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Last week I attended the long-awaited joined conference from CIMdata and Eurostep in Stuttgart. As I mentioned in earlier blog posts. I like this conference because it is a relatively small conference with a focused audience related to a chosen theme.
Instead of parallel sessions, all attendees follow the same tracks and after two days there is a common understanding for all. This time there were about 70 people discussing the themes: Digitalizing Reality—PLM's role in enabling the digital revolution (CIMdata) and Collaboration in the Engineering and Manufacturing Supply Chain –the Extended Digital Thread and Smart Manufacturing (EuroStep)
As you can see all about Digital. Here are my comments:
The State of the PLM Industry:
The Digital Revolution
Peter Bilello kicked off with providing an overview of the PLM industry. The PLM market showed an overall growth of 7.3 % toward 43.6 Billion dollars. Zooming in into the details cPDM grew with 2.9 %. The significant growth came from the PLM tools (7.7 %). The Digital Manufacturing sector grew at 6.2 %. These numbers show to my opinion that in particular, managing collaborating remains the challenging part for PLM. It is easier to buy tools than invest in cPDM.
Peter mentioned that at the board level you cannot sell PLM as this acronym is too much framed as an engineering tool. Also, people at the board have been trained to interpret transactional data and build strategies on that. They might embrace Digital Transformation. However, the Product innovation related domain is hard to define in numbers. What is the value of collaboration? How do you measure and value innovation coming from R&D? Recently we have seen more simplified approaches how to get more value from PLM. I agree with Peter, we need to avoid the PLM-framing and find better consumable value statements.
Nothing to add to Peter's closing remarks:
An Alternative View of the Systems Engineering "V"
For me, the most interesting presentation of Day 1 was Don Farr's presentation. Don and his Boeing team worked on depicting the Systems Engineering process for a Model-Based environment. The original "V" looks like a linear process and does not reflect the multi-dimensional iterations at various stages, the concept of a virtual twin and the various business domains that need to be supported.
The result was the diamond symbol above. Don and his team have created a consistent story related to the depicted diamond which goes too far for this blog post. Current the diamond concept is copyrighted by Boeing, but I expect we will see more of this in the future as the classical systems engineering "V" was not design for our model-based view of the virtual and physical products to design AND maintain.
Sponsor vignette sessions
The vignette sponsors of the conference, Aras, ESI,-group, Granta Design, HCL, Oracle and TCS all got a ten minutes' slot to introduce themselves, and the topics they believed were relevant for the audience. These slots served as a teaser to come to their booth during a break. Interesting for me was Granta Design who are bringing a complementary data service related to materials along the product lifecycle, providing a digital continuity for material information. See below.
The PLM – CLM Axis vital for Digitalization of Product Process
Mikko Jokela, Head of Engineering Applications CoE, from ABB, completed the morning sessions and left me with a lot of questions. Mikko's mission is to provide the ABB companies with an information infrastructure that is providing end-to-end digital services for the future, based on apps and platform thinking.
Apparently, the digital continuity will be provided by all kind of BOM-structures as you can see below.In my post, Coordinated or Connected, related to a model-based enterprise I call this approach a coordinated approach, which is a current best practice, not an approach for the future. There we want a model-based enterprise instead of a BOM-centric approach to ensure a digital thread. See also Don Farr's diamond. When I asked Mikko which data standard(s) ABB will use to implement their enterprise data model it became clear there was no concept yet in place. Perhaps an excellent opportunity to look at PLCS for the product related schema.
A general comment: Many companies are thinking about building their own platform. Not all will build their platform from scratch. For those starting from scratch have a look at existing standards for your industry. And to manage the quality of data, you will need to implement Master Data Management, where for the product part the PLM system can play a significant role. See Master Data Management and PLM.
Systems of Systems Approach to Product Design
Professor Martin Eigner keynote presentation was about the concepts how new products and markets need a Systems of Systems approach combined with Model-Based Systems Engineering (MBSE) and Product Line Engineering (PLE) where the PLM system can be the backbone to support the MBSE artifacts in context. All these concepts require new ways of working as stated below:
And this is a challenge. A quick survey in the room (and coherent with my observations from the field) is the fact that most companies (95 %) haven't even achieved to work integrated for mechatronics products. You can imagine the challenge to incorporate also Software, Simulation, and other business disciplines. Martin's presentations are always an excellent conceptual framework for those who want to dive deeper a start point for discussion and learning.
Additive Manufacturing (Enabled Supply) at Moog
Moog Inc, a manufacturer of precision motion controls for various industries have made a strategic move towards Additive Manufacturing. Peter Kerl, Moog's Engineering Systems Manager, gave a good introduction what is meant by Additive Manufacturing and how Moog is introducing Additive Manufacturing in their organization to create more value for their customer base and attract new customers in a less commodity domain. As you can image delivering products through Additive Manufacturing requires new skills (Design / Materials), new processes and a new organizational structure. And of course a new PLM infrastructure.
Jim van Oss, Moog's PLM Architect and Strategist, explained how they have been involved in a technology solution for digital-enabled parts leveraging blockchain technology. Have a look at their VeriPart trademark. It was interesting to learn from Peter and Jim that they are actively working in a space that according to the Gartner's hype curve is in the early transform phase. Peter and Jim's presentation were very educational for the audience.
For me, it was also interesting to learn from Jim that at Moog they were really practicing the modes for PLM in their company. Two PLM implementations, one with the legacy data and the wrong data for the future and one with the new data model for the future. Both implementations build on the same PLM vendor's release. A great illustration showing the past and the future data for PLM are not compatible
Value Creation through Synergies between PLM & Digital Transformation
Daniel Dubreuil, Safran's CDO for Products and Services gave an entertaining lecture related to Safran's PLM journey and the introduction of new digital capabilities, moving from an inward PLM system towards a digital infrastructure supporting internal (model-based systems engineering / multiple BOMs) and external collaboration with their customers and suppliers introducing new business capabilities. Daniel gave a very precise walk-through with examples from the real world. The concluding slide: KEY SUCCESS FACTORS was a slide that we have seen so many times at PLM events.
Apparently, the key success factors are known. However, most of the time one or more of these points are not possible to address due to various reasons. Then the question is: How to mitigate this risk as there will be issues ahead?
Bringing all the digital trends together. What's next?
The day ended with a virtual Fire Place session between Peter Bilello and Martin Eigner, the audience did not see a fireplace however my augmented twitter feed did it for me:
Some interesting observations from this dialogue:
Peter: "Having studied physics is a good base for understanding PLM as you have to model things you cannot see" – As I studied physics I can agree.
Martin: "Germany is the center of knowledge for Mechanical, the US for Electronics and now China becoming the center for Electronics and Software" Interesting observation illustrating where the innovation will come from.
Both Peter and Martin spent serious time on the importance of multidisciplinary education. We are teaching people in silos, faculties work in silos. We all believe these silos must be broken down. It is hard to learn and experiment skills for the future. Where to start and lead?
The PLM roadmap had some exciting presentations combined with CIMdata's PLM update an excellent opportunity to learn and discuss reality. In particular for new methodologies and technologies beyond the hype. I want to thank CIMdata for the superb organization and allowing me to take part. Next week I will follow-up with a review of the PDT Europe conference part (Day 2)
Ontology example: description of the business entities and their relationships
In my recent posts, I have talked a lot about the model-based enterprise and already after my first post: Model-Based – an introduction I got a lot of feedback where most of the audience was automatically associating the words Model-Based to a 3D CAD Model.
Trying to clarify this through my post: Why Model-Based – the 3D CAD Model stirred up the discussion even more leading into: Model- Based: The confusion.
