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A year ago I wrote a blog post questioning if the construction industry would learn from PLM practices in relation to BIM.
In that post, I described several lessons learned from other industries. Topics like:
- Working on a single, shared repository of on-line data (the Digital Mock Up). Continuity of data based on a common data model – not only 3D
- It is a mindset. People need to learn to share instead of own data
- Early validation and verification based on a virtual model. Working in the full context
- Planning and anticipation for service and maintenance during the design phase. Design with the whole lifecycle in mind (and being able to verify the design)
The comments to that blog post already demonstrated that the worlds of PLM and BIM are not 100 percent comparable and that there are some serious inhibitors preventing them to come closer. One year later, let´s see where we are:
BIM moving into VDC (or BLM ?)
The first trend that becomes visible is that people in the construction industry start to use more and more the term Virtual Design and Construction (VDC) instead of BIM (Building Information Model or Building Information Management?).
The good news here is that there is less ambiguity with the term VDC instead of BIM. Does this mean many BIM managers will change their job title? Probably not as most construction companies are still in the learning phase what a digital enterprise means for them.
Still Virtual Design and Construction focuses a lot on the middle part of the full lifecycle of a construction. VDC does not necessary connect the early concept phase and for sure almost neglects the operational phase. The last phase is often ignored as construction companies are not thinking (yet) about Repair & Maintenance contracts (the service economy).
And surprisingly, last week I saw a blog post from Dassault Systemes, where Dassault introduced the word BLM (Building Lifecycle Management). Related to this blog post also some LinkedIn discussions started. BLM, according to Dassault Systemes, is the combination of BIM and PLM – read this post here.
The challenge however for construction companies is to, what are the related data sets they require and how can you create this continuity of data. This brings us to one of the most important inhibitors.
Data Ownership
Where in other industries a clear product data owner exists, the ownership of data in EPC (Engineering, Procurement, Construction) companies, typical for the construction industry or oil & gas industry is most of the times on purpose vague.
First of all the owner of a construction often does not know which data could be relevant to maintain. And secondly, as soon as the owner asks for more detailed information, he will have to pay for that, raising the costs, which not directly flow back to benefits, only later during the FM (Facility Management) /Operational stage.
And let´s imagine the owner could get the all the data required. Next the owner is at risk, as potentially having the information might makes you liable for mistakes and claims.
From discussion with construction owners I learned their policy is not to aim for the full dataset related to a construction. It reduces the risk to be liable. Imagine Boeing and Airbus would follow this approach. This brings us to another important inhibitor.
A risk shifting business
The construction industry on its own is still a risk shifting business, where each party tries to pass the risk of cost of failure to another stakeholder in the pyramid. The most powerful owners / operators of the construction industry quickly play down the risk to their contractors and suppliers. And these companies then then distribute the risk further down to their subcontractors.
If you do not accept the risk, you are no longer in the game. This is different from other industries and I have seen this approach in a few situations.
For example, I was dealing with an EPC company that wanted to implement PLM. The company expected that the PLM implementer would take a large part of the risk for the implementation. As they were always taking the risk too for their big customers when applying for a project. Here there was a clash of cultures, as PLM implementers learned that the risk of a successful PLM implementation is vague as many soft values define the success. It is not a machine or platform that has to work after some time.
Another example was related to requirements management. Here the EPC company wanted to become clear and specific to their customer. However their customer reacted very strange. Instead of being happy that the EPC company invested in more upfront thinking and analysis, the customer got annoyed as they were not used to be specific so early in the process. They told the EPC company, “if you have so many questions, probably you do not understand the business”.
So everyone in the EPC business is pushed to accept a higher risk and uncertainty than other industries. However, the big reward is that you are allowed to have a cost of failure above 15 – 20 percent without feeling bad. Which this percentage you would be out of business in other industries. And this brings us to another important inhibitor.
Accepted high cost of failure
As the industry accepts this high cost of failure, companies are not triggered to work different or to redesign their processes in order to lower the inefficiencies. The UK government mandates BIM Level 2 for their projects starting in 2016 and beyond, to reduce costs through inefficiencies.
But will the UK government invest to facilitate and aim for data ownership? Probably not, as the aim of governments is not to be extreme economical. Being not liable has a bigger value than being more efficient for governments as I learned. Being more efficient is the message to the outside world to keep the taxpayer satisfied.
It is hard to change this way of thinking. It requires a cultural change through the whole value chain. And cultural change is the “worst” thing that can happen to a company. The biggest inhibitor.
Cultural change
Cultural change is a point that touches all industries and there is no difference between the construction industry and for example a classical discrete manufacturing company. Because of global competition and comparable products other industries have been forced already to work different, in order to survive (and are still challenged)
The cultural change lies in people. We (the older generation) are educated and brought up in classical engineering models that reflect the post second world war best practices. Being important in a process is your job justification and job guarantee.
New paradigms, based on a digital world instead of a document-shifting world, need to be defined and matured and will make many classical data processing jobs redundant. Read this interesting article from the Economist: The Onrushing Wave
This is a challenge for every company. The highest need to implement this cultural change is ironically for those countries with the highest legacy: Western Europe / the United-States.
As these countries also have the highest labor cost, the impact of, keep on doing the old stuff, will reduce their competitiveness. The impact for construction companies is less, as the construction industry is still a local business, as at the end resources will not travel the globe to execute projects.
However cheaper labor costs become more and more available in every country. If companies want to utilize them, they need to change the process. They need shift towards more thinking and knowledge in the early lifecycle to avoid the need for high qualified people to be in the field to the fix errors.
Sharing instead of owning
For me the major purpose of PLM is to provide an infrastructure for people to share information in such a manner that others, not aware of the information, can still easily find and use the information in a relevant context of their activities. The value: People will decide on actual information and no longer become reactive on fixing errors due to lack of understanding the context.
The problem for the construction industry is that I have not seen any vendor focusing on sharing the big picture. Perhaps the BLM discussion will be a first step. For the major tool providers, like Autodesk and Bentley, their business focus is on the continuity of their tools, not on the continuity of data.
Last week I noticed a cloud based Issue Management solution, delivered by Kubus. Issue Management is one of the typical and easy benefits a PLM infrastructure can deliver. In particular if issues can be linked to projects, construction parts, processes, customers. If this solution becomes successful, the extension might be to add more data elements to the cloud solution. Main question will remain: Who owns the data ? Have a look:
Cloud based Issues Management
For continuity of data, you need standards and openness – IFC is one of the many standards needed in the full scope of collaboration. Other industries are further developed in their standards driven by end-user organizations instead of vendors. Companies should argue with their vendors that openness is a right, not a privilege.
