How to build a logistics management course: a complete guide for lecturers

Central question: How should logistics create customer value without optimising one cost at the expense of the system?

Start the course by positioning logistics as the coordinated management of flows, service promises and trade-offs. Students should see transport, inventory, warehousing and information as an integrated system rather than separate functional silos. The lecturer can then return to the same service-cost logic throughout the course.

What this concept covers

• The role of logistics within operations and supply chain management.
• Customer service, order fulfilment, availability and lead-time promises.
• Total logistics cost and trade-offs across inventory, facilities and transport.
• Product characteristics, service segmentation and competitive priorities.
• The difference between local optimisation and end-to-end performance.

Learning outcomes

• Explain how logistics supports customer value and competitive strategy.
• Map the major physical, information and cash flows in a logistics system.
• Evaluate service-cost trade-offs across functions.
• Distinguish logistics decisions from broader supply chain strategy.
• Frame a measurable logistics objective for a business.

How to teach it

• Use one order journey from customer click to final delivery and map every hand-off.
• Ask groups to redesign the service promise for a premium and an economy segment.
• Introduce total cost with a simple trade-off between faster transport and lower inventory.
• End with a short memo: which logistics metric should not be optimised in isolation?

Case-style example

HarbourHome sells flat-pack furniture. Standard delivery costs £18 per order at a 4-day promise; a 2-day option would raise transport cost to £28 but is expected to reduce cancellations from 6% to 3%. Gross margin is £62 per order. Students compute the contribution effect for 10,000 orders, then recommend one service promise or a segmented offer. They discover that faster is not automatically better, but neither is the cheapest logistics option.

Where students struggle

Students often equate logistics excellence with minimum transport cost. Make them state the customer promise first, then judge whether the network, inventory and transport choices support it at an acceptable total cost.

Reading and a quick check

Use Bowersox et al., Chapter 3 on logistics and Chapter 4 on customer accommodation. Quick check: ask students to name one decision that lowers a local cost but can raise total logistics cost.

Applied teaching activity

Use the case-style example as a short seminar decision, model build, critique or memo. Require students to state the decision, the evidence used, the missing information and the trade-off they are accepting.

Where it leads

The service promise creates the demand and availability targets that planning and inventory must support.

Central question: How much should logistics trust a forecast, and how should uncertainty change the plan?

Forecasts are inputs to logistics decisions, not facts. This concept gives students enough demand-planning literacy to connect forecast error with capacity, inventory, delivery reliability and cost. Keep the emphasis managerial: what decision changes when uncertainty rises?

What this concept covers

• Forecast purpose, horizon and aggregation.
• Forecast error, bias and simple accuracy measures.
• Demand variability, seasonality and promotions.
• Sales and operations planning as cross-functional coordination.
• Service levels, lead times and demand segmentation.

Learning outcomes

• Calculate basic forecast error and interpret bias.
• Compare stable and volatile demand profiles.
• Set a service-level objective that reflects customer and cost priorities.
• Explain how forecast error propagates into logistics capacity and inventory.
• Recommend a planning response when demand becomes less predictable.

How to teach it

• Give students twelve months of demand and ask for a baseline forecast plus error measure.
• Change one assumption with a promotion or supplier lead-time increase.
• Have sales, finance and operations teams defend different planning targets.
• Use a one-page S&OP decision note rather than a long forecasting lecture.

Case-style example

NorthPeak Beverages expects monthly demand of 20,000 cases, but actual demand has varied from 16,000 to 27,000. A new campaign may add 15%, while the warehouse can handle only 24,000 without overtime costing £0.80 per case. Students build a base and high-demand forecast, quantify the capacity gap and choose pre-build inventory, overtime or a lower service target. They discover that forecast precision matters less than a plan for forecast error.

Where students struggle

Students can spend too long comparing forecasting methods and too little time on the operational response. Keep asking what inventory, labour, transport or service decision changes because of the forecast.

Reading and a quick check

Use Bowersox et al., Chapter 5 on integrated operations planning. Quick check: give two forecasts with the same average error but different bias and ask which creates the greater logistics risk.

