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For those who have followed my blog over the years, it must be clear that I am advocating for a digital enterprise explaining benefits of a data-driven approach where possible. In the past month an old topic with new insights came to my attention: Yes or No intelligent Part Numbers or do we mean Product Numbers?

 

 

What’s the difference between a Part and a Product?

In a PLM data model, you need to have support for both Parts and Products and there is a significant difference between these two types of business objects. A Product is an object facing the outside world, which can be a company (B2B) or customer (B2C) related. Examples of B2C products are the Apple iPhone 8, the famous IKEA Billy, or my Garmin 810 and my Dell OptiPlex 3050 MFXX8.  Examples of B2B products are the ABB synchronous motor AMZ 2500, the FESTO standard cylinder DSBG.  Products have a name and if there are variants of the product, they also have an additional identifier.

A Part represents a physical object that can be purchased or manufactured. A combination of Parts appears in a BOM. In case these Parts are not yet resolved for manufacturing, this BOM might be the Engineering BOM or a generic Manufacturing BOM. In case the Parts are resolved for a specific manufacturing plant, we talk about the MBOM.

I have discussed the relation between Parts and Products in a earlier post Products, BOMs and Parts which was a follow-up on my LinkedIn post, the importance of a PLM data model. Although both posts were written more than two years ago, the content is still valid. In the upcoming year, I will address this topic of products further, including software and services moving to solutions / experiences.

Intelligent number for Parts?

As parts are company internal business objects, I would like to state if the company is serious about becoming a digital enterprise, parts should have meaningless unique identifiers. Unique identifiers are the link between discipline or application specific data sets. For example, in the image below, where I imagined attributes sets for a part, based on engineering and manufacturing data sets.

Apart from the unique ID, there might be a common set of attributes that will be exposed in every connected system. For example, a description, a classification and one or more status attributes might be needed.

Note 1: A revision number is not needed when you create every time a new unique ID for a new version of the part.  This practice is already common in the electronics industry. In the old mechanical domain, we are used to having revisions in particular for make parts based on Form-Fit-Function rules.

Note 2: The description might be generated automatically based on a concatenation of some key attributes.

Of course if you are aiming for a full digital enterprise, and I think you should, do not waste time fixing the past. In some situations, I learned that an external consultant recommended the company to rename their old meaningful part numbers to the new non-intelligent part numbering scheme. There are two mistakes here. Renumbering is too costly, as all referenced information should be updated. And secondly as long as the old part numbers have a unique ID for the enterprise, there is no need to change. The connectivity of information should not depend on how the unique ID is formatted.

Read more if you want here: The impact of Non-Intelligent Part Numbers

Intelligent numbers for Products?

If the world was 100 % digital and connected, we could work with non-intelligent product numbers. However, this is a stage beyond my current imagination.  For products we will still need a number that allows customers to refer to, for when they communicate with their supplier / vendor or service provider. For many high-tech products the product name and type might be enough. When I talk about the Samsung S5 G900F 16G, the vendor knows which kind of configuration I am referring too. Still it is important to realize that behind these specifications, different MBOMs might exist due to different manufacturing locations or times.

However, when I refer to the IKEA Billy, there are too many options to easily describe the right one consistent in words, therefore you will find a part number on the website, e.g. 002.638.50. This unique ID connects directly to a single sell-able configuration. Here behind this unique ID also different MBOMs might exist for the same reason as for the Samsung telephone. The number is a connection to the sales configuration and should not be too complicated as people need to be able to read and recognize it when you go to a warehouse.

Conclusion

There is a big difference between Product and Part numbers because of the intended scope of these business objects. Parts will soon exist in connected, digital enterprises and therefore do not need any meaningful number anymore. Products need to be identified by consumers anywhere around the world, not yet able or willing to have a digital connection with their vendors. Therefore smaller and understandable numbers will remain needed to support exact communication between consumer and vendor.

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When I started working with SmarTeam Corp.  in 1999, the company had several product managers, who were responsible for the whole lifecycle of a component or technology. The Product Manager was the person to define the features for the new release and provide the justification for these new features internally inside R&D.  In addition the Product Manager had the external role to visit customers and understand their needs for future releases and building and explaining a coherent vision to the outside and internal world. The product manager had a central role, connecting all stakeholders.

In the ideal situation the Product Manager was THE person who could speak in R&D-language about the implementation of features, could talk with marketing and documentation teams to explain the value and expected behavior and could talk with the customer describing the vision, meanwhile verifying the product’s vision and roadmap based on their inputs.All these expected skills make the role of a product manager challenging. Is the person too “techy” than he/she will enjoy working with R&D but have a hard time understanding customer demands. From the other side if the Product Manager is excellent in picking-up customer and market feedback he/she might not be heard and get the expected priorities from R&D. For me, it has always been clear that in software world a “bi-directional” Product Manager is crucial to success.

Where are the Product Managers in the Manufacturing Industry?

Approximate four years ago new concepts related to digitalization for PLM became more evident. How could a digital continuity connect the various disciplines around the product lifecycle and therefore provide end-to-end visibility and traceability? When speaking of end-to-end visibility most of the time companies talked about the way they designed and delivered products, visibility of what is happening stopped most of the time after manufacturing. The diagram to the left, showing a typical Build To Order organization illustrates the classical way of thinking. There is an R&D team working on Innovation, typically a few engineers and most of the engineers are working in Sales Engineering and Manufacturing Preparation to define and deliver a customer specific order. In theory, once delivered none of the engineers will be further involved, and it is up to the Service Department to react to what is happening in the field.

A classical process in the PLM domain is the New Product Introduction process for companies that deliver products in large volumes to the market, most of the time configurable to be able to answer to various customer or pricing segments. This process is most of the time linear and is either described in one stream or two parallel streams. In the last case, the R&D department develops new concepts and prepares the full product for the market. However, the operational department starts in parallel, initially involved in strategic sourcing, and later scaling-up manufacturing disconnected from R&D.

