# Supply Chain Management Elective Course Guide: [Your programme and term] > A course guide for the supply chain management elective of an MBA: what the course is for, the competencies it > builds, a session-by-session roadmap, the key concepts with links to the concept cards, an original mini-case with a > teaching note, the typical assignments and how they are graded, an exam preparation plan and a self-test. It covers > supply chain strategy and fit, demand planning and sales and operations planning, the bullwhip effect and > coordination, multi-echelon inventory and risk pooling, supply contracts, network design, transport and landed cost, > sourcing and procurement, postponement, resilience, revenue management, responsible and low-carbon supply chains, and > digital planning. It builds on the operations core: read mba/mba-operations-guide and mba/mba-inventory-card first, > because this guide assumes EOQ, safety stock and the newsvendor model and does not re-teach them. It is for > second-year students choosing or taking the elective and for managers moving into supply chain roles. Source basis: > the primary works named in each section (Forrester 1961; Lee, Padmanabhan and Whang 1997; Fisher 1997; Pasternack > 1985; Cachon and Lariviere 2005; Eppen 1979; Geoffrion and Graves 1974 and others), written up in original words, > with fictional companies (Northfield Outdoor Co., Corvane Robotics, Tallow Creek Medical Center). Replace every > [bracketed] value and every row marked *Example*. ## Document control | Field | Value | |---|---| | Programme and course | [Full-time MBA, elective: Supply Chain Management] | | Term and dates | [Term], YYYY-MM-DD to YYYY-MM-DD | | Professor and teaching assistant | [Names, office hours] | | Learner | [Your name] | | Study group | [Names] | | Grading split (from the syllabus) | [Participation x% / assignments y% / simulation z% / exam w%] | | Simulation or game used | [Beer distribution game / supply chain simulation named in the syllabus] | | Reviewed by | [Study group / mentor] | | Last updated | YYYY-MM-DD | | Version | [0.1] | ## What the course is for The operations core taught you to analyse one process and one stocking point. The supply chain elective widens the view to the chain of firms that make, move and sell a product: suppliers, plants, distribution centres, carriers, retailers and the information and money that flow between them. Its main lesson is that most supply chain problems are problems of **coordination**: each firm optimises its own costs with its own information, and the chain as a whole does worse than it could. The course gives you the tools to see the whole chain, to measure what poor coordination costs, and to design the contracts, information flows and networks that fix it. After the course you should be able to: 1. Match a supply chain's design to the demand uncertainty of its products (efficient versus responsive, Fisher 1997). 2. Explain the bullwhip effect, measure it from data and name the remedy for each cause. 3. Set inventory across several stocking points and quantify the value of pooling and postponement. 4. Design a supply contract (buyback, revenue sharing) that makes a buyer order what is best for the whole chain. 5. Choose the number and location of facilities, and price a delivery promise. 6. Compare suppliers on landed cost and total cost of ownership, and structure a sourcing strategy. 7. Assess supply chain risk and plan for resilience without paying for redundancy everywhere. 8. Explain how revenue management, sustainability reporting and digital planning change supply chain decisions. **Competency map** (the competency areas used across this library; see mba/mba-core-curriculum-map): | Competency | How this course builds it | Where on this guide | |---|---|---| | Analytical and quantitative reasoning | Bullwhip ratios, newsvendor contracts, location models, landed cost, pooling | Sessions 3 to 8, all cards | | Ethical decision making | Labour conditions and human rights in supplier tiers, honest supplier treatment, emissions claims | Session 12, mini-case question 6 | | Communication | Recommendations to a buying committee, supplier negotiation memos | Assignments, rubric | | Leadership and teamwork | Sales and operations planning across functions, cross-company programmes | Sessions 2 and 4, the simulation | | Global perspective | Global sourcing, trade terms, tariffs, near-shoring, disruption | Sessions 6 to 10 | | Technology, data and AI | Point-of-sale data sharing, forecasting, optimisation, control towers, AI in planning | Sessions 2, 3, 13 | ## Prerequisites - [ ] EOQ, reorder point, safety stock and the newsvendor critical ratio: mba/mba-inventory-card. - [ ] Process flow, bottlenecks and Little's Law: mba/mba-process-analysis-card. - [ ] Probability: mean, standard deviation, the normal distribution, `NORM.INV`, `NORM.S.DIST`: academic/stats-distributions-sheet. - [ ] Linear programming and Solver: mba/mba-decision-analytics-guide. - [ ] Relevant costs and contribution margin: mba/mba-relevant-costs-card. - [ ] Time-value of money for facility decisions: mba/mba-npv-irr-card. - [ ] Case preparation: mba/mba-case-reading-guide and mba/mba-case-prep-sheet. ## Session roadmap | Session | Topic | Card to read | Case or exercise *(Example)* | Deliverable | |---|---|---|---|---| | 1 | Supply chain strategy and fit: efficient versus responsive, implied demand uncertainty | This guide, "Core topics" | A product you know: functional or innovative? | One-page fit diagnosis | | 2 | Demand planning, forecast error, sales and operations planning (S&OP) | academic/stats-time-series-arima | Forecast-error analysis on a product family | Forecast accuracy table | | 3 | The bullwhip effect: causes, measurement, remedies | mba/mba-bullwhip-effect-card | Beer distribution game | Game debrief memo | | 4 | Information sharing and collaboration: VMI, CPFR, data sharing | mba/mba-bullwhip-effect-card | Retailer and supplier negotiation role play | Agreement term sheet | | 5 | Multi-echelon inventory, risk pooling, postponement | mba/mba-inventory-card and this guide | Corvane spare parts (mba/mba-network-design-card, Worked example B) | Problem set 1 | | 6 | Supply contracts: double marginalisation, buyback, revenue sharing | This guide, mini-case | Northfield insulated jacket buy (below) | Group case write-up | | 7 | Network design: facility location, service promise | mba/mba-network-design-card | Northfield distribution network (card, Worked example A) | Solver model | | 8 | Transport, trade terms and landed cost; near-shoring | mba/mba-network-design-card | Backpack sourcing (card, Exercise C) | Problem set 2 | | 9 | Sourcing and procurement: portfolio, supplier selection, total cost of ownership | mba/mba-sourcing-procurement-card | Category strategy for one spend category | Category plan | | 10 | Risk and resilience: time to recover, dual sourcing, stress tests | mba/mba-supply-chain-resilience-card | Tallow Creek critical-supply review | Risk register extract | | 11 | Revenue management and pricing with fixed capacity | mba/mba-revenue-management-card | Booking-limit exercise | Problem set 3 | | 12 | Responsible and low-carbon supply chains: due diligence, Scope 3 | mba/mba-stakeholder-esg-card | Supplier audit findings discussion | Position paper | | 13 | Digital supply chain: planning systems, control towers, AI in forecasting | This guide, "Core topics" | Vendor demo or data exercise | Short reflection | | 14 | Integration and review | mba/mba-formula-sheet | Mixed problem set | Practice exam | ## Key concepts | Concept | One-line meaning | Primary source | Where to learn it | I can explain it | |---|---|---|---|---| | Efficient versus responsive supply chains | Match the chain to the product's demand uncertainty | Fisher (1997) | This guide, "Strategy and fit" | [ ] | | System dynamics of supply chains | Delays and feedback create oscillation | Forrester (1961) | mba/mba-bullwhip-effect-card | [ ] | | Bullwhip effect | Order variability grows upstream; four causes | Lee, Padmanabhan and Whang (1997) | mba/mba-bullwhip-effect-card | [ ] | | Quantified bullwhip | $1 + 2L/p + 2L^2/p^2$ under moving-average forecasts | Chen, Drezner, Ryan and Simchi-Levi (2000) | mba/mba-bullwhip-effect-card | [ ] | | Multi-echelon inventory | Optimal policies across serial stages | Clark and Scarf (1960) | This guide, "Inventory across the chain" | [ ] | | Risk pooling | Centralised stock needs less buffer | Eppen (1979) | mba/mba-network-design-card | [ ] | | Square-root law | Safety stock scales with the square root of the number of sites | Maister (1976) | mba/mba-network-design-card | [ ] | | Postponement | Delay differentiation until demand is known | Feitzinger and Lee (1997) | This guide | [ ] | | Double marginalisation | A buyer facing a wholesale price orders too little for the chain | Spengler (1950) for the pricing form | This guide, mini-case | [ ] | | Buyback contract | Supplier takes back unsold units at a set price | Pasternack (1985) | This guide, mini-case | [ ] | | Revenue sharing | Low wholesale price plus a share of revenue | Cachon and Lariviere (2005) | This guide | [ ] | | Facility location | Open sites and assign flows at minimum cost | Geoffrion and Graves (1974) | mba/mba-network-design-card | [ ] | | Landed cost | Price plus duty, freight, pipeline and safety stock | Standard practice | mba/mba-network-design-card | [ ] | | Purchasing portfolio | Supply strategy by profit impact and supply risk | Kraljic (1983) | mba/mba-sourcing-procurement-card | [ ] | | Resilience and time to recover | Plan for disruptions by recovery time and impact | Sheffi (2005); Simchi-Levi et al. (2014) | mba/mba-supply-chain-resilience-card | [ ] | | Revenue management | Protect capacity for higher-paying demand | Littlewood (1972); Belobaba (1987) | mba/mba-revenue-management-card | [ ] | | Process reference model | Standard process names for benchmarking | ASCM SCOR Digital Standard | This guide, "Digital supply chain" | [ ] | ## Core topics in brief ### Strategy and fit Fisher (1997) separates **functional** products (stable demand, long life cycles, low margins, such as basic socks or groceries) from **innovative** products (unpredictable demand, short life cycles, high margins, such as fashion jackets or new electronics). Functional products need an **efficient** chain: low cost, high utilisation, lean inventory. Innovative products need a **responsive** chain: buffer capacity, short lead times, stock placed to react to demand. A mismatch is costly in both directions: an efficient chain for an innovative product produces stock-outs and markdowns, and a responsive chain for a functional product wastes money on speed nobody pays for. Many companies run both chains side by side for different product lines. ### Demand planning and S&OP Forecasts are always wrong; the questions are by how much and in which direction. Measure forecast error (bias and spread) by product family, plan safety stock and capacity on the spread, and correct the bias. Sales and operations planning is the monthly process in which sales, operations, finance and leadership agree one plan for demand, supply and inventory. The tool matters less than the discipline: one number, owned decisions, and an explicit trade-off when demand exceeds supply. ### Coordination and the bullwhip effect Forrester (1961) showed that delays and feedback make supply chains oscillate. Lee, Padmanabhan and Whang (1997) named four causes of the amplification of orders upstream: demand signal processing, order batching, price variations and the rationing game. Each has its own remedy (share demand data and shorten lead times; smaller, more frequent orders; stable pricing; allocation on past sales). See mba/mba-bullwhip-effect-card for the measurement and the formula of Chen, Drezner, Ryan and Simchi-Levi (2000). ### Inventory across the chain Clark and Scarf (1960) showed how to set optimal policies in a serial chain by working with **echelon inventory**: the stock at a stage plus everything downstream of it. The practical lessons: plan each stage on the inventory position of its whole echelon, not only on its own shelf; hold expensive, differentiated stock late in the chain and generic stock early. **Risk pooling** (Eppen 1979) shows that centralising stock reduces the buffer needed, by up to the square root of the number of locations when demands are independent (Maister 1976). **Postponement** applies the same idea to products: keep a generic product until demand is known, then customise (the localised power supply or language kit added at the regional centre). Feitzinger and Lee (1997) describe how product and process design make that possible. ### Supply contracts When a supplier sells at a wholesale price above its cost, the buyer bears all the risk of unsold stock and orders less than the whole chain would want: the margin is cut twice, which echoes the double marginalisation that Spengler (1950) described for pricing. **Buyback** contracts (Pasternack 1985), where the supplier takes back unsold units at an agreed price, and **revenue-sharing** contracts (Cachon and Lariviere 2005), where the supplier charges a low wholesale price for a share of revenue, can raise the buyer's order to the chain optimum and leave both firms better off. The mini-case below works one through. ### Network design, transport and landed cost How many facilities, where, and which customers each serves: the facility location model (Geoffrion and Graves 1974) trades off fixed cost, inbound and outbound transport, inventory and service. Landed cost compares sources on the delivered cost, including duty, freight, pipeline and safety stock. The trade terms that decide who pays and carries risk are usually written with the ICC Incoterms rules; as of October 2026 the current set is Incoterms 2020, eleven rules in force since 1 January 2020 (check https://iccwbo.org/business-solutions/incoterms-rules/). Duty rates change by product and origin: take them from the official tariff schedule on the date of the study. See mba/mba-network-design-card. ### Sourcing, resilience and revenue management Three cards extend the course. mba/mba-sourcing-procurement-card covers category strategy, the purchasing portfolio (Kraljic 1983), supplier selection and total cost of ownership. mba/mba-supply-chain-resilience-card covers risk identification, time to recover (Simchi-Levi et al. 2014) and the cost of redundancy (Sheffi 2005). mba/mba-revenue-management-card covers capacity allocation when capacity is fixed and perishable (Littlewood 1972; Belobaba 1987). ### Responsible and low-carbon supply chains Most of a company's environmental and human-rights footprint usually sits in its supply