LEAD TIME & INVENTORY REDUCTIONIN APPAREL SUPPLY CHAIN

Team Synergy

Abhishek Kumar

Mangala M

Rashmi Rao

Jisna P Gopi

ABOUT THE COMPANY

• Manufacturing arm of Madura Garments having brands like Louis Philippe , Van Heusen , Allen Solly and Peter England

• It comprises of 4 factories located in Bangalore , with a total employee strength close to 3000.

• The product range offered is men’s and women’s formal – Shirts , Trousers and Suits with annual volumes of 43.6 Lakh garments.

Vision: “To be a global benchmark for apparel industry in manufacturing and innovation providing competitive advantage to stake holders”

1. Fabric Mill

2. Factory

3. Warehouse 4. Retail Store

Fabric

Finished Goods

Finished Goods

Note:-To understand the apparel supply chain in brief which will help to understand the project better

APPAREL SUPPLY CHAIN

NEED FOR PROJECT

No well defined and scientific replenishment model and stocking policyStagnating sales growth year-on-year basisHigh finished goods inventory Increase in discounted sales year on year leading to profit erosion

FG inventory in the range of 5000pcs while Retail sales in

400pcs per month, hence carrying 10 months more

inventory

Due to such high FG inventory level, cash flow for business is blocked and discounted sales

increasing to improve it

PROJECT CHARTER

Business Case : Total Lead time of Apparel supply chain was 130 days and there has been no replenishment leading to high out-of-stock rate. The inventory level was very high carrying 9 months inventory.

This threw up tremendous potential for improving the lead time and inventory reduction

Scope: Fabric procurement to Retail Sales

Relation with business case , approach , scope , CTQ’s and benefits were finalized in the charter

SIPOC

Core

CORE REPLENISHMENT SYSTEM

Factory

Warehouse

Retail Store

Consumer

CRS ( Pull Strategy)

Fabric Mills

Core products would be managed using continuous replenishment at FG as well as fabric stages …

CORE REPLENISHMENT SYSTEM

Core products would be managed using continuous replenishment at FG as well as fabric stages …

Orders are Aggregated Order

Garment Production

PO Preparation

Sends the order

WO is generated

WO

Supplies Fabric PO

FabricOrder

Details

Fabric Status

Garments

Pending Order File

Warehouse Factory

Purchase

Supplier

Customer

FG Stocks, WIPLevels

Fabric StocksIn-Transit, Fabric

RoP Levels

Fabric Avb.

yes

no

Fulfill

CTQ TREE DIAGRAM

Reduction of Lead time and inventory in Apparel Supply chain for Core Replenishment System

Garment Manufacturing

REWORK%

RECUTTING%

MANUFACTURING LEAD TIME

Finished Goods INVENTORY (DAYS)

Fabric LEAD TIME (DAYS)

Retail RETAIL SALES (PCS)

Critical To Quality Characteristics (CTQ)

Operational Definition Measure

Current Status

CTQ Specification

Defect Definition

KANO Status

1. FABRIC PROCUREMENT LEAD TIME (DAYS)

FABRIC LEAD TIME FROM PO CREATION TO DELIVERY AT FACORY STORES

105 Days <15 Days > 15 Days MUST BE

2. MANUFACTURING LEAD TIME (DAYS)

TIME FROM WO CREATION TO DELIERY AT FACTORY WAREHOUSE

18 Days <12 Days > 12 Days MUST BE

3. FINISHED GOODS INVENTORY IN WH (DAYS)

FINISHED GOODS INVENTORY IN DAYS

271Days < 60 Days > 60 Days MUST BE

4. RETAIL SALES (PCS) RETAIL SALES IN PIECES

400 Pcs >500 Pcs < 500 PCS MUST BE

5. RECUTTING (%) PANELSRECUTTING

4% <3% >3% LOWER THE BETTER

BATCH RECUTTING 3% <1% >1%

6. REWORK (%) END OF LINE 7.83% < 7% >7% LOWER THE BETTER

FQC 1.73% <5% >5%

The project objective (Y) is addressed by focusing on the Must Be” CTQ’s (y’s) identified as shown above