I described these two processes because they both illustrate how disconnected the source (R&D/ Sales)  are from the final result in the field. In both cases managed by the service department. A typical story that I learned from many manufacturing companies is that at the end it is hard to get a full picture from what is happening across the whole lifecycle, How external feedback (market & customers) have the option to influence at any stage is undefined. I used the diagram below even  before companies were even talking about a customer-driven digital transformation. Just understanding end-to-end what is happening with a product along the lifecycle is already a challenge for a company.

Putting the customer at the center

Modern business is about having customer or market involvement in the whole lifecycle of the product. And as products become more and more a combination of hardware and software, it is the software that allows the manufacturer to provide incremental innovation to their products. However, to innovate in a manner that is matching or even exceeding customer demands, information from the outside world needs to travel as fast as possible through an organization. In case this is done in isolated systems and documents, the journey will be cumbersome and too slow to allow a company to act fast enough. Here digitization comes in, making information directly available as data elements instead of documents with their own file formats and systems to author them. The ultimate dream is a digital enterprise where date “flows”, advocated already by some manufacturing companies for several years.

In the previous paragraph I talked about the need to have an infrastructure in place for people in an organization to follow the product along the complete lifecycle, to be able to analyze and improve the customer experience. However, you also need to create a role in the organization for a person to be responsible for combining insights from the market and to lead various disciplines in the organization, R&D, Sales, Services. And this is precisely the role of a Product Manager.

Very common in the world of software development, not yet recognized in manufacturing companies. In case a product manager role exists already in your organization, he/she can tell you how complicated it currently is to get an overall view of the product and which benefits a digital infrastructure would bring for their job. Once the product manager is well-supported and recognized in the organization, the right skill set to prioritize or discover actions/features will make the products more attractive for consumers. Here the company will benefit.

Conclusion

If your company does not have the role of a product manager in place, your business is probably not yet well enough engaged in the customer journey.  There will be broken links and costly processes to get a fast response to the market.  Consider the role of a Product Manager, which will emerge as seen from the software business.

NOTE 1: Just before publishing this post I read an interesting post from Jan Bosch: Structure Eats Strategy. Well fitting in this context

NOTE 2: The existence of a Product Manager might be a digital maturity indicator for a company, like for classical PLM maturity, the handling of the MBOM (PDM/PLM/ERP) gives insight into PLM maturity of a company.

Related to the MBOM, please read: The Importance of a PLM data model – EBOM and MBOM

 

 

 

 

 

This post is a rewrite of an article I wrote on LinkedIn two years ago and modified it to my current understanding. When you are following my blog, in particular, the posts related to the business change needed to transform a company towards a data-driven digital enterprise, one of the characteristics of digital is about the real-time availability of information. This has an impact on everyone working in such an organization. My conversations are in the context of PLM (Product Lifecycle Management) however I assume my observations are valid for other domains too.

Real-time visibility is going to be the big differentiator for future businesses, and in particular, in the PLM domain, this requires a change from document-centric processes towards data-driven processes.

Documents have a lot of disadvantages.  Documents lock information in a particular format and document handling results in sequential processes, where one person/one discipline at the time is modifying or adding content. I described the potential change in my blog post: From a linear world to fast and circular?

From a linear world to fast and circular

In that post, I described that a more agile and iterative approach to bring products and new enhancements to the market should have an impact on current organizations. A linear organization, where products are pushed to the market, from concept to delivery, is based on working in silos and will be too slow to compete against future, modern digital enterprises. This because departmental structures with their own hierarchy block fast moving of information, and often these silos perform filtering/deformation of the information.  It becomes hard to have a single version of the truth as every department, and its management will push for their measured truth.

A matching business model related to the digital enterprise is a matrix business model, where multi-disciplinary teams work together to achieve their mission. An approach that is known in the software industry, where parallel and iterative work is crucial to continuous deliver incremental benefits.

Image:  21stcenturypublicservant.wordpress.com/

In a few of my projects, I discovered this correlation with software methodology that I wanted to share. One of my clients was in the middle of moving from a document-centric approach toward a digital information backbone, connecting the RFQ phase and conceptual BOM through design, manufacturing definition, and production. The target was to have end-to-end data continuity as much as possible, meanwhile connecting the quality and project tasks combined with issues to this backbone.

The result was that each individual had a direct view of their current activities, which could be a significant quantity for some people engaged in multiple projects.  Just being able to measure these numbers already lead to more insight into an individual’s workload. At the time we discussed with the implementation team the conceptual dashboard for an individual, it lead to questions like: “Can the PLM system escalate tasks and issues to the relevant manager when needed?” and  “Can this escalation be done automatically? “

And here we started the discussion. “Why do you want to escalate to a manager?”  Escalation will only give more disruption and stress for the persons involved. Isn´t the person qualified enough to make a decision what is important?

One of the conclusions of the discussion was that currently, due to lack of visibility of what needs to be done and when and with which urgency, people accept things get overlooked. So the burning issues get most of the attention and the manager’s role is to make things burning to get it done.

When discussing further, it was clear that thanks to the visibility of data, real critical issues will appear at the top of an individual’s dashboard. The relevant person can immediately overlook what can be achieved and if not, take action. Of course, there is the opportunity to work on the easy tasks only and to ignore the tough ones (human behavior) however the dashboard reveals everything that needs to be done – visibility. Therefore if a person learns to manage their priorities, there is no need for a manager to push anymore, saving time and stress.

The ultimate conclusion of our discussion was: Implementing a modern PLM environment brings first of all almost 100 % visibility, the single version of the truth. This new capability breaks down silos, a department cannot hide activities behind their departmental wall anymore. Digital PLM allows horizontal multidisciplinary collaboration without the need going through the management hierarchy.

It would mean Power to People, in case they are stimulated to do so. And this was the message to the management: “ you have to change too, empower your people.”

What do you think – will this happen? This was my question in 2015.  Now two years later I can say some companies have seen the potential of the future and are changing their culture to empower their employees working in multidisciplinary teams. Other companies, most of the time with a long history in business, are keeping their organizational structure with levels of middle management and maintain a culture that consolidates the past.

