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PLM Benchmark 3 – EPC 2  What did they do and why?

8/2/2018

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This is an article in the series regarding PLM benchmarking among operators, EPC’s and product companies.
The articles cover the motivation for doing as they did, and where their main focus was put in order to achieve their goals.

I will continue to use my information structure map, or the “circle of life” as a client jokingly called it, to explain where the different companies put their focus in terms of information management and why.
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​EPC 2 had a slightly different focus from the EPC in my previous article as they were in an industry where there is a less clear split between Engineering Procurement and Construction companies and Product companies in the capital project value chain.
This company had the challenges of both EPC’s and product companies in ETO (Engineer To Order) projects as they owned several product companies, and naturally used a lot of their products in their EPC projects.
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Figure 2 shows the different data structures that EPC 2 focused on

Their first objective was to respond to fearsome competition from other parts of the world who suddenly emerged on the global scene. In order to do so it was considered crucial to limit the amount of engineering hours used to gain projects. To achieve this, they decided to build a catalog of re-usable data structures with different perspectives (plant, product, execution) in order to promote a controlled re-use of both plant and product engineering data. Similarly, as for EPC 1 they recognized that standardization across disciplines would be necessary to make it all work. The reference/master data put in place for all disciplines to share was a proprietary company standard.

Secondly, they needed to replace a homegrown engineering data hub. This homegrown solution was very impressive indeed and contained a lot of functionality that commercial systems lack even today, however its architecture was built around processes that did no longer work as EPC 2 entered new markets.

Thirdly they wanted to connect their plant engineering disciplines with the various product engineering disciplines throughout their own product companies worldwide. Naturally this meant run-time sharing and consolidation of data on a large scale. The emergence of the catalog with different aspects meant that plant engineering could pick systems and products from the catalog and have auto generated project specific tag information in the functional structure of their projects. It also meant that product engineering would be able to either generate a unique Engineer To Order bill of material if needed, or if plant engineering had not done any major modifications, link it to an already existing Engineering Bill of Material for the full product definition. 
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Their fourth objective was to obtain full traceability of changes across both plant and product engineering disciplines from FEED (Front End Engineering & Design) to delivered project. The reason for this objective was twofold. One part was to be able to prove to clients (operators) where changes originated from (largely from the client itself), and secondly to be able to measure what changes originated from their own engineering disciplines without project planning and execution knowing about it….. Does it sound familiar?
In order to achieve this, engineering data change management was enforced on both FEED functional design structures (yes, there could be several different design options for a project) and the functional structure in the actually executed EPC project. The agreed FEED functional structure was even locked and copied to serve as the starting point for the EPC project. At this point all data in the functional structure was released, subjected to full change management (meaning traceable Change Orders would be needed to change it) and made available to project planning and execution via integration.
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Figure 3 shows the sequence of data structures that was focused on in the implementation project.

Since product design and delivery was a large portion of their projects, the Engineering Bill of Material (EBOM) and variant management (the catalog structures) got a lot more focus compared with EPC 1 in my previous article. This was natural because, as mentioned, EPC 2 owned product companies and wanted to make a shift from Engineer To Order (ETO) towards more Configure To Order (CTO).
It was however decided to defer the catalog structures towards the end because they wanted to gain experience across the other aspects as well before starting to create the catalog itself.

The Functional Structure with the consolidated plant design, project specific data and associated documentation was out next together with the establishment of structures for project execution (WBS), estimation and control (Sales structure), and logistics (Supply structure).

Once the various data structures were in place, the focus was turned to “gluing it all together” with the re-usable catalog structures and the reference data which enabled interoperability across disciplines.

A more comprehensive overview explaining the different structures can be found in the article:
Plant Information Management - Information Structures, and further details regarding each information structure are discussed in:
Plant Engineering meets Product Engineering in capital projects
Handover to logistics and supply chain in capital projects
Plant Information Management - Installation and Commissioning
Plant Information Management – Operations and Maintenance

​Bjorn Fidjeland

The header image used in this post is by 8vfand and purchased at dreamstime.com
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Big Data and PLM, what’s the connection?