CTQ TABLE

MILESTONE

GANTT CHART

  Jun’10 Jul’10 Aug’10 Sep’10 Oct’10 Nov’10 Dec’10 Jan’11

DEFINE                

Project CharterSIPOCCTQ Tree Diagram                

                 

MEASURE                

As-is CTQ Status                

                 

APPROACH/ ANALYSIS                

Cause & Effect Diagram                

FMEA                

                 

DEPLOYMENT/ IMPROVE                

NVA Identification & stratification                

Setting up section wise WIP Norms                

Single piece clearance on daily basis                

Developing system to monitor online                

                 

ASSESSMENT & REVIEW/ CONTROL                

Implementing control mechanism                

Sustenance (Review & Assessment)Replication

1. Fabric Mill2. Factory

3. Warehouse 4. Retail Store

Finished Goods

CTQ 1: FABRIC LEAD TIME REDUCTION AT FABRIC MILL

The Strategic supplier relationship management is required to

Ensure the strategic partnership with fabric mills

Ensure reduced fabric lead time by Vendor managed inventory

Ensure improved service levels for fabric lead time and quality

Ensure building base stock to service peak season demand

ANALYZE

An internal brainstorming session, helped to narrow down the Focus area for

Deployment in spoke from “12 Spoke solution

design” with strong core

IMPROVE

CONTROL

After establishing Strategic supplier relationship, critical vendors identified based on following parameters for rationalization:

Geographic Location Delivery, Quality, Cost

VMI has been adopted for critical vendors by providing them Quarterly projections to deliver stock on-demand

After implementing VMI, due to reduction in Fabric lead time and improved service levels, base stock was built to service

ESCALATION MATRIXLEAD TIME

DELAYVENDOR/ INCHARGE

PURCHASE MANAGER

SOURCING HEAD

BRANDHEAD

14 Days 10 Days 7 Days 2 Days

Safety Stock

Reorder Point

1. Fabric Mill2. Factory

3. Warehouse 4. Retail Store

Finished Goods

CTQ 2 : GMT MFG LEAD TIME REDUCTION AT FACTORY

The reduction in Garment manufacturing lead time is done by

Ensure timely delivery of finished goods at warehouse against SLA

Identifying potential causes for high manufacturing lead time

Identifying root causes by using FMEA

Implementing improvement solutions for those root causes

Implementing controls to ensure that implemented solutions sustain

DIAGNOSING POTENTIAL ROOT CAUSES

An internal brainstorming session, helped to narrow down from 52 x’s to more

likely root causes for further analysis

Single pc clearanceFurther, FMEA was carried out to help in prioritizing the potential root causes

FMEA FOR PRIORITIZING POTENTIAL ROOT CAUSES

Cause

Probability of

Occurrence

Severity Of Conseque

nce

Difficulty of

Detection

(A) Probability

of Occurrenc

e

(B) Consequen

ce(C)

Detection

Risk Priority

No. (RPN)

  (High / Medium / Low)Rating (1-6-9) 1 = lowest, 6 =

medium, 9 = highest A x B x C

Lack of work aid Low Medium Medium 1 6 6 36

Mismatching of material Medium Medium Low 6 6 1 36Fabric Defect & Shade variation Medium High Low 6 9 1 54Improper method due to lack of adherance to SOP Low High Medium 1 9 6 54Planning procedure leading to improper release of work orders Medium High Medium 6 9 6 324High sectional WIP due to lack of adherance to WIP Norms High High Medium 9 9 6 486Delay in single piece clearance due to lack of supervision High High High 9 9 9 729

2 main causes having highest RPNs were selected to work on

High RPN root causes 486 and 729 respectively are identified and

action taken to reduce it

IMPROVEMENT

Delay in single piece clearance due to lack of supervision

Tracking format implemented for tracking down the single piece in the sectionOperators and supervisors were educated to clear single piece Daily review for deviations

Results Statistical Validation

SINGLE PIECE CLEARANCE TRACKING

2-Variance test was done and since p-value (0.498) is greater than 0.05, there is no significant change in the variation

2-sample t-test was done and since the p-value (0.002) is less than 0.05, there is significant reduction in single piece clearance time.

