# Operations Management Course Guide: [Your programme and term] > A course guide for the operations management core 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 process > analysis, capacity and bottlenecks, variability and queues, inventory and the supply chain (including the bullwhip > effect), quality and six sigma, lean and the Toyota Production System, the theory of constraints, service operations > and the basics of project management. It is for admitted students preparing for the core, students taking it, and > working managers who want the core in one place. Source basis: the primary works named in each section (Little 1961; > Harris 1913; Lee, Padmanabhan and Whang 1997; Ohno 1988; Goldratt and Cox 1984; Kelley and Walker 1959 and others), > written up in original words, with fictional companies (Tallow Creek Medical Center, Northfield Outdoor Co.). > Replace every [bracketed] value and every row marked *Example*. ## Document control | Field | Value | |---|---| | Programme and course | [Full-time MBA, core: Operations 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% / exam z%] | | Reviewed by | [Study group / mentor] | | Last updated | YYYY-MM-DD | | Version | [0.1] | ## What the course is for Operations is how an organisation turns resources into the products and services customers pay for. The operations core teaches you to look at any business as a set of processes, to measure them (capacity, flow time, inventory, quality, cost) and to decide where a change creates value. It is the most numerical of the general management courses after finance, but its main lesson is managerial: most performance problems are designed into the process, and most of them can be designed out. After the course you should be able to: 1. Draw a process, find its bottleneck and compute capacity, utilisation, flow time and inventory (Little's Law). 2. Explain why waiting grows sharply as utilisation approaches 100% when arrivals and service times vary, and size capacity for a service level. 3. Set order quantities, reorder points and seasonal buys, and read inventory on a balance sheet. 4. Diagnose the bullwhip effect in a supply chain and propose coordination remedies. 5. Measure quality (defect rates, capability, sigma level) and lead an improvement project with a structured method. 6. Explain the logic of lean and the Toyota Production System and the theory of constraints, and say when each fits. 7. Design a service process that manages customer-introduced variability. 8. Plan a project with a critical path and recognise schedule risk. **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 | Capacity, Little's Law, EOQ, newsvendor, queueing, sigma levels | Sessions 2 to 9, all cards | | Ethical decision making | Patient safety versus throughput, labour conditions in supply chains, honest reporting of service levels | Mini-case question 6, session 11 | | Communication | Case recommendations, operations memos with one exhibit that decides | Assignments, rubric | | Leadership and teamwork | Leading improvement projects, kaizen teams, change on the shop floor | Sessions 8 and 10 | | Global perspective | Global sourcing, lead-time risk, supply chain disruption | Session 6 and 7 | | Technology, data and AI | Data from system logs, forecasting, simulation, automation and AI in planning | Sessions 5 to 7, 12 | ## Prerequisites - [ ] Algebra with rates and units (orders per hour versus minutes per order): mba/pre-mba-quant-refresher. - [ ] Spreadsheet basics: `MIN`, `INDEX`/`MATCH`, absolute references, data tables, Solver: mba/pre-mba-excel-modeling-drills. - [ ] Probability: mean, standard deviation, the normal distribution and its quantiles (`NORM.S.INV`, `NORM.INV`): academic/stats-probability-basics and academic/stats-distributions-sheet. - [ ] Basic cost concepts (fixed, variable, relevant costs): mba/mba-managerial-accounting-guide. - [ ] Reading a case and preparing a recommendation: mba/mba-case-reading-guide and mba/mba-case-prep-sheet. ## Session roadmap | Session | Topic | Card to read | Case or exercise *(Example)* | Deliverable | |---|---|---|---|---| | 1 | Operations strategy: cost, quality, speed, flexibility; product-process matrix (Hayes and Wheelwright 1979) | This guide, "What the course is for" | A company you know: which dimension does it compete on? | One-paragraph reflection | | 2 | Process analysis: flow unit, capacity, bottleneck, utilisation | mba/mba-process-analysis-card | Northfield Ridgeway fulfilment centre (card, Worked example A) | Process map and capacity table | | 3 | Little's Law, flow time, labour content, line balancing | mba/mba-process-analysis-card | Tallow Creek outpatient MRI (card, Worked example B) | Problem set 1 | | 4 | Multiple products, set-ups and batching | mba/mba-process-analysis-card | Northfield bike service workshop (card, Exercise C) | Case preparation | | 5 | Variability and queues: utilisation, waiting, pooling | mba/mba-queueing-card | Call centre or clinic staffing exercise | Problem set 2 | | 6 | Inventory: EOQ, reorder point, safety stock, turns | mba/mba-inventory-card | Northfield headlamp (card, Worked example A) | Spreadsheet model | | 7 | Newsvendor and supply chain: bullwhip effect, coordination, risk pooling | mba/mba-inventory-card and this guide | Beer distribution game (simulation) | Game debrief memo | | 8 | Quality: variation, control charts, capability, six sigma and DMAIC | mba/mba-quality-card | Defect data set from a service process | Problem set 3 | | 9 | Lean and the Toyota Production System | This guide, "Lean and the Toyota Production System" | Tallow Creek Day Surgery Unit mini-case (below) | Group case write-up | | 10 | Theory of constraints and improvement projects | This guide and mba/mba-process-analysis-card | A plant or office with an obvious constraint | Improvement plan | | 11 | Service operations: customer-introduced variability, service design | This guide, "Service operations" | Hospital or bank front office | Case preparation | | 12 | Project management basics: work breakdown, critical path, schedule risk | This guide, "Project management basics" | Turnover-improvement project (below) | Network diagram | | 13 | 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 | |---|---|---|---|---| | Capacity and bottleneck | The slowest resource sets process capacity | Goldratt and Cox (1984) for the managerial logic | mba/mba-process-analysis-card | [ ] | | Utilisation and implied utilisation | Flow rate or demand divided by capacity | Standard process analysis | mba/mba-process-analysis-card | [ ] | | Little's Law | Inventory = flow rate x flow time | Little (1961) | mba/mba-process-analysis-card | [ ] | | Queueing and variability | Waits rise steeply as utilisation nears 100% | Kendall (1953); Kingman (1961) | mba/mba-queueing-card | [ ] | | Economic order quantity | Balances ordering and holding costs | Harris (1913) | mba/mba-inventory-card | [ ] | | Safety stock and reorder point | Buffer for uncertain lead-time demand | Standard inventory theory | mba/mba-inventory-card | [ ] | | Newsvendor and critical ratio | One-period order under uncertain demand | Arrow, Harris and Marschak (1951) | mba/mba-inventory-card | [ ] | | Bullwhip effect | Order variability grows upstream | Lee, Padmanabhan and Whang (1997) | This guide, "Supply chain" | [ ] | | Efficient versus responsive supply chains | Match chain design to product type | Fisher (1997) | This guide, "Supply chain" | [ ] | | Statistical process control | Separate common-cause from special-cause variation | Shewhart (1931) | mba/mba-quality-card | [ ] | | Six sigma and DMAIC | Defect reduction to about 3.4 per million opportunities | Harry and Schroeder (2000) | mba/mba-quality-card and this guide | [ ] | | Lean and the Toyota Production System | Just-in-time and built-in quality (jidoka), waste elimination | Ohno (1988) | This guide | [ ] | | Quick changeover (SMED) | Cut set-up time to make small batches economic | Shingo (1985) | This guide | [ ] | | Theory of constraints | Five focusing steps around the constraint | Goldratt and Cox (1984) | This guide | [ ] | | Customer-introduced variability | Service customers bring variation into the process | Frei (2006) | This guide, "Service operations" | [ ] | | Critical path method | Longest path through a project network sets its duration | Kelley and Walker (1959); PERT: Malcolm et al. (1959) | This guide, "Project management basics" | [ ] | ## Core topics in brief ### Process analysis, capacity and bottlenecks Every process analysis starts with a flow unit and a process map. Capacity of a resource is servers divided by processing time; the lowest capacity is the bottleneck and sets process capacity; flow rate is the smaller of demand and capacity. Little's Law ($I = R \times T$, Little 1961) links inventory, flow rate and flow time for any stable system. The full method with three examples is in mba/mba-process-analysis-card. The strategic frame comes from Hayes and Wheelwright (1979): a job shop, a batch process, an assembly line and a continuous flow process each fit a different product volume and variety, and a mismatch is a strategic problem, not only an operational one. ### Variability and queues Averages hide the main reason customers wait: variability in