Start with something familiar
Imagine planning a meal for a group of friends. You need to know who is coming, what each dish uses, which food is already usable, and when the oven is free. A full pantry does not help if the missing ingredient arrives after dinner. Nor does a long recipe prove that you can cook it before guests arrive. Manufacturing planning applies that same care to parts, unfinished work, available time, and the promises made to customers.
Define the planning boundary
A finished requirement is stated in good bottles. A machine start is a unit entering the process. These are not equal when some starts fail inspection. If expected yield is ninety seven percent, each start contributes an average of zero point nine seven good bottles across repeated comparable runs.
To target twenty nine good bottles, divide twenty nine by zero point nine seven and round starts upward. The result is thirty starts. Expected good output is twenty nine point one. This is an average-based planning estimate; a single batch can produce fewer than twenty nine good bottles. Keep a service recovery option when the due date matters.
Calculate with explicit rules
Do not call a three percent yield loss the same thing as adding three percent to the desired output. Dividing by the retained fraction and multiplying by one plus a loss fraction are different conventions. Their difference grows with losses. Define the convention and denominator in the data dictionary rather than relying on a label such as scrap percent.
Fixed startup scrap is another separate mechanism. If two input units are consumed before productive starts, add those two after calculating productive starts under this lesson’s convention. They are not also included in the observed yield. If the yield history already included setup loss, adding it again would overstate input needs.
Check before committing
Rounding belongs at the physical decision boundary. You can calculate expected output as a decimal, because it is an expectation, but you cannot start a fraction of a bottle in this case. For multiple stages, determine whether intermediate units are indivisible and whether rounding occurs at each stage. State that rule before comparing answers.
Use scenarios to expose the risk. A lower yield raises input needs and may also raise run time. A plan that covers material at the average yield can still fail on capacity. Keep the good-unit target unchanged while varying yield so the effect of the process assumption is visible.
- Read case inputs
- Calculate the baseline
- Change one assumption
- Check and decide
Northstar worked case
All data below are fictional and complete for this calculation.
| Input group | Exact case data |
|---|---|
| 1 | Good target29; yield97%; productive starts are whole bottles. |
| 2 | Each productive start needs1 body; optional fixed startup scrap2 bodies, outside yield. No other losses. |
- 1. Starts=ceil(29/0.97)=30.
- 2. Expected good=30×0.97=29.1; expected reject0.9.
- 3. With separate setup scrap2,body input=30+2=32.
97% starts[30,42,52,68]; expected good[29.1,40.74,50.44,65.96]; bodies[32,44,54,70].
Assumptions
- Only the stated supply and demand exist; no hidden safety stock or open orders.
- Timing, yield, lot size, and setup follow the explicit case; do not import another lesson’s assumptions.
Where this can fail: This deliberately small model checks planning logic. It cannot establish supplier reliability, customer priority, or a statistical service guarantee.
Build, test and explain a plan
Northstar asks you to build a reusable planning model, not just report one answer. Use only the inputs below and the worked example. Good target29; yield97%; productive starts are whole bottles. Each productive start needs1 body; optional fixed startup scrap2 bodies, outside yield. No other losses. Keep full precision in intermediate calculations and round only where the case requires whole units. Use separate tabs or clearly labeled paper grids for the baseline and each scenario. The eight hours are a self-paced practice estimate, not an experimentally validated duration.
Use paper or a spreadsheet. The estimates below include the study and worked example already on this page.
| Estimated hours | Activity |
|---|---|
| 0.5 | Study the terms and mark each input with its unit, date, and status. Write a half-page statement of the boundary of the model and list facts that are deliberately excluded. |
| 0.5 | Recalculate every worked-example step by hand. Compare the hand calculation with your model and explain any difference before moving on. |
| 3 | Build rows for good targets[29,40,50,65] and yield97%; include starts, expected good, expected reject, and bodies including2 fixed scrap. Build formulas, a visible input table, and a reconciliation row. Add notes so another learner can reproduce the model without asking you for missing data. |
| 2 | Repeat all targets at95% and90%; show incremental starts and explain why no row guarantees its target. Save each scenario separately. Compare quantities, dates, and any unresolved shortages. Trace the change from its input through every affected calculation. |
| 1 | Write a one-page decision memo naming the baseline result, the preferred action under each scenario, the remaining uncertainty, and the evidence required before execution. Do not invent costs or customer priorities. |
| 1 | Create a five-minute teach-back with one diagram and one checked number. Then use a second independent calculation to audit the model and revise unclear assumptions. |
Your deliverables
- An auditable input and calculation table with baseline and scenario tabs.
- A comparison table with numerical checks, dates, and unresolved gaps.
- A decision memo and a short teach-back using the supplied facts.
Check your practice work
- 97% starts[30,42,52,68]; expected good[29.1,40.74,50.44,65.96]; bodies[32,44,54,70].
- 95% starts[31,43,53,69];90% starts[33,45,56,73].
- For29 at90%,expected good29.7, productive rejects3.3, body input35 with fixed scrap.
Keep these three ideas
- Translate good-output targets into starts without treating an expectation as a promise.
- A numerical answer must retain its unit, timing, and assumptions.
- Use the scenario checks to test the model before using it to support a decision.
Common mistakes
- Combining values that refer to different dates or different physical units.
- Treating a modeled proposal or expected output as confirmed usable supply.
Optional video study
Translate good-output targets into starts without treating an expectation as a promise.
- A finished requirement is stated in good bottles
- Starts=ceil(29/0.97)=30.
- 97% starts[30,42,52,68]; expected good[29.1,40.74,50.44,65.96]; bodies[32,44,54,70].
First half: definitions and baseline.
Open the module media & quizPut this idea to use
Carry your assumptions into a practical tool, then compare your result with a short guide.
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