ManufacturingDecember 18, 2019

20 in ’20: Digital Manufacturing Tips for 2020

The digital transformation of manufacturing has advanced sufficiently in a number of…
Avatar John Martin

The digital transformation of manufacturing has advanced sufficiently in a number of companies that they are diverting their attention from groundbreaking initiatives to “dotting the i’s and crossing the t’s”—fine-tuning their digital manufacturing operations with innovative accents and refinements that further the vision and execution of a fast-flowing, adaptive, digital manufacturing sphere. It’s been a few years since I had 20/20 vision, but here, in no particular order, are 20 things I think they are, or might consider, sandboxing—or in some cases rolling out, depending on the level of digital maturity—in 2020 across their operations. The objective, as always, is more real-time, granular information, and targeted at the point of production.

Driving down visibility to the shop floor. Let outside customers interact with production teams via messaging channels for questions, order visibility, and digital worker empowerment.

More compute power at edge. Embed machine learning within Industrial Internet of Things (IIoT) assets to analyze sensor readings of real-time circumstances and adapt accordingly.

Microlearning. Provide on-demand, in-task snippets to help digital workers complete jobs.

Augmented Reality (AR) on the line. Display realistic digital twin data to production workers.

Digital twin for Manufacturing Execution Systems (MES) and Manufacturing Operations Management (MOM) systems. Convey iterative improvements to the production process bidirectionally between the virtual and real worlds.

Point of Sale (POS) data. Stream this directly to manufacturing to update the production plan.

POS and consumer sentiment data. Capture these as margin notes in the digital twin to eval suggested changes.

AR- and Virtual Reality (VR)-based instruction. Utilize these to compress training and upskilling cycles.

Microcredits for training hours. Apply them toward community college and vocational competency certificates.

Spare parts models. Continuously update them in the digital twin to reflect IIoT field data.

Manufacturing instructions to outsource partners. Embed your ethical and sustainable sourcing guidelines within the manufacturing models, instructions and specs.

Quality assurance of supplier parts. Location-map every source point along the supply chain, including subcontractors to first-tier outsource suppliers, to track compliance with ethical and sustainable standards as precisely as you perform quality inspection.

Additive-manufacturing-based mini-production cells. Place them in microfulfillment warehouses in densely populated areas to complete last mile/next day delivery with production on-demand, reducing constant, costly inventory moves.

Invite customers to visit “showcase” digital manufacturing sites. Give them the experience of digitally customizing their products and observing their manufacture.

Autonomous, “clued in” apps. Use these to send notices of production delays to downstream functions like packaging, logistics, and customer account execs.

Machine learning. Build into apps that have in-memory computing for “AI on the fly.”

Decentralize intelligence. Take a cue from the human autonomic system and bring more smarts—from that will spring their progeny, autonomy—to the individual production resource.

Self-interested generosity. Share advanced apps like simulation and machine learning with smaller, less tech-savvy partners to further ecosystem-wide digital transformation.

Institutes like MxD (Manufacturing x Digital). Turn to them for sandboxes, and as learning centers for digital manufacturing knowledge and expertise.

Digital collaborative platform. Rein in “rogue” apps, bringing them onto a single, unified scaffolding for the digital transformation of manufacturing.

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