Chap13 APP

8/2/2019 Chap13 APP

1/31

Copyright 2006 John Wiley & Sons, Inc.Beni Asllani

University of Tennessee at Chattanooga

Aggregate Planning

Operations Management - 5thEdition

Chapter 13

Roberta Russell & Bernard W. Taylor, III

8/2/2019 Chap13 APP

2/31

Copyright 2006 John Wiley & Sons, Inc. 13-2

Lecture Outline

Aggregate Planning Process

Strategies for Adjusting Capacity

Strategies for Managing Demand

Quantitative Techniques for AggregateProduction Planning

Hierarchical Nature of Planning

Aggregate Planning for Services

8/2/2019 Chap13 APP

3/31


Aggregate Planning

Determine the resource capacity needed tomeet demand over an intermediate time

horizon Aggregaterefers to product lines or families

Aggregate planning matches supply and demand

Objectives Establish a company wide game plan for allocating

resources Develop an economic strategy for meeting

demand

8/2/2019 Chap13 APP

4/31


Aggregate Planning Process

8/2/2019 Chap13 APP

5/31


Meeting Demand Strategies

Adjusting capacity

Resources necessary to meet demand

are acquired and maintained over thetime horizon of the plan

Minor variations in demand are handledwith overtime or under-time

Managing demand Proactive demand management

8/2/2019 Chap13 APP

6/31


Strategies for Adjusting Capacity

Level production Producing at a constant rate

and using inventory to

absorb fluctuations indemand

Chase demand Hiring and firing workers to

match demand

Peak demand Maintaining resources for

high-demand levels

Overtime and under-time Increasing or decreasing

working hours

Subcontracting Let outside companies

complete the work

Part-time workers Hiring part time workers to

complete the work Backordering

Providing the service orproduct at a later time period

8/2/2019 Chap13 APP

7/31Copyright 2006 John Wiley & Sons, Inc. 13-7

Level Production

Demand

Units

Time

Production

8/2/2019 Chap13 APP


Chase Demand

Demand

Units

Time

Production

8/2/2019 Chap13 APP


Strategies for Managing Demand

Shifting demand intoother time periods Incentives Sales promotions Advertising campaigns

Offering products orservices with counter-cyclical demand patterns

Partnering with suppliersto reduce informationdistortion along thesupply chain

8/2/2019 Chap13 APP


Quantitative Techniques For APP

Pure Strategies

Mixed Strategies

Linear Programming

Transportation Method

Other Quantitative

Techniques

8/2/2019 Chap13 APP


Pure Strategies

Hiring cost = $100 per worker

Firing cost = $500 per worker

Regular production cost per pound = $2.00

Inventory carrying cost = $0.50 pound per quarter

Production per employee = 1,000 pounds per quarter

Beginning work force = 100 workers

QUARTER SALES FORECAST (LB)

Spring 80,000

Summer 50,000

Fall 120,000

Winter 150,000

Example:

8/2/2019 Chap13 APP


Level Production StrategyLevel production

= 100,000 pounds(50,000 + 120,000 + 150,000 + 80,000)

4

Spring 80,000 100,000 20,000

Summer 50,000 100,000 70,000

Fall 120,000 100,000 50,000Winter 150,000 100,000 0

400,000 140,000

Cost of Level Production Strategy

(400,000 X $2.00) + (140,00 X $.50) = $870,000

SALES PRODUCTIONQUARTER FORECAST PLAN INVENTORY

8/2/2019 Chap13 APP


Chase Demand Strategy

Spring 80,000 80,000 80 0 20

Summer 50,000 50,000 50 0 30

Fall 120,000 120,000 120 70 0

Winter 150,000 150,000 150 30 0

100 50

SALES PRODUCTION WORKERS WORKERS WORKERSQUARTER FORECAST PLAN NEEDED HIRED FIRED

Cost of Chase Demand Strategy

(400,000 X $2.00) + (100 x $100) + (50 x $500) = $835,000

8/2/2019 Chap13 APP


Mixed Strategy

Combination of Level Production andChase Demand strategies

Examples of management policies no more than x% of the workforce can be

laid off in one quarter inventory levels cannot exceed xdollars

Many industries may simply shut downmanufacturing during the low demandseason and schedule employeevacations during that time

8/2/2019 Chap13 APP


General Linear Programming (LP)

Model

LP gives an optimal solution, but demandand costs must be linear

Let Wt=workforce size for period t

Pt =units produced in period t

It =units in inventory at the end of period t Ft =number of workers fired for period t

Ht=number of workers hired for period t

8/2/2019 Chap13 APP


LP MODELMinimize Z = $100 (H1 + H2 + H3 + H4)

+ $500 (F

1 +F

2 +F

3 +F

4)+ $0.50 (I1 + I2 + I3 + I4)

Subject to

P1 - I1 = 80,000 (1)

Demand I1 + P2 - I2 = 50,000 (2)

constraintsI2 +

P3 -

I3 = 120,000 (3)

I3 + P4 - I4 = 150,000 (4)

Production 1000 W1 = P1 (5)

constraints 1000 W2 = P2 (6)