Conclusion

A digital enterprise empowers individuals allowing companies to become more proactive and agile instead of working within optimized silos. In silos, it appears that middle management does not trust individuals to prioritize their work.  The culture of a company and its ability to change are crucial for the empowerment of individuals The last two years there is progress in understanding the value of empowered multidisciplinary teams.

Is your company already empowering people ? Let us know !

Note: After speaking with Simon, one of my readers who always gives feedback from reality, we agreed that multidisciplinary teams are very helpful for organizations. However you will still need a layer of strategic people securing standard ways of working and future ways of working as the project teams might be to busy doing their job. We agreed this is the role for modern middle management.
DO YOU AGREE ?

PDT Europe is over, and it was this year a surprising aligned conference, showing that ideas and concepts align more and more for modern PLM. Håkan Kårdén opened the conference mentioning the event was fully booked, about 160 attendees from over 19 countries. With a typical attendance of approx. 120 participants, this showed the theme of the conference: Continuous Transformation of PLM to support the Lifecycle Model-Based Enterprise was very attractive and real. You can find a history of tweets following the hashtag #pdte17

Setting the scene

Peter Bilello from CIMdata kicked-off by bringing some structure related to the various Model-Based areas and Digital Thread. Peter started by mentioning that technology is the least important issue as organization culture, changing processing and adapting people skills are more critical factors for a successful adoption of modern PLM. Something that would repeatedly be confirmed by other speakers during the conference.

Peter presented a nice slide bringing the Model-Based terminology together on one page. Next, Peter took us through various digital threads in the different stages of the product lifecycle. Peter concluded with the message that we are still in a learning process redefining optimal processes for PLM, using Model-Based approaches and Digital Threads and thanks (or due) to digitalization these changes will be rapid. Ending with an overall conclusion that we should keep in mind:


It isn’t about what we call digitalization; It is about delivering value to customers and all other stakeholders of the enterprise

Next Marc Halpern busted the Myth of Digital Twins (according to his session title) and looked into realistic planning them. I am not sure if Marc smashed some of the myths although it is sure Digital Twin is at the top of the hype cycle and we are all starting to look for practical implementations. A digital twin can have many appearances and depends on its usage. For sure it is not just a 3D Virtual model.

There are still many areas to consider when implementing a digital twin for your products. Depending on what and how you apply the connection between the virtual and the physical model, you have to consider where your vendor really is in maturity and avoid lock in on his approach. In particular, in these early stages, you are not sure which technology will last longer, and data ownership and confidentially will play an important role. And opposite to quick wins make sure your digital twin is open and use as much as possible open standards to stay open for the future, which also means keep aiming for working with multiple vendors.

Industry sessions

Next, we had industry-focused sessions related to a lifecycle Model-Based enterprise and later in the afternoon a session from Outotec with the title: Managing Installed Base to Unlock Service opportunities.

The first presentation from Väino Tarandi, professor in IT in Construction at KTH Sweden presented his findings related to BIM and GIS in the context of the lifecycle, a test bed where PLCS meets IFC. Interesting as I have been involved in BIM Level 3 discussions in the UK, which was already an operational challenge for stakeholders in the construction industry now extended with the concept of the lifecycle. So far these projects are at the academic level, and I am still waiting for companies to push and discover the full benefits of an integrated approach.

Concepts for the industrial approach could be learned from Outotec as you might understand later in this post. Of course the difference is that Outotec is aiming for data ownership along the lifecycle, where in case of the construction industries, each silo often is handled by a different contractor.

Fredrik Ekström from Swedish Transport Administration shared his challenges of managing assets for both road and railway transport – see image on the left. I have worked around this domain in the Netherlands, where asset management for infrastructure and asset management for the rail infrastructure are managed in two different organizations. I believe Fredrik (and similar organizations) could learn from the concepts in other industries. Again Outotec’s example is also about having relevant information to increase service capabilities, where the Swedish Transport Administration is aiming to have the right data for their services. When you look at the challenges reported by Fredrik, I assume he can find the answers in other industry concepts.

Outotec’s presentation related to managing installed base and unlock service opportunities explained by Sami Grönstrand and Helena Guiterrez was besides entertaining easy to digest content and well-paced. Without being academic, they explained somehow the challenges of a company with existing systems in place moving towards concepts of a digital twin and the related data management and quality issues. Their practical example illustrated that if you have a clear target, understanding better a customer specific environment to sell better services, can be achieved by rational thinking and doing, a typical Finish approach. This all including the “bi-modal approach” and people change management.

Future Automotive

Ivar Hammarstadt, Senior Analyst Technology Intelligence for Volvo Cars Corporation entertained us with a projection toward the future based on 160 years of automotive industry. Interesting as electrical did not seem to be the only way to go for a sustainable future depending on operational performance demands.

 

Next Jeanette Nilsson and Daniel Adin from Volvo Group Truck shared their findings related to an evaluation project for more than one year where they evaluated the major PLM Vendors (Dassault Systemes / PTC / Siemens) on their Out-of-the-box capabilities related to 3D product documentation and manufacturing.

They concluded that none of the vendors were able to support the full Volvo Truck complexity in a OOTB matter. Also, it was a good awareness project for Volvo Trucks organization to understand that a common system for 3D geometry reduces the need for data transfers and manual data validation. Cross-functional iterations can start earlier, and more iterations can be performed. This will support a shortening of lead time and improve product quality. Personally, I believe this was a rather expensive approach to create awareness for such a conclusion, pushing PLM vendors in a competitive pre-sales position for so much detail.

Future Aerospace

Kenny Swope from Boeing talked us through the potential Boeing journey towards a Model-Based Enterprise. Boeing has always been challenging themselves and their partners to deliver environments close to what is possible. Look at the Boeing journey and you can see that already in 2005 they were aiming for an approach that most of current manufacturing enterprises cannot meet. And now they are planning their future state.