IMPROVEMENT

High Net WIP due to lack of adherance to WIP norms

Section wise norms were fixed based on the daily production of section Implementation of pull system in WIP based WO releaseDaily review for deviations

2-Variance test was done and since p-value (0.587) is greater than 0.05, there is no significant change in the variation

2-sample t-test was done and since the p-value (0.006) is less than 0.05, there is significant reduction in Net WIP (pcs)

Results Statistical Validation

FMEA FOR RESULT VALIDATION

Failure Mode Effect Analysis (FMEA) for Validating the solutions.Cause Probabi

lity Severit

y Of Consequence

Difficulty of

Detection

(A) Poten

tial

(B) Consequenc

e

(C) Detect

ion

Risk Priority

No. (RPN)

Implemented Preventive Action

(A1) Probability

(B1) Severi

ty

(C1) Detection

Residual No.

  (High / Medium / Low) Rating (1-6-9) 1 = lowest, 6 = medium, 9 =

highest

A x B x C

  Rating (1-6-9) 1 = lowest, 6 = medium, 9

= highest

A1 x B1 x C1

Lack of work aid

Low Medium High 1 6 9 54   1 6 9 54

Mismatching of material

Medium Medium Low 6 6 1 36   6 6 1 36

Fabric Defect & Shade variation

Medium High Low 6 9 1 54   6 9 1 54

Improper method due to lack of adherance to SOP

Low High Medium 1 9 6 54   1 9 6 54

Planning procedure leading to improper release of work orders

Medium High Medium 6 9 6 324 Implementation of pull system, WIP based work

order release system

6 1 6 36

High sectional WIP due to lack of adherance to WIP Norms

High High Medium 9 9 6 486 Establishing section wise WIP norms and ensuring

adherance to it

1 9 6 54

Delay in single piece clearance due to lack of supervision

High High High 9 9 9 729 Establishing single piece clearance tracking sheet

and ensuring adherance to it

1 9 6 54

With the implemented solution RPN of root causes came down from 486 and 729 to 54 respectively which

validates the effectiveness of

solution

Sheet to record details of all alterations of same product,

different batches

Single piece clearance of monitoring on a day to day basisRegular training to operators on clearing single pieces on top-priorityOn-site board for supervisors to keep tracking single pieces and Section wise WIPHolding dispatch till 100% single pc clearanceWeekly reviews by Production Manager for ensuring adherance to Section wise WIP norm and single pc clearance

CONTROL

On-site board for production monitoring , style description

and daily wo clearance

Standard operating procedures in all sections to

ensure the process adherence

BENEFITS : MFG LEAD TIME REDUCTION

Two-Sample T-Test and CI: After Tpt time, Before Tpt time

Two-sample T for After vs Before

N Mean StDev SE Mean

After 4 12.50 2.65 1.3

Before 9 29.44 6.37 2.1

Difference = mu (After) - mu (Before)

Estimate for difference: -16.9444

95% upper bound for difference: -12.4122

T-Test of difference = 0 (vs <): T-Value = -6.78

P-Value = 0.000 DF = 10

2-Variance test was done and since p-value is greater than 0.05, there is no significant change in the variation

2-sample t-test was done and since the p-value is less than 0.05, there is significant reduction in throughput time.

