[PPT]Aggregate Planning - Hatem Masri | Hatem Masri · Web viewAggregate Planning for Services Most services cannot be inventoried Demand for services is difficult to predict Capacity

Chapter 14

Aggregate Sales and Operations Planning

Lecture Outline

• The Sales and Operations Planning Process• Strategies for Adjusting Capacity• Strategies for Managing Demand• Quantitative Techniques for Aggregate Planning• Hierarchical Nature of Planning• Aggregate Planning for Services

Copyright 2011 John Wiley & Sons, Inc. 14-2

Sales and Operations Planning

• Determines resource capacity to meet demand over an intermediate time horizon• Aggregate refers to sales and operations planning for

product lines or families• Sales and Operations planning (S&OP) matches supply

and demand• Objectives

• Establish a company wide plan for allocating resources• Develop an economic strategy for meeting demand


Sales and Operations Planning Process


Monthly S&OP Planning Process


Meeting Demand Strategies

• Adjusting capacity• Resources to meet demand are acquired and

maintained over the time horizon of the plan• Minor variations in demand are handled with overtime

or under-time• Managing demand

• Proactive demand management


Strategies for Adjusting Capacity

• Level production• Producing at a constant rate and using inventory to

absorb fluctuations in demand• Chase demand

• Hiring and firing workers to match demand• Peak demand

• Maintaining resources for high-demand levels


Strategies for Adjusting Capacity

• Overtime and under-time• Increase or decrease working hours

• Subcontracting• Let outside companies complete the work

• Part-time workers• Hire part-time workers to complete the work

• Backordering• Provide the service or product at a later time period


Level Production


DemandU

nits

Time

Production

Chase Demand


DemandU

nits

Time

Production

Strategies for Managing Demand

• Shifting demand into other time periods– Incentives– Sales promotions– Advertising campaigns

• Offering products or services with counter-cyclical demand patterns

• Partnering with suppliers to reduce information distortion along the supply chain

14-11Copyright 2011 John Wiley & Sons, Inc.

Quantitative Techniques For AP

• Pure Strategies• Mixed Strategies• Linear Programming• Transportation Method• Other Quantitative Techniques


Pure Strategies


Hiring cost = $100 per workerFiring cost = $500 per worker

Inventory carrying cost = $0.50 pound per quarter Regular production cost per pound = $2.00 Production per employee = 1,000 pounds per quarter Beginning work force = 100 workers

QUARTER SALES FORECAST (LB)

Spring 80,000Summer 50,000Fall 120,000Winter 150,000

Level Production Strategy


Level production

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

4

Spring 80,000 100,000 20,000Summer 50,000 100,000 70,000Fall 120,000 100,000 50,000Winter 150,000 100,000 0

400,000 140,000Cost of Level Production Strategy(400,000 X $2.00) + (140,00 X $.50) = $870,000

SALES PRODUCTIONQUARTER FORECAST PLAN INVENTORY

Chase Demand Strategy


Spring 80,000 80,000 80 0 20Summer 50,000 50,000 50 0 30Fall 120,000 120,000 120 70 0Winter 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

Level Production with Excel


Inventory atend of summer

Input by user=400,000/4

Cost of level production= inventory costs + production costs

Chase Demand with Excel


Workforce requirementscalculated by system

No. of workershired in fall

Production input by user;production =demand

Cost of chasedemand = hiring +firing + production

Mixed Strategy

• Combination of Level Production and Chase Demand strategies

• Example policies• no more than x% of workforce can be laid off in one

quarter• inventory levels cannot exceed x dollars

• Some industries may shut down manufacturing during the low demand season and schedule employee vacations during that time


General Linear Programming (LP) Model

• LP gives an optimal solution, but demand and 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


LP MODEL


Minimize Z = $100 (H1 + H2 + H3 + H4)+ $500 (F1 + F2 + F3 + F4)+ $0.50 (I1 + I2 + I3 + I4)+ $2 (P1 + P2 + P3 + P4)

Subject toP1 - I1 = 80,000 (1)

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

Setting up the Spreadsheet


Target cell;cost of solutiongoes here

Solver will put thesolution here

When model is complete, Solve

Click here nextDemand Constraint

Workforce Constraint

Production Constraint

Cells where solution appears

Setting up the Spreadsheet


Click these boxes

The LP Solution


Optimal production plan

Cost of optimal solution

Solution is a mixof inventory, hiringand firing

Extra report options

Level Production for Quantum


Level = 12,000/12 = 1,000

Input by user

Excel calculates

these

Cost of level production

Chase Demand for Quantum


Input by user

Excel calculates

these

Cost of chase demandNo. workershired in Feb.

LP Solution for Quantum


Solver foundthis solution

Constraintequationsin these

cells

Optimal solution

Transportation Method


1 900 1000 100 5002 1500 1200 150 5003 1600 1300 200 5004 3000 1300 200 500

Regular production cost $20/unitOvertime production cost $25/unitSubcontracting cost $28/unitInventory holding cost $3/unit-periodBeginning inventory 300 units

EXPECTED REGULAR OVERTIME SUBCONTRACTQUARTER DEMAND CAPACITY CAPACITY CAPACITY

Transportation Tableau


Transportation Tableau


Burruss’ Production Plan


1 900 1000 100 0 5002 1500 1200 150 250 6003 1600 1300 200 500 10004 3000 1300 200 500 0

Total 7000 4800 650 1250 2100

REGULAR SUB- ENDINGPERIOD DEMAND PRODUCTION OVERTIME CONTRACT INVENTORY

Copyright 2011 John Wiley & Sons, Inc.

