Capac­ity is the max­i­mum amount of goods or ser­vices an oper­a­tion can pro­duce in a given period. Capac­ity plan­ning is the process of decid­ing how much capac­ity an organ­i­sa­tion needs, when it needs it and in what form, so that future demand can be met with­out short­age or waste.

The topic mat­ters because capac­ity deci­sions are expen­sive and hard to reverse. Too lit­tle capac­ity means delays, over­worked staff and cus­tomers lost to com­peti­tors; too much means idle machines, heavy fixed costs and money locked up in unused facil­i­ties. Capac­ity plan­ning is there­fore about bal­ance, and it links the demand fore­cast to the invest­ment deci­sions of the busi­ness.

What is capac­ity?

Capac­ity sim­ply answers the ques­tion "How much can we do?" It is always stated as a rate, that is, out­put per unit of time:

  • A fac­tory that can make 1,000 bot­tles per day has a capac­ity of 1,000 bot­tles per day.
  • A col­lege that can admit 300 stu­dents a year has a capac­ity of 300 seats.
  • A hos­pi­tal with 100 beds has that as part of its ser­vice capac­ity.

When out­put is var­ied, capac­ity is often mea­sured by an input instead, such as machine-hours avail­able per week in a work­shop or seat-kilo­me­tres in an air­line.

What is capac­ity plan­ning?

Capac­ity plan­ning means decid­ing how much capac­ity is needed in the future so that demand can be met prop­erly. It answers the ques­tion: "Do we have enough abil­ity to meet cus­tomer demand?"

A com­pany should avoid:

  • too lit­tle capac­ity, which leads to short­ages, delays and unhappy cus­tomers;
  • too much capac­ity, which leads to waste, idle machines and extra cost.

Capac­ity plan­ning is usu­ally done at three lev­els:

  • Long-term (more than a year): new plants, major equip­ment, new branches.
  • Medium-term (months up to a year): hir­ing, sub­con­tract­ing, extra shifts, aggre­gate plan­ning.
  • Short-term (days and weeks): over­time, job sched­ul­ing, mov­ing work­ers between tasks.

Why capac­ity plan­ning is impor­tant

If capac­ity is too low

  • orders get delayed;
  • work­ers become over­bur­dened;
  • machines are overused and break down more often;
  • cus­tomers may go to com­peti­tors;
  • over­time cost increases.

If capac­ity is too high

  • machines stay idle;
  • work­ers are under­used;
  • fixed costs become a heavy bur­den;
  • money is blocked in unused facil­i­ties.

Good capac­ity plan­ning helps a busi­ness to meet demand, reduce cost, improve effi­ciency, use resources prop­erly and avoid both over­load and waste. It also affects the abil­ity to com­pete, because spare capac­ity lets a firm respond quickly to new orders.

Capac­ity in man­u­fac­tur­ing and ser­vices

Capac­ity is not only a fac­tory idea. In ser­vices it is often harder to man­age because ser­vices can­not be stored.

  • Hos­pi­tal: num­ber of beds, doc­tors and oper­a­tion the­atres.
  • Bank: num­ber of ser­vice coun­ters, employ­ees and cus­tomers served per hour.
  • Col­lege: num­ber of class­rooms, seats and fac­ulty avail­abil­ity.

An empty hotel room tonight can­not be sold tomor­row, so ser­vice firms rely heav­ily on reser­va­tions, pric­ing and flex­i­ble staffing to match capac­ity with demand.

Mea­sures of capac­ity

This is a very impor­tant exam point. Text­books dis­tin­guish three lev­els.

Design capac­ity

The max­i­mum pos­si­ble out­put under ideal con­di­tions, with no inter­rup­tions. Exam­ple: a machine is designed to pro­duce 100 units per hour. This is a the­o­ret­i­cal fig­ure; in real life con­di­tions are rarely per­fect.

Effec­tive capac­ity

The prac­ti­cal max­i­mum after allow­ing for planned, nor­mal inter­rup­tions such as:

  • tea and lunch breaks;
  • machine set-up and changeover time;
  • sched­uled main­te­nance;
  • qual­ity checks;
  • prod­uct mix and sched­ul­ing lim­its.

If design capac­ity is 100 units per hour, effec­tive capac­ity may be 85 units per hour.

Actual out­put

What the oper­a­tion really pro­duces. It is usu­ally below effec­tive capac­ity because of unplanned prob­lems such as machine break­downs, absen­teeism, mate­r­ial short­ages, qual­ity defects and worker fatigue. If effec­tive capac­ity is 85 units per hour but only 75 units are pro­duced, then design capac­ity is 100, effec­tive capac­ity is 85 and actual out­put is 75. The three fig­ures show the gap between ideal, prac­ti­cal and real per­for­mance.

