Unit 5 Operations management

Operations management transforms inputs such as materials, labour, information and capital into outputs while balancing quality, cost, speed, dependability and flexibility.

Operations choices connect directly to strategy: a low-cost strategy needs efficient flow, while a premium service may prioritise quality and responsiveness. Bottlenecks and variability affect the whole system.

Trace an input through the transformation and identify which performance objective the decision changes.

A meal-delivery firm redesigns kitchen flow to reduce waiting time; the change lowers lead time but must preserve food safety and accuracy.

Operations is not only manufacturing; services also transform resources through people, information and processes.

Operations methods describe how inputs are transformed into outputs: job, batch, mass and flow production are common patterns. The method should fit volume, variety, customisation and required flexibility.

Job production is highly customised, batch groups similar items, mass makes high-volume standardised output, and flow runs continuously. As volume rises, unit cost may fall but flexibility can fall too.

Classify the method from volume, variety, continuity and customer specification, then state the trade-off.

A bespoke wedding cake uses job production; a bakery making 5,000 identical loaves uses batch or mass production; an oil refinery uses flow.

The labels are not determined by industry alone; one business can use different methods for different products.

Mass customization combines high-volume standardized processes with customer-selected variations, often using modular design, flexible technology and delayed final assembly. It can offer personalization with lower unit costs than job production, but requires investment, accurate information and coordinated processes. Evaluate all four methods against context: job production offers uniqueness and close quality control but is slow and costly; batch production balances variety and scale but creates changeover time and stock risk; mass/flow production gives speed, consistency and low unit cost at high volume but is inflexible and costly to stop; mass customization improves customer responsiveness but adds complexity. The best choice depends on expected demand, required quality, acceptable lead time, workforce and technology, not volume alone.

Lean production aims to deliver customer value with minimal waste, using smooth flow, pull, small batches, low inventory and continuous improvement.

Waste includes defects, waiting, overproduction, excess movement, inventory and unused capability. Removing one waste can expose another bottleneck, so the whole flow must be monitored.

Map the process, identify non-value-adding steps and test whether a change improves flow without reducing quality.

A factory rearranges tools at the workstation to cut walking time; output rises only if the new layout does not create safety or inspection delays.

Lean is not simply cutting staff or inventory; it is waste reduction while preserving customer value.

The specified lean production methods are continuous improvement (kaizen) and just-in-time (JIT). Kaizen involves employees making repeated small process improvements; JIT schedules materials and production so inputs arrive and outputs are made only when needed.

Kaizen can reduce waste and defects by using employees' process knowledge, but it needs participation, training, feedback and time for gains to accumulate. JIT lowers inventory, storage and obsolescence costs and can expose quality problems quickly, but it depends on accurate demand information, reliable suppliers, consistent quality and dependable transport.

Choose kaizen when continuous employee-led process learning addresses recurring waste; choose JIT when demand, production and supply can be synchronized. State the dependency and risk introduced by the method.

A component maker asks production teams to test small workstation changes through kaizen, while parts for confirmed orders arrive shortly before assembly under JIT. It retains a disruption plan because a delayed critical supplier could stop production.

Lean production does not require every named operations tool. For this Objective, apply kaizen and JIT only; neither can compensate for unstable processes, unreliable suppliers or poor-quality inputs.

Cradle-to-cradle design treats materials as nutrients for repeated biological or technical cycles, aiming to eliminate waste rather than end in disposal.

Design choices such as modularity, safe materials and take-back systems determine whether components can circulate at useful quality. Recycling a contaminated mix may only downcycle it.

Trace the material after use and test whether the design preserves value for the next cycle.

A phone is designed with replaceable modules and a manufacturer take-back scheme so parts can be recovered rather than shredded.

“Recyclable” on its own does not prove a closed loop; collection and material quality matter.

Quality control detects defects in outputs through inspection or testing; quality assurance designs processes and standards to prevent defects.

Control is reactive at the product stage, while assurance builds reliability into inputs, training and procedures. Both need meaningful specifications and feedback.

Ask whether the activity finds a defect after production or prevents the cause before it occurs.

