Operational and Supply Chain Feasibility

Introduction

A business idea becomes commercially credible only when the entrepreneur can demonstrate that it can be operated reliably, economically, and at an acceptable level of risk. Operational feasibility asks whether the proposed enterprise can transform inputs into customer value through processes, facilities, people, technology, suppliers, and distribution arrangements. Supply chain feasibility extends this question beyond the firm to include upstream suppliers, logistics providers, channel partners, customers, and the flows of materials, information, money, and risk among them.

A feasible operating model must satisfy several conditions simultaneously. It must deliver the required quality and customer experience; possess sufficient capacity; comply with legal, health, safety, and environmental requirements; obtain dependable inputs; control costs; and remain resilient when demand, prices, transport, technology, or suppliers change. The central managerial challenge is therefore not simply to minimize cost. It is to design a system that balances cost, speed, quality, flexibility, sustainability, and resilience.

For an entrepreneur, operational analysis is also an evidence-building exercise. Claims such as “we can produce 1,000 units per month” or “delivery will take two days” must be supported by process maps, capacity calculations, supplier quotations, facility assessments, staffing plans, and risk controls.

Learning Objectives

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

1. Explain the relationship between operations strategy and supply chain feasibility.
2. Map operating processes and identify resources, bottlenecks, controls, and risks.
3. Estimate capacity, utilization, throughput, cycle time, and inventory requirements.
4. Evaluate location, facilities, sourcing, logistics, partnerships, and quality systems.
5. Design scalable, resilient, and ethically governed operating arrangements.
6. Prepare an operational feasibility assessment for a new venture.

1. Operations and Supply Chain as a Feasibility System

Operations management concerns the design and control of processes that create goods and services. A process converts inputs—materials, labor, capital, information, energy, and time—into outputs. Supply chain management coordinates the network through which inputs are sourced, transformed, stored, transported, sold, used, returned, or disposed of.

A useful systems perspective distinguishes four flows:

• Physical flow: movement of materials, products, equipment, and returns.
• Information flow: forecasts, orders, specifications, inventory records, and shipment status.
• Financial flow: payments, credit, working capital, insurance, and settlement terms.
• Risk and responsibility flow: allocation of quality, compliance, disruption, labor, environmental, and reputational risks.

The operating model should be aligned with the venture’s competitive priorities. A premium medical-device firm may prioritize quality and regulatory assurance; a fast-fashion retailer may prioritize speed and variety; a local food business may prioritize freshness and traceability. These priorities create different process, supplier, inventory, and facility requirements.

Core feasibility questions

2. Designing the Operating Process

Process architecture

An entrepreneur should begin with a high-level process architecture before selecting equipment or signing contracts. A typical architecture contains:

1. Demand capture: marketing, customer order, booking, or forecast.
2. Planning: scheduling, purchasing, staffing, and inventory allocation.
3. Inbound activities: receiving, inspection, storage, and preparation.
4. Transformation or service delivery: manufacturing, assembly, consultation, fulfillment, or transaction processing.
5. Quality verification: inspection, testing, approval, and documentation.
6. Outbound fulfillment: packaging, dispatch, installation, or service completion.
7. After-sales and reverse flow: support, returns, repair, replacement, recycling, and complaint handling.

A process map should identify sequence, responsible role, required input, output, decision point, expected time, control, and failure consequence. It should also distinguish value-adding work from waiting, movement, rework, duplication, and unnecessary approval.

Process choice

Process design should match volume and variety. A customized consulting service may use a project or job process, while a standardized bottled product may use a repetitive or continuous process. Common choices include:

• Project process: unique output, high customization, long duration.
• Job or functional process: low volume, high variety, flexible resources.
• Batch process: groups of similar products or services processed together.
• Repetitive process: standardized products moving through a repeated sequence.
• Continuous process: highly standardized, capital-intensive, uninterrupted flow.

Service businesses also require careful design even when no physical product exists. The service blueprint should show customer actions, employee actions visible to customers, backstage activities, technology interactions, and evidence of service quality.

Bottlenecks and process performance

A bottleneck is a resource whose effective capacity limits total system output. Increasing capacity at a non-bottleneck may create idle resources or excess work-in-process rather than higher throughput. Entrepreneurs should therefore locate constraints through observation and data, then ask whether the constraint can be eliminated, relieved, or managed through scheduling.