Conclusion
A year ago, I was more optimistic about the construction industry adopting PLM practices. What I have learned this year, and based on feedback from others, were are not at the turning point yet. Change is difficult to achieve from one day to the other. Meanwhile, the whole value chain in the construction industry has different objectives. Nobody will take the risk or can afford the risk.
I remain interested to see where the construction industry is heading.
What do you think will 2015 be the year of a breakthrough?
Business is changing and becoming digital as you might have noticed. If you haven´t noticed it, you might be disconnected from the world or work in a stable silo. A little bit simplified and provocative otherwise you would not read further.
The change towards digital also has its effect on how PLM is evolving. Initially considered as an extension of PDM, managing engineering data, slowly evolving to an infrastructure to support the whole product lifecycle.
The benefits from a real PLM infrastructure are extremely high as it allows people to work smarter, identify issues earlier and change from being reactive towards proactive. In some industries, this change in working is they only way to stay in business. Others with still enough margin will not act.
Note: I am talking about a PLM infrastructure as I do not believe in a single PLM system anymore. For me PLM is supported through a collection of services across the whole product lifecycle, many potentially in one system or platform.
Changing from an engineering-centric system towards an infrastructure across the departmental silos is the biggest challenge for PLM. PLM vendors and ERP vendors with a PLM offering are trying provide this infrastructure and mainly fight against Excel. As an Excel file can easy pass the border from one department to the other. No vision needed for Excel.
A PLM infrastructure however requires a vision. A company has to look at its core business processes and decide on which information flows through the organization or even better their whole value chain.
Building this vision, understanding this vision and then being able to explain the vision is a challenge for all companies. Where sometime even management says
“Why do we need to have a vision, just fix the problem”
also people working in departments are not looking forward to change their daily routines because they need to share information. Here you here statements like
“Why people feel the need to look at the big picture. I want to have my work done.”
So if current businesses do not change, will there be a change?
Here I see the digital world combined with search-based applications coming up. Search based applications allow companies to index their silos and external sources and get an understanding of the amount of data there exists. And from these results learn that there is a lot of duplicated data or invalid information at different places.
This awareness might create the understanding that if instead of having hundred thousands of Excels in the organization, it would be better to have the data inside a database, uniquely stored and connected to other relevant information.
I have described this process in my past three posts and agreed the context remains complex if you are not involved or interested in it.
To grasp this concept first of all, you need to have the will to understand it. There is a lot of strategic information out there from companies like Accenture, Capgemini and more.
Next if you want to understand it in a more down-to-earth manner it is important to listen and talk with your peers from other companies, other industries. This is currently happening all around the world and I invite you to participate.
Here is a list of events that I am attending or planned to attend but too far away:
October 7th Stockholm – ENOVIA user conference
Here I will participate as a panel member in the discussion around the concept of zero files. Here we want to explain and discuss to the audience what a data-centric approach means for an organization. Also, customers will share their experiences. This conference is focusing on the ENOVIA community – you can still register here
October 14th-15th Paris – Product Data Technology 2014 conference
Here I will speak about the PLM future (based on data) and what PLM should deliver for the future generations. This conference is much broader and addresses all PLM related topics in a broader perspective
October 28 Product Innovation conference in San Diego
I have always enjoyed participating to this conference as like the PDT2014 it brings people together for networking and discussions. Mostly on business topics not on IT-issues.
November 26 Infuseit seminar in Copenhagen
Relative new in the Nordics Infuseit, a PLM consultancy company, is able to attract an audience that wants to work on understanding the PLM future. Instead of listening to presenters, here you are challenged to to discuss and contribute to build a common opinion. I will be there too.
Conclusion: It is time to prepare yourself for the change – it is happening and be educated an investment that will be rewarding for your company
What do you think – Is data-centric a dream ?
This is for the moment the last post about the difference between files and a data-oriented approach. This time I will focus on the need for open exchange standards and the relation to proprietary systems. In my first post, I explained that a data-centric approach can bring many business benefits and is pointing to background information for those who want to learn more in detail. In my second post, I gave the example of dealing with specifications.
It demonstrated that the real value for a data-centric approach comes at the moment there are changes of the information over time. For a specification that is right the first time and never changes there is less value to win with a data-centric approach. Moreover, aren’t we still dreaming that we do everything right the first time.
The specification example was based on dealing with text documents (sometimes called 1D information). The same benefits are valid for diagrams, schematics (2D information) and CAD models (3D information)
1D,2D,3D …..
The challenge for a data-oriented approach is that information needs to be stored in data elements in a database, independent of an individual file format. For text, this might be easy to comprehend. Text elements are relative simple to understand. Still the OpenDocument standard for Office documents is in the background based on a lot of technical know-how and experience to make it widely acceptable. For 2D and 3D information this is less obvious as this is for the domain of the CAD vendors.
CAD vendors have various reasons not to store their information in a neutral format.
- First of all, and most important for their business, a neutral format would reduce the dependency on their products. Other vendors could work with these formats too, therefore reducing the potential market capture. You could say that in a certain manner the Autodesk 2D format for DXF (and even DWG) have become a neutral format for 2D data as many other vendors have applications that read and write back information in the DXF-data format. So far DXF is stored in a file but you could store DXF data also inside a database and make it available as elements.
- This brings us to the second reason why using neutral data formats are not that evident for CAD vendors. It reduces their flexibility to change the format and optimize it for maximal performance. Commercially the significant, immediate disadvantage of working in neutral formats is that it has not been designed for particular needs in an individual application and therefore any “intelligent” manipulations on the data are hard to achieve..
The same reasoning can be applied to 3D data, where different neutral formats exist (IGES, STEP, …. ). It is very difficult to identify a common 3D standard without losing many benefits that an individual 3D CAD format brings currently. For example, CATIA is handling 3D CAD data in a complete different way as Creo does, and again handled different compared to NX, SolidWorks, Solid Edge and Inventor. Even some of them might use the same CAD kernel.
However, it is not only about the geometry anymore; the shapes represent virtual objects that have metadata describing the objects. In addition other related information exists, not necessarily coming from the design world, like tasks (planning), parts (physical), suppliers, resources and more
PLM, ERP, systems and single source of truth
This brings us in the world of data management, in my world mainly PLM systems and ERP systems. An ERP system is already a data-centric application, the BOM is already available as metadata as well as all the scheduling and interaction with resources, suppliers and financial transactions. Still ERP systems store a lot of related documents and drawings, containing content that does not match their data model.