chain, not in its own operations. Two families of rules shape the work. Emissions reporting: purchased goods and transport fall in Scope 3 under the GHG Protocol corporate value chain standard, and logistics emissions are commonly calculated with ISO 14083:2023 and the GLEC Framework (version 3.0 of September 2023 is aligned with it; check smartfreightcentre.org for later versions). Due diligence: the EU Corporate Sustainability Due Diligence Directive (Directive (EU) 2024/1760) requires very large companies to identify and address adverse human-rights and environmental impacts in their chains of activities. As of October 2026 the European Commission's CSDDD page states that Omnibus I (Directive (EU) 2025/794 postponed dates; Directive (EU) 2026/470, in force since 18 March 2026, amended the substance) narrowed the scope to EU companies with at least 5,000 employees and EUR 1.5bn net worldwide turnover (non-EU companies: EUR 1.5bn net turnover in the EU), with transposition by 26 July 2028 and application from 26 July 2029. Check the Commission page (https://commission.europa.eu/business-economy-euro/doing-business-eu/sustainability-due-diligence-responsible-business/corporate-sustainability-due-diligence_en) and the consolidated text on EUR-Lex (https://eur-lex.europa.eu/eli/dir/2024/1760/oj) before you rely on any date or threshold; several jurisdictions also restrict imports linked to forced labour. This guide gives questions, not legal advice: for any obligation, check with counsel. See mba/mba-stakeholder-esg-card. ### Digital supply chain Planning systems, control towers (one view of orders, inventory and shipments across firms) and machine-learning forecasts are now standard in large firms. The course question is managerial: which decisions to automate, which data partners must share, and how to keep humans accountable for exceptions. The ASCM SCOR Digital Standard (SCOR DS) is a widely used process reference model; as of October 2026 the ASCM page describes seven processes (Orchestrate, Plan, Order, Source, Transform, Fulfill, Return) and open access (check https://www.ascm.org/corporate-solutions/standards-tools/scor-ds/). ### How the topics connect The course runs from strategy to execution: the fit decision sets the design, coordination and inventory decisions set the cost of that design, and risk, responsibility and digital planning cut across all of them. ```mermaid flowchart LR A["Strategy and fit (Fisher 1997)"] --> B["Demand planning and S&OP"] B --> C["Coordination: bullwhip, data sharing"] C --> D["Inventory across the chain: pooling, postponement"] D --> E["Supply contracts: buyback, revenue sharing"] A --> F["Network design and landed cost"] F --> G["Sourcing and procurement"] G --> H["Risk and resilience"] E --> H H --> I["Responsible and low-carbon chains"] B --> J["Revenue management"] I --> K["Digital planning and control towers"] J --> K ``` ## Mini-case: Northfield Outdoor Co., the insulated jacket buy ### Situation It is late February and Priya Raman, Northfield Outdoor Co.'s head of merchandise planning, has ten days to commit the season buy for a new insulated jacket for next winter. The jacket is Northfield's most visible launch of the year: the marketing team has booked a campaign, the stores have planned floor space and the online team wants a pre-order page. The jacket is a textbook innovative product. Northfield has sold comparable jackets for five seasons and its forecasts have been far off in both directions (Exhibit 1). Planning's forecast for the new jacket is 10,000 units for the season, with an error of about 30% either way. Unsold jackets go to the outlet channel at the end of winter, where they fetch USD 35. The full retail price is USD 200. The incumbent far-shore manufacturer, a long-standing partner, makes the jacket for Northfield at USD 90 a unit landed at the distribution centre, with one production run: Northfield must commit the full quantity in March, eight months before the season. In Northfield's view the manufacturer's own cost, including freight to Northfield, is about USD 60. Last season Northfield ordered its comparable jacket at the mean forecast and sold out in November, while a sister style sat in the outlet channel until April. This year the manufacturer's account director, Tomás Ferreira, has made a second offer (Exhibit 2): a higher price of USD 98 a unit, but the manufacturer will take back every unsold jacket at the end of the season for a credit of USD 80 and sell them through its own outlet partners. "You can stop ordering scared," he told Priya. "If you're stuck with jackets, so are we." Meanwhile Northfield's sourcing team, led by Grace Okafor, has qualified a near-shore manufacturer. It quotes USD 100 a unit landed, but its lead time is short enough that Northfield can wait until the pre-order page has run for three weeks in September. Past pre-order tests suggest the forecast error