Important performance measures include:

• Throughput: units completed per period.
• Cycle time: elapsed time from process start to completion.
• Lead time: elapsed time from customer order to delivery.
• Yield: proportion of inputs producing acceptable output.
• First-pass yield: proportion completed correctly without rework.
• On-time-in-full (OTIF): orders delivered by the promised time and in the promised quantity.
• Utilization: proportion of available capacity actually used.

A process can have high utilization but poor customer performance if queues, breakdowns, or rework are excessive. Feasibility analysis must therefore avoid evaluating capacity through a single metric.

3. Capacity Planning and Quantitative Feasibility

Design, effective, and actual capacity

Design capacity is the theoretical maximum under ideal conditions. Effective capacity reflects planned breaks, maintenance, product mix, staffing patterns, changeovers, and normal operating constraints. Actual output is what the system achieves after breakdowns, shortages, defects, absenteeism, and demand limitations.

The basic capacity relationship is:

If a machine produces 20 units per hour and is scheduled for 35 hours per week, its design capacity is 700 units per week, assuming a constant rate and no interruptions. If the expected effective-capacity factor is 80%, effective capacity is:

Thus, effective capacity is 700 × 0.80 = 560 units per week.

Capacity utilization is:

Efficiency relative to effective capacity is:

These measures use the same unit of output and time. They should not be interpreted as interchangeable: utilization compares output with theoretical potential, while efficiency compares it with realistic planned potential.

Bottleneck capacity

For a process with sequential resources, the maximum steady-state throughput is constrained by the lowest effective resource capacity:

where Cᵢ is the effective capacity of resource i in consistent units per period. This expression assumes resources are required sequentially and that output is not pooled or bypassed. If parallel resources exist, their capacities should be added only when they can process the same work and are available in the same period.

Worked example: a small food producer

An entrepreneur plans to produce packaged granola. The process has three stages:

The process capacity is the smallest capacity: 165 kg per day. If one package contains 0.5 kg, the maximum stable output is:

Although baking can process 240 kg, packing is the bottleneck. If forecast demand is 300 packages per day, the system has only 30 packages per day of nominal capacity margin. A demand increase, absence, or packaging delay could eliminate that margin. Management could add a packer, redesign packaging, pre-pack during a second shift, or reduce variation in pack sizes. Buying another oven would not solve the current constraint.

Demand variability and capacity cushion

Capacity should be tested against average, peak, and scenario demand. A capacity cushion is capacity deliberately held above expected demand to absorb variability, emergencies, maintenance, or growth. The appropriate cushion depends on demand uncertainty, service promises, cost of lost sales, and ease of adding capacity. A hospital, emergency repair firm, or cloud service generally requires a larger cushion than a made-to-order craft business with flexible delivery dates.

4. Location and Facilities Feasibility

Location decisions influence revenue, labor access, transport cost, customer convenience, regulatory exposure, and future expansion. A structured location assessment should separate non-negotiable requirements from trade-offs.

Location criteria

Relevant criteria may include:

• proximity to target customers and suppliers;
• transport infrastructure, delivery radius, and congestion;
• availability, skill, productivity, and cost of labor;
• rent, land cost, taxes, utilities, insurance, and security;
• zoning, licensing, building standards, environmental restrictions, and operating hours;
• reliability of electricity, water, telecommunications, and waste services;
• climate, flood, fire, earthquake, or other hazard exposure;
• access for employees, customers, disabled persons, and emergency services;
• opportunities for expansion and neighboring land uses.

A weighted-factor method can make assumptions visible. Let wᵢ be the weight assigned to criterion i, with all weights summing to 1, and let rᵢⱼ be the score of location j on criterion i. The location score is:

The method is useful for screening alternatives, but it does not replace legal due diligence or financial analysis. Scores are partly judgmental, and a location with a high total score may still be unacceptable if it fails a mandatory requirement.

Facility layout

The layout should support safe, logical flow rather than merely fit equipment into available space. Common layouts include product or line layout, process or functional layout, cellular layout, fixed-position layout, and service layouts designed around customer movement. A feasibility review should examine receiving, storage, production, inspection, dispatch, waste, maintenance, staff welfare, fire exits, accessibility, and future expansion.

A facility plan must also distinguish usable operating area from circulation, safety clearance, utilities, offices, toilets, storage, and customer space. Underestimating these requirements produces congestion and later relocation costs.

