IB DP Business Management: Sales forecasting (HL only) – Practice Questions

These are original questions written for Marlbridge, for revision and practice on this content. They are not reproduced past-paper questions, and they do not replicate the exam’s exact structure, question count or mark tariffs – the IB holds copyright in its own papers. Use these alongside the official past papers available through your school or the IB store.

These questions cover section 4.3, Sales forecasting (HL only), of the IB Business management guide, first assessment 2024, together with the toolkit’s simple linear regression (HL only). All businesses and figures are invented. Where a question uses the line of best fit, the standard least-squares form y = a + bx is used, with b = Sxy / Sxx and a = ȳ − b·x̄.

Related: study guide, revision notes, Unit 4 overview, course hub, checklist and the free diagnostics.

Questions

1. Define the term “sales forecast”. [2]

2. Outline two benefits to a business of sales forecasting. [4]

3. Mawgan Outdoor sells family tents. Annual sales over five years were as follows.

Year (x)	1	2	3	4	5
Tents sold (y)	84	91	99	105	113

Calculate the line of best fit y = a + bx for these data, and use it to forecast sales in year 6. [6]

4. For Mawgan Outdoor in question 3, the correlation coefficient is r = 0.999. Explain what this suggests about the forecast, and give one reason why the owner should still be careful. [3]

5. Dravik Audio sold the following numbers of a portable speaker.

Year (x)	1	2	3	4	5	6
Speakers sold (y)	240	180	265	170	255	200

The line of best fit is y = 225.33 − 2.00x and r = −0.09. Explain why the owner should be cautious about using this line to forecast sales. [3]

6. Stennack Tiles forecast sales of 1,250 packs of floor tiles for the spring quarter. Actual sales were 1,075 packs.

(a) Calculate by what percentage the forecast exceeded actual sales. Give your answer to 1 decimal place. [2] (b) Explain one consequence for Stennack Tiles of forecasting more sales than it achieved. [3]

7. Isenby Greenhouses sells garden greenhouses. Using five years of data, with x as the year number, it found the line of best fit y = 231.6 − 12.4x, where y is the number of greenhouses sold.

(a) Forecast sales in year 6. [2] (b) The owner extrapolates the line to year 19. Calculate the forecast and explain why the result shows a problem with extrapolating far beyond the data. [3]

8. Marquand Stationers sold 4,800 packs of notebooks last year. Its sales forecast predicts an increase of 15% this year. Its workshop can produce at most 5,000 packs a year.

(a) Calculate the forecast sales. [2] (b) Calculate the shortfall between forecast sales and production capacity. [1] (c) Analyse two implications of this forecast for Marquand Stationers. [4]

9. Olwen Bakehouse bakes fresh loaves each morning. A manager used the last eight weeks of sales to forecast 2,000 loaves for the next week. A new competitor then opened nearby, and Olwen sold 1,840 loaves. Unsold loaves cannot be sold the next day and cost the bakery 1.20 currency units each to make. The forecast took under an hour.

Evaluate the usefulness of sales forecasting to Olwen Bakehouse. [8]

10. Mawgan Outdoor (question 3) must order tent fabric four months before the camping season starts. The owner plans to order enough fabric for the 120 tents forecast for year 6. Camping demand is affected by the weather and by the cost of holidays.

Justify whether the owner should order fabric for exactly 120 tents. [6]

11. Dravik Audio (question 5) must decide how many speakers to produce next year. The owner is considering using the line of best fit and ordering 211 speakers’ worth of components.

Recommend a course of action for the owner. [6]

Answers

1. A sales forecast is a prediction of the sales a business expects [1] over a stated future period, in units, revenue or both [1]. [2] Examiner insight: A definition that says only “an estimate of sales” is incomplete without reference to the future period.

2. Any two benefits, each identified and outlined:

• Production and stock planning: the business can schedule workers and order materials for the expected volume [1], which reduces stockouts and excess stock [1].
• Cash flow planning: expected sales revenue shows when money will come in [1], so the business can plan payments to suppliers or arrange borrowing [1].
• Other valid benefits are accepted. [4] Examiner insight: “Outline” asks for a brief account, so a named benefit with no consequence tends to earn only part of the credit.

3. x̄ = (1 + 2 + 3 + 4 + 5) / 5 = 3 and ȳ = 492 / 5 = 98.4 [1] Deviations from x̄ are −2, −1, 0, 1, 2. Deviations from ȳ are −14.4, −7.4, 0.6, 6.6, 14.6. Sxy = 28.8 + 7.4 + 0 + 6.6 + 29.2 = 72.0 and Sxx = 4 + 1 + 0 + 1 + 4 = 10 [1] b = Sxy / Sxx = 72.0 / 10 = 7.2 [1] a = ȳ − b·x̄ = 98.4 − 7.2 × 3 = 76.8 [1] Line: y = 76.8 + 7.2x. Year 6: y = 76.8 + 7.2 × 6 = 120.0 [1] Forecast: 120 tents in year 6 [1]. [6] Examiner insight: Show Sxy, Sxx, b and a separately; a final line with no working leaves nothing to credit if one slip occurs.

4. r is very close to +1, so there is a very strong positive correlation [1]. The points lie close to the line, so a forecast for the next year is likely to be reasonably reliable [1]. Caution: the line describes the past only, and a change such as a new competitor, poor weather or a rise in prices could move sales away from the trend [1]. [3] Examiner insight: Strong answers separate a good fit to the past from a promise about the future.

5. r is close to zero (−0.09), so there is almost no straight-line relationship between year and sales [1]. The line therefore explains very little of the variation, so its forecast (about 211 for year 7) is hardly better than guessing the average [1]. Actual sales swing between 170 and 265 from one year to the next, so the real figure could be far from the line [1]. [3] Examiner insight: Quote r and link it to the shape of the data in the case.