Reduction in Suits average manufacturing lead time from 18 days to 12 days (0.4 times improvement)

1. Fabric Mill2. Factory

3. Warehouse 4. Retail Store

Finished Goods

CTQ 3: FG INVENTORY REDUCTION AT WAREHOUSE

The Warehouse replenishment system for finished goods to warehouse required to

Ensure work orders are created to fill the inventory gap at warehouse

Ensure that replenishment is aligned to sales as per pull model

Ensure that inventory carrying cost of finished goods is reduced

Warehouse replenishment approach to provide the brands with a competitive advantage of Replenishment Model, which characterizes

Planning Horizon of fortnight with Weekly Wave Model of Work Ordering and Delivery cycle

IMPROVE

Factory Warehouse

Wo Creation

Pre-production activity (1 Days)

11 Days against SLA

Wo Release

Forecasting/ WH Stock Gap against Norm

Every Monday

Production Plan for filling Gap Fabrics &

Trims in-stock

Every Tuesday

1 Day Every Tuesday

Entoma Warehouse

(Retail)

2 DaysRetail Store

1 Day

IMPROVE (Contd) Work ordering cycle : Weekly (on every Tuesday),Delivery cycle : 12 working days from day of work order creation for blazers/

SuitsMOQ per style code : 10 pcs (based on cutting and marker laying efficiency)Fabric Norm: 1 month of salesWarehouse Norm: 1 month of sales

Assessment Mechanism (Daily)OTIFLead Time

Review Mechanism (Quarterly)WH InventoryFabric Inventory

Service level Agreement

GAP REPLENISHMENT

CONTROL

Based on the Scenarios, work order were created to

replenish the gap in warehouse inventory

Daily Status of Work orders were sent to brands for

ensuring the transparency in work order execution

ESCALATION MATRIXLEAD TIME

DELAYSUPERVISOR PRODUCTION

MANAGERFACTORY

MANAGERHEAD OF

MFG4 Days 3 Days 2 Days 1 Day

For process, Smart Transactional Excel Macros were developed for:

Monitoring Retail Sales Monitoring Retail Inventory Monitoring WH Inventory Monitoring Gap in WH inventory

against Norm due to retail sales Work order need to be created

based on gaps on weekly basis

To improve the transparency in work order execution as control sheet, Daily status is sent to all stakeholders

Escalation matrix established for handling lead time delays

BENEFITS : FG INVENTORY REDUCTION

Two-Sample T-Test and CI: After Inventory, Before Inventory

Two-sample T for After Inventory vs Before Inventory

SE

N Mean StDev Mean

After Inventory 7 72.4 28.4 11

Before Inventory 5 208.6 68.8 31

Difference = mu (After Inventory) - mu (Before Inventory)

Estimate for difference: -136.171

95% upper bound for difference: -66.697

T-Test of difference = 0 (vs <): T-Value = -4.18

P-Value = 0.007 DF = 4

2-Variance test was done and since p-value is greater than 0.05, there is no significant change in the variation

2-sample t-test was done and since the p-value is less than 0.05, there is significant reduction in inventory

Reduction in Suits inventory from 9 months to 2 months (4.5 times improvement)

1. Fabric Mill2. Factory

3. Warehouse 4. Retail Store

Finished Goods

CTQ 4: SALES IMPROVEMENT AT RETAIL STORE

The Retail replenishment system for finished goods to retail stores is required to

Ensure timely delivery of finished goods at retail store against SLA

Ensure STO are created to fill the inventory gap at retail stores

Ensure that replenishment is aligned to sales as per pull model

Ensure that sale of finished goods is improved

Retail replenishment approach would ensure that local customer preferences as well as brand’s targeted sales is maintained...