arrivals and service times. With random arrivals, the expected wait grows roughly in proportion to $u / (1 - u)$, so moving a resource from 80% to 90% utilisation more than doubles the wait ($u/(1-u)$ goes from 4 to 9, a factor of 2.25; Kingman 1961 gives the general approximation). Three managerial consequences follow: keep spare capacity where waiting is expensive, pool parallel queues into one, and reduce variability (appointments, standard work). The formulas, worked examples and a chart of wait versus utilisation are in mba/mba-queueing-card. ### Inventory and supply chain Inventory decouples supply from demand. Cycle stock comes from batching (EOQ, Harris 1913), safety stock from uncertainty during the lead time, seasonal stock from one-shot decisions (newsvendor, Arrow, Harris and Marschak 1951). See mba/mba-inventory-card. The **bullwhip effect** is the amplification of order variability as orders move upstream from retailer to wholesaler, distributor and manufacturer. Lee, Padmanabhan and Whang (1997) identified four causes: demand signal processing (each stage updates its forecast from the orders it receives, not from end demand), the rationing game (customers inflate orders when supply is short), order batching (orders placed in full trucks or at period ends) and price variations (promotions that cause forward buying). Remedies follow from the causes: share point-of-sale data, shorten lead times, order smaller and more often, allocate scarce supply on past sales rather than on current orders, and replace promotions with stable everyday pricing. The beer distribution game used in many courses lets you feel the effect: teams that see only their own incoming orders create large swings even when end demand barely moves. Fisher (1997) adds the design question: **functional** products with predictable demand need an **efficient** supply chain (low cost, high utilisation), while **innovative** products with unpredictable demand need a **responsive** one (buffer capacity, short lead times, postponement). ### Quality and six sigma Quality management separates two kinds of variation. Common-cause variation is built into the process; special-cause variation has an assignable cause. Control charts (Shewhart 1931) tell them apart, so managers do not chase noise or ignore signals. Six sigma, developed at Motorola in the 1980s and popularised by Harry and Schroeder (2000), sets a defect target of about 3.4 defects per million opportunities (DPMO) and uses the DMAIC cycle: define, measure, analyse, improve, control. The 3.4 DPMO figure assumes the process mean drifts by 1.5 standard deviations over time; without that convention a six-sigma process would have about 0.002 DPMO. Capability indices, control limits and a full worked example are in mba/mba-quality-card. | Sigma level (with the 1.5-sigma shift convention) | DPMO | Excel | |---|---|---| | 3 | 66,807.2 | `=(1-NORM.S.DIST(3-1.5,TRUE))*10^6` | | 4 | 6,209.7 | `=(1-NORM.S.DIST(4-1.5,TRUE))*10^6` | | 5 | 232.6 | `=(1-NORM.S.DIST(5-1.5,TRUE))*10^6` | | 6 | 3.4 | `=(1-NORM.S.DIST(6-1.5,TRUE))*10^6` | *Example*: Tallow Creek audits 1,200 surgical consent forms, each with 4 opportunities for a defect, and finds 54 defects: DPMO = 54 / (1,200 x 4) x 1,000,000 = 11,250 (`=54/(1200*4)*10^6`), a process sigma of about 3.78 with the shift convention (`=NORM.S.INV(1-11250/10^6)+1.5`). ### Lean and the Toyota Production System Ohno (1988) describes the Toyota Production System as resting on two pillars: **just-in-time** (make only what is needed, when it is needed, in the amount needed) and **jidoka** (automation with a human touch: stop the process at the first abnormality so defects are not passed on). Around them sit the tools most people associate with lean: kanban cards that let downstream demand pull production, levelled production (heijunka), standard work, quick changeover (Shingo 1985) and continuous improvement (kaizen). Ohno's seven wastes are overproduction, waiting, transport, over-processing, inventory, unnecessary motion and defects. Spear and Bowen (1999) argue that the tools are not the system: what is hard to copy is the way every activity, connection and flow path is specified and treated as an experiment to improve. Womack, Jones and Roos (1990) brought the term "lean production" to a wide audience. Lean and process analysis say the same thing in different words: lower inventory means shorter flow time (Little's Law), and smaller batches need short set-ups. The risk is to cut buffers in a process that still has high variability: lean works when variability is reduced first. ### Theory of constraints Goldratt and Cox (1984) told, in the form of a novel, the story of a plant manager who learns that only the constraint determines the output of the system. The later formulation is five focusing steps: (1) identify the constraint, (2) exploit it (never let it idle or work on defective units), (3) subordinate everything else to it (the rest of the process works at the constraint's pace), (4) elevate it (add capacity), (5) when the constraint moves, start again and do not let inertia become the new constraint. Drum-buffer-rope scheduling sets the release of work by the constraint (the drum), protects it with a time buffer and ties material release to its pace (the rope). Compare with lean: lean tries to reduce waste everywhere, the theory of constraints concentrates effort on the one resource that limits output. ### Service operations Services differ from manufacturing because the customer is in the process. Frei (2006) shows that customers introduce variability in when they arrive, what they request, how capable they are, how much effort they make and their subjective preferences, and that a firm can either accommodate this variability (more staff, flexible processes) or reduce it (appointments, menus, self-service), choosing for each type which mix serves its strategy at acceptable cost. In services, flow time is the customer experience, capacity cannot be stored as inventory, and the queueing results in mba/mba-queueing-card matter more than they do in a factory. ### Project management basics A project is a one-off process with a start and an end. The critical path method (Kelley and Walker 1959) and PERT (Malcolm, Roseboom, Clark and Fazar 1959) represent the project as a network of activities with durations and precedence. The longest path through the network, the **critical path**, sets the project duration; activities off it have **slack**. Crashing (adding resources) shortens the project only if it shortens the critical path. Use project/work-breakdown-structure to list the work and project/project-schedule to manage it. *Example*: Tallow Creek's turnover-improvement project (computed with a forward and backward pass): | ID | Activity *(Example)* | Days | Predecessors | Early start | Early finish | Late start | Late finish | Slack | |---|---|---|---|---|---|---|---|---| | A | Map current turnover | 5 | none | 0 | 5 | 0 | 5 | 0 | | B | Train turnover team | 8 | A | 5 | 13 | 9 | 17 | 4 | | C | Buy and stage instrument carts | 12 | A | 5 | 17 | 5 | 17 | 0 | | D | Redesign OR set-up checklist | 6 | A | 5 | 11 | 11 | 17 | 6 | | E | Pilot in two ORs | 10 | B, C, D | 17 | 27 | 17 | 27 | 0 | | F | Roll out to all ORs | 7 | E | 27 | 34 | 27 | 34 | 0 | Critical path A-C-E-F, 34 working days. Speeding up training (B) does nothing for the end date; a faster cart supplier (C) does. In Excel: early finish `=early_start+duration`, early start of E `=MAX(EF_B,EF_C,EF_D)`, slack `=LS-ES`. ```mermaid flowchart LR A["A Map turnover (5 d)"] --> B["B Train team (8 d, slack 4)"] A --> C["C Instrument carts (12 d, critical)"] A --> D["D Set-up checklist (6 d, slack 6)"] B --> E["E Pilot in two ORs (10 d, critical)"] C --> E D --> E E --> F["F Roll out (7 d, critical)"] ``` ## Mini-case: Tallow Creek Medical Center, Day Surgery Unit ### Situation Dr Amara Osei-Lindqvist had been medical director of the Day Surgery Unit at Tallow Creek Medical Center for eight months when the hospital's chief operating officer, Rafael Domínguez, asked her for a plan. The unit performed planned procedures that did not need an overnight stay: knee arthroscopies, hernia repairs, cataract operations and similar cases. Demand was growing. The waiting list for a day-surgery slot had reached nine weeks, and the board wanted more cases done without building new operating rooms. Patients complained too, but not about the surgery. The unit asked every patient to arrive at 06:30 so that no operating room would ever wait for a patient. A typical patient registered, waited, saw a nurse for the pre-operative assessment, waited again, had a 60-minute procedure, spent about two hours in recovery and was discharged. "I was there almost six hours for one hour of surgery," one patient wrote in a feedback form, "and most of it was in a chair in a corridor." The four operating rooms ran from 07:00 to 17:00. Between cases the room had to be cleaned, the instruments for the next case brought in and checked, and the anaesthetic equipment reset. The nursing team called this the turnover and said it took about 30 minutes. The anaesthesia lead, Dr Henrik Vaszary, had seen units elsewhere bring it down to 20 minutes with pre-packed instrument carts and parallel