Existing data prevents companies to improve Part Numbers?

August 15, 2014


Part Numbers is a fascinating topic. I’m coming back to blog about what is the best approach to manage Part Numbers. My last post about it was – Part Numbers are hard. How to think about data first? was just few weeks ago. In that article, I outlined few principles how to keep PN separate from surrounding data focusing on different aspects of parts – description, classification, configurations, suppliers, etc.

Yesterday, my attention was caught by ThomasNet article – Are Part Numbers Too Smart for Their Own Good? The article nailed down a key issue why companies are still having difficulties with management of Part Numbers. Nothing works from scratch in engineering companies. Complexity of characteristics and history of existing Part Numbers and products are making real difficulties to adopt new PN management concepts. The following passage explains the problem:

Another problem with descriptive numbering is that the description can become out of date and irrelevant over time. Individual parts can have their own life cycles; if a part has been identified according to the product, what happens if that product is discontinued but the part continues to be used in a newer product? Or what if a manufacturer changes vendors and the part number contains the name of the vendor that originally provided the piece?

Gilhooley admits that some Ultra Consultants clients have decided that switching from descriptive to auto-generated numbering would require too much organizational change. Some companies stick with old systems, and some opt for hybrid systems that perhaps retain descriptive numbers for existing parts but use auto-generated numbers for new parts.

It looks like there is no single solution or best practice to solve the problem. The "traditional" engineering approach to keep options to manage a diverse set company configuration looks like the only possible way to solve this problem in existing PLM/ERP systems.

What is my conclusion? History keeps customers from moving forward. There are two aspects of complexity in Part Numbers: 1/ complexity of definition and data classification; 2/ historical records of PN in every company including catalogs and existing products. Together, they create a block to make any changes in existing PN schema and prevent companies from migration towards new approaches. New data modeling technologies must be invented to handle existing data as well as supporting customers to migrate into modern PLM and ERP solutions. Just my thoughts…

Best, Oleg

Will public clouds help enterprises to crunch engineering data?

August 6, 2014


The scale and complexity of the data is growing tremendously these days. If you go back 20 years, the challenge for PDM / PLM companies was how to manage revisions CAD files. Now we have much more data coming into engineering department. Data about simulations and analysis, information about supply chain, online catalog parts and lot of other things. Product requirements are transformed from simple word file into complex data with information about customers and their needs. Companies are starting to capture information about how customers are using products. Sensors and other monitoring systems are everywhere. The ability to monitor products in real life creates additional opportunities – how to fix problems and optimize design and manufacturing.

Here is the problem… Despite strong trend towards cheaper computing resources, when it comes to the need to apply brute computing force, it still doesn’t come for free. Services like Amazon S3 are relatively cheap. However, if we you want to crunch and make analysis and/or processing of large sets of data, you will need to pay. Another aspect is related to performance. People are expecting software to work at a speed of user thinking process. Imagine, you want to produce design alternatives for your future product. In many situations, to wait few hours won’t be acceptable. It will be destructing users and they won’t use such system after all.

Manufacturing leadership article Google’s Big Data IoT Play For Manufacturing speaks exactly about that. What if the power of web giants like Google can be used to process engineering and manufacturing data. I found explanation provided by Tom Howe, Google’s senior enterprise consultant for manufacturing quite interesting. Here is the passage explaining Google’s approach.

Google’s approach, said Howe, is to focus on three key enabling platforms for the future: 1/ Cloud networks that are global, scalable and pervasive; 2/ Analytics and collection tools that allow companies to get answers to big data questions in 10 minutes, not 10 days; 3/ And a team of experts that understands what questions to ask and how to extract meaningful results from a deluge of data. At Google, he explained, there are analytics teams assigned to every functional area of the company. “There’s no such thing as a gut decision at Google,” said Howe.

It sounds to me like viable approach. However, it made me think about what will make Google and similar computing power holders to sell it to enterprise companies. Google ‘s biggest value is not to selling computing resources. Google business is selling ads… based on data. My hunch there are two potential reasons for Google to support manufacturing data inititatives – potential to develop Google platform for manufacturing apps and value of data. The first one is straightforward – Google wants more companies in their eco-system. I found the second one more interesting. What if manufacturing companies and Google will find a way to get an insight from engineering data useful for their business? Or even more – improving their core business.

What is my conclusion? I’m sure in the future data will become the next oil. The value of getting access to the data can be huge. The challenge to get that access is significant. Companies won’t allow Google as well as PLM companies simply use the data. Companies are very concerned about IP protection and security. To balance between accessing data, providing value proposition and gleaning insight and additional information from data can be an interesting play. For all parties involved… Just my thoughts..

Best, Oleg

Photo courtesy of Google Inc.

