lsm733-production operations management by: osman bin saif lecture 21 1

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LSM733-PRODUCTION OPERATIONS MANAGEMENT

By: OSMAN BIN SAIF

LECTURE 21

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Summary of Last Session Global Company Profile: Anheuser-

Busch The Planning Process The Nature of Aggregate Planning Aggregate Planning Strategies

Capacity OptionsDemand OptionsMixing Options to Develop a Plan

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Summary of Last Session (Contd.)

Methods for Aggregate PlanningGraphical MethodsMathematical ApproachesComparison of Aggregate Planning

Methods

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Agenda for this Session

Case Study Frito Lays Methods for Aggregate Planning

Graphical MethodsMathematical ApproachesComparison of Aggregate Planning

Methods

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Agenda for this Session (Contd.) Aggregate Planning in Services

RestaurantsHospitalsNational Chains of Small Service FirmsMiscellaneous Services

Airline Industry Yield Management

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Aggregate Planning at Frito-Lay

► More than three dozen brands, 15 brands sell more than $100 million annually, 7 sell over $1 billion

► Planning processes covers 3 to 18 months► Unique processes and specially designed

equipment► High fixed costs require high volumes and high

utilization

© 2014 Pearson Education, Inc.

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Aggregate Planning at Frito-Lay

► Demand profile based on historical sales, forecasts, innovations, promotion, local demand data

► Match total demand to capacity, expansion plans, and costs

► Quarterly aggregate plan goes to 36 plants in 17 regions

► Each plant develops 4-week plan for product lines and production runs

© 2014 Pearson Education, Inc.

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Graphical Methods

Popular techniques Easy to understand and use Trial-and-error approaches that do not

guarantee an optimal solution Require only limited computations

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Graphical Methods

1. Determine the demand for each period2. Determine the capacity for regular time,

overtime, and subcontracting each period3. Find labor costs, hiring and layoff costs, and

inventory holding costs4. Consider company policy on workers and stock

levels5. Develop alternative plans and examine their total

costs

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Roofing Supplier Example 1

Table 13.2

Month Expected DemandProduction

DaysDemand Per Day

(computed)

Jan 900 22 41

Feb 700 18 39

Mar 800 21 38

Apr 1,200 21 57

May 1,500 22 68

June 1,100 20 55

6,200 124

= = 50 units per day6,200124

Average requirement =

Total expected demandNumber of production days

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Figure 13.3

70 –

60 –

50 –

40 –

30 –

0 –Jan Feb Mar Apr May June = Month

22 18 21 21 22 20 = Number ofworking days

Prod

uctio

n ra

te p

er w

orki

ng d

ay

Level production using average monthly forecast demand

Forecast demand

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Table 13.3

Cost Information

Inventory carrying cost $ 5 per unit per month

Subcontracting cost per unit $10 per unit

Average pay rate $ 5 per hour ($40 per day)

Overtime pay rate$ 7 per hour

(above 8 hours per day)

Labor-hours to produce a unit 1.6 hours per unit

Cost of increasing daily production rate (hiring and training)

$300 per unit

Cost of decreasing daily production rate (layoffs)

$600 per unit

Plan 1 – constant workforce

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Table 13.3

Cost Information

Inventory carry cost $ 5 per unit per month







$300 per unit


$600 per unit

Plan 1 – constant workforce

MonthProduction at

50 Units per DayDemand Forecast

Monthly Inventory Change

Ending Inventory

Jan 1,100 900 +200 200Feb 900

700 +200 400

Mar 1,050800 +250 650

Apr 1,0501,200

-150 500

May 1,1001,500

-400 100

June 1,0001,100

-100

0

1,850

Total units of inventory carried over from onemonth to the next = 1,850 units

Workforce required to produce 50 units per day = 10 workers

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Table 13.3

Cost Information








$300 per unit


$600 per unit

MonthProduction at

50 Units per DayDemand Forecast

Monthly Inventory Change

Ending Inventory

Jan 1,100 900 +200 200Feb 900

700 +200 400

Mar 1,050800 +250 650

Apr 1,0501,200

-150 500

May 1,1001,500

-400 100

June 1,0001,100

-100

0

1,850

Total units of inventory carried over from onemonth to the next = 1,850 units

Workforce required to produce 50 units per day = 10 workers

Costs Calculations

Inventory carrying$9,250

(= 1,850 units carried x $5 per unit)

Regular-time labor49,600

(= 10 workers x $40 per day x 124 days)