1/3/2018

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I was challenged the other day to explain the connection between Big Data and PLM by a former colleague. The connection might not be immediately apparent if your viewpoint is from traditional Product Lifecycle Management systems which primarily has to do with managing the design and engineering data of a product or plant/facility.

However, if we first take a look at a definition of Product Lifecycle Management from Wikipedia:

“In industry, product lifecycle management (PLM) is the process of managing the entire lifecycle of a product from inception, through engineering design and manufacture, to service and disposal of manufactured products. PLM integrates people, data, processes and business systems and provides a product information backbone for companies and their extended enterprise.”
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Traditionally it has looked much like this
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Then let’s look at a definition of Big Data
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“Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy. There are three dimensions to big data known as Volume, Variety and Velocity.
Lately, the term "big data" tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem." Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on.”

Included in Big Data you’ll find data sets harvested from sensors within all sorts of equipment and products as well as data fed back from software running within products. One can say that a portion of Big Data is the resulting feedback from the Internet of Things. Data in itself is not of any value whatsoever, but if the data can be analyzed to reveal meaning, trends or knowledge about how a product is used by different customer segments then it has tremendous value to product manufacturers.
If we take a look at the operational phase of a product, and by that, I mean everything that happens from manufactured product to disposal, then any manufacturer would like to get their hands on such data, either to improve the product itself or sell services associated with it. Such services could be anything from utilizing the product as a platform for an ecosystem of connected products to new business models where the product itself is not the key but rather the service it provides. You might sell guaranteed uptime or availability provided that the customer also buys into your service program for instance.
 
The resulting analysis of the data should in my view be managed by, or at least serve as input to the product definition because the knowledge gleamed from all the analytics of Big Data sets ultimately impacts the product definition itself since it should lead to revised product designs that fulfills the customer needs better. It might also lead to the revelation that it would be better to split a product in two different designs going after two distinct end user behavior categories found as a result of data analysis from the operational phase of the products.
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Connected products, Big Data and analysis will to a far greater extent than before allow us to do the following instead:
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It will mean that experience throughout the full lifecycle can be made available to develop better products, tailor to new end user behavior trends and create new business models.

Note: the image above focuses on the feedback loops to product engineering, but such feedback loops should also be made available from for instance service and operation to manufacturing.

Most companies I work with tell me that the feedback loops described in the image above is either too poor, or virtually nonexistent. Furthermore, they all say that such feedback loops are becoming vital for their survival as more and more of their revenue comes from services after a product sale and not from the product sale itself. This means that it is imperative for them to have as much reliable and analyzed data as possible about their products performance in the field, how their customers are actually using them and how they are maintained.

For these companies at least, the connection between Big Data analysis and its impact on Product Lifecycle Management is becoming clearer and clearer.

Bjorn Fidjeland

The header image used in this post is by garrykillian and purchased at dreamstime.com

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Digital Twin - What needs to be under the hood?

10/22/2017

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In the article Plant Information Management – Information Structures, and the following posts regarding Plant Information Management (see Archive) I explained in more detail the various information structures, the importance of structuring the data as object structures with interconnecting relationships to create context between the different information sources. 

​What does all of this have to do with the digital twin? - Let's have a look.

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​Information structures and their interconnecting relationships can be described by one of the major fashion word these days, the digital thread or digital twin.
The term and concept of a digital twin was first coined by Michael Grieves at the University of Michigan in 2002, but has since taken on a life of its own in different companies.
 
Below is an example of what information can be accessed from a digital twin or rather what the digital twin can serve as an entry point for:
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​If your data is structured in such a way with connected objects, attributes and properties, an associated three-dimensional representation of the physically delivered instance is a tremendously valuable asset as a carrier of information. It is however, not a pre- requisite that it is a 3D model, a simple dashboard giving access to the individual physical items might be enough. The 3D stuff is always promoted in the glossy sales representations by various companies, but it’s not needed for every possible use case. In a plant or aircraft, it makes a lot of sense, since the volume of information and number of possible entry points to the full data set is staggering, but it might not be necessary to have individual three-dimensional representations for all mobile phones ever sold. It might suffice to have each data set associated with each serial number.
 