1000 W3 = P3 (7)

1000 W4 = P4 (8)100 + H1 - F1 = W1 (9)

Work force W1 + H2 - F2 = W2 (10)

constraints W2 + H3 - F3 = W3 (11)

W3 + H4 - F4 = W4 (12)

8/2/2019 Chap13 APP


Transportation Method

1 900 1000 100 500

2 1500 1200 150 5003 1600 1300 200 500

4 3000 1300 200 500

Regular production cost per unit $20

Overtime production cost per unit $25

Subcontracting cost per unit $28

Inventory holding cost per unit per period $3

Beginning inventory 300 units

EXPECTED REGULAR OVERTIME SUBCONTRACT

QUARTER DEMAND CAPACITY CAPACITY CAPACITY

8/2/2019 Chap13 APP

18/31

18

Transportation Tableau

UnusedPERIOD OF PRODUCTION 1 2 3 4 Capacity Capacity

Beginning 0 3 6 9

Inventory 300 300

Regular 600 300 100 1000

Overtime 100 100

Subcontract 500

Regular 1200 1200

Overtime 150 150

Subcontract 250 250 500

Regular 1300 1300

Overtime 200 200

Subcontract 500 500

Regular 1300 1300

Overtime 200 200

Subcontract 500 500

Demand 900 1500 1600 3000 250

1

2

3

4

PERIOD OF USE

20 23 26 29

25 28 31 34

28 31 34 37

20 23 26

25 28 31

28 31 34

20 23

25 28

28 31

20

25

28

8/2/2019 Chap13 APP

19/31


Burruss Production Plan

1 900 1000 100 0 5002 1500 1200 150 250 600

3 1600 1300 200 500 1000

4 3000 1300 200 500 0

Total 7000 4800 650 1250 2100

REGULAR SUB- ENDINGPERIOD DEMAND PRODUCTION OVERTIME CONTRACT INVENTORY

8/2/2019 Chap13 APP

20/31


Other Quantitative Techniques

Linear decision rule (LDR)

Search decision rule (SDR)

Management coefficients model

8/2/2019 Chap13 APP

21/31


Hierarchical Nature of Planning

Items

Product linesor families

Individualproducts

Components

Manufacturingoperations

Resource

Level

Plants

Individualmachines

Criticalworkcenters

Production

Planning

Capacity

Planning

Resourcerequirements

plan

Rough-cutcapacityplan

Capacityrequirements

plan

Input/outputcontrol

Aggregateproduction

plan

Masterproductionschedule

Materialrequirements

plan

Shopfloor

schedule

Allwork

centers

8/2/2019 Chap13 APP

22/31


Available-to-Promise (ATP)

Quantity of items that can bepromised to the customer

Difference between plannedproduction and customer ordersalready received

AT in period 1 = (On-hand quantity + MPS in period 1)

- (CO until the next period of planned production)ATP in period n = (MPS in period n)

- (CO until the next period of planned production)

8/2/2019 Chap13 APP

23/31


ATP: Example

8/2/2019 Chap13 APP

24/31


ATP: Example (cont.)

8/2/2019 Chap13 APP

25/31


ATP: Example (cont.)

ATP in April = (10+100) 70 = 40

ATP in May = 100 110 = -10ATP in June = 100 50 = 50

= 30

= 0

Take excess units from April

8/2/2019 Chap13 APP

26/31


Rule Based ATPProductRequest

Is the productavailable at

this location?

Is an alternativeproduct available

at an alternatelocation?

Is an alternativeproduct availableat this location?

Is this productavailable at a

differentlocation?

Available-to-promise

Allocateinventory

Capable-to-promise date

Is the customerwilling to wait for

the product?

Available-to-promise

Allocateinventory

Revise masterschedule

Trigger production

Lose sale

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

8/2/2019 Chap13 APP

27/31


Aggregate Planning for Services

1. Most services cant be inventoried

2. Demand for services is difficult to predict

3. Capacity is also difficult to predict

4. Service capacity must be provided at theappropriate place and time

5. Labor is usually the most constrainingresource for services

8/2/2019 Chap13 APP

28/31


Yield Management

8/2/2019 Chap13 APP

29/31


Yield Management (cont.)

8/2/2019 Chap13 APP

30/31


Yield Management: Example

NO-SHOWS PROBABILITY P(N< X)

0 .15 .00

1 .25 .15

2 .30 .40

3 .30 .70

Optimal probability of no-shows

P(n< x) = = .517Cu

Cu + Co

75

75 + 70

.517

Hotel should be overbooked by two rooms

8/2/2019 Chap13 APP

31/31

Copyright 2006 John Wiley & Sons, Inc.All rights reserved. Reproduction or translation of this

work beyond that permitted in section 117 of the 1976United States Copyright Act without express permissionof the copyright owner is unlawful. Request for furtherinformation should be addressed to the Permission

Department, John Wiley & Sons, Inc. The purchasermay make back-up copies for his/her own use only andnot for distribution or resale. The Publisher assumes noresponsibility for errors, omissions, or damages causedby the use of these programs or from the use of the

information herein

Chap13 APP

Documents