PLM systems have gradually becoming more and more data centric as the origin was around engineering data, mostly stored in files. In a data-centric approach, there is the challenge to exchange data between a PLM system and an ERP system. Usually there is a need to share information between two systems, mainly the items. Different definitions of an item on the PLM and ERP side make it hard to exchange information from one system to the other. It is for that reason why there are many discussions around PLM and ERP integration and the BOM.
In the modern data-centric approach however we should think less and less in systems and more and more in business processes performed on actual data elements. This requires a company-wide, actually an enterprise-wide or industry-wide data definition of all information that is relevant for the business processes. This leads into Master Data Management, the new required skill for enterprise solution architects
The data-centric approach creates the impression that you can achieve a single source of the truth as all objects are stored uniquely in a database. SAP solves the problem by stating everything fits in their single database. To my opinion this is more a black hole approach: Everything gets inside, but even light cannot escape. Usability and reuse of information that was stored with the intention not to be found is the big challenge here.
Other PLM and ERP vendors have different approaches. Either they choose for a service bus architecture where applications in the background link and synchronize common data elements from each application. Therefore, there is some redundancy, however everything is connected. More and more PLM vendors focus on building a platform of connected data elements, where on top applications will run, like the 3DExperience platform from Dassault Systèmes.
As users we are more and more used to platforms as Google, Apple provide these platforms already in the cloud for common use on our smartphones. The large amount of apps run on shared data elements (contacts, locations …) and store additional proprietary data.
Platforms, Networks and standards
And here we enter an interesting area of discussion. I think it is a given that a single database concept is a utopia. Therefore, it will be all about how systems and platforms communicate with each other to provide in the end the right information to the user. The systems and platforms need to be data-centric as we learned from the discussion around the document (file centric) or data-centric approach.
In this domain, there are several companies already active for years. Datamation from Dr. Kais Al-Timimi in the UK is such a company. Kais is a veteran in the PLM and data modeling industry, and they provide a platform for data-centric collaboration. This quote from one of his presentations, illustrates we share the same vision:
“……. the root cause of all interoperability and data challenges is the need to transform data between systems using different, and often incompatible, data models.
It is fundamentally different from the current Application Centric Approach, in that data is SHARED, and therefore, ‘NOT OWNED’ by the applications that create it.
This means in a Data Centric Approach data can deliver MORE VALUE, as it is readily sharable and reusable by multiple applications. In addition, it removes the overhead of having to build and maintain non-value-added processes, e.g. to move data between applications.”
Another company in the same domain is Eurostep, who are also focusing on business collaboration between in various industries. Eurostep has been working with various industry standards, like AP203/214, PLCS and AP233. Eurostep has developed their Share-A-space platform to enable a data-centric collaboration.
This type of data collaboration is crucial for all industries. Where the aerospace and automotive industry are probably the most mature on this topic, the process industry and construction industry are currently also focusing on discovering data standards and collaboration models (ISO 15926 / BIM). It will be probably the innovators in these industries that clear the path for others. For sure it will not come from the software vendors as I discussed before.
Conclusion
If you reach this line, it means the topic has been interesting in depth for you. In the past three post starting from the future trend, an example and the data modeling background, I have tried to describe what is happening in a simplified manner.
If you really want to dive into the PLM for the future, I recommend you visit the upcoming PDT 2014 conference in Paris on October 14 and 15. Here experts from different industries will present and discuss the future PLM platform and its benefits. I hope to meet you there.
Some more to read:
https://us.sogeti.com/wp-content/uploads/2014/04/PLM-Systems-White-Paper.pdf
In my previous post, I talked about the unstoppable trend towards digital information and knowledge based on data becoming the new business paradigm.
Building knowledge based on information extracted from data, instead of working with documents and people, who need to manipulate these documents.
Moreover, the reasons to move towards a digital data-oriented approach are the immense business benefits it can bring to an organization. Having online visibility on information in context of other information from different stakeholders allows companies to be more proactive.
A proactive company will react faster to the market or customer. This will reduce waste and resources (materials / people) and therefore in the end be more competitive. This is all described in my first post, with relevant links to various global references.
In this post, I want to describe in an example what the differences are between a document-oriented and a data-oriented approach and how it affects people and business. This might give you an impression of the expected business benefits.
The ultimate goal behind a data-oriented approach is to have a single version of the truth for a product, project or plant. This can be realized by treating information as data elements in various connected database, where on demand reports or dashboards can be created based on actual information, instead of documents generated by duplicating data in new systems and locations. Digital data will provide paperless processes accessible almost anywhere around the world.
As an example, I will explain the difference between document-centric and data-centric when dealing with specifications.
The specification
Everyone knows the challenge with specifications. Most of the time in printed documents describing how a product or service should work from the client point of view. There are two principles behind specifications:
- Complexity. The more complex product or service is, the bigger chance that specifications are not complete or hundred percent understood, leading to an iterative change process. The challenge here is to manage the change and the consistency of the full specifications.
- Industry and margins. In a repetitive business, for example, the automotive or other mass consumer products, products can be quiet complex and once sold hard to maintain and repair. In a competitive business, an error in the field can consume a lot of the expected profit. In the construction industry, where most of the time single projects are executed by a chain of disciplines, the industry (still) accepts the costs overrun and the high costs of fixing issues in the field, instead of being clearer upfront during the design and planning.
Let’s stay with the example in the middle of complexity and industry volume. In color the various stages of the process.
The document based specification – 1
When the document based specification arrives, the company has to get an understanding of the content. The project manager has a first read through the document (100+ pages) and decides to send the document (it is a pdf) to sales, engineering, legal and planning. Engineering decides to distribute the document internally to mechanical, electrical and quality (for compliance).
The project manager stresses everyone on a weekly base to deliver the responses and tries to understand if the answers will come in time. There are some meetings needed with the stakeholders as the whole understanding needs to be consistent. Based on several iterations a response is compiled.
The data-oriented approach – 1
When the document based specification arrives, the project leader first stores the document as a reference in the PLM system and extracts all the customer requirements as data elements in the system. While extracting the requirements, the projects manager groups them into digital folders (functional / non-functional, contractual, regulations, etc.) and assigns them to the relevant stakeholders, who get notified by the system. Each of the persons assigned, again the engineering manager has distributed the discipline specific requirements internally.
