How to build an operations management course: a complete guide for lecturers

Central question: How should the operating system translate business strategy into choices about cost, quality, speed, dependability and flexibility?

Operations Management begins with strategic alignment. Students need to see processes, capacity, inventory and technology as linked choices rather than isolated techniques. This concept gives lecturers a language for connecting customer value, competitive priorities and operating trade-offs before the course moves into analytical tools.

What this concept covers

• The role of operations in manufacturing, services and platform businesses.
• Competitive priorities: cost, quality, speed, dependability and flexibility.
• Order winners, qualifiers and the risk of trying to maximise every objective at once.
• Structural choices such as capacity, process technology, network design and vertical scope.
• Infrastructure choices such as planning systems, improvement routines, workforce practices and performance measures.

Learning outcomes

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

• Explain how operations contributes to competitive advantage.
• Diagnose conflicts between operating objectives.
• Translate a market promise into measurable operating priorities.
• Evaluate whether an operating model is internally consistent.
• Recommend a small set of performance measures aligned to strategy.

Supports the course learning outcomes through assessable analysis, evaluation and recommendation.

How to teach it

• Open with two organisations serving similar customers through different operating models.
• Ask teams to rank five performance objectives for each organisation and defend the ranking.
• Use a strategy-to-operations matrix to connect customer promises to process choices.
• End with a short memo in which students identify one operating trade-off management must accept.

Case-style example

Aster Bikes sells premium commuter bicycles. Management promises 48-hour delivery, high customisation and prices 8% below specialist rivals. The plant currently runs at 92% utilisation, changeovers take 55 minutes and late orders have risen to 14%. Students rank the five competitive priorities, identify the contradiction in the promise, and recommend one operating objective to protect and one to relax. Two defensible answers emerge depending on whether brand value comes from speed or customisation. Students discover that strategy becomes operational only when trade-offs are explicit.

Where students struggle

Students often treat performance objectives as a checklist and conclude that every operation should maximise all five. The teaching task is to make opportunity cost visible: extra variety can increase changeovers, extra utilisation can damage responsiveness, and lower cost can conflict with resilience.

Reading and a quick check

Set the operations strategy and performance chapters from the core textbook. Quick check: ask students to write one sentence linking a customer promise, an operating objective and a process metric.

Applied teaching activity

Use a case discussion, process workshop, model critique or short decision memo here rather than forcing a simulation before students have the relevant integrated decision context.

Where it leads

Once priorities are explicit, students can inspect the processes that must deliver them and identify where flow is being created or lost.

Central question: How does work actually flow through an operation, and what should be redesigned when the process cannot deliver the required performance?

Process analysis is the bridge between strategy and day-to-day operations. Rather than teach flowcharts as a descriptive exercise, use them to expose handoffs, rework, variability, waiting and capacity loss. Students should leave able to turn a process map into a diagnosis and a practical redesign.

What this concept covers

• Process boundaries, inputs, outputs, customers and suppliers.
• Flowcharts, swimlanes, service blueprints and value-stream views.
• Cycle time, throughput time, work-in-process and first-pass yield.
• Handoffs, rework loops, batching and sources of delay.
• Process choice, standardisation, customisation and front-office/back-office design.

Learning outcomes

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

• Map a process at an appropriate level of detail.
• Calculate basic flow and throughput measures.
• Identify non-value-adding activity and rework.
• Compare alternative process designs.
• Recommend a redesign linked to a stated performance objective.

Supports the course learning outcomes through assessable analysis, evaluation and recommendation.

How to teach it

• Give students a messy process narrative and make them create the map before seeing any numbers.
• Add times, defect rates and handoff probabilities after the first map.
• Ask groups to redesign for different objectives such as speed, quality or flexibility.
• Use before-and-after metrics to force students to quantify the consequence of their redesign.

Case-style example

Harbour Health processes 600 outpatient appointments each week. Registration averages 6 minutes, triage 9, consultation 18 and checkout 5, but 22% of patients return to registration because insurance data are incomplete. Students map the process, calculate the expected extra registration load and redesign the handoff. One group may add pre-visit validation; another may cross-train triage staff. Students discover that the visible bottleneck is not always the root cause and that rework can create hidden capacity demand.

Where students struggle

Students can produce visually neat process maps without making a decision. Require every map to end with a quantified diagnosis: what is the constraint, how much capacity is lost and which redesign changes the outcome?

