JAC - Just Another MES, LIMS, WMS, CMMS, etc. These solutions that will at best be “just as good” as the other MES, LIMS, WMS, CMMS, etc., and will inherently have all the associated shortcomings.

A Composable solution is built from the bottom up in an iterative manner. It is inherently agile and adaptable and provides the most efficient way to digitize manufacturing operations. It provides a solution in which the manufacturing execution is organically integrated with the operations and business processes. It provides the most robust and effective way to increase productivity with a modern digital tools specifically a Frontline Operations Platform.

This is in stark contrast to a Monolithic approach where top down hierarchical process is used to provide a solution that fits within specific constraints that is hard to change. The goal is to fit the solution to the process in contrast to fitting the process to the solution. Composability removes the difficulties associated with adhering to complicated standards and systems. It frees the engineers to focus on rapidly building targeted apps that solve a specific problem, fit the process, and increases the rate of solution development by an order of magnitude.

• Tailored specifically to each process, activity, operation - no compromises.
• Instrumentation of each discrete process - capturing granular data about each activity
• Complexity is distributed across the solution's Apps and easier to maintain
• Highly adaptable and agile - easy to change, minimal impact to overall system behavior

From: "The 5 Trademarks of Agile Organizations"

This brings us back to Holonics and holarchies which explain the fundamental principle enabling agility and why monolithic system will never be able to support agility. We talk all day long about digital transformation but until we understand that the technologies we use have to enable these fundamental principles we will not get the promised order of magnitude productivity increases. Let me close with a quote from what was once the Agility Forum, one of the research initiatives that is the foundation for Industry 4.0: 

“Instead of building something that anticipates a defined range of requirements based on ten or twelve contingencies, build it so it can be deconstructed and reconstructed as needed.” 

                                                                                                   -Rick Dove, Agility Forum 

Stating the obvious, a post with many quotes and cliches!

This is an exciting week, I am preparing for the different panels for an event this week at Operations Calling. I found myself searching for ways to drive home the main point about adoption of digital platforms.  I have tried in so many ways to explain this in this blog, at events, with industry influencers and of course with all my customers. I have used technical explanations, analogies, theories, examples and more.  At the end I find myself repeating cliches and quotes that I believe provide a concise and memorable phrasing and drive a point home - and I love them. They are also very effective in the face of all the debate and skepticism around digital transformation, a topic a write a lot about.

So this post is going to be a bit full of what seems like cliches and obviosity but at the same time points out how ridiculous it is that we are still dealing with skepticism and denial about what digital technologies can do for our industry. 

Digital Transformation requires a commitment to a fundamental change that results in an order of magnitude productivity increase. 

And it does and can happen! I have seen it with my own eyes - two recent examples of manufacturing sites that have successfully transformed show how true this is (you can watch one here, the other is still not public). Once you see and experience it, the reality of it seems so easy, so obvious. At the end of the day what make this a reality is the collective human organization's quest to achieve a goal and the focused effort that it takes. If you will it, it will come true! And let's be clear it is a collective team's and organization's effort to achieve a common goal. "Unless someone like you cares a whole awful lot, nothing is going to get better. It's not.", from The Lorax, 1971, Dr Seuss.

Its not a secret that change requires a focused strategy, just listen to Walker Reynolds. Strategy is nothing without a clear goal. So many companies and people talk about strategy but they have no well defined objective or goal.  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.” 

So defining goals and objectives are key. Arnold Schwarzenegger says that you should "visualize the person you want to become". In other words visualize your goals, draw a picture of what your manufacturing operations will look like when you are fully transformed and has achieved three digit precent improvements, for example increasing throughput by 100%. I can just hear the critics, its not easy, its complicated, you don't understand. Well here is quote from Clint Eastwood: "If you want a guarantee, buy a toaster". Nelson Mandela said: "It always seems impossible until its done. Nelson Mandela".

But remember that "culture eats strategy for breakfast", another of Drucker's famous quotes (borders on being a cliche). In the examples mentioned above it was clear that culture was a critical factor in the transformation. It works hand in hand, driving change with a clear goal ignites people and that helps create a culture of change. Also aligning a culture of continuous improvement with digital tools is an explosive catalyst for change. That is exactly what happened in this case - its just incredible to watch. 