To approach the future state Boeing aims to align their business with a single architecture for all aspects of the company. Starting with collecting capabilities (over 400 in 6 levels) and defining value streams (strategic/operational) the next step is mapping the capabilities to the value streams.  Part of the process would be to look at the components of a value stream if they could be fulfilled by a service. In this way you design your business for a service-oriented architecture, still independent from any system constraints. As Kenny states the aerospace and defense industry has a long history and therefore slow to change as its culture is rooted in the organization. It will be interesting to learn from Kenny next hear how much (mandatory) progress towards a model-based enterprise has been achieved and which values have been confirmed.

Gearing up for day 2

Martin Eigner took us in high-speed mode through his vision and experience working in a bi-modular approach with Aras to support legacy environments and a modern federated layer to support the complexity of a digital enterprise where the system architecture is leading. I will share more details on these concepts in my next post as during day 2 of PDT Europe both Marc Halpern and me were talking related to this topic, and I will combine it in a more extended story.

The last formal presentation for day one was from Nigel Shaw from Eurostep Ltd where he took us through the journey of challenges for a model-based enterprise. As there will not be a single model that defines all, it will be clear various models and derived models will exist for a product/system.  Interesting was Nigel’s slide showing the multiple models disciplines can have from an airplane (1948). Similar to the famous “swing” cartoon, used to illustrate that every single view can be entirely different from the purpose of the product.

Next are these models consistent and still describing the same initial specified system. On top of that, even the usage of various modeling techniques and tools will lead to differences in the system. And the last challenge on top is managing the change over the system’s lifecycle. From here Nigel stepped into the need for digital threads to govern relations between the various views per discipline and lifecycle stage, not only for the physical and the virtual twin.  When comparing the needs of a model-based enterprise through its lifecycle, Nigel concluded that using PLCS as a framework provides an excellent fit to manage such complexity.

Finally, after a panel discussion, which was more a collection of opinions as the target was not necessary to align in such a short time, it was time for the PDT dinner always an excellent way to share thoughts and verify them with your peers.

Conclusion

Day 1 was over before you knew it without any moment of boredom and so I hope is also this post. Next week I will close reviewing the PDT conference with some more details about my favorite topics.

 

Last week I published a dialogue I had with Flip van der Linden, a fellow Dutchman and millennial, eager to get a grip on current PLM. You can read the initial post here: A PLM dialogue.  In the comments, Flip continued the discussion (look here).  I will elaborate om some parts of his comments and hope some others will chime in. It made me realize that in the early days of blogging and LinkedIn, there were a lot of discussions in the comments. Now it seems we become more and more consumers or senders of information, instead of having a dialogue. Do you agree? Let me know.

Point 1

(Flip) PLM is changing – where lies the new effort for (a new generation of) PLM experts.  I believe a huge effort for PLM is successful change management towards ‘business Agility.’ Since a proper response to an ECR/ECO would evidently require design changes impacting manufacturing and even after-sales and/or legal.  And that’s just the tip of the iceberg.

 

You are right, the main challenge for future PLM experts is to explain and support more agile processes, mainly because software has become a major part of the solution. The classical, linear product delivery approach does not match the agile, iterative approach for software deliveries. The ECR/ECO process has been established to control hardware changes, in particular because there was a big impact on the costs. Software changes are extremely cheap and possible fast, leading to different change procedures. The future of PLM is about managing these two layers (hardware/software) together in an agile way. The solution for this approach is that people have to work in multi-disciplinary teams with direct (social) collaboration and to be efficient this collaboration should be done in a digital way.

A good article to read in this context is Peter Bilello’s article: Digitalisation enabled by product lifecycle management.

 

(Flip) What seems to be missing is an ‘Archetype’ of the ideal transformed organization. Where do PLM experts want to go with these businesses in practice? Personally, I imagine a business where DevOps is the standard, unique products have generic meta-data, personal growth is an embedded business process and supply chain related risks are anticipated on and mitigated through automated analytics. Do you know of such an evolved archetypal enterprise model?

I believe the ideal archetype does not exist yet. We are all learning, and we see examples from existing companies and startups pitching their story for a future enterprise. Some vendors sell a solution based on their own product innovation platform, others on existing platforms and many new vendors are addressing a piece of the puzzle, to be connected through APIs or Microservices. I wrote about these challenges in Microservices, APIs, Platforms and PLM Services.  Remember, it took us “old PLM experts” more than 10-15 years to evolve from PDM towards PLM, riding on an old linear trajectory, caught up by a new wave of iterative and agile processes. Now we need a new generation of PLM experts (or evolving experts) that can combine the new concepts and filter out the nonsense.

Point 2

(Flip) But then given point 2: ‘Model-based enterprise transformations,’ in my view, a key effort for a successful PLM expert would also be to embed this change mgt. as a business process in the actual Enterprise Architecture. So he/she would need to understand and work out a ‘business-ontology’ (Dietz, 2006) or similar construct which facilitates at least a. business processes, b. Change (mgt.) processes, c. emerging (Mfg.) technologies, d. Data structures- and flows, e. implementation trajectory and sourcing.

And then do this from the PLM domain throughout the organization per optimization.  After all a product-oriented enterprise revolves around the success of its products, so eventually, all subsystems are affected by the makeup of the product lifecycle. Good PLM is a journey, not a trip. Or, does a PLM expert merely facilitates/controls this enterprise re-design process? And, what other enterprise ontologism tools and methods do you know of?

Only this question could be a next future blog post. Yes, it is crucial to define a business ontology to support the modern flow of information through an enterprise. Products become systems, depending on direct feedback from the market. Only this last sentence already requires a redefinition of change processes, responsibilities. Next, the change towards data-granularity introduces new ways of automation, which we will address in the upcoming years. Initiatives like Industry 4.0 / Smart Manufacturing / IIoT all contribute to that. And then there is the need to communicate around a model instead of following the old documents path. Read more about it in Digital PLM requires a Model-Based Enterprise. To close this point:  I am not aware of anyone who has already worked and published experiences on this topic, in particular in the context of PLM.

