increase cat spare parts inventory turn (itr) v2.0
TRANSCRIPT
Increase Caterpillar Spare Parts Inventory Turn (ITR)
A 6 SIGMA project
The Burning Platform4
Module 3.0
BurningPlatform
TheBurningPlatform
The
PARTS ITR IS 2.31 & HIGH INTEREST BURDEN ON INVENTORY HOLDING
PROJECT SCOPE
TEAM SELECTIONPROJECT PLAN
OPPORTUNITY STATEMENT
GOAL STATEMENT
BUSINESS CASE
Increase Caterpillar Spare Parts Inventory Turn (ITR)
High Temp. & Exhaust, Non Moving and Protective Stock of Caterpillar Spare parts leads to an average month end Inventory of Rs. 100.29 Crs (Approx.) which in turn results to high interest on inventory carrying cost.
The focus of the project is to maintain Allocated, Non Moving, Protective and Consignment stock at optimum level which will help to reduce interest burden and enhance Inventory Turnover (ITR), without effecting existing service levels.
The Project has direct implication on quality & cost of PQVC matrices. The Project will result to an estimated Level1 benefit of Rs. 4.24 Crs by reducing interest burden on average inventory holding cost.
Our Current (April– Dec‘14) Caterpillar Spare Parts Inventory Turn is 2.31 against budgeted growth plan of 4 for FY 2015-16. The current interest on inventory holding for the past 9 months has been approximately Rs. 12.63 Crs. which has an adverse impact on the bottom line.
Opportunity exists to reduce average Inventory from Rs. 100.29 Crs to estimated Rs. 66.6 Crs, thereby saving interest (@12.6%) on inventory carrying cost by approximately Rs. 4.24 Cr during 2015-16.
Y: Increase Parts ITR from 2.31 to 4 and thereby reduce interest cost by Rs. 4.24 Crs within 2015-16.
X1 = Parts Inventory Module in SAP X2 = Temp. & Exhaust StockX3 = Consignment Stock X4 = Protective StockX5 = Back to Back Ordering ProcessX6 = Stock Order ProcessX7 = Manual Ordering Process
In-scope: Spare Parts Inventory for Caterpillar Equipment.
Out of Scope: Spare Parts Inventory for Extended Mining Products and AI Parts Sales.
Project Sponsor – Subir Kumar Dutta & Shekhar AgarwalProcess Owner - Saibal MitraMaster Black Belt – Biswajit Mukherjee & Rajeev KwatraBlack Belt – Krishnendu ChakrabortySubject Matter Expert – Prodyot Haldar & Harish Awadhani.Green Belts – Supriyo Majumdar, Niloy Ghosh, Sumit Sharma & Biswadip Mukherjee.
June July August OctoberW1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4
Define MeasureAnalyseImproveControl
SeptemberFebruary March April May
Current Project Status
PROJECT : Increase Caterpillar Spare Parts Inventory turn (ITR) Black Belt Month
Week 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
Target
Actual
Legend
Measure by : Analyse by :Improve by :Control by :
September OctoberAugustJune July
Control
Revised Targets :
February March April May
Krishnendu Chakraborty
Define Measure Analyse
Remarks :
Improve
Parts Inventory & Supply Process – Functional Deployment Plan
Parts Inventory & Supply Process – Functional Deployment Map
PROCUREMENT PROCESS LOGISTICS WAREHOUSEBACK ORDER
PROCESS
Weekly Stock Order Report
Review of Stock Order
Stock Available at Branch?
Release of Stock Transfer Advice
Shipping Exception by
Branch?
Issues relating to Price difference, non-availability, replacement of
Parts etc.
Resolve Issues with Pricipal
PO Release on Principal
Acknowledge the Order
Invoicing/ Dispatch by Principal
Receipt of Material by Dealer
Mismatch between MOQ & Order Quantity?
Modify Stock Order
GIR
Discrepancy if Any?
Packing Slip by Branch
Road Permit Availability?
Dispatch of Material
Short Supply from Principal
Wrong Supply from Branch/
Principal
Excess Supply from Branch/
Principal
Damaged Items During Transit
Receive Material into Inventory
Collection of Road Permit
Resolve Issues with Branch/
Principal
No
Yes
NoYes
No
Yes
YesYes
No
Yes
No
Start
Stop
All Issues Resolved?
