Industry 4.0: Toward More Data-Driven Decision Making

Lionel Grealou Industry 4.0 PLM 4 minutes

“In God we trust, all others must bring data.” – W. Edwards Deming

Data is everywhere

In most industries (and including the manufacturing industry), data leads to knowledge, and ultimately to Intellectual Property (IP) and competitive advantage. There is a lot to do, in business understanding terms, to differentiate valuable or useful data from non value-added data.

There are plenty of opportunities to leverage data and convert it into value with the proliferation of connected devices, big data, inexpensive storage solutions, data mining and analytics tools to visualise and navigate (or ‘drill into‘) it to the required level of detail. However, the race to value generation in the engineering and product development domain (upstream) is yet to catchup with the manufacturing domain (downstream). Often Industry 4.0 and Internet of Things (IoT) frameworks and connected devices are ‘things‘ that relate to manufacturing activities, mostly downstream of new product introduction activities.

Data supporting different decisions at different stage of the product lifecycle

The above perhaps translates into the following questions:

  • How is data used in the engineering and product development space (upstream) vs in the manufacturing space (downstream)?
  • What decisions are made upstream and downstream, and are they more data-driven or intuitive in one area and the other?
  • Are data models, types and structures similar or different and, if different, how are decisions made in the respective areas?
  • Considering that a lot of information flows downstream from engineering and product development to manufacturing, how much from the cascaded data is used downstream in the decision making process? In other words, do the decisions made downstream rely on data created (or authored / mastered) upstream?
  • How does data and trust inform the decision making process, both upstream and downstream of the product development cycle?

Insights are tacit knowledge, most decisions are intuitive

Many management practitioners (…) argue that key decisions are mostly made based on intuitions or tacit knowledge rather than rational or data-driven criteria. Obviously, that depends on the type of decision, importance and potential implications (with associated risks).

Intuitions relate to something that is known, experience-driven, perceived, understood or believed by instinct, feelings or nature without actual evidence, rather than by use of conscious thought, reason, or rational processes.

Informed decisions are data-driven

Decisions are made by people, hence they must be driven by insights, not only data, information and knowledge. Decision are informed by data, the ability to retrieve the relevant data at the relevant time, in the relevant format, accessible to the relevant people. Data that people can understand and trust.

What does industry 4.0 may bring to the decision making process? Generically, it will drive a combination of:

  • Better data backbone across functions and domains – upstream data designed for downstream usage, and downstream data feedback loops (e.g. design for manufacturing, design for assembly, etc.)
  • More robust bridge between Engineering and Manufacturing with decision-driven data intelligence – rather than data looking for a decision
  • Recognition of “data effective decisions” rather than “data-aided decisions” – from which systems (and people) can improve themselves, leveraging machine learning, and soon some sort of artificial intelligence

From informing to triggering decision making

Typically, business users and related stakeholders can see ‘data’, but they can barely trust it due to various technical and process limitations which have repetitively raised key concerns in the manufacturing industry. These concerns are due to the lack of data transparency and understanding:

  • Can users trace data back to its source and understand its dependencies and lifecycle?
  • Can they understand and make good use of its ever growing complexity?
  • Can they rely on digital tools to, not only store it, but manage and control it?
  • Can they rely on lean processes to create, update, manage it, and can they expect the right level of flexibility to support the entire product realisation cycle?
  • Can they experience the relevance and value of how it is made available to them?

Enterprise data seems more and more complex in the background and made easy to consume for human, but what do you do with it? How is the underlying knowledge captured, managed (or not), and maintained? How much do users need to understand of what machines are doing to be effective in their decision making? (…) Data traceability might not equate to simplicity and visibility: users can search and find it, but can’t they understand it and do they see the implications.

Going forward, process and data micromanagement won’t be possible anymore. Complex decisions used to be mostly irrational… Informed decisions need trusted data.

No data, No relevant data; maybe it used to be true but things are changing. 

Today one can get nearly all the data one wants and can process it to get descriptive, predictive or prescriptive insights…provided one know what one is looking for. Tomorrow, decision makers will use data to challenge what used to be led by intuition. Big data and machine-to-machine will certainly provide means to interpret complex data and help simplify and provide access and accessibility to data. Predictive analysis is going to have a huge impact on the role of future leaders and managers.

What are your thoughts?

This post was originally published on LinkedIn on 4 November 2016.

About the Author

Lionel Grealou


Lionel Grealou, a.k.a. Lio, helps original equipment manufacturers transform, develop, and implement their digital transformation strategies—driving organizational change, data continuity and process improvement, managing the lifecycle of things across enterprise platforms, from PDM to PLM, ERP, MES, PIM, CRM, or BIM. Beyond consulting roles, Lio held leadership positions across industries, with both established OEMs and start-ups, covering the extended innovation lifecycle scope, from research and development, to engineering, discrete and process manufacturing, procurement, finance, supply chain, operations, program management, quality, compliance, marketing, etc.

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