Reading and a quick check

Use the process design chapter in the core textbook. Quick check: give a five-step process and ask students to identify one value-adding step, one necessary non-value-adding step and one removable delay.

Applied teaching activity

Use a case discussion, process workshop, model critique or short decision memo here rather than forcing a simulation before students have the relevant integrated decision context.

Where it leads

A process map exposes flow. The next step is to quantify its constraint, utilisation, queue behaviour and capacity choices.

Central question: How much capacity is enough, where is the constraint, and how much utilisation can an operation tolerate before service or flow deteriorates?

Capacity decisions make the cost-responsiveness trade-off tangible. Lecturers can combine manufacturing bottlenecks with service queues to show that high utilisation is not automatically efficient. Students should learn to distinguish design capacity from effective capacity and to recognise that variability changes what a safe utilisation level looks like.

What this concept covers

• Design capacity, effective capacity, utilisation and efficiency.
• Bottleneck analysis, line balancing and theory-of-constraints logic.
• Little's Law and the relationship between throughput, inventory and flow time.
• Queue drivers: arrival variability, service variability, pooling and priority rules.
• Layout choices and their effects on movement, visibility, flexibility and congestion.

Learning outcomes

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

• Calculate process capacity and identify the bottleneck.
• Apply Little's Law to a stable process.
• Explain why waiting grows as utilisation approaches full capacity.
• Evaluate capacity cushions and pooling choices.
• Recommend a layout or capacity intervention for a stated objective.

Supports the course learning outcomes through assessable analysis, evaluation and recommendation.

How to teach it

• Use a physical or spreadsheet flow exercise with unequal workstation times.
• Increase demand until queues appear, then ask why local efficiency creates system-wide delay.
• Compare a dedicated-server service design with a pooled queue.
• Finish with a decision brief on whether to add capacity, rebalance work or manage demand.

Case-style example

Mosaic Kitchens has three stations: cutting 45 units/hour, assembly 36 and finishing 42. Demand is 40 units/hour and average work-in-process is 120 units. Students identify the bottleneck, calculate implied flow time using Little's Law, and compare a £60,000 assembly upgrade to a £15,000 cross-training plan that raises assembly to 40 units/hour but increases variability. Both can be defensible depending on demand confidence. Students discover that relieving one constraint can simply move the bottleneck and that utilisation alone is not a service-level target.

Where students struggle

Students often optimise a workstation rather than the system, or assume 100% utilisation is desirable. Use queues and WIP to make the system consequence of that assumption visible.

Reading and a quick check

Set the capacity and process-flow chapters. Quick check: ask what happens to system throughput when a non-bottleneck improves by 20% but the bottleneck does not change.

Applied teaching activity

Use a case discussion, process workshop, model critique or short decision memo here rather than forcing a simulation before students have the relevant integrated decision context.

Where it leads

Capacity is only useful when it is matched to demand, so the course next moves into forecasting and demand planning.

Central question: How should an operation plan when demand is uncertain and every forecast contains error?

Forecasting should be taught as a decision input, not a hunt for a perfect number. Students need enough technique to compare methods, but the richer learning comes from choosing the forecast horizon, measuring error and deciding how operations should respond when forecasts are wrong.

What this concept covers

• Forecast purpose, horizon and aggregation level.
• Moving averages, exponential smoothing and simple trend/seasonality logic.
• Forecast error measures and bias.
• Judgemental overrides and the risks of gaming the forecast.
• Sales and operations planning as cross-functional reconciliation of demand and supply.

Learning outcomes

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

• Select a forecasting approach appropriate to a decision.
• Calculate and interpret common forecast-error measures.
• Distinguish random error from persistent bias.
• Evaluate a management override using evidence.
• Translate forecast uncertainty into capacity or inventory choices.

Supports the course learning outcomes through assessable analysis, evaluation and recommendation.

How to teach it

• Give teams the same demand history but different decision horizons.
• Have them compare a simple baseline with a management forecast.
• Introduce a promotion or supply constraint after forecasts are submitted.
• Debrief on whether the best statistical forecast was also the best operational decision.

Case-style example

BrightBean Coffee supplies 40 university outlets. Weekly demand averages 18,000 cups, but term starts create 25% spikes and recent forecast bias is +8%. A marketing team predicts a further 20% launch uplift for a new drink. Students create a baseline forecast, measure the cost of over- versus under-forecasting, and decide whether to accept the override. One defensible view protects service; another avoids perishability. Students discover that forecast accuracy matters because different errors have different operating costs.