A Digital Twin of the Organization
At that time, I briefly touched on business models and business processes that also need to be reshaped and build for a digital enterprise. Business modeling is necessary if you want to understand and streamline large enterprises, where nobody can overview the overall company. This approach is like systems engineering where we try to understand and simulate complex systems.
With this post, I want to close on the Model-Based series and focus on the aspects of the business model. I was caught by this catchy article: How would you like a digital twin of your organization? which provides a nice introduction to this theme. Also, I met with Steve Dunnico, Creator and co-founder of Clearvision, a Swedish startup company focusing on modern ways of business modeling.
Jos (VirtualDutchman): Steve can you give us an introduction to your company and the which parts of the model-based enterprise you are addressing with Clearvision?
Steve (Clearvision): Clearvision started as a concept over two decades ago – modeling complex situations across multiple domains needed a simplistic approach to create a copy of the complete ecosystem. Along the way, technology advancements have opened up big-data to everyone, and now we have Clearvision as a modeling tool/SaaS that creates a digital business ecosystem that enables better visibility to deliver transformation.
As we all know, change is constant, so we must transition from the old silo projects and programs to a business world of continuous monitoring and transformation.
Clearvision enables this by connecting the disparate parts of an organization into a model linking people, competence, technology services, data flow, organization, and processes.
Complex inter-dependencies can be visualized, showing impact and opportunity to deliver corporate transformation goals in measured minimum viable transformation – many small changes, with measurable benefit, delivered frequently. This is what Clearvision enables!
Jos: What is your definition of business modeling?
Steve: Business modeling historically, has long been the domain of financial experts – taking the "business model" of the company (such as production, sales, support) and looking at cost, profit, margins for opportunity and remodeling to suit. Now, with the availability of increased digital data about many dimensions of a business, it is possible to model more than the financials.
This is the business modeling that we (Clearvision) work with – connecting all the entities that define a business so that a change is connected to process, people, data, technology and other dimensions such as cost, time, quality. So if we change a part, all of the connected parts are checked for impact and benefit.
Jos: What are the benefits of business modeling?
Steve: Connecting the disparate entities of a business opens up limitless opportunities to analyze "what is affected if I change this?". This can be applied to simple static "as-is" gap analyses, to the more advanced studies needed to future forecast and move into predictive planning rather than reactive.
The benefits of using a digital model of the business ecosystem are applicable to the whole organization. The "C-suite" team get to see heat-maps for not only technology-project deliveries but can use workforce-culture maps to assess the company's understanding and adoption of new ways of working and achievement of strategic goals. While at an operational level, teams can collaborate more effectively knowing which parts of the ecosystem help or hinder their deliveries and vice-versa.
Jos: Is business modeling applicable for any type or size of the company?
The complexity of business has driven us to silo our way of working, to simplify tasks to achieve our own goals, and it is larger organizations which can benefit from modeling their business ecosystems. On that basis, it is unlikely that a standalone small business would engage in its own digital ecosystem model. However, as a supplier to a larger organization, it can be beneficial for the larger organizations to model their smaller suppliers to ensure a holistic view of their ecosystem.
The core digital business ecosystem model delivers integrated views of dependencies, clashes, hot-spots to support transformation
Jos: How is business modeling related to digital transformation?
Digital transformation is an often heard topic in large corporations, by implication we should take advantage of the digital data we generate and collect in our businesses and connect it, so we benefit from the whole not work in silos. Therefore, using a digital model of a business ecosystem will help identify areas of connectivity and collaboration that can deliver best benefit but through Minimum Viable Transformation, not a multi-year program with a big-bang output (which sometimes misses its goals…).
Today's digital technology brings new capabilities to businesses and is driving competence changes in organizations and their partner companies. So another use of business modeling is to map competence of internal/external resources to the needed capabilities of digital transformation. Mapping competence rather than roles brings a better fit for resources to support transformation. Understanding which competencies we have and what the gaps are pr-requisite to plan and deliver transformation.
Jos: Then perhaps close with your Clearvision mission where you fit (uniquely)?
Having worked on early digital business ecosystem models in the late 90's, we've cut our teeth on slow processing time, difficult to change data relationships and poor access to data, combined with a very silo'd work mentality. Clearvision is now positioned to help organizations realize that the value of the whole of their business is greater than the sum of their parts (silos) by enabling a holistic view of their business ecosystem that can be used to deliver measured transformation on a continual basis.
Jos: Thanks Steve for your contribution and with this completing the series of post related to a model-based enterprise with its various facets. I am aware this post the opinion from one company describing the importance of a model-based business in general. There are no commercial relations between the two of us and I recommend you to explore this topic further in case relevant for your situation.
Companies and their products are becoming more and more complex, most if it happening now, a lot more happening in the near future. In order to understand and manage this complexity models are needed to virtually define and analyze the real world without the high costs of making prototypes or changes in the real world. This applies for organizations, for systems, engineering and manufacturing coordination and finally in-field operating systems. They all can be described by – connected – models. This is the future of a model-based enterprise
Coming up next time: CIMdata PDM Roadmap Europe and PDT Europe. You can still register and meet a large group of people who care about the details of aspects of a digital enterprise
What I want to discuss this time is the challenging transformation related to product data that needs to take place.
The top image of this post illustrates the current PLM world on the left, and on the right the potential future positioning of PLM in a digital enterprise. How the right side will behave is still vague – it can be a collection of platforms or a vast collection of small services all contributing to the performance of the company. Some vendors might dream, all these capabilities are defined in one system of systems, like the human body; all functions are available and connected.
Coordinated or connected?
This is THE big question for a future digital enterprise. In the current PLM approach, there are governance structures that allow people to share data along the product lifecycle in a structured way.
These governance structures can be project breakdown structures, where with a phase-gate approach the full delivery is guided. Deliverables related to task and gates will make sure information is stored available for every stakeholder. For example, a well-known process in the automotive industry, the Advanced Product Quality Process ( APQP process) is a standardized approach to make sure parts or products are introduced with the right quality for the customer.
Deliverables at any stage in the process can be reviewed or consumed by another stakeholder. The result is most of the time a collection of approved documents (Office-type, Design & Test files) stored centrally. This is what I would call a coordinated data approach.
In complex environments, besides the project governance, there will be product structures and Bill of Materials, where each object in such a structure will be the placeholder for related information. In case of a product structure it can be its specifications per component, in case of a Bill of Materials, it can be its design specification (usually in CAD models) and its manufacturing specifications, in case of an MBOM.
Although these structures contain information about the product composition themselves, the related information makes the content understandable/realizable.
Again it is a coordinated approach, and most PLM systems and implementations are focusing on providing these structures.
Sometimes with their own system only – you need to follow the vendor portfolio to get the full benefit or sometimes the system is positioned as an overlay to existing systems in the company, therefore less invasive.
Providing the single version of the truth is often associated with this approach. The question is: Is the green bin on the left the single version of the truth?
The Coordinated – Single Version of the Truth – problem
The challenge of a coordinated approach is that there is no thorough consistency checking if the data delivered is representing the real truth. Through serious review procedures, we do our best to make sure every deliverable has the required content and quality. As information inside these deliverables is not connected to the outside world, there will be discrepancies between reality and what has been stored. Still, we feel comfortable enough as an organization to pretend we know where the risks are. Until the costly impossible happens !
The connected enterprise
The ultimate dream of a digital enterprise is that everything relevant is connected in context. This means no more documents or files but a very granular information model for linking data and keeping it in context. We can apply algorithms and automation to connected data and use Artificial Intelligence to make sense of massive amounts of data.
Connected data allows us to share combined sets of information that are relevant to a particular role. Real-time dashboarding is one of the benefits of such an infrastructure. There are still a lot of challenges with this approach. How do we know which information is valid in the context of other information? What are the rules that describe a valid product or project baseline at a particular time?