The project manager watches the progress of the requirements analyses which are around a virtual model. There is still a need for meetings with the stakeholders to agree on the solution approach. Everything is stored and visible online in the system. This visibility has helped some of the stakeholders to be better-aligned upfront. In the end, the response is generated and converted to the customer’s format.
Not much benefit for step 1
If you compare the two approaches, there is mainly one person happy: the project manager. Instead of spending time to collect the status of all information, direct visibility on the response helps him/her to prioritize of focus where attention is needed, instead of discovering it on a weekly base.
There is some small benefit from the virtual model as other stakeholders can have a better understanding of the actual progress.
However, for the rest, all stakeholders are complaining. It is difficult to work. (Fl)Excel was much easier. Moreover, thinking about a virtual model takes time as we are not used working in this way. Typically something for aerospace you might think.
And now the benefits come – step 2
The customer has placed the order, and the project has started. The design has started, and people start to discover discrepancies or ambiguous demands that need to be negotiated with the customer. Is it part of the project and if not, should it become part of the project and at which costs (for whom)
The document oriented approach – 2
Several engineers are now discussing with the counterparts at the customer the detailed interpretation of the requirements, either through face-to-face meetings or emails. Changes are collected and sent to the project manager, who tries to understand what has changed and how to merge it in an on-going specification document. To avoid many revisions, he/she tries to update the document on a bi-weekly base, send it to the internal stakeholders for review and with their feedback generates a specification document for the customer that supposed to cover the latest agreements.
Unfortunate not all changes have reached the document as some of the stakeholders were busy and forgot to include some of the changes agreed with the customer as they were in a lost email. Also, a previous change of a requirement was overwritten as an update from quality used the old data. Finally, some design solutions were changed, which raised the costs. And not sure if the product with all its changes will be compliant after delivery. However, luckily nobody noticed so far, not even the customer
The data-oriented approach – 2
Thanks to the virtual model and the relations between all the requirements, any change in a requirement gets notified in the system. When a requirement is further clarified, it is updated in the system. When a requirement needs to be changed, it is clear what the impact of this change is. A change workflow assures that decisions are made visible and approved. Potentially changes that lead to more work were quoted to the customer for acceptance. Luckily the compliancy engineer noted that the change of materials used would lead to a compliancy issue. On a bi-weekly base, the project manager generates an agreed specification for the customer based on the data in the system.
Benefits are growing.
The project manager remains the happiest person and is even happier as less discussion is needed about who changed what and why. Alternatively, discussions about changes that should exist and cannot be found. The time saved by the project manager could be used to collaborate even better with the teams (without annoying them) or perhaps a second project to manage in parallel.
Other stakeholders start to enjoy the data-oriented approach too. Less ambiguity on their side too, fewer iterations because changes were not apparent. As all information is related to the virtual model online, the actual status is clear when making a decision. Less fixing afterwards and luckily still project meeting between the stakeholders to synchronize. The PLM system does not eliminate communication; it provides a reliable baseline of the truth. No need (and option) to look in your archives.
At this stage, benefits start to become clear. Fewer iterations and better decisions will have an impact on the costs and project stress. Still a complaint from the engineers might be that they need to do too much upfront thinking although some years later they might discover that this will be their main job. Fixing issues from the past have diminished.
And then the ultimate benefits
Now the project has reached the physical state. It is manufactured or under commissioning.
The document oriented approach – 3
In the document oriented approach, many issues might pop-up because they have not been considered in the early phase, or they got lost during document exchanges. Does the product work as specified? Is the building certified as specified?
The customer is king and for manufacturing companies this might lead to product recalls or launch delays. In the construction world, people in the field, will fix the issues by using skilled resources and creating a waste of materials and/or resources.
Data handover to the owner is a nightmare for a the project-centric delivery. Several people have been searching for documents, specifications and emails to build and compile the required documents for handover.
The data-oriented approach – 3
In the data-centric approach, the behavior of the physical product works as expected as most of the issues have been solved in the virtual model. When testing the product it works as specified as the specifying requirements have always been linked to the product. Moreover, they have been agreed and approved by the relevant stakeholders. Where relevant, the customer has paid for the extra work specified.
The handover process was not so stressful as before with the document-oriented approach. As the required information was known and specified upfront related to the requirements, the maturity process of the virtual model assured this data exists in the system. Now the as-built information matches the as-specified information. What a relief.
Conclusion
It is clear that the significant benefits can be found in step 3. I wrote the comparison in an extreme manner, knowing that reality lies in the middle. Excellent people can comprehend and fix more upfront because of their experience. Building the ultimate virtual model is not yet an easy achievement either.
The savings in materials and required resources are significant in a data-oriented approach. The time savings and the quality enhancements might change your company into a market-leader. The cost savings achieved through a pro-active approach will make your margin growing (unless competition does the same) and enable you to innovate.
One final remark on business change
If business change could be achieved by selecting the right tool or system without a business change, you will never get a competitive advantage. As your competitor can buy these too.
However, if you change to a data-centric approach, it will be a though change process and therefore once implemented you will leave competitors behind that keep on hanging on the past.
My holidays are over. After reading and cycling a lot, it is time to focus again on business and future. Those of you who have followed my blog the past year must have noticed that I have been talking on a regular base about business moving to a data-oriented approach instead of a document / file-based approach. I wrote an introduction to this topic at the beginning of this year: Did you notice PLM has been changing?
It is part of a bigger picture, which some people might call the Second Machine Age, Industry 4.0, The Third Wave or even more disturbing The onrushing wave.
This year I have had many discussions around this topic with companies acting in various industries; manufacturing, construction, oil & gas, nuclear and general EPC-driven companies. There was some commonality in all these discussions:
- PLUS: Everyone believes it is a beautiful story and it makes sense
- MINUS: Almost nobody wants to act upon it as it is an enormous business change and to change the way a company works you need C-level understanding
- PLUS: Everyone thinks the concept is clear to them
- MINUS: Few understand what it means to work data-oriented and what the impact on their business would be
Therefore, what I will try to do in the upcoming blog posts (two-three-four ??) is to address the two negative observations and how to make them more precise.
What is data / information / knowledge?
Data for me is a collection of small artifacts (numbers, characters, lines, sound bits, …) which have no meaning at all. This could be bundled together as a book, a paper drawing, a letter but also bundled together as a digital format like an eBook, a CAD file, an email and even transmission bytes of a network / internet provider can be considered as data.