would then shrink to about 15%. The near-shore plant is new to Northfield; its quality was good in a pilot but it has never made an insulated jacket at volume. Finance wants a recommendation that maximises expected contribution and says what Northfield is paying for. The chief operating officer, Daniel Mbeki, adds two questions of his own. "If the buyback is so good for us, why is the supplier offering it? And are we sure the cheap option is cheap once we count the risk of being stuck with one factory on the other side of the world?" Priya also knows that the far-shore plant has had a labour audit finding on overtime hours, now under a corrective plan, and that the sustainability team is preparing Northfield's first supply chain emissions inventory. Priya asks her analyst to prepare the numbers for the buying committee. ### Exhibit 1: comparable jackets, forecast versus actual (*Example*) | Season | Forecast (units) | Actual demand (units) | Actual / forecast | |---|---|---|---| | 1 | 8,000 | 11,200 | 1.40 | | 2 | 9,000 | 6,300 | 0.70 | | 3 | 9,500 | 9,975 | 1.05 | | 4 | 10,500 | 7,560 | 0.72 | | 5 | 9,000 | 10,620 | 1.18 | | Mean / SD of the ratio | | | 1.01 / 0.3012 (`=AVERAGE(D2:D6)`, `=STDEV.S(D2:D6)`) | Planning models the new jacket's demand as normal with mean 10,000 and SD 3,000 when ordering in March (0.30 x 10,000), and SD 1,500 after three weeks of pre-orders. ### Exhibit 2: supply options (*Example*) | Option | Price per unit (landed) | Leftover jackets | Commitment | Demand SD at commitment | |---|---|---|---|---| | O1 Far-shore, wholesale | USD 90 | Northfield sells at outlet for USD 35 | March, one run | 3,000 | | O2 Far-shore, buyback | USD 98 | Returned to manufacturer for USD 80 credit; manufacturer recovers USD 35 | March, one run | 3,000 | | O3 Near-shore, wholesale | USD 100 | Northfield sells at outlet for USD 35 | September, one run | 1,500 | ### Exhibit 3: prices and costs (*Example*) | Item | Value | |---|---| | Retail price | USD 200 | | Outlet value of a leftover jacket | USD 35 | | Far-shore manufacturer's unit cost, landed (Northfield estimate) | USD 60 | | Mean season demand | 10,000 units | | Order quantities | Rounded to the nearest 10 units | ### Discussion questions 1. Under O1, how many jackets should Northfield order, and what are its expected sales, leftovers and contribution? Why is the answer above the mean forecast but still "too low" from the chain's point of view? 2. Under O2, what is Northfield's optimal order and expected contribution? What does the manufacturer gain or lose compared with O1? Why would a supplier offer this? 3. What buyback credit, at a wholesale price of USD 90, would make Northfield order exactly the chain-optimal quantity? Would the manufacturer accept it? What does that tell you about designing a contract? 4. Under O3, what is Northfield's expected contribution? At what near-shore price would O3 match O2? 5. Which factors outside the expected-contribution model (risk, quality, flexibility, the bullwhip of a single run, relationship) should the buying committee weigh, and how would you present them? 6. How should the labour audit finding and the emissions inventory enter the recommendation, if at all? ### Teaching note **Method.** Each option is a newsvendor problem (mba/mba-inventory-card). The underage cost is the margin lost on a missed sale and the overage cost is the loss on a leftover jacket. Excel: order `=NORM.INV(CR, 10000, SD)`; expected lost sales `=SD*(NORM.S.DIST(z,FALSE)-z*(1-NORM.S.DIST(z,TRUE)))` with `z=(Q-10000)/SD`; expected sales = 10,000 minus lost sales; leftovers = $Q$ minus expected sales. All results below come from the re-runnable calculation script. **Question 1 (O1).** $C_u$ = 200 - 90 = 110, $C_o$ = 90 - 35 = 55, critical ratio 110 / 165 = 0.6667, $z$ = 0.4307, $Q^*$ = 11,292.18, so order 11,290 (`=NORM.INV(110/165,10000,3000)`). Expected sales 9,339.20, leftovers 1,950.80, lost sales 660.80. Northfield's expected contribution = 200 x 9,339.20 + 35 x 1,950.80 - 90 x 11,290 = USD 920,018.06. The manufacturer earns (90 - 60) x 11,290 = USD 338,700.00. The chain earns USD 1,258,718.06. For the chain as a whole, a missed sale costs 200 - 60 = 140 and a leftover costs 60 - 35 = 25: critical ratio 140 / 165 = 0.8485 and an optimal quantity of 13,089.87. Northfield orders 1,800 fewer jackets because it alone carries the leftover risk while the manufacturer keeps its margin on every unit: double marginalisation. **Question 2 (O2).