A final inspection rejects a cracked bottle (control); supplier checks and machine calibration prevent cracks (assurance).

More inspection cannot substitute for a poorly designed process.

Quality management methods such as TQM, benchmarking, quality circles and continuous improvement make quality a shared, measurable responsibility.

TQM links customer requirements, leadership, staff involvement and process data; benchmarking compares performance to learn, not to copy blindly.

Select a method that matches the quality problem and identify how learning will change the process.

A hotel compares check-in waiting times with a strong competitor, then runs a staff quality circle to redesign its own process.

A benchmark is a reference point, not proof that another firm’s method fits your context.

Lean and TQM can reduce waste, defects and cost while improving reliability, but they require investment, training and cultural change.

Benefits depend on stable processes and employee involvement; excessive targets can increase stress, hide defects or shift costs to suppliers.

Balance measurable gains with effects on people, resilience, suppliers and customer experience.

A hospital standardises equipment locations and invites nurses to improve the layout; errors fall, but emergency stock is retained to protect resilience.

Short-term cost reduction is not the same as total quality or sustainable improvement.

Quality standards are agreed requirements used to specify, measure and demonstrate consistent performance. Certification can signal a controlled system but does not guarantee every output is excellent.

A standard clarifies evidence, responsibilities and corrective action; its value depends on scope, audit quality and actual practice.

Identify the requirement, evidence and consequence of non-conformance before judging the standard.

A food business uses a documented hygiene standard, records temperatures and investigates deviations rather than relying on a certificate on the wall.

Compliance is a baseline; customer value and continuous improvement still need direct evaluation.

A production location should support the strategy by balancing labour, materials, markets, infrastructure, regulation, community and total cost.

A low-cost site may increase transport or coordination costs; a market-facing site may improve service but raise rent.

List the dominant cost and service drivers, then compare sites using consistent weights and sensitivity checks.

A bakery locates near customers for freshness, while a bulk factory locates near raw materials and transport links.

The cheapest rent is not the cheapest operation once logistics and risk are included.

Analyse the complete location system: labour availability, skills, productivity and wages; proximity and reliability of suppliers; transport, energy and digital infrastructure; access to customers and delivery speed; land and operating costs; government taxes, grants, planning rules and trade policy; and environmental effects such as emissions, resource use, waste and exposure to climate risk. A factor matters through its effect on total cost, quality, capacity, resilience, reputation or strategic objectives, and its importance differs by product and market.

Outsourcing or subcontracting transfers an activity to an external provider; offshoring moves an activity to another country, whether internally owned or outsourced; insourcing brings an activity under the business's own control; reshoring returns an activity from abroad to the home country.

Outsourcing or offshoring may reduce cost, add specialist capability or increase flexibility, but can weaken quality control, expose supply chains and create coordination, ethical or reputation risks. Insourcing or reshoring may improve control, speed, intellectual-property protection and local employment, but requires investment, skills and potentially higher operating costs.

Separate the ownership decision from the geographic decision, then evaluate total cost, quality, control, flexibility, capacity, supply risk, employment and reputation. Include transition costs and whether the firm has the capability to perform the work internally.

A clothing business can outsource sewing to a specialist in its home country without offshoring, or reshore overseas production and keep it outsourced to a domestic supplier. These choices have different effects on ownership, logistics and employment.

Outsourcing does not necessarily mean abroad, and offshoring does not necessarily mean using an external supplier. Lower wage rates do not prove lower total cost once logistics, defects, delays and risk are included.

Contribution per unit is selling price minus variable cost per unit; total contribution pays fixed costs before profit.

A higher contribution can come from price or lower variable cost, but demand may change. Contribution is useful for short-run decisions when fixed costs are unchanged.

Calculate price−variable cost, multiply by volume, then check whether the decision changes fixed costs or demand.

A $10 meal with$6 variable cost contributes $4; 2,000 meals contribute$8,000 toward fixed costs.

Contribution is not profit until fixed costs are covered.

Break-even output is fixed costs divided by contribution per unit; at that volume total revenue equals total cost. Margin of safety shows how far expected sales are above break-even.