Applied teaching activity

Use the case-style example as a short seminar decision, model build, critique or memo. Require students to state the decision, the evidence used, the missing information and the trade-off they are accepting.

Where it leads

Planning translates into concrete inventory policies, where service, cash and disruption risk become visible.

Central question: How much inventory is enough when holding cost, stockout risk and cash are all real constraints?

Inventory is where service and finance meet. Students should be able to calculate the operating logic of cycle stock and safety stock, but the teaching aim is judgement: when should the firm carry more buffer, and when does inventory become an avoidable use of cash?

What this concept covers

• Cycle stock, safety stock, reorder points and service levels.
• Inventory turnover, days inventory outstanding and obsolescence.
• Demand and lead-time uncertainty.
• Stockout costs, backorders and lost sales.
• Working capital and the link between inventory, receivables and payables.

Learning outcomes

• Calculate basic reorder and safety-stock measures from supplied data.
• Evaluate the cost and service consequences of inventory policies.
• Interpret DIO and inventory turnover alongside DSO and DPO.
• Recommend an inventory buffer under uncertainty.
• Explain how inventory decisions affect expansion readiness and cash flow.

How to teach it

• Start with one SKU and a simple lead-time demand problem.
• Add a supplier delay and ask students whether the safety stock should change.
• Compare a high-service policy with a cash-preservation policy.
• Place the Working Capital Management Simulation after the calculations so the metric becomes a decision.

Case-style example

Alpine Medical Supplies sells 1,200 units a week. Lead time is 3 weeks, demand standard deviation is 180 units per week and each unit costs £42. A 95% service policy requires more safety stock than a 90% policy, but finance has set a £175,000 inventory cap. Students calculate approximate lead-time demand and compare two buffers, then recommend the policy and identify the service risk. They discover that safety stock is a strategic use of cash, not free insurance.

Where students struggle

Students often minimise inventory without pricing the stockout or disruption consequence, or maximise service without recognising cash and obsolescence. Require both sides of the trade-off in every recommendation.

Reading and a quick check

Assign Guo, Liu, Song and Wang (2025) on inventory strategies for supply chain resilience alongside Bowersox et al., Chapter 7. Quick check: ask which assumption would most change the safety-stock recommendation.

Where a simulation fits

The Working Capital Management Simulation fits directly here. Students act as CFO of Northwood Furnishings, calculate DSO, DIO, DPO and the Cash Conversion Cycle, then make monthly receivables, inventory and payables decisions before recommending whether the company should expand.

Where it leads

Once inventory policy is set, students can examine the facilities and fulfilment processes that physically hold and move that stock.

Central question: What should a warehouse do, where should effort be spent, and when does automation improve the system?

Warehousing is more than storage. It combines receiving, put-away, replenishment, picking, packing, staging and information control. Students should learn to diagnose where time, errors and congestion enter the flow before recommending technology.

What this concept covers

• Warehouse roles and facility types.
• Receiving, put-away, storage, replenishment, picking and dispatch.
• Slotting, travel distance, order profiles and labour productivity.
• Automation, robotics and warehouse management systems.
• Safety, packaging, damage and quality control.

Learning outcomes

• Map a warehouse process and identify non-value-adding movement.
• Select a picking or storage approach for a stated order profile.
• Compare manual and automated fulfilment options.
• Evaluate capacity and congestion constraints.
• Recommend operational metrics for warehouse performance.

How to teach it

• Give a process map with travel times and ask where the bottleneck sits.
• Run a simple ABC slotting exercise using SKU velocity.
• Compare person-to-goods and goods-to-person logic.
• Use Perotti et al. (2022) to frame the benefits and barriers of Logistics 4.0 in warehousing.

Case-style example

MetroParts processes 8,000 order lines a day. Fast-moving A-items are 20% of SKUs but 68% of picks; they are currently stored at the back of the facility. Pickers walk an average 11.5 km per shift, and overtime begins above 8,500 lines. Students re-slot the top 30 SKUs, estimate the change in travel and argue whether a £450,000 automated picking module is justified. They discover that layout discipline should precede automation.

Where students struggle

Students are attracted to automation before defining the problem. Require a baseline flow, volume profile and bottleneck before any technology recommendation.