IMPROVE

Provide information on store traffic, demand patterns, gaps in merchandise

Provide with weekly control reports to monitor inventory and store look

IMPROVE

Retail management system -Micro strategy and Business warehouse was implemented in year 2009 to provide transparency across the 68 retail stores of LP

Store Sales Store Inventory In-transit inventory

To replenish retail stock in northern and western region, couriers are used to reduce replenishment time within 3 days

All India One Stock (AIOS) system deployed for cross-docking, one retail store to another retail store

Micro strategy software to monitor store sales,

inventory

Business warehouse software to monitor in-transit inventory and AIOS system

CONTROL

ESCALATION MATRIXOUT OF STOCK

VENDOR/ INCHARGE

RETAIL MERCHANT

RETAIL HEAD

BRANDHEAD

4 Days 3 Days 2 Days 1 Day

For process, Out-of-stock and Transit time is monitored and reviewed on daily basis based on:

Retail Sales Retail Inventory Retail inventory norm Monitoring gap in Retail inventory

against Norm Stock transfer order need to be

created based on gaps

Escalation matrix established for handling transit time delays

Store Transit Time (Days) Before After

% Improvement

APK 2.56 1.38 46.09%TN/ KERALA 2.45 1.28 47.76%

WEST 6.28 2.96 52.87%NORTH 7.43 3.21 56.80%

AVERAGE 4.68 2.21 52.83%

(IN PCS)

Month NormActual Stock

Out-of-stock

Sales Loss %

Jul 1367 932 435 32%Aug 1367 830 537 39%Sep 1367 1067 300 22%Oct 2734 2568 166 6%Nov 2734 2876 0 0%Dec 2734 2754 0 0%

BENEFITS : SALES IMPROVEMENT

Two-Sample T-Test and CI: Before Sale, After Sale Two-sample T for Before Sale vs After Sale SE N Mean StDev MeanBefore Sale 12 174.7 43.0 12After Sale 3 717.7 38.7 22Difference = mu (Before Sale) - mu (After Sale)Estimate for difference: -543.00095% upper bound for difference: -482.904T-Test of difference = 0 (vs <): T-Value = -21.26 P-Value = 0.000 DF = 3

2-Variance test was done and since p-value is greater than 0.05, there is no significant change in the variation

2-sample t-test was done and since the p-value is less than 0.05, there is significant improvement in sales

Increase in Suits retail sales from avg 400 pcs sales to avg 785pcs in peak season (2 times improvement)

OVERALL BENEFIT

FINANCIAL BENEFIT FOR SUITS & BLAZER IN RETAIL CHANNELBUDGETED SALES PROJECTION (JAN’11-DEC’11) – 3375 pcs (Rs. 1.68 crores/ Annum)

DUE TO RETAIL REPLENISHMENT, 40% REDUCTION IN BUDGETED SALES LOSSNOV’10- JAN’11 ACTUAL SALES IMPROVEMENT – 800 PCS, ACTUAL EXTRA REVENUE – Rs20 Lacs

EXPECTED SALES IMPROVEMENT DUE TO RETAIL REPLENISHMENT – 1350 pcs/ Annum

EXPECTED PROFIT DUE TO SALES IMPROVEMENT– Rs33.6 Lacs/ Annum (Profit@Rs 2500/pc)

NET LEAD TIME REDUCTION(FABRIC + MANUFACTURING + RETAIL REPLENISHMENT) LEAD TIME

BEFORE, 105 + 18 + 7 (Max in case of Delhi) – 130 Days

AFTER, 15+12+3 (Max in case of Delhi) – 30 Days

IMPROVEMENT IN LEAD TIME – 4 TIMES

FUTURE ROADMAPAutomating the mechanism for forecasting Channel wise sales trend by identifying the best tool among the several tools by Q1

Automating the process of setting the inventory norm across the supply chain quarterly by Q1

Automating the transaction, which triggers the stock transfer order, work order for style codes and purchase order for fabric codes by Q2

