Product, Service and Process Design

Introduction

Product, service and process design are among the most consequential decisions in operations and supply chain management. A product or service is the customer-facing result of an organization’s activities, while a process is the coordinated set of resources and actions used to create and deliver that result. Design decisions determine not only what an organization offers, but also its cost structure, quality, speed, flexibility, environmental impact, employee experience and ability to scale.

For entrepreneurs, design is especially important because resources are limited and early design choices are difficult and expensive to reverse. A poorly designed product may require excessive customization, generate avoidable returns or depend on unreliable suppliers. A poorly designed service may create queues, inconsistent experiences and employee frustration. A poorly designed process may contain bottlenecks that prevent the venture from meeting demand even when the product itself is attractive.

Effective design therefore integrates customer needs, technical feasibility, operational capability and lifecycle consequences. This chapter examines product and service design, process mapping, service blueprinting, volume-variety analysis, bottleneck management, standardization, mass customization, design for manufacturability and concurrent engineering.

Learning Objectives

By the end of this chapter, you should be able to:

1. Explain the relationship between product, service and process design.
2. Apply lifecycle thinking to design decisions.
3. Map a process and identify waste, failure points and bottlenecks.
4. Construct and interpret a service blueprint.
5. Use volume-variety analysis to select an appropriate process structure.
6. Calculate basic capacity, utilization, takt time and flow-time measures.
7. Distinguish standardization, modularity and mass customization.
8. Explain design for manufacturability and concurrent engineering.
9. Evaluate design decisions using operational, commercial, ethical and environmental criteria.

1. The Design System: Product, Service and Process

A product is a physical or digital offering whose characteristics can be specified and inspected. Examples include a bicycle, a software application and packaged food. A service is an activity or experience delivered for a customer, often involving customer participation and simultaneous production and consumption. Examples include a haircut, a logistics delivery and business consulting.

The distinction is useful but not absolute. Many modern offerings are bundles. A smartphone combines hardware, software, warranty and support. A restaurant provides food, an environment and an experience. A subscription software firm delivers a digital product through an ongoing service process.

Product and service design answer the question: What value will be offered? Process design answers: How will that value be created and delivered? The two must be designed together. A promise of same-day delivery, for example, requires appropriate inventory positioning, order-processing technology, transport capacity and exception-management procedures.

A design should satisfy several dimensions of performance:

These objectives may conflict. A highly customized product may increase customer value but reduce speed and raise cost. A process optimized for maximum utilization may create long queues and reduce responsiveness. Design is therefore a process of making explicit trade-offs rather than maximizing one criterion in isolation.

2. Lifecycle Thinking

Lifecycle thinking evaluates an offering from the extraction of inputs through production, distribution, use, maintenance and end-of-life. It prevents designers from shifting costs or environmental impacts from one stage to another.

A product lifecycle commonly includes:

1. Concept and development: customer research, specifications, feasibility assessment and prototyping.
2. Introduction: launch, initial production, market testing and service support.
3. Growth: capacity expansion, supplier development and process stabilization.
4. Maturity: cost reduction, differentiation and reliability improvement.
5. Decline or renewal: withdrawal, redesign, reuse, refurbishment or replacement.
6. End-of-life: collection, recycling, remanufacturing, safe disposal or data deletion for digital services.

Lifecycle thinking applies to services as well. A digital service has design, deployment, use, maintenance, upgrades and decommissioning stages. A delivery service must consider packaging, transport, failed deliveries, returns and reverse logistics.

A basic lifecycle assessment identifies material and energy inputs, outputs and impacts at each stage. A simplified net environmental-impact model can be expressed as:

Here, I_total is total lifecycle impact; I_input represents impacts from inputs and production; I_recovered represents credits or avoided impacts from reuse and recovery; I_use is impact during customer use; and I_end is impact from disposal or end-of-life processing. The equation is a conceptual accounting identity, not a universal environmental-conversion formula. The terms must be measured using a defined functional unit, system boundary and impact-assessment method.