 

Point 3

(Flip) Where to draw the PLM line in a digital enterprise? I personally think this barrier will vanish as Product Lifecycle Management (as a paradigm, not necessarily as a software) will provide companies with continuity, profitability and competitive advantage in the early 21st century. The PLM monolith might remain, but supported by an array of micro services inside and outside the company (next to IoT, hopefully also external data sets).

I believe there is no need to draw a PLM line. As Peter’s article: Digitalisation enabled by product lifecycle management already illustrated there is a need for a product information backbone along the whole (circular) lifecycle, where product information can interact with other enterprise platforms, like CRM, ERP and MES and BI services. Sometimes we will see overlapping functionality, sometimes we will see the need to bridge the information through Microservices. As long as these bridges are data-driven and do not need manual handling/transformation of data, they fit in the future, lean digital enterprise.

Conclusion:

This can be an ongoing dialogue, diving into detailed topics of a modern PLM approach. I am curious to learn from my readers, how engaged they are in this topic? Do you still take part in PLM dialogues or do you consume? Do you have “tips and tricks” for those who want to shape the future of PLM?


Let your voice be heard! (and give Flip a break)

 

elevator_thumb.jpgRecently I connected with a fellow countryman, Flip, through LinkedIn and we had a small dialogue related to PLM. Flip describes himself as a millennial thinking loud about PLM and shared some of his thoughts trying to define “the job of PLM.” Instead of keeping it a Dutch dialogue, I would like to open the dialogue to all (millennials), as we need a new generation of PLM consultants

Point 1

observation_thumb.png(Flip) You cannot automate design activities easily, but the rest you can. Isn’t PLM an evolution of 3D Design tooling (and with that the next step in design – theory)

think_thumb.pngYou are right. Historically PLM originated from managing 3D design in a collaborative manner, although at that time we would call it cPDM (Collaborative Product Data Management).  PDM was very design focused. However, PDM also supported the connection to an Engineering Bill of Materials (EBOM) and connected engineering change processes (Engineering Change Request / Engineering Change Order – read more: ECR/ECO for Dummies)

PTC’s Windchill was the first modern cPDM software that still exists. At the same time, Dassault Systemes and Siemens extended the support for design towards the manufacturing planning and execution, introducing the term PLM (Product Lifecycle Management). In the following years, PLM systems started to support the full go-to-market lifecycle as the figure shows below.

lifecycle

This linear go-to-market process is currently rapidly changing because PLM is changing.

plm_txt_thumb.pngThe P standing for Product now represents a System (hardware & software interacting with the environment). The L standing for Lifecycle is also under change.

Support for the Lifecycle of a “product” has changed in two ways. First, the lifecycle is no longer going to be a linear process, but also be more iterative and incremental for the same “product.” Secondly, the lifecycle is stretched to support the “products” in the fields thanks to feedback from sensors (IoT – Internet of Things). That’s why PTC now claims IoT is PLM. Read more: Best Practices or Next Practices.

Finally, the M from Management is under change as thanks to a data-driven approach we should be able to (semi-)automate processes using algorithms. Favorite buzz words here are machine-learning, cobots (collaborative robots) and preventive actions thanks to data analysis & trends.

Point 2

observation_thumb.png (Flip) Storing data in a structured manner creates more complexity (you need to choose what to store). With simulation, complexity could be reduced to make meaningful (design) decisions, so PLM is about clever data hoarding?

image_thumb.pngI believe there is always a challenge with managing structured data for two reasons. People often only create the data they require.  Adding more context more data or a richer context is often considered “extra work,” for with the department is not rewarded or adding more data is not known as these persons do not know the future use of their information. This is a typical exercise for companies now engaging in a digital transformation. (read more: The importance of accurate data)

think_thumb.pngWhen you talk about simulation, I immediately thought about the current trend to work towards a model-based enterprise, where the model is the center of all information. And with the model, we do not only mean the 3D Model but also the functional and logical model which we can simulate. (Read more: Digital PLM requires a Model-Based Enterprise)

Point 3

observation_thumb.png(Flip) Automation from manufacturing with more and more resources requires new ways to drive manufacturing so a team of 8 people can do the work of 80 people through a PLM system?

Industry4Here you are addressing exactly the point that initiatives like Industry 4.0 or in the Netherlands Smart Industry are addressing. Instead of a linear, document-driven process, where each step new versions of information need to be created, the dream is to work around a model (the model-based enterprise).

The idea is that data is flowing through the organization – digital continuity / digital thread – without conversion and by using algorithms and machine learning, the data is consumed and created during the manufacturing process in an automated manner. Indeed, reducing the amount of people involved drastically.

think_thumb.pngI am not sure of we still would call this PLM, it is more a digital enterprise, where digital platforms interact together. PLM could be considered the source for the Product Innovation Platform, but there will also be Execution platforms (ERP and MES as the main source) and customer related platform (CRM as a source). As vendors from all these platforms will provide overlapping functionality, it will be hard to draw exact lines. The main goal for a company will be that the data is flowing and not locked into a proprietary format or systems. And here we still have a lot of work to do,

Conclusion

No conclusion this time as it is an on-going dialogue. Feel free to comment or send your questions, and we can all learn from the dialogue (always better than a monologue).

Your thoughts?

Although I have a PLM-twisted brain, I try to read in my free time books and articles that have no direct link with PLM. My main interest goes to people. How do they behave and decide in a society, in a company? What makes them decide to change an existing business?

SapiensI am currently reading the book from Yuval Noah Harari, called Sapiens: A Brief History of Humankind. I still have to finish the book but got intrigued by the following text when he tried to explain why homo sapiens was able to motivate and mobilize larger groups than a tribe:

How did Homo sapiens manage to a critical threshold, eventually founding cities comprising tens of thousands of inhabitants and empires ruling hundreds of millions? The secret was probably the appearance of fiction. Large numbers of strangers can cooperate successfully by believing in common myths.