** Northfield's $C_u$ = 200 - 98 = 102, $C_o$ = 98 - 80 = 18, critical ratio 102 / 120 = 0.8500, $z$ = 1.0364, $Q^*$ = 13,109.30, so order 13,110. Expected sales 9,767.02, leftovers 3,342.98, lost sales 232.98. | Option *(Example)* | Order | In-stock probability | Northfield contribution | Manufacturer profit | Chain profit | |---|---|---|---|---|---| | O1 Far-shore wholesale (USD 90) | 11,290 | 66.64% | USD 920,018.06 | USD 338,700.00 | USD 1,258,718.06 | | O2 Far-shore buyback (USD 98, credit USD 80) | 13,110 | 85.01% | USD 936,062.84 | USD 347,746.06 | USD 1,283,808.90 | | O3 Near-shore wholesale (USD 100) | 10,400 | 60.51% | USD 904,771.75 | not known | not comparable | Northfield gains USD 16,044.77 and the manufacturer gains USD 9,046.06; the chain gains USD 25,090.84 because more jackets are available when demand is high. Manufacturer profit under O2 = (98 - 60) x 13,110 - (80 - 35) x 3,342.98. The supplier offers the contract because the chain profit grows and the terms give it part of the gain. Note also that O2 nearly coordinates the chain: the maximum chain profit at 13,090 units is USD 1,283,811.51. **Question 3.** With wholesale price $w$, retail price $p$ and outlet value $s$, Northfield's critical ratio is $(p - w)/(p - b)$; setting it equal to the chain's $(p - c)/(p - s)$ gives $$ b^{*} = p - \frac{(p - w)(p - s)}{p - c} = 200 - \frac{110 \times 165}{140} = 70.36 $$ At $w$ = 90 and $b$ = 70.36 Northfield orders 13,090 and earns USD 1,008,709.05, but the manufacturer earns only USD 275,102.47, less than under O1, so it will refuse. Coordinating contracts form a family: each pair ($w$, $b$) divides the same maximal chain profit in a different proportion (here Northfield's share is $(p - w)/(p - c)$). With $b$ = 80, the exactly coordinating price is $w$ = 98.18; the offer of 98 is close. The negotiation is about the split, not about the order quantity, and both firms gain only if the split leaves each above its O1 profit: Northfield's share must lie between 71.7% and 73.6% of USD 1,283,811.51. **Question 4 (O3).** $C_u$ = 100, $C_o$ = 65, critical ratio 0.6061, $z$ = 0.2691, $Q^*$ = 10,403.60, so order 10,400 (`=NORM.INV(100/165,10000,1500)`). Expected sales 9,580.43, leftovers 819.57, contribution USD 904,771.75. Halving the forecast error is worth a lot (lost sales 419.57 and leftovers 819.57 against 660.80 and 1,950.80 under O1), but not USD 10 more per jacket than O1 on every unit. O3 matches O2 at a near-shore price of about USD 97.00. A price negotiation, or a split (a smaller far-shore buy plus a near-shore top-up in September), could change the answer: the split is a natural exam extension. **Question 5.** Present the expected values with their spread: under O1 the in-stock probability is only 66.64%. Outside the model: dependence on one far-shore factory and one production run (a disruption leaves no recovery option; see mba/mba-supply-chain-resilience-card); the near-shore plant's volume risk; the value of reacting to a surprise hit; the information value of pre-orders for the next season; a buyback contract that hands leftover jackets to the manufacturer's outlet partners, where they may undercut Northfield's outlet stores (channel conflict); and the long relationship with the incumbent. A strong answer recommends O2 for this season, a written plan to build the near-shore option for part of the volume, and contract terms on return condition and outlet channels. **Question 6.** The labour finding is not a number in the model but a gate: Northfield should confirm the corrective plan is on track before committing a larger order to that plant (O2 raises the volume by 1,820 jackets). The emissions inventory adds a reason to record freight mode and distance for each option; the near-shore route usually has lower transport emissions per unit, but that has to be measured, not assumed. Any legal duties under due diligence or forced-labour rules are questions for counsel, not for this model. ## Typical assignments and how they are graded | Assignment *(Example)* | What it asks | Typical weight | |---|---|---| | Problem sets (3) | Bullwhip, pooling, contracts, location, landed cost, revenue management calculations | 20% | | Simulation or game | Run a supply chain in a team; debrief memo on what went wrong and why | 15% | | Case write-ups (2) | Recommendation memo with one exhibit that decides | 25% | | Class participation | Case discussion, game debrief | 15% | | Final exam | Mixed problems and a short case | 25% | **Rubric for case write-ups** (score each 1 to 4): | Criterion | 4: excellent | 2: weak | |---|---|---| | Diagnosis | Names the coordination failure and its cause with evidence from the exhibits | Restates the case facts | | Analysis | Correct model, correct numbers, Excel formulas traceable, sensitivity shown | Formulas without a model choice; no sensitivity | | Trade-offs | Weighs cost, service, risk and relationships explicitly | One dimension only | | Recommendation | Clear decision, contract terms, implementation steps and owners | "It depends" without a decision | | Communication | One page, one decisive exhibit, plain language | Long, exhibit-heavy, no headline | ## Exam preparation plan | Week | Focus | Activities | |---|---|---| | 4 weeks before | Rebuild the models | Redo Worked examples A on mba/mba-bullwhip-effect-card and mba/mba-network-design-card in Excel from a blank sheet | | 3 weeks before | Contracts and pooling | Redo the mini-case teaching note from the exhibits only; change one input (outlet value USD 50) and recompute | | 2 weeks before | Mixed problems | Self-test of this guide and of each card, interleaved; log misses in mba/mba-error-log | | 1 week before | Cases and judgement | Two past cases: write the headline and the one exhibit in 45 minutes each | | Exam day minus 1 | Light review | mba/mba-formula-sheet, flashcards (mba/mba-concept-flashcards), sleep | ## Common struggles - **Optimising one stage.