--- title: "OPMT 130 · Run operations that work" publisher: Maestro College (Maestro Open) lessons: 37 source: https://open.maestrocollege.edu/run-operations-that-work full: https://open.maestrocollege.edu/run-operations-that-work/llms-full.txt --- # Run operations that work (OPMT 130) See a business as a system: map how work flows, find the bottleneck, plan capacity and inventory, and make trade-offs you can defend. A practical first course in operations management. You'll learn to think like an operations manager, seeing a business as a system that turns demand into customer value through workflows that run under constraints and uncertainty. You'll map real workflows, find bottlenecks, size capacity, make defensible trade-offs with incomplete information, run small improvement loops and bring leaders along with you. Real scenarios run throughout (a food delivery app, a coffee subscription box, a SaaS support team and a capstone case), with AI as a working partner you stress-test rather than blindly trust. ## Course objectives - Operations management principles - Analyzing and optimizing business processes - Supply chain and inventory management - Quality management and control - Efficiency and capacity planning ## Course teaching approach - Start from a real-world problem, then introduce the concept as the solution. - Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning. - Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks. - End with one fun closed question about the topic. ## Lessons ### Unit 1: Seeing operations as a system #### 1. Operations as a system - Define operations management in plain language as the design and running of the system that transforms inputs into customer value. - Identify the inputs, transformation steps, and customer-visible outputs in a familiar small business and name the transformation in one sentence. #### 2. Product, service, and hybrid operations - Distinguish product, service, and hybrid operations by what is produced, when the customer is present, and whether output can be stored. - Classify a given small business on the product-service spectrum and name the operational implications of that position (e.g., inventory matters more, customer-facing variability matters more). #### 3. The customer journey through operations - Trace the customer journey through a small-business operation and identify the moments where operational design directly shapes how the customer experiences the business. - Recommend one operational change that would improve customer experience by changing the system, without resorting to a 'try harder' or 'hire better people' answer. #### 4. Operations strategy and business goals - Connect a stated business goal (grow revenue, improve loyalty, lower cost, scale) to the specific operational choices that would support or undermine it. - Identify at least one operational decision in a small business that is misaligned with its stated strategy and name the misalignment in one sentence. #### 5. Cross-functional operations - Map how operations connects to marketing, finance, HR, and customer service in a small business. - Identify one cross-functional friction point (e.g., marketing promises something operations cannot deliver) and describe its operational consequence. #### 6. KPIs and the five trade-off axes - Name the five core operational performance axes (efficiency, quality, responsiveness, customer experience, scalability) and explain why they cannot all be maximized at once. - Choose two or three KPIs that would best signal performance for a stated small-business goal, and name one KPI that could push behavior in the wrong direction. #### 7. AI as your operational thinking partner - Use a free AI chat tool as a thinking partner on an operations situation — describing the situation and prompting the AI to surface factors, questions, or angles you had not considered, then judging which are useful and which it got wrong before relying on them. - Distinguish where AI is helpful as a thinking partner from where the human manager must own the call. Teaching notes: Have the learner use a real free AI chat tool (ChatGPT, Claude, or Gemini free tier) as a genuine thinking partner, not a summarizer — they should not hand the AI the answer and ask it to restate it, but use it to surface angles, questions, or risks they had not considered. The point is to feel both the usefulness and the limits. #### 8. Challenge: read a business as a system - Given a small business, produce a short operational reading that names the value being created, traces the workflow at a high level, identifies the operational choices supporting or undermining the stated strategy, names two trade-offs the current design is making, and names one or two KPIs that would reveal how well the system is performing. - Identify at least one cross-functional connection or friction point (where operations meets marketing, finance, HR, or customer service) and explain its operational consequence. - Defend the reading in language a non-operations owner would accept, and reflect on one specific moment from a business you know where this lens would have changed what you noticed. Teaching notes: Let the learner choose a small business they