Start with something familiar
When estimating bread to buy, you can copy last week, average recent weeks, or gradually adjust your usual amount after each trip. Copying reacts quickly but can overreact to a party. A long average smooths the party but adapts slowly when the household grows. A weighted update offers another memory rule. The question is not which rule sounds cleverest, but which performs well when applied before you know the next shopping outcome.
Words to know: Naive forecast, Moving average, Exponential smoothing.
Three memory rules
Naive uses the latest actual. A three-period moving average takes the latest three actuals. Simple exponential smoothing updates a level: alpha times the latest actual plus one minus alpha times the previous level. The next forecast equals the updated level. Specify the initial level so another analyst can reproduce every row.
With actuals 42,48,54, initial level 42 before week one, and alpha 0.5, updated levels are 42,45,49.5. Week-four forecasts are naive 54, moving average 48, and smoothing 49.5. Their differences reflect different weights, not an arithmetic contradiction. They are alternatives until validation gives grounds to prefer one.
Put forecasts before observations
Score each week's forecast before updating its level with that week's actual. If a same-row formula uses the actual to generate the current forecast, it has seen the answer. Label origin and target beside each formula. At an origin after week three, week-four actual is unavailable, even if it appears elsewhere in your workbook.
Simple smoothing without trend or seasonality gives the same level for all future horizons from one origin. Do not feed forecasts back as if they were new observations. A flat baseline is useful as a reference even when demand later changes. More complex methods should demonstrate a practical improvement over that reference.
Choose responsiveness fairly
Higher alpha emphasizes the newest actual. A shorter moving window likewise responds faster but can magnify a temporary spike. Compare a small declared candidate set on historical origins and leave a final holdout untouched. A setting chosen to match a manager's preferred next number has not been fairly tested.
Retain decimals when comparing forecasts. Expected demand of 49.5 is meaningful even though shipments need whole units. Apply operational rounding separately. Also keep promotion adjustments and old backlog outside the baseline method, so users can see whether a changed plan came from the statistical update or a separate business decision.
- Define target
- Check evidence
- Calculate
- Challenge assumptions
- Record decision
Build three transparent baselines — worked case
All inputs below describe fictional Northstar bottles. Keep the stated units and timing when reproducing the calculation.
| Week | Actual | Updated alpha-0.5 level |
|---|---|---|
| 1 | 42 | 42 |
| 2 | 48 | 45 |
| 3 | 54 | 49.5 |
- 1. Naive uses latest actual 54.
- 2. Moving average = (42 + 48 + 54)/3 = 48.
- 3. New level = 0.5×54 + 0.5×45 = 49.5.
Week-four forecasts: 54,48,49.5 respectively.
Assumptions
- Periods and quantities use the definitions stated in this example.
- Only explicitly supplied events or adjustments are included.
Where this can fail: The arithmetic checks the stated assumptions; it does not establish that uncertain future events will occur.
Build, test and explain a plan
A actuals over twelve weeks: [42,48,54,48,51,57,54,60,57,63,60,66]. B: [20,21,19,20,22,18,20,21,19,20,22,18]. Initial levels before week one are 42 and 20. Candidate alphas 0.2,0.5,0.8. All units are bottles unless stated otherwise. Use a spreadsheet or ruled paper. Eight hours includes construction, comparison, writing, and teach-back; this is a planning estimate, not an experimentally validated completion time.
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 lesson and write a data dictionary defining every field, time bucket, unit, and cutoff. Separate observed facts from assumptions. Identify two ways a colleague could misread the target and rewrite its definition to prevent those errors. |
| 0.5 | Reproduce the worked example independently, showing formulas and intermediate values. Compare against the answer and keep a correction log explaining each difference. Check one result a second way using a total, balance, or direct manual calculation. |
| 3 | Build naive, three-week average, and three smoothing columns for both SKUs. Score the shared targets weeks four to twelve using absolute error. Publish week-thirteen forecasts with level-update and forecast columns distinct. Keep inputs separate from formulas, label every origin and target, and add independent row and total checks. Produce an annotated chart with at least two observations tied to specific rows; include a brief explanation of why each check matters. |
| 2 | Replace only A week six with 87, then separately add twelve to A weeks seven through twelve. Compare responsiveness to a one-week spike versus a lasting shift. Preserve each variant separately. Create an output comparison table and explain which changed assumption caused each movement. Identify a scenario that affects timing differently from totals and state what the planner should do with that distinction. |
| 1 | Write a 400-word decision memo using your own numerical outputs. State the proposed action, evidence, two limitations, an owner for the next check, and a measurable condition that would change your recommendation. Attach the model so a reader can audit every cited value. |
| 1 | Prepare a five-minute teach-back using one worked row and one scenario comparison. Explain it aloud to a colleague or recording. Write three skeptical questions, answer them with evidence, and revise the memo or model where your explanation exposes an unsupported assumption. |
Your deliverables
- Auditable input and formula model with checks
- Scenario comparison and annotated chart
- Decision memo, correction log, and teach-back notes
Check your practice work
- A week-four naive, average, alpha-0.5 forecasts are 54,48,49.5.
- A week-thirteen naive 66; three-week mean 63.
- A alpha-0.5 week-thirteen forecast is 62.9912109375.
- B week-thirteen naive 18; three-week mean 20.
- The extra thirty in A week six raises alpha-0.5 week-seven forecast 15, week-eight 7.5, and week-nine 3.75 with later actuals fixed.
Keep these three ideas
- Calculate naive, moving-average, and exponential-smoothing forecasts with correct timing.
- Keep original evidence distinct from modeled values and assumptions.
- A precise output remains conditional on definitions and timing.
Common mistakes
- Overwriting the original evidence after a revision.
- Reporting a number without its unit, period, cutoff, and assumptions.
Optional video study
Calculate naive, moving-average, and exponential-smoothing forecasts with correct timing.
- Three memory rules
- Put forecasts before observations
- Choose responsiveness fairly
One half of the module narration, with a distinct Northstar calculation and a bridge to the paired lesson.
Open the module media & quizPut this idea to use
Carry your assumptions into a practical tool, then compare your result with a short guide.
Open a related planning toolRead the related planning guidePractice with the planning gameThis saves a completion marker in this browser only. It does not verify time spent.