Here my PLM-twisted brain woke up. What if we could create a  digital PLM myth? Currently, a lot of the PLM arguments are about functions and features, technical capabilities and perceived Return On Investment (ROI). For a digital transformation ROI is hard to estimate as the future state is not known and stable. What if the future state is a myth?  I will think about it when I finish the book and write the myth 🙂

Meanwhile, the rest of this blog post will be a reprint of a post I wrote almost five years ago in a similar context. PLM (old and new) are concepts against our evolution history. Enjoy and discover.

Our brain blocks PLM acceptance (Aug 2012)

tacit_logo.pngThe brain has become popular in the Netherlands in the past two years. Brain scientists have been publishing books sharing their interpretations on various topics of human behavior and the brain.

The common theme of all: The brain is influencing your perceptions, thoughts, and decisions without you even being aware of it.

clip_image005.jpg< added this post: in April 2013 Daniel Kahneman published his book Thinking Fast and Slow I referred in my post from May 2014 to this book – PLM is doomed, unless …>

Some even go that far by claiming certain patterns in the brain can be a proof if you have a certain disorder. It can be for better or for worse.

“It was not me that committed this crime; it was my brain and more…”

Anyway, this post will be full of quotes as I am not the brain expert, still giving the brain an important role (even in PLM)

Our brain blocks PLM acceptance

“My brain? That´s my second favorite organ” – Woody Allen

It is good to be aware of the influence of the brain. I wrote about this several times in the past, when discussing PLM vendor/implementer selection or when even deciding for PLM. Many of my posts are related to the human side of justifying and implementing PLM.

As implementing PLM for me primary is a business change instead of a combination of IT-tools to implement, it might be clear that understanding the inhibitors for PLM change are important to me.

In the PLM communities, we still have a hard job to agree between each other what is the meaning of PLM and where it differs from ERP. See for example this post, and in particular, the comments on LinkedIn (if you are a member of this group): PLM is a business process, not a (software) tool

Moreover, why it is difficult for companies to implement PLM beside ERP (and not as an extension of ERP) – search for PLM and ERP and you find zillions of thoughts and answers (mine too).

Charles_Roxburgh.jpgThe brain plays a major role in the Why PLM we have ERP battle (blame the brain). A week ago I read an older publication from Charles Roxburgh (published in May 2003 by McKinsey) called: Hidden flaws in strategy subtitle: Can insights from behavioral economics explain why good executives back bad strategies.

COULD read, hear and download the full article when you are a registered user. Unfortunate the link has been broken now>

The article has been written long before the financial and global crises were on the agenda and Mr. Roxburgh describes 8 hidden flaws that influence our strategic decision making (and PLM is a strategy).  Note all quotes below are from his publication.

Flaw 1: Overconfidence

We often make decisions with too much confidence and optimism as the brain makes us feel overconfident and overoptimistic about our own capabilities.

Flaw 2: Mental accounting

Avoiding mental accounting traps should be easier if you adhere to a basic rule: that every pound (or dollar or euro) is worth exactly that, whatever the category. In this way, you will make sure that all investments are judged on consistent criteria and be wary of spending that has been reclassified. Be particularly skeptical of any investment labeled “strategic.”

Here I would relate to the difference in IT-spending and budget when you compare ERP and PLM. ERP spending is normal (or strategic) where PLM spending is not understood.

Flaw 3: The status quo bias

People would rather leave things as they are. One explanation for the status quo bias is an aversion to loss—people are more concerned about the risk of loss than they are excited by the prospect of gain.

Another reason why adopting and implementing PLM in an organization is more difficult than for example just automating what we already do.

Flaw 4: Anchoring

Anchoring can be dangerous—particularly when it is a question of becoming anchored to the past

PLM has been anchored with being complex and expensive. Autodesk is trying to change the anchoring. Other PLM-like companies stop talking about PLM due to the anchoring and name what they do differently: 3DExperience, Business Process Automation, …..

Flaw 5: The sunk-cost effect

A familiar problem with investments is called the sunk-cost effect, otherwise known as “throwing good money after bad.” When large projects overrun their schedules and budgets, the original economic case no longer holds, but companies still keep investing to complete them.

I have described several cases in the past anonymously; where companies kept on investing and customizing their ERP environment to achieve PLM goals. Although it never reached the level of acceptance and quality a PLM system could offer, stopping these projects was impossible.

Flaw 6: The herding instinct

This desire to conform to the behavior and opinions of others is a fundamental human trait and an accepted principle of psychology.

Warren Buffett put his finger on this flaw when he wrote, “Failing conventionally is the route to go; as a group, lemmings may have a rotten image, but no individual lemming has ever received bad press.”

A quote in a quote but so true. Innovative thinking, introducing PLM in a company requires a change. Who needs to be convinced? If you do not have consensus (which usually happens as PLM is vague) you battle against the other lemmings.

Flaw 7: Misestimating future hedonistic states

Social scientists have shown that when people undergo major changes in circumstances, their lives typically are neither as bad nor as good as they had expected—another case of how bad we are at estimating. People adjust surprisingly quickly, and their level of pleasure (hedonistic state) ends up, broadly, where it was before

A typical situation every PLM implementation faces: users complaining they cannot work as efficient anymore due to the new system and their work will be a mess if we continue like this. Implementers start to customize quickly, and we are trapped. Let these people ‘suffer’ with the right guidance and motivation for some months (but this is sometimes not the business model the PLM implementer pushes as they need services as income)

Flaw 8: False consensus

People tend to overestimate the extent to which others share their views, beliefs, and experiences—the false-consensus effect. Research shows many causes, including these:

  • confirmation bias, the tendency to seek out opinions and facts that support our own beliefs and hypotheses

  • selective recall, the habit of remembering only facts and experiences that reinforce our assumptions

  • biased evaluation, the quick acceptance of evidence that supports our hypotheses, while contradictory evidence is subjected to rigorous evaluation and almost certain rejection; we often, for example, impute hostile motives to critics or question their competence