Although all data is stored as unique information objects in a network of information, we cannot apply the old mechanisms for a coordinated approach all the time. Generated reports from a connected environment can still serve as baselines or records related to a specific state, such as when the design was approved for manufacturing, we can generate approved Product Baselines structures or Bill of Materials structures.
However, this linearity in lifecycle for passing information through an enterprise will not exist anymore. It might be there are various design alternatives and the delivery process is already part of the design phase. Through integrated virtual simulation and testing, we reach a state that the product satisfies the market for that moment and the delivery process is known at the same time
Almost immediately and based on first experiences from the field, new features can be added virtually tested and validated for the next stage. We need to design new PLM infrastructures that can support this granularity and therefore complexity.
The connected – Single Version of the Truth – problem
The concepts I described related to the connected enterprise made me realize that this is analogue to how the brain works. Our brain is a giant network of connected information, dynamically maintaining associations, having different abstraction levels and always pretending there is one truth.
If you want to understand a potential model of the brain, please read On Intelligence from Jeff Hawkins. With the possible upcoming of the Quantum Computer, we might be able to create performing brain models.
In my earlier post: Are we blocking our future, I referred to the book; The Idiot Brain: What Your Head is Really Up To from Dean Burnett, where Dean is stating that due to the complexity of stored information our brain continuously adapts "non-compliant" information to make sure the owner of the brain feels comfortable.
What we think that is the truth might be just the creation from the brain, combining the positive parts into a compelling story and suppressing or deleting information that does not fit. Although it sounds absurd, I believe if we are able to create a connected digital enterprise we will face the same symptoms. Due to the complexity of connected information, we are looking for the best suitable version, and as all became so complex, ordinary human beings will no longer be able to distinguish this
As part of the preparation for the upcoming PDT Europe 2018, I was investigating the topics coordinated and connected enterprise to discover potential transformation steps. We all need to explore the future with an open mind, and the challenge is: WHERE and HOW FAST can we transform from coordinated to connected? I am curious if you have experiences or thoughts on this topic.
At this moment we are in the middle of the year. Usually for me a quiet time and a good time to reflect on what has happened so far and to look forward.
Three themes triggered me to write this half-year:
- The changing roles of (PLM) consultancy
- The disruptive effect of digital transformation on legacy PLM
- The Model-driven approaches
A short summary per theme here with links to the original posts for those who haven't followed the sequence.
The changing roles of (PLM) consultancy
Triggered by Oleg Shilovitsky's post Why traditional PLM ranking is dead. PLM ranking 2.0 a discussion started related to the changing roles of PLM choice and the roles of a consultant. Oleg and I agreed that using the word dead in a post title is a way to catch extra attention. And as many people do not read more than the introduction, this is a way to frame ideas (not invented by us, look at your newspaper and social media posts). Please take your time and read this post till the end.
Oleg and I concluded that the traditional PLM status reports provided by consultancy firms are no longer is relevant. They focus on the big vendors, in a status-quo and most of them are 80 % the same on their core PLM capabilities. The challenge comes in how to select a PLM approach for your company.
Here Oleg and I differ in opinion. I am more looking at PLM from a business transformation point of view, how to improve your business with new ways of working. The role of a consultant is crucial here as the consultant can help to formalize the company's vision and areas to focus on for PLM. The value of the PLM consultant is to bring experience from other companies instead of inventing new strategies per company. And yes, a consultant should get paid for this added value.
Oleg believes more in the bottom-up approach where new technology will enable users to work differently and empower themselves to improve their business (without calling it PLM). More or less concluding there is no need for a PLM consultant as the users will decide themselves about the value of the selected technology. In the context of Oleg's position as CEO/Co-founder of OpenBOM, it is a logical statement, fighting for the same budget.
The discussion ended during the PLMx conference in Hamburg, where Oleg and I met with an audience recorded by MarketKey. You can find the recording Panel Discussion: Digital Transformation and the Future of PLM Consulting here.
Unfortunate, like many discussions, no conclusion. My conclusion remains the same – companies need PLM coaching !
The related post to this topic are:
The disruptive effect of digital transformation on legacy PLM
A topic that I have discussed the past two years is that current PLM is not compatible with a modern data-driven PLM. Note: data-driven PLM is still "under-development". Where in most companies the definition of the products is stored in documents / files, I believe that in order to manage the complexity of products, hardware and software in the future, there is a need to organize data related to models not to files. See also: From Item-centric to model-centric ?
For a company it is extremely difficult to have two approaches in parallel as the first reaction is: "let's convert the old data to the new environment".
This statement has been proven impossible in most of the engagements I am involved in and here I introduced the bimodal approach as a way to keep the legacy going (mode 1) and scale-up for the new environment (mode 2).
A bimodal approach is sometimes acceptable when the PLM software comes from two different vendors. Sometimes this is also called the overlay approach – the old system remains in place and a new overlay is created to connect the legacy PLM system and potentially other systems like ALM or MBSE environments. For example some of the success stories for Aras complementing Siemens PLM.
Like the bimodal approach the overlay approach creates the illusion that in the near future the old legacy PLM will disappear. I partly share that illusion when you consider the near future a period of 5 – 10+ years depending on the company's active products. Faster is not realistic.
And related to bimodal, I now prefer to use the terminology used by McKinsey: our insights/toward an integrated technology operating model in the context of PLM.
The challenge is that PLM vendors are reluctant to support a bimodal approach for their own legacy PLM as then suddenly this vendor becomes responsible for all connectivity between mode 1 and mode 2 data – every vendors wants to sell only the latest.
I will elaborate on this topic during the PDT Europe conference in Stuttgart – Oct 25th . No posts on this topic this year (yet) as I am discussing, learning and collecting examples from the field. What kept me relative busy was the next topic:
The Model-driven approaches
Most of my blogging time I spent on explaining the meaning behind a modern model-driven approach and its three main aspects: Model-Based Systems Engineering, Model-Based Definition and Digital Twins. As some of these aspects are still in the hype phase, it was interesting to see the two different opinions are popping up. On one side people claiming the world is still flat (2D), considering model-based approaches just another hype, caused by the vendors. There is apparently no need for digital continuity. If you look into the reactions from certain people, you might come to the conclusion it is impossible to have a dialogue, throwing opinions is not a discussion..
One of the reasons might be that people reacting strongly have never experienced model-based efforts in their life and just chime in or they might have a business reason not to agree to model-based approached as it does not align with their business? It is like the people benefiting from the climate change theory – will the vote against it when facts are known ? Just my thoughts.
There is also another group, to which I am connected, that is quite active in learning and formalizing model-based approaches. This in order to move forward towards a digital enterprise where information is connected and flowing related to various models (behavior models, simulation models, software models, 3D Models, operational models, etc., etc.) . This group of people is discussing standards and how to use and enhance them. They discuss and analyze with arguments and share lessons learned. One of the best upcoming events in that context is the joined CIMdata PLM Road Map EMEA and the PDT Europe 2018 – look at the agenda following the image link and you should get involved too – if you really care.
And if you are looking into your agenda for a wider, less geeky type of conference, consider the PI PLMx CHICAGO 2018 conference on Nov 5 and 6. The agenda provides a wider range of sessions, however I am sure you can find the people interested in discussing model-based learnings there too, in particular in this context Stream 2: Supporting the Digital Value Chain
My related posts to model-based this year were:
- Model-Based – an introduction
- Why Model-Based? The 3D CAD Model
- Model-Based – The Confusion
- Model-Based: Systems Engineering (MBSE)
- Model-Based – Connecting Engineering and Manufacturing
- Model-Based – The Digital Twin
- Model-Based: Digital Twin – the PLM side
I spent a lot of time demystifying some of PLM-related themes. The challenge remains, like in the non-PLM world, that it is hard to get educated by blog posts as you might get over-informed by (vendor-related) posts all surfing somewhere on the hype curve. Do not look at the catchy title – investigate and take time to understand HOW things will this work for you or your company. There are enough people explaining WHAT they do, but HOW it fit in a current organization needs to be solved first. Therefore the above three themes.