The chart makes assumptions visible: constant price, unit variable cost, fixed costs and a relevant output range.

Compute the point, label revenue/cost lines, then interpret what happens if volume or assumptions change.

Fixed costs are $20,000 and contribution$5, so break-even is 4,000 units; expected sales of 5,000 give a 1,000-unit margin of safety.

Break-even is a modelled threshold, not a forecast of demand.

Complete the model with these relationships: contribution per unit = selling price − variable cost per unit; break-even output = fixed costs ÷ contribution per unit; margin of safety = actual or forecast sales − break-even output; target profit output = (fixed costs + target profit) ÷ contribution per unit; profit at a stated output = total contribution − fixed costs; and target price = variable cost per unit + (fixed costs + target profit) ÷ target output. On the chart, output is on the horizontal axis and costs/revenue on the vertical axis: fixed cost is horizontal, total cost starts at fixed cost, total revenue starts at zero, and their intersection is break-even. For fixed costs of $20,000, variable cost of$6 and a $10 selling price, a$4 contribution gives break-even of 5,000 units. A $4,000 target profit needs (20,000 + 4,000) ÷ 4 = 6,000 units; at 7,000 units profit is 7,000 ×$4 − $20,000 =$8,000.

Changing price, variable cost or fixed cost changes contribution, break-even and profit; the direction is mechanical but the sales response may not be.

A price cut lowers contribution per unit but may raise volume; a fixed-cost rise shifts break-even without changing unit contribution.

Recalculate contribution and break-even, then test whether the assumed volume response is credible.

Price falls from $10 to$9 while variable cost stays $6: contribution falls from$4 to $3, so break-even rises unless volume grows enough.

Do not infer higher profit from higher sales without recalculating contribution.

Show each change graphically and quantitatively while holding other factors constant. A higher selling price steepens the total-revenue line, raises contribution, lowers break-even output and increases profit and margin of safety at a stated sales volume; a lower price does the reverse unless extra demand compensates. A higher variable cost steepens the total-cost line, lowers contribution and raises break-even; a higher fixed cost shifts the total-cost line upward in parallel and also raises break-even. Example: with fixed costs of $12,000, price$10 and variable cost $6, break-even is$12,000 ÷ $4 = 3,000 units. If variable cost rises to$7, contribution falls to $3 and break-even rises to 4,000 units; forecast sales of 5,000 then have a 1,000-unit rather than 2,000-unit margin of safety.

Break-even analysis simplifies reality by assuming linear revenue/cost relationships, stable prices and costs, one product or a constant mix, and known output.

Demand, capacity, step costs, quality, uncertainty and multiple products can invalidate the chart. It is best used with scenarios and sensitivity analysis.

State the assumption most likely to fail, then explain how it could change the decision.

A factory reaches overtime capacity, so variable cost rises in steps; the straight-line chart understates the true break-even point.

A precise break-even number can create false confidence when the assumptions are weak.

A supply chain links suppliers, production, distribution and customers; managing it coordinates material, information and cash flows across organisations.

Lead times, quality, dependency and visibility determine resilience. A local saving can increase total cost if it creates delays or defects elsewhere.

Map the chain, identify the constraint and ask how a change affects total flow rather than one stage.

A manufacturer changes supplier for a lower unit price but delivery variability causes stoppages and expediting costs.

Supply chain management is broader than purchasing; upstream and downstream effects matter.

Just-in-time keeps inventory low by receiving inputs near use; just-in-case holds buffers against uncertainty. The choice balances carrying cost with disruption risk.

JIT needs reliable suppliers and predictable flow; JIC protects continuity but ties up cash and may hide waste. Hybrid buffers can protect critical items.

Identify uncertainty and the cost of stockout versus holding stock before choosing the policy.

A hospital keeps emergency masks as JIC but orders routine stationery JIT.

JIT is not “no inventory,” and JIC is not automatically inefficient.

Stock control charts track inventory over time, showing maximum, reorder and minimum levels; the reorder point should allow for lead time and demand uncertainty.

Usage, delivery reliability and safety stock determine when to order. A chart is useful only if data and lead times are current.