Change is infectious because the results and accomplishments are real and rewarding. Organization and their people that change are eager and proud to share their feedback and observations. This is something that you can use to gauge the impact of a digital technology as well as the other way around if it affects change. 

Why Pharma is primed to capitalize on Digital

Digital and GxP compliance - a topic that is in high demand and interest these days. The general skepticism and misunderstanding of people in the quality functions of life sciences manufacturing companies is simply put; the biggest barrier currently in digital adoption. I spend so much time explaining and trying to convey the potential in digital technologies and why they are much more compliant than any paper based documentation method. The paper mindset is a shackle that all pharma and biotech companies have to cut off, now and fast otherwise the implications are dire! And, this is not news; regulators know it, executives know it, operations know it, even the quality leadership in most companies know it. That is why such concepts and guidances as Pharma 4.0, Control Strategy, CSA, Continuous Validation, etc. are being introduced and advocated.  

The industries dependency on paper as a mean of documented evidence for compliance is so engrained that it more than a solution it has become weaved into the culture. The selected attempts to introduce electronic solution for compliance such as eBR, LIMS, QMS, etc have been so painful, expensive and unwieldy that they have left us grasping and finding comfort in the paper based solutions. This has left the industry is in a constant dilemma where we know we need to step into the digital era (the unknown) but feel most comfortable staying in warmth of the paper world (known). While some opt for the compromise of traditional systems as an alternative (another known). 

Pharma and Biotech operations and processes are inherently dependent on data. Unlike other industries where the products are physical and you can see problems and defects, the processes can in most cases only be seen or observed through data. Yet we choose to observe by capturing this data mostly on paper and resist the adoption of digital means - why? Because of preconceived notions of compliance and painful experiences with other systems? This is mind boggling - its like someone with poor vision refusing to get a pair of glasses or contacts with obscure excuses that its "not allowed" (reference to regulatory guidance).

In other manufacturing industries operational excellence is the only way to stay profitable. In the life sciences industries the profit margins allow some leeway in productivity levels but of course have the additional burden of compliance. It means that the opportunity to gain significant productivity improvements are very high, e.g. in the CPG industry productivity increase of less than 1% are welcome. In life science not many will make an effort for such a again. With that in mind think about "the waste of documentation" in most processes - I would venture to say that there is more than 10% productivity increase potential by digitizing all paper records across most operations. 

With this I am making a plea to all my peers in the life sciences manufacturing industries. Adopt a "digital anything" mindset and start the transformation. If you see a piece of paper being used that is your opportunity to start digitizing. It does not have to be an intricate complicated "system" that needs IT and top down approvals. Start small and digitize a paper a day, one paper at a time and you will become digital faster than you have ever imagined. Yes there are regulatory implication but do no use that as an excuse, start the digitization process where the skepticism (or maybe fear) is greatest, e.g. the QA or documentation departments.

If one thing you should take aways from the changing paradigm is that transforming paper to digital is not only a must but also significantly easier than it was in the past. I have said this many times, there are no excuses not to start adopting digital technology, and that means the improvements can be incredibly rewarding.  Eliminate paper there are easier way to capture evidence in the digital world!

To Data Model or not to Data Model Part II - What to Data Model

The previous post about Data Models included a bit of a long winded discussion of why strict monolithic data models are not the alternative in the new paradigm. The main conclusion was that rather than focus on design of a strict data model for all of manufacturing lets step back and understand the problem that needs to be solved. The discussion is a bit theoretic and that is why I am compelled to go one level of detail deeper in an attempt to clarify some of the concepts. 

What do we need to help us in the transformation journey to maturity, how can we achieve Visibility, Transparency, Predictive Capacity and Adaptability? We need to shift the thinking from "what is the correct data model?" to "what do we need to become  predictive, and adaptable?". First of all we need more data, start digitizing your operation - the majority of the data we need is still on paper and diverse electronic documents and spreadsheets. Second, and this is the topic of this post, understand the informational elements of your operation and define a loose data dictionary that supports your digitization initiatives and citizen developers. With that and modern and emerging technologies for data analysis including AI/ML you will be able to gain the required insights and intelligence without a strict standardized monolithic relational data model. This will allow freedom within an organization for people to capture data without having to spend immense efforts in curating and micro managing how the data is stored and structured. Remember democratization and citizen development are a key enabler of digital transformation, their creative abilities with no-code technologies is the fastest way to digitize and instrument the operations. Get more digital data fast, its more important than how its structured and don't forget variety, multi media etc.