This is my concluding post related to the various aspects of the model-driven enterprise. We went through Model-Based Systems Engineering (MBSE) where the focus was on using models (functional / logical / physical / simulations) to define complex product (systems). Next we discussed Model Based Definition / Model-Based Enterprise (MBD/MBE), where the focus was on data continuity between engineering and manufacturing by using the 3D Model as a master for design, manufacturing and eventually service information.
And last time we looked at the Digital Twin from its operational side, where the Digital Twin was applied for collecting and tuning physical assets in operation, which is not a typical PLM domain to my opinion.
Now we will focus on two areas where the Digital Twin touches aspects of PLM – the most challenging one and the most over-hyped areas I believe. These two areas are:
- The Digital Twin used to virtually define and optimize a new product/system or even a system of systems. For example, defining a new production line.
- The Digital Twin used to be the virtual replica of an asset in operation. For example, a turbine or engine.
Digital Twin to define a new Product/System
There might be some conceptual overlap if you compare the MBSE approach and the Digital Twin concept to define a new product or system to deliver. For me the differentiation would be that MBSE is used to master and define a complex system from the R&D point of view – unknown solution concepts – use hardware or software? Unknown constraints to be refined and optimized in an iterative manner.
In the Digital Twin concept, it is more about a defining a system that should work in the field. How to combine various systems into a working solution and each of the systems has already a pre-defined set of behavioral / operational parameters, which could be 3D related but also performance related.
You would define and analyze the new solution virtual to discover the ideal solution for performance, costs, feasibility and maintenance. Working in the context of a virtual model might take more time than traditional ways of working, however once the models are in place analyzing the solution and optimizing it takes hours instead of weeks, assuming the virtual model is based on a digital thread, not a sequential process of creating and passing documents/files. Virtual solutions allow a company to optimize the solution upfront instead of costly fixing during delivery, commissioning and maintenance.
Why aren't we doing this already? It takes more skilled engineers instead of cheaper fixers downstream. The fact that we are used to fixing it later is also an inhibitor for change. Management needs to trust and understand the economic value instead of trying to reduce the number of engineers as they are expensive and hard to plan.
In the construction industry, companies are discovering the power of BIM (Building Information Model) , introduced to enhance the efficiency and productivity of all stakeholders involved. Massive benefits can be achieved if the construction of the building and its future behavior and maintenance can be optimized virtually compared to fixing it in an expensive way in reality when issues pop up.
The same concept applies to process plants or manufacturing plants where you could virtually run the (manufacturing) process. If the design is done with all the behavior defined (hardware-in-the-loop simulation and software-in-the-loop) a solution has been virtually tested and rapidly delivered with no late discoveries and costly fixes.
Of course it requires new ways of working. Working with digital connected models is not what engineering learn during their education time – we have just started this journey. Therefore organizations should explore on a smaller scale how to create a full Digital Twin based on connected data – this is the ultimate base for the next purpose.
Digital Twin to match a product/system in the field
When you are after the topic of a Digital Twin through the materials provided by the various software vendors, you see all kinds of previews what is possible. Augmented Reality, Virtual Reality and more. All these presentations show that clicking somewhere in a 3D Model Space relevant information pops-up. Where does this relevant information come from?
Most of the time information is re-entered in a new environment, sometimes derived from CAD but all the metadata comes from people collecting and validating data. Not the type of work we promote for a modern digital enterprise. These inefficiencies are good for learning and demos but in a final stage a company cannot afford silos where data is collected and entered again disconnected from the source.
The main problem: Legacy PLM information is stored in documents (drawings / excels) and not intended to be shared downstream with full quality.
Read also: Why PLM is the forgotten domain in digital transformation.
If a company has already implemented an end-to-end Digital Twin to deliver the solution as described in the previous section, we can understand the data has been entered somewhere during the design and delivery process and thanks to a digital continuity it is there.
How many companies have done this already? For sure not the companies that are already a long time in business as their current silos and legacy processes do not cater for digital continuity. By appointing a Chief Digital Officer, the journey might start, the biggest risk the Chief Digital Officer will be running another silo in the organization.
So where does PLM support the concept of the Digital Twin operating in the field?
For me, the IoT part of the Digital Twin is not the core of a PLM. Defining the right sensors, controls and software are the first areas where IoT is used to define the measurable/controllable behavior of a Digital Twin. This topic has been discussed in the previous section.
The second part where PLM gets involved is twofold:
- Processing data from an individual twin
- Processing data from a collection of similar twins
Processing data from an individual twin
Data collected from an individual twin or collection of twins can be analyzed to extract or discover failure opportunities. An R&D organization is interested in learning what is happening in the field with their products. These analyses lead to better and more competitive solutions.
Predictive maintenance is not necessarily a part of that. When you know that certain parts will fail between 10.000 and 20.000 operating hours, you want to optimize the moment of providing service to reduce downtime of the process and you do not want to replace parts way too early.
The R&D part related to predictive maintenance could be that R&D develops sensors inside this serviceable part that signal the need for maintenance in a much smaller time from – maintenance needed within 100 hours instead of a bandwidth of 10.000 hours. Or R&D could develop new parts that need less service and guarantee a longer up-time.
For an R&D department the information from an individual Digital Twin might be only relevant if the Physical Twin is complex to repair and downtime for each individual too high. Imagine a jet engine, a turbine in a power plant or similar. Here a Digital Twin will allow service and R&D to prepare maintenance and simulate and optimize the actions for the physical world before.
The second part where R&D will be interested in, is in the behavior of similar products/systems in the field combined with their environmental conditions. In this way, R&D can discover improvement points for the whole range and give incremental innovation. The challenge for this R&D organization is to find a logical placeholder in their PLM environment to collect commonalities related to the individual modules or components. This is not an ERP or MES domain.
Concepts of a logical product structure are already known in the oil & gas, process or nuclear industry and in 2017 I wrote about PLM for Owners/Operators mentioning Bjorn Fidjeland has always been active in this domain, you can find his concepts at plmPartner here or as an eLearning course at SharePLM.
To conclude:
- This post is way too long (sorry)
- PLM is not dead – it evolves into one of the crucial platforms for the future – The Product Innovation Platform
- Current BOM-centric approach within PLM is blocking progress to a full digital thread
More to come after the holidays (a European habit) with additional topics related to the digital enterprise
Model-based continued: Model-Based Definition
After a short celebration, 10 years blogging and 200 posts, now it is time to continue my series related to the future of model-based. So far my introduction and focus on the bigger picture of the term Model-Based has led to various reactions. In particular, related to Model-Based Definition, the topic I am going to discuss in this post. Probably this is the topic where opinions vary the most as it is more close to the classical engineering and manufacturing processes.
What is Model-Based Definition?
There are various definitions of the term Model-Based Definition. Often the term Model-Based Enterprise is used in the same context. Where some people might stop thinking because the terminology is not 100 % aligned, I propose to focus on content. Let's investigate what it is.
In the classical product lifecycle, a product is first designed for its purpose based on specifications. The product can be simple, purely mechanical or more complex, requiring mechanical design, electronic components, and software to work together. For the first case, I will focus on Model-Based definition, for the second case I recommend to start reading about Model-Based Systems Engineering approaches where the mechanical design is part of a more complex system.
