# Jay Byam

Jay Byam shares practical perspectives on manufacturing operations, with a focus on improving OEE and downtime data so teams can identify problems and make better-informed improvement decisions.

- Profile: https://b2binfluencer.marketing/creators/jay-byam/
- LinkedIn: https://www.linkedin.com/in/jay-byam-0a107b122
- LinkedIn followers: 1,734
- Topics: operational-excellence, continuous-improvement, lean-manufacturing
- Based on 11 dated posts, latest 2026-10-06

## Summary

Jay Byam is most useful for practical manufacturing content on OEE and downtime data decisions.

- Best for: Manufacturing software brands wanting thought leadership on OEE, downtime data, and operational behavior.
- Not for: It does not fit broad consumer or general-business brands seeking high-volume, short-form product demos or lifestyle content.
- Check first: Confirm engagement, audience demographics, sponsorship terms, and whether they accept sponsorships.

### Takeaways

- OEE tracking fails when logging is cumbersome and operators never see useful results.
- Plants losing most money to downtime often keep weakest downtime records.
- Put a dollar figure on each specific failure cause, not just total production.

## Main ideas

- **OEE tracking fails when the system gives operators nothing back**: Jay Byam argues that the usual explanation for abandoned OEE tracking — operators who are "not disciplined enough" or "not bought in" — misses the real cause. Spreadsheet-and-paper tracking works for a while, then the binder fills, the spreadsheet gets messy, downtime reasons collapse into "misc," and operators stop logging because no improvement comes from it. The fix they describe is a system that fits how the work actually flows, makes logging fast and low-friction, and turns entries into visible feedback. The standard is deliberately modest: data collection does not have to be perfect; it only has to be worth doing.
- **The plants that lose the most to downtime often keep the weakest downtime records**: Jay Byam explains why downtime costs and downtime data quality move in opposite directions. Leadership at expensive plants requires proof before approving improvements — downtime data to justify replacing a machine, lost hours to justify more operators — so the data has to withstand scrutiny. But when a line is down, restarting it takes priority over documenting it. Details get lost, failures are simplified, and events are missed. The result: a weak case for the fix, repeated questions, delayed spending, and the same failures recurring.
- **Put a dollar figure on each specific failure cause, not just total production**: Byam separates a data problem from a prioritization problem: even when plants have data, it often fails to answer "What should we fix first?" OEE reports how a plant is performing, but not what matters most. Pricing individual downtime causes shifts the conversation from opinion to evidence, moves managers from lobbying to proving, and gives operators numbers behind them. The key is specificity: dollars tied to particular causes, not just aggregate production.
- **The team, not the software, decides whether a data project sticks**: Byam argues that successful downtime-data projects depend on engaged people on site. In the project they describe, production engineers helped shape how the data should work, and operators improved the system while on shift — flagging entries that did not reflect reality, simplifying steps so they worked in practice, and catching edge cases early. The payoff was better data, faster insight, and far less friction getting the change to hold. Byam's conclusion is blunt: the difference is not the software, it is the team. These posts carry the Kartorium name, but they point to the team, not the tooling, as the decisive factor. Since Byam describes this engagement as rare, ask for evidence that it can be repeated.
- **Short face-to-face conversations are where manufacturing ideas actually move**: Byam is candid that conferences are not a favorite, yet values the ten-minute, in-person conversations that teach more than formal sessions. The focus is less on events as marketing than on learning and exchanging operational experience.

## Work to read

- [Why OEE tracking quietly fails](https://www.linkedin.com/posts/jay-byam-0a107b122_many-manufacturing-teams-have-tried-tracking-activity-7431866318649425920-KRlF)
- [The $800,000-an-hour mill with weak downtime data](https://www.linkedin.com/posts/jay-byam-0a107b122_manufacturing-operations-operationalexcellence-activity-7437559911959654400-hXfv)
- [Turning downtime hours into dollars](https://www.linkedin.com/posts/jay-byam-0a107b122_when-every-issue-has-a-price-tag-priorities-activity-7443012651582136320-gqPa)
- [Data projects that stick because the team shapes them](https://www.linkedin.com/posts/jay-byam-0a107b122_manufacturing-operations-continuousimprovement-activity-7448501581437759488-iksM)
- [Why the ten-minute conference conversation matters](https://www.linkedin.com/posts/jay-byam-0a107b122_manufacturing-operations-oee-activity-7453106448286289920-W_bL)

## Brand relationships

- **Kartorium** (disclosed_partnership)
- **MD&M Trade Shows** (mention)

## Partnerships

Jay Byam's five posts offer operational commentary on OEE, downtime data and manufacturing prioritisation, and close with the name Kartorium, which appears as the company associated with the writing. They do not document a partnership offer or disclose a paid partnership, sponsor policy, rate card, media kit or inquiry route. A buyer planning a collaboration on OEE and downtime data would need to agree terms directly with Jay Byam; ask whether they accept sponsorships.

## Alternatives

- Eric Ciampoli (MBA): A better choice when a collaboration needs to connect OEE and downtime data with plant documentation, procedures, and ERP alignment, or when the brand wants a partner already attached to a named operations-intelligence platform. The posts link operational improvement to a platform that brings plant data, documentation, and knowledge together, and ask whether procedures kept pace as equipment and processes changed. That framing suits a campaign about knowledge systems and process consistency around the data, rather than data-capture behavior itself.
- Daryl Holley: A better choice when a campaign targets leadership alignment and metric governance above the floor: why lean and continuous-improvement programs stall when the executive team never settles on one priority, and why reviews track whatever gets reported instead of what actually predicts downtime. Both posts use first-person plant examples: a strategy review where sales, finance, and operations each held a different definition of priority, and a weekly ops review that left a yellow metric untouched for eleven weeks because it did not roll up to corporate. That is the right register for a campaign aimed at plant and operations leaders.
- Tim Stuart: A better choice when a campaign needs technical implementation depth for process engineers, controls engineers, IIoT developers, or manufacturing IT/OT teams. The posts cover contextualizing raw PLC signals into machine states and events, event-driven alerts and workflows in Ignition, and lightweight analytics for downtime, fault duration, and micro-stops. They also offer an article-backed view that continuous improvement fails because it does not reach finance, planning, IT, and HR—not because of the methodology. That speaks directly to the audience that would build or configure a downtime-data system.

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