In my close to 30 years of studying the manufacturing domain it has become clear that there are just a few main and critical informational elements to a manufacturing operation. With that in mind I recommend an approach that uses generalization to help create transparency and interpretability but still allow for flexibility for specific use cases and varying degrees of complexity. The following generalization allows for a top down perspective into the complexity of a manufacturing operation. 

With this thinking, data about the artifacts represents the current status of each artifact, a single unique set of data (e.g. a row in a table). The processes that impact the artifact are captured in a historical record, a set of data for each significant transaction that transformed the state of the artifact (e.g. a running log). This results in a data set that represents real world artifact in a one to one relationship while everything that has happened to this artifact is captured in logs.  

If you create simple templates that allow contextualization of data at the source based on these simple rules you can with modern analytics tools rapidly get the insights that you need to mature digitally. I find that it works for both human driven analytics, from charting and graphing in Excel to Tableau, Sigma or whatever tool you prefer. Taking this even further you can super charge that with AI/ML driven analytics. I urge you to try, the good and easy thing is that the effort to build and use something like this with modern operational platforms is minimal compared to a building and using a complex relational data model. 

I also find that this model and generalization is a helpful tool to rapidly gain an understanding of a specific manufacturing operation. In fact I use it as a mental model when I do plant walk-thrus (Gemba walk) after which digital improvement ideas to observed operational challenges can be defined much faster and accurately. If you look closely many of the prevailing standards have the similar generalization but unfortunately have been overengineered past the point where they are practical.

Quickly understanding how a specific manufacturing system operates, from the machine to line and to the plant levels is the basis of digitization, its secret to gaining Visibility, Transparency, Predictive Capacity and Adaptability. Remember that is what we are after, the technology is just a means. If we can make it easier, more democratic, and adopted by the frontline masses then the network effect kicks-in and transformation happens faster, we gain productivity faster and we are well on our way to cross the digital divide.

To Data Model or not to Data Model

The ongoing debate about where and how MES fits in new era of digital technologies is raging. Its not surprising and in fact to be expected in any kind of change, basically the old guard vs the new guard. Of course you have to believe that the 4th industrial revolution is really a paradigm change. Something that I clearly align with and have some background to do so since I have been studying this phenomena since the 1990s.

As in other paradigm shifts there will always be a bit of the old that is part of the new. Steam power has not completely disappeared, it still relevant in specialized application but it is not the main source of energy powering industrial operations. This leads us to ISA-95 that I believe is a relic of the current "industry 3.0" era and not directly relevant in the new digital paradigm. (note I purposefully am trying to minimize the use of "Industry 4.0" since it starting to get a negative connotation with all the hype going on). But, that being said there are elements of ISA-95 and other best practices that may be relevant in the new paradigm, ie the old in the new?

If we let history be our teacher we can probably come up with some prediction and that is where the data model topic is interesting. ISA-95 includes a data model and all the established MOM solution include a data model that based on the available technologies at the time that seemed appropriate. The question is then; is the quest to achieve the nirvana of one standard monolithic data model for all manufacturing achievable and is it still relevant with the new digital technologies? The answer I think is clearly no and no, as far as I know there are very few, if any, examples of an organization achieving a real working standard data repository for all its operation and its not because of lack of trying.

The bottom line here is that striving for a single standard data model in a monolithic repository is a fools errand, regardless of if we try to implement it with modern digital technologies. That being said a common, shared and interpretable view of manufacturing operations is still needed and critical. In fact it's at the core of Industry 4.0, in that its the data and information that gives us the Visibility, Transparency, Predictive Capacity and Adaptability. This holistic view into the manufacturing operations is also at the core of the CIM concept from the 80s that advocated a common "shared knowledge" that all operational activities in plant uses in order to streamline to manufacturing of products. That means that both paradigms are aligned around the same challenge that to improve manufacturing operations we need to all have a common understanding and view into the operation!   

The CIM Enterprise Wheel (c)1993, SME.

