Manufacturing Operations Consulting
Most manufacturing consultants walk in with a framework and walk out with a slide deck. They map your processes onto their methodology, hand you a binder of recommendations, and leave you to figure out the implementation. Six months later, nothing has changed except your consulting budget is lighter.
Manufacturing operations are different from every other business category. The physics are real. The tolerances are measured in thousandths. The tribal knowledge lives in the heads of machinists who have been running those machines for twenty years. And the gap between what your ERP says is happening and what is actually happening on the shop floor is almost always wider than leadership realizes.
Vectis Works exists to close that gap. We diagnose the operational constraint that is actually throttling your output, your margins, or your win rate — and then we build the system that eliminates it. Not a recommendation. A working system, deployed and running.
Common Manufacturing Operational Challenges
Mid-market manufacturers — companies running between $10M and $100M in revenue — face a specific set of operational pressures that differ from both small job shops and large-scale OEMs. You are big enough to have real complexity but often too lean to have dedicated teams solving it. According to the National Association of Manufacturers, manufacturers contribute $2.9 trillion to the U.S. economy, yet Deloitte’s Manufacturing Outlook consistently finds that the majority of mid-market manufacturers cite operational inefficiency and workforce challenges as their top constraints on growth.
Jeffrey Liker documented in The Toyota Way (2004) that most manufacturing processes are 90% waste and 10% value-added work — a finding consistent across discrete, process, and assembly environments. For mid-market manufacturers without dedicated continuous improvement teams, that waste is not just a theoretical concern. It shows up as real dollars lost to rework, real capacity consumed by manual processes, and real growth constrained by systems that have not kept pace with volume.
Bidding and Quoting Bottlenecks
In discrete and fabrication environments, the bidding process is frequently the first bottleneck. Government and commercial bid preparation involves navigating pricing schedules, compliance requirements, material cost escalations, and margin targets — often in compressed timelines. The people who can actually price jobs accurately are usually the same people who are also managing production, which means every bid cycle pulls capacity from the floor.
According to Salesforce’s State of Sales Report (6th Edition, 2024), only 28% of sales professionals expect their teams to hit 100% of annual quota. In manufacturing, where “sales” often means technical estimating and bid preparation, this number reflects something deeper: the bidding process itself is often so labor-intensive that your best technical people cannot process enough opportunities to keep the pipeline full.
Quality Costs That Hide in Plain Sight
The American Society for Quality (ASQ) reports that quality-related costs typically run 15-20% of sales revenue, with some organizations experiencing costs as high as 40% of total operations. In manufacturing, quality costs show up as scrap, rework, warranty claims, customer returns, inspection labor, and — most expensively — lost customers who never tell you why they left.
The challenge for mid-market manufacturers is that quality data is often fragmented. Inspection records live in one system, customer complaints in another, scrap reports in a third. Nobody has stitched the picture together to see which product lines, which machines, or which shifts are actually driving the cost. The information exists. It just has not been connected.
Shop Floor Tribal Knowledge
Every manufacturer has processes that depend on specific people. The machinist who knows the quirks of that particular CNC. The engineer who remembers why that tolerance was set where it is. The estimator who carries fifteen years of pricing history in their head.
This tribal knowledge is not a strength — it is a liability. When those people retire, take vacation, or leave, the knowledge walks out with them. Gallup estimates that replacing a departing employee costs 40% to 200% of their annual salary — with technical roles averaging 80% and managers up to 200% (Gallup, 2024). In manufacturing, where the knowledge is deeply technical and experience-dependent, the true cost of losing a key person often exceeds those estimates.
Documentation and Drawing Gaps
Fabrication and assembly environments generate enormous documentation requirements. Assembly drawings, work instructions, inspection sheets, material certifications, process specifications. In many mid-market shops, these documents are still created manually or maintained in disconnected systems that require duplicate data entry. The time spent creating and maintaining documentation is time not spent producing parts.