5. Sourcing, Supplier Selection, and Partnerships

Sourcing decisions concern what the venture should make, buy, license, outsource, or obtain through a strategic partner. Outsourcing may reduce fixed investment and provide expertise, but it can increase dependency, coordination difficulty, intellectual-property exposure, and quality risk.

Supplier evaluation should consider total cost rather than purchase price alone. Total cost may include transport, duties, inspection, minimum-order commitments, storage, defects, late deliveries, payment terms, switching cost, and disposal. A low-price supplier can be economically unattractive if its defect rate or lead time is high.

Supplier due diligence

A practical due-diligence process includes:

1. Define specifications, tolerances, service levels, certifications, and ethical requirements.
2. Identify alternative suppliers and assess geographic concentration.
3. Verify capacity, financial stability, ownership, labor practices, and regulatory history.
4. Request samples, references, quotations, and evidence of quality systems.
5. Conduct a pilot order or service trial.
6. Negotiate price, lead time, minimum order, payment, warranty, confidentiality, audit, and termination provisions.
7. Establish performance measures and a corrective-action process.

A supplier agreement should clarify who owns tools, designs, data, inventory, and customer information. It should also specify responsibilities for product liability, recalls, cybersecurity, force majeure, continuity, and subcontracting.

Make-or-buy analysis

A make-or-buy decision should consider relevant avoidable costs, capacity opportunity cost, strategic capability, quality, flexibility, and risk. Fixed costs that will continue regardless of the decision are not relevant to a short-term comparison, whereas avoidable labor, materials, energy, inspection, and external logistics generally are. Long-term decisions must also consider investment, learning, intellectual property, and dependence.

Partnerships can be especially valuable for early ventures. Examples include contract manufacturing, shared kitchens, fulfillment providers, distributors, franchising, platform partnerships, and university or incubator facilities. The entrepreneur should identify the partner’s incentives, information needs, governance rights, exit conditions, and possible conflicts of interest.

6. Inventory, Logistics, and Working Capital

Inventory exists to decouple supply and demand, support production, achieve purchasing economies, and protect against uncertainty. However, inventory also ties up cash and creates holding, damage, obsolescence, insurance, and shrinkage costs.

Inventory categories include raw materials, work-in-process, finished goods, maintenance supplies, and goods in transit. The appropriate policy depends on demand variability, replenishment lead time, product perishability, supplier reliability, minimum order quantities, and the cost of stockouts.

Reorder point

Under a simple deterministic model with constant demand and lead time:

If average demand is 40 units per day and lead time is 6 days, the reorder point is 240 units. This assumes demand and lead time are known and stable, and it excludes safety stock.

With safety stock:

Safety stock should be based on documented uncertainty rather than an arbitrary percentage. In a simplified model with independent, normally distributed daily demand and constant lead time:

where z is the desired service-level factor, σ_d is the standard deviation of daily demand in units, and L is lead time in days. This formula assumes demand observations are independent, lead time is fixed, and the selected probability model is reasonable. If lead time is also variable, a different variance expression is required; the simple formula should not be used without adjustment.

Economic order quantity

For relatively stable demand, a basic economic order quantity is:

where D is annual demand in units per year, S is ordering or setup cost per order, and H is annual holding cost per unit. The model assumes constant demand, constant lead time, immediate replenishment, no quantity discounts, no stockouts, and a meaningful separation between ordering and holding costs. It is a benchmark, not an automatic purchasing instruction.

Logistics design

Logistics decisions include transport mode, shipment frequency, consolidation, packaging, delivery ownership, returns, and tracking. A feasible distribution plan should state the promised service level, delivery geography, cut-off times, contingency carrier, proof of delivery, and treatment of failed deliveries. Incoterms or other contractual delivery rules should be used accurately where international trade is involved.

7. Quality, Compliance, and Process Control

Quality is conformance to requirements and fitness for customer use. It must be designed into the process rather than inspected only at the end. Prevention generally costs less than repeated rework, returns, complaints, and reputational damage.

A basic quality system includes:

• clear specifications and acceptance criteria;
• approved materials and supplier controls;
• standard operating procedures and training;
• calibration and maintenance of measurement equipment;
• inspection, testing, sampling, and traceability;
• nonconformance records and root-cause analysis;
• corrective and preventive action;
• customer complaint and recall procedures.

Entrepreneurs may use the Plan–Do–Check–Act cycle: plan requirements and controls, do the work, check results against evidence, and act to correct or improve the system. Statistical process control can be appropriate when repeated measurements are available, but control charts should not be applied mechanically to small, irregular, or highly customized operations.