Increasing the Scope of this project to different products and channels

BRAND CHANNEL PRODUCT EXP TIMELP RETAIL S&B LP TRADE S&B Q1LP RETAIL SHIRT Q2LP TRADE SHIRT Q2LP RETAIL TROUSER Q1LP TRADE TROUSER Q1VH RETAIL S&B Q1VH TRADE S&B Q1VH RETAIL SHIRT Q1VH TRADE SHIRT Q1VH RETAIL TROUSER Q2VH TRADE TROUSER Q2

THANK YOU

SUPPORTING DATA

NORMS CALCULATION FOR FABRIC & FG

REORDER POINT = SAFETY STOCK + AVERAGE DEMAND DURING LEAD TIMESAFETY STOCK = SERVICE LEVEL x STD DEV OF PROJECTION x SQRT(LEAD TIME)

NORMS CALCULATION FOR RETAIL STORERETAIL INVENTORY NORM = 5 x BUDGETED SALES (ADOPTED BY MKTNG DEPTT)

FORECAST FOR LP CORE STYLE CODES

LINEAR REGRESSION

Y = AX + B

A = 5.0852174B = 632.18478

MONTHLY ADJUSTMENT

2008-09 2009-10 MTH. AVE CUM AVE MONTHLY FACTOR

APRIL 676 379 528 696 0.758174632

MAY 580 400 490 696 0.704022989

JUNE 577 487 532 696 0.764367816

JULY 610 745 678 696 0.97341954

AUGUST 752 781 767 696 1.101293103

SEPTEMBER 423 458 441 696 0.632902299

OCTOBER 669 494 582 696 0.835488506

NOVEMBER 1,009 1,029 1,019 696 1.46408046

DECEMBER 985 1,016 1,001 696 1.4375

JANUARY 1,014 1,308 1,161 696 1.668103448

FEBRUARY 845 714 780 696 1.119971264

MARCH 374 373 374 696 0.536637931

ADJUSTED FORECAST

2010-11(R) 2010-11(A)

APRIL 759 576

MAY 764 538

JUNE 769 588

JULY 775 754

AUGUST 780 859

SEPTEMBER 785 497

OCTOBER 790 660

NOVEMBER 795 1164

DECEMBER 800 1150

JANUARY 805 1343

FEBRUARY 810 907

MARCH 815 437

GRAPH

SALES % CALCULATION

BRAND AVERAGE Sales%

LP CORE 326 44.10%

ROLL DOWN

BRAND SALES%

APR 2010

MAY 2010

JUN 2010

JUL 2010

AUG 2010

SEP 2010

OCT 2010

NOV 2010

DEC 2010

JAN 2011

FEB 2011

MAR 2011

576 538 588 754 859 497 660 1164 1150 1343 907 437

LP 44.10% 254 237 259 333 379 219 291 513 507 592 400 193

ROLL DOWN (LAST YEAR %)

APR 2010

MAY 2010

JUN 2010

JUL 2010

AUG 2010

SEP 2010

OCT 2010

NOV 2010

DEC 2010

JAN 2011

FEB 2011

MAR 2011

576 538 588 754 859 497 660 1164 1150 1343 907 437

LP 43.28% 249 233 255 326 372 215 286 504 498 581 393 189

ROLL DOWN (LAST YEAR % M-O-M)

APR 2010

MAY 2010

JUN 2010

JUL 2010AUG 2010

SEP 2010

OCT 2010

NOV 2010

DEC 2010

JAN 2011

FEB 2011

MAR 2011

576 538 588 754 859 497 660 1164 1150 1343 907 437

LP(%) 45.12% 53.25% 64.89% 45.23% 58.00% 58.30% 55.06% 45.77% 32.78% 27.98% 35.71% 69.44%

LP 260 287 382 341 498 290 363 533 377 376 324 304

COMPARISON OF ROLL-DOWN METHODS w.r.t LP

APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MARLP CORE 2009-10

171 213 316 337 453 267 272 471 333 366 255 259 LP CORE 2010-11

254 237 259 333 379 219 291 513 507 592 400 193

LP CORE 2010-11(LY SP) 249 233 255 326 372 215 286 504 498 581 393 189LP CORE 2010-11(LY SP M-O-M)