Designers should ask:

• Can the product use fewer materials or less energy?
• Can components be repaired, replaced or upgraded?
• Can packaging be reduced or reused?
• Are suppliers meeting labor, safety and environmental standards?
• Can the offering support circular flows rather than a linear “take–make–dispose” model?

Lifecycle thinking also includes economic and social consequences. A low-cost component that fails frequently may create warranty expense, customer harm and electronic waste. A service that relies on unpredictable gig work may appear flexible but raise concerns about income security, safety and fairness.

3. Translating Customer Needs into Design Requirements

Design begins with understanding customer problems rather than merely collecting requests. Customers often describe solutions (“I need a larger battery”) when the underlying need is functional (“I need the device to operate during a long journey”). Techniques include interviews, observation, complaint analysis, ethnographic research, surveys, lead-user research and analysis of usage data.

A useful design requirement should be:

• specific enough to guide decisions;
• measurable where possible;
• relevant to a customer or stakeholder outcome;
• feasible within available technology and resources; and
• traceable to evidence.

Quality function deployment (QFD) is a structured approach for translating the “voice of the customer” into technical characteristics and process requirements. It commonly uses a matrix linking customer requirements to engineering or operational characteristics. The purpose is not to produce a decorative matrix but to expose priorities, conflicts and responsibility.

For example, “easy to assemble” might translate into fewer fasteners, clear orientation features, reduced tool requirements and a target assembly time. “Fast response” in a service may translate into staffing rules, queue-management technology and a service-level target.

Specifications should distinguish critical-to-quality characteristics from desirable features. Feature accumulation can increase complexity without creating meaningful customer value. A minimum viable product is not simply a product with fewer features; it is a deliberately bounded design used to test important assumptions while controlling resource commitment.

4. Process Mapping and Analysis

Process mapping represents the sequence of activities, decisions, information flows and physical movements used to produce an outcome. It makes invisible work visible and supports shared understanding among employees, suppliers and customers.

A basic flowchart uses symbols such as:

• rectangle: activity or operation;
• diamond: decision;
• arrow: flow or sequence;
• document or data symbol: information generated or used;
• delay or queue: waiting before the next activity.

A process map should identify the process boundary, customer or user, inputs, outputs, responsible roles, systems and exception paths. Mapping only the “happy path” can conceal the work that creates most delay and cost.

Value-Added and Non-Value-Added Work

An activity is value-adding when it changes the product or service in a way the customer values and it is performed correctly the first time. Necessary but non-value-adding activities may include regulatory checks, security controls or unavoidable movement. Pure waste includes avoidable waiting, rework, searching, excess motion, duplication, overproduction and unnecessary transport.

A process map can be extended into value-stream mapping by recording cycle time, waiting time, inventory, information signals and quality losses. The goal is not to remove every non-value-adding activity mechanically. Controls that protect safety, privacy or legality should not be eliminated merely because they do not directly transform the product.

Process Performance Measures

If a process completes X units during a period of length T, its average throughput is:

Throughput must specify the unit, such as orders per hour or customers per day.

Capacity is the maximum sustainable output under stated conditions. If one resource performs a task in t minutes per unit and is available for A minutes per period, its theoretical capacity is:

This assumes stable task time, no setup, no breakdowns and continuous availability. Practical capacity is lower when breaks, maintenance, changeovers, absenteeism, quality losses and other constraints are included.

Utilization is commonly expressed as:

Utilization above 100% is not sustainable under the stated capacity definition; it indicates that demand is exceeding capacity or that capacity has been measured incorrectly.

Little’s Law relates average work-in-process, throughput and flow time in a stable system:

WIP is average work-in-process, Throughput is average completion rate and Flow time is average time in the system. Units must be consistent—for example, orders, orders per hour and hours. The law applies to a stable system over the period considered, where average entries and exits are approximately equal. It does not by itself identify causes of delay.

5. Service Blueprinting

A service blueprint is a customer-centered map of the service experience and the organizational processes that support it. It is especially valuable because services are often produced through interactions among customers, frontline employees, back-office employees, technology and external partners.