actually know (a cafe, salon, clinic, micro-agency); the tutor can offer one if they don't have one. Make sure the KPIs and the cross-functional link are actually applied to the chosen business — named and used inside the reading, not just defined in the abstract. ### Unit 2: Seeing flow and constraints #### 9. Process thinking - Reframe a 'people problem' in a small business as a process problem and name the underlying repeatable steps. - Identify at least one symptom that points to a flow issue rather than a motivation or staffing issue. Teaching notes: Use the Bite scenario — reframe 'drivers are unreliable' or 'restaurants are slow' as a process problem (order timing, handoffs, unclear status updates, dispatch logic, demand spikes). #### 10. Process mapping - Create a labeled process map of a small-business workflow using a diagramming tool, showing steps, handoffs, and decision points with every branch (both the yes and the no path) resolved. - Capture the actual flow accurately by walking the process end-to-end and recording who does what, in what order, and where handoffs happen. Teaching notes: Use the Bite scenario — map order placement → restaurant confirm → prep → driver assignment → pickup → delivery → customer confirmation. Build the map in Mermaid (the Mermaid Live Editor, mermaid.live): introduce the site and basic Mermaid flowchart syntax, show how to use the (+) icon to connect shapes, and have the learner paste Mermaid code back into the chat. Every decision diamond must include both exits — the 'No'/failure path as well as the 'Yes' path — and all steps must be connected (including the arrows into 'Send order to restaurant' and 'Restaurant accepts order') so the final map has no dangling steps. This map is the unit's running artifact, reused in the next lessons for the bottleneck hunt, utilization, service design, and AI critique. #### 11. Bottlenecks and throughput - Identify the bottleneck in a multi-step process by comparing step times and explain why it sets overall throughput. - Recommend one change at the bottleneck and predict its effect on throughput and on a relevant KPI (e.g., on-time delivery rate). Teaching notes: Use the Bite scenario — hunt for the bottleneck on the process map the learner built in the previous lesson rather than starting fresh; candidates include restaurant prep time, driver availability, dispatch timing, pickup handoff, traffic, or status-update inaccuracy. This is the first KPI use in the unit — briefly recall what a KPI is before connecting the bottleneck fix to one. #### 12. Capacity and utilization - Calculate a simple utilization figure (output divided by capacity) for a small-business operation using basic ratios and average times, and interpret what it says about headroom or strain. - Estimate the capacity of a process step from scratch, predict how it changes when a resource is added or removed, and distinguish effective capacity from theoretical maximum output. - Read a high-utilization warning sign (queues, late deliveries, missed handoffs) and connect it back to the bottleneck. Teaching notes: Use the Bite scenario — calculate capacity and utilization at the bottleneck identified in the previous lesson, not a new step. Have the learner estimate capacity from scratch, then test a change (e.g., adding a driver) to see how capacity and utilization move, and distinguish effective capacity from theoretical maximum. Use rounded numbers — the arithmetic is a means to judgment, not the focus. #### 13. Matching capacity to demand - Build a simple schedule for a small-business shift pattern that matches capacity to expected demand peaks. - Identify the trade-off in the schedule between staff cost and customer wait time, name which side it favors, and name the KPI that would reveal the cost of that choice. Teaching notes: Use the Bite scenario — to make capacity concrete, frame it as a partner restaurant scheduling its kitchen/prep staff across lunch and dinner peaks (more intuitive than scheduling drivers, where capacity is abstract); compare that capacity against expected order volume. Surface the core trade-off — labor cost vs customer wait time — and explain the relevant KPI as you go, not only at the end. #### 14. Layout and service design - Evaluate a small-business layout (counter, kitchen, waiting area, back-of-house) or a digital service surface (app screens, notifications) for how it supports or fights the workflow. - Propose one layout or service-design change and connect it to a flow or customer-experience outcome. Teaching notes: Use the Bite scenario — apply the lesson's service-design framework to the workflow mapped earlier in the unit. Have the learner pick one surface — either the physical layout (restaurant pickup shelf, driver arrival flow) or the digital surface (in-app order-status screen, notifications, support path) — and apply the framework fully to that one context, rather than starting both physical and digital and finishing neither. #### 15. AI-supported process analysis - Use a free AI tool to critique a process map or workflow you have already produced — feeding the artifact in and reading the AI's suggested flow issues or bottleneck candidates. - Use a triage grid to