On the other hand, if you have a 3D representation, it can become a front end used by end users for finding, searching and analyzing all connected information from the data structures described in my previous blog posts. Such insights takes us to a whole new level of understanding of each delivered products life, their challenges and opportunities in different environments and the way they are actually being used by end customers.
 
Let’s say that we via the digital twin in the figure above select a pump. The tag of that pump uniquely identifies the functional location in the facility. An end user can pull information from the system the pump belongs to in the form of a parametric Piping & Instrumentation Diagram (P&ID), the functional specification for the pump in the designed system, information about the actually installed pump with serial number, manufacturing information, supplier, certificates, performed installation & commissioning procedures and actual operational data of the pump itself.
 
The real power in the operational phase becomes evident when operational data is associated with each delivered pump. In such a case the operational data can be compared with environmental conditions the physical equipment operates in. Let’s say that the fluid being pumped contains more and more sediments, and our historical records of similar conditions tells us that the pump will likely fail during the next ten days due to wear and tear of critical components. However, it is also indicated that if we reduce the power by 5 percent we will be able to operate the full system until the next scheduled maintenance window in 15 days. Information like that gives real business value in terms of increased uptime.
 
Let’s look at some other possibilities.
If we now consider a full facility with a three-dimensional representation:
During the EPC phase it is possible to associate the 3D model with a fourth dimension, time, turning it into a 4D model. By doing so, the model can be used to analyze and validate different installation execution plans, or monitor the actual ongoing installation of the Facility. We can actually see the individual parts of the model appearing as time progresses.
 
A fifth dimension can also be added, namely cost. Here the cost development over time according to one or several proposed installation execution plans or the actual installation itself can be analyzed or monitored.
This is already being done by some early movers in the construction industry where it is referred to as 5D or Virtual Design & Construction.
 
The model can also serve as an important asset when planning and coordinating space claims made by different disciplines during the design as well as during the actual installation. It can easily give visual feedback if there is a conflict between space claims made by electrical engineering and mechanical engineering, or if there is a conflict in the installation execution plan in terms of planned access by different working crews.
More and more companies are also making use of laser scanning in order to get an accurate 3D model of what has been actually installed so far. This model can easily be compared with the design model to see if there are any deviations. If deviations are found, they can be acted upon by analyzing how it will impact the overall system if it is left as it is, or will it require re-design? Does the decision to leave it as it is change the performance of the overall system? Are we still able to perform the rest of the installation, due to less available space?
Answers to these questions might entail that we will have to dismantle the parts of the system that has deviations. It is however a lot better and cost effective to identify such problems as early as possible.
 
This is just great, right? Such insights as mentioned would have huge impacts on how EPC’s manage their projects, operators run their plants and how product vendors can operate or service their equipment in the field, as well as feeding information back to engineering to make better products.
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​New business models can be created in the likes of: “We sell power by the hour, dear customer, you don’t even have to buy the asset itself”!
(Power-by-the-Hour is a trademark of Rolls-Royce, although the concept itself is 50 years old you can read about a more recent development here)
 
So why haven’t more companies already done it?
 
Because in order to get there, the underlying data must be connected, and in the form of… yes data as in objects, attributes and relationships. It requires a massive shift from document orientation to connected data orientation to be at its most effective.
 
On the bright side, several companies in very diverse industries have started this journey, and some are already starting to harvest the fruits of their adventure.
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My advice to any company thinking about doing the same would be along the lines of:
When eating this particular elephant, do it one bite at the time, remember to swallow and let your organization digest between each bite.

Bjorn Fidjeland

The header image used in this post is by Elnur and purchased at dreamstime.com

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Who owns what data when…..?