Other costs (overtime, hiring, layoffs, subcontracting) 0

Total cost$58,850

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Figure 13.4

Cum

ulati

ve d

eman

d un

its7,000 –

6,000 –

5,000 –

4,000 –

3,000 –

2,000 –

1,000 –

–Jan Feb Mar Apr May June

Cumulative forecast requirements

Cumulative level production using average monthly

forecast requirements

Reduction of inventory

Excess inventory

6,200 units

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Table 13.2


DaysDemand Per Day

(computed)

Jan 900 22 41

Feb 700 18 39

Mar 800 21 38

Apr 1,200 21 57

May 1,500 22 68

June 1,100 20 55

6,200 124

Minimum requirement = 38 units per day

Plan 2 – subcontracting

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70 –

60 –

50 –

40 –

30 –



Prod

uctio

n ra

te p

er w

orki

ng d

ay

Level production using lowest monthly

forecast demand

Forecast demand

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Table 13.3

Cost Information








$300 per unit


$600 per unit

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Table 13.3

Cost Information








$300 per unit


$600 per unit

In-house production = 38 units per day x 124 days

= 4,712 units

Subcontract units = 6,200 - 4,712= 1,488 units

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Table 13.3

Cost Information








$300 per unit


$600 per unit


In-house production = 38 units per day x 124 days

= 4,712 units

Subcontract units = 6,200 - 4,712= 1,488 units

Costs Calculations

Regular-time labor$37,696

(= 7.6 workers x $40 per day x 124 days)

Subcontracting14,880

(= 1,488 units x $10 per unit)

Total cost$52,576

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Table 13.2


DaysDemand Per Day

(computed)

Jan 900 22 41

Feb 700 18 39

Mar 800 21 38

Apr 1,200 21 57

May 1,500 22 68

June 1,100 20 55

6,200 124

Production = Expected Demand

Plan 3 – hiring and firing

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70 –

60 –

50 –

40 –

30 –



Prod

uctio

n ra

te p

er w

orki

ng d

ay

Forecast demand and monthly production

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Table 13.3

Cost Information








$300 per unit


$600 per unit

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Table 13.3

Cost Information








$300 per unit


$600 per unit

MonthForecast

(units)

Daily Prod Rate

Basic Production

Cost (demand x

1.6 hrs/unit x $5/hr)

Extra Cost of Increasing Production (hiring cost)

Extra Cost of Decreasing Production (layoff cost) Total Cost

Jan 900 41 $ 7,200 — — $ 7,200

Feb 700 39 5,600 — $1,200 (= 2 x $600) 6,800

Mar 800 38 6,400 — $600 (= 1 x $600) 7,000

Apr 1,200 57 9,600$5,700

(= 19 x $300) — 15,300

May 1,500 68 12,000$3,300

(= 11 x $300) — 15,300

June 1,100 55 8,800 — $7,800 (= 13 x $600) 16,600

$49,600 $9,000 $9,600 $68,200

Table 13.4

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Comparison of Three Plans

Table 13.5

Cost Plan 1 Plan 2 Plan 3

Inventory carrying $ 9,250

$ 0

$ 0

Regular labor 49,600 37,696 49,600

Overtime labor 0 0 0

Hiring 0 0 9,000

Layoffs 0 0 9,600

Subcontracting 0 14,880 0

Total cost $58,850 $52,576 $68,200Plan 2 is the lowest cost option

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Mathematical Approaches

Useful for generating strategiesTransportation Method of Linear

Programming Produces an optimal plan

Management Coefficients Model Model built around manager’s experience and

performance

Other Models Linear Decision Rule Simulation

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Transportation Method

Table 13.6

CostsRegular time $40 per tireOvertime $50 per tireSubcontracting $70 per tireCarrying $ 2 per tire per month

Sales PeriodMar Apr May

Demand 800 1,000 750Capacity: Regular 700 700 700 Overtime 50 50 50 Subcontracting 150 150 130Beginning inventory 100

tires

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Transportation Example

Important points1. Carrying costs are $2/tire/month. If goods are

made in one period and held over to the next, holding costs are incurred

2. Supply must equal demand, so a dummy column called “unused capacity” is added

3. Because back ordering is not viable in this example, cells that might be used to satisfy earlier demand are not available

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Important points4. Quantities in each column designate the levels

of inventory needed to meet demand requirements

5. In general, production should be allocated to the lowest cost cell available without exceeding unused capacity in the row or demand in the column

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Table 13.7

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Management Coefficients Model

Builds a model based on manager’s experience and performance

A regression model is constructed to define the relationships between decision variables

Objective is to remove inconsistencies in decision making

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Other Models

Linear Decision Rule

Minimizes costs using quadratic cost curves Operates over a particular time period

Simulation Uses a search procedure to try different combinations of variables Develops feasible but not necessarily optimal solutions

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Summary of Aggregate Planning Methods

TechniquesSolution

Approaches Important Aspects

Graphicalmethods

Trial and error

Simple to understand and easy to use. Many solutions; one chosen may not be optimal.