Where students struggle

Students can become absorbed in method selection and ignore the decision. Keep asking: what would you do differently if the forecast were 10% wrong?

Reading and a quick check

Assign Goltsos et al. (2022) alongside the forecasting chapter. Quick check: ask students to explain why a biased forecast can be more damaging than an equally large but unbiased error.

Applied teaching activity

Use a case discussion, process workshop, model critique or short decision memo here rather than forcing a simulation before students have the relevant integrated decision context.

Where it leads

Demand uncertainty creates the inventory problem: how much stock, where, and at what service level?

Central question: How should an operation balance availability, working capital, obsolescence and uncertainty when deciding how much inventory to hold?

Inventory makes a strong integrative topic because it connects service, forecasting, cost, cash and risk. Teach the formulas, but keep the managerial question visible: inventory is not automatically waste, and a lower stock level is not automatically better if service or resilience collapses.

What this concept covers

• Cycle stock, safety stock, pipeline stock and anticipation inventory.
• EOQ logic, reorder points and review systems.
• Service levels, stockout cost and uncertainty.
• ABC segmentation and differentiated control.
• Inventory turns, days inventory outstanding and the cash consequence of stock.

Learning outcomes

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

• Calculate a basic EOQ and reorder point.
• Explain the role of safety stock under uncertainty.
• Compare service-level and working-capital implications of inventory policies.
• Segment inventory using managerial criteria.
• Recommend an inventory policy and state the assumptions it depends on.

Supports the course learning outcomes through assessable analysis, evaluation and recommendation.

How to teach it

• Start with a stockout story before introducing formulas.
• Make students calculate both the operating and cash consequence of an inventory change.
• Use a sensitivity table for demand variability, lead time and service level.
• Ask students to defend which SKUs deserve differentiated control rather than one universal rule.

Case-style example

Vela Homeware sells 10,000 units a year of a £120 mixer. Ordering cost is £180, annual holding cost is 24% of unit cost, lead time is 10 days and daily demand averages 32 units with a standard deviation of 8. Students calculate EOQ and a service-based reorder point, then compare the cash tied up with a more conservative policy. One group may prioritise 98% availability; another may release cash. Students discover that inventory policy is a risk choice, not merely a formula.

Where students struggle

Students often report an EOQ or safety-stock number without checking whether assumptions such as stable demand, lead time and shortage cost are credible.

Reading and a quick check

Use the inventory chapter plus Goltsos et al. (2022). Quick check: ask which input has to change before a higher service level can reduce total cost.

Where a simulation fits

The Working Capital Management Simulation fits as a bridge after inventory and receivables/payables have been introduced. Students can see how inventory choices affect cash conversion, liquidity and the expansion decision rather than treating inventory as an isolated operations metric. View the Working Capital Management Simulation.

Where it leads

Inventory sits inside a wider network. The next concept expands the unit of analysis from one operation to suppliers, logistics partners and customers.

Central question: Which activities should the organisation own, where should capacity sit, and how should suppliers be selected and governed?

Supply chain design turns local operations decisions into network decisions. The key teaching move is to avoid reducing the topic to purchasing price. Students should compare landed cost, lead time, quality, flexibility, geopolitical exposure, information sharing and relationship design.

What this concept covers

• Network configuration and make-or-buy decisions.
• Strategic sourcing, supplier segmentation and total cost of ownership.
• Lead-time, geographic and concentration risk.
• Coordination, contracts, information sharing and the bullwhip effect.
• Supplier development, responsible sourcing and multi-tier visibility.

Learning outcomes

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

• Evaluate make-or-buy choices using operational and strategic criteria.
• Compare suppliers on total cost rather than price alone.
• Diagnose sources of supply-chain amplification and delay.
• Assess concentration and dependency risk.
• Recommend a supplier portfolio and governance approach.

Supports the course learning outcomes through assessable analysis, evaluation and recommendation.

How to teach it

• Give students three suppliers with different price, lead-time, defect and risk profiles.
• Make each team write its weighting before calculating a score.
• Introduce a disruption and see which sourcing logic remains defensible.
• Use the debrief to distinguish resilience from indiscriminate duplication.

