14. Process Selection, Economics and Operations Strategy

Introduction: From Technical Possibility to Industrial Choice

Advanced manufacturing provides an expanding set of technically feasible processes: additive manufacturing, high-speed machining, electrochemical machining, laser processing, hybrid systems, automated composite manufacture, and digitally integrated production. Yet technical feasibility alone does not justify adoption. A process may produce an excellent part but fail economically because of low throughput, expensive consumables, unstable quality, difficult maintenance, or dependence on a fragile supply chain. Conversely, a process with a higher unit cost may be strategically preferable when it reduces lead time, enables a product that conventional methods cannot make, or protects the organization against supply disruption.

Process selection is therefore a multidisciplinary decision. It connects design intent, materials engineering, manufacturing capability, cost accounting, operations management, quality assurance, sustainability, and corporate strategy. At postgraduate level, the central question is not simply “Which process can make the part?” but “Which configuration of process, equipment, people, suppliers, and operating policies creates the greatest value over the required life cycle and under realistic uncertainty?”

This chapter develops an evidence-based approach to that question. It examines capability matrices, economic models, throughput and capacity, life-cycle costing, risk and resilience, technology readiness, make-or-buy decisions, and the strategic implementation of manufacturing investments.

Learning Objectives

After studying this chapter, the student should be able to:

1. Translate product requirements into measurable process-selection criteria.

2. Construct and interpret capability matrices for competing manufacturing processes.

3. Compare process alternatives using cost, throughput, capacity, and life-cycle models.

4. Incorporate uncertainty, risk, technology readiness, and supply-chain resilience into investment decisions.

5. Conduct make-or-buy and outsourcing analyses without confusing price with total cost.

6. Evaluate digital and advanced-manufacturing investments using strategic as well as financial criteria.

7. Recommend an operations strategy and justify it using evidence, assumptions, and sensitivity analysis.

14.1 The Decision Architecture of Process Selection

A manufacturing decision begins with the product requirement rather than with a preferred machine or fashionable technology. Requirements normally include material, geometry, dimensions, tolerances, surface integrity, mechanical performance, production volume, delivery time, traceability, regulatory approval, and expected product life. These requirements should be separated into three categories:

• Must-have constraints, such as a specified alloy, minimum fatigue life, dimensional tolerance, or certification requirement.
• Performance preferences, such as lower surface roughness, shorter setup time, or reduced energy consumption.
• Strategic objectives, such as developing internal capability, reducing dependence on a single supplier, or creating a platform for future products.

This distinction prevents a common error: assigning numerical scores to a process that is actually disqualified by a mandatory requirement. A process incapable of meeting a critical tolerance should not remain competitive merely because it is inexpensive.

The decision can be represented as a chain:

Product requirements → feasible process family → process route → equipment and resources → operating system → economic and strategic evaluation → implementation decision.

A process family describes the principal transformation, such as material removal, forming, joining, deposition, or chemical/electrochemical processing. A process route adds the sequence of operations, inspection, heat treatment, finishing, and handling. The route is often more important than the individual process because auxiliary operations may dominate cost and lead time.

For example, laser powder-bed fusion may create a complex titanium component in one build, but the complete route can include powder preparation, build removal, stress relief, support removal, heat treatment, machining, surface finishing, inspection, and powder management. Comparing only the build time with the cycle time of a machined blank would produce a misleading conclusion.

14.2 Capability Matrices and Feasibility Screening

A capability matrix provides a structured comparison between process alternatives and product requirements. Typical rows include material compatibility, maximum and minimum feature size, achievable tolerance, surface finish, aspect ratio, wall thickness, part envelope, production rate, batch flexibility, automation potential, inspection requirements, operator skill, environmental burden, and certification maturity.

The matrix should distinguish measured capability from nominal machine specification. A machine may advertise a positioning accuracy of a few micrometres, while actual part accuracy depends on thermal drift, fixturing, tool wear, material distortion, calibration, and process control. Capability evidence should therefore come from production trials, qualified supplier data, statistically controlled studies, or validated technical literature.

A useful screening method is a two-stage matrix. First, each process is tested against hard constraints. A process receives “pass,” “conditional pass,” or “fail.” Second, the surviving alternatives are evaluated using weighted criteria. A simple weighted score is

\[

S_j=\sum_{i=1}^{n} w_i r_{ij}

\]

where \(S_j\) is the score of process \(j\), \(w_i\) is the importance weight of criterion \(i\), and \(r_{ij}) is the rating of process \(j\) against that criterion. The weights should sum to one. Ratings may use a five-point or ten-point scale, but the scale must be defined clearly.

