How to Escape Old Enterprise Platform Thinking and Switch to Cloud, Data, and SaaS?

How to Escape Old Enterprise Platform Thinking and Switch to Cloud, Data, and SaaS?

As the world moves towards more and more cloud-based solutions, manufacturers are looking for ways to take advantage of this technology in order to improve their operations. However, there is a lot of confusion about what exactly “the cloud” is and how it can be used in manufacturing. In this blog post, we will break down the cloud, data, and enterprise concepts for you and show how they can be used together to improve your manufacturing business.

We are a decade into cloud development in the engineering and manufacturing industry. But, in fact, I think we only starting these days. Until now, it was mostly a way to deliver expensive PLM solutions to small and medium-sized companies, but things are changing. Enterprise manufacturing companies have started to adopt cloud solutions, to learn what it means for them, what is the right architecture and how to get SaaS providers aligned with their business goals.

There are multiple topics on the agenda of enterprise manufacturing companies starting to adopt cloud models. It includes important questions about how multiple enterprise solutions can work together, how to connect multi-cloud, how to organize security, networking paradigms, data sharing, and many others. Let’s talk about them today.

From PLM to New Platforms

For the last 2-3 decades the paradigm of PLM remained the same – to become a single source of truth database to hold all information and control processes. It worked, but it had its limits. Because no one can create a platform big enough to make everything. This is how we get into the troubles of data integration, questions like “who owns the eBOM vs mBOM?” and many others. Enterprise applications providers built siloes of applications and tried to rule the world by calling themselves “platforms”.

Cloud is coming to enterprises and companies are looking for new platform paradigms and architectures. The focus is shifting from applications (PLM, ERP, CRM, etc.) to information “data” that resides inside of these products. That is more important than “applications”. Data is at the center and it requires a new way of thinking about platforms.

Big cloud infrastructure platforms (AWS, Azure, GCP, and a few others) are quickly becoming a center of platform thinking, while all applications running on top of these platforms are now “services” to provide the data and help to organize an organizational workflow. Enterprise IT likes these platforms because it gives them a better way to manage IT resources. Service providers like these platforms because it gives them seamless development infrastructure. Finally, all new SaaS applications are running on top of these platforms and also make them available to enterprise companies to build their solutions. Altogether, it transforms the vision of old PLM platforms into a new way of thinking about them as services available for AWS, GCP, and Azure.

Data, Data, Data…

Data is the fuel of digital transformation. Manufacturing companies are looking at how to transform their operation, to make them more efficient, intelligent, and competitive. How to make it happen? Data is the answer. To be able to answer intelligent questions on time about any aspect of design, manufacturing planning, operation, and maintenance, you need to get access to the information you need. This is where companies are starting to be obsessed with data access, data sharing, data consolidation, analysis, and visualization.

Data is needed everywhere. Therefore, everyone in the new enterprise platforms will become a data service provider. Moving from “data hoarding” to “data sharing” will allow us to make the data available, but also to transform the business models. In the past legacy, PLM platforms were holding data and upselling their applications. New platforms will create and share data to fuel solutions and upsell data access (services).

Enterprise SaaS and Web Services

The beginning of SaaS in PLM started with finding a way to host existing platforms in the cloud (eg. AWS) and continue to do business with the same and new customers by selling subscriptions. It quickly became obvious that the SaaS model or on-demand software model proposed by “hosting vendors” only can solve the problem of IT, upgrades, and business models. From everything else, these platforms remained “closed silos” exactly how they have been before when companies installed them in their IT departments.

SaaS and Web services are different because they bring agility, collaboration, and modern multi-tenant architecture to deal with customer data in the most robust way. Building a multi-tenant platform capable to provide solutions to the entire enterprise while keeping each company owning its own data is a big shift from old silos. Now data is connected and shared, applications can get access to the data in multiple systems to support decision-making on the enterprise levels. All these can be possible because of the new architecture and modern technological approach that is finally getting adopted by enterprise manufacturing companies.

What is my conclusion?

Enterprise manufacturing companies are getting into the cloud. Old platform thinking of hosted silos wont work there. The first thing they need to do is to understand how to manage multiple networks of applications, silos of data, and cross-company processes and make all these things sustainable. Hosting multiple applications with 20+ years old architecture could be a good first step, but it will not be around for a long time. Enterprise companies will be looking for new computing solutions capable to manage data, and processes, and focusing on the data and data availability, and intelligence rather than data control in silos. Just my thoughts…

Best, Oleg

Disclaimer: I’m co-founder and CEO of OpenBOM developing a digital cloud-native PDM & PLM platform that manages product data and connects manufacturers, construction companies, and their supply chain networksMy opinion can be unintentionally biased.


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