7/7/2017

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​A vital questions when looking at cross departmental process optimization and integration is in my view: who owns what data when in the overall process? 
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Usually this question will spark up quite a discussion between the process owners, company departments, data owners and the different enterprise architects. The main reason for this is that depending on where the stakeholders have their main investment, they tend to look at “their” part of the process as the most important and the “master” for their data.

Just think about sales with their product configurators, engineering with CAD/PLM, supply chain, manufacturing & logistics with ERP and MES. Further along the lifecycle you encounter operations and service with EAM, Enterprise Asset Management, systems sometimes including MRO, Maintenance Repair and Operations/Overhaul. The last part being for products in operational use. Operations and service is really on the move right now due to the ability to receive valuable feedback from all products used in the field (commonly referred to as Internet of Things) even for consumer products, but hold your horses on the last one just for a little while.
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The different departments and process owners will typically have claimed ownership of their particular parts of the process, making it look something like this:
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This would typically be a traditional linear product engineering, manufacturing and distribution process. Each department has also selected IT tools that suit their particular needs in the process.
This in turn leads to information handovers both between company departments and IT tools, and due to the complexity of IT system integration, usually, as little as possible of data is handed from one system to the next.
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So far it has been quite straight forward to answer “who owns what data”, especially for the data that is actually created in the departments own IT system, however, the tricky one is the when in “ who owns what data when”, because the when implies that ownership of certain data is transferred from one department and/or IT system to the next one in the process. In a traditional linear one, such information would be “hurled over the wall” like this:
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Now, since as little information as possible flowed from one department / IT system to the next, each department would make it work as best as they could, and create or re-create information in their own system for everything that did not come directly through integration.
Only in cases where there were really big problems with lacking or clearly faulty data, an initiative would be launched to look at the process and any system integrations that would be affected.

The end result being that the accumulated information throughout the process that can be associated with the end product, that is to say the physical product sold to the consumer, is only a fraction of the actual sum of information generated in the different department’s processes and systems.
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Now what happens when operations & services get more and more detailed information from each individual product in the field, and starts feeding that information back to the various departments and systems in the process?
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The process will cease to be a linear one, it becomes circular with constant feedback of analyzed information flowing back to the different departments and IT systems.

Well what’s the problem you might ask.

The first thing that becomes clear is that each department with their systems does not have enough information to make effective use of all the information coming from operations, because they each have a quite limited set of data concerning mainly their discipline.

Secondly, the feedback loop is potentially constant or near real-time which will open up for completely new service offerings, however, the current process and infrastructure going from design through engineering and manufacturing was never built to tackle this kind of speed and agility.

Ironically, from a Product Lifecycle Management perspective, we’ve been talking about breaking down information and departmental silos in companies to utilize the L in PLM for as long as I can remember, however the way it looks now, it is probably going to be operations and the enablement of Internet Of Things and Big Data analytics that will force companies to go from strictly linear to circular processes.

And when you ultimately do, please always ask yourself “who should own what data when”, because ownership of data is not synonymous with the creation of data. Ownership is transferred along the process and accumulates to a full data set of the physically manufactured product until it is handed back again as a result of fault in the product or possible optimization opportunities for the product.

 – And it will happen faster and faster
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Bjorn Fidjeland

The header image used in this post is by Bacho12345 and purchased at dreamstime.com
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Plant Information Management – Operations and Maintenance

1/29/2017

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This post is a continuation of the posts in the Plant Information Management series of:
“Plant Information Management - Installation and Commissioning”
“Handover to logistics and supply chain in capital projects”
“Plant Engineering meets Product Engineering in capital projects”
 “Plant Information Management - What to manage?”