Model-Based Definition for Mechanical Designs – the role of 2D
Historically designs were done on the drawing board in 2D. After the introduction of 2D CAD and later affordable 3D CAD systems at the end of the previous century, companies made a shift from designing in 2D towards 3D. The advantages were clear. A much better understanding of products. Reading a 2D drawing requires special skills and sometimes they were not unambiguous. Therefore, 3D CAD models lead to increased efficiency and quality combined with the potential to reuse and standardize parts or sub-assemblies in a design.
These benefits were not always observed as complementary to the design (the engineering point of view), there was still the need to describe and define how a product needs to be manufactured. The manufacturing definition remained in a set of 2D drawings, and the 2D Drawings were the legal authority describing the product.
An interesting side note observation:
You will still see in industrial machinery companies, a pure EBOM does not exist, as designs were made to target the manufacturing drawings, not the 3D Model, engineering focused, intent. In this type of companies, the discussion EBOM/MBOM is challenging to explain.
Once the 3D Model becomes the authority, the split between design and manufacturing information will create extra work if you keep on creating 2D drawings for manufacturing. It requires non-value added extra work, i.e., reinterpreting 3D data in 2D formats (extra engineering hours) and there is the risk for new errors (interpretations/versioning issues). This non-value added engineering time can add up to over 30 percent of the time spent by engineering. You can find these numbers through the links below this post. I will not be the MBD teacher in this post, I will focus on the business impact.
Model-Based Definition based on 3D
The logical step is to use the 3D Model and add manufacturing information attached to the model, through different views. This can be Geometric Dimensioning and Tolerancing information (GF&T), Quality measurement information, Assembly instructions and more, all applied to different views of the model.
Of course here you become dependent on the chosen environments that support the combination of a 3D CAD model combined with annotation views that can be selected in the context of the model. There are existing standards how to annotate a model, find your most practical standard to your industry / Eco-system. Next, most CAD vendors and PLM vendors have their proprietary 3D formats and when you stay within their solution range working with a model-based definition will bring direct benefits, however …..
Model-Based Definition data standards
Every company needs to be able to combine and share information internally with other teams or with partners and suppliers, so a single vendor solution is a utopia. Even if your company has standardized themselves to one system, the next acquisition might be disturbing this dream. Anticipating for openness is crucial and when you start working according to a model-based definition, make sure that at least you have import or export capabilities from within your environment towards model-based definition standards.
The two major standards for model-based definition are 3DPDF and AP242/JT based. Don't expect these standards to be complete. They will give you a good foundation for your model-based journey and make sure you are part of this journey. (Listen to the CIMdata webinar also listed below)
The Model-Based journey
It took almost 20 years for 3D CAD to become the mainstream for mechanical design. Engineers are now trained in 3D and think in 3D. Now it is time to start the journey to abandon 2D and connect engineering, manufacturing and service more efficient. Similar gains can be expected. Follow the links below this article, here already a quote from an old post by Isha Gupta Ray (Capgemini) related to MBD:
MBE Drivers: The need for consumption of 3D product data by non-engineering departments and the elimination of 2D drawing related rework and costs are driving companies to adopt 3D MBE methods rapidly. DoD predicts that the move away from 2D Drawings and into open and free-to-view 3D MBE documents will reduce the cost of its internal engineering activities by up to 30%, reduce the scrap and rework it currently deals with from its supply channel by nearly 20% and improves supplier response times by up to 50%.
Model-Based Definition is not as challenging as becoming a Model-Driven enterprise, that I described in my introduction post to this theme. It is a first step to challenge or energize your company to become a digital enterprise, as sharing between engineering and manufacturing needs to be orchestrated, even with your external parties. It is easy to do nothing and to wait till your company is pushed or pushed out, which would cause extra stress (or relieve forever). For me Model-Based Definition is a first (baby) step towards a digital enterprise, warming-up your company to change a look at your data in a different way. Next when you combine parameters and simulation to your models, you will make the next step towards a model-driven digital enterprise.
Below a selection of links related to the theme of Model-Based Definition. If you feel I missed some crucial links, please provide them through the comments section of this post, and I will add them to the post if relevant.
Tech-Clarity: The How-to Guide for Adopting Model Based Definition (MBD)
Action Engineering: Articles, Blog plus training
Engineering.com: How Model-Based Definition Can Fix Your CAD Models
Lifecycle Insights: Quantifying the value of Model-Based definitions
CIMdata: Webinar on Model-Based Definition and Standards
Capgemini: Model-Based Enterprise with 3D PDF
if you want to learn more in-depth the advanced usage and potential of MBD, try to understand:
I wrote in my previous posts about the various aspects of a model-based enterprise. In case you missed this post you can find it here: Model-Based an introduction. In this post I will zoom in on the aspects related to the 3D model, probably in the context of PLM, the most anticipated approach.
3D CAD vs 3D CAD Model
At the time 3D CAD was introduced for the mid-market, the main reason why 3D CAD was introduced was to provide a better understanding of the designed product. Visualization and creating cross-sections of the design became easy although the "old" generation of 2D draftsmen had to a challenge to transform their way of working. This lead often to 3D CAD models setup with the mindset to generate 2D Manufacturing drawings, not taking real benefits from the 3D CAD Model. Let's first focus on Model-Based Definition.
Model-Based Definition
We talk about Model-Based Definition when the product and manufacturing information is embedded / connected to the 3D CAD model, allowing the same source of information to be used downstream for manufacturing, analysis and inspection. The embedded information normally contains geometric dimensions, annotations, surface finish and material specifications. Instead of generating easy to distribute 2D drawings, you would be using the 3D model now with its embedded information.
According to an eBook, sponsored by SolidWorks and published by Tech-Clarity: "The How-to Guide for Adopting Model-Based Definition MBD", Tech-Clarity's research discovered that 33 percent of design time is spent on drawing generation. Imagine you do not need this time anymore to specify manufacturing processes and operations. Does this mean the design activities can be reduced by 30 % ? Probably not, the time could be used to spend on design alternatives too, at the end contributing to better designs.
Still this is not the reason why companies would move to MBD. Companies that have implemented MBD report fewer manufacturing mistakes/less rework (61 %) – here is where the value becomes visible. In addition, improved communication with suppliers was reported by 50 % of the companies. More clarity in the communication, however as some of the suppliers are not used to MBD either, this excuse is used not to implement MBD. Instead of creating a win-win situation a status-quo is created.
Read the eBook to demystify Model-Based Definition and realize that although it might look like a complex change, within 8 to 9 months the company might have gone through this change, assuming you have found the proper trainers / coaches for that.
When discussion a roadmap towards a digital enterprise, this is one of the "easier" steps to take as it does not force the organization to change their primary processes. They become more efficient, lean and integrated, delivering rapid benefits within a year.
In the same context of MBD, in my post: Digital PLM requires a Model-Based Enterprise I referred to two articles in engineering.com written by Dick Bourke with the support from Jennifer Herron. The first article: How Model-based Definition Can Fix Your CAD Models digs into more detail and provides additional insights into benefits realizable by implementing MBD. As I am not the expert, I would recommend if you agree on the benefits and necessity for your company's future, find the right literature. There is a lot of information related to MBD coming from vendors but also vendor-neutral sources. Technology Is not the issue. You just have to study, digest and implement it with your suppliers.
Beyond MDB using a 3D CAD Model
Although the post gets long, it is crucial to understand that the 3D CAD model should also be built in a more sophisticated manner. Using parameters in the model instead of hard-coded values allows the model to be used and interact with other disciplines in a digital manner.
A parametric model, combined with business rules can be accessed and controlled by other applications in a digital enterprise. In this way, without the intervention of individuals a set of product variants can be managed and not only from the design point of view. Geometry and manufacturing parameters are also connected and accessible. This is one of the concepts where Industry 4.0 is focusing on: intelligent and flexible manufacturing by exchanging parameters
The 3D CAD Model and Simulation
The last (short) part related to the 3D CAD Model is about its relation to simulation. If you do no use simulation together with your 3D CAD Models, you are still designing in the past. No real advantage between 2D and 3D, just better understanding?