Yes
No
The Life Cycle of Parts Inventory
Tem
pora
ry S
tock
Par
ts
Exhaust Stock PartsStocked Parts
Made Stock Parts
Non Stock Parts
Definitions of Record Types of a Part
Record types of a part:
• Non-stock (N) - Part not stocked & Cannot have an on-hand, in process, in return, or on-order qty.
• Made-stock (M) - Part qualified to become stock.
• Stocked (S) - Parts can be stocked as per The Inventory Planning resultant qty.
• Exhaust Stock (E) - Part no longer qualified for routine replenishment.
• Temporary Stock (T) - Non-stock returns.
• Dead Stock (D) - When a part is replaced by new one, then old materials record type will get changed to D if stock in not available.
SIPOC
Suppliers Input Process Steps Output Customers Direct Customer Order Commercial Terms & ConditionsScope of Supply
Direct Customer Order Debtors Outstanding Credit days & Credit Limit Manual Parts Requistion Approved Purchase OrderApproved Purchase RequisitionCall / Demand Analysis from Stocking Module
CAT/CIPL Invoices Delivery Challan from branches PDI Clearance Sheet Parts Allocation to Customer OrderTax details / Discount related info Invoicing / Despatch Documents
Parts Invoice details Parts List as per CAT return Policy CAT approval (PRA) for Parts return Return documents
Principal/ TIPL Other Branches
Parts Store/C&L Delivery ProcessDelivery of Parts - Delivery Challan/
Packing Slip
TIPL Stocking Location/ Customer
Approved Customed Order Parts Department
Customer
Parts Department / Customer
Parts Return Return of Parts -
Return Order/Credit Note
TIPL Stocking Location/ Principal
Parts DepartmentStock Order & Back
Order Process
Purchase Order on Principal/
Requistion to other Branches
Sale Order Record Process
Order Receipt & Acknowlegement -
Purchase Requistion/ Purchase Order
Creation
Branch Parts C&L
Start Boundary : Parts Ordering End Boundary: Parts Return
Other Branches/Principal
Goods Receipt Process Goods Receipt Note Parts Store/ C&L
Customer/ SAPFinance Module/
CSE/ PSSR/ Parts C&L
Credit Lock Process
Input & Output Parameters
INPUT INDICATORS
PROCESS INDICATORS
OUTPUT INDICATORS
• # Parts Ordered through in SAP
# Stock Orders Executed
# Back Order Processed
• # Manual Orders
Processed
• # of Good Inwards Receipt (GIR) processed
• # Parts return request processed
• % Purchase Order Raised of Total
• % of Manual Orders Raised (on Branch/ Principal) of Total
• % Allocated Stock of Total
• % Protective Stock of Total
• % Consignment Stock of Total
• % Exhaust Stock/ Non Stocks of Total
• % Credit Notes Raised of Total
• Parts ITR
• % Workshop & Service Returns
• % Order Cancellations
• Stock Ageing
• # Valid Customer Orders
• # Debtor Outstanding
• # Credit Locks in SAP System
• # Approvals taken for Manual Orders
• # Parts return request from Customers
• # Cost of Sale of Parts
• # Average Inventory
Measurement PlanPerformance Operational Data Source and Location Sample Who will When will How will
Measure Definition Size Collect the data?
Data be collected? Data be collected?
COS Sales - SAP Data
Month Wise, Model Wise Cost of Sales Data of Parts Sales - CRM Service, FOC, MARC, Rental & Charge Offs. EMP Data to be excluded.
Data to be downloaded In Excel from SAP ECC System
All Models Sumit Sharma 1st Day of Every Month. 2 Years Historical Data Needed.
Data would be collected Directly from SAP as a download
COS Data from Accounts
Month Wise, Model Wise Cost of Sales Data of Parts Sales. EMP Data to be excluded.
Data to be collected from Accounts Dept. All Models
Baidyanath Dhabal/ TIPL South East Accounts
After month end closing by accounts i.e. 5th of Every Month. 2 Years Historical Data Needed.
Data would be collected Directly from Mr. Baidyanath Dhabal
Average Inventory Data
Monthwise Inventory Summary which includes Branchwise, Distribution Wise, SOS Wise, Ageing Wise Parts Inventory Break up.
SAP System (All Territories, All Models)
All Models Sumit Sharma Monthwise YTD Data. 2 Years Historical Data Needed.
Data is prepared by Mr. Prodyut Halder. Data needs to be collected from him.