Transportation method of linear programming

Optimization LP software available; permits sensitivity analysis and new constraints; linear functions may not be realistic.

Table 13.8

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Summary of Aggregate Planning Methods

TechniquesSolution

Approaches Important Aspects

Management coefficients model

Heuristic Simple, easy to implement; tries to mimic manager’s decision process; uses regression.

Simulation Change parameters

Complex; may be difficult to build and for managers to understand.

Table 13.8

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Aggregate Planning in Services

Controlling the cost of labor is critical

1. Accurate scheduling of labor-hours to assure quick response to customer demand

2. An on-call labor resource to cover unexpected demand

3. Flexibility of individual worker skills4. Flexibility in rate of output or hours of work

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Five Service Scenarios

Restaurants Smoothing the production processDetermining the optimal workforce size

HospitalsResponding to patient demand

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National Chains of Small Service FirmsPlanning done at national level and at

local level Miscellaneous Services

Plan human resource requirementsManage demand

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Law Firm Example

Table 13.9

Labor-Hours Required Capacity Constraints(2) (3) (4) (5) (6)

(1) Forecasts Maximum Number ofCategory of Best Likely Worst Demand in Qualified

Legal Business (hours) (hours) (hours) People Personnel

Trial work 1,800 1,500 1,200 3.6 4Legal research 4,500 4,000 3,500 9.0 32Corporate law 8,000 7,000 6,500 16.0 15Real estate law 1,700 1,500 1,300 3.4 6Criminal law 3,500 3,000 2,500 7.0 12Total hours 19,500 17,000 15,000Lawyers needed 39 34 30

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Airline industryExtremely complex planning problem Involves number of flights, number of

passengers, air and ground personnel, allocation of seats to fare classes

Resources spread through the entire system

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Yield Management

Allocating resources to customers at prices that will maximize yield or revenue

1. Service or product can be sold in advance of consumption

2. Demand fluctuates3. Capacity is relatively fixed4. Demand can be segmented5. Variable costs are low and fixed costs are

high

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Demand Curve

Yield Management Example

Figure 13.5

Passed-up contribution

Money left on the table

Potential customers exist who are willing to pay more than the $15 variable cost of the room

Some customers who paid $150 were actually willing to pay more for the roomTotal

$ contribution =(Price) x (50

rooms)=($150 - $15)

x (50)=$6,750

Price

Room sales

100

50

$150Price charged

for room

$15Variable cost

of room

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Total $ contribution =(1st price) x 30 rooms + (2nd price) x 30 rooms =

($100 - $15) x 30 + ($200 - $15) x 30 =$2,550 + $5,550 = $8,100

Demand Curve

Yield Management Example

Figure 13.6

Price

Room sales

100

60

30

$100Price 1

for room

$200Price 2

for room

$15Variable cost

of room

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Yield Management Approaches

▶Airlines, hotels, rental cars, etc.▶ Tend to have predictable duration of service

and use variable pricing to control availability and revenue

▶ Movies, stadiums, performing arts centers▶ Tend to have predicable duration and fixed

prices but use seating locations and times to manage revenue

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Yield Management Approaches

▶Restaurants, golf courses, ISPs▶ Generally have unpredictable duration of

customer use and fixed prices, may use “off-peak” rates to shift demand and manage revenue

▶Health care businesses, etc.▶ Tend to have unpredictable duration of

service and variable pricing, often attempt to control duration of service

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Yield Management MatrixD

urati

on o

f use

Unp

redi

ctab

le

Pred

icta

ble

PriceTend to be fixed Tend to be variable

Quadrant 1: Quadrant 2:

Movies HotelsStadiums/arenas Airlines

Convention centers Rental carsHotel meeting space Cruise lines

Quadrant 3: Quadrant 4:

Restaurants Continuing careGolf courses hospitals

Internet serviceproviders

Figure 13.7

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Making Yield Management Work

1. Multiple pricing structures must be feasible and appear logical to the customer

2. Forecasts of the use and duration of use

3. Changes in demand

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Summary of this Session

Case Study Frito Lays for Aggregate Planning

Graphical MethodsMathematical ApproachesComparison of Aggregate Planning

Methods

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Summary of this Session (Contd.) Aggregate Planning in Services

RestaurantsHospitalsNational Chains of Small Service FirmsMiscellaneous Services

Airline Industry Yield Management

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THANK YOU

lsm733-production operations management by: osman bin saif lecture 21 1

Documents

daily production rate

costsquarterly aggregate

cost informationinventory

monthsubcontracting

working daylevel production

fritolaydemand profile

session contd

pearson education