The difference is how we achieve this common and shared view (information and knowledge). In the old paradigm it was the notion of a strict and rigidly structured data model, in the new paradigm we have relaxed these requirements to allow for analysis from both structured and unstructured data. I can hear the skeptics already; how can you gain any insights with different solution each having their own data structures? A few things to consider here: We do need context and this context should be defined at the source. We need to simplify data structures and get away from multiple levels of abstractions needed to run monolithic process driven solution.  Adhere to some simple shared guidelines using a consistent data dictionary that allows for flexibility within your organization. (I know this sounds overly simplistic and see part II of this blog post). With these principles we can adopt many of the modern digital tools to curate views into our data, on demand with the flexibility needed for common and personalized views and insights including of course AI.     

Let's take at an example where different solutions all represent some data about a lot of materials and its product code. The material can be referenced as Lot, Batch, Units, Pack, Kit, etc and the product code can be references as SKU, Item ID, Product, Material Number, etc. We of course immediately recognize these different names as similar because we understand how they are used. In the old paradigm we had to enforce strict rules in structure and semantics for software solution in order to visualize and analyze this data. That is however changing with new digital technologies and modern analytics platforms.  

It is also where AI can help, you see simply put AI is good at finding patterns. Its not that AI understands what the meaning of Item and Material Number is. It simply is looking for similarities in the relationship to other data structure and how its used to see that Item and Material Number really are very similar. With enough data volume and variety this can be easily detectable. Notice I said volume and variety this is where Cloud based system are important. Using isolated traditional monolithic system data sources will never get you to this point, even if they are lift and shifted to the cloud. You need a modern cloud native operational platforms that provides easy access to the their data that can be amassed and used to identifying these patterns.

I know there are a number of concepts discussed here and there may be some lack of depth in the discussion. I promised a follow up on this post with some more detail. But assuming this is true, just think about it. It means we can relax the strict data type and structure requirements and allow citizen developers to extend template data structures to create solution to solve operational problems and know that we can still gain valuable insights about operations, and again the more data we have to more insight we have. The conclusion here is: prioritize data volume and variety and not monolithic structures.

4 Questions As a Guide Towards True Digital Transformation

Most people that have heard me talk about digital transformation are probably sick of hearing me talk about the "order of magnitude" productivity gain that is promised by the ongoing digital industrial revolution. But here again it is a key principle that can be used to understand transformation and navigate through the existing maze of confusion of what is and is not digital technology. Simply put digital technology is a technology that can directly impact industrial or manufacturing operations to bring about an order of magnitude productivity increase. 

I have compiled 4 critical elements that can help you sort through the maze of different technologies that are touted as digital, Industry 4.0 or Smart Manufacturing technologies based on this principle. These 4 elements can be framed as question that guide evaluation and selection of technologies:

Is it adopted and implemented in a "Bottom Up" manner?

Adoption and implementation are performed in an agile method, starting small in an iterative manner and building on outcome of each iteration. Agile approaches are an inherent part of the digital transformation and advocate a way to learn faster by short and rapid test-fail-learn cycles. The overall manufacturing systems solution is built from the bottom up in an iterative manner. 

This is in stark contrast to the traditional system approaches, including MES, where top down hierarchical processes are used to provide a solution that fits within specific constraints that is hard to change. There should be no "gap assessments", the technology is adapted to the process in contrast to fitting the process to the solution. It also removes the difficulties associated with adhering to complicated standards and systems. It frees engineers to focus on building solutions rapidly that fit the process and increases the rate of implementation by an order of magnitude (here I go again...). There are some interesting implications to this approach one of which is that Industry 3.0 standards such as ISA-95 becomes less relevant in this context.

Does it inherently support and enable Continuous Improvement?

Lean principles are still the most effective way to achieve productivity increases in an industrial operation and therefore the technology should be a tool to implement these operational improvements. The adoption of the technology should be done in a methodical PDCA or DMAIC cycle with each improvement supporting the next. Changes and modifications to a solution are easy and support iterative and constant improvement. The technology solutions are targeted at improvement areas with clear and quantified goals.   

It should be no surprise that regardless of the paradigm shift that is going on Lean and the principles of TPS are still real and valid. There is a close connection between the continuous improvement process and agile (bottom up) development approach of using the technology/solution. Th technology should be a Lean tool that allows engineers to rapidly iterate thru solutions to problem building digital content to an effective solution. 

