virtual+digital (v+d) is a semi-technical / geeky(-ish!), leadership and business strategy blog which focuses on digitalization, business transformation and enterprise operations platforms, including (albeit not limited to) master data strategies and business change across the PLM+ERP+MES+CRM stack — the four cornerstones​ of manufacturing.

Product Lifecycle Management (PLM)

Product development requirements: how product attributes drive the new product introduction process

Enterprise Resource Planning (ERP)

Strategic requirements: how the corporate objectives are derived in strategic requirements

Manufacturing Execution System (MES)

Manufacturing requirements: how the product is manufactured and / or assembled

Customer Relationship Management (CRM)

Customer requirements: how they feed into product development, in terms of product and process quality

Broadly speaking, PLM knows "what" (technical requirements), ERP knows "why" (strategic requirements), MES knows "how to" (operational requirements), while CRM knows "who" (customer requirements). Read more about these principles here (blog post initially published via LinkedIn on 22-Feb-2017).

Latest insights

Need for data interpretation and traceability The Industrial Internet of Things (IIoT) is the use of Internet of Things (IoT) technologies in manufacturing. It incorporates machine learning and big data technology, harnessing ‘real-time’...


Manufacturing organisations aim to create more and better innovative products and bring them faster to market. This is enabled by enterprise product development and manufacturing tools and technologies to automate core business administr...


What is ITIL Information Technology Infrastructure Library (ITIL) provides a framework for organizing service management in an IT environment and incorporates processes such as configuration, change, capacity and release management. ITIL...


A Bill of Materials (BoM) is often compared to a recipe – both identify and list the components of a finished product. While the recipe identifies the required ingredients, preparation steps and presentation recommendations, in manufactu...


Quotes from Engineering VPs and Chief Engineers -alike, captured in various contexts while discussing Product Life-cycle Management (PLM)… Engineering efficiency, I want more PLM. I do believe you’re making my PLM twitch. Global business...


What is the difference between Product Life-cycle Management (PLM) and Enterprise Resource Planning (ERP)? There are many schools of thought around the broader PLM vs ERP topic, here are a few perspectives to consider: ERP and PLM system...


What is the difference between Product Data Management (PDM) and Product Life-cycle Management (PLM)? There are many schools of thought around the topic, and here are a few perspectives to consider: PDM was there before PLM, so it might ...


All things digital

The Digital Thread, Digital Twins, and Model-Based Systems Engineering (MBSE) are key enablers that allow program and product managers to reduce both technical and programmatic risk through better business / IT interfaces and more robust collaboration — while getting more visibility on implications from design choices on cost and schedule. 

Digitalization is all about leveraging the virtual to create, simulate and enhance the real, across digital engineering, smart operations and connected products.


From product attribute strategy, requirements, concept design to engineering, managing product development process and data lifecycle — enabled by Virtual Twins to simulate future product capabilities, performance, safety compliance and user experience.


Data joins the dots horizontally between front-end and back-end of the manufacturing execution, and vertically from shop floor to top floor — and every "thing" in between, leveraging Digital Twins with the Internet of Things and machine-to-machine communications.


From smart factories to smart products, complemented by digitally enabled services, operating in connected buildings, based in smart cities: embedded sensors, software and analytics are joining the dots between a data network of opportunities.

Connecting data sources (input devices) and data engines (machines) with digital models provides opportunities for continuous improvement, end-to-end business analytics and close feedback loops for data alignment, traceability and optimisation across the Digital Thread. Overall insights and analytics become exponentially available, leading to even more possibilities in complex operations or product interactions.

  • What data set is mastered where (it could be a number of databases or files, this is not about having a single system, but a clear original source of authoring for each data set)?
  • How data mastership evolves / changes throughout the product or service lifecycle (i.e. as one data set matured, it is combined with other data sets and transformed from its original purpose to a new purpose)?
  • How data set are combined to support business capabilities (e.g. product development, service execution, etc.)?
  • How platforms are integrated within their own components and between themselves, how data is sewed across the digital thread, and what interfaces enable this and how open / agile are these data models and connections?
  • How to motivate to embrace change and train business users to create better operations?


Virtual Reality (VR) describes an interactive computer-added experience or immersive simulation of the real, sharing important functional aspects with other things (real or imagined); by contrast of Augmented Reality (AR) which is about overlaying virtual things onto the real. VR immerses users inside virtual worlds, while AR augments the real world with virtual things. Not all virtual objects that are used today are digital models of physical objects, sometimes these objects are new concepts designed for any type of task or information encapsulation.


As technology advances, more and more things appear in digital format; any thing or object, conveniently tagged, may be able to communicate with other objects equally tagged through internet or any other protocols. These devices, objects and associated machines form the Internet of Things (IoT) which rely on data interconnections and machine-to-machine communication. Simply put, virtual objects are digital elements with a specific purpose, comprised of data series and which can perform dedicated actions.


As products and services mature, virtual twins evolve into full digital representations of the real. More and more objects called things which are merely physical start to be seen also in digital format. Digital twins are representations of product or service attributes, informing decision making from ideation to  experimentation and service optimization. Digital twins combined with IoT enable communication and integration of physical objects (with each other) and people to automate tasks and improve efficiency.  

virtual digital