Key capac­ity for­mu­las

Capac­ity for a period:

Capacity=Production rate per hour×Hours worked per period\text{Capacity} = \text{Production rate per hour} \times \text{Hours worked per period}

For exam­ple, a machine that pro­duces 40 units per hour and works 8 hours a day has a capac­ity of 40×8=32040 \times 8 = 320 units per day.

Util­i­sa­tion com­pares actual out­put with design capac­ity:

Utilisation=Actual outputDesign capacity×100\displaystyle \text{Utilisation} = \frac{\text{Actual output}}{\text{Design capacity}} \times 100

Effi­ciency com­pares actual out­put with effec­tive capac­ity:

Efficiency=Actual outputEffective capacity×100\displaystyle \text{Efficiency} = \frac{\text{Actual output}}{\text{Effective capacity}} \times 100

Exam­ple: if design capac­ity is 1,000 units and actual out­put is 800 units, util­i­sa­tion is 800/1,000×100=80800 / 1{,}000 \times 100 = 80 per cent. The com­pany is using 80 per cent of its total capac­ity.

Because effec­tive capac­ity is never more than design capac­ity, effi­ciency is always at least as high as util­i­sa­tion.

Worked exam­ple: util­i­sa­tion, effi­ciency and machines required

Sup­pose a pack­ag­ing unit runs one machine rated at 40 units per hour, for 8 hours a day, 6 days a week. After allow­ing for set-ups and sched­uled main­te­nance, 8 machine-hours are lost every week. Last week the machine actu­ally pro­duced 1,280 units.

Step 1: design capac­ity.

40×8×6=1,920 units per week40 \times 8 \times 6 = 1{,}920 \text{ units per week}

Step 2: effec­tive capac­ity. Planned losses are 8×40=3208 \times 40 = 320 units, so:

1,920320=1,600 units per week1{,}920 - 320 = 1{,}600 \text{ units per week}

Step 3: util­i­sa­tion.

1,2801,920×100=66.67 per cent\displaystyle \frac{1{,}280}{1{,}920} \times 100 = 66.67 \text{ per cent}

Step 4: effi­ciency.

1,2801,600×100=80 per cent\displaystyle \frac{1{,}280}{1{,}600} \times 100 = 80 \text{ per cent}

Horizontal bar chart for one machine per week: design capacity 1,920 units, effective capacity 1,600 units, actual output 1,280 units; utilisation 66.67%, efficiency 80%
Design capac­ity, effec­tive capac­ity and actual out­put for the pack­ag­ing machine in the worked exam­ple.

Step 5: machines needed for next year. The demand fore­cast is 3,000 units per week. Using effec­tive capac­ity only:

3,0001,600=1.8752 machines\displaystyle \frac{3{,}000}{1{,}600} = 1.875 \Rightarrow 2 \text{ machines}

But each machine really deliv­ers only 80 per cent of its effec­tive capac­ity, that is 1,600×0.80=1,2801{,}600 \times 0.80 = 1{,}280 units. So:

3,0001,280=2.343 machines\displaystyle \frac{3{,}000}{1{,}280} = 2.34 \Rightarrow 3 \text{ machines}

Two machines would give only 2×1,280=2,5602 \times 1{,}280 = 2{,}560 units, a short­fall of 440 units a week. The unit must either buy a third machine or raise effi­ciency; with two machines it would need an effi­ciency of 3,000/3,200=93.753{,}000 / 3{,}200 = 93.75 per cent. This shows why real­is­tic out­put, not design fig­ures, should drive capac­ity deci­sions.

Why util­i­sa­tion mat­ters

High util­i­sa­tion

High util­i­sa­tion is gen­er­ally good, because resources are being used well. But if it stays too high for too long, machines wear out faster, work­ers get tired, qual­ity may drop, break­downs increase and wait­ing times grow sharply.

Low util­i­sa­tion

Low util­i­sa­tion means idle resources, wasted invest­ment and lower profit.

The goal is there­fore not 100 per cent all the time but healthy, bal­anced util­i­sa­tion with a sen­si­ble capac­ity cush­ion (spare capac­ity kept to absorb demand peaks and break­downs).

A sim­ple illus­tra­tion: a bak­ery

Sup­pose a bak­ery can make 500 cakes per day.

CaseDaily demandSit­u­a­tionResult
1450 cakesCapac­ity is enoughDemand met com­fort­ably, 90 per cent util­i­sa­tion
2700 cakesCapac­ity too lowMissed orders, late deliv­er­ies, lost cus­tomers
3200 cakesCapac­ity too highOvens, work­ers and elec­tric­ity under­used

The bak­ery must then ask: should it buy a new oven, add a shift, reduce idle capac­ity or train more work­ers? That think­ing process is capac­ity plan­ning.