  • group-think, the pressure to agree with others in team-based cultures

Although positioned as number 8 by Mr. Roxburgh, I would almost put it on the top when referring to PLM and PLM selection processes. So often a PLM decision has not been made in an objective manner, and PLM selection paths are driven to come to the conclusion we already knew. (Or is this my confirmation bias too )

Conclusion

As scientists describe, and as Mr. Roxburgh describes our strategic thinking is influenced by the brain, and you should be aware of that. PLM is a business strategy and when rethinking your PLM strategy tomorrow, be prepared to avoid these flaws mentioned in this post today.

simpleMy recent posts were around the words Simple (PLM is not simple) and Simplicity  (Human Beings, PLM and Simplicity).  Combined with a blog dialogue with Oleg Shilovitsky (Small manufacturers and search of simple solutions)  and comments to these posts, the theme Simple has been discussed in various ways. Simple should not be confused with Simplicity. The conclusion: A PLM implementation should reduce complexity for an organization, aiming for increasing simplicity. The challenge: Achieving more simplicity is not simple (the picture related to this paragraph)

What does simplicity mean in the context of PLM?

My definition would be that compared to the current state, the future state should bring measurable benefits by reducing or eliminating non-value added activities. Typical non-value added PLM activities are collecting data from various disciplines to get a management understanding, conversion of file formats to support other disciplines or collecting and distributing data for change and approval processes.

If you can reduce or eliminate these steps, significant benefits can be achieved: reducing iterations, increasing quality and (re)acting faster to changes. These benefits are the whole idea behind Digital PLM. See Accenture’s explanation or read my post: Best Practices or Next Practices.DigitalPLM

Simplicity comes from the fact that the user does not need to depend on intermediate people or data formats to have an understanding of “the best so far truth.” Empowered users are a characteristic of modern digital processes. Empowered users need to have different skills than persons working in a traditional environment where exchange and availability of information are more controlled through communication between silos.  Some people can make the change, some will never make the change.

What can you do?

On LinkedIn, I found some good suggestions from Peter Weis in his CIO article: The most painful, gut-wrenching part of leading transformation. Peter’s post is about the challenges within a company going through a transformation and to keep the pace. My favorite part:

For me, the most difficult and gut-wrenching part of leading our transformation was not the technology involved. It was making and acting on those tough decisions about who was not going to succeed. In some cases, people had been with the company for decades and had been rewarded and encouraged for the very work they were no longer required to do. These were good people, skilled talent, who provided a great service to the company – but the technology and the cultural gap were just too wide for them to bridge.

Peter describes a dilemma that many of us consultants should face when implementing a business change. Keeping on board all employees is a mission impossible. But what if you want to keep them all on board?

Reducing complexity by making the system rigid?

One of the companies, I am currently working with, decided to keep all employees on board by demanding for a PLM system that is so rigid and automated that a user cannot make mistakes or wrong decisions. For example: Instead of allowing the user to decide which approval path should be chosen, the predefined workflow should be started where all participants are selected by automation. The idea: reducing the complexity for the (older) user. The user does not have to learn how to navigate in a new environment to decide what is the best option. There is always one option. Simple isn’t it?

I believe it reduces any user to a person that clicks on buttons and writes some comments. It is not about real empowerment.

There are two downsides to this approach

  • To make the PLM system, so incredibly rigid additional customizations are needed (which come with a cost). However more costly will be the upgrades in the future and the maintenance of every change in business process which is hard coded currently.
  • The system will be so rigid that even future, more digital native users, will dislike the system as it does not challenge them to think. Implementing the past or pushing for the future?

My challenge:

  • A rigid system creates the illusion that the system is secure and simple for the existing employees (who you do not want to challenge to change)
  • A rigid system leads by default to complexity in the future with high costs of change.

I am curious to learn how you would approach my challenge (a PLM consultant’s challenge)
Making the customer happy or being the “bad news” guy who creates fear for the future?
I assume a topic many PLM consultants should face nowadays – your opinion?

My last blog post was about reasons why PLM is not simple. PLM supporting a well-planned business transformation requires business change / new ways of working. PLM is going through different stages. We are moving from drawing-centric (previous century), through BOM-centric (currently) towards model-centric (current and future). You can read the post here: PLM is not simple!

I was happy to see  my blog buddy Oleg Shilovitsky chimed in on this theme, with his post: Who needs Simple PLM? Oleg reviewed the stakeholders around a PLM implementation. An analytical approach which could be correct in case predictive human beings were involved. Since human beings are not predictive and my focus is on the combination of PLM and human beings, here are some follow comments on the points Oleg made:

 

Customers (Industrial companies)

Oleg wrote:

A typical PLM customer isn’t a single user. A typical PLM buyer is engineering IT organization purchasing software to solve business problem. His interest to solve business problem, but not really to make it simple. Complex software requires more people, an increased budget and can become an additional reason to highlight IT department skills and experience. End-users hate complex software these days,therefore, usability is desired, but not top priority for enterprise PLM.

My comments on this part: PLM becomes more and more an infrastructure for product information along the whole lifecycle. PLM is no longer an engineering tool provided by IT.

There are now many other stakeholders that need product data, in particular when we are moving to a digital enterprise. A model-based approach connects Manufacturing and Service/Operations through a digital thread. It is the business demanding for PLM to manage their complexity. IT will benefit from a reduction in silo applications.

 

PLM Vendors

Oleg wrote:

…most PLM vendors are far away from a desired level of simplicity. Marketing will like “simple” messages, but if you know how to sell complex software, you won’t be much interested to see “simple package” everyone can sell. However, for the last decade, PLM vendors were criticized a lot for complexity of their solutions, so they are pretty much interested how to simplify things and present it as a competitive differentiation.

 

Here we are aligned. All PLM vendors are dreaming of simplifying their software. Imagine: if you have a simple product everyone can use, you would be the market leader and profitable like crazy without a big effort as the product is simple. Of course, this only works, assuming this dream can be realized.