Case-style example

Orion MedTech buys a critical sensor from Supplier A at £42 with a 4-week lead time and 0.4% defects. Supplier B charges £49, delivers in 10 days and has 0.2% defects. A third supplier in a higher-risk region charges £37 but has a 9-week lead time. Students estimate total operating exposure and choose single, dual or regional sourcing. Different answers are defensible under different disruption probabilities. Students discover that the cheapest supplier can create the most expensive operating system.

Where students struggle

Students frequently convert every risk into a score and hide judgement inside weightings. Require them to explain which criterion is non-compensatory: the condition that a supplier must meet regardless of price.

Reading and a quick check

Use the supply network and sourcing chapters, then add Holgado et al. (2024) for resilience and sustainability. Quick check: ask why dual sourcing can reduce one risk while increasing coordination cost.

Where it leads

A supply network still depends on process capability. The next concept focuses on quality and the routines that prevent defects rather than inspect them out.

Central question: How should an operation define quality, detect variation and build a system that prevents recurring failure?

Quality gives lecturers a way to connect customer requirements with process capability and organisational learning. Students should learn both analytical tools and the management system around them: standards, root-cause analysis, feedback, visual control and continuous improvement.

What this concept covers

• Conformance and perceived quality in products and services.
• Cost of quality: prevention, appraisal, internal failure and external failure.
• Process variation and the logic of statistical process control.
• Root-cause analysis, PDCA and structured problem solving.
• Quality culture, standard work and employee involvement.

Learning outcomes

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

• Distinguish common-cause and special-cause variation conceptually.
• Calculate and interpret simple quality measures.
• Use root-cause analysis rather than symptom fixing.
• Evaluate the economics of prevention versus failure.
• Design a continuous-improvement response with ownership and measures.

Supports the course learning outcomes through assessable analysis, evaluation and recommendation.

How to teach it

• Use a recurring defect where the obvious explanation is wrong.
• Have students build a cause-and-effect diagram, then ask which causes they can actually test.
• Compare the cost of inspection with the cost of prevention.
• End with a PDCA cycle that names the measure and review point.

Case-style example

Northline Appliances ships 50,000 kettles a month. Warranty returns have risen from 1.2% to 2.6%; 70% of failures involve the thermostat. Incoming inspection finds no change in supplier defects, but overtime on assembly increased 35% last quarter. Students quantify failure cost at £38 per return, develop competing root-cause hypotheses, and design a test. One view focuses supplier variation; another focuses process discipline. Students discover that correlation is not root cause and that prevention economics can justify operational change.

Where students struggle

Students jump from a symptom to a solution. Insist on an evidence path: observation, hypothesis, test, countermeasure and follow-up metric.

Reading and a quick check

Assign the quality and improvement chapters. Quick check: ask students to distinguish a control limit from a customer specification in one sentence.

Applied teaching activity

Use a case discussion, process workshop, model critique or short decision memo here rather than forcing a simulation before students have the relevant integrated decision context.

Where it leads

Quality improvement leads naturally into lean operations, where students examine flow, waste, standardisation and pull as a system rather than a toolkit.

Central question: How can an operation improve flow and responsiveness without simply cutting resources or transferring risk elsewhere?

Lean is frequently taught as a list of tools. A stronger course treats it as an operating system: clarify value, expose flow, reduce waste, stabilise work, solve problems and develop capability. The course should also examine where lean can become brittle if buffers are removed without understanding variability and disruption risk.

What this concept covers

• Value, flow, pull and the seven/eight wastes.
• Standard work, visual management, 5S and problem-solving routines.
• Setup reduction, small batches and flow.
• Just-in-time and the relationship between inventory, variability and reliability.
• Lean culture, employee involvement and the boundary between efficiency and resilience.

Learning outcomes

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

• Diagnose waste in a process without confusing all inventory with waste.
• Explain how setup time changes batch economics.
• Evaluate a pull system against demand and process stability.
• Design a lean improvement that protects quality and people.
• Critique a lean proposal under disruption or high variability.

Supports the course learning outcomes through assessable analysis, evaluation and recommendation.

How to teach it

• Run a short batch-versus-flow exercise.
• Add a quality problem or supplier disruption after teams have removed buffers.
• Ask students which lean practices create dynamic capability rather than only cost reduction.
• Use Camuffo and Poletto (2024) to connect lean systems with performance evidence.