During operations and maintenance, the two main structures of information needed in order to operate the plant in a safe and reliable manner is the functional or tag structure and the physically installed structure.
The functional tag structure is a multidiscipline consolidated view of all design requirements and criteria, whereas the physically installed structure is a representation of what was actually installed and commissioned together with associated data. It is important to note that the physically installed structure evolves over time during operations and maintenance, so it is vital to make baselines of both structures together to obtain “As-Installed” and “As-Commissioned” documentation
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Figure 1.
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Let’s zoom in on some of the typical use cases of the two structures.
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Figure 2.
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The requirements in the blue tag structure are fulfilled by the physical installation, the yellow structures. In a previous post I promised to get back to why they are represented as separate objects. The reason for this is that during operations one would often like to replace a physical individual on site with another physical individual. This new physical individual still has to fulfill the tag requirements, as the tag requirements (system design) have not changed. In addition we need full traceability of not only what is currently installed, but also what used to be installed at that functional location (see figure 3).
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Figure 3.

Here we have replaced the vacuum pump during operations with another vacuum pump from another vendor. The new vacuum pump must comply with the same functional requirements as the old one even if they might have different product designs.
This is a very common use case where a product manufacturing company comes up with a new design a few years later. The product might be a lot cheaper and still fulfills the requirements, so if the operator of the plant has 500 instances of such products in the facility, it makes perfect sense to replace them when the old product nears end of life or have extensive maintenance programs.
 
Another very important reason to keep the tag requirements and physically installed as separate objects is if….or rather when the operator wishes to execute a modification or extension project to the plant.
In such cases one must still manage and record the day to day operation of the plant (work requests and work orders performed on physical equipment in the plant) while at the same time performing a plant design and execution project. This entails Design, Engineering, Procurement, Construction and Commissioning all over again.
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Figure 4.
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The figure shows, that when the blue functional tag structure is kept separate from the yellow physically installed structure we can still operate the current plant on a day to day basis, and at the same time perform new design on the revised system (Revision B).
This allows us to execute all the processes right up until commissioning on the new revision, and when successfully commissioned, the revision B becomes operational.
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This all sounds very good in theory, but in practice it is a bit more challenging, as there in the meantime might have been made change orders that effected the design of the previous revision as a result of operations. This is one of the use cases where structured or linked data instead of a document centric approach really pays off, because such a change order would immediately indicate that it would affect the new design, and thus,  appropriate measures can be taken at an early stage instead of nasty surprises popping up during installation and commissioning of the new system.

Bjorn Fidjeland

The header image used in this post is by nightman1965 and purchased at dreamstime.com
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Plant Engineering meets Product Engineering in capital projects

9/30/2016

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This post is a follow up of “Plant Information Management - What to manage?”.

It focuses on the needed collaboration between Plant Engineering (highly project intensive) and Product Engineering which ideally should be “off the shelf” or at least Configure To Order (CTO), but in reality is more often than not, Engineer To Order (ETO) or one-offs.
More and more EPC’s (Engineering Procurement Construction companies), and product companies exposed to project intensive industries are focusing hard on ways to  re-use product designs from one project to the next or even internally in the same project through various forms of configuration and clever use of master data, see “Engineering Master Data - Why is it different?”.
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However, we will never get away from the fact that the product delivery in a capital project will always have to fulfill specific requirements from Plant Engineering, and especially in safety classed areas of the plant.
If you look at the blue object structure, it represents a consolidated view of multi-discipline plant engineering. The system might consist of several pumps, heat exchangers, sensors, instrumentation and pipes, but we are going to focus on a specific tag and it’s requirements, namely one of the pumps in the system.​
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At one point in the plant engineering process the design is deemed fit for project procurement to start investigating product designs that might fulfill the requirements stated in the plant system design.
If the plant design is made by an EPC that does not own any product companies, the representing product is typically a single article or part with associated preferred vendors/manufacturers who might be able to produce such a product or have it in stock. If the EPC does own product companies, the representing product might be a full product design. In other words a full Engineering Bill Of Material (EBOM) of the product.
 