Purchase Order Data
Month Wise, Model Wise Purchase Order Data (PO Raised on Caterpillar) with Order Value SAP System or Manually
All Models Sumit Sharma 2 Years Data to be collected one time
Data would be collected Directly from SAP as a download or to be created manually.
Customer Order Data
Month Wise, Model Wise Customer Order Placed on TIPL by Customers with order value SAP System or Manually
All Models Sumit Sharma 2 Years Data to be collected one time
Data would be collected Directly from SAP as a download or created manually
Parts Return Data
Month Wise, Item Wise Parts List Returned by Customers with reasons SAP System or Manually
All Models Sumit Sharma 2 Years Data to be collected one time
Data would be collected Directly from SAP as a download or created manually
Customer Credit Lock Data
How many customers have credit locks in SAP System. SAP System or Manually
As on Date Sumit Sharma 2 Years Data to be collected one time
Data would be collected Directly from SAP as a download or created manually
Manual Order Data
How many instances the customer credit lock status has been over ruled for raising manual orders or for advance procurement with justification. Data required month Wise, Model Wise what parts are manually Ordered and what is the order value. SAP System or Manually
All Models Sumit Sharma
2 Years Data to be collected one time
Data would be collected Directly from SAP as a download or created manually
Allocated Stocks Data
Model Wise - What is the allocated quantity of stocks
SAP System
All Models Sumit Sharma 2 Years Data to be collected one time
Data would be collected Directly from SAP as a download
Protective Stocks Data
Model Wise - What is the Protected quantity of stocks
SAP System
All Models Sumit Sharma 2 Years Data to be collected one time
Data would be collected Directly from SAP as a download
Stock Orders
Month Wise, Model Wise what is the Stock Order Maximum and Minimum Value generated in SAP System and what is the actual order value raised on Principal
SAP System All Models Sumit Sharma 2 Years Data to be collected one time
Data would be collected Directly from SAP as a download
Exhaust Stock and Non - Stocks Data
Model Wise - What is the Exaust Stock and Non Stock quantity of stocks
SAP System All Models Sumit Sharma 2 Years Data to be collected one time
Data would be collected Directly from SAP as a download
Dead Stock Data (If Any)
Model Wise - What is the Dead Stock and Non Stock quantity of stocks
SAP System All Models Sumit Sharma 2 Years Data to be collected one time
Data would be collected Directly from SAP as a download
Voice of Customer & Voice of Business
VOB
•Non Stocking Parts blocks the Capital• Inventory Turnover ratio (ITR) is currently not satisfactory•Huge interest is being paid on the non stocking parts inventory•External customers are dissatisfied
KBI
• % of Allocated & Protective Stocks needs to be reduced• To improve the working capital• Require higher ITR of 4.0• Require to achieve a better mix of parts inventory to increase customer serviceability & satisfaction
CBR
•To reduce of reduce average Inventory from Rs. 96.98 Crs to estimated Rs. 66.6 Crs.
•To improve current ITR from 2.39 to 4 & sustain the same•Require to reduce the interest burden to the tune of Rs. 3.82 Crs
CCR
•Receipt of 100% defect free parts•100% Availability of Parts •100% adaptability of parts as per the order
KCI•No transit damage or pilferage of parts.
•Parts ordered should be technically OK in all respects.
VOC•Receipt of parts in good condition
•Parts must comply to the requirements of the customer.
Mapping VOC& VOB
VOB: Voice Of Business KBI: Key Business Issues CBR: Critical Business Requirement CCR: Critical Customer Requirement KCI: Key Customer Issues VOC: Voice Of Customer
WIN - WIN
1050
99
90
50
10
1840
99
90
50
10
1432
99
90
50
10
1
531
99
90
50
10
1210
99
90
50
10
1
EastPe
rcen
tSouth East North
North Central Taratolla General Office
Mean 4.954StDev 1.677N 12AD 1.583P-Value <0.005
East
Mean 3.917StDev 1.170N 12AD 1.140P-Value <0.005
South East
Mean 2.862StDev 0.3555N 12AD 0.252P-Value 0.672
North
Mean 2.846StDev 0.5588N 12AD 0.203P-Value 0.838
North Central
Mean 1.069StDev 0.3373N 12AD 0.892
Taratolla General Office
Normal - 95% CITerritorial Probability Plot of ITR
Territorial Probability Distribution Chart of ITR
The above Probability Distribution Chart shows that the ITR for Territories East & South East are not Normally Distributed, with East Showing the Highest Standard Deviation.
