# Prasad Velaga

Prasad Velaga writes about improving manufacturing operations, with a focus on production scheduling in high-mix job shops. Their posts explain how scheduling and bottleneck management can affect throughput, work in progress, and lead times.

- Profile: https://b2binfluencer.marketing/creators/prasad-velaga/
- LinkedIn: https://www.linkedin.com/in/prasadvelaga
- LinkedIn followers: 7,232
- YouTube: https://www.youtube.com/channel/UCdXnzOfMB8mnbRZz-nuQAFA
- Topics: lean-manufacturing
- Based on 13 dated posts, latest 2026-10-06

## Summary

Prasad Velaga is most useful for explaining production scheduling and bottleneck decisions in high-mix job shops.

- Best for: A manufacturing software company planning a technical article or webinar on production scheduling.
- Not for: A Lean training business seeking beginner-friendly quick-win posts for early-career production staff.
- Check first: Confirm whether paid or sponsored collaborations are available and ask for recent audience analytics.

### Takeaways

- A job shop is a network of moving work, not a production line.
- Diagnose the bottleneck before adding capacity.
- Improvement options can affect throughput, work in progress, and lead time differently.

## Main ideas

- **Test an improvement idea against production outcomes before pursuing it**: Velaga starts from Lean’s principle of continuous waste reduction but objects to pursuing every improvement opportunity without first assessing its potential benefit. In nontrivial production systems, they argue, the benefits are not obvious, and Lean offers no equivalent of what-if analysis to estimate them. The result can be effort spent on changes that barely move performance, disappointing managers, frustrating them, and eroding faith in continuous improvement. Velaga proposes a production model that predicts work progress, fluctuating WIP, and bottleneck formation so teams can estimate the effect of a change before making it.
- **A job shop is a network of moving work, not a production line**: Velaga argues that people trained in Lean manufacturing struggle to shed the conscious or subconscious picture of production as a line making products sequentially, one after another. They trace that picture to Lean’s origins in production lines and say it persists when consultants discuss high-mix, low-volume work in job shops. Without an adequate mental model of true HMLV production, they argue, neither Lean principles nor the Theory of Constraints can do much to help control it. Velaga recommends scientific scheduling instead, which depends on sensible job routings and accurate shop-floor job status.
- **Diagnose the bottleneck before adding capacity**: Velaga offers managers a short diagnostic sequence before they add people or equipment to address a capacity shortfall. Check whether the bottleneck is losing useful time: whether it sits nonoperational while work waits for it, does frequent rework because of quality problems, runs below its standard speed, or sits idle waiting for work. If availability losses and idling, rather than raw capacity, are the problem, they argue, intelligent scheduling can reduce bottleneck idle time and improve plant performance without adding resources.
- **Shop-floor performance has several interacting causes**: Velaga lists the factors that shape how work progresses on a shop floor: wasteful activities, bottleneck capacity, material release times for orders, randomness or uncertainty, and irrational managerial decisions. They argue that understanding how these factors interact helps identify efficient ways to improve work in progress, lead times, throughput, and on-time delivery. Velaga frames the issue as a systems problem, then invites readers to respond to the practical difficulty of working through the details.
- **Best use of the resource set versus highest use of every resource**: Velaga asks readers which they would pursue: the best utilization of the existing set of production resources, or the highest utilization of each individual resource. The post raises the question without resolving it, framing a debate they consider important: whether local utilization targets serve system performance.

## Work to read

- [Waste reduction needs a way to test its likely effects](https://www.linkedin.com/posts/prasadvelaga_continuousimprovement-lean- toc-activity-7510370837284212736-9gO6)
- [Why job-shop production is not a production line](https://www.linkedin.com/posts/prasadvelaga_continuousimprovement-leanmanufacturing-activity-7511455064947896320-XGF6)
- [Check bottlenecks before adding capacity](https://www.linkedin.com/posts/prasadvelaga_bottlenecks-capacity-manufacturing-activity-7507097221574754304-9ZlX)
- [A general model for understanding production performance](https://www.linkedin.com/posts/prasadvelaga_continuousimprovement-lean-toc-activity-7510370837284212736-9gO6)
- [Highest utilization is not always the right goal](https://www.linkedin.com/posts/prasadvelaga_resourceutilization-resourcemanagement-activity-7506740052656275456-6nQA)

## Brand relationships

- **Schedlyzer** (mention)

## Partnerships

Prasad Velaga's five LinkedIn posts explain production scheduling, bottlenecks, capacity, resource use, and high-mix low-volume job shops, but do not describe partnership formats, terms, deliverables, or lead times. One post recommends a scheduling software product by name as part of an argument about job shop control; it does not disclose a paid relationship. The posts include no sponsorship policy, rate card, media kit, or inquiry route. Buyers should ask directly whether paid or sponsored collaborations are available and, if so, what formats, terms, deliverables, and lead times they offer.

## Alternatives

- Eric Ciampoli: A better choice when the collaboration should frame production scheduling as part of a wider operational-intelligence and data-consistency story. Ciampoli’s posts cover consultancy-led work on operational improvement, ERP alignment, process optimization and plant visibility, as well as an operations-intelligence platform that connects plant data, documentation and knowledge so procedures stay current as equipment and processes change. Vendors seeking a scheduling piece about documentation risk, ERP-to-shop-floor alignment, or the adoption and change-management side of a deployment get a consulting voice and a live platform context.
- Santi Adavani: A better choice when the vendor wants to frame scheduling through digital twins and simulation for a broader operations-technology audience. Adavani’s posts explain what a factory digital twin is, how live data from PLCs, MES systems, sensors and inventory feeds a virtual replica, and how what-if testing can examine sequencing, bottlenecks, layout, predictive maintenance and staffing without disrupting production. One post also lays out a descriptive first level of digital twins, with automotive examples in asset modeling, physics-based design simulation and virtual showrooms. A webinar presenting scenario modeling as the entry point to better schedules fits this material more directly than an argument about scheduling methods.
- Nicholas Parton: A better choice when the vendor wants a practitioner deployment story rather than an argument about methods. Parton’s posts describe building and rolling out a SAP-fed production execution tracker that connects planning, NC programming, manufacturing engineering, order release, material issuance, machining, outside processing, quality inspection and final inventory in one operational view, and report gains in work readiness and executable machine backlog. A second post covers a move into a digital transformation role at Spirit Defense, a Boeing Company, alongside an existing program. For a webinar on adopting a scheduling or execution tool across functions in a real plant—and the leadership and conversations that make it stick—this is closer to the buyer’s question than a diagnostic checklist.

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