Source page: McKinsey & Company

Commentary

Visual form

Diagrammatic workflow chart.

Layout / body structure

The page uses a single left-to-right workflow-style chart rather than a conventional axis chart. Reader follows the logic from a digital model of a part through the evaluation and redesign steps that uncover sourcing and standardization opportunities.

What is being compared

It compares alternative component designs and sourcing decisions inside a digital-twin process, with the emphasis on how AI can reveal opportunities to standardize parts or restructure bills of materials.

Measurement system

This chart leans more on categorical labels and decision logic than on a numeric axis. The important labels are the part categories, design relationships, and sourcing or volume-buy implications identified along the workflow.

Visible structure inside the graphic

The graphic is built from part illustrations, labeled workflow stages, and directional links showing how one design or sourcing decision leads to another. The internal pieces are the component views, the AI or digital-twin stage, and the callouts that identify standardization or restructuring opportunities.

Main takeaway from the visual

The chart is designed to show that AI is valuable here because it exposes duplication and redesign opportunities that are hard to see in a static parts list. The visual logic moves from model to decision, making the productivity gain a matter of structure rather than just speed.

Key standout values or extremes

The page does not revolve around one dominant numeric label. Its standout element is the explicit visual link between the digital twin and the opportunities for part standardization, bill-of-material restructuring, and larger volume buys.

Controls / sequence, when applicable

This is a static chart image with no in-chart controls to operate.

Companion media, when applicable

There is no separate companion audio or video; the chart image is the full visual on this page.


Seeing double with AI

Advanced industries | Artificial Intelligence

January 30, 2023 – AI has the potential to accelerate problem solving for industrial companies in a few key ways, find senior partners Alex Singla and Bill Wiseman and colleagues. For example, AI can create a “digital twin” of a component, as a way to more quickly evaluate the component’s design across different performance metrics. The technology could help companies reduce sourcing costs for parts and bring higher-performing products to market faster.

A digital twin visualization helps identify opportunities for part standardization or restructuring of bills of materials and volume buys.

To read the article, see “The future is now: Unlocking the promise of AI in industrials,” December 6, 2022.


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