A typical blueprint includes:

1. Customer actions: steps performed by the customer.
2. Frontstage actions: visible employee or system interactions.
3. Backstage actions: activities invisible to the customer but necessary for delivery.
4. Support processes: supplier, IT, finance, compliance and other enabling activities.
5. Physical or digital evidence: receipts, interfaces, facilities, messages and other cues.
6. Lines of interaction and visibility: boundaries showing who can see or influence each action.

Consider an online grocery order. Customer actions include searching, selecting a delivery slot, paying and receiving the order. Frontstage actions include order confirmation and delivery communication. Backstage activities include inventory allocation, picking, substitution decisions and route planning. Support processes include payment authorization, supplier replenishment and platform maintenance.

Blueprinting reveals failure points. If inventory information is not synchronized, the customer may order an unavailable item. If substitution rules are unclear, the picker and customer may disagree. If delivery promises ignore route capacity, late deliveries become predictable rather than exceptional.

Service design should also address customer participation. Customers may provide inaccurate information, fail to appear, misuse equipment or create variability in demand. Clear instructions, self-service interfaces, error-proofing and appropriate recovery policies can reduce this variability without blaming the customer.

6. Volume–Variety Analysis and Process Choice

Volume and variety are central determinants of process design. Volume is the quantity of output, while variety is the number and diversity of products, services or specifications. In general, high volume and low variety support repetition, specialization and automation. Low volume and high variety require flexible resources, skilled employees and broader process capability.

Common process types form a continuum:

The continuum is not a rigid classification. A firm may use a hybrid process: a standardized base product, flexible final configuration and customized delivery.

The product–process matrix links product characteristics with process characteristics. Competitive performance usually improves when the process structure fits the required volume and variety. A high-volume standardized product produced entirely through a job shop may be too expensive. Conversely, a highly customized service forced through a rigid automated system may produce poor experiences and excessive exceptions.

Entrepreneurs should avoid selecting automation solely because it is technologically impressive. Automation is justified when demand, quality requirements, labor economics, safety and process stability support it. Flexible digital tools can sometimes provide customization without dedicated equipment, but they still require data integrity, cybersecurity and capable employees.

7. Bottleneck Analysis and Flow Improvement

A bottleneck is a resource or stage whose effective capacity limits the throughput of the entire process. Bottlenecks may be physical, informational, financial or organizational. A slow approval, unavailable software interface or limited specialist can constrain output just as a machine can.

To identify a bottleneck:

1. Map the end-to-end process.
2. Measure demand, processing time, availability and quality at each stage.
3. Estimate effective capacity rather than theoretical capacity.
4. Observe queues and work accumulation.
5. Confirm whether the constraint is persistent or caused by temporary variation.

Suppose three sequential workstations have capacities of 20, 15 and 18 units per hour. The process capacity is:

The second workstation is the bottleneck. Increasing the first workstation to 25 units per hour will not increase total throughput; it will increase accumulation before the bottleneck.

The Theory of Constraints proposes five focusing steps:

1. Identify the constraint.
2. Exploit it by using its existing capacity effectively.
3. Subordinate other activities to the constraint’s pace.
4. Elevate the constraint by adding capacity or changing the process.
5. Repeat because once one constraint is removed, another may emerge.

Practical actions include reducing setup time, preventing defective work from reaching the bottleneck, scheduling high-priority work appropriately, cross-training employees, adding parallel capacity and redesigning the product to reduce work at the constrained stage.

A balanced line may use takt time. If available production time per period is A and required customer demand is D, takt time is:

For example, if a facility has 420 productive minutes per day and demand is 140 units per day:

Takt time is a demand rate, not the actual processing time of every task. To meet demand, the paced line must have sufficient effective capacity and task assignments whose workload does not exceed the available cycle time. It is inappropriate when demand is highly erratic unless an appropriate planning period or capacity strategy is used.

8. Standardization, Modularity and Mass Customization

Standardization reduces unnecessary variation by specifying common components, methods, interfaces or service procedures. It can improve quality, purchasing leverage, training, speed and predictability. However, excessive standardization may reduce responsiveness and make the offering insensitive to legitimate customer differences.