ITR Process Capability Chart of TIPL (Without MARC & Taratolla Plant)
The above Process Capability Chart shows that the defects per million of TIPL ITR process is 975330 and the process is operating at -0.47 Sigma Level.
5.04.54.03.53.02.52.01.5
LSL, Target USL
LSL 4Target 4USL 5Sample Mean 2.81677Sample N 12StDev(Within) 0.591867StDev(Overall) 0.60273
Process Data
Cp 0.28CPL -0.67CPU 1.23Cpk -0.67
Pp 0.28PPL -0.65PPU 1.21Ppk -0.65Cpm 0.00
Overall Capability
Potential (Within) Capability
PPM < LSL 916666.67PPM > USL 0.00PPM Total 916666.67
Observed PerformancePPM < LSL 977204.28PPM > USL 112.69PPM Total 977316.97
Exp. Within PerformancePPM < LSL 975184.09PPM > USL 146.03PPM Total 975330.13
Exp. Overall Performance
WithinOverall
Process Capability of TIPL ITR
Stock Value Vs ITR Trend – Territory Level (Without MARC)
With Time, the Inventory Increases and ITR Decreases
With Time, the Inventory and ITR Decreases
With Time, the Inventory Increases and ITR also Increases Marginally
With Time, the Inventory Increases and ITR Decreases
Considering the January-December’14 ITR of North is 2.9 & North Central is 2.8, we need to focus on the said Territories.
COS 80.67 49.39 36.33 30.53 24.13Percent 36.5 22.3 16.4 13.8 10.9Cum % 36.5 58.8 75.3 89.1 100.0
Territory
Tarato
lla Ge
neral O
ffice
North T
otal
East To
tal
North C
entral
Total
South
East T
otal
250
200
150
100
50
0
100806040200
Jan-
Dec-
14 C
OS
Perc
ent
Pareto Chart of Territorial Cost Of Sale
ITR 4.922 3.910 2.861 2.826 1.072Percent 31.6 25.1 18.3 18.1 6.9Cum % 31.6 56.6 75.0 93.1 100.0
Territory
1614121086420
100
80
60
40
20
0
Jan-
Dec-
14 IT
R
Perc
ent
Pareto Chart of Territorial ITR
Avg. Inventory 22.50 20.63 17.47 10.67 7.38Percent 28.6 26.2 22.2 13.6 9.4Cum % 28.6 54.8 77.1 90.6 100.0
Territory
80706050403020100
100
80
60
40
20
0Jan-
Dec-
14 A
vg. I
nven
tory
Perc
ent
Pareto Chart of Territory Wise J an-Dec'14 Avg. Inventory
Stock Value, COS & ITR by Territory (Excluding MARC)
The 80-20 Rule shows that the Highest Stock Value lies at Taratolla & South East & North Central; Highest COS is at South East, North Central & East & the Lowest ITR is for Taratolla, North Central & North.
From the above Charts we can sight that the Highest ITR & Lowest Inventory is for East Territory, whereas the highest COS lies at South East.
Focus Territories for TIPL are North & North Central, also includes Taratolla Stock and this fact is confirmed by the Peretos too..
Temp E
xhaust
Value
Stock i
n Tran
sit Val
ue
Stock a
t Vendo
r Valu
e
Service
Stock V
alue
SaleRe
turn S
tock V
alue
SaleO
rder S
tock V
alue
Protec
tive Va
lue
Poisso
n Valu
e
Non Movi
ng Val
ue
Excise
FG Val
ue
Consign
ment Sto
ck Valu
e
BlockS
tock-S
crapin
g Valu
e
40
30
20
10
0
Stock Distribution
Inve
ntor
y Va
lue
Boxplot of Stock Value by Stock Distribution
Stock Value by Stock Distribution
The above Boxplot & Main Effects Plots also confirms that the Highest Stock Value is for Temp Exhaust, Poisson, Non Moving, Protective & Consignment Stock.