PLM, ERP and the death of over the wall engineering

July 31, 2014


Do you remember "throw over the wall of manufacturing" statement? This is a traditional engineering world. Pretty much sequential. Engineers are doing their job and throw it over the wall to the next stage. Traditional manufacturing was driven by sales forecast. This is was the world that formed a traditional domains of PDM/PLM and ERP. The engineering job was a black box – product design delivered to manufacturing. Manufacturing people supposed to take design and make it work to production. The processes required lots of back and forth communication. The result you know – skyrocketing cost of change requests, suboptimal design and after all production delays.

There are lot of changes in manufacturing environment these days. One of the most interesting example is growing number of smaller manufacturing companies / startups. I wrote about that few months ago in my post – Why Kickstarter projects need PLM? Today, I want to speak more about that. LineShapeSpace article – Manufacturing Inventory Management: How Much Inventory Do You Need? caught my attention. The question sounds obvious. However, article speaks about looking on inventory from completely different perspective – engineering and growth.

Growth is an essential part of every startup. This is probably one and the most important goal to stay focused on. However, the specific part of manufacturing company is the cost of parts and size of the inventory. To hack the growth path is not simple. To go on the wrong path means to literally to die. Here is my favorite passage from the article

This mismatch is expensive. It usually means high inventory carrying costs while you chase down a lot of little customers and invest resources into getting—and keeping—their relatively small orders. The inverse relationship impacts cash flow and energy level significantly, as well as your ability to feed yourself. Long term, this kind of business will most likely be a hobby, not something that sustains you, absent significant investment or luck.

In order to develop a successful product and find a right inventor path, you need to re-think a traditional engineering-manufacturing process. No more over the wall process. You need to design for optimal manufacturing, sourcing, inventory and many other factors. Which means engineering and manufacturing team to work together. My hunch, there is no traditional PLM/ERP boundary any more. Here is another quote to emphasize that:

“We used every fancy prototyping technology, investigated multiple production scenarios, and ultimately landed our production with great manufacturing partners near Hong Kong…utilizing ‘traditional manufacturing’ for production [was] an ordeal to set up, but yields quality, repeatable parts thereafter. The decision to move at this scale of production required that we grow a global sales and fulfillment network.

That wasn’t exactly an ambition for a first our product…but it’s certainly an interesting, if occasionally harrowing, game.” The takeaway from all of this? Do your best to match the inventory risk to your channel risk. It’s a lot easier, faster, and cheaper to go back to the design drawing board than it is to return a container ship to China.

What is my conclusion? We are going to see the world of manufacturing changing in front of us these days. It may change (and probably already changing) the traditional engineering, production planning and manufacturing boundaries. What was true in an old PLM/ERP world will change. The new forms of manufacturing will require re-thinking of current software. Interesting time for PLM and ERP analysts, product managers and vendors. Just my thoughts…

Best, Oleg

PLM and Manufacturing Startups: Potential Mismatch?

July 14, 2014


Selling PLM for SME was always a very controversial topic among PLM vendors. No consensus here. I wrote about it few months ago in my Why PLM stuck to provide solution for SME post and got interesting follow up conversations with few industry pundits.

Every PLM vendor has some special product offering ready for SME market segment. But did it work well to anybody? My hunch, most of "successful PLM SME" implementations are focusing on basic CAD/PDM features. Very few SME organizations successfully implemented a complete PLM system including BOM, change management, configurations, manufacturing integration, requirement management and more. If you got a chance to see one, it is typically result of huge effort of people in the organization itself committed to make it work.

One of the most typical reasons for PLM vendors to sale to SME was high cost of implementation and sales multiplied by absence of IT people ready to handle PLM implementation. In my view, PLM vendors have a great hope to make it easier with modern cloud based PLM offering, but jury is still out to watch results.

Meantime, manufacturing landscape is getting even more interesting. Hardware is the new software. Nest, GoPro, Beats, Jawbone, Oculus… You’re welcome in the world of manufacturing startups. I touched it in my earlier post – Why Kickstarter projects need PLM? Yesterday, my attention was caught by TechCrunch article – Hardware Case Study: Why Lockitron Has Taken So Long To Ship. Read the article – I found it very interesting. The following passage explains basically that from "limited assembly", manufacturing startups are moving towards full manufacturing cycle:

In our initial RFQs (“request for quote”) we leaned heavily towards manufacturing entirely in the United States. Our impetus for this was largely around logistics; if we could make everything domestically, we wouldn’t have to travel far and wide to ensure the quality we expected. It quickly became apparent that manufacturing domestically would cost far beyond what we had budgeted for. Given the number of parts, required touch time (the amount of time it takes someone to assemble a product), various materials and processes used, building entirely in the U.S. wasn’t viable. Potential domestic suppliers still looked East for most of the components we needed, albeit with longer lead times.