Case-style example

Kestrel Components runs batches of 600 units because each machine changeover takes 90 minutes. Average WIP is £1.8 million and customer lead time is 18 days. An SMED pilot could cut changeover to 25 minutes for £120,000. Students calculate the operational case for smaller batches and identify the reliability conditions needed before reducing buffers. One answer moves aggressively to flow; another stages the change. Students discover that lean gains depend on process stability and problem-solving capability, not inventory reduction alone.

Where students struggle

Students equate lean with headcount reduction or zero inventory. Reframe lean around flow, learning and problem exposure, then ask what buffer is protecting the system from.

Reading and a quick check

Assign Gutierrez et al. (2022) and Camuffo and Poletto (2024). Quick check: ask students to name one buffer that should be removed and one that should be retained, with reasons.

Applied teaching activity

Use a case discussion, process workshop, model critique or short decision memo here rather than forcing a simulation before students have the relevant integrated decision context.

Where it leads

Once flow and quality are improved, students can tackle the recurring planning problem: how to allocate people, time and money across competing operational needs.

Central question: How should managers translate demand and priorities into feasible plans when labour, time, machines and budgets are constrained?

Planning and scheduling brings together forecasts, capacity and cost. It is a useful point to introduce managerial accounting as operational decision support: contribution, break-even, margin of safety and return measures help managers compare products and allocate scarce resources without losing sight of non-financial constraints.

What this concept covers

• Aggregate planning and capacity-demand reconciliation.
• Master scheduling, sequencing and priority rules.
• Finite capacity and the difference between a plan and a feasible schedule.
• Contribution margin, break-even, CVP and margin of safety for operating choices.
• Resource allocation across products, functions and strategic opportunities.

Learning outcomes

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

• Build a feasible high-level plan from demand and capacity data.
• Apply a simple sequencing rule and explain its consequence.
• Use contribution and break-even analysis to support an operating decision.
• Compare resource-allocation choices from different functional perspectives.
• Defend a plan that balances financial and operational constraints.

Supports the course learning outcomes through assessable analysis, evaluation and recommendation.

How to teach it

• Give teams a demand plan that exceeds one resource and make them choose overtime, subcontracting, inventory or delayed demand.
• Add product economics so the capacity choice has a financial consequence.
• Use the Managerial Accounting Simulation after students can calculate contribution and break-even.
• Debrief differences between the CFO, COO and CMO perspectives before asking for one integrated CEO decision.

Case-style example

Pioneer Nutrition makes Bars A, B and C on one packaging line with 2,400 hours available. Contribution per unit is £3.20, £4.10 and £5.30, but hours per 1,000 units are 12, 18 and 30, with minimum customer commitments of 40,000, 30,000 and 20,000 units. Students build a feasible mix and compare contribution per unit with contribution per constrained hour. Different mixes can be defended if strategic customer commitments are weighted differently. Students discover why local product margin is not enough for constrained-resource planning.

Where students struggle

Students may treat the highest unit margin as the priority without identifying the constrained resource or the service commitment. Make capacity feasibility the first test.

Reading and a quick check

Use the planning and control chapters. Quick check: ask why a financially attractive plan can still be operationally infeasible.

Where a simulation fits

The Managerial Accounting Simulation is a primary fit here. Students first compare three products using contribution margin, break-even, NPV, margin of safety and ROI, then allocate a fixed $100 million budget across five opportunities through CFO, COO and CMO perspectives before making an integrated CEO allocation. View the Managerial Accounting Simulation.

Where it leads

Resource allocation often culminates in a longer-term capacity or technology investment. The next concept moves from operating the current system to changing its asset base.

Central question: When should an operation invest in new capacity or technology, and how should financial appraisal be combined with flexibility, risk and operating fit?

Capital investment is where Operations Management meets Corporate Finance. Students should use NPV and related measures without letting the spreadsheet erase operational consequences such as learning curves, ramp-up risk, flexibility, maintenance, quality and strategic option value.

What this concept covers

• Process technology choices and automation.
• Capacity expansion timing, scale and flexibility.
• Project cash flows, NPV, IRR, profitability index and payback.
• Sensitivity, scenario and implementation risk.
• Portfolio selection when capital is rationed.

Learning outcomes

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

• Compare process-technology alternatives using operational criteria.
• Evaluate a capital project using core investment metrics.
• Identify assumptions that matter most to the appraisal.
• Allocate a constrained capital budget across projects.
• Recommend an investment with an implementation and risk rationale.

Supports the course learning outcomes through assessable analysis, evaluation and recommendation.