sort the AI's suggestions (accept, verify, or reject) against what you actually observed, and decide which to trust. Teaching notes: Use the Bite scenario — feed the process map the learner built earlier in the unit into a real free AI chat tool by pasting the Mermaid code as text (there is no file or screenshot upload, so do not ask the learner to upload an image). Have the AI surface flow issues or bottleneck candidates, then use a triage grid to accept, verify, or reject each suggestion against what was actually observed. #### 16. Challenge: map flow, find the bottleneck, fix it - Produce a labeled process map of a real small-business workflow in a free diagramming tool, identify the bottleneck, estimate capacity at that step, and recommend a specific change (at the bottleneck, in the schedule, or in the layout/service design) that would improve throughput. - Predict the effect of the change, name what it trades off, and name one KPI that would show whether it worked. - Reflect on one specific moment from a small business you know where this unit's tools would have changed the outcome. Teaching notes: Use the Bite scenario, but apply the toolkit to a Bite workflow the unit lessons did NOT map — e.g., the order cancellation/refund flow, restaurant onboarding, or the customer-complaint/support flow — so this is fresh end-to-end application, not a reassembly of the delivery map built across the unit. Map the chosen workflow in Mermaid, identify the biggest bottleneck, estimate capacity at that step, propose a capacity, schedule, or layout fix, predict its throughput effect, and name a relevant KPI. ### Unit 3: Deciding under uncertainty #### 17. Framing the operational problem - Frame a vague operational complaint (e.g., 'we're always behind') as a specific problem statement with a measurable gap. - Identify what kind of analysis (quantitative, qualitative, or AI-supported) would best fit the framed problem. Teaching notes: Don't use the unit scenario — let the student bring a vague operational complaint they've heard and frame it. #### 18. Quantitative and qualitative evidence - Distinguish when a quantitative read (averages, ratios, simple counts) and when a qualitative read (interviews, observation, customer comments) is the right tool. - Read basic summaries (counts, averages, trends) of a small set of operational numbers — letting a spreadsheet or free AI tool do the arithmetic — and explain what each summary tells the operator. - Apply both lenses to a small-business operational issue and reconcile what they say. Teaching notes: Don't use the unit scenario — let the student pick their own small set of operational numbers to summarize. The focus is interpreting what the numbers say, not doing the arithmetic by hand — let the spreadsheet or AI tool compute the summaries. #### 19. Reading demand patterns - Identify the demand pattern (steady, peaky, seasonal, lumpy) in a small business from a simple time series. - Connect a small business's demand pattern to its supplier lead-time risk and choose a planning response (staffing, prep, inventory, or supplier-side buffer) that protects customers without overbuying. Teaching notes: Use the Pour Over Club scenario — read weekly order patterns; identify spikes around campaigns, holidays, product drops, or subscription deadlines. #### 20. Supplier coordination and lead times - Identify the key suppliers and typical lead times for a small business and map them onto the operations system. - Spot the operational risk to customers, capacity, and costs when a supplier slips, and name one practical mitigation. Teaching notes: Use the Pour Over Club scenario — map key roaster suppliers, shipping lead times, and the risk to customers and fulfillment when stock is delayed. #### 21. Inventory and reorder rules - Explain the trade-off between holding more inventory and risking a stockout in plain language, using a small-business example. - Calculate a simple reorder point from scratch — lead-time demand plus a safety-stock buffer — for a realistic small-business situation. - Explain how safety stock buffers against variable demand and lead times, and choose a buffer size that balances stockout risk against holding cost. Teaching notes: Use the Pour Over Club scenario — have the student calculate a reorder point from scratch (lead-time demand plus a safety-stock buffer) for a featured origin, naming safety stock explicitly and accounting for variable demand and roaster lead times. When offering reorder/buffer options to choose between, keep them close together so the trade-off is genuinely hard — avoid obviously-wrong extremes. #### 22. Forecasting and uncertainty - Produce a simple moving-average forecast for a small business with a spreadsheet or a free AI tool, letting the tool do the arithmetic. - Identify the uncertainty around the forecast (range, not a single number) and decide how to plan against it — buffer, hedge, or accept the risk — rather than just trusting the point estimate. Teaching notes: Use the Pour Over Club scenario — produce a moving-average forecast for upcoming orders using a real free tool (spreadsheet or AI chat), letting the tool do the arithmetic. Keep the focus on managing the uncertainty