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OM AGG Planning

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    Lecture Outline Agg.

    Plng Aggregate Planning Process

    Strategies for Adjusting Capacity

    Strategies for Managing Demand

    Quantitative Techniques for AggregateProduction Planning

    Hierarchical Nature of Planning

    Aggregate Planning for Services

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    Aggregatee Planning PRJ

    13-2

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    13-3

    Aggregate Planning

    Determine the resource capacity needed tomeet demand over an intermediate time

    horizon Aggregate refers 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

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    13-4

    Aggregate Planning Process

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    13-5

    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

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    13-6

    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

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    Level Production

    Demand

    Units

    Time

    Production

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    Chase Demand

    Demand

    Units

    Time

    Production

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    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

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    Quantitative Techniques For APP

    Pure Strategies

    Mixed Strategies

    Linear Programming

    Transportation Method

    Other Quantitative

    Techniques

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    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:

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    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

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    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

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    Mixed Strategy

    Combination of Level Production andChase Demand strategies

    Examples of management policies no more thanx% of the workforce can be

    laid off in one quarter inventory levels cannot exceedxdollars

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

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    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

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    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)

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    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

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    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

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    13-19

    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

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    Other Quantitative Techniques

    Linear decision rule (LDR)

    Search decision rule (SDR)

    Management coefficients model

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    13-21

    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

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    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)

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    13-23

    ATP: Example

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    ATP: Example (cont.)

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    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

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    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

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    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

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    13-28

    Yield Management

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    Yield Management (cont.)

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    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

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    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


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