Modularity divides an offering into relatively independent components with defined interfaces. A modular architecture allows a firm to standardize common elements while combining them in different configurations. Examples include configurable software packages, furniture systems and vehicles built from common platforms.

Mass customization seeks to provide meaningful customer variety with efficiency approaching that of standardized production. Its main mechanisms include:

• Postponement: delay final differentiation until demand information is available.
• Modular design: combine standardized modules in multiple ways.
• Configure-to-order: use rules to assemble an approved configuration.
• Flexible manufacturing: change outputs with limited setup cost.
• Digital configuration: allow customers to select options within feasible boundaries.
• Customer co-production: transfer selected activities to customers through self-service.

Mass customization fails when the number of options exceeds the organization’s ability to manage information, inventory, quality and service. A useful design question is not “How many options can we offer?” but “Which differences create customer value, and can they be delivered reliably?”

9. Design for Manufacturability and Assembly

Design for manufacturability (DFM) and design for assembly (DFA) integrate production considerations into product design. The objective is to make the product easier, safer and more economical to produce while preserving required performance.

Typical principles include:

• reduce the number of parts and fasteners;
• use standard, readily available components;
• design clear orientation and mistake-proofing features;
• minimize difficult movements, precision adjustments and special tools;
• provide access for assembly, inspection and maintenance;
• select materials and tolerances appropriate to the process;
• avoid unnecessary surface finishes or operations;
• design for disassembly, repair and recycling where relevant.

A design that is elegant for the customer may be difficult to manufacture. Conversely, a product that is easy to assemble but difficult to repair may create lifecycle costs. DFM should therefore be combined with design for quality, serviceability, sustainability and safety.

Tolerance design is important because every dimension cannot be manufactured exactly. Designers specify acceptable variation and determine whether accumulated variation could prevent assembly or impair performance. Tolerances must be realistic for the chosen process; overly tight tolerances increase cost, while loose tolerances may cause failure.

Error-proofing, or poka-yoke, designs the process so that an error is prevented or immediately detected. Examples include connectors that fit only one way, software validation of required fields and barcode checks that prevent incorrect order picking.

10. Concurrent Engineering and Cross-Functional Design

Traditional sequential development passes a design from marketing to engineering, then to operations, purchasing and service. This may appear orderly but often discovers problems late, when changes are expensive.

Concurrent engineering brings relevant functions into the design process early. A cross-functional team may include customers, designers, engineers, operations specialists, procurement professionals, quality staff, service employees, finance and sustainability experts. Suppliers and key customers may also participate.

Benefits include:

• earlier identification of manufacturing and service constraints;
• fewer engineering changes after launch;
• better supplier integration;
• improved maintainability and customer support;
• shorter development lead time;
• stronger ownership of implementation.

Concurrent engineering requires governance. Without clear decision rights, cross-functional participation can create delay or compromise. Useful practices include a shared requirements document, design reviews, failure-mode analysis, prototype testing, version control, documented assumptions and stage-gate decisions.

Digital tools such as computer-aided design, product lifecycle management systems, simulation, digital twins and collaborative platforms can support concurrent engineering. Artificial intelligence may assist with concept generation, demand analysis, defect detection or design-space exploration. However, AI-generated designs require human validation, reliable training data, intellectual-property review, cybersecurity controls and testing against safety and fairness requirements. AI should support accountable design decisions, not obscure who is responsible for them.

11. Design Risk, Quality and Governance

Design risk includes the possibility that an offering will be unsafe, illegal, unreliable, unaffordable, discriminatory or impossible to deliver consistently. Risk assessment should occur before launch and continue throughout the lifecycle.

Failure mode and effects analysis (FMEA) identifies potential failure modes, their effects, causes and existing controls. Teams commonly assess severity, occurrence and detectability using agreed scales and prioritize high-risk items. Numerical risk-priority scores can support discussion, but they should not replace judgment: a low-frequency safety hazard may deserve immediate action even if a simple score ranks it below frequent minor defects.

Governance issues include:

• product and workplace safety;
• accessibility and inclusive design;
• consumer protection and truthful claims;
• privacy and responsible use of customer data;
• intellectual-property ownership;
• supplier labor and environmental standards;
• cybersecurity of connected products and services;
• traceability, recall and incident-response capability.