However, even more interesting quote is the following one explaining the level of challenges during the development processes.

We spent the next few months redesigning our gearbox to reduce noise while increasing power to deal with sticky or hard-to-close locks. While the choice was the right one to make, it cost us valuable time; a few parts had to be retooled and there were cascading effects on our electronics and supplier choices. We selected an ultra-efficient, powerful motor to place at the lock’s heart, but this also impacted our timeline. Most challenging, however, was the meshing of electronic and mechanical worlds. An initial circuit board design proved overly complex and underpowered.

As you noted the complexity of product including mechanical and electronic parts is very high. In addition to that, even it wasn’t stated explicitly by the article, I can see a growing complexity of integration between electromechanical and software components.

What is my conclusion? The complexity of manufacturing startups is growing. To scale product development and manufacturing is a very challenging job. And all must be done in a craziest timeline – the reality of every startup. Manufacturing startups is an interesting niche that clearly different from typical SME organizations we’ve seen before. The challenge of PLM with a typical manufacturing SME is to compete with a status quo of existing processes and tools. Manufacturing startups are different – absence of processes, startup culture and an absolutely need to get job done in a very short timeframe. It would be interesting to see a growing demand for PLM tools as well as growing complexity of product development and supply chain in these organizations. What PLM tools will provide an answer? Good question for PLM strategists these days. Just my thoughts…

Best, Oleg

PLM Best Practices and Henry Ford Mass Production System

April 6, 2014


If you are in PLM business, I’m sure you are familiar with term called "best practices". The term is widely used to explain how PLM system can be deployed, how to manage data and how to organize and optimize product development processes. So, where are roots of PLM best practices and why PLM vendors like them so much? Remember, the original PLM (and even PDM) systems started as a glorified data management toolkit with elements of CAD and ERP integrations. To get such system in product was very expensive and it required lot of time and implementation services. The reason is simple – every manufacturing company is different. It takes time for service provider to understand company landscape, processes, data requirements, legacy systems and suggest a solution. Put heavy price tag next to this activity. You can think about this process as something similar to organizing mass production assembly line. It is costly and complicated. Once you’ve get it done, your objective will be simple – run it to the largest possible quantity without re-configuration (which will cost you money, again). The same happened with first large PLM implementations.

The invention of "best practices" helped to figure out how to move from heavy and complicated PLM assembly line to more configurable and flexible mechanisms of PLM deployment. Technologically, toolkit approach was a underline product foundation. PLM companies and especially service providers and PLM consultants liked the approach. To create OOTB (out-of-the-box) pre-configured environments was relatively easy based on the practices gathered from existing large customers. However, to get it to the field and implement wasn’t so simple. Marketing and sales used OOTB environments to demonstrate and make sales. However, implementations and fine tuning was failing to apply it after that. The implementation devil was in details and service teams were required to bring to production. Similar to manufacturing mass production environment, customizing and services was a straightforward answer to solve the problem of product and requirement diversity.

As we know from the history of manufacturing, mass customization won and left mass production system in a dust. What was clear innovation 100 years ago was replaced by new forms of manufacturing, customization and flexible manufacturing units. I believe this is still very hot topic in the industry and every manufacturing company. The diversity of product requirements is skyrocketing, product lifecycle is getting even shorter. To produce PLM system that will fit this type of environment is probably one of the most important innovation that might happen in engineering and manufacturing software technologies these days.

What is my conclusion? I think software companies can learn something from the history of manufacturing companies. The move from from mass product to mass customization is one of them. PLM software made a turn from from complicated preconfigured assembly lines to expensive data management toolkits that require services. Manufacturing is getting different these days. Next step can be hardly achieved by pure technology or process organization. My hunch it is going to be a hybrid of new data management technologies empowered by crowdsourcing and customer innovation. Just my thoughts…

Best, Oleg

Photo source.

How not to miss PLM future?

March 23, 2014


The world around us is very disruptive these days. Nothing stands still. You cannot stop innovation and progress. Engineering and manufacturing software is not fastest changing domains. It explained by slow changing process, high level of complexity in product development and significant capital investment manufacturing companies made in existing PLM and other enterprise software. Nevertheless, to think PLM will stand still is probably a mistake that potentially can happen in the community PLM vendors and experts.

I’ve been reading Google CEO Larry Page Spoke At TED article. Unfortunately, TED didn’t stream his talk, so everything based on twitter stream. My favorite passage was related to the Page’s explanation about why companies are failing. Here is the quote:

"The main thing that has caused companies to fail, in my view, is that they missed the future," Page said.