This is where it becomes very interesting indeed because the product design (EBOM) is generic in nature. It represents a blueprint or mold if you will, used to produce many physical products or instances of the product design. The physical products typically have serial numbers, and you are able to touch them. However, due to requirements from the Owner/Operator, the EPC will very often dictate both project and tag specific documentation from the product company supplying to the project, which in turn often leads to replication of the product designs X number of times to achieve compliance with the documentation requirements in the project (Documentation For Installation and Operations).​
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So, even if it is exactly the same product design it ends up being copied each time there is a project specific delivery. This often happens even if let’s say 40 pumps are being supplied by the same vendor to the same project, as responses to the requirements on 40 different tags in the plant design……
Needless to say it becomes a lot of Engineering Bill Of Materials in order to comply with documentation requirements in capital projects. Even worse, for the product companies it becomes virtually impossible to determine exactly what they have delivered each time, since it is different Engineering Bills Of Materials all the time, yet 97% of the information might be the same. The standardized product has now become an Engineer To Order product.
So how is it possible to avoid this monstrous duplication of work?
More and more companies are looking into ways to make use of data structures used in different contexts. The contexts might be different deliveries to the same project or across multiple projects, but if one is able to identify and separate the generic information from what information that needs to be project specific it is also possible to facilitate re-use.​
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​The image above shows how a generic product design (EBOM) is able to fulfill three different project specific tags or functional locations in a plant. Naturally three physical instances or serial numbers must then be manufactured based on the generic product design, but since we have the link or relationship between the project specific requirements (the tags) and the generic (the EBOM), one can generate project specific data and documentation without making changes to the generic representation of the product (the EBOM).
This approach even enables the product company to identify and manufacture one of the pumps which happens to be in a safety classed area in the plant design according to regulatory requirements without having to make changes or duplicate the product design, however more on that next time.
 
Bjorn Fidjeland

The header image used in this post is by Nostal6ie and purchased at dreamstime.com
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PLM - the KPI's, the ROI and the simple

8/20/2016

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It is often hard to measure since PLM affects so many different and interchanging aspects of an engineering and manufacturing company.

​However I would like to share a story about a multi-billion dollar company who was about to embark on a global PLM implementation journey.
They were required to come up with a series of KPI’s and to report the progress to top management. The PLM project spent considerable effort in doing so.
One of the 10 KPI’s they identified was their engineering change process. They measured time spent from the engineering change was requested to it was released, and furthermore the impact it had on the project execution schedules in their customer projects (The company is heavily engaged in project intensive industries).

All measurements were done on a monthly basis before and after the PLM platform went live.

Before the introduction of the platform it was largely a manual process where the project and product managers would gather and have so called “sign fests” where engineering changes was simply signed without much review since the magnitude of changes was staggering, and so they had to rely on the judgement of their junior managers. This sounds like a rather risky approach, but the fact was that the cost of not approving was far greater than just approving. Needless to say this manual process still took quite some time since the junior managers spent a lot of time evaluating before submitting to the “sign fest”.

After the introduction of the PLM platform, they continued measuring. Top management was still eager to get reports on all the KPI’s, but then they started to notice something. The engineering change process showed a relatively modest drop in time spent globally in the beginning, but the calculation of impact on time and money saved in the projects showed impressive results. As the organization got more and more familiar with the new engineering change process, the time spent from change request to release fell rapidly, and the result in ongoing customer projects was even greater.

After less than a year they concluded that the improvements on throughput of Engineering Changes and time spent on them alone paid for the entire project.
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In my view, yes there are a lot of important benefits like “increased collaboration” using a PLM platform that are hard to measure, but a lot of times it makes even more sense to measure smaller but more identifiable benefits and then trace the impact they have further down the lifecycle of the product.

Bjorn Fidjeland

The header image used in this post is by Andrewgenn and purchased at dreamstime.com

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PLM platforms, the difficult organizational rollout

3/6/2016

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What is PLM really about? In my view it is about tying relevant information to business processes, you know, the stuff that makes your company truly unique and then tying your employees to those very same processes throughout the life of a product.