Temp E
xhaust
Value
Stock i
n Tran
sit Val
ue
Stock a
t Vendo
r Valu
e
Service
Stock V
alue
SaleRe
turn S
tock V
alue
SaleO
rder S
tock V
alue
Protec
tive Va
lue
Poisso
n Valu
e
Non Movi
ng Val
ue
Excise
FG Val
ue
Consign
ment Sto
ck Valu
e
BlockS
tock-S
crapin
g Valu
e
35302520151050
Stock Distribution
Inve
ntor
y M
ean
Valu
e
Main Effects Plot for Stock Value by Stock DistributionData Means
17 Marketing
One-way ANOVA: Stock Value versus Stock Distribution
Source DF SS MS F PStock Distribution 11 15000.28 1363.66 952.26 0.000Error 132 189.03 1.43Total 143 15189.31
S = 1.197 R-Sq = 98.76% R-Sq(adj) = 98.65%
Level N Mean StDevBlockStock-Scraping Valu 12 0.080 0.261Consignment Stock Value 12 5.748 1.657Excise FG Value 12 2.552 0.706Non Moving Value 12 12.459 1.176Poisson Value 12 24.539 2.610Protective Value 12 7.418 1.113SaleOrder Stock Value 12 4.776 0.525SaleReturn Stock Value 12 0.195 0.178Service Stock Value 12 5.573 1.175Stock at Vendor Value 12 0.230 0.098Stock in Transit Value 12 2.058 0.508Temp Exhaust Value 12 34.213 1.575
P Value is Less than 0.05 and hence we reject the null Hypothesis i.e. that the mean of all stock distributions are same.
R-Sq (adj) tells us that the type of Stock Distribution has significant impact (98.65%) on Inventory.
18 Marketing
Individual 95% CIs For Mean Based on Pooled StDevLevel -+---------+---------+---------+--------BlockStock-Scraping Valu (*)Consignment Stock Value (*Excise FG Value (*Non Moving Value *)Poisson Value (*Protective Value *)SaleOrder Stock Value (*SaleReturn Stock Value *)Service Stock Value (*Stock at Vendor Value *)Stock in Transit Value (*)Temp Exhaust Value *) -+---------+---------+---------+-------- 0 10 20 30
Pooled StDev = 1.197
One-way ANOVA: Stock Value versus Stock Distribution
Focus Areas as any of the given Stock Distributions might have a significant impact on Inventory.
420-2-4
99.999
90
50
10
10.1
Residual
Perc
ent
3020100
4
2
0
-2
-4
Fitted Value
Resi
dual
4.53.01.50.0-1.5-3.0
60
45
30
15
0
Residual
Freq
uenc
y
1401301201101009080706050403020101
4
2
0
-2
-4
Observation Order
Resi
dual
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for Stock Value
Residual Plots for Stock Value – Check for ANOVA
It is verified from above charts that residuals are normally and randomly distributed and have a mean of zero, variance is equal for all factor levels and the data points come from a Normally Distributed Population.
Matrix Plots for Stock Value – Without MARC
From the Matrix Plot we can identify that there is a strong positive correlation between total stock and Poisson Stock, Temp. Exhaust Stock, Non-Moving Stock and Service Stock Values.
1050 3.01.50.0 1050 840 420 3.01.50.0 0.160.080.0040
20
010
503.0
1.5
0.010
5
08
4
04
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1.50.0
Total Value
Poisson Value
Protective Value
Temp Exhaust Value
Nonmoving Value
Consignment Stock Value
Service stock Value
Sale Return Stock Value
Matrix Plot of Stock Value
21 Marketing
Total Value Poisson Value Protective ValuePoisson Value 0.907 0.000
Protective Value 0.296 0.005 0.022 0.967
Temp Exhaust Val 0.911 0.804 0.318 0.000 0.000 0.013
Nonmoving Value 0.897 0.841 0.145 0.000 0.000 0.268
Consignment Stoc 0.425 0.309 -0.043 0.001 0.016 0.747
Service stock Va 0.805 0.797 0.052 0.000 0.000 0.691
Sale Return Stoc 0.146 0.099 -0.093 0.266 0.450 0.477
Correlation of Individual Stock Values with Total Stock Value (Without MARC)
Strong Correlations Exists.
This confirms what we saw in Matrix Plot. There exists a strong correlation between Poisson Value, Temp. Exhaust Value, Nonmoving Value, Service Stock with Total Stock Value i.e. Individual Stocks (Poisson, Temp. Exhaust, Non Moving & Service Stock) are strongly affecting the Total Stock.
22 Marketing
Temp Exhaust Val Nonmoving Value Consignment StocNonmoving Value 0.835 0.000
Consignment Stoc 0.336 0.204 0.009 0.119
Service stock Va 0.571 0.773 0.302 0.000 0.000 0.019
Sale Return Stoc 0.193 0.238 0.171 0.139 0.067 0.193
Service stock VaSale Return Stoc 0.150 0.252
Cell Contents: Pearson correlation P-Value
Correlation of Individual Stock Values with Total Stock Value (Without MARC)
Strong Correlations Exists.