Data becomes significant once provided in the context of each other or in the context of other data. At that time, we start calling it information. For that reason, a book or a drawing provides information as the data has been structured in such a manner to become meaningful. The data sent through the network cable only becomes information when it is filtered and stripped from the irrelevant parts.
Information is used to make decisions based on knowledge. Knowledge is the interpretation of information, which combined in a particular way, helps us to make decisions. And the more decisions we make and the more information we have about the results of these decisions, either by us or other, it will increase our knowledge.
Data and big data
Now we have some feeling about data, information and knowledge. For academics, there is room to discuss and enhance the definition. I will leave it by this simple definition.
Big data is the term for all digital data that is too large to handle in a single data management system, but available and searchable through various technologies. Data can come from any source around the world as through the internet an infrastructure exists to filter and search for particular data.
By analyzing and connecting the data coming from these various sources, you can generate information (placing the data in context) and build knowledge. As it is an IT-driven activity, this can be done in the background and give almost actual data to any person. This is a big difference with information handling in the old way, where people have to collect and connect manual the data.
The power of big data applies to many business areas. If you know how your customers are thinking and associating their needs to your products, you can make them better and more targeted to your potential market. Or, if you know how your products are behaving in the field during operation (Internet of Things) you can provide additional services, instant feedback and be more proactive. Plus the field data once analyzed provide actual knowledge helping you to make better products or offer more accurate services.
Wasn’t there big data before?
Yes, before the big data era there was also a lot of information available. This information could be stored in “analogue” formats ( microfiche, paper, clay tablets, papyrus) or in digital formats, better known as files or collections of files (doc, pdf, CAD-files, ZIP….).
Note the difference. Here I am speaking about information as the data is contained in these formats.
You have to open or be in front of information container first, before seeing the data. In the digital world, this is often called document management, content management. The challenge of these information containers is that you need to change the whole container version once you modify one single piece of data inside it. And each information container holds duplicated information from a data element. Therefore, it is hard to manage a “single version of the truth” approach.
And here comes the data-oriented approach
The future is about storing all these pieces of data inside connected data environments, instead of storing a lot of data inside a (versioned) information container (a file / a document).
Managing these data elements in the context of each other allow people to build information from any viewpoint – project oriented, product oriented, manufacturing oriented, service oriented, etc.
The data remains unique, therefore supporting much closer the single version of the truth approach. Personally I consider the single version of the truth as a utopia, however reducing the amount of duplicated data by having a data-oriented approach will bring a lot more efficiency.
In my next post, I will describe an example of a data-oriented approach and how it impacts business, both from the efficiency point of view and from the business transformation point of view. As the data-oriented approach can have immense benefits . However, they do not come easy. You will have to work different.
Some more details
An important point to discuss is that this data-oriented approach requires a dictionary, describing the primary data elements used in a certain industry. The example below demonstrates a high-level scheme for a plant engineering environment.
Data standards exist in almost any industry or they are emerging and crucial for the longevity and usage of the data. I will touch it briefly in one of the upcoming posts, however, for those interested in this topic in relation to PLM, I recommend attending the upcoming PDT Europe. If you look at the agenda there is a place to learn and discuss a lot about the future of PLM.
I hope to see you there.
Last week I attended the PI Apparel conference in London. It was the second time this event was organized and approximate 100 participants were there for two full days of presentations and arranged network meetings. Last year I was extremely excited about this event as the different audience, compare to classical PLM events, and was much more business focused.
Read my review from last year here: The weekend after PI Apparel 2013
This year I had the feeling that the audience was somewhat smaller, missing some of the US representatives and perhaps there was a slightly more, visible influence from the sponsoring vendors. Still an enjoyable event and hopefully next year when this event will be hosted in New York, it will be as active as last year.
Here are some of my observations.
Again the event had several tracks in parallel beside the keynotes, and I look forward in the upcoming month to see the sessions I could not attend. Obvious where possible I followed the PLM focused sessions.
First keynote came from Micaela le Divelec Lemmi, Executive Vice President and Chief Corporate Operations Officer of Gucci. She talked us through the areas she is supervising and gave some great insights. She talked about how Gucci addresses sustainability through risk and cost control. Which raw materials to use, how to ensure the brands reputation is not at risk, price volatility and the war on talent. As Gucci is a brand in the high-end price segment, image and reputation are critical, and they have the margins to assure it is managed. Micaela spoke about the short-term financial goals that a company as Gucci has related to their investors. Topics she mentioned (I did not write them down as I was tweeting when I heard them) were certainly worthwhile to consider and discuss in detail with a PLM consultant.
Micaela further described Gucci´s cooperate social responsibility program with a focus on taking care of the people, environment and culture. Good to learn that human working conditions and rights are a priority even for their supply chain. Although it might be noted that 75 % of Gucci´s supply chain is in Italy. One of the few brands that still has the “Made in Italy” label.
My conclusion was that Micaela did an excellent PR job for Gucci, which you would expect for a brand with such a reputation. Later during the conference we had a discussion would other brands with less exclusivity and more operating in the mass consumer domain be able to come even close to such programs?
Next Göktug and Hakan gave us their insights deploying their first PLM system at the AYDINLI group.
The company is successful in manufacturing and selling licensed products from Pierre Cardin, Cacharel and US Polo Association mainly outside the US and Western Europe.
Their primary focus was to provide access to the most accurate and most updated information from one source. In parallel, standardization of codes and tech packs was a driver. Through standardization quality and (re)use could be improved, and people would better understand the details. Additional goals are typical PLM goals: following the product development stages during the timeline, notify relevant users about changes in the design, work on libraries and reuse and integrate with SAP.
Interesting Hakan mentioned that in their case SAP did not recommend to use their system for the PLM related part due to lack of knowledge of the apparel industry. A wise decision which would need followup for other industries.
In general the PLM implementation described by Göktug and Hakan was well phased and with a top-down push to secure there is no escape to making the change. As of all PLM implementations in apparel they went live in their first phase rather fast as the complex CAD integrations from classical PLM implementations were not needed here.
Next I attended the Infor session with the title: Work the Way you Live: PLM built for the User. A smooth marketing session with a function / feature demo demonstrating the flexibility and configuration capabilities of the interface. Ease of use is crucial in the apparel industry, where Excel is still the biggest competitor. Excel might satisfy the needs from the individual, it lacks the integration and collaboration aspect a PLM system can offer.