So it’s about information, processes, people and an IT platform, in this case a PLM platform.
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To be successful, ALL areas must intersect.

It does not matter if you have the perfect PLM system with perfectly defined processes if the information you need to manage is bad.

Just as little as it will help to have good quality data with perfectly defined processes and an organization ready to adopt it if the PLM platform is unable to scale to your needs.

It will not help to have good quality data tied to perfectly defined processes and a state of the art PLM system either if nobody is using it….

So going back to the headline: PLM platforms, the difficult organizational rollout.
I’ve seen far too many PLM implementations underperform due to unsuccessful rollout in the organization.
I find it strange that although the projects are often run iteratively to develop or customize smaller chunks of functionality in each iteration to ensure success, one expects the end users to devour the full elephant of the project in more or less one big bite…

In my view a rollout of such a large and business critical platform should also be considered iterative and with time for the end users to come to terms with what they have learned after each iteration before the next iteration starts.
I would compare it to building a house.
​ You would never start erecting the walls before the concrete slab is sufficiently cured.
The same is true for an organization. If more functionality and new processes are put on top before the previously learned functionality and processes has had time to settle, you get resistance, and the foundation becomes weak.

Another important factor is to not only train the end users in a classroom environment and then expect them to perform well in their new system… Because they won’t.
They’re still afraid to do something wrong, and they will struggle to remember what they learned in the classroom.
Then they will try to find solutions in the manuals, and growing more and more frustrated by the minute.

If this frustration is allowed to continue for too long, you can be sure that the end result is that they feel that the system is too difficult to use and basically suck. It might sound childish, but holding hands work! Have some super users or trainers available in the everyday work situation to help and guide the users the first few weeks.​
That will mitigate the fear factor of doing something wrong, and steadily build confidence and ability.

​Bjorn Fidjeland
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Challenges when going from entrepreneur to industrialized manufacturer

1/3/2016

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​In my neck of the woods there are a lot of very talented engineers, and a lot of entrepreneurial spirit. My region (South Western part of Norway) is very much exposed to oil & gas industry and the delivery of products to plants, oil platforms etc. This means that it is very project focused and ETO (Engineer To Order) intensive.
The entrepreneurial spirit I mentioned has led to a whole host of startups with good ideas of how to solve some problem with a new product in better and more cost effective ways.

One story I keep hearing from such product companies, not only in Norway, but also in other countries and project intensive industries goes something like this:
So we won our first contract and the customer is really impressed with our product and our technology. It became a bit more expensive to deliver the project than we thought, but we managed and we were sure we would have better returns on the next project.
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As time progresses we expect our cost in the projects to drop significantly.
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​But what happens in a lot of such product companies?
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​There is nothing strange in expecting such a development. One would instinctively think that one would be able to shorten the project execution time as one gains experience and have successfully delivered such a product before. The organization knows what suppliers can deliver and which cannot. Engineers and employees in installation and commissioning are becoming more and more experienced etc.
​It becomes very, very hard to drive down the cost of delivering projects, even if the product delivered from project to project is very similar. The transition from entrepreneur to industrialized manufacturer becomes hard for a lot of these companies.
Why is that?

Personally I think there are several factors
  • It was too hard to say no to those small insignificant changes that the client required in the next project….. That for engineering or manufacturing turned out to be not so insignificant.
  • Product development is constantly being performed in the projects. Engineers will always search for the perfect and most elegant solution. That does not mean that it is the best or most cost effective way to manufacture the product.
  • Clients or operators documentation requirements in terms of LCI (LifeCycle Information) deliveries. If the product company is unable to define a process to deal with shifting requirements from operator to operator, this becomes a manual nightmare that constantly diverts resources. Such a process should be an integrated part of the project execution process, and not as it mostly is today, a separated process.
  • It is in my view paramount that smaller parts of the product at least, is standardized and modularized  in such a way that the engineering information can be re-used from project to project (You can read more about my views here: “Engineering Master Data - Why is it different?” and “Can PLM help industrializing Oil & Gas projects?”   )
  • Last but not least there is a screaming need to manage project specific engineering data (Tag structures, P&ID’s, D&ID’s, electrical) together with, but NOT in a one to one relation with more generic product development data

I’ve seen the three last bullets addressed with PLM platforms at various companies, however the technology itself is just one factor. The organizational processes and how they are enforced in the platform is of far bigger importance.