Some vendors believe that easy customization or configuration of the system means simplification. Others believe a simple user-interface is the key differentiator. Compared to mass-consumer software products in the market, a PLM system is still a niche product, with a limited amount of users working with the exact same version of the software. Combined with the particular needs (customizations) every company has (“we are different”), there will never be a simple PLM solution. Coming back to the business transformation theme, human beings are the weakest link.

 

Implementation and Service Providers

Oleg wrote:

Complex software, customization, configuration, know-hows, best practices, installation… you name it.More of these things can only lead to more services which is core business of PLM service providers. PLM industry is very much competitive, but simplicity is not a desired characteristic for PLM when it comes to service business. Guess what… customer can figure it out how to make it and stop paying for services.

Here we are totally aligned. In the past, I have been involved in potential alliances where certain service providers evaluated SmarTeam as a potential tool for their business. In particular, the major PLM service providers did not see enough value in an easy to configure and relatively cheap product. Cheap means no budget for a huge amount of services.

Still, the biggest problem SmarTeam had after ten years was the fact that every implementation became a unique deployment. Hard to maintain and guarantee for the future. In particular, when new functionality was introduced which potentially already existed as customization.  Implementation and service providers will never say NO to a customer when it comes to further customization of the system. Therefore, the customer should be in charge and own the implementation. For making strategic decision support can come from a PLM consultant or coach.

 

PLM Consultants

Here Oleg wrote:

Complex software can lead to good consulting revenues. It was true many years for enterprise software. Although, most of PLM consultants are trying to distant from PLM software and sell their experience “to implement the future”, simplicity is not a favorite word in consulting language. Customer will hire consulting people to figure out the future and how to transform business, but what if software is simple enough to make it happen without consultant? Good question to ask, but most of them will tell you it is not a realistic scenario. Which is most probably true today. But here is the hint – remember the time PC technicians knew how to configured jumpers on PC cards to make printer actually print something?

Here we are not aligned. Business transformations will never happen because of simple tools. People are measured and pushed to optimize their silos in the organization. A digital transformation, which is creating a horizontal flow and transparency of information, will never happen through a tool. The organization needs to change, and this is always driven by a top-down strategy. PLM consultants are valuable to explain the potential future, to coach all levels of the organization. In theory, a PLM consultant’s job is tool independent. However, the challenge of being completely disconnected from the existing tools might allow for dreams that never can be realized. In reality, most PLM consultants are experienced in one or more specific tools they have been implementing. The customer should be aware of that and make sure they own the PLM roadmap.

My conclusion:

Don’t confuse PLM with a tool, simple or complex. All PLM tools have a common base and depending on your industry and company’s vision there will be a short list. However, before you touch the tools, understand your business and the transformation path you want to take. And that is not simple !!

 

Your opinion?

Oleg and I can continue this debate for a long time.  We would be interested in learning your view on PLM and Simplicity – please tune in through the comments section below:

simple

In my previous post, I shared my thoughts Why PLM is the forgotten domain in digital transformation. Legacy data, (legacy) people and slow organizations are the main inhibitors to moving forward. Moreover, all this legacy makes it hard to jump on the digital wagon.

When you talk with vendors and implementers of PLM solutions, they will all focus on the fact that with their solution and support PLM is simple. It is simple because:plm-vendor_thumb.jpg

  • We have the largest market share in your industry segment
  • We have the superior technology
  • We are cloud-based
  • We are insane customizable
  • Gartner is talking about us
  • We have implemented at 100+ similar companies

For my customers, implementing PLM was never simple as every PLM implementation was driving a business change. In the early days of SmarTeam, we had the theme “We work the way you work”, which is in hindsight a very bad statement. You do not want to automate the way a company is currently working. You want to use a PLM implementation to support a business change.

Never implement the past, implement the future

And there are changes ……

When I was discussing PLM with my potential customers ten years ago, the world was different. PLM was in a transition from being a PDM-tool from engineering into an extended PDM-tool centered around product development. A major theme for this kind of implementations was to move from a document-driven environment towards an item-centric environment. Instead of managing documents (CAD files and other files like Excel) the implementation was based on providing a data continuity, where the item (the physical part or in SAP terms the material) would be the main information placeholder. The continuity is implemented around EBOMs and MBOMs and thanks to automation the MBOM can be connected to the ERP system in a continuous flow.

Just search for item-centric or BOM-centric, and you will find many references from vendors and consultants for this approach.  Implementing PLM item-centric is already a big step forward in efficiency and quality for companies. However,…

Never implement the past, implement the future

And there will be changes …..

youtube

Digital Transformation & PLM on YouTube

Digital transformation is changing the way we do business and is changing the way companies should organize their data. A BOM-centric approach is no longer the ultimate implementation concept. To support a digital enterprise, the next step is a model-based enterprise. The model (not necessary the 3D-model) and its maturity and configurations are intended to be the reference for an organization. The model and its representation can connect hardware and software in a data-driven environment through the whole lifecycle. A model is needed to support smart manufacturing and the digital twin concept.There are many impressive marketing movies on YouTube explaining how companies/vendors implement digital continuity. Unfortunate the gap between marketing and reality is big at this time because moving to a model based enterprise is not an easy step. Coming back to the LEGACY-statement at the beginning of this post, it is not simple.

We all have to learn

PDT2017Digital transformation is just starting in the domain of PLM. Sharing and collecting knowledge is crucial, independent from particular solutions. For me, the upcoming PDT-conference in October is going to be a reference point where we are on this journey. In case your company has the experience to share related to this topic, please react to this link: http://pdteurope.com/call-for-abstract-now-open/

In case you want to learn and believe it is not simple, wait till the program it will be announced. The PDT conference has always been a conference where details are discussed. Looking forward and discuss with you.

Conclusion

Implementing and continuing with PLM is not simple for a company due to changes in paradigms. Digital transformation forces companies to investigate the details how to make it happen. Implementing PLM in scope of a digital transformation requires learning and time, not products first.

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