Period 2’s endinginventory

Regular productionfor period 1

Cost of solution

Excel and Transportation Method

14-31

Other Quantitative Techniques

• Linear decision rule (LDR)• Search decision rule (SDR)• Management coefficients model


Hierarchical Nature of Planning


Items

Product lines or families

Individual products

Components

Manufacturing operations

Resource Level

Plants

Individual machines

Critical work

centers

Production Planning

Capacity Planning

Resource requirements

plan

Rough-cut capacity

plan

Capacity requirements

plan

Input/ output control

Sales and Operations

Plan

Master production schedule

Material requirements

plan

Shop floor

schedule

All work centers

Disaggregation


• Breaking an aggregate plan into more detailed plans

• Create Master Production Schedule for Material Requirements Planning

Collaborative Planning

• Sharing information and synchronizing production across supply chain

• Part of CPFR (collaborative planning, forecasting, and replenishment)• involves selecting products to be jointly managed,

creating a single forecast of customer demand, and synchronizing production across supply chain


Available-to-Promise (ATP)• Quantity of items that can be promised to customer• Difference between planned production and customer

orders already 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) • Capable-to-promise

• quantity of items that can be produced and mad available at a later date

ATP


ATP


ATP


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


Rule Based ATPProduct Request

Is the product available at

this location?

Is an alternative product available

at an alternate location?

Is an alternative product available at this location?

Is this product available at a

different location?

Available-to-promise

Allocate inventory

Capable-to-promise date

Is the customer willing to wait for the

product?

Available-to-promise

Allocate inventory

Revise master schedule

Trigger production

Lose sale

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Aggregate Planning for Services

• Most services cannot be inventoried• Demand for services is difficult to predict• Capacity is also difficult to predict• Service capacity must be provided at the

appropriate place and time• Labor is usually the most constraining resource

for services



Yield Management


Yield Management

Yield Management


NO-SHOWS PROBABILITY P(N < X)

0 .15 .001 .25 .152 .30 .403 .30 .70

Optimal probability of no-shows

P(n < x) = = .517Cu

Cu + Co

7575 + 70

.517

Hotel should be overbooked by two rooms

Chapter 14 Supplement

Linear Programming Decision Analysis

Tools and Techniques

Lecture Outline

• Model Formulation• Linear Programming Model Solution• Solving Linear Programming Problems with

Excel• Sensitivity Analysis

Copyright 2011 John Wiley & Sons, Inc. Supplement 14-46

Linear Programming (LP)

• A model consisting of linear relationships representing a firm’s objective and resource constraints

• A mathematical modeling technique which determines a level of operational activity in order to achieve an objective, subject to restrictions called constraints


Types of LP


Types of LP


Types of LP


LP Model Formulation

• Decision variables• symbols representing levels of activity of an operation

• Objective function• linear relationship for the objective of an operation• most frequent business objective is to maximize profit• most frequent objective of individual operational units

(such as a production or packaging department) is to minimize cost

• Constraint• linear relationship representing a restriction on

decision making


LP Model Formulation

Max/min z = c1x1 + c2x2 + ... + cnxn

subject to:a11x1 + a12x2 + ... + a1nxn (≤, =, ≥) b1

a21x1 + a22x2 + ... + a2nxn (≤, =, ≥) b2

: an1x1 + an2x2 + ... + annxn (≤, =, ≥) bn

xj = decision variablesbi = constraint levelscj = objective function coefficientsaij = constraint coefficients


Constraints

Highlands Craft Store


Labor Clay RevenueProduct (hr/unit) (lb/unit) ($/unit)Bowl 1 4 40Mug 2 3 50

There are 40 hours of labor and 120 pounds of clay available each day

Decision variablesx1 = number of bowls to producex2 = number of mugs to produce

Resource Requirements

Highlands Craft Store


Maximize Z = $40 x1 + 50 x2

Subject tox1 + 2x2 40 hr (labor constraint)

4x1 + 3x2 120 lb (clay constraint)x1 , x2 0

Solution is x1 = 24 bowls x2 = 8 mugsRevenue = $1,360

Simplex Method

• Mathematical procedure for solving LP problems• Follow a set of steps to reach optimal solution• Slack variables added to ≤ constraints to represent

unused resources• x1 + 2x2 + s1 = 40 hours of labor• 4x1 + 3x2 + s2 = 120 lb of clay

• Surplus variables subtracted from ≥ constraints to represent excess above resource requirement. • 2x1 + 4x2 ≥ 16 is transformed into• 2x1 + 4x2 - s1 = 16

• Slack/surplus variables have a 0 coefficient in the objective function• Z = $40x1 + $50x2 + 0s1 + 0s2


Solving LP Problems with Excel


Objective function

=C6*B10+D6*B11

=E6-F6

=E7-F7

=C7*B10+D7*B11Decision variables bowls (X1) = B10mugs (x2) = B11

Click on “Data” to invoke “Solver”

Solving LP Problems with Excel


After all parameters and constraintshave been input, click on “Solve”

Objective function

Decision variables

C6*B10+D6*B11≤40and

C7*B10+D7*B11≤120 Click on “Add” toinsert constraints

Click on “Options” to addnon-negativity and linear

conditions

LP Solution


Sensitivity Analysis


Sensitivity range for labor;30 to 80 lbs.

Sensitivity range for clay;60 to 160lbs.

Shadow prices – marginalvalues – for labor and clay.

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[PPT]Aggregate Planning - Hatem Masri | Hatem Masri · Web viewAggregate Planning for Services Most services cannot be inventoried Demand for services is difficult to predict Capacity