23 Marketing
The regression equation isTotal Value = 0.517 + 0.891 Poisson Value + 1.85 Protective Value + 1.08 Temp Exhaust Value + 0.950 Nonmoving Value + 1.19 Consignment Stock Value + 1.37 Service stock Value
Predictor Coef SE Coef T P VIFConstant 0.5165 0.4134 1.25 0.217Poisson Value 0.8915 0.1161 7.68 0.000 7.058Protective Value 1.8477 0.2190 8.44 0.000 1.573Temp Exhaust Value 1.0789 0.1849 5.83 0.000 7.554Nonmoving Value 0.9504 0.2176 4.37 0.000 6.816Consignment Stock Value 1.1875 0.1618 7.34 0.000 1.366Service stock Value 1.3744 0.2738 5.02 0.000 4.450
S = 0.975503 R-Sq = 98.5% R-Sq(adj) = 98.3%
Analysis of Variance
Source DF SS MS F PRegression 6 3324.35 554.06 582.24 0.000Residual Error 53 50.44 0.95Total 59 3374.79
Regression Model for Individual Stock Values (Without MARC)
P < 0.05 indicates that the model is statistically significant
Individual Stocks Value affecting the Total Stock Value by 98%
3.01.50.0-1.5-3.0
99.999
90
50
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10.1
Residual
Perc
ent
40302010
3
2
1
0
-1
Fitted Value
Resi
dual
3210-1
16
12
8
4
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Residual
Freq
uenc
y
605550454035302520151051
3
2
1
0
-1
Observation Order
Resi
dual
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for Total Value
Regression Model – Analysis of Residuals (Without MARC)
This above plots confirms there are few outliers in the Normal Probability Plot however, the data is uniformly scattered around zero and there are no patterns. Also, there are no observations with high leverage or influence.
3.01.50.0 210 1050 3.01.50.0 0.0100.0050.000 0.20.10.0 0.50.0-0.520
10
03.0
1.5
0.02
1
010
5
03.0
1.5
0.00.0100.0050.0000.20.10.0
Sum of Total Value
Sum of Poisson Value
Sum of Protective Value
Sum of Temp Exhaust Value
Sum of Nonmoving Value
Sum of Consignment Stock Value
Sum of Service stock Value
Sum of Sale Return Stock Value
Matrix Plot of Stock Value - MARC
Matrix Plots for Stock Value – MARC
From the Matrix Plot we can identify that there is a strong positive correlation between total stock and Poisson Stock, Protective Stock, Temp. Exhaust Stock and Non-Moving Stock Values.
Sum of Total Sum of Poisson Sum of ProtectiveSum of Poisson 0.929 0.000
Sum of Protecti 0.936 0.785 0.000 0.000
Sum of Temp Exh 0.990 0.897 0.916 0.000 0.000 0.000
Sum of Nonmoving 0.974 0.861 0.960 0.000 0.000 0.000
Sum of Consignm -0.094 -0.167 -0.118 0.476 0.202 0.371
Sum of Service 0.885 0.724 0.955 0.000 0.000 0.000
Sum of Sale Ret * * * * * *
26 Marketing
Correlation of Individual Stock Values with Total Stock Value (MARC)
Strong Correlations Exists.
This confirms what we saw in Matrix Plot. There exists a strong correlation between Poisson Value, Protective Value, Temp. Exhaust Value, Nonmoving Value with Total Stock Value i.e. Individual Stocks (Poisson, Protective, Temp. Exhaust & Non Moving) are strongly affecting the Total Stock.
27 Marketing
Correlation of Individual Stock Values with Total Stock Value (MARC)
Strong Correlations Exists.
Sum of Temp Exh Sum of Nonmovin Sum of ConsignmSum of Nonmovin 0.952 0.000
Sum of Consignm -0.063 -0.055 0.632 0.679
Sum of Service 0.858 0.938 -0.087 0.000 0.000 0.510
Sum of Sale Ret * * * * * *
Sum of ServiceSum of Sale Ret * *
Cell Contents: Pearson correlation P-Value
* NOTE * All values in column are identical.