around the forecast and the planning trade-offs it forces, not on the calculation itself. #### 23. Choosing a trade-off position - Map a decision onto the five trade-off axes (efficiency, quality, responsiveness, customer experience, scalability) and choose a defended position. - Name explicitly what the chosen position sacrifices and why that sacrifice is right for this business right now. Teaching notes: Don't use the unit scenario — let the student pick a decision from a context they know so the trade-off axes feel like their own call. #### 24. AI as a decision co-pilot - Use a free AI tool to frame an operational decision and stress-test its uncertainty before you commit — having the AI surface hidden assumptions, alternative readings, and failure modes while you, the human, own the final call. - Produce a short written decision that cites the AI input, names its limits, and defends the human-owned call. Teaching notes: Don't use the unit scenario — let the student frame their own decision for AI stress-testing so the human-owned judgment feels real. #### 25. Challenge: forecast, decide, defend - Given a realistic small-business demand scenario, run the full operational decision loop: frame the problem, gather one quantitative and one qualitative read, forecast demand with uncertainty, name the supply and capacity constraints, and recommend an operational response that names its trade-off position. - Defend the recommendation in writing against at least one realistic counter-argument, citing one quantitative read and one AI-supported stress-test of your own thinking. - Reflect on one specific moment from a small business you know where this unit's decision loop would have changed the outcome. Teaching notes: Use the Pour Over Club scenario — forecast next month's volume with uncertainty, name the supplier lead-time and inventory constraints, recommend an operational response, and defend the trade-off with one quantitative read and one AI-supported check. ### Unit 4: Improving the system #### 26. Variability and the hidden cost - Identify variation (inconsistency in time, output, or quality) as a hidden cost of a small-business process and propose one upstream-design fix rather than a downstream check. - Define defect rate and first-time-right rate in plain language and read what each one says about a process. Teaching notes: Use the Tasklane scenario — define defects such as repeated login failures, unclear onboarding steps, unresolved tickets, reopened tickets, or incorrect support answers. #### 27. Quality designed in, not inspected in - Distinguish designing quality into a process from inspecting quality at the end, using a small-business example. - Explain why defects come from how the work is designed (not from people trying less hard), and identify one upstream design change that would prevent a recurring defect. Teaching notes: Use the Tasklane scenario — separate where the team is doing inspection (catching bad tickets after they exist) from where they could change the design (preventing the bad ticket from ever opening). #### 28. Lean thinking and waste - Identify value-adding versus non-value-adding steps in a small-business workflow from the customer's point of view. - Name common categories of operational waste (waiting, overprocessing, defects, extra motion, excess inventory) using familiar small-business examples, and spot at least three forms of waste in a described workflow. Teaching notes: Use the Tasklane scenario — separate value-adding support from waste (repeated explanations, duplicate tickets, rework, unnecessary escalation, waiting). #### 29. Root cause analysis - Apply a structured root cause technique (5 Whys or simple fishbone) to a recurring small-business problem. - Distinguish a true root cause from a convenient surface cause and justify the distinction. Teaching notes: Use the Tasklane scenario — run 5 Whys or a fishbone on why users keep opening the same ticket type. Treat fishbone rib placement as interpretive: a cause can reasonably sit on more than one rib (e.g., Process vs Tools/Tech) depending on framing, so don't present one classification as the single correct answer. Include at least one case where the root cause turns out to be a people or behavior cause rather than a systems or design cause, so students don't assume the answer is always design. #### 30. Improvement loops with KPIs - Apply a PDCA-style cycle (plan, do, check, act) to a realistic small-business improvement, naming what each step contains. - Choose a confirming KPI that would show the improvement worked — including at least one measure of designed-in quality or variation (e.g., defect rate, first-time-right rate, throughput per shift) — and a balancing KPI that would catch a side effect moving in the wrong direction. Teaching notes: Use the Tasklane scenario — plan a PDCA loop and choose a confirming KPI and a balancing KPI (e.g., first-response time and reopened-ticket rate). At the Check step, give the student mixed or contradictory results (e.g., the confirming KPI improves while the balancing KPI worsens) so the Act decision requires real judgment rather than an obvious 'it worked'. #### 31. AI for KPI monitoring - Use a free AI tool to