Reading and a quick check

Use Bowersox et al., Chapter 9 and Perotti et al. (2022). Quick check: ask students to name one warehouse automation benefit and one adoption barrier that could offset it.

Applied teaching activity

Use the case-style example as a short seminar decision, model build, critique or memo. Require students to state the decision, the evidence used, the missing information and the trade-off they are accepting.

Where it leads

Warehousing decisions connect directly to transport, because shipment size, frequency, consolidation and cut-off times shape both systems.

Central question: Which transport option best balances cost, speed, reliability, capacity and risk for the shipment?

Transport is usually the most visible logistics activity, but students need a structured way to choose between modes, carriers and service levels. The key is to connect freight economics with inventory, customer service and network design rather than teaching mode facts in isolation.

What this concept covers

• Road, rail, air, sea and intermodal characteristics.
• Freight rates, shipment size, distance and density.
• Carrier selection, service reliability and contractual performance.
• Consolidation, routing, scheduling and backhaul logic.
• Time-in-transit, inventory implications and disruption exposure.

Learning outcomes

• Compare transport modes using cost and service criteria.
• Estimate how transit time affects inventory and customer promise.
• Evaluate carrier performance beyond headline rate.
• Recommend a transport strategy for different product profiles.
• Identify when consolidation or intermodal transport creates value.

How to teach it

• Give three transport quotes with different transit time and reliability.
• Ask groups to price the inventory-in-transit consequence of the slower option.
• Use a carrier scorecard with service, claims and cost.
• Close with a short tender recommendation.

Case-style example

Vector Electronics must move 40 pallets from Poland to the UK each week. Road costs £7,800 with 3-day transit and 96% on-time delivery; intermodal costs £5,900 with 6-day transit and 91% on-time delivery. Inventory is valued at £320,000 per weekly shipment and the annual carrying rate is 18%. Students calculate the extra in-transit holding cost, then decide whether the saving compensates for service risk. They discover that cheapest freight is not necessarily cheapest logistics.

Where students struggle

Students compare rates without valuing transit time, reliability or the cost of failure. Make every transport recommendation include at least one inventory and one service consequence.

Reading and a quick check

Use Bowersox et al., Chapter 8. For current application, pair it with the World Bank (2026) Logistics Performance Indicators 2.0 report. Quick check: ask which transport KPI would make them switch carriers even if the rate were unchanged.

Applied teaching activity

Use the case-style example as a short seminar decision, model build, critique or memo. Require students to state the decision, the evidence used, the missing information and the trade-off they are accepting.

Where it leads

Mode and carrier decisions make more sense once students see the network of facilities, lanes and last-mile nodes they are serving.

Central question: How many facilities, where should they sit, and how should the network balance responsiveness with cost?

Network design turns individual logistics decisions into a system. Students should see how facility location, inventory pooling, customer proximity and transport distance interact. Last-mile logistics then makes the service-cost tension especially concrete.

What this concept covers

• Centralised versus decentralised distribution.
• Facility location, capacity and customer assignment.
• Inventory pooling and responsiveness.
• Hub-and-spoke, cross-docking, parcel networks and urban fulfilment.
• Last-mile productivity, delivery density and customer choice.

Learning outcomes

• Compare centralised and decentralised network options.
• Explain the inventory and transport effects of adding a facility.
• Evaluate a last-mile operating model.
• Use service zones and density to support a network recommendation.
• Defend a network design under uncertain growth.

How to teach it

• Plot customers on a simple map and compare one- versus two-DC designs.
• Use a total-cost table rather than a full optimisation model for undergraduate cohorts.
• Introduce last-mile delivery with the Paack case.
• Ask students to state which data they would need before changing a network.

Case-style example

SwiftHome serves 75% of orders from one Midlands DC. Average delivery is 2.8 days and parcel cost is £6.40. A second southern DC would cost £1.2 million a year but cut southern parcel cost by £1.60 and average delivery by 0.9 days for 520,000 annual orders. Students calculate the direct transport saving, identify missing inventory and fixed-cost effects, then recommend build, outsource or defer. They discover why network design cannot be justified from one cost line.