6. (a) Difference = 1,250 − 1,075 = 175 packs [1]. Percentage of actual sales = 175 / 1,075 × 100 = 16.3% [1]. [2] (b) Stennack Tiles may have bought or made too many tiles [1]. The unsold stock ties up cash and needs storage space [1]. This reduces the cash available to pay suppliers or invest, and it may force price cuts to clear the stock [1]. [3] Examiner insight: A consequence earns most credit when it runs from forecast error to stock to cash or profit.

7. (a) y = 231.6 − 12.4 × 6 = 231.6 − 74.4 = 157.2 [1], so about 157 greenhouses [1]. [2] (b) y = 231.6 − 12.4 × 19 = 231.6 − 235.6 = −4.0 [1]. Sales cannot be negative, so the line cannot be valid this far out [1]. Markets change over long periods, so a line from five years of data should not be extended far [1]. [3] Examiner insight: Show the substitution, then explain why the negative figure is impossible.

8. (a) Forecast = 4,800 × 1.15 [1] = 5,520 packs [1]. [2] (b) Shortfall = 5,520 − 5,000 = 520 packs [1]. [1] (c) Any two developed implications:

• Operations: the shortfall is about 9.4% of the forecast (520 / 5,520), so Marquand must expand capacity, outsource or accept unmet demand [1]. Missed orders could lose customers to rivals [1].
• Finance: expanding capacity or outsourcing needs spending before the sales revenue arrives [1], so the cash flow forecast and any borrowing plans need updating [1].
• Marketing: limit promotion until capacity is secured. [4] Examiner insight: “Analyse” asks for the parts to be broken down and linked to the case. Use the 520-pack shortfall in your reasoning rather than writing about capacity in general.

9. Indicative content, with a maximum of 8 marks:

• Benefit developed [2]: the forecast tells the manager how many loaves to bake each morning, which helps limit waste and avoid selling out. Using eight weeks of data gives some evidence rather than a guess.
• Second benefit [1]: it is quick and cheap to prepare, so the benefit is large compared with the cost.
• Limitation developed [2]: the forecast relied on past data and could not foresee the new competitor. Actual sales were 160 loaves lower, so the forecast was 8.7% above actual sales (160 / 1,840 × 100). The 160 unsold loaves cost 160 × 1.20 = 192 currency units in wasted baking.
• Second limitation [1]: bread is perishable, so errors cost more here than for goods that can be stored.
• Judgement [2]: the forecast is still worth making because it is cheap and gives a base for daily baking. The bakery should adjust it each week, keep the baking quantity flexible and review it when a competitor appears, rather than relying on the number alone. [8] Examiner insight: Answers that weigh both sides and then commit to a reasoned judgement tend to do better than answers that list points and stop.

10. Indicative content:

• Case for ordering 120 [2]: five years of data fit a line almost exactly (r = 0.999), and the long lead time means the order must be placed before sales are known, so a forecast is better than guessing. The forecast of 120 is also a short step beyond the data.
• Case against [2]: weather and holiday costs can change demand and the line does not include them. The order is placed four months ahead, so there is time for conditions to change, and unused fabric ties up cash.
• Justified decision [2]: the owner should use 120 as the central estimate but not treat it as certain. For example, order most of the fabric now and keep a smaller second order for later, if the supplier allows. [6] Examiner insight: “Justify” asks for valid reasons supporting a conclusion, so end with a clear decision and give the reason for it rather than only listing for and against.

11. Indicative content:

• Weakness of the line [2]: r = −0.09 shows almost no trend, and sales moved between 170 and 265, a range of 95, so ordering to the 211 forecast risks stockouts and unsold stock.
• Recommended action [2]: do not rely on the regression line. Find out why high and low years differed, and use shorter-term forecasts or smaller, more frequent component orders so production can follow actual demand.
• Judgement [2]: this approach costs more time and may mean higher unit costs for smaller orders. It is still better than committing to a number the data does not support, since a large stock error probably costs more. [6] Examiner insight: A recommendation gains credit when it follows from evidence in the case and acknowledges a cost or risk of the course chosen.

Where marks are usually lost

• Writing general benefits and limitations that could apply to any business.
• Showing a final regression equation with no Sxy, Sxx or means, so errors cannot be traced.
• Treating r near 1 as proof that the forecast will be right, or extrapolating far without comment.
• Giving “evaluate”, “justify” or “recommend” answers with no decision or reason.
• Ignoring numbers given in the case.

Next steps

• Review the revision notes and the study guide.
• Open the course hub and the printable checklist.
• Take one of the free 10-minute diagnostics to find your weakest areas.
• Book a free trial class if you would like help from a teacher.

Frequently asked questions

Is sales forecasting HL only in IB Business Management? Yes. Section 4.3, Sales forecasting, is HL only in the IB Business management guide, first assessment 2024, and the simple linear regression tool used to calculate trends is also an HL tool.

What should you show when calculating a line of best fit? Show the means, Sxy and Sxx as well as the final equation y = a + bx, so that any error can be traced and method marks can still be awarded.

Does a correlation close to 1 mean a sales forecast will be accurate? No. A high correlation shows that past data fit a straight line closely, not that the future will follow it. Extrapolating far beyond the data without comment is a common mistake.

What are common mistakes on sales forecasting evaluate questions? Students often write general benefits and limitations that could apply to any business, ignore the numbers in the case, and give “evaluate”, “justify” or “recommend” answers with no decision or reason.

Official syllabus

IB Diploma Programme Business management guide, first assessment 2024, published by the International Baccalaureate Organization. Sections used: 4.3 Sales forecasting (HL only), and the business management tools listing for simple linear regression (HL only).