1. Capacity Planning

2. Capacity
Capacity (A): is the upper limit on the load that an operating unit can handle.
Capacity (B): the upper limit of the quantity of a product (or product group) that an operating unit can produce (= the maximum level of output)
Capacity (C): the amount of resource inputs available relative to output requirements at a particular time
The basic questions in capacity handling are:
What kind of capacity is needed?
How much is needed?
When is it needed?
How does productivity relate to capacity?

3. Importance of Capacity Decisions
Impacts ability to meet future demands
Affects operating costs
Major determinant of initial costs
Involves long-term commitment
Affects competitiveness
Affects ease of management
Impacts long range planning

4. Examples of Capacity Measures

5. Capacity Designed capacity Effective capacity
maximum output rate or service capacity an operation, process, or facility is designed for
= maximum obtainable output
= best operating level
Effective capacity
Design capacity minus allowances such as personal time, maintenance, and scrap
Actual output = Capacity used
rate of output actually achieved. It cannot exceed effective capacity.

6. Capacity Efficiency and Capacity Utilization
Actual output
Efficiency =
Effective capacity
Utilization =
Design capacity

7. Numeric Example Design capacity = 10 tons/week
Effective capacity = 8 tons/week
Actual output = 6 tons/week
Actual output tons/week
Efficiency = = = 75%
Effective capacity tons/week
Actual output tons/week
Utilization = = = 60% Design capacity tons/week

8. Determinants of Effective Capacity
Facilities
Product and service factors
Process factors
Human factors
Operational factors
Supply chain factors
External factors

9. Key Decisions of Capacity Planning
Amount of capacity needed
Timing of changes
Need to maintain balance
Extent of flexibility of facilities

10. Capacity Cushion
level of capacity in excess of the average utilization rate or level of capacity in excess of the expected demand.
extra demand intended to offset uncertainty
Cushion = (designed capacity / capacity used) - 1
High cushion is needed:
service industries
high level of uncertainty in demand (in terms of both volume and product-mix)
to permit allowances for vacations, holidays, supply of materials delays, equipment breakdowns, etc.
if subcontracting, overtime, or the cost of missed demand is very high

11. Steps for Capacity Planning
Estimate future capacity requirements
Evaluate existing capacity
Identify alternatives
Conduct financial analysis
Assess key qualitative issues
Select one alternative
Implement alternative chosen
Monitor results (feedback)

12. Sources of Uncertainty
Manufacturing
Customer delivery
Supplier performance
Changes in demand

13. The „Make or Buy” problem
Available capacity
Expertise
Quality considerations
Nature of demand
Cost
Risk

14. Developing Capacity Alternatives
Design flexibility into systems
Take stage of life cycle into account (complementary product)
Take a “big picture” approach to capacity changes
Prepare to deal with capacity “chunks”
Attempt to smooth out capacity requirements
Identify the optimal operating level

15. Economies of Scale Economies of scale Diseconomies of scale
If the output rate is less than the optimal level, increasing output rate results in decreasing average unit costs
Diseconomies of scale
If the output rate is more than the optimal level, increasing the output rate results in increasing average unit costs

16. Evaluating Alternatives
Production units have an optimal rate of output for minimal cost.
Minimum average cost per unit
Average cost per unit
Minimum cost
Rate of output

17. Evaluating Alternatives II.
Minimum cost & optimal operating rate are
functions of size of production unit.
Small
plant
Medium
plant
Average cost per unit
Large
plant
Output rate

18. Planning Service Capacity
Need to be near customers
Capacity and location are closely tied
Inability to store services
Capacity must be matched with timing of demand
Degree of volatility of demand
Peak demand periods

19. Some examples of demand / capacity

20. Adapting capacity to demand through changes in workforce
PRODUCTION RATE (CAPACITY)

21. Adaptation with inventory
DEMAND
Inventory reduction
Inventory accumulation
CAPACITY

22. Adaptation with subcontracting
DEMAND
SUBCONTRACTING
PRODUCTION (CAPACITY)

23. Adaptation with complementary product
DEMAND
PRODUCTION (CAPACITY)
DEMAND
PRODUCTION (CAPACITY)

24. Seminar exercises
Homogeneous Machine

25. Designed capacity in calendar time
CD= N ∙ sn ∙ sh ∙ mn ∙ 60 (mins / planning period)
CD= designed capacity (mins / planning period)
N = number of calendar days in the planning period (≈ 250 wdays/yr)
sn= maximum number of shifts in a day (= 3 if dayshift + swing shift + nightshift)
sh= number of hours in a shift (in a 3 shifts system, it is 8)
mn= number of homogenous machine groups

