A Systems Engineer’s Guide on How to Reduce Manufacturing Costs

Attempting to reduce manufacturing costs by squeezing the Bill of Materials (BOM) is a tactical error that misses the strategic picture. Last-minute BOM scrubbing yields incremental gains at best, but the significant, systemic cost drivers are locked in by architectural decisions made weeks or months earlier. Ignoring this reality leads to predictable and expensive consequences: late-stage engineering change orders (ECOs), catastrophic yield busts during production ramp, and field failures that erode margins and customer trust. So we will cover how to reduce manufacturing costs.

This guide is for the engineering decision-makers—VPs of Engineering, CTOs, Program Managers, and Lead Engineers—who are responsible for shipping complex, reliable hardware. It is not for teams focused solely on procurement or commodity sourcing. We will dissect how to control costs by focusing on the high-leverage engineering and operational decisions that have the largest downstream impact on your cost of goods sold (COGS). The core recommendation is to embed manufacturability and testability into your process from day one, treating them as primary design constraints, not afterthoughts.

You will learn to:

• Identify and mitigate the systemic cost drivers that exist outside the BOM.
• Implement Design for Manufacturability (DFM) and Design for Test (DFT) as early-gate engineering disciplines.
• Develop a supplier qualification and management strategy that prioritizes total cost of ownership over per-unit price.

Master Design for Manufacturability and Assembly (DFM/DFMA) to Control Downstream Costs

The single greatest point of leverage you have to reduce manufacturing costs is the design phase. An estimated 70-80% of a product’s total lifecycle cost is determined by architectural and design choices made long before a single component is ordered. Treating cost reduction as a post-design activity is a recipe for expensive tooling changes, low yield, and schedule slips. High-performing teams embed DFM and Design for Assembly (DFMA) into their workflow from the initial system definition.

This is a technical discipline focused on slashing assembly time, reducing fixture complexity, and achieving high first-pass yield from the first production run.

Actionable Criteria for Cost-Driven Design

Effective DFM requires engineers to view their design through the lens of the production line. It moves the evaluation beyond a component’s unit price to its total cost of implementation. A microcontroller may be a few cents cheaper, but if it requires a complex bring-up sequence, a unique programming fixture, or a non-standard package, it will cost far more in labor, tooling, and potential yield loss.

Every design choice must be evaluated against production reality.

Here are the core areas we scrutinize on every project:

• Component Selection: Are parts readily available from multiple qualified sources? Are they compatible with standard automated pick-and-place equipment? Every manual placement or secondary operation designed out is a direct cost saving and a source of error removed.
• PCB Layout: Are fiducials included for automated optical inspection (AOI)? Is there sufficient clearance around tall components for machine nozzles? Is component orientation consistent to minimize rotation during placement? Grouping test points in a logical sequence simplifies fixture design.
• Mechanical and Enclosure Design: Standardize fasteners. Using a single screw type across an assembly is a classic, high-impact DFM win. It reduces inventory SKUs, simplifies tooling on the line, and eliminates time-wasting tool changes. Features like self-aligning parts, poka-yoke (mistake-proofing) geometries, and clear orientation markers make assembly faster and more reliable.

At Sheridan Technologies, we find that single-threaded technical ownership is critical for effective DFM. When one lead engineer owns the product from architecture through the PVT build, critical details are not lost in handoffs. This continuity is invaluable for cost control and risk reduction.

Real-World Scenario: Ruggedized Industrial Sensor

A client developing a ruggedized industrial sensor had a design that functioned perfectly in the lab but was fundamentally unmanufacturable at scale. The initial design required three different fastener types, a high-insertion-force press-fit connector requiring a custom arbor press, and critical test points that were inaccessible after the PCB was installed in its housing.

The contract manufacturer’s initial quote estimated 25 minutes of assembly labor per unit, with a projected first-pass yield below 90% due to the high risk of assembly errors. This was commercially non-viable.