Read the stock level against reorder and minimum lines, then explain the consequence of ordering now or later.

If stock falls to the reorder level while supplier lead time is ten days, the order should cover expected demand plus safety stock for variability.

A reorder line is not a guarantee against stockout when demand or delivery changes.

Read the four specified quantities precisely. Lead time is the interval between placing and receiving an order; buffer stock is the minimum reserve against uncertainty; reorder level is the stock level that triggers an order; reorder quantity is the amount ordered, shown by the vertical rise when delivery arrives. With steady demand, reorder level = expected demand during lead time + buffer stock. If usage is 20 units per day, lead time is 5 days and buffer stock is 30 units, reorder at 20 × 5 + 30 = 130 units. An order of 200 units is the reorder quantity; it does not mean the reorder level is 200. Interpret sloping falls as usage, vertical rises as deliveries, and test whether changing demand or lead time makes the settings unsafe or unnecessarily costly.

Capacity utilization rate = actual output ÷ maximum possible output × 100. Defect rate = defective output ÷ total output × 100. Labour productivity = output ÷ labour input, and capital productivity = output ÷ capital input; always state whether the input is workers, labour-hours or a monetary amount.

A productivity rate compares output with the specified total input, commonly expressed as output ÷ input × 100 when the question requires a percentage. Operating leverage = total contribution ÷ profit: a high value means fixed costs are large relative to profit, so a given percentage change in sales can create a larger percentage change in operating profit, in either direction.

Select the denominator named by the measure, keep units consistent and compare like with like across time or businesses. Then interpret the cause: unused capacity, defects, process methods, workforce skills, technology, product mix and fixed-cost structure can change the result.

A plant makes 8,000 units from capacity of 10,000, so utilization is 8,000 ÷ 10,000 × 100 = 80%. If 160 units are defective, the defect rate is 160 ÷ 8,000 × 100 = 2%. With 100 labour-hours, labour productivity is 80 units per labour-hour. If total contribution is $60,000 and profit is$15,000, operating leverage is 60,000 ÷ 15,000 = 4 times.

Higher utilization or productivity is not automatically better if quality, safety, flexibility or resilience falls. A falling defect rate is normally favourable, while high operating leverage increases both profit upside and loss risk; never compare productivity figures with different input definitions or units without adjustment.

A make-or-buy decision compares producing an input internally with purchasing it, using relevant costs, capacity, quality, control, reliability and strategic dependence.

Avoidable variable and fixed costs matter; allocated overhead that remains after outsourcing should not drive the decision. Supplier risk and opportunity cost can outweigh a lower quoted price.

Compare relevant total cost at the required volume, then include capacity released, quality, lead time and dependency.

Buying a component saves $2 per unit but uses a supplier with long lead times; if internal capacity could make a higher-margin product, the opportunity cost changes the answer.

The lowest purchase price is not automatically the lowest total or strategic cost.

Calculate only relevant costs at the required output. Cost to make (CTM) = avoidable fixed costs of internal production + variable cost per unit × quantity. Cost to buy (CTB) = supplier price per unit × quantity + relevant ordering, transport, inspection or contract costs. For 5,000 units, avoidable make fixed costs of $8,000 and variable cost of$6 give CTM = $8,000 + 5,000 ×$6 = $38,000. A supplier price of$7 plus $1,000 delivery gives CTB = 5,000 ×$7 + $1,000 =$36,000, a $2,000 cost advantage to buy. The final judgment must also weigh quality, control, reliability, capacity released, intellectual property and supplier dependence; allocated fixed overhead that remains under both choices is not a saving.

Crisis management responds to an event that threatens people, operations, finances or reputation; contingency planning prepares actions and resources before disruption occurs.

Planning identifies critical activities, dependencies, triggers, owners and communication routes. A crisis plan must be usable under pressure, not a long document nobody rehearses.

Identify the threat, the critical function and the first decision; then connect the contingency action to recovery.

A cloud outage plan names a backup system, decision owner and customer message, then tests whether data can be restored.

A contingency plan reduces impact but cannot predict every event; it needs review and exercises.