Weighted scoring is not a substitute for engineering judgment. It is sensitive to the chosen weights, can disguise poor data, and may imply false precision. A robust practice is to report the score together with the evidence, confidence level, and sensitivity to changes in weights. Multi-criteria decision analysis is most useful when it makes assumptions visible rather than when it produces a single apparently objective number.

Important capability dimensions

Geometric capability concerns complexity, internal channels, thin walls, free-form surfaces, and feature accessibility. Material capability concerns melting range, conductivity, hardness, chemical reactivity, residual stress, and anisotropy. Quality capability includes tolerance, surface integrity, repeatability, defect population, and inspection confidence. Production capability includes rate, batch size, changeover time, uptime, and scalability. Organizational capability includes skills, maintenance, software, qualification procedures, and supplier support.

Capability should also be expressed statistically. The process capability index

\[

C_p=\frac{USL-LSL}{6\sigma}

\]

compares specification width with process spread, while

\[

C_{pk}=\min\left(\frac{USL-\mu}{3\sigma},\frac{\mu-LSL}{3\sigma}\right)

\]

also considers centering. These indices are meaningful only when the process is stable and the sampling assumptions are appropriate. They do not prove that a process is suitable if measurement error, drift, or non-normal defect mechanisms have been ignored.

14.3 Economic Models: From Unit Cost to Life-Cycle Value

The simplest comparison uses unit manufacturing cost:

\[

C_u=C_m+C_l+C_o+C_t+C_q+C_s

\]

where material, labour, overhead, tooling, quality, and subcontracting costs are included. This expression is useful for initial screening but is inadequate for capital-intensive or uncertain processes.

A more complete annual cost model is

\[

C_{annual}=C_{fixed}+Q(C_{variable})+C_{quality}+C_{logistics}+C_{risk}

\]

where \(Q\) is accepted production volume. Fixed cost includes depreciation, facility modifications, software, and salaried support. Variable cost includes material, consumables, direct labour, machine time, utilities, and tooling. Quality cost includes inspection, scrap, rework, warranty, and failure investigation. Risk cost represents expected losses from disruption, failure, delay, or regulatory non-compliance.

The distinction between cash cost and economic cost is essential. A process may appear attractive because existing equipment has no additional accounting charge. For a rational comparison, the relevant cost is the opportunity cost of using that equipment, including the value of displaced production and future capacity. Similarly, sunk costs should not determine a new decision.

Break-even analysis

If two alternatives have fixed costs \(F_1,F_2\) and variable costs per unit \(v_1,v_2\), the break-even volume is

\[

Q^*=\frac{F_2-F_1}{v_1-v_2}

\]

provided the denominator is positive. Below this volume, the lower-fixed-cost route may be preferable; above it, the lower-variable-cost route may dominate. Break-even analysis becomes unreliable if yield, demand, product mix, or cycle time changes substantially with volume. It should therefore be accompanied by scenario analysis.

Net present value and total cost of ownership

Capital decisions should use discounted cash flow where timing is important:

\[

NPV=-I_0+\sum_{t=1}^{T}\frac{CF_t}{(1+r)^t}+\frac{SV}{(1+r)^T}

\]

Here, \(I_0\) is initial investment, \(CF_t\) is net cash flow in year \(t\), \(r\) is the discount rate, \(T\) is project life, and \(SV\) is terminal or salvage value. Cash flows should include installation, training, validation, maintenance, software subscriptions, energy, tooling, upgrades, decommissioning, and working capital where relevant.

Total cost of ownership extends the analysis beyond purchase price. It includes acquisition, integration, operation, maintenance, downtime, cybersecurity, consumables, calibration, compliance, and disposal. In advanced manufacturing, integration and qualification costs can be substantial and should be estimated before investment approval.

Environmental life-cycle costing adds external or internally priced effects such as energy, material waste, emissions, water use, hazardous waste, and end-of-life treatment. It does not automatically make a process sustainable, but it reveals trade-offs that conventional costing omits.

14.4 Throughput, Capacity and the Operations Strategy

A process that has a low nominal cycle time may still deliver poor output if it suffers from long setup, inspection, waiting, or maintenance intervals. Effective capacity is better represented by

\[

Q_{effective}=Q_{design}\times A\times P\times Q_y

\]

where availability \(A\), performance rate \(P\), and quality yield \(Q_y\) represent the major components of overall equipment effectiveness. The exact definition of each component must be consistent with the organization’s measurement system.