Capac­ity flex­i­bil­ity

Capac­ity flex­i­bil­ity is how eas­ily a busi­ness can adjust its capac­ity, or switch it between prod­ucts, when demand changes. A gar­ment fac­tory that can quickly move its lines from school uni­forms to office shirts has good capac­ity flex­i­bil­ity. Flex­i­bil­ity comes from:

  • flex­i­ble plants with quick changeovers;
  • flex­i­ble processes using gen­eral-pur­pose equip­ment;
  • flex­i­ble work­ers trained in sev­eral tasks.

Fac­tors affect­ing capac­ity plan­ning

Inter­nal fac­tors

  • num­ber and con­di­tion of machines;
  • worker skill and moti­va­tion;
  • plant lay­out and facil­ity design;
  • qual­ity of main­te­nance;
  • work­ing hours and shifts;
  • mate­r­ial avail­abil­ity;
  • pro­duc­tion method and prod­uct design.

Exter­nal fac­tors

  • mar­ket demand and sea­sonal changes;
  • sup­plier delays;
  • gov­ern­ment reg­u­la­tions and safety rules;
  • power short­ages;
  • trans­port prob­lems;
  • com­pe­ti­tion.

Some fac­tors, such as machine break­downs, are only partly con­trol­lable. They reduce actual out­put below effec­tive capac­ity and must be allowed for in the plan.

Link between fore­cast­ing and capac­ity plan­ning

Fore­cast­ing asks: how much demand may come in the future? Capac­ity plan­ning asks: do we have enough abil­ity to meet that demand? Fore­cast­ing comes first and capac­ity plan­ning fol­lows. For exam­ple, if the fore­cast says next mon­th's demand will rise by 30 per cent, capac­ity plan­ning decides whether to add work­ers, increase shifts, rent new machines or sub­con­tract work.

Steps in capac­ity plan­ning

  1. Fore­cast future demand for each prod­uct or ser­vice.
  2. Esti­mate the present capac­ity (design and effec­tive).
  3. Cal­cu­late the gap between required and avail­able capac­ity.
  4. Develop alter­na­tives to close the gap (over­time, shifts, new machines, sub­con­tract­ing, doing noth­ing).
  5. Eval­u­ate the alter­na­tives in money terms (for exam­ple, break-even or cost com­par­i­son) and qual­i­ta­tively.
  6. Select and imple­ment the best alter­na­tive, then mon­i­tor results.

Capac­ity tim­ing strate­gies

Once extra capac­ity is needed, the firm must decide when to add it.

Three step charts against a rising demand line over 8 years: lead capacity steps stay above demand, lag steps stay below, match steps straddle demand
Lead, lag and match strate­gies dif­fer in whether capac­ity is added before, after or along­side demand growth.

Lead strat­egy

Capac­ity is added before demand increases. Exam­ple: a school builds new class­rooms before admis­sions rise. Advan­tage: the firm is ready for growth and can win cus­tomers from slower rivals. Dis­ad­van­tage: risk of unused capac­ity if demand does not grow as fore­cast.

Lag strat­egy

Capac­ity is added only after demand has already increased. Exam­ple: a restau­rant hires more staff only after the cus­tomer rush grows. Advan­tage: less risk of waste and higher util­i­sa­tion. Dis­ad­van­tage: cus­tomers may face delays, and some may be lost, before expan­sion hap­pens.

Match (track­ing) strat­egy

Capac­ity is added in small steps as demand grows, so that it is some­times slightly above and some­times slightly below demand. Exam­ple: a clinic adds one doc­tor first, then another later. Advan­tage: a bal­anced approach with mod­er­ate risk. Dis­ad­van­tage: needs care­ful plan­ning and mon­i­tor­ing, and fre­quent small expan­sions can cost more per step.

Strat­egyTim­ingMain ben­e­fitMain risk
LeadBefore demandReady for growthIdle capac­ity
LagAfter demandHigh util­i­sa­tion, low riskLost sales, delays
MatchAlong­side demandBal­anceNeeds close mon­i­tor­ing

Prob­lems caused by poor capac­ity plan­ning

Under-capac­ity prob­lemsOver-capac­ity prob­lems
Cus­tomer dis­sat­is­fac­tionIdle labour
Delayed pro­duc­tion and rush jobsUnused machines
Over­time costHigh fixed costs
Qual­ity issuesLow return on invest­ment
Loss of salesWasted space and money

Poor capac­ity plan­ning harms both ser­vice qual­ity and prof­itabil­ity.