Does it offer a Democratized approach and how does it enable "Citizen Developers"?

Users and implementors of the technology do not need to have unique and specific skills that are common for software, IT or automation engineers. Engineers and SMEs can rapidly adopt the technology to develop solution for the operations. The technology is so easy to use and learn that it is effectively accessible to most people with a basic level of technical aptitude. This allows the people that are closest to the process to craft solutions that are focused on solving a problem or provide an improvement. The technology should be adopted by people from within the operation rather than implemented by external parties.

Democratization and the citizen developer is an important aspect of the digital transformation. With modern digital technology we all can become builders of digital content. We already do this when using office tools such as Word and Excel and now we can even easily program our IoT door to open automatically when we get within range so we don’t have to take our key out. This is a big change compared to the high level of skills and expertise needed to build even simple automation tasks in traditional systems. No-Code/Low-Code is a key enabler of Democratization, it allows people with no programming or IT skills to build content that automates manufacturing processes in a simple and intuitive way. 

I find that for bigger organizations citizen development may be alarming, i.e. they feel it is akin to "arming the rebels". However there is no way around it, the benefits far outweigh the risks in this case and democratization of technology is key element of the new digital age. At the same time most of the new platform technologies provide accessible and transparent control and management of content being created and consumed. 

Is the technology able to provide Human Centric solutions?

The use of the technology should result in solution that serve humans or specifically frontline operators. It has to be intuitive, simple, easy to understand and easy to use. It has to serve the frontline operator by making him more productive, the operator is the key to the productivity gains promised by digital technologies in I4.0  

Modern digital technologies and tools are built on the principle of supporting human activity, that is what makes them so effective and so widely adopted. People are the key to unlocking productivity gains from digital technologies, that therefore have to focus on supporting human activity. The premise is that in order to increase productivity technology needs to support the human operator. In the new digital age manufacturing needs to enable the connected worker whose tasks are monitored and supported by a larger network of digital tools. In addition the technology should be used to capture additional digital data streams such as instrumentation of the human activity, the data that human operators collect, input they can provide about the process, and more. 

In conclusion, if a technology is not able to impact your operations in this significant way then its not digital technology - simply drop it from the list. Let's take a simple example: SaaS MES that is purportedly in the cloud and requires experts with specific skills set to configure and use with a 6+ months implementation time frame. This is not and example of digital technology. You should be seeing quantifiable productivity increase results within weeks of adopting any technology.  Another example is if the technology implementation requires a waterfall/phased method that requires design of the substantial parts of the solution upfront then it is not a digital technology!  

You can watch me talk about these topics on the Manufacturing IT Podcast with Daniel Langley.

Maybe this will also nudge the skeptics out there since speed, effort and real world double and triple productivity gains are becoming real and undeniable. Charlie Chaplin once said "if you look down, you will not see the rainbow".

The Genius of the Toyota Production System Explained

So much has been written about the Toyota Production System (TPS) that you may be wondering why in this day and age were the focus of most of this blog is on digitalization and Industry 4.0, I am bringing this topic back up? It should be no surprise that regardless of the paradigm shift that is going on Lean and the principles of TPS are still real and valid. The reason is that under all the tactical concepts that we in general refer to as "Lean Manufacturing" it addresses a basic characteristic of all manufacturing systems and that is that they are inherently chaotic! In fact they are a special type of chaotic system called Complex Adaptive System or CAS, a topic that I believe is critically important to understand in these transformational times.

McElroy, Mark. (2000). Integrating Complexity Theory, Knowledge Management, and Organizational Learning. Journal of Knowledge Management. 

Before explaining what Chaos and CAS are and why I believe this to be true, I wanted to state the conclusion. The genius of TPS is that the concepts and strategies that it employees are directly built to use the characteristics of the CAS to its advantage, ie to increase value and productivity. The two fundamental concepts that are central to Lean and TPS that back this hypothesis are:

• Understanding that adaptability to change is critical. Lean provides for effective management small deviation and big changes with a focused and controlled manner with strong discipline, clear objectives and effective execution. Examples are Andons, Kaizen, Gemba, Hoshin Kanri, Poke Yoke, etc. that are designed to provide an effective way to deal with the repeatable patterns (good or bad) within the system.
• Understanding the boundaries between chaotic dynamics and order. Lean strives to coral the unpredictable nature of CAS by making a discrete non linear system more linear. Reducing batch size, making value flow, JIT, Heijunka, Kanban, SMED, etc. makes the system more continuous and less discrete and therefore more predictable.