More interesting was the next session that I attended from Marcel Oosthuis, who was responsible as Process Re-Engineering Director (read PLM leader). Marcel described how they had implemented PLM at Tommy Hilfiger, and it was an excellent story (perhaps too good to be true).
I believe larger companies with the right focus and investment in PLM resources can achieve this kind of results. The target for Tommy Hilfiger´s PLM implementation was beyond 1000 users, therefore, a serious implementation.
Upfront the team defined first what the expected from the PLM system to select (excellent !!). As the fashion industry is fast, demanding and changing all the time, the PLM system needs to be Swift, Flexible and Prepared for Change. This was not a classical PLM requirement.
In addition, they were looking for a high-configurable system, providing best practices and a vendor with a roadmap they could influence. Here I got a little more worried as high-configurable and best practices not always match the prepared for change approach. A company might be tempted to automate the way they should work based on the past (best practices from the past)
It was good to hear that Marcel did not have to go into the classical ROI approach for the system. His statement, which I fully endorse that it is about the capability to implement new and better processes. They are often not comparable with the past (and nobody measured the past)
Marcel described how the PLM team (eight people + three external from the PLM vendor) made sure that the implementation was done with the involvement of the end users. End user adoption was crucial as also key user involvement when building and configuring the system.
It was one of the few PLM stories where I hear how all levels of the organization were connected and involved.
Next Sue Butler, director from Kurt Salmon, described how to maximize ROI from your PLM investment. It is clear that many PLM consultants are aligned, and Sue brought up all the relevant points and angles you needed to look at for successful PLM implementation.
Main points: PLM is about changing the organization and processes, not about implementing a tool. She made a point that piloting the software is necessary as part of the learning and validation process. I agree on that under the condition that it is an agile pilot which does not take months to define and perform. In that case, you might be already locked in into the tool vision too much – focus on the new processes you want to achieve.
Moreover, because Sue was talking about maximize ROI from a PLM implementation, the topics focus on business areas that support evolving business processes and measure (make sure you have performance metrics) came up.
The next session Staying Ahead of the Curve through PLM Roadmap Reinvention conducted by Austin Mallis, VP Operations, Fashion Avenue Sweater Knits, beautifully completed previous sessions related to PLM.
Austin nicely talked about setting the right expectations for the future (There is no perfect solution / Success does not mean stop / Keeping the PLM vision / No True End). In addition, he described the human side of the implementation. How to on-board everyone (if possible) and admitting you cannot get everyone on-board for the new way of working.
Next in row was my presentation with potential the longest title: “How to transform your Business to ensure you Benefit from the Value PLM can deliver”.
Luckily the speakers before me that day already addressed many of the relevant topics, and I could focus on three main thoughts completing the story:
1. Who decides on PLM and Why?
I published the results from a small survey I did a month ago via my blog (A quick PLM survey). See the main results below.
It was interesting to observe that both the management and the users in the field are the majority demanding for PLM. Consultants have some influence and PLM vendors even less. The big challenge for a company is that the management and consultants often talk about PLM from a strategic point of view, where the PLM vendor and the users in the field are more focused on the tool(s).
From the expectations you can see the majority of PLM implementations is about improving collaboration, next time to market, increase quality and centralizing and managing all related information.
2. Sharing data instead of owning data
(You might have read about it several times in my blog) and the trend that we move to platforms with connected data instead of file repositories. This should have an impact on your future PLM decisions.
3. Choosing the right people
The third and final thought was about choosing the right people and understanding the blocker. I elaborated on that topic already before in my recent blog post: PLM and Blockers
My conclusions for the day were:
A successful PLM implementation requires a connection in communication and explanation between all these levels. These to get a company aligned and have an anchored vision before even starting to implement a system (with the best partner)
The day was closed by the final keynote of the day from Lauren Bowker heading T H E U N S E E N. She and her team are exploring the combinations of chemistry and materials to create new fashion artifacts. Clothes and materials that change color based on air vent, air pollution or brain patterns. New and inspiring directions for the fashion lovers.
Have a look here: http://seetheunseen.co.uk/
The morning started with Suzanne Lee, heading BioCouture who is working on various innovative methodologies to create materials for the apparel industry by using all kind of live micro-organisms like bacteria, fungi and algae and using materials like cellulose, chitin and protein fibers, which all can provide new possibilities for sustainability, comfort, design, etc. Suzanne´s research is about exploring these directions perhaps shaping some new trends in the 5 – 10 years future ahead. Have a look into the future here:
Renate Eder took us into the journey of visualization within Adidas, with her session: Utilizing Virtualization to Create and Sell Products in a Sustainable Manner.
It was interesting to learn that ten years ago she started the process of having more 3D models in the sales catalogue. Where classical manufacturing companies nowadays start from a 3D design, here at Adidas at the end of the sales cycle 3D starts. Logical if you see the importance and value 3D can have for mass market products.
Adidas was able to get 16000 in their 3D catalogue thanks to the work from 60 of their key suppliers who were fully integrated in the catalogue process. The benefit from this 3D catalogue was that their customers, often the large stores, need lesser samples, and the savings are significant here (plus a digital process instead of transferring goods).
Interesting discussion during the Q&A part was that the virtual product might even look more perfect than the real product, demonstrating how lifelike virtual products can be.
And now Adidas is working further backwards from production patterns (using 3D) till at the end 3D design. Although a virtual 3D product cannot 100 % replace the fit and material feeling, Renate believes that also introducing 3D during design can reduce the work done during pilots.
Finally for those who stayed till the end there was something entirely different. Di Mainstone elaborating on her project: Merging Architecture & the Body in Transforming the Brooklyn Bridge into a Playable Harp. If you want something entirely different, watch here:
Conclusion
The apparel industry remains an exciting industry to follow. For some of the concepts – being data-centric, insane flexible, continuous change and rapid time to market are crucial here.
This might lead development of PLM vendors for the future, including using it based on cloud technology.
From the other side, the PLM markets in apparel is still very basic and learning, see this card that I picked up from one of the vendors. Focus on features and functions, not touching the value (yet)
In my previous post, I wrote about the different ways you could look at Service Lifecycle Management (SLM), which, I believe, should be part of the full PLM vision. The fact that this does not happen is probably because companies buy applications to solve issues instead of implementing a consistent company wide vision (When and Where to start is the challenge). Oleg Shilovitsky just referred one more time to this phenomena – Why PLM is stuck in PDM.
I believe PLM as the enterprise information backbone for product information. I will discuss the logical flow of data that might be required in a PLM data model, to support SLM. Of course all should be interpreted in the context of the kind of business your company is in.