Where the Biggest Leaks Hide
After working across discrete, process, assembly, and fabrication environments, patterns emerge. The biggest profit leaks in manufacturing are rarely where leadership thinks they are.
The Estimating-to-Production Handoff
Most manufacturers focus improvement efforts on either the front office (sales, estimating) or the shop floor (throughput, quality). The handoff between them gets less attention, but it is where some of the largest leaks occur. Information that was gathered during the estimating process does not flow cleanly to production planning. Assumptions that were made during quoting do not match the reality of how jobs actually run. The result is that your actual margins on completed jobs rarely match your estimated margins — and the variance is almost always negative.
Pricing Discipline
McKinsey research published in Harvard Business Review found that across 2,400 companies, a 1% improvement in price realization yields an average 11.1% improvement in operating profit (Marn & Rosiello, 1992). In manufacturing, price realization leaks through inconsistent quoting practices, outdated cost models, untracked material cost changes, and sales teams that discount to win work without understanding the margin impact. This is not a sales problem or an accounting problem — it is a systems problem. When your people do not have accurate, current cost data at the point of decision, they make bad pricing decisions with good intentions.
Manual Processes That Nobody Questions
Every shop has processes that are done manually because “that is how we have always done it.” Drawing creation that takes hours because templates are not parameterized. Data that gets keyed into three different systems because nobody built the bridge between them. Reports that someone assembles every Friday by pulling numbers from four different spreadsheets. These manual processes are invisible in most improvement initiatives because they are not bottlenecks in the traditional sense — they are diffuse, spread across the organization, each one small enough to ignore individually but collectively consuming thousands of hours per year.
What We Have Seen Work
The following are real engagements — not hypothetical examples. Company names are withheld per client agreements, but the work and the outcomes are documented.
Automating Government Bid Preparation
A mid-market aerospace components manufacturer was spending hours on every government bid package. The pricing involved navigating complex schedules, applying escalation factors, calculating compliance-driven cost adjustments, and assembling documentation — all done manually by senior estimators who were also managing active production.
We reverse-engineered the bidding system, identified the underlying mathematical relationships between the pricing variables, and built an automated system that performed the calculations and assembled the bid packages. Bid preparation time dropped from hours to minutes. More critically, the senior estimators were freed to focus on production management and strategic pricing rather than mechanical bid assembly.
This was not a software implementation. It was a fundamentals problem — understanding the math behind the system — that happened to result in an automated tool.
Eliminating Manual Drawing Production
A custom valve manufacturer was producing assembly drawings manually. Each drawing required an engineer to specify components, dimensions, and configurations — repetitive work that followed predictable rules but consumed significant engineering hours.
We programmed an automated drawing generation system that took order specifications and produced complete assembly drawings without manual intervention. The result eliminated the manual drawing assembly work entirely. Engineers who had been spending their days on repetitive documentation were redeployed to design work and customer technical support — higher-value activities that had been waiting in the queue.
Data-Driven Product Placement
A consumer products manufacturer needed to optimize retail placement decisions across a large distribution network. Placement decisions were being made based on historical convention and sales rep relationships rather than data.
We built predictive analytics that identified placement patterns correlated with performance, giving the company a data-driven approach to placement optimization. The shift from gut-feel to data-informed decisions changed the conversation between the manufacturer and their retail partners from opinion-based negotiation to evidence-based planning.
Our Approach to Manufacturing Operations
We do not come in with a pre-built methodology that we map onto your operation. Every manufacturer is different — different products, different processes, different constraints, different competitive dynamics.
Our approach follows a straightforward sequence:
Diagnose the actual constraint. Not the symptom. Not the department that leadership is frustrated with. The actual systemic bottleneck that is limiting throughput, margin, or growth. This requires going deep — talking to the people who do the work, understanding the real process (not the documented process), and tracing the data.