Compliance may include product safety, food safety, occupational health, environmental permits, data protection, accessibility, tax, employment law, labeling, intellectual property, and sector-specific licensing. Legal obligations vary by jurisdiction; feasibility work should identify the relevant authority and obtain professional advice where necessary.

8. Scalability, Resilience, and Business Continuity

Scalability is the ability to increase output or service volume while maintaining acceptable cost, quality, speed, and control. Scaling may be achieved through modular equipment, standardized work, cloud systems, cross-training, additional shifts, multiple sites, contract capacity, or redesigned products.

A venture should distinguish:

• replication: opening another similar unit;
• capacity expansion: adding resources to the existing system;
• platform scaling: serving more users through technology;
• scope scaling: adding products, markets, or channels.

Scaling too early can create idle assets and complexity. Scaling too late can produce stockouts, poor service, employee burnout, and lost trust.

Resilience is the capacity to prepare for, absorb, adapt to, and recover from disruption. A continuity plan should identify critical products, processes, dependencies, recovery priorities, maximum tolerable downtime, minimum service levels, responsible decision-makers, communication channels, and recovery resources.

Typical controls include:

• dual or geographically diverse suppliers;
• approved substitutes and alternative specifications;
• emergency inventory for critical items;
• backup power, communications, data, and payment systems;
• preventive maintenance and spare parts;
• cross-trained staff and documented procedures;
• alternative logistics routes and carriers;
• cyber incident response and data restoration;
• insurance, emergency cash, and supplier continuity clauses.

Redundancy has a cost, so risk controls should be proportionate to likelihood and impact. A risk register can record the event, cause, consequence, probability, impact, early warning indicator, owner, preventive control, response, and recovery action. Business continuity should be tested through drills rather than left as an unverified document.

9. Ethical, Environmental, and Digital Considerations

Operational feasibility includes the legitimacy and sustainability of the operating model. Entrepreneurs should examine wages, working hours, worker safety, forced labor, discrimination, community effects, emissions, water use, waste, packaging, and supplier transparency. A supply chain that is financially feasible but dependent on unsafe or exploitative practices is not professionally acceptable and may create legal and reputational liabilities.

Technology can improve forecasting, inventory visibility, route planning, quality traceability, and predictive maintenance. Artificial intelligence may support demand forecasting or anomaly detection, but its outputs depend on data quality and assumptions. Human oversight is necessary where decisions affect safety, employment, credit, privacy, or regulatory compliance. Data governance should define access rights, retention, cybersecurity, model validation, bias monitoring, and responsibility for errors.

Contemporary ventures increasingly consider circular operations: repair, reuse, remanufacturing, take-back, recyclable packaging, and product-as-a-service models. These arrangements can reduce waste and create customer value, but they require reverse logistics, product traceability, cleaning or refurbishment capacity, and clear ownership responsibilities.

10. An Integrated Feasibility Assessment

An operational feasibility report should connect evidence rather than present isolated descriptions. A recommended sequence is:

1. State the customer promise and operating requirements.
2. Map the end-to-end process and identify critical resources.
3. Estimate demand by period, including peak and scenario cases.
4. Calculate capacity and locate bottlenecks.
5. Compare location and facility alternatives.
6. Define sourcing, supplier, inventory, and logistics arrangements.
7. Specify quality, compliance, safety, and environmental controls.
8. Estimate operating costs, working capital, and implementation investment.
9. Analyze risks, dependencies, scalability, resilience, and continuity.
10. Identify assumptions, evidence gaps, milestones, and go/no-go conditions.

The report should distinguish facts, estimates, assumptions, and decisions. For example, a supplier quotation is evidence of a quoted price, not proof of future reliability. A demand forecast is an estimate, not an observed order. Sensitivity analysis should show how feasibility changes if demand, wages, rent, lead time, yield, exchange rates, or transport cost vary.

Applied Case: GreenBite Meal Kits

The following is an illustrative case.

Background

GreenBite is a proposed venture selling refrigerated meal kits to urban households. Customers order online by Wednesday for delivery on Saturday. The venture plans to purchase ingredients, assemble kits in a rented certified kitchen, and use a contracted courier.

Problem

The founders initially estimated that the kitchen could produce 500 kits per week because the assembly tables appeared capable of that volume. However, they had not analyzed ingredient receiving, cold storage, quality checks, packing, courier cut-off times, or demand peaks.