The first two bullets are a lot harder, as they require a shift in mindset from entrepreneur to industrialized manufacturer of the organization. This includes going from quick and nimble to more standardized processes, and continuous process improvement. If you consider PLM as a mindset rather than just a technology, you will also harvest  benefits here, but it is hard work.

Bjorn Fidjeland



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Customization – Upgradeability

10/24/2015

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Picture
In one of my previous posts “Customization – Do you fit in the box?” I touched upon an important aspect when deciding to customize a software platform (same principles apply whether it is a PLM platform or for instance a CRM platform).



How will my customization impact the upgradeability of the platform?
The reason why this becomes very important becomes obvious when you want to upgrade the platform from an old release to a newer release. Most companies skip one or two releases in each release cycle from the vendor before upgrading, and that makes it all the more important to perform an upgrade analysis beforehand.

If no customization is made it becomes an analysis of:
  •  What does our current data model look like, and what will it look like after the upgrade
  • What does our data look like, and how will the modifications from the vendor impact our data set.


If customizations have been made the analysis becomes a bit more complex:
  • What did the original data model of the software platform look like
  •  What does our current and customized data model look like?
  •  What will the data model look like after the upgrade?
  • Are there any conflicts between our current customized data model and how the data model will look like after the upgrade?
  • Should some of our customizations be removed since the new release covers some of our customizations?
  • What does our data look like, and how will our customizations and the modifications from the vendor impact our new data set?

In my view there are a few rules that should be followed to avoid too many problems with upgrades.

  • Try as much as possible to avoid changing the OOTB (Out Of The Box) data model itself, it is usually a lot safer to add to the OOTB data model and GUI.
  • Avoid to make changes in the OOTB business logic itself. If you have to make changes, then override the OOTB logic and create your own separately, BUT make sure to document such overrides so that you can switch back to OOTB. Remember that if you change the business logic directly, chances are those modifications will be overwritten by an upgrade.
  • If you decide to make your own data model completely customized with GUI and Business logic you are generally safe from an upgrade point of view, but you then stand the risk of not being able to benefit from the software vendors new releases of the platform in the future if they have decided to incorporate the same kind of functionality in the OOTB platform. In such cases you will be faced with a migration project, not an upgrade….
  • One of the things that always cause a hassle when upgrading is changes to the user interface (GUI). I would offer the same advice here. Do not change the original user interface! Make a copy instead, implement the changes and override the original. This is because if you change the original, your modifications will probably be overwritten during an upgrade. In addition when performing an upgrade analysis you’ll have to perform a three way comparison between the old OOTB, your customizations and the new OOTB to reveal the consequence of the changes.
  • If the software platform comes with a framework that rapidly enables you to build a user interface, data model and associate business logic, this is often preferable, BUT make sure to always analyze what new functionality is provided in the new release. Can you remove some of your older customizations? If you have overridden or hidden the old OOTB user interface, you will not be able to see the new juicy stuff that came as a result of an upgrade.
  • Never upgrade the production environment without having tested extensively in a sandbox first (a copy of the production environment). Not even the best upgrade analysis in the world will find all issues or problems when performing an upgrade

Conclusion:
When dealing with software platforms you should always perform an upgrade analysis to determine how the upgrade will impact your installation. In my view, this should be done even if you have gone strictly OOTB. Such an analysis can help you weed out the worst of the problems, and should serve as a decision point for the upgrade project. Test your upgrade and procedures in a sandbox environment extensively first before upgrading the production environment.

Some points to ponder
Bjorn Fidjeland

The image used in this post is by Dirk Ercken and purchased at dreamstime.com

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