Ethical design asks who benefits, who bears risk and who may be excluded. For example, a service optimized for smartphone users may disadvantage people with limited connectivity or disabilities. A pricing algorithm may create unfair outcomes if it uses biased data. Entrepreneurs should include affected stakeholders in testing and provide channels for complaints and remedy.

12. Applied Case: FreshBox Subscription Meals

This is an illustrative case. FreshBox is a fictional entrepreneurial venture offering weekly meal kits to urban households. Customers choose meals online, and the company purchases ingredients, assembles boxes and delivers them in scheduled time windows.

Background and Problem

FreshBox’s initial offering allowed customers to choose any combination of ingredients. Sales increased rapidly, but operations became unstable. Pickers searched for many low-volume ingredients, boxes were sometimes missing items, and delivery promises were missed. The founders considered buying a larger warehouse, but did not know whether space was the main constraint.

Analysis

The team mapped the process from online order cutoff to delivery. It identified customer actions, platform confirmation, supplier receiving, cold storage, picking, packing, quality checks, route planning and delivery. The map showed that picking was the bottleneck. The large variety of ingredients created long walking distances and frequent replenishment interruptions. Packing also experienced rework because the order information did not clearly distinguish substitutions.

The team recorded the following simplified capacities:

The process capacity was therefore 720 boxes per day, determined by picking. Purchasing a larger delivery fleet would not solve the immediate problem.

Approach and Solution

FreshBox introduced a modular menu. Core ingredients were standardized across several recipes, while sauces and flavor modules created variety. It postponed final box configuration until orders were consolidated. The company redesigned ingredient locations using demand frequency, introduced barcode scanning and created a clear substitution rule visible to customers.

A cross-functional team involving menu design, procurement, warehouse operations, customer service and delivery reviewed recipes concurrently. Recipes that required rare ingredients or unusually complex picking were redesigned. The team also measured actual picking time and used the results to set realistic order cutoffs.

Outcome

The redesigned process reduced ingredient variety without eliminating meaningful customer choice. Picking capacity increased to 900 boxes per day, while packing capacity rose to 950 boxes through fewer exceptions. The company still monitored delivery as a potential future constraint. Customer complaints about missing items declined, and the firm postponed major warehouse expansion until demand justified it.

Learning Points

1. The visible customer offering can create hidden operational complexity.
2. Capacity is constrained by the lowest effective-capacity stage.
3. Modularity and postponement can support mass customization.
4. Process mapping should include information and exception flows.
5. Cross-functional design is more effective than optimizing menu, procurement or delivery independently.

13. Implementation Roadmap for Entrepreneurs

A practical design project can follow these stages:

1. Define the customer problem and design boundary. Identify users, stakeholders and the outcome to be created.
2. Gather evidence. Observe use, analyze complaints, interview customers and study competing offerings.
3. Translate needs into requirements. Separate essential performance from attractive but noncritical features.
4. Generate alternatives. Consider product architecture, service channels, process types and sourcing options.
5. Assess feasibility. Test technical, operational, financial, legal and sustainability assumptions.
6. Map the process or blueprint the service. Include exceptions, queues, data and support processes.
7. Identify constraints and risks. Estimate effective capacity and conduct failure analysis.
8. Prototype and test. Use physical prototypes, simulations, pilot services or minimum viable releases.
9. Design for production, delivery and lifecycle. Involve suppliers, operators, service staff and maintainers.
10. Launch with measurement. Monitor quality, throughput, lead time, customer outcomes, safety and environmental indicators.
11. Learn and redesign. Treat post-launch data as input to continuous improvement.

A design dashboard may include first-pass yield, defect rate, average flow time, on-time delivery, customer effort, warranty or rework cost, employee safety, energy use and return or disposal rates. Measures should be linked to decisions; collecting data without ownership and action creates administrative burden rather than improvement.