monitor a small-business KPI trend and flag where to look first — having the AI propose candidate anomalies or drift in the numbers, then investigating root cause yourself rather than acting on the AI's flag blindly. - Critique the AI's suggestions and choose which to pursue in a PDCA-style next loop. Teaching notes: Use the Tasklane scenario — feed a ticket-volume or first-response-time KPI trend into a real free AI chat tool and probe its proposed anomalies. #### 32. Challenge: run a full improvement loop - Plan, simulate, and write up a full PDCA-style improvement loop for a recurring small-business problem: diagnose the issue, identify the root cause, design an upstream fix, choose 1–2 KPIs (including a designed-in-quality KPI), evaluate the result, and recommend the next loop. - Decide what you would do next if the loop did not work, demonstrating that improvement is a habit, not a one-shot fix. - Reflect on one specific moment from a small business you know where this unit's improvement loop would have changed the outcome. Teaching notes: Use the Tasklane scenario — diagnose, root-cause, design an upstream fix, run a PDCA loop, name the KPI, and explain what you would do next if the first loop doesn't work. ### Unit 5: Leading and communicating operations #### 33. Cross-functional friction and alignment - Identify the operational friction created when marketing, finance, or customer service makes a decision without operations input. - Recommend a cross-functional resolution that names the operational trade-off, the affected KPIs, and which function owns which decision right. #### 34. Communicating to non-operations audiences - Structure an operational recommendation for a non-operations audience using a clear headline, the trade-off, the evidence, and the next step. - Translate operational vocabulary (bottleneck, capacity, utilization, KPI, variability) into plain business language without losing the point. #### 35. Handling pushback and building buy-in - Use a structured move (surfacing trade-offs, naming the decision rights, agreeing on the KPI of success) to drive a cross-functional team toward an operational decision. - Defend a chosen position against at least one teammate's counter-argument and name one realistic source of resistance to the change and how you would address it without abandoning the human-owned judgment. Teaching notes: The tutor should play the role of one or more teammates with different priorities (e.g., a marketing lead, a finance lead, a customer-service lead) so the learner has to actually negotiate the call and address pushback. #### 36. Capstone, part 1: diagnose the operation - For a fresh small-business case, read the system and observe the flow end to end — how value is created, in what order, and where work moves and waits. - Identify the bottleneck and estimate capacity and utilization at it, using one quantitative read (capacity OR a KPI snapshot). - State the core operational problem as a specific, evidence-backed gap that the recommendation in part 2 must address. Teaching notes: Meridian Clinics is a four-clinician primary-care practice serving a growing suburban community. It is open Monday–Friday, 8:00 a.m.–5:00 p.m., with a one-hour lunch closure. The clinic has six exam rooms, two medical assistants (MAs), one front-desk coordinator, and one checkout/referral coordinator. Patients book 20-minute clinician appointments. Before an appointment, the front desk checks the patient in, an MA takes vitals and updates the record, the patient waits in an exam room for the clinician, the clinician sees the patient, and the MA or checkout coordinator completes follow-up, referrals, and payment. Demand is heaviest from 8:00–10:30 a.m. and 3:00–5:00 p.m. Meridian normally books 88 appointments per day. Its new AI scheduling and overbooking tool predicts no-shows from past attendance and has added two extra bookings per clinician each day; 96 patients are now booked. On a typical day, about 8% do not attend, so about 88 patients arrive. The tool does not account for late arrivals, incomplete intake information, room turnover, or the fact that some visits run longer than scheduled. A clinician spends an average of 20 minutes with a patient, which gives four clinicians theoretical capacity for 96 visits in the eight clinical hours. Yet each patient occupies an exam room for about 35 minutes on average: 8 minutes for intake and vitals, 20 minutes with the clinician, and 7 minutes for documentation, cleaning, and handoff before the room can be used again. Six rooms therefore provide capacity for about 82 completed visits per day. At peak periods, all rooms are occupied, MAs wait to room patients, clinicians sometimes wait for a room, and patients wait 25 minutes on average after check-in; 22% wait more than 30 minutes. The clinic's on-time-start rate has fallen from 81% to 58%, while patient complaints about waiting have doubled. Finance supports the overbooking because it has increased completed visits by roughly 6% and reduced unused clinician time. The lead clinician says rushed visits and delayed documentation are harming quality. The operations director wants a recommendation that improves flow without simply adding