26. Designed capacity in working minutes (machine minutes), with given work schedule
CD= N ∙ sn ∙ sh ∙ mn ∙ 60 (mins / planning period)
CD= designed capacity (mins / planning period)
N = number of working days in the planning period (≈ 250 wdays/yr)
sn= number of shifts in a day (= 3 if dayshift + swing shift + nightshift)
sh= number of hours in a shift (in a 3 shifts system, it is 8)
mn= number of homogenous machine groups

27. Effective capacity in working minutes
CE = CD - tallowances (mins / planning period)
CD= designed capacity
tallowances = allowances such as personal time, maintenance, and scrap (mins / planning period)

28. The resources we can count with in product mix decisions
b =  ∙ CE
b = expected capacity
CE = effective capacity
 = performance percentage
Product types
Expected Capacities
Resources
Resource utilization coefficients

29. Exercise 1.1
Set up the product-resource matrix using the following data!
RU coefficients: a11: 10, a22: 20, a23: 30, a34: 10
The planning period is 4 weeks (there are no holidays in it, and no work on weekends)
Work schedule:
E1 and E2: 2 shifts, each is 8 hour long
E3: 3 shifts
Homogenous machines:
1 for E1
2 for E2
1 for E3
Maintenance time: only for E3: 5 hrs/week
Performance rate:
90% for E1 and E3
80% for E2

30. Solution (bi) Ei = N ∙ sn ∙ sh ∙ mn ∙ 60 ∙ 
N=(number of weeks) ∙ (working days per week)
E1 = 4 weeks ∙ 5 working days ∙ 2 shifts ∙ 8 hours per shift ∙ 60 minutes per hour ∙ 1 homogenous machine ∙ 0,9 performance = = 4 ∙ 5 ∙ 2 ∙ 8 ∙ 60 ∙ 1 ∙ 0,9 = minutes per planning period
E2 = 4 ∙ 5 ∙ 2 ∙ 8 ∙ 60 ∙ 2 ∙ 0,8 = mins
E3 = (4 ∙ 5 ∙ 3 ∙ 8 ∙ 60 ∙ 1 ∙ 0,9) – (5 hrs per week maintenance ∙ 60 minutes per hour ∙ 4 weeks) = – 1200 = mins

31. Solution (RP matrix) 10 20 30 T1 T2 T3 T4 b (mins/y) E1 17 280 E2T1 T2 T3 T4
b (mins/y) E1 10
17 280 E2 20 30
30 720 E3
24 720

32. Exercise 1.2
Complete the corporate system matrix with the following marketing data:
There are long term contract to produce at least:
50 T1
100 T2
120 T3
50 T4
Forecasts says the upper limit of the market is:
units for T1
1 500 for T2
1 000 for T3
3 000 for T4
Unit prices: T1=100, T2=200, T3=330, T4=100
Variable costs: E1=5/min, E2=8/min, E3=11/min

33. Solution (CS matrix) 10 20 30 T1 T2 T3 T4 b (mins/y) E1 17 280 E2T1 T2 T3 T4
b (mins/y) E1 10
17 280 E2 20 30
30 720 E3
24 720
MIN (pcs/y) 50
100
120
MAX (pcs/y)
10 000
1 500
1 000
3 000 p
200
330 f 40 90
-10

34. What is the optimal product mix to maximize revenues?
T3= ( ∙20-120∙30)/30= 837<MAX
T4=24 720/10=2472<3000

35. What if we want to maximize profit?
The only difference is in T4 because of its negative contribution margin.
T4=50

36. Exercise 2 6 3 2 4 1 T1 T2 T3 T4 T5 T6 b (hrs/y) E1 2 000 E2 3 000 E3T1 T2 T3 T4 T5 T6
b (hrs/y) E1 6
2 000 E2 3 2
3 000 E3 4
1 000 E4
6 000 E5 1
5 000
MIN (pcs/y)
200
100
250
400
MAX (pcs/y)
20000
500
1000
2000
p (HUF/pcs) 50
f (HUF/pcs) 80 40 30 20
-10

37. Solution Revenue max. T1=333 T2=500 T3=400 T4=250 T5=900 T6=200
Contribution max.
T1=333
T2=500
T3=400
T4=250
T5=966
T6=100