Product data, machine learning and Manufacturing as a Service

Product data, machine learning and Manufacturing as a Service


Earlier this year I shared some of my thoughts how machine learning can be used to improve the quality and understanding of product data. Check out my blog – Product Data and Machine Learning as a Service. With the latest development of cloud PLM, more data about product is available via cloud services, which makes data integration much easier. Machine learning cloud services is an interesting opportunity that can lower barrier for cloud PLM systems to use machine learning in some practical examples – engineering change order, product cost analysis and others.

Forbes article – 10 ways machine learning is revolutionizing manufacturing speaks about variety of manufacturing improvement that can be driven by machine learning. Have a read and draw your own opinion. In a nutshell, it is about how usage of data can improve everything in manufacturing. The following passage is a good summary:

“Machine learning’s core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months. The ten ways machine learning is revolutionizing manufacturing include the following.”

However, one paragraph caught my special attention. It speaks about Manufacturing-as-a-Service. Read it below.

The vision of Manufacturing-as-a-Service will become a reality thanks to machine learning enabling subscription models for production services. Manufacturers whose production processes are designed to support rapid, highly customized production runs are well positioning to launch new businesses that provide a subscription rate for services and scale globally. Consumer Packaged Goods (CPG), electronics providers and retailers whose manufacturing costs have skyrocketed will have the potential to subscribe to a manufacturing service and invest more in branding, marketing, and selling.

The idea of MaaS (Manufacturing as a service) is getting more real with the latest development of cloud data data management and other communication technologies. While manufacturing is even more global and distributed than ever, the question how to use data to orchestrate this distributed environment is becoming very interesting and important.

It made me think about product data – a fundamental element of Manufacturing as a Service. Multi-disciplinary product data is combined from bills of materials, dependencies, information about product components, variants, suppliers, manufacturing option, cost and many others. The information represented by PLM, ERP and other related enterprise systems. To connect all these pieces of information together and to support a process which will interplay between many systems and organization is a very challenging tasks manufacturing companies are going to face very soon.

What is my conclusion? In order to turn the vision of Manufacturing as a Service (MaaS) into reality, manufacturing companies have to bring an information about product to the next level. It will require integration of product data beyond enterprise silos, to enable communication between companies, manufacturing and supply chain processes with granular coordination based on product data.This is a note to data and product lifecycle architects. Future manufacturing of demand services can be real, but feeding MaaS with right data will be a challenging task. Just my thoughts….

Best, Oleg

Want to learn more about PLM? Check out my new PLM Book website.

Disclaimer: I’m co-founder and CEO of openBoM developing cloud based bill of materials and inventory management tool for manufacturing companies, hardware startups and supply chain.


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