Strategic Capacity PlanningDefined

• Capacity: the ability to hold, receive, store, or produce.

• Strategic capacity planning is an approach for determining the overall capacity level of capital intensive resources, including facilities, equipment, and overall labor force size.

The Experience Curve

Total accumulated production of units

Cost orpriceper unit

As plants produce more products, they gain experience in the best production methods and reduce their costs per unit.

Capacity Focus• The concept of the focused factory holds

that production facilities work best when they focus on a fairly limited set of production objectives.

• Plants Within Plants (PWP) (from Skinner)– Extend focus concept to operating level

Example of a Decision Tree Problem

A glass factory specializing in crystal is experiencing a substantial backlog, and the firm's management is considering three courses of action:

A) Arrange for subcontracting,B) Construct new facilities.C) Do nothing (no change)

The correct choice depends largely upon demand, which may be low, medium, or high. By consensus, management estimates the respective demand probabilities as .10, .50, and .40.

Example of a Decision Tree Problem: The Payoff Table

Probability 0.1 0.5 0.4Low sales Medium sales High sales

Subcontract 10 50 90Build new -120 25 200No change 20 40 60

The management also estimates the profits when choosing from the three alternatives (A, B, and C) under the differing probable levels of demand. These costs, in thousands of dollars are presented in the table below:

Example of a Decision Tree Problem: Step 1. We start by drawing the three decisions

Subcontract

Build New

No Change

Example of Decision Tree Problem: Step 2. Add our possible states of nature, probabilities, and payoffs

Subcontract

Build New

No Change

High demand (.4)

Medium demand (.5)

Low demand (.1)

$90k$50k

$10k

High demand (.4)

Medium demand (.5)

Low demand (.1)

$200k$25k

-$120k

High demand (.4)

Medium demand (.5)

Low demand (.1)

$60k$40k

$20k

Example of Decision Tree Problem: Step 3. Determine the expected value of each decision

High demand (.4)

Medium demand (.5)

Low demand (.1)

Subcontract

$90k$50k

$10k

EVA=.4(90)+.5(50)+.1(10)=$62k

$62k

Example of Decision Tree Problem: Step 4. Make decision

High demand (.4)

Medium demand (.5)

Low demand (.1)

High demand (.4)

Medium demand (.5)

Low demand (.1)

A

B

CHigh demand (.4)

Medium demand (.5)

Low demand (.1)

$90k$50k

$10k

$200k$25k

-$120k

$60k$40k

$20k

$62k

$80.5k

$46k

Alternative B generates the greatest expected profit, so our choice is B or to construct a new facility.

Capacity Utilization & Service Quality

• Best operating point is near 70% of capacity - balances efficiency and reserve

• From 70% to 100% of service capacity, what do you think happens to service quality? - customers are serviced, but service quality often declines

Components of Demand• Average demand for a period of time

• Trend

• Seasonal element

• Cyclical elements

• Random variation

• Autocorrelation

Delphi Methodl. Choose the experts to participate. There should be

a variety of knowledgeable people in different areas.

2. Through a questionnaire (or E-mail), obtain forecasts (and any premises or qualifications for the forecasts) from all participants.

3. Summarize the results and redistribute them to the participants along with appropriate new questions.

4. Summarize again, refining forecasts and conditions, and again develop new questions.

5. Repeat Step 4 if necessary. Distribute the final results to all participants.

2 Period Moving Average( Weekly Video Rentals)

650

660

670

680

690

700

710

720

3 4 5 6 7 8 9 10 11 12 13

Week

Vid

eo

Ren

tals

Forecast

Actual

Which Forecast Would You Prefer?

4 Period Moving Average(Weekly Video Rentals)

650

660

670

680

690

700

710

720

5 6 7 8 9 10 11 12 13

Week

Vid

eo

Ren

tals

Forecast

Actual

Which Forecast Would You Prefer?Exponential Smoothing (Weekly Video Rentals)