Capacity analysis should include bottlenecks. In a route with operations of different capacities, the bottleneck limits system throughput. Increasing capacity at a non-bottleneck operation may create inventory rather than output. Little’s Law,

\[

WIP=TH\times LT,

\]

relates work-in-process to throughput and lead time. It reminds the decision-maker that high work-in-process is often a symptom of long lead time, unstable flow, or excessive batch sizes rather than a sign of productivity.

Operations strategy determines how the process will serve demand. A make-to-stock system values stable high-volume output; make-to-order values flexibility and responsiveness; engineer-to-order emphasizes design integration and low-volume customization. The same manufacturing technology can be appropriate in one strategic context and inappropriate in another.

For high-mix, low-volume production, flexibility, rapid programming, modular fixturing, and short changeovers may be more valuable than maximum machine speed. For high-volume production, dedicated tooling, automation, parallel capacity, and statistical control may justify higher fixed investment. Strategic fit is therefore a central criterion.

14.5 Risk, Resilience and Technology Readiness

Manufacturing decisions are made under uncertainty. Important uncertainties include demand, material prices, learning rate, yield, maintenance cost, regulatory approval, labour availability, and supplier performance. A deterministic business case should be challenged using sensitivity analysis, scenarios, and where justified, probabilistic simulation.

A risk register identifies the event, cause, consequence, likelihood, detectability, owner, mitigation, and residual risk. Failure mode and effects analysis can prioritize technical failure modes, while fault-tree analysis examines how combinations of events produce system failure. These tools are most valuable when linked to actions such as design changes, dual sourcing, spare-parts policies, process monitoring, or qualification testing.

Supply-chain resilience is more than having two suppliers. It includes geographic diversity, financial stability, capacity visibility, commonality of materials, substitutability, logistics alternatives, cyber protection, and recovery time. A cheaper supplier may produce a higher total risk cost if qualification is difficult or disruption would stop the plant.

Technology readiness should be assessed separately from commercial attractiveness. Technology readiness levels are commonly used to distinguish laboratory concepts, demonstrated subsystems, relevant-environment demonstrations, and proven operational systems. A high readiness level does not guarantee economic success, while a lower level may be acceptable for research or pilot production if the organization has the capability to absorb learning risk.

A practical adoption path is:

1. Evidence review: establish physical principles, known failure modes, and comparable applications.

2. Laboratory validation: demonstrate repeatability and critical material or process behaviour.

3. Pilot production: produce representative parts using intended equipment and personnel.

4. Qualification: verify capability, inspection, documentation, and compliance.

5. Controlled scale-up: introduce the process with performance gates and contingency capacity.

Real-options thinking can improve decisions under uncertainty. Instead of treating an investment as an irreversible commitment, management may purchase a smaller pilot system, reserve supplier capacity, or conduct a joint development project. The option to expand, defer, abandon, or switch routes has economic value, especially when demand and technology maturity are uncertain.

14.6 Make-or-Buy and Network Design

Make-or-buy analysis compares internal production with external sourcing. The relevant comparison includes avoidable internal costs, supplier price, logistics, incoming inspection, supplier management, inventory, qualification, intellectual-property protection, disruption exposure, and strategic capability.

A short-run decision should normally exclude unavoidable fixed costs. If a factory already owns equipment and salaries cannot be changed, the immediate make decision may depend on variable cost and opportunity cost. A long-run decision must include avoidable capital, labour, facilities, and technology costs. Confusing these horizons produces incorrect recommendations.

The decision should also consider whether the activity is strategically distinctive. Processes containing proprietary know-how, critical quality knowledge, or a major source of differentiation may be retained internally even when suppliers quote a lower price. Conversely, standardized operations with mature supplier markets may be better outsourced, allowing the organization to focus on design, integration, and customer value.

Hybrid arrangements are often superior to an absolute make-or-buy choice. A firm may retain a core process internally, qualify two external suppliers for surge capacity, and outsource non-critical overflow. Another option is equipment-as-a-service or a technology partnership, particularly when capital cost and learning risk are high.

14.7 Digital Decision Support and Data Governance

Digital manufacturing systems can strengthen process-selection decisions by connecting product data, process plans, machine records, quality results, maintenance history, and cost information. Digital twins and simulation can estimate cycle time, material flow, thermal behaviour, energy demand, and bottleneck effects before physical deployment. However, a digital model is only as reliable as its assumptions and calibration data.