** Students compute the buyer's optimum and stop. Fix: always compute the chain optimum too and explain the gap. - **Forgetting who carries the leftovers.** In buyback and revenue-sharing contracts the overage cost changes. Fix: write $C_u$ and $C_o$ for each party before any formula. - **Using the formula for every bullwhip case.** The variance formula covers forecasting with lead times only. Fix: look for batching, promotions and rationing in the order data first. - **Comparing suppliers on price.** Fix: landed cost plus pipeline and safety stock, then risk. - **Over-precision in network models.** Options within 1 to 2% are ties. Fix: show the flat region and decide on service and risk. - **Treating sustainability as a separate topic.** Fix: add an emissions and human-rights line to every recommendation, with the question you would ask counsel. - **Normal distribution mechanics.** `NORM.INV` versus `NORM.S.INV`, the loss function. Fix: academic/stats-distributions-sheet. ## Reading list **Classic works (primary sources):** - Spengler, J. J. (1950). Vertical integration and antitrust policy. *Journal of Political Economy*, 58(4), 347-352. - Clark, A. J., and Scarf, H. (1960). Optimal policies for a multi-echelon inventory problem. *Management Science*, 6(4), 475-490. - Forrester, J. W. (1961). *Industrial Dynamics*. MIT Press. - Littlewood, K. (1972). Forecasting and control of passenger bookings. *AGIFORS Symposium Proceedings*, 12. Reprinted in *Journal of Revenue and Pricing Management* (2005), 4(2), 111-123. - Geoffrion, A. M., and Graves, G. W. (1974). Multicommodity distribution system design by Benders decomposition. *Management Science*, 20(5), 822-844. - Maister, D. H. (1976). Centralisation of inventories and the "square root law". *International Journal of Physical Distribution*, 6(3), 124-134. - Eppen, G. D. (1979). Effects of centralization on expected costs in a multi-location newsboy problem. *Management Science*, 25(5), 498-501. - Kraljic, P. (1983). Purchasing must become supply management. *Harvard Business Review*, September-October 1983. - Pasternack, B. A. (1985). Optimal pricing and return policies for perishable commodities. *Marketing Science*, 4(2), 166-176. - Belobaba, P. P. (1987). Airline yield management: an overview of seat inventory control. *Transportation Science*, 21(2), 63-73. - Sterman, J. D. (1989). Modeling managerial behavior: misperceptions of feedback in a dynamic decision making experiment. *Management Science*, 35(3), 321-339. - Fisher, M. L. (1997). What is the right supply chain for your product? *Harvard Business Review*, March-April 1997. - Feitzinger, E., and Lee, H. L. (1997). Mass customization at Hewlett-Packard: the power of postponement. *Harvard Business Review*, January-February 1997. - Lee, H. L., Padmanabhan, V., and Whang, S. (1997). Information distortion in a supply chain: the bullwhip effect. *Management Science*, 43(4), 546-558. - Chen, F., Drezner, Z., Ryan, J. K., and Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times, and information. *Management Science*, 46(3), 436-443. - Cachon, G. P., and Lariviere, M. A. (2005). Supply chain coordination with revenue-sharing contracts: strengths and limitations. *Management Science*, 51(1), 30-44. - Sheffi, Y. (2005). *The Resilient Enterprise: Overcoming Vulnerability for Competitive Advantage*. MIT Press. - Simchi-Levi, D., Schmidt, W., and Wei, Y. (2014). From superstorms to factory fires: managing unpredictable supply chain disruptions. *Harvard Business Review*, January-February 2014. **Reference standards and rules (dated facts, check the official pages):** ASCM SCOR Digital Standard (https://www.ascm.org/corporate-solutions/standards-tools/scor-ds/); ICC Incoterms 2020 (https://iccwbo.org/business-solutions/incoterms-rules/); Directive (EU) 2024/1760 on corporate sustainability due diligence as amended by Directive (EU) 2026/470 (https://eur-lex.europa.eu/eli/dir/2024/1760/oj). **Textbooks (further reading, by name only):** Chopra and Meindl, *Supply Chain Management: Strategy, Planning, and Operation*; Simchi-Levi, Kaminsky and Simchi-Levi, *Designing and Managing the Supply Chain*; Cachon and Terwiesch, *Matching Supply with Demand*; Sheffi, *The Power of Resilience*. **Open resources:** MIT OpenCourseWare, 15.762J Supply Chain Planning (Spring 2011), https://ocw.mit.edu/courses/15-762j-supply-chain-planning-spring-2011/, and ESD.273J Logistics and Supply Chain Management (Fall 2009), https://ocw.mit.edu/courses/esd-273j-logistics-and-supply-chain-management-fall-2009/. ## Self-test 1. Classify each as functional or innovative and name the supply chain it needs: (a) printer paper, (b) a limited-edition trail shoe, (c) generic surgical gloves. 