The article made me think about what potential "future" that PLM companies can miss today in our fast moving engineering and manufacturing software ecosystem. So, I decided to look into my ‘crystal ball’ today and pickup top 3 things that potentially can be missed by PLM vendors:

1- Downturn in premium price of PLM software

The price of PLM software is a challenging factor. Which is true, in general, about enterprise software. I think, customers are worrying about what will be total cost of ownership for PLM software. Result – huge interest to develop ‘predictable business models’, which include scalable parameters identifying how to pay for PLM software. The strategic mistake that can be done by PLM vendors is to miss the point where new TCO models will be conflicting with existing business and revenue models.

2- Switch from data ownership to openness and data share business values

Openness is another heavily discussed topic in engineering software. The demand of customers is not be locked on a specific vendor. The situation when company is using software from different vendors is not rare and if we include supply chain scenarios, openness requirements is probably one of the most critical. However, most of business models today are fundamentally assuming customer lock on a particular type of software, file types, databases, etc. Technology and business disruption in this space can remove lock and become a surprising factor for existing vendors.

3- The importance of vertical integration.

Integration of enterprise business and information systems becomes more and more important. Manufacturing and production environment is moving towards digital forms of mass customization. The involvement of engineers into the process of manufacturing is getting more tight. The future cost saving is in even deeper optimization between product design and manufacturing. By missing the importance of these aspects existing vendors can be outperformed by modern cloud (and not only) vendors and newcomers.

What is my conclusion? Some people calling what happens these days in manufacturing as the next industrial revolution. I don’t want to put specific stickers. Nevertheless, engineering and manufacturing business is getting even more competitive. Internet, cloud, diverse competition, cost pressure and new business models – this is only short list of disruptive factors that will be very important in the future of digital manufacturing. Just my thoughts…

Best, Oleg

Why PLM vendors might decide to beat Amazon?

March 21, 2014


Amazon is an absolutely marketshare leader in cloud computing. Because "cloud" is such a big and vague word these days, we must clarify and say "public cloud". So, you may think for most of us, cloud is equal to Amazon. AWS EC2 allows us to spin new servers quickly and provide great services to everybody interested in development of SaaS packages.

Not so fast… Questions are coming too. I can see two major ones – cost and strategy. I’ve been posted Cloud PLM and battle for cost recently. Amazon public cloud is coming with challenging cost sticker to some of us. Strategy question is connected to many factors – PLM PaaS opportunity, security and storage alternatives. Finally, with huge respect to Amazon, I’m not sure how many CAD / PLM companies are interested in catholic marriage between cloud PLM platforms and AWS. To provide PLM solution independent from Amazon IaaS and to control data storage is an interesting option for many vendors and partners. How to do so? I think, this is part of strategy for every PLM vendor these days looking how to develop long term relationships with manufacturing OEMs and suppliers.

My attention caught Gigaom article – Want to beat Amazon in the cloud? Here are 5 tips. Read the article. It provides some interesting opportunities how to compete AWS. It raises the point that in 2014 AWS became an elastic service commodity competing on cost. This is an interesting quote explaining that -

But fast-forward to 2014: there are dozens of IaaS providers offering similar capabilities. The selling points — like self-service, zero CAPEX and elasticity — that once made the cloud look exciting are not as appealing anymore, and they are no longer the differentiating factors. In the current context, selling cloud for its self-service capabilities is similar to Microsoft trying to sell the latest version of Windows only for its graphical interface.

Cost is important. However, for enterprise, value is often even more important. Therefore, speaking from the perspective of PLM players, my favorite passage is related to how to support scale-up and shared storage:

AWS’s philosophy of throwing more VMs at an application is not ideal in many scenarios. It might work wonders for marketing websites and gaming applications but not for enterprise workloads. Not every customer use case is designed to run on a fleet of servers in a scale-out mode. Provide a mechanism to add additional cores to the CPU, more RAM and storage to the VM involving minimal downtime. The other feature that’s been on the wish list of AWS customers for a long time is shared storage. It’s painful to setup a DB cluster with automatic failover without shared storage.

Here is my point. I think, CAD and PLM vendors will have to discover how to provide a balanced and scalable cloud platform. This platform will have to answer on questions how to scale from the solution for small manufacturers and mid-size companies to enterprise OEMs and Tier 1 suppliers. The border between these segments is vague. It is hard to develop two distinct PLM offerings and support two separate platforms. It was hard in the past on premise software and it is even more complicated on the cloud.

What is my conclusion? PLM providers will have to discover how to grow up from AWS-based offering and develop scalable cloud PLM platforms. It must include diverse options for data storage as well as computing power. So, to beat Amazon can be not such a dream option for PLM vendors like it looks from the beginning. Just my thoughts…

Best, Oleg


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