We conducted a DFM/DFMA review before the EVT build and implemented three high-impact changes:

1. Standardized Fasteners: All screws were replaced with a single M3 Torx drive. This eliminated tool swaps on the assembly line.
2. Connector Swap: The press-fit component was replaced with a latching connector that provided clear tactile and audible feedback upon mating, drastically reducing the risk of an incomplete connection.
3. Dedicated Test Interface: We added a small daughterboard with pogo-pin pads that exposed all necessary test points to a single, robust connector, accessible even after final assembly.

The results were immediate and measurable. The revised design cut assembly labor by 18% (to 20.5 minutes) and, more importantly, reduced projected field failures by 30% by designing out a primary source of defects. The marginal cost of the daughterboard was recovered within the first production run through labor savings and yield improvement alone.

For teams looking to build this discipline, our deep dive into Design for Manufacture and Assembly best practices offers a more structured framework.

Build a Resilient and Cost-Effective Supply Chain

Your supply chain is a critical system that can either be a source of competitive advantage or a significant drain on your budget. A transactional, price-first procurement model is a common failure mode. The path to sustainably lower manufacturing costs requires a strategic shift to partnerships built on rigorous supplier qualification, transparent cost models, and a mutual commitment to quality.

This means evaluating suppliers on total cost of ownership, not just per-unit price. A cheap component from an unreliable supplier becomes the most expensive part in your BOM when it shuts down your production line.

Beyond Price: The Reality of Supplier Qualification

A robust qualification process vets a partner’s technical capabilities, their quality management system (QMS), and their ability to mitigate supply chain disruptions. I’ve seen teams get burned repeatedly by selecting the cheapest vendor, only to face line-down situations due to poor quality parts, wiping out any initial savings.

Before issuing a Request for Quote (RFQ), your team should be vetting potential partners on key metrics:

• Quality Management System (QMS): Do they have verifiable quality processes? Look for certifications like ISO 9001 or industry-specific standards like ISO 13485 (medical devices) or AS9100 (aerospace). Request their quality manual and recent audit results.
• Technical Capability: Can their engineering team provide meaningful DFM feedback? A partner who helps you optimize your design for their manufacturing line is infinitely more valuable than one who just builds to print.
• Supply Chain Visibility: How do they manage their own upstream suppliers? A partner with an opaque Tier 2 and Tier 3 supply chain introduces unmanaged risk into your product.

A classic mistake is treating the RFQ as the start of the supplier conversation. By the time you issue an RFQ, you should have already down-selected to 2-3 suppliers you are confident can meet your technical and quality requirements. The RFQ then becomes a tool for an apples-to-apples cost comparison, not a discovery exercise.

Structuring Your RFQ for True Cost Comparison

A vague RFQ yields noisy, incomparable quotes. To get actionable data, your RFQ package must be explicit, forcing suppliers to break out all cost drivers. Your cost model must include line items for non-recurring engineering (NRE), tooling, test fixture development, and projected yield ramps. A low unit price can easily hide tens of thousands in upfront NRE. A well-structured RFQ provides a transparent view of the total cost. For a truly cost-effective supply chain, adopting advanced and efficient methodologies like implementing robust systems for understanding your last mile delivery optimization strategies is paramount to reducing logistical expenses.

The Single-Source vs. Dual-Source Tradeoff

The decision to single-source or dual-source a critical component is a fundamental tradeoff between cost and risk. There is no universally correct answer; the decision depends on your product’s volume, margin, and the operational cost of a line-down event.

The following table illustrates why early-stage decisions carry so much weight in cost control.

Cost Reduction Impact Across the Product Lifecycle

As the table shows, making a change during the design phase is low-cost with high downstream impact. Fixing the same issue in mass production is an expensive, high-risk endeavor. For high-stakes applications like medical devices or aerospace, the catastrophic cost of a line-down event often makes the overhead of dual-sourcing a necessary insurance policy. Understanding these tradeoffs is essential for a smooth transition from prototype to product readiness.