Where students struggle

Students may treat additional facilities as automatically faster or centralisation as automatically cheaper. Require them to account for inventory duplication, fixed costs, density and service value.

Reading and a quick check

Assign Boysen, Fedtke and Schwerdfeger (2021) on last-mile delivery concepts. Quick check: ask which last-mile innovation changes customer convenience but may worsen operational complexity.

Applied teaching activity

Use the case-style example as a short seminar decision, model build, critique or memo. Require students to state the decision, the evidence used, the missing information and the trade-off they are accepting.

Where it leads

Networks depend on suppliers, logistics service providers and contracts, so the next step is to manage external partners and bargaining power.

Central question: Which logistics activities should the firm own, contract or collaborate on, and how much power sits with critical suppliers?

Logistics performance depends on external partners. This concept connects supplier selection, service-level agreements, 3PL and 4PL relationships, outsourcing risk and supplier power. Students should learn to distinguish a cheap contract from a resilient relationship.

What this concept covers

• Supplier segmentation and logistics-service-provider selection.
• 3PL and 4PL roles, make-or-buy and control trade-offs.
• Service-level agreements, KPIs, penalties and incentives.
• Supplier concentration, switching costs and bargaining power.
• Collaboration, information sharing and contingency arrangements.

Learning outcomes

• Evaluate a logistics outsourcing decision.
• Design a supplier or carrier scorecard.
• Assess supplier power and switching risk.
• Recommend contract metrics that align service and cost.
• Identify where dual sourcing or contingency capacity is justified.

How to teach it

• Give two 3PL bids with different price, capacity guarantees and data visibility.
• Ask students to map supplier power before negotiating terms.
• Use Porter's Five Forces selectively to analyse structural bargaining pressure.
• Require a one-page outsourcing recommendation with exit risks.

Case-style example

BrightBox Retail can renew its national 3PL at £9.4 million a year with 99.1% fill-rate performance, or switch to a challenger at £8.5 million with 97.8% historic fill rate and a £600,000 transition cost. The incumbent also controls a scarce temperature-controlled site. Students compute first-year savings, assess supplier power and decide renew, renegotiate or dual-source. They discover that switching cost and capacity scarcity can outweigh the headline bid.

Where students struggle

Students often equate procurement with price negotiation. Make them analyse dependence, switching, information integration and continuity before judging the contract.

Reading and a quick check

Use Bowersox et al., Chapters 6 and 12. Quick check: ask which fact would most increase the logistics supplier’s bargaining power.

Where a simulation fits

The Porter’s Five Forces Simulation can be used as a short supplier-power and industry-structure application. Growth and Risk teams score the five forces, provide evidence-based reasoning and defend a Go, Conditional Go or Reject recommendation. In a Logistics Management course, the debrief should focus specifically on supplier power, switching costs, barriers and margin pressure.

Where it leads

External partners are easier to manage when the firm has reliable data and visibility across orders, inventory and transport.

Central question: Which digital capability improves the logistics decision, rather than simply producing more data?

Digital logistics should be taught as a decision architecture, not a technology catalogue. Students need to connect ERP, WMS, TMS, IoT, tracking, analytics, digital twins and AI to concrete problems such as visibility, routing, labour, inventory and exception management.

What this concept covers

• ERP, WMS and TMS roles and data flows.
• Track-and-trace, IoT and real-time visibility.
• Analytics for demand, inventory, routing and exception management.
• Automation, robotics and digital twins.
• Data quality, cybersecurity, adoption barriers and human oversight.

Learning outcomes

• Map the information systems needed for an end-to-end logistics process.
• Identify a visibility gap and its operational consequence.
• Evaluate a digital investment against a defined decision problem.
• Explain the limits of automation and algorithmic recommendations.
• Design a small dashboard for logistics exception management.

How to teach it

• Map one shipment across order, warehouse and transport systems.
• Give students a dashboard with noisy alerts and ask which three deserve intervention.
• Use Helo and Thai (2024) for tracking and tracing, then Hu et al. (2024) for advanced analytics at scale.
• Require benefits, dependencies and failure modes for every digital proposal.