How to teach it

• Present a manual, semi-automated and automated process option with different fixed costs, variable costs and flexibility.
• Have students calculate financial metrics, then add demand uncertainty and ramp-up risk.
• Use Capital Budgeting as the secondary simulation when time permits.
• Require a recommendation that explains both project economics and operational fit.

Case-style example

Vertex Packaging can expand with Line A for £4.0 million, capacity 1.2 million units and variable cost £1.80, or flexible Line B for £5.6 million, capacity 1.0 million units and variable cost £1.55 with 30% faster changeovers. Forecast demand ranges from 0.7 to 1.15 million units. Students build cash-flow scenarios and recommend a line. One answer values scale; another values flexibility. Students discover that the highest base-case NPV can be the wrong operating choice when product mix is uncertain.

Where students struggle

Students may assume an NPV ranking fully resolves the decision. Require them to state what the investment locks the operation into and what uncertainty it can absorb.

Reading and a quick check

Use the process-technology and project chapters in the core text, plus a capital-budgeting technical note. Quick check: ask what information is absent from NPV if implementation delays are likely.

Where a simulation fits

The Capital Budgeting Simulation is a useful secondary application. Students appraise competing projects with NPV, IRR, profitability index and payback, then allocate a fixed $10 million portfolio, which makes capital rationing and project interaction visible. View the Capital Budgeting Simulation.

Where it leads

A capacity investment changes cash, inventory and supplier/customer terms. The next concept makes the operating-financial link explicit through working capital.

Central question: How do inventory, receivables, payables and operating decisions translate into cash, liquidity and the ability to grow?

Working capital is a particularly strong capstone for an operations course because the levers are operational while the consequence is financial. Students can see how supplier terms, collections, inventory, customer credit and expansion choices affect the cash conversion cycle and the organisation's room to act.

What this concept covers

• DSO, DIO, DPO and the cash conversion cycle.
• Accounts receivable, accounts payable and inventory as operating policies.
• Liquidity versus relationship and service trade-offs.
• Working-capital improvement without damaging suppliers or customers.
• Expansion readiness and the difference between accounting profit and cash availability.

Learning outcomes

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

• Calculate core working-capital metrics.
• Explain how operational choices change cash conversion.
• Evaluate policies for inventory, receivables and payables.
• Interpret monthly financial outcomes alongside operational events.
• Recommend whether an expansion is financially and operationally supportable.

Supports the course learning outcomes through assessable analysis, evaluation and recommendation.

How to teach it

• Begin with a profitable company that is short of cash.
• Make students calculate the cash released by changing DSO, DIO or DPO before discussing side effects.
• Run the Working Capital Management Simulation as the primary integrative application.
• Debrief the final expansion recommendation by asking what trade-off created the strongest and weakest cash outcome.

Case-style example

Riverton Furnishings has annual sales of £36 million, COGS of £24 million, DSO 52 days, DIO 78 and DPO 39. A new retailer could add £6 million of sales but demands 75-day terms and extra stock equal to 20 days of COGS. Students calculate the current and pro-forma cash conversion cycle, estimate additional working-capital need, and recommend Expand, Expand with Caution or Delay. Both growth and caution can be defensible. Students discover why revenue growth can consume cash faster than it creates profit.

Where students struggle

Students often optimise DSO, DIO and DPO independently. The better question is which change preserves service, supplier health and long-run economics while releasing cash.

Reading and a quick check

Use the working-capital material in the core text and the simulation preparation materials. Quick check: ask why stretching payables can improve cash today but weaken operations tomorrow.

Where a simulation fits

This is the main placement for the Working Capital Management Simulation. Students become the CFO of Northwood Furnishings, calculate working-capital metrics and then make 12 monthly operating and financial decisions before recommending Expand, Expand with Caution or Delay Expansion. View the Working Capital Management Simulation.

Where it leads

The final concept asks students to test the operating system against disruption, sustainability constraints and digital change rather than a single steady-state objective.

Central question: How should an operation remain productive and financially viable while absorbing disruption, meeting stakeholder expectations and adopting new technology?

A modern Operations Management course should close by challenging steady-state optimisation. Resilience, sustainability and digital technology all change the boundary of a good decision. Students need to judge redundancy, carbon and workforce choices, data quality and automation without assuming that every buffer is inefficiency or every digital tool is an improvement.