Design a Lean and Effective Manufacturing Test Strategy

A manufacturing test strategy is not merely a quality gate; it is a high-leverage investment for process control and cost reduction. Treating test as a final pass/fail check is a reactive posture that inflates costs through long test times, poor fault coverage, and an inability to perform rapid root cause analysis.

The goal is a lean Design for Test (DFT) strategy that maximizes fault coverage without compromising production throughput. This requires architecting a test plan that provides actionable data for continuous process improvement.

Layering Tests for Maximum Efficiency and Coverage

No single test can detect every possible defect. A robust, cost-effective strategy layers multiple test methodologies, each designed to find specific faults at the earliest and cheapest point in the process. Attempting to find a solder bridge at a final system-level test is incredibly inefficient.

A typical tiered test hierarchy includes:

• Automated Optical Inspection (AOI): First line of defense post-reflow, catching visual defects like missing components, incorrect polarity, and solder bridging.
• In-Circuit Test (ICT): Uses a flying probe or bed-of-nails fixture to verify individual components are placed correctly and have the correct values. Excellent for detecting shorts, opens, and wrong/missing passives.
• Functional Circuit Test (FCT): Powers up the board to verify that specific circuits (e.g., power supplies, communication buses) behave according to specification.
• System-Level Test: The final check, where the fully assembled product is tested to ensure it meets all end-user performance requirements.

The optimal mix depends on product complexity and volume. High-volume consumer products may rely heavily on fast, automated ICT, whereas low-volume, high-complexity industrial systems will require more extensive FCT and system-level testing.

The process flow below shows how foundational steps like supplier qualification create a resilient base upon which a good test strategy is built.

This structured supplier engagement directly impacts test strategy by ensuring component quality before parts reach the factory floor.

Closing the Loop with a FRACAS Process

A test strategy that only yields a pass/fail count is a missed opportunity. Real value is unlocked by creating a closed-loop system for continuous improvement. This is the role of a Failure Reporting, Analysis, and Corrective Action System (FRACAS).

FRACAS is a formal engineering process for logging every failure, performing root cause analysis, and implementing a corrective action to prevent recurrence. It transforms test data from a simple quality metric into an engine for yield improvement.

Every failed unit at your FCT station is an invaluable data point. Ignoring this data is equivalent to throwing away free engineering consulting. A disciplined FRACAS process ensures this data is captured, analyzed, and used to harden your design and manufacturing process.

This requires tight collaboration between design, test, and manufacturing engineering. When a failure trend emerges—for example, a specific power rail consistently failing at FCT—the FRACAS process triggers an investigation that could lead back to a design margin issue, a bad component lot, or a process drift in assembly.

Real-World Scenario From a Connected Device Program

On a high-volume IoT device program, the final assembly yield was stuck at 92%. The dominant failure mode was a communication issue with an RF module, caught only during the final system-level test. The initial impulse was to blame the module supplier.

A disciplined FRACAS process forced a deeper investigation.

Test engineering data from hundreds of failed units revealed a strong correlation: failures were far more prevalent on boards from one specific location within the multi-unit PCB panel. This immediately shifted the focus from the component to the PCB fabrication or assembly process.

Failure analysis, including cross-sectioning failed boards, revealed microfractures in the solder joints of the module’s ground pins. The root cause was not the module itself, but a thermal imbalance during the reflow process caused by a large ground plane on the PCB acting as a heat sink. This created thermal stress that cracked the solder joints.

The corrective action was a minor PCB layout change: adding thermal reliefs to the ground pins. This no-cost design modification stabilized the reflow process. Final yield jumped from 92% to over 98.5%.

For a product with a BOM cost over $50, that 6.5% yield improvement translated directly to over $250,000 in annual savings from reduced scrap and rework. That is the power of a data-driven failure analysis loop.