Case-style example

Orion Appliances has GPS data for 480 trucks, WMS scans for 18,000 daily picks and customer ETA complaints rising by 22%. A vendor offers an AI control tower for £750,000 plus £180,000 a year. Students identify three decisions the control tower must improve, estimate the minimum annual benefit needed over three years and list the data-quality conditions for success. They discover that visibility creates value only when it changes an operational decision.

Where students struggle

Students list technologies without naming the decision, user or data dependency. Mark the problem definition before the technology choice.

Reading and a quick check

Assign Helo and Thai (2024) and Hu et al. (2024). Quick check: ask students to separate a visibility metric from an action trigger.

Applied teaching activity

Use the case-style example as a short seminar decision, model build, critique or memo. Require students to state the decision, the evidence used, the missing information and the trade-off they are accepting.

Where it leads

Digital visibility becomes especially valuable when flows cross borders and the external environment changes.

Central question: How do borders, infrastructure, regulation, geopolitics and market conditions change logistics design?

International logistics exposes students to constraints that do not appear in a domestic network: customs, documentation, trade lanes, infrastructure quality, exchange-rate effects, sanctions, port congestion and regulatory change. Teach the external environment as a set of logistics consequences, not just a list of macro factors.

What this concept covers

• International transport corridors, ports and border processes.
• Incoterms awareness and documentation responsibilities.
• Customs, duties, trade compliance and regulatory change.
• Infrastructure, political risk, labour, technology and environmental regulation.
• Lead-time variability and the impact of border friction on inventory.

Learning outcomes

• Map the stages of an international shipment.
• Identify external factors that change cost, lead time or feasibility.
• Compare trade lanes using reliability as well as cost.
• Recommend buffers or alternative routes under border uncertainty.
• Translate a PESTLE factor into a logistics decision.

How to teach it

• Use one import lane and ask students to identify every external dependency.
• Compare a low-cost lane with a more reliable alternative.
• Run the PESTLE Analysis Simulation after students know how external factors translate into operational consequences.
• Require each macro factor to end with a decision implication.

Case-style example

GreenWave Bikes imports components from Asia into Rotterdam and serves plants in Germany and the UK. A new customs process could add 1 to 4 days, while an alternative northern port raises freight cost by €220 per container but has more reliable clearance. With 180 containers a month and a stockout cost of €7,500 per day, students compare expected logistics exposure and recommend route diversification, extra buffer or no change. They discover why reliability can dominate average freight rate.

Where students struggle

Students produce generic PESTLE lists. Insist that every factor change a route, inventory, supplier, compliance or capacity decision, or it is not yet useful logistics analysis.

Reading and a quick check

Use the World Bank (2026) Logistics Performance Indicators 2.0 report. Quick check: ask which external factor in a chosen country most directly affects shipment reliability.

Where a simulation fits

The PESTLE Analysis Simulation fits here. Growth Strategy and Risk Management teams analyse the same market-entry decision, score political, economic, social, technological, legal and environmental factors, allocate weights totalling 100, then negotiate a single recommendation. The Logistics Management debrief should ask which factors would alter infrastructure, lead time, sourcing, transport or compliance choices.

Where it leads

Global exposure sets up the formal study of disruption, resilience and continuity planning.

Central question: Which buffers, alternatives and recovery actions are worth paying for before disruption occurs?

Resilience is not simply more inventory or more suppliers. It is the ability to anticipate, absorb, recover and adapt while preserving critical service. Students should compare mitigation options using both cost and recovery logic.

What this concept covers

• Risk identification, likelihood, impact and time-to-recover.
• Single points of failure and critical nodes.
• Inventory, capacity, supplier and route buffers.
• Dual sourcing, rerouting, postponement and contingency contracts.
• Stress testing, incident response and post-disruption learning.

Learning outcomes

• Identify critical logistics vulnerabilities.
• Estimate the service consequence of a disruption.
• Compare prevention, buffer and recovery options.
• Design a simple logistics stress test.
• Defend a resilience investment using cost and recovery evidence.

How to teach it

• Map a network and remove one critical node.
• Ask teams to restore service under a fixed contingency budget.
• Compare redundancy with flexibility rather than assuming more buffer is always better.
• Use Ivanov et al. (2023) to connect resilience with viability and adaptation.