260 287 382 341 498 290 363 533 377 376 324 304

SIMPLE REGRESSION MODELSIMPLE REGRESSION MODELEXAMPLE FOR A DEPARTMENT SOTRE:EXAMPLE FOR A DEPARTMENT SOTRE:

YEARYEAR QUARTERQUARTER SALES SALES (RS.1,000)(RS.1,000)

YEARYEAR QUARTERQUARTER SALES SALES (RS.1,000)(RS.1,000)

11 2020 11 4040

11 22 3030 33 22 6060

33 5050 33 8080

44 7070 44 9090

11 3030 11 5050

22 22 4040 44 22 8080

33 6060 33 9090

44 8080 44 100100

TREND LINE : Y = a + bt

n

t b -y a Where,

22 ttn

yt -ty n b

tt yy tyty yy22 tt22 tt yy tyty yy22 tt22

11 2020 2020 400400 11 99 4040 360360 16001600 8181

22 3030 6060 900900 44 1010 6060 600600 36003600 100100

33 5050 150150 25002500 99 1111 8080 880880 64006400 121121

44 7070 280280 49004900 1616 1212 9090 10801080 81008100 144144

55 3030 150150 900900 2525 1313 5050 650650 25002500 169169

66 4040 240240 16001600 3636 1414 8080 11201120 64006400 196196

77 6060 420420 36003600 4949 1515 9090 13501350 81008100 225225

88 8080 640640 64006400 6464 1616 100100 16001600 1000100000

156156

Σ y = 970, Σ t = 136, Σ ty = 9600, Σ y2 = 67,900 and Σ t2 = 1496

3.9853 (136) - (1496) (16)

(970) (136) - (9600) (16) b

2

t3.98553 26.75 y

26.75 (16)

(136) (3.9853) -970 a

2

125

100

75

50

25

0

TREND LINEY = 26.75 + 3.9853{T)

PROJECTEDTREND

LINE

FORECASTADJUSTEDSEASONAL

VARIATIONSACTUALSALES

4 8 12 16

TT ACTUAL ACTUAL SALES SALES

y y

SALES SALES ESTIMATED ESTIMATED

ŷŷ

Y Y ŷ ŷ %%

tt ACTUAL ACTUAL SALESSALES

SALES SALES ESTIMATED ESTIMATED

ŷŷ

Y Y ŷ ŷ

11 2020 30.7330.73 65.00865.008 1111 8080 70.5970.59 133.3133.344

22 3030 34.7234.72 86.4086.40 1212 9090 74.5774.57 120.6120.699

33 5050 38.7038.70 129.19129.19 1313 5050 78.5578.55 63.6563.65

44 7070 42.6942.69 163.97163.97 1414 8080 82.5282.52 96.9296.92

55 3030 46.6746.67 64.2864.28 1515 9090 86.5386.53 104.4104.488

66 4040 50.6650.66 78.9578.95 1616 100100 90.5190.51 110.4110.488

77 6060 54.6454.64 109.80109.80 1717 94.5094.50

88 8080 58.6358.63 136.44136.44 1818 98.4898.48

99 4040 62.6162.61 63.8863.88 1919 102.47102.47

1100

6060 66.6066.60 90.0990.09 2020 106.64106.64

SEASONAL ADJUSTMENTS:

SEASONAL ADJUSTMENT FACTOR:SEASONAL ADJUSTMENT FACTOR:

QUARTER SEASONAL ADJUSTMENT FACTORQUARTER SEASONAL ADJUSTMENT FACTOR

132.89% 4

48.110 120.69 .44136 163.97 4

114.08% 4

104.01 113.34 09.80 129.19 3

88.09% 4

96.92 90.09 78.95 86.40 2

64.22% 4

63.65 63.88 64.28 65.08 1