a = .1

620

630

640

650

660

670

680

690

700

710

720

2 3 4 5 6 7 8 9 10 11 12 13

Week

Vid

eo R

enta

ls

Forecast

Actual

Exponential Smoothing (Weekly Video Rentals)a = .6

620

630

640

650

660

670

680

690

700

710

720

2 3 4 5 6 7 8 9 10 11 12 13

Week

Vid

eo R

enta

ls

Forecast

Actual

In Class Example

3 Period Moving Average

July 110

August 115

September 105

October 110

November 125

December 110

January

(110 +115 + 105)/3 = 110

(115 + 105 + 110)/3 =110

(105 + 110 + 125)/3 = 113.333

(110 + 125 + 110)/3 = 115

In Class Example

Exponential Smoothing, a = .4 Ft = aAt-1 + (1- a)Ft-1

July 110

August 115

September 105

October 110

November 125

December 110

January

110

.4(110) + (1-.4)110 = 110

.4(115) + (1-.4)110 = 112

.4(105) + (1-.4)112 = 109.2

.4(110) + (1-.4)109.2 = 109.52

.4(125) + (1-.4)109.52 = 115.71

.4(110) + (1-.4)115.71 = 113.43

In Class 2 a = .4

At Ft

Forecast Error RSFE

Absolute Deviation MAD TS

July 110 110.0

August 115 110.0

September 105 112.0

October 110 109.2

November 125 109.5

December 110 115.7

January 113.4

a = .4

At Ft Forecast Error

July 110 110.0

August 115 110.0 115-110 = 5.0

September 105 112.0 105 – 112 = -7.0

October 110 109.2 110-109.2 = 0.8

November 125 109.5 125 – 109.5 = 15.5

December 110 115.7 110-115.7 = -5.7

January 113.4

a = .4

At Ft

Forecast Error RSFE

July 110 110.0

August 115 110.0 5.0 5.0

September 105 112.0 -7.0 5 – 7 = -2.0

October 110 109.2 0.8 -2+.8 = -1.2

November 125 109.5 15.5 -1.2 + 15.5 = 14.3

December 110 115.7 -5.7 14.3 – 5.7 = 8.6

January 113.4

a = .4

At Ft

Forecast Error RSFE

Absolute Deviation

July 110 110.0

August 115 110.0 5.0 5.0 5.0

September 105 112.0 -7.0 -2.0 7.0

October 110 109.2 0.8 -1.2 0.8

November 125 109.5 15.5 14.3 15.5

December 110 115.7 -5.7 8.6 5.7

January 113.4

a = .4

At Ft

Forecast Error RSFE

Absolute Deviation MAD

July 110 110.0

August 115 110.0 5.0 5.0 5.0 5/1 = 5.0

September 105 112.0 -7.0 -2.0 7.0 (5+ 7)/2 = 6.0

October 110 109.2 0.8 -1.2 0.8 (5+7+.8)/3 = 4.3

November 125 109.5 15.5 14.3 15.5 (5+7+.8+15.5)/4 = 7.1

December 110 115.7 -5.7 8.6 5.7 (5+7+.8+15.5+5.7)/5 = 6.8

January 113.4

a = .4

At Ft

Forecast Error RSFE

Absolute Deviation MAD TS

July 110 110.0

August 115 110.0 5.0 5.0 5.0 5.0 5/5 = 1.00

September 105 112.0 -7.0 -2.0 7.0 6.0 -2/6 = -0.33

October 110 109.2 0.8 -1.2 0.8 4.3 -1.2/4.3 = -0.28

November 125 109.5 15.5 14.3 15.5 7.1 14.3/7.1 = 2.02

December 110 115.7 -5.7 8.6 5.7 6.8 8.6/6.8 = 1.26

January 113.4

Master Production Scheduling

Material Requirements Planning

Order SchedulingWeekly Workforce &Customer Scheduling

Daily Workforce &Customer Scheduling

Process Planning

Strategic Capacity Planning

Sales and Operations (Aggregate) Planning

Long-range

Intermediate-range

Short-range

Manufacturing Services

Sales Plan Aggregate Operations Plan

Forecasting and Demand

Mgmt.

Aggregate Planning

• Goal: Specify the optimal combination of– production rate (units completed per unit of time)– workforce level (number of workers)– inventory on hand (inventory carried from

previous period)

• Product group or broad category - family (Aggregation)

• Intermediate-range planning period: 6-18 months

Key Strategies for Meeting Demand

• Chase - Match production to customer order rate by hiring and laying off employees

• Level - Stable workforce with constant output, inventory and backlogs absorb fluctuations in demand

• Some combination of the two - Stable workforce, variable hours - vary output through overtime or flexible schedules