In order to better understand this premise, I really need to provide a primer about Chaos and Complex Adaptive Systems. First a disclaimer - this is a diverse and complex topic and the following explanation is very high level and simplistic. Chaos sometimes is confused with randomness, however it is different. Here is a simple comparison:

Random	Chaotic	Periodic
Unpredictable Path Unpredictable Pattern	Unpredictable Path Predictable Pattern	Predictable Path Predictable Pattern

This means that systems that exhibit chaotic behavior are impossible to predict but they have predictable patterns that repeat. On a side note; we as humans are very good at identifying patterns, which means we are inherently very good at operating in chaos. That is also what AI is good at and why its application in manufacturing context is so interesting and offers so much potential. 

As mentioned a CAS is a special type of chaotic system that exhibit chaotic dynamics and emergent behavior and includes of course our favorite system the manufacturing organization. Other examples of CAS are weather, traffic, ant colonies, the stock market, social and organizations. A CAS behaves and evolves according to three key principles:

• Order and control is emergent. The overall behavior of the system of elements is not predicted by the behavior of the individual elements. There is a natural transition between equilibrium points through environmental adaptation and self-organization.
• The system's history is irreversible or irreducible. Irreversible process transformations cannot be reduced back to its original state. They evolve and their past is co-responsible for their present behavior.
• The system's future is often unpredictable. Non linear and therefore not predictable but yet have repeatable identifiable pattern. Predetermined patterns within their complexity describe potential evolutions of the system. 

OK, now with this information we can reflect on what this means in the context of Lean and TPS. Jim Womack famously defined the 5 principle of Lean as a result of studying TPS and how it works. By looking at how each of these principles is directly designed to use the characteristic of a CAS the genius of TPS is revealed.

 

Lean Thinking (1996), James P. Womack and Daniel T. Jones

• Identify Value: This is in fact not directly related to the manufacturing being a CAS, it is really just a sound business principles. I.e. make sure to have clear objectives and know what to focus on- similar to Deming's "consistency of purpose". Identifying the value stream provides the framework for prioritization of how and where to optimize.
• Map the Value Stream: Understanding the value creation process and the details in the value stream helps in understanding the specific key attributes that impact the behavior of the system. In a CAS there are dependencies between key attributes and specific patterns that indicate either good or bad behaviors. This is to understand what in the value stream may trigger unwanted states, ie.e the famous "butterfly effect". For example the rectangles drawn on the weather forecast indicating that a Tornado is likely.   
• Create Flow: Make sure that the production value stream flows since systems that flow are continuous and more linear in contrast to nature of discrete "stop and go" type of behavior. Reducing the unit size (one piece flow) eases flow, with the smaller the unit the better. Without consistent flow the system becomes more non-linear and may exhibit catastrophic patterns. For example getting stuck behind the guy who can't out of the way when boarding a plane (exemplified by Mythbusters). 
• Establish Pull: Consistent flow can be nearly guaranteed if you pull rather than push the elements of the system. It secures blockages are identified immediately and removed, simply because you can't pull with something in the way. Think what happens when you are behind two trucks overtaking on a highway causing traffic to backup.  
• Seek Perfection: This principle is to ensure that we can keep the CAS in states that we can predict. By striving to keep the manufacturing system as close as possible to linear (consistent flow) we can predict and guarantee performance and behavior. However it also provide tools to identify unwanted patterns and ways to quickly address states that are undesirable.     

Toyota has always advocated a cautious approach to new technology and that is a a problem. The n-1  approach to technology advocated by Lean (the use of proven technology, not the latest and greatest) is sometimes used as an excuse to maintain status quo. In these times of change with all the new digital technology available this seemingly puts Lean companies at a disadvantage. However if you approach these technologies as a way to Lean, to reduce waste, to create flow and execute pull then the value of the technology can easily outweigh the risk. In addition the n-1 concept was really meant for production equipment, i.e. to not increase the risk of interrupting the production flow and quality issues.