Architect the system that eliminates it. Once we understand the constraint, we design the approach. Sometimes that is automation. Sometimes it is a process redesign. Sometimes it is a simple tool that gives the right information to the right person at the right time. We use open-source tools — n8n for workflow automation, Python for custom applications, and Excel/VBA where it makes sense — because they are flexible, they do not lock you into vendor contracts, and they can be maintained and extended by your team after we leave.
Build and deploy it. We do the implementation. Not a handoff to your already-stretched IT team. Not a recommendation that you hire a developer. We build the working system, test it in your environment, train your people, and make sure it runs.
This is the difference between manufacturing operations consulting that produces reports and manufacturing operations consulting that produces results. We are interested in the latter.
Frequently Asked Questions
Do you work with our specific type of manufacturing?
We have worked across discrete manufacturing, process manufacturing, assembly, and fabrication environments. The specific industry — aerospace components, valves, consumer products, industrial equipment — matters less than the operational patterns. Mid-market manufacturers between $10M and $100M face remarkably similar structural challenges regardless of what they make. The constraint is usually in the information flow, the decision-making process, or the manual work that has never been automated — not in the product itself.
How is this different from lean consulting or Six Sigma?
Lean and Six Sigma are valuable frameworks for specific problems — waste reduction, variation control, process standardization. We are not replacing those disciplines. What we do differently is start from the business constraint rather than the methodology. If your biggest profit leak is in your bidding process, a kaizen event on the shop floor is not going to fix it. We find the actual constraint first, then apply whatever approach — automation, process redesign, analytics, tooling — fits the problem. Sometimes that overlaps with lean. Sometimes it does not.
What does the engagement look like for a manufacturer?
It starts with understanding your operation. We spend time with the people who do the work — estimators, planners, shop floor supervisors, engineers — not just leadership. From there, we identify the highest-impact constraint and scope a project to address it. Engagements are project-based, typically running weeks to a few months depending on complexity. We deliver working systems, not reports. By the end, something in your operation works differently and measurably better than it did before.
Will your systems work with our existing ERP and shop floor systems?
We build around what you already have. We are not selling you a platform or asking you to replace your ERP. The tools we build — whether automation workflows, custom applications, or optimized spreadsheet systems — are designed to connect with your existing technology stack. We use open-source tools specifically because they integrate with virtually anything and do not create vendor dependencies.
Next Steps
If you run a manufacturing operation between $10M and $100M and you suspect there is a constraint costing you more than it should, here are two ways to start:
Profit Multiplier Session — A half-day intensive where we identify the single highest-impact constraint in your operation. This is the fastest way to get clarity on where your biggest profit leak actually is.
Profit Leak Fix — If you already know where the problem is and you want it diagnosed, built, and deployed in a week, this is the engagement.
Or, if you want to have a conversation first: Schedule a 30-minute fit call.
Related pages: Process Optimization Consulting | Business Process Automation | Aerospace and Defense Operations
Sources
- Salesforce. State of Sales Report, 6th Edition (2024). Survey of 5,500 sales professionals across 27 countries.
- American Society for Quality. “Cost of Quality (COQ).” ASQ Quality Resources.
- McFeely, S. & Wigert, B. (2019). “This Fixable Problem Costs U.S. Businesses $1 Trillion.” Gallup Workplace.
- Yi, R. (2024). “Employee Retention Depends on Getting Recognition Right.” Gallup Workplace.
- Marn, M.V. & Rosiello, R.L. (1992). “Managing Price, Gaining Profit.” Harvard Business Review, September-October 1992.
- Liker, J.K. (2004). The Toyota Way: 14 Management Principles from the World’s Greatest Manufacturer. McGraw-Hill. Documents that most manufacturing processes are 90% waste and 10% value-added work.
- Ohno, T. (1988). Toyota Production System: Beyond Large-Scale Production. Productivity Press. Foundational framework for waste identification and elimination in manufacturing.
Vectis Works — The bridge between insight and implementation.