Artificial intelligence can support demand forecasting, anomaly detection, predictive maintenance, parameter recommendation, and supplier-risk monitoring. It should assist rather than replace engineering accountability. Models require representative data, control of bias, explainability appropriate to the decision, and protection against data leakage or cyberattack. A machine-learning system trained on one material batch, machine, or product family may not generalize to another.

A sound digital governance system defines data ownership, version control, access rights, validation procedures, audit trails, and cybersecurity responsibilities. Configuration control is particularly important when process parameters affect safety-critical or regulated products.

14.8 Illustrative Case Study: Selecting a Route for a Low-Volume Aerospace Bracket

Background

The following is an illustrative case. A manufacturer must produce 1,200 titanium brackets annually for a new aircraft subsystem. The bracket has internal weight-reduction pockets, curved surfaces, tight positional tolerances, and a qualification requirement. Annual demand is expected to remain moderate, but design revisions are likely during the first three years.

Situation and alternatives

Three routes are considered:

• five-axis machining from titanium billet;
• investment casting followed by machining;
• laser powder-bed fusion followed by heat treatment, support removal, finishing, and inspection.

Machining has high material waste but mature quality and predictable qualification. Casting may reduce material waste at volume but requires tooling and has risks associated with defects and dimensional variation. Additive manufacturing can consolidate geometry and reduce material removal, but it requires process qualification, powder control, post-processing, and specialized inspection.

Analysis

The team first applies hard constraints. All three routes can potentially meet the material requirement, but casting requires evidence that thin internal sections and defect acceptance criteria are achievable. Additive manufacture requires demonstration of fatigue performance, build orientation effects, and repeatability. Machining passes the initial capability screen because comparable titanium parts are already produced in the facility.

A capability matrix is then constructed using criteria weighted for the program: qualification maturity, total cost, design-change flexibility, lead time, material utilization, production capacity, and supply risk. The team avoids assigning a high score to additive manufacturing merely for geometric novelty. Instead, it requests pilot parts and measures dimensional accuracy, surface condition, residual stress, and post-processing time.

The economic model includes material yield, machine time, tooling, inspection, qualification, engineering changes, and expected scrap. At the forecast volume, casting has a potentially attractive unit cost after tooling is amortized, but its break-even volume is sensitive to design stability. Machining has the lowest qualification risk and can absorb design changes rapidly, but its material and cutting-tool costs are high. Additive manufacturing has higher initial qualification cost than machining but shortens the transition from revised design to physical prototype.

A scenario analysis is performed. In the stable-demand scenario, casting becomes attractive after tooling amortization. In the design-change scenario, machining has the lowest total cost because casting tooling must be modified. In the disruption scenario, an additive supplier provides valuable capacity diversification but cannot yet replace the qualified machining route immediately.

Strategy and outcome

The company adopts a staged strategy rather than a single permanent choice. Machining is selected for initial production because it meets qualification and delivery requirements. Additive manufacturing is funded as a controlled pilot for design iterations and future weight-reduced variants. Casting is deferred until the design is frozen and demand is demonstrated. A second machining supplier is qualified for resilience, and a review gate is established after twelve months using measured yield, cycle time, qualification progress, and forecast demand.

Learning points

The case illustrates five principles. First, process selection must evaluate the complete route, not the primary transformation alone. Second, design instability changes the economic ranking of processes. Third, technology readiness and strategic flexibility can justify pilot investment even when immediate unit cost is not lowest. Fourth, resilience may favour a portfolio of capabilities. Finally, staged investment can preserve optionality while avoiding premature commitment.

14.9 Common Errors, Governance and Professional Responsibility

Several recurring errors weaken manufacturing investment decisions. These include comparing machine purchase prices rather than total ownership cost; using supplier brochures as proof of production capability; ignoring yield and inspection; treating forecast demand as certain; excluding engineering and qualification labour; counting sunk costs; assuming automation eliminates all labour; and selecting the process with the lowest unit cost despite poor strategic fit.

Governance is especially important for safety-critical, medical, nuclear, automotive, and aerospace products. Decisions must respect applicable standards, material traceability, validation requirements, worker safety, environmental regulations, and record-retention obligations. A process that is technically effective but not auditable may be unusable in a regulated environment.