Its important to understand how digital technology can be used to support a Lean system. Remember the new paradigm offers an order of magnitude productivity increase and herein lies key motivator. Digital transformation needs to support a lean system, it has provide additional tools in the Lean toolbox. Digital technology can be catalyst to a Lean organization where continuous improvement happens faster and more effectively and that is how the "order of magnitude increase" is realized.

So the genius of TPS is simply put that Toyoda understood behavioral dynamics of his manufacturing operation even without knowing its a CAS and was able to put together a management strategy to directly impact the characteristics of the system with optimization in mind. When operational principles are nicely aligned with the science behind it there its not a surprise that its successful. We should now do the same to transform lean operations with digital tools so that we can hit the digital transformation jackpot. 

How Electricity Goes Around the Bend & Where is the Electricity Manager?

The Ghost Town

I recently was on a motorcycle ride to Bodie, a gold rush ghost town in California that is now a state park. I was fortunate enough to be with Mark, who apart from running motorcycle adventure rides, who is also is a bit of a gold rush history buff. He told a story about how electricity was perceived during the boom days at Bodie.

Bodie SHP, CA (California State Parks)

Electricity first came to the gold rush mining towns in the California desert of the Eastern Sierras in the 1890s and it was, as you would expect, quite a spectacle. It brought with it a major change in the way gold was mined and processed and offered great productivity increases.  At the the time there was a common misconception that the electricity power lines had to be run straight because the electricity would shoot out if it there was a bend in the line. Basically it could not flow around a bent or wire that changes direction.  Here is a bit more background from Chat GPT: "Did people believe that electricity can't flow through a bent wire in the gold rush towns?"

"One popular misconception of the time was that electricity followed the path of least resistance. In this context, the notion that electricity could not flow through a bent wire might have arisen. People might have believed that the bent shape of the wire created a higher resistance, hindering the flow of electricity."

Considering that this was a new technology that was brought to remote towns where the living, to say the least, was hard and dangerous such a misconception seems reasonable. Yet we can draw some striking similarities with this scenario and the introduction of digital technologies to manufacturing plants. Manufacturing plants are operational islands where financial survival is always a top priority and digital technology is not fully understood, or maybe understanding it is not the most important priority. Like electricity in the gold rush town, its hard to relate to a new technology that has lofty and even ungrounded promises such as "a fundamental change to how we live and operate" and "an order of magnitude productivity increase".

Managing Electricity

During the 2nd industrial revolution, where we transformed from steam to electricity all plants had an "Electricity Manager". Again Chat-GPT for some wisdom: "what was the role of the electricity manager during the 2nd industrial revolution?"

"Overall, the role of an electricity manager during the Second Industrial Revolution involved overseeing the generation, distribution, and management of electricity. Their responsibilities encompassed technical, safety, operational, and financial aspects to ensure the reliable and efficient supply of electrical power to support industrial and societal advancements during this transformative period."

So clearly we do not have this role in our manufacturing plants today, we simply pay for electricity as a service. Does this sounds eerily similar to the current roles of CIO or CDO in managing digital technology? What is the destiny of IT organization and CIOs? Will XaaS (Anything as a Service) become common place and make IT redundant?

Oh the Skepticism

I tell these stories to most skeptics that I meet in an attempt to explain that a new paradigm requires new thinking. We will not be able to experience productivity increases until we realize that what we have at our hands is so different than anything we have seen before. In other words electricity does flow around bent wires, data is safe in the cloud, citizen developers can build complex systems, you can validate a solution in hours, control of democratized technology is easy, IIoT can be safe, it's also for all size companies, etc. Oh and one more that is quite controversial; MES, LIMS, WMS, etc. are not digital technologies - they are relics of the previous industrial age (industry 3.0). They are what steam was to electricity!

The challenge that I face on a daily basis is how to dispel the myths of digital technology and relieve the skepticism that is inherent in most manufacturing organizations? This is in the perspective of the bigger challenge that is how do we plan to transform industry so they can start capitalizing at the order of magnitude productivity gains. In the words of Søren Kierkegaard: "Life can only be understood backwards; but it must be lived forwards."

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.