In engineering we often talk about Form, Fit and Function – the three dimensions to decide on a change. With 2D (and 3D without simulation) we manage Form and Fit disconnected from Function. Once we use 3D combined with Simulation we are able to manage these three parameters in relation.
For example, when designing product, first simulations can provide direct feedback on shape and dimension constraints. Where to save material costs, choose from another design solution? The ultimate approach is Generative Design where the Functional constraints and the Fit are the given constraints and the Form is optimized based on artificial intelligence rules.
In case a company has a close relation between 3D Design and Simulation, the concept of Design of Experiments (DOE) will help to find the optimal product constraints. The more integrated the 3D CAD model and the simulation are, the more efficient alternatives can be evaluated and optimized.
In this post we focused on model-based in relation to the 3D CAD Model. Without going to the expert level for each of the topics discussed, I hope it creates the interest and enthusiasm for further investment in model-based practices. One commonality for all model-based practices: it is about parameters. Parameters provide digital continuity where each discipline (design, simulation, manufacturing) can build upon in almost real-time without the need for people to convert or adjust information. Digital Continuity – one of the characteristics of the future digital enterprise
The recent years I have been mentioning several times addressing the term model-based in the context of a modern, digital enterprise. Posts like: Digital PLM requires a model-based enterprise (Sept 2016) or Item-Centric or Model-Centric (Sept 2017) describe some of the aspects of a model-based approach. And if you follow the PLM vendors in their marketing messages, everyone seems to be looking for a model-based environment.
This is however in big contrast with reality in the field. In February this year I moderated a focus group related to PLM and the Model-Based approach and the main conclusion from the audience was that everyone was looking at it, and only a few started practicing. Therefore, I promised to provide some step-by-step education related to model-based as like PLM we need to get a grip on what it means and how it impacts your company. As I am not an academic person, it will be a little bit like model-based for dummies, however as model-based in all aspects is not yet a wide-spread common practice, we are all learning.
What is a Model?
The word Model has various meanings and this is often the first confusion when people speak about Model-Based. The two main interpretations in the context of PLM are:
- A Model represents a 3D CAD Model – a virtual definition of a physical product
- A Model represents a scientific / mathematical model
And although these are the two main interpretations there are more aspects to look at model-based in the context of a digital enterprise. Let's explore the 3D CAD Model first
The role of the 3D CAD Model in a digital enterprise
Just designing a product in 3D and then generating 2D drawings for manufacturing is not really game-changing and bringing big benefits. 3D Models provide a better understanding of the product, mechanical simulations allow the engineer to discover clashes and/or conflicts and this approach will contribute to a better understanding of the form & fit of a product. Old generations of designers know how to read a 2D drawing and in their mind understand the 3D Model.
Modern generations of designers are no longer trained to start from 2D, so their way of thinking is related 3D modeling. Unfortunate businesses, in particular when acting in Eco-systems with suppliers, still rely on the 2D definition as the legal document. The 3D Model has brought some quality improvements and these benefits already justify most of the companies to design in 3D, still it is not the revolution a model-based enterprise can bring.
A model-based enterprise has to rely on data, so the 3D Model should rely on parameters that allow other applications to read them. These parameters can contribute to simulation analysis and product optimization or they can contribute to manufacturing. In both cases the parameters provide data continuity between the various disciplines, eliminating the need to create new representations in different formats. I will come back in a future post to the requirements for the 3D CAD model in the context of the model-based enterprise, where I will zoom in on Model-Based Definition and the concepts of Industry 4.0.
The role of mathematical models in a digital enterprise
The mathematical model of a product allows companies to analyze and optimize the behavior of a product. When companies design a product they often start from a conceptual model and by running simulations they can optimize the product and define low-level requirements within a range that optimizes the product performance. The relation between design and simulation in a virtual model is crucial to be as efficient as possible. In the current ways of working, often design and simulation are not integrated and therefore the amount of simulations is relative low, as time-to-market is the key driver to introduce a new product.
In a digital enterprise, design and simulations are linked through parameters, allowing companies to iterate and select the optimal solution for the market quickly. This part is closely related to model-based systems engineering (MBSE) , where the focus is on defining complex systems. In the context of MBSE I will also zoom in on the relation between hardware and software, which at the end will deliver the desired functionality for the customer. Again in this part we will zoom in on the importance of having a parameter model, to ensure digital continuity.
Digital Twin
There is still a debate if the Digital Twin is part of PLM or should be connected to PLM. A digital twin can be based on a set of parameters that represent the product performance in the field. There is no need to have a 3D representation, despite the fact that many marketing videos always show a virtual image to visualize the twin.
Depending on the business desire, there can be various digital twins for the same products in the field, all depending on the parameters that you want to monitor. Again it is about passing parameters, in this case from the field back to R&D and these parameters should be passed in a digital manner. In a future post I will zoom in on the targets and benefits of the digital twin.
There are various aspects to consider related to "model-based". The common thread for each of the aspects is related to PARAMETERS. The more you can work with parameters to connect the various usages of a product/system, the closer you are related to the digital enterprise. The real advantages of a digital enterprise are speed (information available in real-time), end-to-end visibility (as data is not locked in files / closed systems).
PARAMETERS the objects to create digital continuity
When PLM – Product Lifecycle Management – was introduced, one of the main drivers was to provide an infrastructure for collaboration and for sharing product information across the whole lifecycle. The top picture shows my impression of what PLM could mean for an organization at that time. The PLM circle was showing a sequential process from concept, through planning, development, manufacturing towards after sales and/or services when relevant. PLM would provide centralization and continuity of data. Through this continuity we could break down the information silos in a company.
Why do we want to break down the silos?
You might ask yourself what is wrong with silos if they perform in a consistent matter? Oleg Shilovitsky recently wrote about it: How PLM can separate data and organization silos. Read the post for the full details, I will stay at Oleg's conclusion:
Keep process and organizational silos, but break data silos. This is should be a new mantra by new PLM organization in 21st century. How to help designers, manufacturing planners and support engineers to stay on the same BOM? By resolving this problem, organization will preserve current functional structure, but will make their decisions extremely data drive and efficient. The new role of PLM is to keep organizational and process silos, but connect data silos. This is a place where new cloud based multi-tenant technologies will play key role in the future organization transformation from the vision of no silo extended enterprise to organized functional silos connected by common understanding of data.
When I read this post I had so much to comment, which lead to this post. Let me share my thoughts related to this conclusion and hopefully it helps in future discussions. Feel free to join the discussion:
Keep process and organizational silos, but break data silos. This is should be a new mantra by new PLM organization in 21st century
For me "Keep process and organizational silos ….. " is exactly the current state of classical PLM, where PLM concepts are implemented to provide data continuity within a siloed organization. When you can stay close to the existing processes the implementation becomes easier. Less business change needed and mainly a focus on efficiency gains by creating access to information.
Most companies do not want to build their data continuity themselves and therefore select and implement a PLM system that provides the data continuity, currently mainly around the various BOM-views. By selecting a PLM system, you have a lot of data integration done for you by the vendor. Perhaps not as user-friendly as every user would expect, however no company has been able to build a 100% user-friendly PLM system yet, which is the big challenge for all enterprise systems. Therefore PLM vendors provide a lot of data continuity for you without the need for your company to take responsibility for this.
And if you know SAP, they go even further. Their mantra is that when using SAP PLM, you even do not need to integrate with ERP. You can still have long discussions with companies when it comes to PLM and ERP integrations. The main complexity is not the technical interface but the agreement who is responsible for which data sets during the product lifecycle. This should be clarified even before you start talking about a technical implementation. SAP claims that this effort is not needed in their environment, however they just shift the problem more towards the CAD-side. Engineers do not feel comfortable with SAP PLM when engineering is driving the success of the company. It is like the Swiss knife; every tool is there but do you want to use it for your daily work?
