Future PLM selection: It is like to get married or buy a smartphone

November 17, 2014

decision-making

Ask people about PLM selection process. You can get an impression it is not a simple process. The same can be said about any decision related to enterprise software – it is complex. Today, I want to take a look on that from a bit different perspective.

I know few manufacturing companies that literally spending years to make a decision. In PLM domain, it is quite regular to invest few months in investigation about what is the right PLM system for your organization. You can find many articles and presentations from vendors, service providers and industry analysts about PLM selection process. Here are just few of them (priority order by Google search) – Gartner PLM selection criteria; The art and science of selecting the right PLM for your organization; PLM selection – do this.

Jos Voskuil (you might know him as Virtual Dutchman) just posted another story that caught my attention during this weekend – PLM selection proof of concept observation. Have a read – this is very thoughtful article. I found it as a very good advise how to manage complex enterprise software selection process. For good and for bad, it can be applied to any piece of software, hardware and probably even to situations beyond that like decision about business partnership, etc. I replaced word “PLM” in the article by “ERP” and found it as a very good piece of advise too. I tried to applied it by “mobile solution” or “home theater system” – hold your breath… with some stretch, I think you can use it as well. Because.. the process of decision making is very similar.

It made me think about lifecycle of things and solutions around us. So, I want to come with two examples – ERP system and smartphone. Navigate your browser to the following article by fairphone – Next step in lifecycle assessment. Article speaks about average lifecycle of smartphone is about 2-3 years these days.

average-lifespan-of-phone

The following article – Why your new smartphone is already obsolete by MyPhoneMd brings you an interesting breakdown of smartphone lifecycle by country. From absolutely extreme case of 21 months in U.S. it goes to some more conservative numbers in Europe, which is about 40-50 weeks.

Another example – ERP system (I wanted to come with PLM and CRM, but didn’t find any meaningful data). Bluelinkerp blog – When should you replace your ERP software brings an interesting diagram – the majority of ERP implementations is up to 7 years old. The chart based on data provided by Aberdeen study – Aging ERP – When your ERP is too old.

erp-system-age

Now, I want to take it to extreme. Lets speak about marriage. For most of us, this is a fundamental decision we supposed to take for a very long time (I hope most of my readers would agree). However, modern statistic provide a bit different data points. Navigate to the article – 32 shocking divorce statistics article and you learn that average marriage lifespan is 8 years (almost like ERP system in my example above). My hunch, we can improve this situation by applying of some recommendation and observations from Jos’ article about PLM proof of concept, but this is already different story for another blog post.

What is my conclusion? The lifespan of things in our life is getting shorter. While it is sad in some situations like family, it is probably good for most of other examples I mentioned. In my view, this is a reflection of speed of changes in technology and industry. What it means for your PLM selection process? Do it faster and think what pains you can kill and what processes you can improve in your organizations in a short period of time. Modern trends in software development – cloud and SaaS will make software lifecycle easier and replacement less painful. Just my thoughts…

Best, Oleg

photo credit: toprankonlinemarketing via photopin cc


Will PLM crunch untapped data in manufacturing organizations?

January 24, 2013

Do you remember the golden era of desktop searches? I remember first time I had a chance to run Google Desktop on my computer. The most inspiring moment was to see documents and emails that you completely forgot about. Today, desktop search solutions are not as popular as before. Our personal digital life moved to the cloud. Application search, such as Outlook search and others improved significantly (thanks to open search solutions reused by many vendors). The focus of "data crunch" moved from a single desktop solution to cloud and mobile devices. Despite a huge promise of enterprise search solutions, majority of them are experiencing difficulties to provide efficient, reliable and cost-effective solution that can help to organization to capture and search trough massive amount of digital data. Focused search solutions are more efficient and we can see them coming from enterprise software vendors.

However, it doesn’t solve the problem of huge amount of existing data in organizations. I’ve been reading Crowdshifter article Behold The Untapped Big Data Gap. It shows some data coming from IDC study. Here is an interesting quote:

…article reported that 23% of data within the digital universe of 2012 could be useful for big data collection and analysis purposes if tagged. However, there is a huge gap in the amount that has been tagged versus the amount that remains without semantic enrichment. Only 3% has been tagged and only .5% has been analyzed.

Source: IDC/EMC.

Manufacturing organizations are desperately looking how to improve their decision management process. To leverage the existing data in an organization can be an interesting approach. I can bring many examples from PLM space where data about change management history, maintenance, suppliers, etc. can help to make a better decisions. For the moment, the majority of the information stored in application silos and cannot be used in an easy way. This data can easy become digital garbage similar to last year papers on your desktop and similar to old documents and email on your desktop before desktop search era.

What is my conclusion? To analyze data is a tough job. It requires computing resources, time, investment and smart algorithms. Google laundry list of results won’t be helpful. The new methods of data crunching and data discovery need to be developed. With only .5% of data analyzed and 3% of data tagged, we have a huge potential to tap in. Just my thoughts…

Best, Oleg


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