Challenges and Limitations

Design methods simplify reality. A process map may become outdated when suppliers, software or customer behavior changes. Capacity estimates based on averages can conceal variability and peak-demand failure. Volume–variety categories may not fit digital or platform businesses with near-zero replication cost but substantial service and infrastructure constraints.

Standardization can suppress innovation or exclude users whose needs differ from the majority. Mass customization can create choice overload and increase carbon emissions through fragmented production and delivery. Lifecycle assessments depend on assumptions about use, transport, energy sources and end-of-life behavior. AI-supported design may reproduce historical bias or generate solutions that are technically plausible but legally or socially unacceptable.

Managers should therefore combine quantitative analysis with direct observation, stakeholder consultation, pilot testing and professional judgment. No single framework replaces responsible decision-making.

Contemporary Developments

Several developments are reshaping product, service and process design:

• Servitization: manufacturers offer maintenance, subscriptions, performance contracts and digital monitoring rather than one-time products.
• Circular design: products are designed for durability, repair, reuse, remanufacturing and material recovery.
• Additive manufacturing: small batches and complex geometries can be produced with reduced tooling, although material, speed and quality constraints remain.
• Digital twins and simulation: virtual models support capacity, layout and failure analysis before physical changes are made.
• Platform and ecosystem design: value is created through interactions among customers, providers, developers and logistics partners.
• Human-centered automation: technology augments employees while preserving judgment, accessibility and meaningful work.
• Resilient design: firms design alternative suppliers, substitution rules and flexible capacity rather than optimizing only for normal conditions.

Chapter Summary

Product, service and process design must be treated as an integrated system. Product and service design define the value proposition; process design determines how reliably and economically that value is delivered. Lifecycle thinking evaluates impacts from concept through end-of-life and encourages attention to repair, reuse, safety and social consequences.

Process maps expose activities, information flows, queues and exceptions. Service blueprints add customer actions, visible and invisible interactions, support processes and service evidence. Volume–variety analysis helps match offerings with appropriate process structures. Bottleneck analysis identifies the stage limiting throughput, while capacity, utilization, takt time and Little’s Law provide useful quantitative perspectives when their assumptions are respected.

Standardization improves consistency, whereas modularity and postponement allow variety with controlled complexity. Mass customization is successful only when customer-valued differences can be delivered reliably. Design for manufacturability, assembly, serviceability and sustainability brings operational realities into product design. Concurrent engineering brings functions and stakeholders together early, reducing late changes and improving implementation.

Finally, design is an ethical and governance responsibility. Safety, accessibility, privacy, labor conditions, environmental impacts and accountability must be considered alongside cost and speed. Entrepreneurs who design the offering and the operating system together are better positioned to create scalable, resilient and responsible ventures.

Key Terms

Product design: Specification of the features, functions, materials and performance of an offering.

Service design: Planning the customer experience and organizational resources required to deliver a service.

Process design: Definition of activities, resources, sequence and controls used to produce an output.

Lifecycle thinking: Evaluation of an offering across development, production, use and end-of-life.

Process map: Visual or textual representation of activities and flows in a process.

Service blueprint: Customer-centered map of customer, frontstage, backstage and support activities.

Volume: Quantity of output produced or delivered in a specified period.

Variety: Breadth or diversity of products, services or specifications.

Bottleneck: Resource or stage that constrains the throughput of a system.

Capacity: Maximum sustainable output under stated operating conditions.

Utilization: Actual output expressed as a proportion of available capacity.

Takt time: Available production time divided by required customer demand.

Standardization: Use of common specifications, components or methods to reduce unnecessary variation.

Modularity: Design of an offering from components with defined interfaces.

Mass customization: Efficient delivery of meaningful variety to individual customers.

Postponement: Delaying final differentiation until demand information is available.

Design for manufacturability: Designing products to be produced efficiently and consistently.

Concurrent engineering: Cross-functional and overlapping development of product, service and process decisions.

Poka-yoke: Error-proofing that prevents or immediately detects mistakes.

Lifecycle assessment: Systematic evaluation of environmental impacts across an offering’s lifecycle.