Ethical responsibility includes honest reporting of uncertainty, avoidance of manipulated business cases, protection of worker health, and consideration of environmental and social consequences. Engineers should distinguish verified evidence from assumptions and should state who bears the consequences if the process underperforms.

14.10 Best-Practice Decision Procedure

A disciplined procedure can be organized as follows:

1. Define product, volume, quality, delivery, regulatory, and strategic requirements.

2. Separate hard constraints from weighted preferences.

3. Identify feasible process families and complete process routes.

4. Collect capability evidence and conduct trials where evidence is weak.

5. Estimate capacity, yield, cycle time, lead time, and bottleneck effects.

6. Build a total-cost and life-cycle model with transparent assumptions.

7. Evaluate risk, supply-chain resilience, technology readiness, and organizational capability.

8. Compare make, buy, partnership, and hybrid alternatives.

9. Perform sensitivity, scenario, and, when appropriate, probabilistic analysis.

10. Select implementation gates, ownership, metrics, and contingency plans.

11. Review the decision using actual production data and update the model.

The final recommendation should state not only which process is selected, but also why alternatives were rejected, what assumptions are critical, what evidence remains incomplete, and what conditions would trigger a reassessment.

Current Developments and Strategic Implications

Several developments are changing process-selection practice. Hybrid manufacturing combines additive deposition with machining or other finishing operations. Distributed production and digital work instructions can reduce dependence on centralized factories, although they increase cybersecurity and configuration-control requirements. More organizations are using carbon accounting and circularity criteria alongside conventional cost. Autonomous process control is improving consistency in selected applications, but qualification and explainability remain important barriers.

The strategic implication is that manufacturing capability is becoming a portfolio rather than a single route. Organizations require architectures that combine internal competence, qualified external capacity, digital visibility, and the ability to transfer production when demand or risk changes. The strongest decision is consequently not always the process with the best static economics; it is often the system that creates robust value across plausible futures.

Chapter Summary

Process selection is an integrated engineering and operations decision. Product requirements should be translated into hard constraints, preferences, and strategic objectives before alternatives are compared. Capability matrices support transparent feasibility screening, while weighted decision methods help compare surviving processes when evidence and priorities are explicit.

Economic evaluation must extend beyond unit price to include fixed and variable costs, yield, quality, capacity, opportunity cost, total cost of ownership, life-cycle effects, and discounted cash flow. Throughput is governed by effective capacity and bottlenecks, not nominal machine speed. Risk analysis should address technical uncertainty, technology readiness, supply-chain resilience, and organizational capability. Make-or-buy decisions must distinguish short-run avoidable costs from long-run strategic economics.

Digital tools, simulation, analytics, and AI can improve evidence and visibility, but they require validated models, sound data governance, and human accountability. Staged investment, pilot production, qualification gates, and portfolio strategies are valuable when technology or demand is uncertain. Ultimately, an effective operations strategy aligns process capability, economics, resilience, sustainability, and organizational purpose.

Key Terms

Capability matrix: A structured comparison of process performance against product, quality, operational, and strategic criteria.

Process route: The complete sequence of manufacturing, treatment, inspection, handling, and support operations used to produce a part.

Hard constraint: A mandatory requirement that disqualifies an alternative if it cannot be met.

Weighted decision matrix: A multi-criteria method that combines criterion importance with process ratings.

Process capability: The demonstrated ability of a stable process to remain within specified limits.

Cp: An index comparing specification width with six standard deviations of process variation.

Cpk: A capability index that considers both process variation and displacement from the specification centre.

Total cost of ownership: The complete cost of acquiring, integrating, operating, maintaining, upgrading, and retiring an asset.

Life-cycle costing: Economic evaluation of costs incurred throughout a product, process, or asset life.

Break-even volume: The production quantity at which two alternatives have equal economic cost.

Net present value: The present value of future cash flows less the initial investment.

Effective capacity: Achievable output after accounting for availability, performance, and quality losses.

Bottleneck: The resource that limits the throughput of a production system.

Overall equipment effectiveness: A performance measure combining availability, performance rate, and quality yield.

Technology readiness: The maturity of a technology demonstrated through progressively realistic evidence.

Supply-chain resilience: The ability of a supply network to withstand, recover from, and adapt to disruption.

Make-or-buy analysis: Comparison of internal production and external sourcing using relevant economic and strategic factors.

Real option: The value of retaining the ability to expand, defer, abandon, or change an investment.

Qualification: Documented demonstration that a process consistently meets specified technical and regulatory requirements.

