# Benjamin Reynolds

Benjamin Reynolds develops spatial-computing tools for manufacturing, using phone-based 3D scanning, motion tracking, and AI to help teams study and improve work processes.

- Profile: https://b2binfluencer.marketing/creators/benjamin-reynolds/
- LinkedIn: https://www.linkedin.com/in/benolds
- LinkedIn followers: 693
- Topics: continuous-improvement, lean-manufacturing, customer-research
- Based on 10 dated posts, latest 2026-10-06

## Summary

Most useful for manufacturing spatial-computing content that explains phone-based 3D scanning, motion tracking, and AI for work process studies.

- Best for: Enterprise software or AI brands building spatial computing tools for manufacturing operations.
- Not for: Consumer AR or VR entertainment brands and general AI productivity tools seeking broad paid creator reach.
- Check first: Confirm independent measurement of reported productivity gains and audience breakdown before contacting.

### Takeaways

- Overlaying 3D graphics was not enough; computer vision and AI capture workspace shape, movement, context.
- Process: scan with iPhone or iPad, record, analyze in browser-based 3D tools, then view insights.
- Lean-production study covers 3 Ms, 5 Ss, 7 Wastes, value stream mapping, Little's Law.

## Main ideas

- **Spatial computing matters when it helps people improve physical work**: Reynolds describes augmented reality as less useful than they had hoped when it mainly placed 3D graphics over the real world. They say computer vision and AI made a more practical approach possible: using a phone camera to capture a space’s shape, movement, and context, then combining that data with tools for analysis and interaction.
- **Start with a specific work problem, then make improvement easier to repeat**: Rather than describing a general-purpose technology, Reynolds focuses on the manufacturing tasks the tools are meant to support. The proposed value is that engineers can study more processes, more often, and use analysis to guide changes instead of relying on manual methods alone.
- **Customer understanding should shape what gets built**: Reynolds says understanding customers’ problems is necessary to help them well. They connect that principle to product decisions, describing customer feedback as a way to distinguish ideas that can become useful products from experiments that should remain experiments.
- **Lean methods can help explain improvement beyond manufacturing**: Reynolds treats lean production as more than a set of factory-specific terms. While studying the subject, they suggest its ideas can also help people build and maintain well-running systems more broadly. Product posts use related language, including kaizen and continuous improvement.

## Work to read

- [Launching Data Time Space: from an MIT lab email to a spatial-computing company](https://www.linkedin.com/posts/benolds_12-years-ago-when-i-was-a-sophomore-in-college-activity-7313185136403963904-T7s7)
- [Why AR needed computer vision: taking Kaizen enhancement live on the Azure Marketplace](https://www.linkedin.com/posts/benolds_spatialai-kaizen-manufacturing-activity-7391528556474032128-_jsp)
- [A four-step walkthrough of the process](https://www.linkedin.com/posts/benolds_we-just-launched-a-faster-easier-way-for-activity-7379152153732612096-hnV_)
- [Studying lean production on the side](https://www.linkedin.com/posts/benolds_the-deeper-you-understand-your-customers-activity-7414114782854967296-D1eS)

## Brand relationships

- **Data Time Space** (owned_business)
- **PTC** (employment)
- **MIT Media Lab** (employment)
- **Microsoft Azure Marketplace** (disclosed_partnership)

## Partnerships

No sponsorship program, media kit, rate card, or partnership inquiry route is published in the posts. Instead, Reynolds and the Data Time Space team invite companies to request demos, describe ten months of adding customer-requested features to make the system enterprise-ready, point to distribution through the Microsoft Azure Marketplace, and ask readers to send manufacturing teams their way for safety and efficiency work. For buyers exploring spatial computing in manufacturing, the practical route is a product and domain conversation, not a paid creator placement. Expect discussion to centre on trialling an iPhone-based 3D scan and motion-tracking tool on real work processes, ergonomics and time-motion studies, and enterprise rollout requirements. The posts don’t specify partnership terms, deliverables, or pricing, so ask the team directly and confirm feasibility before committing a budget.

## Alternatives

- Devin Bhushan: Choose Bhushan when a buyer needs an AI-agent-led continuous-improvement vendor already selling automated time-and-motion analysis, or wants to understand how a direct competitor positions AI on the factory floor. They describe an AI agent that takes the stopwatch and clipboard out of engineers' hands and runs full time-and-motion studies, and cite an AI that watches 18 minutes of a changeover and returns a study classifying every segment against the seven wastes. They also describe the staffing gap this creates, hiring a Head of Lean Six Sigma to bridge factory experts and AI builders—a useful perspective for a buyer deciding whether its own product needs domain expertise built in.
- Prasad Velaga, PhD: Choose Velaga when a buyer needs production-scheduling and operations-research depth: throughput, WIP and lead-time tradeoffs, advance what-if assessment of improvement projects, and control of true high-mix, low-volume job shop production. Velaga argues that lean practitioners pursue every visible improvement opportunity without predicting its benefit, and proposes scheduling software and discrete-event simulation to test the impact on throughput, lead time and WIP before committing. For a buyer whose product produces process data, Velaga supplies an analytical framework for deciding which process is worth optimizing.
- Eric Ciampoli (MBA): Choose Ciampoli when the buyer needs a services-led route into manufacturers: ERP alignment, process optimization, plant visibility, and keeping procedures current as equipment and processes change. ECIC's OPSIDIAN platform is positioned as turning operational knowledge into something accessible, current and actionable. The Manufacturing Day post shows the team in direct conversation with manufacturers about the challenges shaping their operations, offering a plausible channel to operations leaders who would host a spatial computing pilot.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/