What this concept covers

• Operational resilience, recovery time and critical dependencies.
• Redundancy, flexibility, buffers and business-continuity design.
• Environmental and social effects of production, logistics, labour and sourcing.
• ESG trade-offs and economic constraints.
• AI, analytics, automation, digital twins and the governance of operational data.

Learning outcomes

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

• Map critical operational dependencies and failure modes.
• Compare efficiency and resilience interventions.
• Evaluate an operating decision using financial, environmental and social criteria.
• Identify when digital technology changes a process rather than merely digitises it.
• Defend an integrated operating recommendation under stakeholder conflict.

Supports the course learning outcomes through assessable analysis, evaluation and recommendation.

How to teach it

• Return to an earlier efficient process and introduce a disruption.
• Ask students which buffers now look valuable and which remain waste.
• Use the ESG Simulation as an optional closing stakeholder negotiation.
• End with a board-style operating recommendation that names financial, service, people and environmental consequences.

Case-style example

Solara Textiles can cut unit cost by 6% by concentrating production in one high-efficiency site, or retain two sites at an extra £1.1 million annual cost. The single site cuts energy use 9% but creates a 14-week recovery exposure after a major outage; the dual-site option protects service but duplicates equipment. Students quantify the expected exposure, consider workforce and emissions effects, and recommend the network. Different risk appetites support different conclusions. Students discover that resilience and sustainability can pull in opposite directions and require explicit governance.

Where students struggle

Students can turn ESG or resilience into values statements detached from operating economics. Require every recommendation to name a metric, a trade-off and a trigger for revisiting the decision.

Reading and a quick check

Assign Holgado et al. (2024), Dubey et al. (2023) and Cannas et al. (2024). Quick check: ask students to name one digital capability that improves resilience and one new risk it creates.

Where a simulation fits

The ESG Simulation is a secondary fit at the end of the course. Management, Investor, Regulator and Union teams negotiate an ESG package covering production, additional investment, ESG budget, management compensation, wages and workforce choices while keeping the business economically viable. View the ESG Simulation.

Where it leads

This closes the lifecycle. Students should now be able to diagnose an operation, quantify trade-offs, allocate resources, improve flow and defend an integrated decision.

10-week course structure

A 10-week version should compress adjacent ideas rather than remove the operating lifecycle. Keep the quantified diagnosis, resource-allocation, investment, working-capital and final integration decisions.

14-week course structure

A 14-week version creates space to separate service queues, sales and operations planning, supplier risk and simulation debrief. Use the extra time to raise cognitive demand and defence rather than simply adding more frameworks.

The Working Capital Management Simulation turns working-capital calculations into a 12-month CFO decision. Students take responsibility for Northwood Furnishings, first establish the mechanics of receivables, inventory, payables and the cash conversion cycle, then manage a sequence of operating and financial events before deciding whether the company should Expand, Expand with Caution or Delay Expansion.

Why it fits here

By Session 11, students already understand inventory, supplier relationships, customer terms and operating trade-offs. The simulation lets them see those levers move cash, liquidity and expansion capacity. It is especially useful for an Operations Management course because the financial outcome is generated by operational choices rather than by a separate finance case.

What students do

• Complete a working-capital basics stage using annual sales, COGS and starting DSO, DIO and DPO.
• Calculate receivables, inventory, payables, turnover ratios and cash conversion cycle for alternative strategies.
• Submit calculation answers, strategy choices and short rationales.
• Move through 12 simulated months, making decisions on supplier relationships, payment terms, receivables, inventory, customer credit, collections, cash flow, capex and operating risk.
• Watch metrics and financial statements update after monthly decisions.
• Finish with an expansion verdict: Expand, Expand with Caution or Delay Expansion, supported by a written rationale.

Recommended course placement

• At the end of Session 11 after DSO, DIO, DPO and cash conversion have been taught.
• Split across pre-class calculation preparation and a live decision/debrief session if contact time is limited.
• Before a final integrated operations memo so students can carry simulation evidence into a defensible recommendation.

Knowledge students should have before the simulation

• The operating meaning of receivables, inventory and payables.
• How to calculate DSO, DIO, DPO and the cash conversion cycle.
• Why faster collections, lower stock or longer supplier terms can create second-order commercial effects.
• The difference between accounting profit, cash availability and expansion readiness.