Deploying Lean Methodologies and Smart Automation on the Factory Floor

As you scale from prototype builds to mass production, the factory floor becomes the primary battleground for cost control. Manual workflows that were acceptable for small batches become critical bottlenecks, and hidden inefficiencies start to erode margins on every unit shipped.

This is where you must apply two powerful, related disciplines: Lean manufacturing and smart automation.

Lean is not about massive, disruptive overhauls. It is a systematic methodology for identifying and eliminating “waste” (muda in Japanese)—any activity that consumes resources but adds no value to the final product. A recent Porsche Consulting study highlights the urgency: 87% of industrial manufacturers list factory cost reduction as a top priority, targeting average savings of 12%. Top performers are achieving over 30% reduction in material waste. You can review the full factory cost reduction findings for more detail.

Pinpointing Waste with Value Stream Mapping (VSM)

The most effective starting point is Value Stream Mapping (VSM). This is a diagnostic tool that involves physically walking the production line—from receiving inspection to final pack-out—and documenting every single step.

The objective is to visualize the flow of materials and information to identify the seven classic forms of manufacturing waste:

• Transportation: Excessive movement of PCBs and sub-assemblies between stations.
• Inventory: Work-in-progress (WIP) accumulating between process steps.
• Motion: An operator repeatedly walking to retrieve a tool or component.
• Waiting: An expensive test fixture sitting idle due to an upstream bottleneck.
• Over-production: Building sub-assemblies faster than the next station can consume them.
• Over-processing: Performing a manual inspection that is redundant to an existing automated test.
• Defects: Any unit requiring rework or scrap, representing a total loss of materials, labor, and machine time.

A VSM clearly identifies the bottlenecks and non-value-added time, allowing you to focus cost-reduction efforts with surgical precision.

Deploying Automation with a Clear ROI

Once you have identified waste, automation is your primary tool for eliminating it. The key is to be strategic. Automating a broken or inefficient process only allows you to produce bad parts faster. Instead, target high-impact, repeatable tasks that consume significant labor or are a source of human error.

A common mistake is to fixate on complex robotics. Often, the highest ROI is in software automation: automating data collection from test fixtures, streamlining firmware flashing and provisioning, or creating digital traceability records. These projects typically have a lower NRE and deliver immediate gains in quality and efficiency.

Before committing capital, perform a simple Return on Investment (ROI) calculation:

ROI (%) = [(Gain from Investment – Cost of Investment) / Cost of Investment] x 100

Consider a manual firmware flashing process that takes three minutes per unit and has a 2% error rate requiring rework. An automated fixture might cost $20,000 in NRE but cuts the process to 30 seconds and eliminates errors. At a production volume of 50,000 units a year and a burdened labor rate of $40/hour, the savings in labor and rework can deliver a payback period of just a few months. That is a clear business case.

Real-World Example from the Trenches

A team building a complex medical device was throttled by their final system test. A technician had to manually probe a dozen test points, record voltage readings on a paper log, and then transcribe the data into a spreadsheet. The process took 15 minutes per unit.

Our VSM immediately flagged this as a major source of “waiting” and “motion.” The solution was not a complex robot but a semi-automated test fixture using pogo pins and a simple LabVIEW script.

• The Solution: The new fixture engaged all test points simultaneously. The script executed the test sequence, captured all readings, compared them against pass/fail limits, and logged the complete, serialized data set to a central database.
• The Outcome: Test time dropped from 15 minutes to under 90 seconds. Paper logs, a frequent source of transcription errors, were eliminated. This targeted automation project not only slashed labor costs but also provided a rich, reliable dataset for statistical process control (SPC) and failure analysis—a core tenet of the Sheridan philosophy for building robust manufacturing systems.

Leveraging Predictive Analytics for Proactive Cost Control

The most advanced stage of manufacturing cost reduction is moving from reactive problem-solving to proactive prevention. This is the practical application of the “smart factory” concept, where data from the production line is used to predict and prevent equipment failures, optimize workflows, and avoid costly disruptions.