Analysis

The team mapped the process and measured effective weekly capacity:

Cold packing is the bottleneck at 460 kits. The founders also identified a single supplier for a key protein, a two-day replenishment lead time, and insufficient refrigerated storage for a 20% demand surge. A food-safety review showed that temperature logging and allergen segregation were required controls.

Approach and solution

GreenBite decided to launch at 350 kits per week, leaving a capacity cushion of 110 kits relative to the current bottleneck. It approved a second ingredient supplier, established receiving-temperature checks, introduced batch and allergen labels, and contracted a backup courier. A small amount of safety stock was authorized only for shelf-stable packaging, not highly perishable ingredients. The founders negotiated access to an additional cold room on peak production days and created a recall and customer-notification procedure.

Outcome

The revised plan required less initial sales ambition but produced a more credible launch. The operating budget included additional quality labor, cold-storage rental, and supplier qualification. The venture’s milestones were: complete a pilot of 100 kits, achieve at least 98% on-time delivery, document temperature compliance, maintain an agreed defect threshold, and confirm the backup supplier before expanding beyond 350 kits.

Learning points

The case demonstrates that apparent facility capacity is not the same as end-to-end capacity. The binding constraint may be quality, packing, storage, or dispatch rather than the visible production activity. It also shows why food ventures require traceability, contingency suppliers, cold-chain controls, and staged scaling. A conservative launch can be more feasible than an ambitious launch unsupported by operating evidence.

Challenges and Limitations of Operational Feasibility Analysis

Operational forecasts are uncertain, particularly when the venture lacks historical data. Supplier promises may be optimistic, customer demand may be seasonal, and early processes may improve through learning. Quantitative models can create false precision if assumptions are hidden. Weighted scoring can conceal disagreement about criteria, while capacity calculations may ignore changeover, maintenance, quality losses, or shared-resource conflicts.

Feasibility is also dynamic. A design that works at 100 units per week may fail at 1,000 units because supervision, layout, software, working capital, and supplier governance change. Entrepreneurs should therefore use pilots, test orders, time studies, supplier trials, and staged investment to replace assumptions with evidence.

Best-Practice Checklist

A strong operational feasibility assessment:

• begins with the customer promise;
• maps the complete process, including returns and complaints;
• uses realistic effective capacity rather than theoretical capacity;
• identifies the bottleneck and tests peak demand;
• evaluates total cost and working-capital impact;
• qualifies suppliers and avoids unjustified single-source dependence;
• embeds quality, safety, ethics, and compliance controls;
• documents assumptions and validates critical ones through pilots;
• plans for scalability without sacrificing control;
• tests continuity arrangements through drills and measurable milestones.

Chapter Summary

Operational and supply chain feasibility evaluates whether a venture can reliably convert resources into the promised customer value. It requires process design, capacity analysis, location and facility planning, sourcing, inventory, logistics, quality management, partnerships, scalability, resilience, and continuity planning. Capacity must be assessed using effective resources and bottlenecks, not visual impressions or theoretical maximums. Inventory and logistics decisions must balance service with cash and risk. Supplier selection must consider total cost, quality, ethics, dependence, and recovery options. Quality and compliance are designed into processes through specifications, training, traceability, inspection, and corrective action. Finally, a credible feasibility report makes assumptions explicit, tests them through evidence, and connects operational choices to financial, market, legal, and strategic viability.

Key Terms

Capacity: The amount of output a process can produce in a specified period.

Capacity cushion: Deliberate capacity held above expected demand to absorb variability.

Bottleneck: The resource or activity that limits total process throughput.

Cycle time: Elapsed time required to complete a process activity or unit.

Design capacity: Theoretical maximum output under ideal conditions.

Effective capacity: Realistic planned capacity after normal operating constraints.

First-pass yield: Percentage of output completed correctly without rework.

Inventory: Materials, work-in-process, finished goods, or supplies held for future use or sale.

Lead time: Time between placing an order or starting a process and receiving or completing it.

Logistics: Planning and control of the movement and storage of goods and related information.

Operational feasibility: Assessment of whether a venture can deliver its value proposition through workable resources and processes.

OTIF: On-time-in-full delivery performance.

Process map: Visual or structured representation of activities, sequence, decisions, inputs, and outputs.

Resilience: Ability to absorb, adapt to, and recover from disruption.