This post is probably not the easiest to digest as it assumes you are somehow aware and familiar with the issues relevant for the ETO (Engineering To Order) /EPC (Engineering Procurement Construction) /BTO (Build To Order) business
A collection of systems or a single device
The first significant differentiation I want to make is between managing an installation or a single device as I will focus only on installations.
An installation can be a collection of systems, subsystems, equipment and/or components, typically implemented by companies that deliver end-to-end solutions to their customers. A system can be an oil rig, a processing production line (food, packages, …), a plant (processing chemicals, nuclear materials), where maintenance and service can be performed on individual components providing full traceability.
Most of the time a customer specific solution is delivered to a customer, either direct or through installation / construction partners. This is the domain I will focus on.
I will not focus on the other option for a single device (or system) with a unique serial number that needs to be maintained and serviced as a single entity. For example a car, a computer device. Usually a product for mass consumption, not to be traced individually.
In order to support SLM at the end of the PLM lifecycle, we will see a particular data model is required which has dependencies on the early design phases.
Let´s go through the lifecycle stages and identify the different data types.
The concept / sales phase
In the concept/sales phase the company needs to have a template structure to collect and process all the information shared and managed during their customer interaction.
In the implementations that I guided, this was often a kind of folder structure grouping information into a system view (what do we need), a delivery view (how and when can we deliver), a services view (who does what ) and a contractual view (cost, budget, time constraints). Most of these folders had initially relations to documents. However the system view was often already based on typical system objects representing the major systems, subsystems and components with metadata.
In the diagram, the colors represent various data types often standard available in a rich PLM data model. Although it can be simplified by going back to the old folder/document approach shared on a server, you will recognize the functional grouping of the information and its related documents, which can be further detailed into individual requirements if needed and affordable. In addition, a first conceptual system structure can already exist with links to potential solutions (generic EBOMs) that have been developed before. A PLM system provides the ideal infrastructure to store and manage all data in context of each other.
The Design phase
Before the design phase starts, there is an agreement around the solution to be delivered. In that situation, an as-sold system structure will be leading for the project delivery, and later this evolved structure will be the reference structure for the as-maintained and as-services environment.
A typical environment at this stage will support a work breakdown structure (WBS), a system breakdown structure (SBS) and a product breakdown structure (PBS). In cases where the location of the systems and subsystems are relevant for the solution, a geographical breakdown structure (GBS) can be used. This last method is often used in shipbuilding (sections / compartments) and plant design (areas / buildings / levels) and is relevant for any company that needs to combine systems and equipment in shared locations.
The benefit of having the system breakdown structure is that it manages the relations between all systems and subsystems. Potentially when a subsystem will be delivered by a supplier this environment supports the relationship to the supplier and the tracking of the delivery related to the full system / project.
Note: the system breakdown structure typically uses a hierarchical tag numbering system as the primary id for system elements. In a PLM environment, the system breakdown elements should be data objects, providing the metadata describing the performance of the element, including the mandatory attributes that are required for exchange with MRO (Maintenance Repair Overhaul) systems.
Working with a system breakdown structure is common for plant design or a asset maintenance project and this approach will be very beneficial for companies delivering process lines, infrastructure projects and other solutions that need to be delivered as a collection of systems and equipment.
The delivery phase
During the delivery phase, the system breakdown structure supports the delivery of each component in detail. In the example below you can see the relation between the tag number, the generic part number and the serial number of a component.
The example below demonstrates the situation where two motors (same item – same datasheet) is implemented at two positions in a subsystem with a different tag number, a unique serial number and unique test certificates per motor.
The benefit of a system breakdown structure here is that it supports the delivery of unique information per component that needs to be delivered and verified on-site. Each system element becomes traceable.
The maintenance phase
For the maintenance phase the system breakdown structure (or a geographical breakdown structure) could be the place holder to follow up the development of an installation at a customer site.
Imagine that, in the previous example, the motor with tag number S1.2-M2 appears to be under dimensioned and needs to be replaced by a more powerful one. The situation after implementing this change would look like the following picture:
Through the relationships with the BOM items (not all are shown in the diagram), there is the possibility to perform a where-used query and identify other customers with a similar motor at that system position. Perhaps a case for preventive maintenance?
Note: the diagram also demonstrates that the system breakdown structure elements should have their own lifecycle in order to support changes through time (and provide traceability).
From my experience, this is a significant differentiator PLM systems can bring in relation to an MRO system. MRO and ERP (Enterprise Resource Planning)systems are designed to work with the latest and actual data only. Bringing in versioning of assets and traceability towards the initial design intent is almost impossible to achieve for these systems (unless you invest in a heavy customized system).
Conclusion
In this post and my previous post, I tried to explain the value of having at least a system breakdown structure as part of the overall PLM data model. This structure supports the early concept phase and connects data from the delivery phase to the maintenance phase.
Where my mission in the past 8 years was teaching non-classical PLM industries the benefits of PLM technology and best practices, in this situation you might say it is where classical BTO companies can learn from best practices from the process and oil & gas industry.
Note: Oleg just published a new blog post: PLM Best Practices and Henry Ford Mass Production System where he claims PLM vendors, Service partners and consultants like to sell Best Practices and still during implementation discover mass customization needs to be made to become customer specific, therefore, the age of Best Practices is over.
I agree with that conclusion, as I do not believe in an Out-Of-The-Box approach, to lead a business change.
Still Best Practices are needed to explain to a company what could be done and in that context without starting from a blank sheet.
Therefore I have been sharing this Best Practice (for free)
Everyone wants to be a game changer and in reality almost no one is a game changer. Game changing is a popular term and personally I believe that in old Europe and probably also in the old US, we should have the courage and understanding changing the game in our industries.
Why ? Read the next analogy.
1974
With my Dutch roots and passion for soccer, I saw the first example of game changing happening in 1974 with soccer. The game where 22 players kick a ball from side to side, and the Germans win in the last minute.
My passion and trauma started that year where the Dutch national team changed the soccer game tactics by introducing totaalvoetbal.
The Dutch team at that time coached by Rinus Michels and with star player Johan Cruyff played this in perfection.
Defenders could play as forwards and they other way around. Combined with the offside-trap; the Dutch team reached the finals of the world championship soccer both in 1974 and 1978. Of course losing the final in both situations to the home playing teams (Germany in 74 – Argentina in 78 with some help of the referee we believe)
This concept brought the Dutch team for several years at the top, as the changed tactics brought a competitive advantage. Other teams and players, not educated in the Dutch soccer school could not copy that concept so fast
At the same time, there was a game changer for business upcoming in 1974, the PC.