For teams building complex systems like industrial robotics or aerospace hardware, this data-driven approach is a significant competitive advantage.

The first step is instrumenting your production line. Embedding IoT sensors on critical equipment to monitor parameters like vibration, temperature, and power consumption creates a real-time data stream of machine health. This data is the fuel for predictive maintenance.

Predictive Maintenance with IoT and AI

Predictive maintenance uses AI models to analyze sensor data and identify subtle patterns that precede equipment failure. Instead of waiting for a critical machine to fail and halt production, the system can flag it for service during a planned maintenance window. This proactive approach yields massive savings by reducing emergency repairs and unplanned downtime. A solid grasp of the fundamentals is required, starting with understanding predictive modeling.

The financial impact is well-documented. Studies show predictive maintenance can reduce equipment downtime by up to 50% and cut overall maintenance costs by 40%. For our clients in high-reliability sectors, these numbers represent a critical operational advantage.

The Power of the Digital Twin

This rich data stream also enables the creation of a digital twin—a virtual, high-fidelity model of your entire manufacturing process. This simulation environment allows you to test and optimize changes without disrupting the live production line.

Potential applications include:

• Scenario Modeling: Evaluate the throughput impact of a new line layout before moving any physical equipment.
• Process Optimization: Test new firmware flashing sequences or optimize robotic arm paths in the digital twin to find efficiency gains with zero risk.
• Operator Training: Onboard new technicians by allowing them to practice on the virtual line, where mistakes have no cost.

The path to a smart factory is typically iterative. High-performing teams start with a pilot project on a single critical machine, prove the ROI of predictive maintenance, and then scale the solution across the factory floor.

Common Questions on Manufacturing Cost Reduction

We work with engineering leaders and program managers shipping complex hardware, and the same critical questions arise repeatedly. The answers consistently point away from last-minute cost-cutting tactics and toward strategic, early-stage engineering decisions.

What is the single biggest mistake teams make when trying to reduce costs?

The most common and costly mistake is treating cost reduction as a late-stage activity. Too many teams focus on negotiating the BOM price after the design is frozen. By that point, 70-80% of the product’s total lifecycle cost is already locked in by the design architecture.

Meaningful, sustainable cost reduction comes from integrating DFM and DFMA disciplines from the very beginning of the development process. A close second is neglecting Design for Test (DFT). This oversight leads directly to expensive custom fixtures, long test cycle times, and poor fault coverage, which inflates rework costs and allows quality escapes to reach customers.

How do you balance cost reduction against product quality?

This question presents a false dichotomy. The most effective cost reduction strategies almost always improve product quality and reliability.

When you design for simpler assembly (DFMA), you are inherently reducing opportunities for human error on the production line. When you implement a robust test strategy (DFT), you are detecting defects at the earliest, cheapest possible point, preventing them from becoming expensive field failures.

The key is to use data, not intuition. Tools like a Failure Modes and Effects Analysis (FMEA) should be used to rigorously assess the risk of any proposed cost-saving change. The objective is to eliminate non-value-added costs—waste, rework, excessive handling—not to compromise the product’s core reliability. When teams get stuck, a structured approach to root cause analysis in engineering is often the key to exposing systemic flaws.

When should we engage our contract manufacturer (CM)?

Far earlier than most teams think. A common failure mode is to “throw the design over the wall” to a CM after it is considered “final.” This approach is a recipe for expensive, last-minute ECOs and schedule delays.

High-performing teams engage key manufacturing partners during the architecture or early design phase (e.g., during the EVT planning stage). This allows the CM to provide critical DFM feedback based on their specific equipment set, processes, and operational expertise. This early collaboration is not a courtesy; it is a foundational requirement for a smooth and predictable production ramp.

At Sheridan Technologies, we specialize in de-risking the journey from prototype to production. If your team is facing manufacturing challenges or wants to build a more cost-effective development process from the ground up, consider our Manufacturing Readiness Assessment.

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