cleaning and set-up, and he wanted to try. The surgeons were sceptical: "Every minute we cut from turnover is a minute of safety checks we lose," said one. The unit's operations analyst, Mei-Ling Castellanos, had pulled data from the patient tracking system. On an average weekday the unit treated 24 patients, and a head count taken every 30 minutes showed on average 14 patients in the unit (Exhibit 2). She had also noticed something odd about the sterile surgical kits used in every procedure. Weekly kit usage was steady, but the unit's own orders to the hospital's central store swung from week to week, and the central store's orders to the regional distributor swung even more (Exhibit 3). The distributor had started to complain about "unpredictable demand" from Tallow Creek and was proposing a 6% price increase for "volatility handling". Osei-Lindqvist had a meeting with Domínguez in two weeks. She wanted a plan that would raise the number of cases, shorten the time patients spent in the unit, settle the kit ordering problem and keep the surgeons on board. ### Exhibit 1: Day Surgery Unit resources and times (average weekday, *Example*) | Step | Resources | Minutes per patient | Notes | |---|---|---|---| | Registration | 2 clerks | 10 | | | Pre-operative nursing assessment | 3 nurses | 25 | | | Operating room | 4 rooms | 90 | 60 minutes of surgery plus 30 minutes of turnover | | Recovery (post-anaesthesia care unit) | 8 recovery bays | 120 | | | Discharge | 2 nurses | 15 | | Operating hours: 07:00 to 17:00 (10 hours). Contribution per additional case after variable costs: USD 1,800. The unit operates 250 days a year. ### Exhibit 2: Operating data (average of the last 60 weekdays, *Example*) | Measure | Value | |---|---| | Patients treated per day | 24 | | Average number of patients in the unit (30-minute head counts, 07:00 to 17:00) | 14 | | Arrival policy | All patients asked to arrive at 06:30 | | Waiting list for a day-surgery slot | 9 weeks | | Turnover time, median (nursing estimate) | 30 minutes | ### Exhibit 3: Sterile surgical kits, weekly units (last 12 weeks, *Example*) | Week | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | |---|---|---|---|---|---|---|---|---|---|---|---|---| | Kits used in procedures | 112 | 128 | 121 | 115 | 126 | 118 | 124 | 113 | 127 | 120 | 116 | 125 | | Unit's orders to central store | 118 | 130 | 140 | 105 | 125 | 100 | 150 | 110 | 135 | 120 | 105 | 130 | | Central store's orders to distributor | 120 | 160 | 200 | 40 | 160 | 40 | 240 | 40 | 200 | 120 | 40 | 160 | The central store orders in pallets of 40 kits. Unit nurses place orders on Friday by looking at the shelf. ### Discussion questions 1. Which resource is the bottleneck of the Day Surgery Unit? What is its utilisation, and what is the daily capacity of the unit? 2. How long does an average patient spend in the unit, and how much of that time is waiting? Which data in the exhibits let you answer without timing a single patient? 3. What would cutting turnover from 30 to 20 minutes do to capacity, and is any other step at risk of becoming the new bottleneck? What is the annual value of three more cases a day? 4. What would you change about the 06:30 arrival policy, and what risk does your change create? 5. Measure the bullwhip effect in Exhibit 3. Which of the four causes identified by Lee, Padmanabhan and Whang (1997) do you see, and what would you change? How should Tallow Creek respond to the proposed price increase? 6. How would you address the surgeons' safety concern, and what should the plan say about safety and throughput? ### Teaching note **Q1. Bottleneck.** At an arrival rate of 24 / 10 = 2.4 patients an hour, capacities and utilisations are: | Step | Capacity (patients per hour) | Capacity per 10-hour day | Utilisation | Excel | |---|---|---|---|---| | Registration | 12.00 | 120.0 | 20.0% | `=2*60/10` | | Pre-operative assessment | 7.20 | 72.0 | 33.3% | `=3*60/25` | | Operating rooms | 2.67 | 26.7 | 90.0% | `=4*60/90` | | Recovery bays | 4.00 | 40.0 | 60.0% | `=8*60/120` | | Discharge | 8.00 | 80.0 | 30.0% | `=2*60/15` | The operating rooms are the bottleneck at 26.67 cases a day and 90% utilisation (`=24/26.67`). With variable case lengths, 90% is already high: late finishes and overtime are to be expected. **Q2. Flow time.** By Little's Law, $T = I / R$ = 14 / 2.4 = 5.83 hours = 350 minutes (`=14/2.4*60`). Value-added time for the patient is 10 + 25 + 60 + 120 + 15 = 230 minutes (turnover is not patient time), so waiting is 120 minutes and flow time efficiency is 230 / 350 = 65.7%. The head count and the daily throughput are enough; no stopwatch needed. Most of the waiting happens in the morning, because all patients arrive at 06:30 while the operating rooms release them one at a time over ten hours. **Q3. Turnover.