Effective crisis management depends on speed, clear authority, accurate information, stakeholder communication, ethical judgement and learning after the event.

Conflicting messages and unclear ownership increase harm; transparent updates preserve trust even when the solution is incomplete. Decisions should protect safety first and document trade-offs.

Check who decides, what evidence is reliable, how stakeholders are informed and how the response adapts.

During a product recall, one incident lead coordinates technical evidence while customer support gives a consistent safety message and records affected batches.

A confident public statement without verified information can deepen the crisis.

Contingency planning can shorten downtime, protect people and reputation, and clarify priorities, but it also consumes time and resources and may create false confidence.

Plans expose dependencies and improve resilience when rehearsed; unused plans still need updating as suppliers, systems and regulations change.

Weigh preparation cost against likelihood and impact, then test the plan’s recovery time and single points of failure.

A retailer pays for a second payment provider; the cost is justified if a failed provider would stop all sales during peak season.

A plan stored in a folder is not resilience; capability must be available and tested.

Research and development creates or improves products, processes and knowledge; it can support differentiation, productivity and future revenue but has uncertain returns.

R&D ties up cash before benefits are known and may be copied or made obsolete. Portfolio choices balance technical feasibility, customer value, time and risk.

State the problem or opportunity, identify evidence of value and assess the time and uncertainty of payoff.

A firm funds a prototype that cuts energy use, but pilots it with customers before committing to full-scale production.

R&D spending is not automatically innovation or profit; outcomes and adoption matter.

An unmet customer need is a problem or desired outcome not adequately served by current alternatives. Discovering it can create opportunity, but the need must be real, reachable and valuable enough to pay for.

Observation, interviews and usage data reveal friction customers may not articulate. The business must distinguish a genuine need from a preference with no viable market.

Describe the user, problem, evidence and willingness-to-pay or adoption constraint before designing a solution.

Commuters struggle with unreliable transfer information; a simple live alert service solves a specific problem if users trust its accuracy.

An interesting idea is not evidence of an unmet need.

Copyright protects original creative expression such as text, music, software code or artwork; a patent protects a qualifying new invention or technical process; a trademark protects a brand identifier such as a name, logo or symbol that distinguishes the source of goods or services.

These rights can deter copying, support licensing revenue and make R&D or branding investment easier to recover. Protection and enforcement cost money, rights differ by jurisdiction, patents are time-limited and require disclosure, and competitors may legally design around protected inventions.

Match the asset to the right: copyright for original expression, patent for an eligible invention, and trademark for brand identity. Then consider registration where applicable, target countries, expected commercial value and enforcement cost.

A technology business may copyright its software code, seek a patent for a genuinely novel technical invention used by the product, and trademark the product name and logo. Each right protects a different asset rather than the whole business idea.

Intellectual-property protection does not guarantee demand, profit or complete freedom from imitation. Do not use copyright to protect an invention or a patent to protect a brand name.

Incremental innovation makes successive improvements to an existing good, service or process for current markets. Disruptive innovation begins with a different, often simpler, cheaper or more accessible value proposition and can reshape a market as its performance and adoption grow.

Incremental innovation can improve quality, efficiency or features with lower uncertainty and use existing capabilities, but rivals may copy it and gains may be small. Disruptive innovation can create new customers or business models and weaken established advantages, but adoption, technology, timing and profitability are highly uncertain.

Identify what changed, the initial target users, the value proposition and whether the innovation improves the existing trajectory or changes who can access and value the offer. Evaluate capability, cannibalization, investment, adoption and competitor response.

Improving an existing delivery app's route suggestions is incremental. A low-cost self-service model that initially serves customers ignored by full-service providers and later improves enough to challenge them may be disruptive.

Disruptive does not simply mean dramatic, digital or successful, and a major technical invention may not disrupt a market. Innovation requires implementation and adoption, not novelty alone.

Data analytics examines data to identify patterns, explain performance or support predictions and decisions. A database is an organized electronic collection of related data that can be stored, updated and queried. Cybersecurity protects systems, networks and data; cybercrime is illegal activity using or targeting digital systems or information.

