Capacity Planning

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?

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

Examples of Capacity Measures

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.

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

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

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

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

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

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)

Sources of Uncertainty Manufacturing Customer delivery Supplier performance Changes in demand

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

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

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

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

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

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

Some examples of demand / capacity

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

Adaptation with inventory DEMAND Inventory reduction Inventory accumulation CAPACITY

Adaptation with subcontracting DEMAND SUBCONTRACTING PRODUCTION (CAPACITY)

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

Seminar exercises Homogeneous Machine

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

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

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)

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

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

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 = 17 280 minutes per planning period E2 = 4 ∙ 5 ∙ 2 ∙ 8 ∙ 60 ∙ 2 ∙ 0,8 = 38 720 mins E3 = (4 ∙ 5 ∙ 3 ∙ 8 ∙ 60 ∙ 1 ∙ 0,9) – (5 hrs per week maintenance ∙ 60 minutes per hour ∙ 4 weeks) = 25 920 – 1200 = 24 720 mins

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

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: 10 000 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

Solution (CS matrix) 10 20 30 T1 T2 T3 T4 b (mins/y) E1 17 280 E2   T1 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

What is the optimal product mix to maximize revenues? T3= (30 720-100∙20-120∙30)/30= 837<MAX T4=24 720/10=2472<3000

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

Exercise 2 6 3 2 4 1 T1 T2 T3 T4 T5 T6 b (hrs/y) E1 2 000 E2 3 000 E3   T1 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

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