In theory a company does not need to buy a PLM system. You could build your own PLM-system, based on existing infrastructure capabilities. CAD integrations might be trickier, however this you could solve by connecting to their native environments. For example, Microsoft presented at several PDT conferences an end-to-end PLM story based on Microsoft technology. Microsoft "talks PLM" during these conferences, but does not deliver a PLM-system – they deliver the technologies.
The real 21st-century paradigm
What is really needed for the 21st century is to break down the organizational silos as current ways of working are becoming less and less applicable to a modern enterprise. The usage of software has the major impact on how we can work in the future. Software does not follow the linear product process. Software comes with incremental deliveries all the time and yes the software requires still hardware to perform. Modern enterprises try to become agile, being able to react quickly to trends and innovation options to bring higher and different value to their customers. Related to product innovation this means that the linear, sequential go-to-market process is too slow, requires too much data manipulation by non-value added activities.
All leading companies in the industry are learning to work in a more agile mode with multidisciplinary teams that work like startups. Find an incremental benefit, rapidly develop test and interact with the market and deliver it. These teams require real-time data coming from all stakeholders, therefore the need for data continuity. But also the need for data quality as there is no time to validate data all the time – too expensive – too slow.
Probably these teams will not collaborate along the various BOM-views, but more along digital models, both describing product specifications and system behavior. The BOM is not the best interface to share system information. The model-based enterprise with its various representations is more likely to be the backbone for the new future in the 21st century. I wrote about this several times, e.g. item-centric or model-centric.
And New cloud-based multi-tenant technologies …
As Oleg writes in his conclusion:
This is a place where new cloud-based multi-tenant technologies will play key role in the future organization transformation from the vision of no silo extended enterprise to organized functional silos connected by common understanding of data.
From the academic point of view, I see the beauty of new cloud-based multi-tenant technologies. Quickly build an environment that provides information for specific roles within the organization – however will this view be complete enough? What about data dictionaries or is every integration a customization?
When talking with companies in the real world, they are not driven by technology – they are driven by processes. They do not like to break down the silos as it creates discomfort and the need for business transformation. And there is no clear answer at this moment. What is clear that leading companies invest in business change first before looking into the technology.
Sometimes too much academic and wishful thinking from technology providers is creating excitement. Technology is not the biggest game changer for the 21st century. It will be the new ways of working and business models related to a digital enterprise that require breaking organizational silos. And these new processes will create the demand for new technologies, not the other way around.
Break down the walls !


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Spring Cleaning
Spring has sprung. The equinox was at the weekend and I also had my first proper work visit since before the winter.
There's not much management to do to the trees themselves at this time of year, they're all a bit too small yet to need pruning or coppicing or pollarding. I'm deliberately waiting until there is a bit of coverage before I think about what to do with the undergrowth. I anticipate planting some woodland shrubs and flowers if I can legally and safely obtain some of local provenance (my granny lives reasonably locally and she has an overabundance of snowdrops and cowslips which I think I can transplant).
I was hoping to do some weed-killing on Saturday, but when I got up the grass hadn't started growing enough to permit it. Probably for the best, my brother-in-law was with me to help and most of the family are vehemently anti-herbicides. I'll end up doing it on my own in April and May (and again in August/September).
Our aim on Saturday was to straighten up a lot of the trees. The wood is on the Lagan flood plain, and I'm sure it was under water quite a lot during the winter. Lots of the trees had been pushed over, so we had to walk around them all straightening the tubes. It's funny really, the top end of the wood is damaged by local children pulling up the bamboo canes and stamping on the plastic tubes, and the bottom of the wood is subject to the same damage but the offender is the river.
We also had to clear a lot of rubbish away, as you'll see from the photos below.
I finally got the "for sale" sign down, something I'd been meaning to do for a while.
I seem to be incapable of taking a photo that isn't blurry…

subset: commoncrawl


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text: Calendar of Events ←Back to Search Results Super Smash Bros Ultimate Tournament Date: 9/18/2019 Time & Place:4:30PM - 5:30 PM East Waco Library Description:Duke it out as your favorite Nintendo characters in a high-energy tournament. Sign in and we'll get you in a bracket. Then, face off against the other competitors until we find the Ulitmate Smash Bro! On the Web: calendar.wacolibrary.org/event/5535706


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A Superman of Two Worlds
The second installment of two, new, high-profile Superman series, Superman: Year One and Superman's Pal Jimmy Olsen, arrive in stores this week. In addition to showcasing some of the most venerated talents in modern comics, both of these miniseries provide radically different perspectives on the most recognizable character in their titles (sorry, Jimmy).
Superman: Year One, from writer Frank Miller and artist John Romita Jr., is an over-sized three-part series that reexamines the life of Clark Kent before the world knew him as Superman. The first issue follows him from the destruction of Krypton through his high school graduation, while the second portrays a new career in the Navy along with a new take on Superman's mermaid friend Lori Lemaris.
Superman's Pal Jimmy Olsen is a heightened take that fits within the modern continuity of DC Comics, albeit with a story that can easily be read without much outside knowledge of history or characters. Writer Matt Fraction and artist Steve Lieber are focusing on Jimmy Olsen, cub photographer and one of Superman's closest friends, in order to deliver a series of humorous, interconnected short stories in each issue.
Taken separately these stories reflect two very different visions of Superman, both in terms of narrative and artistic rendering. Viewed side-by-side, as they'll likely land in many pull file this week, they represent something more significant about how Superman is depicted in comics today. Superman is the character that laid the foundation for the DC Comics empire and is the foundational figure in the superhero genre, a genre that seems to have never been more popular than it is right now with so many global movie successes. Superman is recognizable to almost any person on the planet and can be seen on merchandise and media in almost any imaginable circumstance.
These two series provide very different approaches to such a figure as recognizable as Superman, approaches that can be recognized in almost any other Superman story today, whether it is in comics, cartoons, or movies. Superman: Year One emphasizes the grandiose legend that has risen around Superman, both in comics and reality, while Superman's Pal Jimmy Olsen undermines that legend by embracing the silliness and minutiae surrounding the character. Both series offer their own style and strengths, but reading them together helps clarify Superman's place in pop culture and how modern creators may approach a character whose legacy looms so large.
Reinforcing Mythology
It's apparent in the first few pages of Superman: Year One how important this story considers itself to be. Superman, as an infant, narrates much of his own origin (although the captions alternate narrative perspective regularly). This rewriting of Krypton's demise and the potential hero being delivered to Earth are filled with sturm und drang, heightening the events to an almost absurd degree. This culminates with the discovery of an infant Kal-El presenting himself in a panel (seen above) where he appears to both be entirely self-aware and the more powerful character in front of his adoptive parents-to-be. Whatever a reader's response to this presentation may be, it provides a clear tone that the rest of the first two issues of the series have maintained. Each new segment of this story, including fights with high school bullies and training at boot camp, is aware that it is important because it is a part of Superman's story.
This approach to Superman as a mythological figure is no more silly than new fictional presentations of historical figures in similar fashions. Superman is one of the most recognizable and influential characters in western culture today, comparable only to the likes of Mickey Mouse or Luke Skywalker. Rather than ignore reader's previous awareness, this story seeks to reinforce existing beliefs about Superman. He is the ur-text of the superhero here: all good, all knowing, and always significant. Given that Superman: Year One is an origin, it attempts to hold the weight of every other Superman story as something that could potentially be connected to this retelling of Action Comics #1 and the 80 years of stories that would follow.