Review Questions

Short Answer Questions

1. Why should hard constraints be separated from weighted preferences in a capability matrix?

2. Define total cost of ownership in the context of advanced manufacturing equipment.

3. What is the significance of a bottleneck in throughput analysis?

4. Distinguish technology readiness from commercial attractiveness.

5. Why can a lower supplier price fail to represent the lowest make-or-buy cost?

Long Answer Questions

1. Explain how a capability matrix can be constructed and used to screen competing manufacturing processes.

2. Develop a comprehensive economic model for comparing two capital-intensive manufacturing routes.

3. Discuss the role of effective capacity, yield, bottlenecks, and lead time in operations strategy.

4. Explain how technology readiness, risk, and supply-chain resilience should influence investment decisions.

5. Describe a staged process-selection procedure for an uncertain advanced-manufacturing project.

Analytical / Application Questions

1. A machining route has a fixed annual cost of 120,000 currency units and a variable cost of 85 per part. An alternative automated route has a fixed annual cost of 420,000 and a variable cost of 35 per part. Calculate the break-even volume and explain which route is preferable below and above that volume.

2. A proposed additive process has excellent geometric flexibility but uncertain yield and qualification status. Design a decision framework that determines whether it should be adopted immediately, piloted, outsourced, or rejected.

3. A company can either make a component internally or buy it from a single low-cost supplier. Identify the cost, risk, quality, and strategic information required before making the recommendation, and propose a resilient sourcing arrangement.

22. Multiple Choice Questions with Answers

Question 1

Which statement best describes the purpose of a capability matrix?

A. To calculate depreciation for a machine

B. To compare process performance against product and strategic requirements

C. To replace all production trials with subjective ratings

D. To determine only the fastest machine cycle

Correct Answer: B. To compare process performance against product and strategic requirements

Question 2

A process fails a mandatory tolerance requirement but receives a high weighted score for low cost. What should normally happen?

A. It should be selected because cost has the highest score

B. It should remain equal to all other alternatives

C. It should be rejected or marked infeasible

D. Its tolerance should be removed from the analysis

Correct Answer: C. It should be rejected or marked infeasible

Question 3

Which cost is most likely to be omitted in a simple machine purchase-price comparison?

A. Machine colour

B. Integration, maintenance, qualification, and downtime

C. Part geometry

D. Material density only

Correct Answer: B. Integration, maintenance, qualification, and downtime

Question 4

If a production system has high machine speed but poor availability and yield, its effective capacity will most likely be:

A. Equal to design capacity

B. Greater than design capacity

C. Lower than design capacity

D. Independent of quality yield

Correct Answer: C. Lower than design capacity

Question 5

What does the break-even volume indicate in a comparison of two process routes?

A. The maximum possible machine speed

B. The volume at which the two alternatives have equal cost

C. The minimum acceptable product quality

D. The number of suppliers required

Correct Answer: B. The volume at which the two alternatives have equal cost

Question 6

Which situation most strongly supports a staged investment strategy?

A. Demand and technology performance are both certain

B. The process is fully qualified and capacity is stable

C. Technology maturity and future demand are uncertain

D. The organization has no need for pilot data

Correct Answer: C. Technology maturity and future demand are uncertain

Question 7

In a make-or-buy decision, which factor is strategic rather than merely transactional?

A. Quoted unit price alone

B. Protection of proprietary process knowledge

C. The colour of supplier packaging

D. The number of pages in the purchase order

Correct Answer: B. Protection of proprietary process knowledge

Question 8

Why may a process with a lower unit cost still be strategically inferior?

A. It may have higher disruption, qualification, or flexibility risk

B. Low unit cost always indicates poor quality

C. Unit cost is never relevant

D. Strategic decisions cannot use economic data

Correct Answer: A. It may have higher disruption, qualification, or flexibility risk

Question 9

What is the primary purpose of sensitivity analysis in a capital investment decision?

A. To hide uncertain assumptions

B. To test how changes in important assumptions affect the recommendation

C. To eliminate the need for cost estimates

D. To guarantee the forecast outcome

Correct Answer: B. To test how changes in important assumptions affect the recommendation

Question 10

In the illustrative aerospace bracket case, why was machining selected for initial production?

A. It had no material cost

B. It provided mature qualification and rapid response to design changes

C. It required no inspection

D. It was guaranteed to have the lowest long-run cost

Correct Answer: B. It provided mature qualification and rapid response to design changes