Case-style example

MedRoute distributes temperature-sensitive medicines from one national DC. A flood could close the site for 8 days with an estimated annual probability of 6%. A backup 3PL arrangement costs £180,000 a year and can restore 70% of volume within 24 hours; holding regional emergency stock costs £260,000 a year and covers 3 days. Students estimate expected exposure, then recommend one, both or neither option. They discover that resilience depends on recovery objective, not only expected cost.

Where students struggle

Students often treat every risk as justification for redundancy. Require a service objective, time-to-recover and clear trigger before funding resilience.

Reading and a quick check

Assign Ivanov, Dolgui, Blackhurst and Choi (2023). Quick check: ask students to distinguish resilience, robustness and long-term viability in one sentence each.

Where a simulation fits

The PESTLE Analysis Simulation can be revisited as a pre-disruption scanning exercise, especially where political, legal, environmental or technological changes alter logistics feasibility. Use the reflection to separate early-warning evidence from the later operational response plan.

Where it leads

Resilience should be designed alongside environmental and social responsibility, not treated as a separate end-of-course concern.

Central question: How should logistics reduce environmental and social impact without hiding the operational and financial trade-offs?

Sustainability belongs inside logistics decisions because transport, facilities, packaging, labour and reverse flows all carry environmental and social consequences. Students should learn to evaluate concrete trade-offs rather than write generic sustainability statements.

What this concept covers

• Freight emissions, mode shift, fleet transition and route efficiency.
• Energy, waste and packaging in warehouses and fulfilment.
• Reverse logistics, returns, repair, reuse and circular flows.
• Labour, safety and stakeholder considerations.
• ESG metrics, supplier requirements and financial viability.

Learning outcomes

• Identify material environmental and social impacts in a logistics system.
• Compare logistics decarbonisation options using cost and service criteria.
• Design a reverse-flow process for a product category.
• Evaluate stakeholder trade-offs in an ESG logistics decision.
• Recommend measurable sustainability KPIs.

How to teach it

• Give students a lane-level emissions and cost comparison.
• Map the reverse flow for returns, repair and recycling.
• Use Prataviera et al. (2024) to frame stakeholder influence.
• Run the ESG Simulation where you want students to negotiate trade-offs rather than write an ideal policy.

Case-style example

LoopTech ships 600,000 devices a year. Switching 40% of outbound volume from air to rail/sea saves an estimated 2,100 tCO2e and £1.1 million but adds 6 days of lead time. A repair-and-reuse programme costs £18 per returned unit and avoids £46 of replacement cost for 55% of eligible returns. Students calculate the direct economics, then propose service segmentation and reverse-logistics rules. They discover that greener logistics can improve economics, but only if service and recovery flows are designed carefully.

Where students struggle

Students may accept any lower-carbon option without testing service or total cost, or reject sustainability because one transition cost is visible. Require a quantified operational consequence and a quantified environmental outcome.

Reading and a quick check

Assign Prataviera, Creazza and Perotti (2024). Quick check: ask which stakeholder could block a green-logistics initiative even when the financial case is positive.

Where a simulation fits

The ESG Simulation fits here. Management, Investor, Regulator and Union teams analyse role-specific evidence, model connected financial and operating choices, negotiate environmental, workforce and funding terms, and approve one ESG Policy Statement while keeping the company financially viable.

Where it leads

The final concept brings service, cost, resilience and sustainability together through performance measurement and continuous improvement.

Central question: Which measures reveal whether the logistics system is actually improving, and which measures create harmful local optimisation?

The course should close with an integrated performance system. Students need cost-to-serve, service, asset, working-capital, reliability, resilience and sustainability measures that support decisions rather than dashboards full of disconnected KPIs.

What this concept covers

• Logistics cost categories and cost-to-serve.
• Service metrics such as OTIF, fill rate and order-cycle time.
• Asset and productivity metrics for inventory, warehousing and transport.
• Cash Conversion Cycle and working-capital indicators.
• Balanced scorecards, root-cause analysis and continuous improvement.

Learning outcomes

• Select logistics KPIs aligned to strategy.
• Calculate simple cost-to-serve and service measures.
• Diagnose a trade-off hidden by a single metric.
• Design a balanced logistics dashboard.
• Recommend an improvement priority and evidence plan.