Deflating a Legend
The Superman of Superman's Pal Jimmy Olsen is shown to be powerful and admired, but the story itself does not want to be either simply because it features the character. Instead, the series regularly finds opportunities to poke fun at many tropes and ideas from Superman's long history. In the first issue there is a moment where Superman breaks from the comic's internal reality altogether, breaking the fourth wall to acknowledge his role in saving so many others in Metropolis to the confusion of one such resident. This moment serves to both allow readers to laugh with (and at) Superman while still recognizing that even in a story where Jimmy Olsen is the focus, Superman remains an all-important figure.
It is not that Superman's Pal Jimmy Olsen doesn't acknowledge or care for what Superman means, to his fictional universe or our very real one. Rather the series doesn't appear to find much value in acknowledging or reinforcing this meaning. If Superman is important to people, then that importance is accepted as something that will be read into the specific parts of this text. Small moments of humor allow readers to laugh alongside the character or to chuckle at how much they might care themselves. Allowing the passion that so many feel to go unspoken means offers a story that is more accessible and easily enjoyed, as it does not beg for comparison to prior superhero stories or similar icons.
A Multiverse of Icons
Whether creators choose to remind audiences that think Superman is significant, like Miller and Romita, or to open that idea to japes, like Fraction and Lieber, every creator who constructs a Superman story is forced to reckon with all of the history that has transformed Superman into a universally recognized name. There is a meta-textual conversation occurring about the meaning of such a character, a meaning that is being rewritten today.1comments
While we can easily observe the work of contextualizing and recontextualizing a legend in Superman: Year One, it's occurring within less obviously serious series too. The narrative efforts found in that series are transformed into subtext in Superman's Pal Jimmy Olsen, as found in a brief sequence in its second issue where after a night of fun gags, Superman flies away. That sequence ends with a long distance between the figure of Jimmy in the foreground and Superman in the distance, only seen in the reader's mind as he appears to be a speck on the page. The idea of Superman remains and looms over Jimmy even when he is not present. It's a rarely poignant moment for the story, one that reminds readers that this too-good-to-be-real hero is too impactful to be ignored, even when he's simply spending time performing card tricks with a pal.
Superman is a modern myth, a legend that both informs the entire superhero genre and our own ideas of heroism and valor. No creator can touch the character without some awareness of this legend. However, that awareness can be filtered into the purposeful depiction of why and how that greatness matters like we see in Superman: Year One, the self-effacing fun of Superman's Pal Jimmy Olsen, or a blend of both. Every new Superman story both acknowledges what came before and provides something new that expands an already sprawling mythos. Watching the legend evolve is a key part of appreciating and enjoying Superman comics today, no matter how they depict the Man of Steel.


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- Many people think that pickles are simply a side dish and are not healthy
- Try some healthy alternatives of salt like lemon zest, chili or pepper
- Pickles are a store house of essential nutrients
Celebrated all over the world on the 29th September, World Heart Day is all about spreading the awareness about and importance of maintaining a healthy heart. In the past decades, heart diseases used to affect only a certain group of people whereas today the scenarios are different. It is a matter of shock that even the young generation is falling prey to heart diseases at a faster rate. The primary causes of heart diseases include excessive smoking and alcohol, lack of exercise, unhealthy diet, stress, high blood pressure and drug abuse.
Also read: World Heart Day 2017: How To Avoid Heart Disease Later In Life
Celebrity nutritionist Rujuta Diwekar in her recent Instagram post talks about some health benefits of pickles. Many people are under the impression that pickles are simply a side dish and are not healthy. While some think that pickles are high in sodium and can lead to stomach cancer. Whereas, pickles offer several health benefits like weight loss, they are filled with healthful antioxidants, and can prevent some kinds of cancer. Pickles also retain the nutrients of the ingredients like mango, amla, lemon, ginger or a local green and have certain antioxidants like vitamins C and A. Pickles can boost your gut flora with probiotics, or good bacteria, while vinegary pickles can lower your blood sugar levels after meals. Also, having a salty lime or ginger pickles to in the morning may help you deal with morning sickness and nausea. Mango and lemon are two types of pickles which prevents morning sickness.
Also read: World Heart Day 2017: Can You Reverse Heart Disease?
This World Heart Day, we have debunked some common myths about pickles:
1. Myth: Pickles are full of salt and oil
Fact: Without the oil and salt, the gut friendly bacteria would not grow and you will not get the benefits of pickles. Also try using seasonal fresh and local vegetables with less salt and oil.
2. Myth: The salt will cause blood pressure
Fact: Many people think that salt causes blood pressure, but unhealthy habits like lack of physical exercise, poor sleep, unhygienic foods, packaged and processed food causes high blood pressure. You can use unprocessed 'jada or kala or sendha salt' as per your taste. Also, try using some healthy alternatives of salt like lemon zest, chili or pepper.
3. Myth: Oil is not good for heart health
Fact: Again many people are under the impression that consumption of fat or oil cause heart problems. Whereas, it is generally the unhealthy eating habits that are associated with a risk of heart diseases. Some common unhealthy eating habits include late night dinners, skipping meals, junk food or unhealthy fats in your diet and of course, lack of physical exercise. You can always use kacche ghani ka ground nut oil, mustard oil, til oil, gingley oil according to your food heritage.
4. Myth: But pickle is unhealthy
Fact : Pickles are a store house of essential minerals, vitamins and healthy bacteria. One to two tablespoon of pickle everyday can help reduce bloating, anaemia, Vitamin D and B12 deficiencies and is even helpful for IBS. As an added benefit, no heat is involved in the production of traditionally fermented pickles which helps to preserve the nutrients in the vegetables.
Also read: World Heart Day 2017: Cut Down On Salt And See The Benefits For Your Heart
Disclaimer: This content including advice provides generic information only. It is in no way a substitute for qualified medical opinion. Always consult a specialist or your own doctor for more information. NDTV does not claim responsibility for this information.


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Posted: July 30th, 2015
THE Association of Mirror Pensioners regrets that the forthcoming and future issues of Mirror Pensioner magazine will be sent to AMP members only. Pensioners who have not joined AMP will no longer receive our bumper autumn newsletter, writes chairman TOM BROWN.
With generous financial help from Trinity Mirror, previous issues had been printed and mailed to around 9,000 Mirror pensioners and deferred pensioners, irrespective of AMP membership.
But Trinity Mirror says it is having to review every one of its costs and has informed us that, as part of a cost-cutting programme, it can no longer provide that support. The company's latest report showed increased cost-cutting – from £10M to £20M.
A director explained: "As you will be aware, trading is tough and we are having to take further cost actions to support the business. In light of this we have given serious consideration to our continued funding of the newsletter and regret that we will be unable to fund the cost."
AMP feels it would be unfair to our loyal paid-up members to use association funds to continue to extend distribution of Mirror Pensioner to non-members.
The AMP fund has been contributed by members for professional advice and legal services, which can be expensive, and for supporting members who have to take action to receive their pension entitlement. In today's fast-changing pension environment, it is vital to have that financial protection.
Our annual spring newsletter is unaffected because the printing and postage are paid for by AMP, and it will continue to be distributed to AMP members only, as now.
If you are a member of Mirror Group "Old" Scheme with its linked Past Service Scheme (PSS), and/or the MGN "New" Pension Scheme, you are eligible to join the Association of Mirror Pensioners.
There's a joining form on the "Join Us" page (see drop-down menu above). Simply double click on the PDF and print it out.
This autumn's issue of Mirror Pensioner, now in production, will include all the latest news about our pensions, plus stories about our ex-colleagues (and what they get up to!) and a special members-only travel offer.
DON'T MISS IT! If you are not an AMP member – JOIN NOW!
Merry Christmas and a
happy and healthy 2020
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