** At 20 minutes, the room time per case falls to 80 minutes and capacity rises to 4 x 60 / 80 = 3.00 an hour, 30 cases a day (`=4*60/80*10`). At 24 cases utilisation falls to 80%; at 27 cases it is back at 90%. Recovery would run at 3 / 4 = 75% if the rooms ran flat out, so it is not the next bottleneck yet, though recovery bays become tight if long cases cluster in the afternoon. Three more cases a day are worth 3 x 1,800 x 250 = USD 1.35m a year in contribution (`=3*1800*250`). The project to get there takes 34 working days on its critical path (see "Project management basics"). A strong answer also notes the choice between more cases (higher utilisation, longer waits) and the same cases with more slack (shorter waits, fewer late finishes). **Q4. Arrival policy.** Staggered arrival times (for example, each patient asked to arrive 90 minutes before the planned start of the case) cut the waiting before surgery without changing capacity. If the average time in the unit fell from 5.83 to 4.5 hours, the average census would fall from 14 to 10.8 patients (`=4.5*2.4`), freeing chairs, nurses' attention and space. The risk is a room waiting for a late patient: protect the bottleneck with a small buffer (one patient ready ahead of each room), which is exactly the theory of constraints' "exploit the constraint". **Q5. Bullwhip.** Standard deviations of weekly quantities: kits used 5.62, unit orders 15.49, central store orders 72.03 (`=STDEV.S(range)`). Variance amplifies by 7.61 from usage to unit orders (`=VAR.S(orders)/VAR.S(usage)`) and by 21.63 from unit orders to distributor orders. Causes visible here: demand signal processing (nurses order by looking at the shelf, reacting to last week), order batching (the central store orders in pallets of 40, producing the 40-200-40 pattern) and possibly rationing behaviour after short weeks. Remedies: order from procedure schedules (the unit knows next week's cases), give the central store and the distributor visibility of actual usage, replace the Friday shelf check with a two-bin kanban, and order smaller mixed pallets or more often. The price increase is a symptom of Tallow Creek's own ordering; fixing the ordering is the better negotiation position. **Q6. Safety and throughput.** Turnover reduction in the lean tradition (Shingo 1985) separates internal set-up (only possible with the room empty) from external set-up (can be done while the previous case is still running), and moves work from the first to the second: pre-packed instrument carts are checked before the room is free, not after. The safety checks remain; they move in time, and the pilot should measure safety events alongside minutes. Stating safety as a constraint, not a variable, is also the ethical answer: the plan should commit to stop the pilot if safety indicators worsen. **Recommendation.** Pilot the 20-minute turnover in two rooms with the surgeons who are willing, stagger arrivals with a one-patient buffer per room, move kit ordering to schedule-based two-bin replenishment with shared data, and use the added capacity first to shorten the waiting list, keeping utilisation near 85% to protect patients from long delays. ## Assignments and grading Typical assessed work in an operations core: | Assignment | Typical weight *(Example)* | What it tests | |---|---|---| | Class participation in cases | [25%] | Structured argument, use of numbers in discussion, building on others (mba/mba-class-participation-log) | | Problem sets (3 to 4) | [15%] | Correct use of the tools, units, Excel models | | Group case write-up (2 to 5 pages) | [20%] | Diagnosis, analysis, recommendation (mba/mba-case-write-up) | | Simulation game (supply chain or factory) | [10%] | Decisions under uncertainty, debrief quality | | Final exam (open or closed book) | [30%] | Process, inventory, queueing and quality problems plus a short case | **Rubric for a written operations case analysis (*Example*):** | Criterion | Excellent (4) | Good (3) | Adequate (2) | Weak (1) | |---|---|---|---|---| | Problem definition | States the decision and the measure that matters (capacity, wait, cost, service) | Decision stated, measure vague | Describes symptoms | Retells the case | | Process and data analysis | Correct map, flow unit, bottleneck and Little's Law, units consistent | Minor errors that do not change the conclusion | Partial analysis, some unit errors | Numbers without logic | | Use of variability and risk | Considers peaks, variability and what could go wrong | Mentions variability | Averages only | None | | Recommendation | Clear, feasible, ranked options with trade-offs | Clear but one-sided | Vague | Missing | | Implementation and people | Sequencing, pilot, measures, stakeholders | Some implementation detail | Generic | Missing | | Communication | One exhibit decides the argument; concise | Clear but long | Hard to follow | Unclear | ## Exam preparation plan | Week | Focus | Activities | Check | |---|---|---|---| | T-4 weeks | Process analysis and Little's Law | Redo every class problem without notes; build one capacity spreadsheet from scratch | 10 problems, 80% correct | | T-3 weeks | Queueing and inventory | Card exercises; one newsvendor and one EOQ problem a day | Can choose the model in 30 seconds | | T-2 weeks | Quality, lean, theory of constraints, service | Explain each concept aloud to your group; one short case a day | Concept table above fully ticked | | T-1 week | Mixed past exams under time | Two full practice exams, then error log review (mba/mba-error-log) | Score trend up; no repeated error type | | Exam day | Formula sheet and calm | Read all questions first; start with the one you are surest of; write units everywhere | | Use mba/mba-exam-prep for the general plan and mba/mba-concept-flashcards for spaced review of the key concepts. ## Common struggles - **Units.** Rates (per hour) and times (minutes per unit) are inverses. Fix: write units in every spreadsheet header and every line of working. - **Choosing the model.** Students know the formulas but pick the wrong one. Fix: start every problem with "repeated or one-off? known or uncertain? waiting or stock?" (see the decision diagrams on the cards). - **Averages versus variability.** Analyses on averages say a process works; the queue says otherwise. Fix: ask "what happens at the peak and when times vary?" in every case. - **Treating lean as a toolbox.** Kanban cards without stable processes fail. Fix: explain the principle behind each tool. - **Over-calculating in class.** A case discussion rewards a clear number that decides the question, not every number. Fix: prepare one decisive exhibit per case. - **Ignoring people.** Process changes fail when staff are not involved. Fix: add an implementation and stakeholder paragraph to every recommendation (the surgeons in the mini-case). - **Normal distribution mechanics.** `NORM.S.INV` versus `NORM.INV`, critical ratio versus z. Fix: academic/stats-distributions-sheet. ## Reading list **Classic works (primary sources):** - Harris, F. W. (1913). How many parts to make at once. *Factory, The Magazine of Management*, 10(2), 135-136, 152. - Shewhart, W. A. (1931). *Economic Control of Quality of Manufactured Product*. Van Nostrand. - Arrow, K. J., Harris, T., and Marschak, J. (1951). Optimal inventory policy. *Econometrica*, 19(3), 250-272. - Kendall, D. G. (1953). Stochastic processes occurring in the theory of queues and their analysis by the method of the imbedded Markov chain. *The Annals of Mathematical Statistics*, 24(3), 338-354. - Kelley, J. E., and Walker, M. R. (1959). Critical-path planning and scheduling. *Papers presented at the Eastern Joint IRE-AIEE-ACM Computer Conference*, December 1959, 160-173. - Malcolm, D. G., Roseboom, J. H., Clark, C. E., and Fazar, W. (1959). Application of a technique for research and development program evaluation. *Operations Research*, 7(5), 646-669. - Little, J. D. C. (1961). A proof for the queuing formula: L = λW. *Operations Research*, 9(3), 383-387. - Kingman, J. F. C. (1961). The single server queue in heavy traffic. *Mathematical Proceedings of the Cambridge Philosophical Society*, 57(4), 902-904. - Hayes, R. H., and Wheelwright, S. C. (1979). Link manufacturing process and product life cycles. *Harvard Business Review*, January-February 1979, 133-140. - Goldratt, E. M., and Cox, J. (1984). *The Goal: A Process of Ongoing Improvement*. North River Press. - Shingo, S. (1985). *A Revolution in Manufacturing: The SMED System*. Productivity Press. - Ohno, T. (1988). *Toyota Production System: Beyond Large-Scale Production*. Productivity Press. - Womack, J. P., Jones, D. T., and Roos, D. (1990). *The Machine That Changed the World*. Rawson Associates. - Lee, H. L., Padmanabhan, V., and Whang, S. (1997). Information distortion in a supply chain: the bullwhip effect. *Management Science*, 43(4), 546-558. - Fisher, M. L. (1997). What is the right supply chain for your product? *Harvard Business Review*, March-April 1997. - Spear, S., and Bowen, H. K. (1999). Decoding the DNA of the Toyota Production System. *Harvard Business Review*, September-October 1999. - Harry, M., and