Reorder point: Inventory level at which replenishment should be initiated.

Safety stock: Additional inventory held to protect against uncertainty.

Scalability: Ability to increase volume while maintaining acceptable performance and control.

Supply chain: Network of organizations and activities involved in sourcing, transformation, delivery, and return flows.

Throughput: Rate at which a system completes acceptable output.

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

Review Questions

Short Answer Questions

1. Distinguish design capacity from effective capacity.
2. Why is a bottleneck important in operational feasibility analysis?
3. Identify four flows coordinated by supply chain management.
4. What information should a supplier due-diligence process establish?
5. Why should quality be designed into a process rather than left to final inspection?

Long Answer Questions

1. Explain how an entrepreneur should map and evaluate an end-to-end operating process.
2. Discuss the factors that should influence a location and facility decision.
3. Explain how inventory policy affects customer service, cash flow, and operational risk.
4. Compare the benefits and risks of outsourcing a critical operating activity.
5. Describe how a new venture can build scalability and business continuity into its operating model.

Analytical / Application Questions

1. A three-stage process has effective capacities of 420, 360, and 390 units per week. Identify the bottleneck, calculate process capacity, and recommend two possible interventions.
2. A venture has daily demand of 50 units and a supplier lead time of 4 days. If it holds 30 units of safety stock, calculate the reorder point under the constant-demand assumption and explain one limitation of the calculation.
3. Design an operational feasibility plan for a small online retailer, including process stages, capacity evidence, supplier controls, inventory policy, delivery arrangements, quality measures, and two continuity risks.

Multiple Choice Questions with Answers

Question 1

Which resource normally determines the steady-state capacity of a sequential process?

A. The resource with the highest theoretical capacity

B. The resource with the lowest effective capacity

C. The resource with the lowest purchase price

D. The resource closest to the customer

Correct Answer: B. The resource with the lowest effective capacity

Question 2

A machine has a design capacity of 800 units per week and an effective-capacity factor of 75%. What is its effective capacity?

A. 200 units

B. 600 units

C. 725 units

D. 1,067 units

Correct Answer: B. 600 units

Question 3

Under the simple constant-demand model, which expression represents the reorder point when safety stock is included?

A. Annual demand ÷ ordering cost

B. Demand during lead time + safety stock

C. Holding cost × lead time

D. Design capacity − actual output

Correct Answer: B. Demand during lead time + safety stock

Question 4

Which statement best describes a capacity cushion?

A. Capacity deliberately held above expected demand

B. Inventory that has passed its expiry date

C. Unused warehouse space caused by poor layout

D. The difference between purchase price and selling price

Correct Answer: A. Capacity deliberately held above expected demand

Question 5

A supplier offers the lowest unit price but has frequent defects and late deliveries. Which principle is most relevant?

A. Evaluate total cost and risk, not purchase price alone

B. Always select the supplier with the lowest quoted price

C. Ignore delivery performance if demand is strong

D. Replace quality inspection with larger orders

Correct Answer: A. Evaluate total cost and risk, not purchase price alone

Question 6

Which activity is most directly associated with process resilience?

A. Eliminating all spare capacity

B. Using only one geographically concentrated supplier

C. Maintaining alternative suppliers and recovery procedures

D. Removing preventive maintenance to reduce cost

Correct Answer: C. Maintaining alternative suppliers and recovery procedures

Question 7

In the location weighted-factor method, what does the weight assigned to a criterion represent?

A. The legal cost of the location

B. The relative importance of the criterion

C. The number of employees at the site

D. The annual output of the facility

Correct Answer: B. The relative importance of the criterion

Question 8

Why is cold-chain temperature logging important in the GreenBite case?

A. It replaces the need for supplier qualification

B. It supports food-safety control and traceability

C. It increases the theoretical capacity of the kitchen

D. It eliminates the need for a backup courier

Correct Answer: B. It supports food-safety control and traceability

Question 9

A process has design capacity of 1,000 units and actual output of 700 units. What is utilization?

A. 30%

B. 70%

C. 100%

D. 1,429%

Correct Answer: B. 70%

Question 10

Which is an appropriate use of artificial intelligence in operations?

A. Making safety decisions without human oversight

B. Replacing all legal and compliance review

C. Supporting forecasting while monitoring data quality and bias

D. Guaranteeing that forecasts are always accurate

Correct Answer: C. Supporting forecasting while monitoring data quality and bias