On the picture, you see Steve Jobs and Steve Wozniak testing their Apple 1 design. The abbreviation IT was not common yet and the first mouse device and Intel 8008 processor were coming to the market.
This was disruptive innovation at that time, as we would realize 20 years later. The PC was a game changer for business.
2006
Johan Cruyff remained a game changer and when starting to coach and influence the Barcelona team, it was his playing concept tika-taka that brought the Spanish soccer team and the Barcelona team to the highest, unbeatable level in the world for the past 8 years
![clip_image002[6]](https://virtualdutchman.com/wp-content/uploads/2014/01/clip_image0026.gif "clip_image002[6]")
Instead of having strong and tall players to force yourself to the goal, it was all about possession and control of the ball. As long as you have the ball the opponent cannot score. And if you all play very close together around the ball, there is never a big distance to pass when trying to recapture the ball.
This was a game changer, hard to copy overnight, till the past two years. Now other national teams and club teams have learned to use these tactics too, and the Spanish team and Barcelona are no longer lonely at the top.
Game changers have a competitive advantage as it takes time for the competition to master the new concept. And the larger the change, the bigger the impact on business.
Also, PLM was supposed to be a game changer in 2006. The term PLM became more and more accepted in business, but was PLM really changing the game ?
PLM at that time was connecting departments and disciplines in a digital manner with each other, no matter where they were around the globe. And since the information was stored in centralized places, databases and file sharing vaults, it created the illusion that everyone was working along the same sets of data.
The major successes of PLM in this approach are coming from efficiency through digitization of data exchange between departments and the digitization of processes. Already a significant step forward and bringing enough benefits to justify a PLM implementation.
Still I do not consider PLM in 2006 a real game changer. There was often no departmental or business change combined with it. If you look at the soccer analogy, the game change is all about a different behavior to reach the goal, it is not about better tools (or shoes).
The PLM picture shows the ideal 2006 picture, how each department forwards information to the next department. But where is PLM supporting after sales/services in 2006 ? And the connection between After Sales/Services and Concept is in most of the companies not formalized or existing. And exactly that connection should give the feedback from the market, from the field to deliver better products.
The real game changer starts when people learn and understand sharing data across the whole product or project lifecycle. The complexity is in the word sharing. There is a big difference between storing everything in a central place and sharing data so other people can find it and use it.
People are not used to share data. We like to own data, and when we create or store data, we hate the overhead of making data sharable (understandable) or useful for others. As long as we know where it is, we believe our job is safe.
But our job is no longer safe as we see in the declining economies in Europe and the US. And the reason for that:
Data is changing the game
In the recent years the discussion about BI (Business Intelligence) and Big Data emerged. There is more and more digital information available. And it became impossible for companies to own all the data or even think about storing the data themselves and share it among their dispersed enterprises. Combined with the rise of cloud-based platforms, where data can be shared (theoretically) no matter where you are, no matter which device you are using, there is a huge potential to change the game.
It is a game changer as it is not about just installing the new tools and new software. There are two major mind shifts to make.
- It is about moving from documents towards data. This is an extreme slow process. Even if your company is 100 % digital, it might be that your customer, supplier still requires a printed and wet-signed document or drawing, as a legal confirmation for the transaction. Documents are comfortable containers to share, but they are killing for fast and accurate processing of the data that is inside them.
- It is about sharing and combining data. It does not make sense to dump data again in huge databases. The value only comes when the data is shared between disciplines and partners. For example, a part definition can have hundreds of attributes, where some are created by engineering, other attributes created by purchasing and some other attributes directly come from the supplier. Do not fall in the ERP-trap that everything needs to be in one system and controlled by one organization.
Because of the availability of data, the world has become global and more transparent for companies. And what you see here is that the traditional companies in Europe and the US struggle with that. Their current practices are not tuned towards a digital world, more towards the classical, departmental approach. To change this, you need to be a game changer, and I believe many CEOs know that they need to change the game.
The upcoming economies have two major benefits:
- Not so much legacy, therefore, building a digital enterprise for them is easier. They do not have to break down ivory towers and 150 years of proud ownership.
- The average cost of labor is lower than the costs in Europe and the US, therefore, even if they do not do it right at the first time; there is enough margin to spend more resources to meet the objectives.
The diagram I showed in July during the PI Apparel conference was my interpretation of the future of PLM. However, if you analyze the diagram, you see that it is not a 100 % classical PLM scope anymore. It is also about social interaction, supplier execution and logistics. These areas are not classical PLM domains and therefore I mentioned in the past, the typical PLM system might dissolve in something bigger. It will be all about digital processes based on data coming for various sources, structured and unstructured. Will it still be PLM or will we call it different ?
The big consultancy firms are all addressing this topic – not necessary on the PLM level:
2012 Cap Gemini – The Digital advantage: …..
2013 Accenture – Dealing with digital technology’s disruptive impact on the workforce
2014 McKinsey – Why every leader should care about digitization and disruptive innovation
For CEOs it is important to understand that the new, upcoming generations are already thinking in data (generation Y and beyond). By nature, they are used to share data instead of owning data in many aspects. Making the transition to the future is, therefore, also a process of connecting and understanding the future generations. I wrote about it last year: Mixing past and future generations with a PLM sauce
This cannot be learned from an ivory tower. The easiest way is not to be worried by this trend and continue working as before, losing business and margin slowly year by year.
As in many businesses people are fired for making big mistakes, doing nothing unfortunate is most of the time not considered as a big mistake, although it is the biggest mistake.
During the upcoming PI Conference in Berlin I will talk about this topic in more detail and look forward to meet and discuss this trend with those of you who can participate.
The soccer analogy stops here, as the data approach kills the the old game.
In soccer, the maximum remains 11 players on each side and one ball. In business, thanks to global connectivity, the amount of players and balls involved can be unlimited.
![clip_image002[8]](https://virtualdutchman.com/wp-content/uploads/2014/01/clip_image0028.gif "clip_image002[8]")
A final observation:
In my younger days, I celebrated many soccer championships, still I am not famous as a soccer player.
Why ?
Because the leagues I was playing in, were always limited in scope: by age, local,regional, etc. Therefore it was easy to win in a certain scope and there are millions of soccer champions beside me. For business, however, there are almost no borders.
Global competition will require real champions to make it work !!!
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