six sigma approach for replenishment in supply chain
Post on 29-Dec-2015
106 Views
Preview:
DESCRIPTION
TRANSCRIPT
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
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
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
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)
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
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:
top related