Start with something familiar
Your travel time may differ between Monday and Friday every week, while roadworks gradually make all days slower. A crash can create one spike without changing either the weekly rhythm or the long-run level. Looking only at the last trip confuses these stories. A planner asks what repeats, what drifts, and what was unusual before extending a line into the future. The same distinction matters when a seasonal sales peak happens during growth.
Words to know: Trend, Seasonality, Naive forecast.
Plot the relevant time scale
Put observations in order and inspect the decision frequency. Monthly totals can hide weekly stockouts, while daily movements can make a stable monthly business appear noisy. Mark holidays, promotions, closures, and distribution changes. An annotation proposes an explanation; it does not prove an event caused the observed movement.
Level is a typical amount, trend is persistent movement, and seasonality repeats at a known calendar cycle. Two high months do not establish annual seasonality. More than one cycle helps distinguish recurrence from coincidence. With little history, acknowledge weak evidence and use cautious scenarios instead of declaring a stable pattern.
Compare like periods
Northstar quarterly units are 20,30,40,30 in year one and 24,34,44,34 in year two. Every matching quarter rises four units while the shape repeats. Seasonal naive repeats year two, predicting 24 next first quarter. Adding another four gives a growth scenario of 28, which assumes persistence rather than proving it.
An additive description uses unit differences; a multiplicative description uses relative changes. Constant four-unit increments favor an additive story. If every quarter rises twenty percent, a relative story may be clearer. Neither should be chosen merely for an attractive fit. Compare forecasts issued before their outcomes and consider the business mechanism.
Connect patterns to operations
Repeating the prior season can capture peaks a moving average would smooth away. Extending a trend can overshoot if growth slows. Treat these as competing explanations and test at the horizon matching replenishment or staffing. Keep decimals during comparison; operational rounding can be applied later under an explicit rule.
Calendar alignment matters. Holidays move between weeks and periods contain different numbers of selling days. Product launches can change the customer mix and invalidate old seasonal shares. Ask which part of the old pattern should survive, which changed, and what future observations would make you revise the forecast.
- Define target
- Check evidence
- Calculate
- Challenge assumptions
- Record decision
Recognize level, trend, and seasonality — worked case
All inputs below describe fictional Northstar bottles. Keep the stated units and timing when reproducing the calculation.
| Quarter | Year one | Year two |
|---|---|---|
| Q1 | 20 | 24 |
| Q2 | 30 | 34 |
| Q3 | 40 | 44 |
| Q4 | 30 | 34 |
- 1. Annual totals are 120 and 136.
- 2. Each matching quarter rises four.
- 3. Seasonal naive year three is [24,34,44,34].
- 4. Additive-growth scenario is [28,38,48,38].
Year-three totals: seasonal naive 136; persistent four-unit growth 152.
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
Use the example as SKU A. SKU B year-one quarters are [10,15,20,15], year-two [12,18,24,18]. All periods are unconstrained demand. Forecast four year-three quarters. 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 a twelve-row quarterly table for both SKUs. Compare last-value naive, seasonal naive, additive same-quarter growth, and multiplicative same-quarter growth. Mark actual and forecast boundaries. 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 | For A, test a one-off additional ten units in year-two Q3 and a separate permanent five-unit shift starting year-three Q1. For B test growth slowing from twenty percent to ten percent. 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 seasonal naive total 136; additive total 152. Last-value naive is [34,34,34,34], also total 136 but wrong seasonal shape if recurrence holds.
- B seasonal naive total 72; twenty-percent growth [14.4,21.6,28.8,21.6], total 86.4.
- B ten-percent growth total 79.2.
- A seasonal naive plus five each quarter is [29,39,49,39], total 156.
- Repeating a one-off Q3 uplift requires an explicit recurrence assumption.
Keep these three ideas
- Distinguish repeated calendar patterns from gradual change and isolated events.
- 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
Distinguish repeated calendar patterns from gradual change and isolated events.
- Plot the relevant time scale
- Compare like periods
- Connect patterns to operations
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.
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