staff or removing all overbooking. Begin by fully presenting and explaining these fixed facts, including the operational context, workflow, available evidence, room-capacity constraint, AI tool limitations, stakeholder positions, and decision constraint. Do not ask the learner to diagnose the operation or guess at solutions until they understand the case's facts and scope. Then use the fixed facts to trace the patient flow, identify and support the bottleneck with a quantitative read, and state the core operational problem as a specific evidence-backed gap that part 2 must address. Communicate the diagnosis clearly. #### 37. Capstone, part 2: decide, improve, and win adoption - Building on the part 1 diagnosis, choose a defended trade-off position and design at least one improvement loop with one KPI (including at least one upstream-design fix), stress-testing the decision with at least one AI-supported check and citing where the AI helped and where the human overrode it. - Communicate the recommendation to a cross-functional leadership group, translating operational vocabulary into business language with a clear headline, trade-off, evidence, and next step. - Address the most likely pushback from at least two named stakeholder roles (e.g., the owner, a clinician lead, a finance lead) using the cross-functional debate moves from this unit, and name what each role needs to see to buy in. Teaching notes: Meridian Clinics is a four-clinician primary-care practice serving a growing suburban community. It is open Monday–Friday, 8:00 a.m.–5:00 p.m., with a one-hour lunch closure. The clinic has six exam rooms, two medical assistants (MAs), one front-desk coordinator, and one checkout/referral coordinator. Patients book 20-minute clinician appointments. Before an appointment, the front desk checks the patient in, an MA takes vitals and updates the record, the patient waits in an exam room for the clinician, the clinician sees the patient, and the MA or checkout coordinator completes follow-up, referrals, and payment. Demand is heaviest from 8:00–10:30 a.m. and 3:00–5:00 p.m. Meridian normally books 88 appointments per day. Its new AI scheduling and overbooking tool predicts no-shows from past attendance and has added two extra bookings per clinician each day; 96 patients are now booked. On a typical day, about 8% do not attend, so about 88 patients arrive. The tool does not account for late arrivals, incomplete intake information, room turnover, or the fact that some visits run longer than scheduled. A clinician spends an average of 20 minutes with a patient, which gives four clinicians theoretical capacity for 96 visits in the eight clinical hours. Yet each patient occupies an exam room for about 35 minutes on average: 8 minutes for intake and vitals, 20 minutes with the clinician, and 7 minutes for documentation, cleaning, and handoff before the room can be used again. Six rooms therefore provide capacity for about 82 completed visits per day. At peak periods, all rooms are occupied, MAs wait to room patients, clinicians sometimes wait for a room, and patients wait 25 minutes on average after check-in; 22% wait more than 30 minutes. The clinic's on-time-start rate has fallen from 81% to 58%, while patient complaints about waiting have doubled. Finance supports the overbooking because it has increased completed visits by roughly 6% and reduced unused clinician time. The lead clinician says rushed visits and delayed documentation are harming quality. The operations director wants a recommendation that improves flow without simply adding staff or removing all overbooking. Start by restating and explaining this complete fixed case, including the operational context, workflow, available evidence, room-capacity constraint, AI tool limitations, stakeholder positions, and decision constraint. Do not ask the learner to make decisions, recommend improvements, or anticipate stakeholder objections until they understand the facts and scope and can ground their work in the established part 1 diagnosis. Then make and defend an operational decision, propose an improvement loop and KPI, and use a real free AI tool to stress-test the decision while retaining human judgment. Build stakeholder adoption by presenting a clear business recommendation to the operations director, lead clinician/medical director, and finance lead, addressing their likely pushback and the evidence each needs to support the change. Produce the recommendation and communication in a shared document. ## How to use this document You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates. Start with lesson 1 unless the learner asks for a specific lesson. Keep track of which lesson you are on. ## When a lesson is done Teach one lesson at a time. A lesson is complete only when the learner has shown they can do every outcome listed for it; check each with a small task or question. When it is complete: 1. Say clearly: "Lesson N complete: ." (use the lesson's number and title). 2. Give a two-line recap of what they learned. 3. Name the next lesson, say "Lesson N+1 of 37: ", and ask if they're ready to start it. Start it only when they say yes. After the last lesson, congratulate them on finishing the course.