Review Questions

Short Answer Questions

1. Why must product or service design be integrated with process design?
2. What information does a service blueprint add to a conventional process map?
3. Define a bottleneck and explain why increasing non-bottleneck capacity may not increase throughput.
4. Distinguish standardization from modularity.
5. State two principles of design for manufacturability.

Long Answer Questions

1. Explain lifecycle thinking and discuss how it can influence product, service and process decisions.
2. Compare project, job-shop, batch, repetitive and continuous processes using volume and variety.
3. Describe how concurrent engineering can reduce development risk and explain the conditions needed for it to work.
4. Discuss the advantages and limitations of mass customization for an entrepreneurial venture.
5. Explain how ethical, environmental and governance issues should be incorporated into design decisions.

Analytical / Application Questions

1. A process has four sequential stages with effective capacities of 30, 24, 28 and 26 units per hour. Identify the bottleneck and process capacity. Recommend two actions, ensuring that at least one addresses the bottleneck directly.
2. A service business promises 180 customer visits per day and has 540 available service minutes per day. Calculate takt time and explain why takt time should not be confused with the processing time of every task.
3. Design a service blueprint for an online grocery order. Identify at least three customer actions, three frontstage actions, three backstage activities, two support processes and two likely failure points.

Multiple Choice Questions with Answers

Question 1

Which statement best describes the relationship between product design and process design?

A. Product design determines customer value, while process design determines how that value is created and delivered.

B. Product design concerns only marketing, while process design concerns only accounting.

C. Process design is required only for manufacturing, not for services.

D. The two decisions are independent after a product specification is approved.

Correct Answer: A. Product design determines customer value, while process design determines how that value is created and delivered.

Question 2

A process has sequential capacities of 20, 15 and 18 units per hour. What is its capacity under the stated conditions?

A. 15 units per hour

B. 18 units per hour

C. 20 units per hour

D. 53 units per hour

Correct Answer: A. 15 units per hour

Question 3

Which activity is most characteristic of a service blueprint?

A. Mapping customer actions and separating visible from backstage activities

B. Calculating depreciation on production equipment

C. Ranking suppliers only by purchase price

D. Determining the market capitalization of a firm

Correct Answer: A. Mapping customer actions and separating visible from backstage activities

Question 4

A facility has 420 productive minutes per day and demand is 140 units per day. What is the takt time?

A. 2 minutes per unit

B. 3 minutes per unit

C. 140 minutes per unit

D. 560 minutes per unit

Correct Answer: B. 3 minutes per unit

Question 5

Which process type is generally associated with low volume and high variety?

A. Continuous process

B. Repetitive line

C. Job shop

D. Mass production line

Correct Answer: C. Job shop

Question 6

What is the primary purpose of postponement in mass customization?

A. To delay final differentiation until better demand information is available

B. To eliminate all product variety

C. To increase finished-goods inventory before demand occurs

D. To prevent customers from selecting options

Correct Answer: A. To delay final differentiation until better demand information is available

Question 7

Which practice is most consistent with design for manufacturability?

A. Increasing the number of unique fasteners

B. Using standard components and simplifying assembly access

C. Specifying unnecessarily tight tolerances for every feature

D. Ignoring maintenance because it occurs after production

Correct Answer: B. Using standard components and simplifying assembly access

Question 8

Little’s Law is expressed as which relationship?

A. WIP = Throughput × Flow time

B. WIP = Throughput ÷ Flow time

C. Flow time = WIP × Throughput

D. Throughput = WIP × Flow time

Correct Answer: A. WIP = Throughput × Flow time

Question 9

What is a central feature of concurrent engineering?

A. Functions work sequentially and do not share design information

B. Relevant functions participate early and develop interdependent decisions together

C. Only the engineering department approves all operational decisions

D. Production begins before requirements are defined

Correct Answer: B. Relevant functions participate early and develop interdependent decisions together

Question 10

Which statement about utilization is correct?

A. It is actual output divided by available capacity, expressed as a percentage.

B. It is always equal to customer satisfaction.

C. It can exceed 100% sustainably without changing the capacity definition.

D. It measures variety divided by volume.

Correct Answer: A. It is actual output divided by available capacity, expressed as a percentage.