For information and reference only – not to be submitted as your own work. Use this extract to study how a Forecasting and Scheduling answer is structured and how the OEE working is laid out, then write your own answer in your own words and with your own organisation. Follow your college’s academic integrity and AI-use rules.

What this extract covers

This extract covers Question 1 (the OEE calculation) and Question 2 (capacity management) of an Irish college operations assignment on forecasting and scheduling, based on the Pizzamatic OEE and Capacity Management Assignment 1 brief. Only these two questions are covered here. The companion guide, how to calculate OEE and answer capacity management questions, explains the method step by step.

What the brief asks

Question 1: OEE of the Pizzamatic machine

Approach

OEE multiplies three ratios: availability, performance rate and quality rate (Nakajima, 1988; Slack, Brandon-Jones and Burgess, 2022). Start with the time the machine is scheduled to run, remove the stoppages, then apply the speed and quality ratios to what remains.

Working

Answer: the Pizzamatic’s OEE is approximately 74.9%.

What each loss category means

Nakajima (1988) grouped equipment losses into six big losses. Two relate to availability (breakdowns, and set-up and adjustment), two to performance (idling and minor stoppages, and reduced speed) and two to quality (defects and rework, and start-up or yield losses). In this question the 8 hours of changeovers and the 4 hours of breakdowns map directly onto the first two. Waiting for ingredients is a starved-machine stoppage; some textbooks fold it into availability, as done here, and others treat it as an idling loss. Check how your lecturer’s notes classify it, because the arithmetic is the same either way.

Where the lost hours went

The table is a useful sanity check. It reaches the same 74.9% by a different route, and it shows that the speed loss is almost as costly as all the stoppages combined.

Sanity checks and an alternative reading

• OEE must be lower than the lowest of the three factors (87%), and 74.9% is.
• Do not add the percentages or average them; the factors are multiplied.
• Some textbooks apply the 2% reject rate to the units produced rather than to time. Because output is proportional to running time and speed, the result does not change here: both routes give 74.9%.
• Round only at the end. Using 0.88 for availability gives 75.0%, which a strict marker may penalise.

How the result compares with the benchmark

An OEE of 85% is commonly cited as a world-class target for discrete manufacturing, often built from roughly 90% availability, 95% performance and 99% quality (0.90 × 0.95 × 0.99 ≈ 0.85). Treat it as a reference point and not a universal law, since sensible targets differ by process and industry (Muchiri and Pintelon, 2008). On that yardstick the Pizzamatic sits about ten percentage points short. Availability (87.8% against 90%) is close, performance (87% against 95%) is the largest gap, and quality (98% against 99%) is near target. The first improvement priority would therefore be running speed, followed by shorter changeovers and reliable ingredient supply.

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Question 2: Capacity management in your own organisation

Illustrative example: the organisation below is a composite hospital outpatient clinic, written to show structure and depth. You must describe your own workplace, or one you know well, and use real details from it.

Why capacity management matters

Capacity is the maximum level of value-added activity an operation can complete in a given period (Slack, Brandon-Jones and Burgess, 2022). In the clinic, it is the number of appointments that clinicians, rooms and equipment can deliver each week. Too little capacity produces long waiting lists, cancellations and unhappy patients. Too much leaves consultants and rooms idle while salaries and overheads still have to be paid. Capacity management is the continuous effort to balance these two costs, so it directly affects service quality, cost per patient and staff morale.

Measuring capacity and forecasting demand

The clinic measures capacity in appointment slots per session, adjusted for the real constraint, which is usually the consultant or the diagnostic room. Utilisation (slots used divided by slots available) is tracked weekly alongside the “did not attend” rate. Demand is forecast from referral data, previous years’ seasonal patterns (for example winter respiratory peaks) and known events such as a new referral pathway. Forecasts are treated as ranges, not single numbers, because uncertainty is the reason buffers are needed.

Synchronising demand and capacity

Slack, Brandon-Jones and Burgess (2022) describe three broad responses to a mismatch. Level capacity keeps staffing constant and lets a queue absorb the fluctuations. The clinic uses this for its core consultant rota, because specialist skills cannot be flexed quickly. Chase demand adjusts capacity to follow demand; the clinic does this through overtime sessions, agency nurses and extra Saturday lists during peaks. Demand management changes demand to fit capacity, through appointment booking rules, reminder texts that lower missed appointments, and moving routine follow-ups to quieter weeks.

In practice, the clinic uses a mixed plan. A level baseline is chosen at the annual aggregate planning stage, using forecast referrals and budgets. A monthly sales and operations planning (S&OP) style meeting, adapted here as an activity and capacity review between clinicians and managers, compares the forecast with the rota and decides whether to add sessions or limit bookings. Queue discipline and a prioritisation system for urgent cases protect the highest-risk patients, and a small amount of planned overbooking offsets expected non-attendance, an idea borrowed from yield management.

Queues are managed deliberately and not left to chance. Patients are triaged into urgent and routine groups, routine waits are published, and a small buffer of unbooked slots is held each day for urgent referrals. When the queue for a specialty grows beyond its target, the review meeting treats this as a signal to add capacity or reduce intake, and records the decision so that the next forecast can be compared with what actually happened. This feedback loop is what turns a plan into a managed process.

Performance measures

The clinic monitors utilisation, waiting time to first appointment, the proportion of patients seen within target, did-not-attend rate and overtime cost. Reviewing them together stops one measure being improved at the expense of another: raising utilisation to 100% would almost certainly lengthen waits.

Evaluation

The approach works because it combines a stable core with flexible edges, but it relies on accurate forecasts and willing staff. Its main weakness is dependence on overtime, which carries cost and burnout risks. Better forecasting, cross-training and more shared scheduling would reduce this reliance (Slack, Brandon-Jones and Burgess, 2022).

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How to adapt this extract

• Replace the clinic with your own organisation and use real examples, such as shift patterns, peak periods or bottleneck resources.
• Keep the maths layout for Q1 (givens, subtotals, three factors, final percentage) but check each figure against your own brief.
• State your classification of “waiting for materials” and apply it consistently.
• Name the level, chase and demand management strategies and say which ones your organisation actually uses.
• Stay close to the stated word count and keep every claim tied to a cited source or a concrete example.

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References

Muchiri, P. and Pintelon, L. (2008) ‘Performance measurement using overall equipment effectiveness (OEE): literature review and practical application discussion’, International Journal of Production Research, 46(13), pp. 3517–3535. doi:10.1080/00207540601142645.

Nakajima, S. (1988) Introduction to TPM: Total Productive Maintenance. Cambridge, MA: Productivity Press.

Slack, N., Brandon-Jones, A. and Burgess, N. (2022) Operations Management. 10th edn. Harlow: Pearson.