A management information system combines people, processes, databases and technology to collect, process and present timely information for planning, decisions and organizational control. Analytics is only as reliable as the database's accuracy, completeness and relevance, while cybersecurity protects confidentiality, integrity and availability from cybercrime and other threats.

Start with the management decision, identify the minimum relevant data and database fields, define access and quality controls, and decide which analysis can validly answer the question. Protect the system through proportionate technical, procedural and human controls.

A retailer queries its sales-and-stock database and analyses demand by store to plan replenishment. Access controls, backups and staff phishing awareness reduce the risk that cybercriminals steal or alter the data.

Raw data is not automatically useful information, analytics does not prove causation, and cybersecurity cannot eliminate all risk. A database is the organized data store, not the whole MIS.

Critical digital infrastructure includes data centres that house computing and storage, cloud computing that supplies scalable remote resources, and artificial neural networks that learn weighted patterns from data. Business technologies also include virtual reality (VR), the internet of things (IoT), artificial intelligence (AI) and big data.

VR creates simulated environments for training, design or customer experiences; IoT connects sensor-equipped objects that collect and exchange data; AI performs tasks involving prediction, recognition or decision support; big data describes datasets whose volume, variety or speed require advanced processing. These applications rely on infrastructure, connectivity, skills, quality data and governance.

Match the technology to a precise business problem, then analyse implementation cost, compatibility, skills, energy and infrastructure needs, vendor dependence, cybersecurity, privacy, bias, reliability and stakeholder impact.

A manufacturer sends IoT sensor data through cloud infrastructure to an AI model that predicts equipment failure. The system may reduce downtime, but managers need secure connections, reliable data, human verification and a fallback if the data centre or provider is unavailable.

An artificial neural network is one AI approach, not a human brain, and big data is not automatically good data. Cloud use transfers some infrastructure work but not the business's accountability for security, privacy or decisions.

Customer data can improve segmentation, personalisation, service and retention when collected lawfully and used transparently.

Tracking creates value only if the insight changes an offer or interaction; misuse damages trust and may breach privacy rules.

Identify the customer decision, consent basis, data minimisation and expected benefit.

A loyalty app recommends products from purchase history while allowing customers to opt out.

Personalisation does not justify collecting every available data point.

A customer loyalty programme rewards repeat behaviour and records transactions, which can improve retention, targeting and personalized offers; however, rewards cost money, may attract deal-seeking rather than loyal customers, and can raise privacy or fairness concerns. Data mining examines large datasets to discover patterns or relationships that inform segmentation, demand forecasts and decisions. Its conclusions depend on data quality and interpretation: correlation may not show causation, historical patterns may encode bias, and opaque collection can damage trust. Evaluate whether the insight changes a useful decision and whether consent, access, retention and customer benefit are proportionate.

Employee data can support scheduling, training, safety and performance decisions, but monitoring affects autonomy, fairness and trust.

Metrics may be incomplete or biased; workers need clear purpose, access and routes to challenge errors.

Check whether the measure reflects the job and whether the use is proportionate and explained.

A warehouse uses workload data to plan breaks, not to rank workers without context about equipment faults.

A precise metric can still be an unfair measure.

Digital Taylorism uses digital systems to divide, standardize, measure and closely monitor work, extending scientific-management ideas through real-time data, algorithms or automated targets. It may identify bottlenecks, support scheduling, improve consistency and raise productivity, but narrow metrics can ignore quality and context, intensify work, reduce autonomy, create stress and weaken trust. Employee data should have a clear purpose, proportionate collection, transparent criteria, human review and a way to correct errors or challenge decisions; productivity gains must be weighed against motivation, retention, ethics and long-term performance.

MIS can improve coordination, speed, forecasting and control, but creates cyber, privacy, dependency, bias and surveillance risks.

Good governance sets access, retention, audit, security and accountability; ethical use balances efficiency with rights and stakeholder harm.

Identify the benefit, threat, affected stakeholder and control, then judge residual risk.

A firm encrypts customer data, limits access and audits model decisions rather than assuming the software is neutral.

Compliance alone does not prove ethical or socially responsible use.