Schroeder, R. (2000). *Six Sigma: The Breakthrough Management Strategy Revolutionizing the World's Top Corporations*. Currency Doubleday. - Frei, F. X. (2006). Breaking the trade-off between efficiency and service. *Harvard Business Review*, November 2006. **Textbooks (further reading, by name only):** Cachon and Terwiesch, *Matching Supply with Demand: An Introduction to Operations Management*; Anupindi, Chopra, Deshmukh, Van Mieghem and Zemel, *Managing Business Process Flows*; Hopp and Spearman, *Factory Physics*; Chopra and Meindl, *Supply Chain Management: Strategy, Planning, and Operation*. **Open resource:** MIT OpenCourseWare, 15.761 Introduction to Operations Management (Spring 2013), https://ocw.mit.edu/courses/15-761-introduction-to-operations-management-spring-2013/. ## Self-test 1. A process has three steps with capacities of 40, 25 and 30 units per hour. Demand is 20 per hour. Name the bottleneck and the utilisation of each step. 2. An online retailer ships 300 orders a day and has 600 orders in process on average. What is the average flow time? 3. Why can a help desk at 95% utilisation have long queues even though it has spare capacity on average? 4. $D$ = 10,000 units a year, $S$ = USD 50, $H$ = USD 4. Compute the EOQ. 5. A newsvendor item sells at USD 50, costs USD 20 and has a salvage value of USD 5. Demand is normal with mean 500 and SD 100. Compute the critical ratio and the order quantity. 6. Name the four causes of the bullwhip effect identified by Lee, Padmanabhan and Whang (1997) and one remedy for each. 7. What are the two pillars of the Toyota Production System according to Ohno (1988)? 8. State the five focusing steps of the theory of constraints. 9. Why does a six-sigma process correspond to 3.4 DPMO in most practitioner material, and not to a much smaller number? 10. In the project network of this guide, activity B is delayed by 3 days. Does the project end later? And if it is delayed by 5 days? 11. Give two ways a bank branch can reduce customer-introduced variability and two ways it can accommodate it. 12. Interleaving: which tool would you use first? (a) A hospital asks how many beds keep the probability of a full ward low. (b) A retailer's supplier complains about erratic orders. (c) A factory asks which machine to buy. ## Answer key 1. The step with capacity 25 per hour is the bottleneck. Utilisations: 20 / 40 = 50%, 20 / 25 = 80%, 20 / 30 = 66.7%. 2. $T = I / R$ = 600 / 300 = 2 days. 3. Because arrivals and service times vary: when work arrives in clusters, a queue forms that the desk clears only slowly with 5% spare capacity. Waits grow roughly with $u / (1 - u)$ (mba/mba-queueing-card). 4. $Q^* = \sqrt{2 \times 10{,}000 \times 50 / 4}$ = 500 units (`=SQRT(2*10000*50/4)`). 5. $C_u$ = 30, $C_o$ = 15, critical ratio = 30 / 45 = 0.6667, $z$ = 0.4307, $Q^*$ = 543.07, so order 544 (`=NORM.INV(30/45,500,100)`). 6. Demand signal processing (share end-customer demand data); rationing game (allocate on past sales, not current orders); order batching (smaller, more frequent orders, mixed loads); price variations (everyday low prices instead of promotions). 7. Just-in-time and jidoka (built-in quality: stop at the first abnormality). 8. Identify the constraint; exploit it; subordinate everything else to it; elevate it; if it moves, go back to step 1 and do not let inertia become the constraint. 9. By convention the process mean is assumed to drift by 1.5 standard deviations in the long run, so a six-sigma process is treated as 4.5 sigma from the nearest limit: one-sided tail = 3.4 per million. Without the shift the two-sided rate is about 0.002 per million. 10. B has 4 days of slack. A 3-day delay does not move the end date (34 days); a 5-day delay makes B critical and the project ends 1 day later (35 days). 11. Reduce: appointments for complex requests, online forms that collect information in advance. Accommodate: a flexible staff pool for peaks, a dedicated desk for complex cases. 12. (a) Queueing (mba/mba-queueing-card). (b) Bullwhip diagnosis and supply chain coordination (this guide). (c) Process analysis to find the bottleneck first (mba/mba-process-analysis-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 process at my last job I would now analyse differently | [ ] | | 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 operations management core common to leading international MBA programmes and with the competency areas used across this library; the primary works cited are named for their ideas. 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 or course; check your own syllabus for the topics and weights that apply. Template licence: MIT.