How to teach it

• Give a dashboard with fifteen metrics and force students to choose six.
• Ask what behaviour each metric could unintentionally encourage.
• Use a cause-and-effect tree from service failure to process driver.
• Close with an integrated logistics improvement memo and oral defence.

Case-style example

Apex Distribution improved OTIF from 91% to 96% by adding premium freight and extra safety stock. Freight cost rose 19%, inventory days rose from 42 to 57, and complaints fell 35%. Students calculate the cash tied up if inventory value is £24 million, then build a six-metric dashboard and recommend whether to keep, refine or reverse the policy. They discover that one excellent service metric can hide a deteriorating logistics system.

Where students struggle

Students either use too many KPIs or treat the highest service score as proof of improvement. Make them identify the metric, its decision owner and the trade-off it could conceal.

Reading and a quick check

Use Bowersox et al., Chapter 13 and the Working Capital Management Simulation debrief. Quick check: ask for one leading and one lagging indicator for a logistics improvement plan.

Where a simulation fits

The Working Capital Management Simulation provides strong individual evidence here because each student’s calculations, monthly choices, rationales and ending financial profile can be compared in the debrief. The platform evidence supports academic judgement; it does not replace it.

Where it leads

This completes the course: students can now define the service promise, design and operate the logistics system, manage disruption and defend improvement choices with evidence.

10-week course structure

Compress by combining planning with inventory, transport with last mile, and network design with procurement. Keep the Working Capital, global-environment, resilience and sustainability decisions because they distinguish applied logistics from a descriptive survey course.

14-week course structure

Use the extra time to separate planning from service design, inventory from the working-capital simulation, transportation from last mile, and resilience from sustainability. Add more reflection and assessment time rather than more disconnected theory.

The Working Capital Management Simulation is a 2-3 hour single-player simulation in which every student acts as CFO of Northwood Furnishings. The exercise fits Logistics Management because students must manage receivables, inventory and payables across a 12-month scenario, then decide whether the company is financially ready to expand. It makes the logistics-finance link explicit rather than treating inventory as an isolated stock-control calculation.

Why it fits here

• Students have already studied safety stock, inventory turnover and service trade-offs.
• The simulation adds DSO, DIO, DPO and the Cash Conversion Cycle to show how operating choices accumulate into liquidity.
• The final expansion recommendation forces students to balance cash, margins, supplier continuity, customer discipline and inventory reliability.

What students do

• Watch a short introduction and review the Northwood Furnishings CFO mission.
• Complete the WC Basics stage by calculating receivables, inventory, payables, turnover measures and the Cash Conversion Cycle, then choose initial Accounts Receivable, Accounts Payable and Inventory strategies with rationales.
• Review the first reflection and compare why the lowest Cash Conversion Cycle is not automatically the best decision.
• Manage Northwood across 12 simulated months, responding to decisions involving supplier relationships, payment terms, receivables, inventory, customer credit, collections, cash flow, capital expenditure and risk.
• Submit Expand, Expand with Caution or Delay Expansion with a final rationale.
• Use the final Reflection to compare the starting and ending financial profile and explain which decisions created the result.

Recommended course placement

• At the end of Session 3 or as the main applied activity in Session 4.
• After students know inventory logic but before they treat working capital as a finance-only topic.
• Before a written logistics improvement memo so students can use their own decision path as evidence.

Knowledge students should have before the simulation

• Basic inventory and service-level logic.
• Working-capital definitions and the purpose of DSO, DIO and DPO.
• A basic ability to read income-statement and balance-sheet outputs.
• The difference between improving one metric and improving the overall logistics-financial profile.

Suggested debrief questions

• Which inventory decision released cash but created the largest service or continuity risk?
• Where did supplier terms improve liquidity but weaken the operating relationship?
• Why was the lowest Cash Conversion Cycle not necessarily the strongest overall outcome?
• Which month changed your expansion recommendation most?
• Which metric would you watch first if the company expands?