A sec­ond illus­tra­tion: school seats

Sup­pose a school has 10 class­rooms and each can hold 40 stu­dents, giv­ing a seat capac­ity of 10×40=40010 \times 40 = 400 stu­dents. If only 250 stu­dents join, capac­ity is under­used (util­i­sa­tion 62.5 per cent). If 500 stu­dents apply, capac­ity is insuf­fi­cient by 100 seats. The school must then decide whether to add class­rooms, run two shifts, limit admis­sions or open another branch. That deci­sion is capac­ity plan­ning.

Capac­ity, capac­ity plan­ning and util­i­sa­tion com­pared

  • Capac­ity: the abil­ity to pro­duce.
  • Capac­ity plan­ning: the deci­sion process of how much capac­ity is needed, and when.
  • Capac­ity util­i­sa­tion: the per­cent­age of capac­ity actu­ally being used.

These three terms are often con­fused in exams, so learn them clearly.

Exam answer for­mats

5-mark answer. Capac­ity is the capa­bil­ity of an organ­i­sa­tion to pro­duce goods or ser­vices in a given period. Capac­ity plan­ning is the process of deter­min­ing the capac­ity required to meet future demand. It is impor­tant because it bal­ances demand and pro­duc­tion capa­bil­ity, reduces cost, improves resource use and avoids both under-capac­ity and over-capac­ity. Capac­ity is mea­sured as design capac­ity, effec­tive capac­ity and actual out­put. Util­i­sa­tion and effi­ciency show how much of the capac­ity is actu­ally used. Capac­ity plan­ning is influ­enced by demand, machines, labour and break­downs, and firms may fol­low lead, lag or match strate­gies.

Mem­ory line. Capac­ity plan­ning means arrang­ing enough pro­duc­tion abil­ity to meet demand with­out waste or short­age. Remem­ber four words: design capac­ity, effec­tive capac­ity, actual out­put, util­i­sa­tion.

Key terms

Capac­ity
The max­i­mum out­put rate an oper­a­tion can achieve in a given period.
Design capac­ity
The max­i­mum out­put pos­si­ble under ideal con­di­tions.
Effec­tive capac­ity
Design capac­ity less planned allowances such as set-ups, breaks and main­te­nance.
Actual out­put
The out­put really achieved, after unplanned losses.
Util­i­sa­tion
Actual out­put as a per­cent­age of design capac­ity.
Effi­ciency
Actual out­put as a per­cent­age of effec­tive capac­ity.
Capac­ity cush­ion
Spare capac­ity kept above expected demand to han­dle peaks and uncer­tainty.
Lead strat­egy
Adding capac­ity in antic­i­pa­tion of demand growth.
Lag strat­egy
Adding capac­ity only after demand has grown.

Com­mon ques­tions

What is the dif­fer­ence between util­i­sa­tion and effi­ciency?

Util­i­sa­tion divides actual out­put by design capac­ity; effi­ciency divides it by effec­tive capac­ity. In the worked exam­ple, 1,280 units gives 66.67 per cent util­i­sa­tion and 80 per cent effi­ciency.

Why should a firm not aim for 100 per cent util­i­sa­tion?

Run­ning flat out leaves no room for break­downs, main­te­nance or demand peaks. Wait­ing times rise sharply, qual­ity can suf­fer and equip­ment wears faster. A capac­ity cush­ion gives sta­bil­ity.

Why is effec­tive capac­ity lower than design capac­ity?

Because real oper­a­tions need planned time for breaks, set-ups, changeovers and main­te­nance, and the prod­uct mix and sched­ul­ing may also limit out­put.

Which capac­ity strat­egy is best?

It depends on the sit­u­a­tion. A lead strat­egy suits grow­ing mar­kets where los­ing cus­tomers is costly; a lag strat­egy suits firms that want to avoid risk; a match strat­egy bal­ances the two but needs close mon­i­tor­ing.

How is capac­ity planned in a ser­vice firm?

Because ser­vices can­not be stored, ser­vice firms plan capac­ity for peak demand and man­age demand through appoint­ments, reser­va­tions, pric­ing and part-time or cross-trained staff.

Ref­er­ences

  1. Steven­son, W. J. Oper­a­tions Man­age­ment. McGraw-Hill Edu­ca­tion.
  2. Heizer, J., Ren­der, B. and Mun­son, C. Oper­a­tions Man­age­ment: Sus­tain­abil­ity and Sup­ply Chain Man­age­ment. Pear­son.
  3. Kra­jew­ski, L. J., Mal­ho­tra, M. K. and Ritz­man, L. P. Oper­a­tions Man­age­ment: Processes and Sup­ply Chains. Pear­son.
  4. Slack, N., Bran­don-Jones, A. and Burgess, N. Oper­a­tions Man­age­ment. Pear­son.
  5. Pan­neer­sel­vam, R. Pro­duc­tion and Oper­a­tions Man­age­ment. PHI Learn­ing.

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