2. Name the four causes of the bullwhip effect and one remedy for each. 3. Why does a buyer facing a wholesale price order less than the chain-optimal quantity? 4. A product sells at USD 100, costs the supplier USD 30, the wholesale price is USD 60 and leftovers are worth USD 10. What buyback credit coordinates the chain? 5. Four regional warehouses each face independent weekly demand with SD 100. What is the SD of demand if stock is pooled in one warehouse? 6. A stage forecasts with a 4-week moving average and has a 2-week lead time. What is the variance ratio of its orders to demand under the formula of Chen et al. (2000)? 7. A supplier ships 300 units a week and transit takes 8 weeks. How much stock is in the pipeline? 8. What is postponement, and which kind of uncertainty does it address? 9. Why can a revenue-sharing contract work well for a video rental chain but poorly when the retailer can also push competing products? 10. Which Incoterms set is current as of October 2026, and why should a buyer care which rule appears in a contract? 11. In the mini-case, what does Northfield pay for when it chooses O1 over O2? 12. Interleaving: which tool first? (a) A distributor's orders spike before every price increase. (b) A retailer asks whether to open a fourth warehouse. (c) A buyer compares a far-shore and a near-shore offer. ## Answer key 1. (a) Functional, efficient chain. (b) Innovative, responsive chain. (c) Functional, efficient chain, but a hospital may add resilience buffers because a stock-out is a patient-safety risk. 2. Demand signal processing (share end-demand data, shorten lead times); order batching (smaller, more frequent orders); price variations (stable pricing, pay on sell-through); rationing game (allocate on past sales). 3. The buyer carries all the leftover risk but earns only its own margin on extra sales, so its critical ratio is lower than the chain's: double marginalisation. 4. $b = 100 - (100 - 60)(100 - 10)/(100 - 30)$ = 48.57 (`=100-(100-60)*(100-10)/(100-30)`). 5. $100\sqrt{4}$ = 200, against 400 summed across the four warehouses (`=SQRT(4*100^2)`). 6. $1 + 2(2)/4 + 2(2^2)/4^2$ = 2.5 (`=1+2*2/4+2*2^2/4^2`). 7. 300 x 8 = 2,400 units (Little's Law). 8. Keeping a product generic until demand is known, then customising; it addresses uncertainty about the mix of variants. 9. Revenue sharing lowers the wholesale price, so the retailer has less incentive to push the product; it works when the retailer's effort matters little, and poorly when effort can be shifted to competing products (Cachon and Lariviere 2005). 10. Incoterms 2020 (eleven rules, in force since 1 January 2020; check the ICC page). The rule fixes who pays freight and insurance, who handles customs and where risk passes, which changes landed cost and who bears a loss in transit. 11. Northfield gives up USD 16,044.77 of expected contribution and accepts a lower in-stock probability (66.64% against 85.01%) to avoid paying USD 8 more per jacket; in exchange it keeps control of leftover jackets and outlet pricing. 12. (a) mba/mba-bullwhip-effect-card (price variations, forward buying). (b) mba/mba-network-design-card. (c) Landed cost in mba/mba-network-design-card, then mba/mba-sourcing-procurement-card. ## My notes and next steps | Item | My entry | |---|---| | Self-test score before the course / before the exam | [ ] / [ ] | | Concepts I still cannot explain without notes | [ ] | | A supply chain from my last job I would now analyse differently | [ ] | | Elective plan link (mba/mba-elective-concentration-plan, mba/mba-electives-roadmap) | [ ] | | Next review date | YYYY-MM-DD | ## Revision history | Version | Date | Author | Change summary | |---|---|---|---| | 0.1 | YYYY-MM-DD | [Your name] | First draft from the template | | 0.2 | YYYY-MM-DD | [Your name] | *Example:* Roadmap replaced with the actual syllabus, mini-case answers from study group added | ## Standard and licence note Structure aligned with the supply chain management electives common to leading international MBA programmes and with the competency areas used across this library; the primary works cited are named for their ideas, and the ASCM SCOR DS, ICC Incoterms and EU rules are named as dated facts only. All text, the mini-case, data and examples are original and fictional, and textbooks are named as further reading only. This template is not affiliated with or endorsed by any business school, publisher, standards body or course, and it is not legal advice; check your own syllabus for the topics and weights that apply. Template licence: MIT.