Delivery format

Suggested debrief questions

• Which monthly decision released the most cash, and what operating risk did it create?
• Where did an apparently attractive working-capital move damage a customer or supplier relationship?
• What metric changed your expansion recommendation?
• Which event made the base plan least credible?
• Would you make the same expansion decision if demand were 10% lower?
• What evidence from the individual decision record belongs in the assessment, and what still needs written defence?

The Managerial Accounting Simulation gives students an individual operating-resource-allocation problem. They first compare three products using contribution margin, break-even, NPV, margin of safety and ROI. They then move to a fixed $100 million allocation across five opportunities, seeing the decision through CFO, COO and CMO perspectives before making a final CEO allocation.

Why it fits here

Operations managers routinely need to translate product economics and capacity choices into priorities for scarce resources. Placing the simulation after planning and scheduling makes the accounting measures decision evidence, not a separate finance topic. The multiple executive perspectives are useful for showing why the financially highest-ranked option may not be the only defensible operating choice.

What students do

• Review proposition, features, target market, selling price, variable costs, fixed costs, sales forecasts and projected financial information for product options.
• Calculate or enter contribution margin, break-even, NPV, margin of safety and ROI to compare three products.
• Record selections and reasons rather than only a numerical ranking.
• Allocate a fixed $100 million budget across five opportunities from CFO, COO and CMO perspectives.
• Make an integrated CEO allocation and compare how the functional lenses changed priorities.
• Use comparative outputs to explain whether the final allocation is robust or dependent on one metric.

Recommended course placement

• At the end of Session 9 after contribution, break-even and constrained-resource thinking.
• As a 1.5-hour applied class with a separate 20-30 minute debrief.
• Before capital budgeting so students can distinguish operating-resource allocation from longer-term project appraisal.

Knowledge students should have before the simulation

• Contribution margin and break-even logic.
• Basic interpretation of ROI and NPV.
• The idea of scarce resources and competing operating priorities.
• Why different functions can evaluate the same opportunity differently.

Delivery format

Suggested debrief questions

• Which metric changed your product ranking most?
• Where did the COO perspective disagree with the CFO perspective, and why?
• Did the final CEO allocation maximise one metric or balance several objectives?
• Which assumption would most easily reverse your allocation?
• What would you need to know about capacity or service before approving the final budget?
• How should the individual decision record be combined with a written or oral defence?

Platform evidence supports academic judgement; it does not replace your rubric. For the two single-player primary simulations, individual calculations, choices, rationales and outcomes can provide attributable evidence. For team-based ESG, use team outcomes with an individual reflection or defence where you need an individual mark. Do not convert a leaderboard position directly into a grade.

Grading with the Working Capital Management Simulation

Students calculate working-capital metrics and then manage 12 monthly decisions before an expansion verdict.

• Accuracy and interpretation of DSO, DIO, DPO, turnover and cash conversion.
• Quality and consistency of monthly operating choices.
• Recognition of customer, supplier, inventory and liquidity trade-offs.
• Evidence used to support Expand, Expand with Caution or Delay.
• Quality of reflection on one decision that should be revised.

Grading with the Managerial Accounting Simulation

Students compare products using contribution, break-even and return metrics, then allocate a fixed $100 million budget across five opportunities from multiple executive perspectives.

• Accuracy and interpretation of product-economics calculations.
• Ability to identify the constrained resource or decision criterion.
• Quality of CFO, COO and CMO allocation logic.
• Coherence of the final CEO allocation and stated trade-offs.
• Individual rationale and response to challenge.

Grading with the Capital Budgeting Simulation

Students appraise competing projects using NPV, IRR, profitability index and payback before allocating a fixed $10 million portfolio.

• Calculation accuracy and project ranking.
• Recognition of capital rationing and project interactions.
• Sensitivity to assumptions and rejected alternatives.
• Quality of the final portfolio recommendation.

Grading with the ESG Simulation

Management, Investor, Regulator and Union teams negotiate an ESG package covering production, investment, budgets, compensation, wages and workforce choices while keeping the business economically viable.

• Use of role-specific evidence and objectives.
• Quality of negotiation and trade-off reasoning.
• Financial and operating viability of the agreed package.
• Connection between ESG choices and operational consequences.
• Individual reflection or oral defence where individual evidence is required.

Moderation and free-riding

Use a common rubric, retain the decision evidence, sample rationales and apply the same debrief or viva prompts. Where group work is used, add an attributable component and follow institutional rules for adjusting marks. The platform should be described as a source of evidence, not as an automatic grader.