The PESTLE Analysis Simulation is a 2-hour role-based activity built around an unresolved market-entry decision. Growth Strategy and Risk Management teams assess the same evidence, score political, economic, social, technological, legal and environmental factors, allocate weights totalling 100 and then negotiate a common recommendation. In Logistics Management, the teaching value comes from translating external factors into infrastructure, sourcing, lead-time, transport, compliance and resilience consequences.

Why it fits here

• Students already know the physical logistics system, so PESTLE can focus on materiality rather than definition recall.
• The fixed weighting budget forces teams to decide which external factors actually change the logistics answer.
• Opposing Growth and Risk mandates create useful disagreement about infrastructure, regulation, market access and disruption exposure.

What students do

• Receive a common briefing and an opposing Growth Strategy or Risk Management mandate.
• Work through all six PESTLE factors using common and role-specific evidence.
• Score each factor from 1 to 10 and assign weights that total exactly 100, recording a written rationale for every score and weight.
• Enter a paired Approval Committee where teams compare factor-by-factor positions, challenge assumptions and respond to live developments.
• Commit to enter, delay or do not enter, then compare the agreed analysis with cohort weighting patterns and recommendations.
• In the logistics debrief, identify which external factors would change route, inventory, supplier, facility or compliance decisions.

Recommended course placement

• At the end of Session 10 after global logistics and trade exposure.
• As a two-hour class with a short pre-read and a focused logistics debrief.
• Before the resilience session so the agreed PESTLE factors become inputs to a stress test.

Knowledge students should have before the simulation

• A basic introduction to PESTLE.
• Understanding of logistics lead time, infrastructure, sourcing, transport and compliance.
• Ability to distinguish an external factor from its operational consequence.

Suggested debrief questions

• Which PESTLE factor changed a concrete logistics decision rather than simply describing the environment?
• Where did Growth and Risk teams use the same evidence differently?
• Did the 100-point weighting rule reveal any hidden assumptions?
• Which external risk should become a resilience stress-test scenario?
• What additional logistics data would you want before committing to the market?

Grading with the Working Capital Management Simulation

Each student independently acts as CFO, calculates working-capital measures, makes monthly policy choices and submits an expansion recommendation. The simulation produces individual evidence, but the academic grade should come from a lecturer-defined rubric.

• Calculation accuracy and interpretation of DSO, DIO, DPO and the Cash Conversion Cycle.
• Quality of inventory, receivables and supplier-term decisions.
• Consistency between the ending financial profile and the expansion recommendation.
• Clarity of rationale and recognition of trade-offs or rejected alternatives.

Grading with the Porter’s Five Forces Simulation

Growth and Risk teams score all five forces, record written reasoning and defend a Go, Conditional Go or Reject recommendation. In this course, assess the supplier-power and logistics-partner implications rather than treating the exercise as a generic strategy task.

• Application of supplier power, buyer power, rivalry, substitutes and entry logic.
• Evidence supporting each score and the consistency of the final recommendation.
• Ability to translate industry structure into logistics sourcing or outsourcing implications.
• Optional individual reflection on where role bias changed the analysis.

Grading with the PESTLE Analysis Simulation

Teams score and weight six external factors, negotiate a common assessment and commit to a market-entry recommendation. For Logistics Management, the strongest evidence is whether students can convert macro evidence into operational consequences.

• Quality and materiality of factor scoring and weighting.
• Evidence-based written rationales.
• Ability to identify which factors change route, inventory, facility, supplier or compliance decisions.
• Individual post-simulation memo that explains one disagreement and one unresolved risk.

Grading with the ESG Simulation

Management, Investor, Regulator and Union teams analyse role-specific evidence, model connected choices, negotiate terms and approve or fail to approve an ESG operating package. Use the results as evidence of decision quality and trade-offs, not as an automatic mark.

• Role-specific decision quality and use of evidence.
• Financial and operational consistency of the negotiated package.
• Recognition of environmental, workforce and governance consequences.
• Individual reflection on compromise, collective viability and the logistics implications of the final terms.

Platform evidence: The platform records what each team decided, the terms they agreed and comparative outcomes across groups. That evidence supports your academic judgement; it does not replace it, and it does not establish which individual student made which argument. Where attribution matters, add an individual memo, oral defence or short reflection.