The regression equation isSum of Total Value = 0.0066 + 1.06 Sum of Poisson Value + 0.997 Sum of Protective Value + 1.00 Sum of Temp Exhaust Value + 1.09 Sum of Nonmoving Value
Predictor Coef SE Coef T P VIFConstant 0.00659 0.01814 0.36 0.718Sum of Poisson Value 1.06448 0.02637 40.37 0.000 5.852Sum of Protective Value 0.99658 0.07282 13.69 0.000 14.547Sum of Temp Exhaust Value 1.00496 0.01773 56.69 0.000 14.662Sum of Nonmoving Value 1.09089 0.06037 18.07 0.000 23.994
S = 0.0952127 R-Sq = 100.0% R-Sq(adj) = 100.0%
Analysis of Variance
Source DF SS MS F PRegression 4 1761.76 440.44 48584.40 0.000Residual Error 55 0.50 0.01Total 59 1762.26
28 Marketing
Regression Model for Individual Stock Values (MARC)
P < 0.05 indicates that the model is statistically significant
Individual Stocks Value affecting the Total Stock Value by 100%
0.300.150.00-0.15-0.30
99.999
90
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10.1
Residual
Perc
ent
20151050
0.30
0.15
0.00
-0.15
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Fitted Value
Resi
dual
0.240.120.00-0.12-0.24
40
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Residual
Freq
uenc
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605550454035302520151051
0.30
0.15
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-0.15
-0.30
Observation Order
Resi
dual
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for Sum of Total Value
This above plots confirms there are few outliers in the Normal Probability Plot however, the data is uniformly scattered around zero and there are no patterns. Also, there are no observations with high leverage or influence.
Regression Model – Analysis of Residuals (MARC)
ISHIKAWA DIAGRAM
ROOT CAUSE ANALYSIS - FMEA
Advance / Manual Procurement because of Wrong anticipation of Customer Order OR Procurement done against Bargain Offer
Wrong Procurement. Inflated Inventory. 8
Failure to understand parts specifications. Wrong Recommendation. Wrong Anticipation of Customer Order. Customer Credit Lock.
10 6 480
Discrepancy in Min / Max Ordering
Procurement under misleading min/max. 8
Wrong recommendation of parts utilized under warranty.
10 5 400
PSSR/ PSM Skill Gap
Wrong recommendation of parts leading to wrong procurement.
9 Wrong unerstanding of Parts Specifications.
5 Training Required. 5 225
Protective Stock / Float / CAT RO / PSP / NPI Parts
Inflated Inventory. 7 Recommendation from Sales Team/ Principal.
10 4 280
Shelf life and Scrap Increase in scrap parts and revenue loss.
8Inability to clear shelf life parts before expiry date.
9 1 72
Physical Stock discrepancies in Inventory
Stock Looks inflated even if the material is non-existant. Loss of revenue.
7 No system to check physical inventory.
5 5 175
Parts Sales
Customer not lifting parts or Customer Orders cancelled
Inflated Inventory. 6
Customer's financial status not stable/customer requirement non-existent.
10 5 300
WO Management
Lack of monitoring Open WO leads to increase in Consignment Stock
Increase in Consignment Stock in System. Revenue not getting adjusted against Service WO.
8Lack of System Training/ System Knowledge.
9 5 360
Parts Return ProcessParts not returned to Principal
Inflated Inventory & loss of revenue. 4 Parts Return Missed. 5 5 100
Ordering
Inventory Management
RPN
Process Function Potential Failure ModeDet
ec
Potential Effect(s) of Failure
Sev
Potential Cause(s)/ Mechanism(s) of Failure
Occ
ur
Current Process Controls
ROOT CAUSES OF INCREASING PARTS INVENTORY
1. Advance / Manual Procurement because of Wrong anticipation of
Customer Order, wrong recommendation from CSE or procurement
done against Bargain Offer
2. Protective Stock / Float / CAT Recommended Order / PSP / NPI Parts
3. Discrepancy in Min / Max Ordering. Warranty Parts also Captured under
Min / Max Ordering
4. Customer not lifting parts/ Customer Orders cancelled/ Parts return by
Customer
5. Lack of monitoring Open WO leads to increase in Consignment
6. Physical Stock discrepancies in Inventory
7. Parts not timely returned to Principal
8. PSSR/ PSM Skill Gap
9. Parts not returned to Principal
IMPROVEMENT SUGGESTIONS RECEIVED FROM TEAM
1. Accountability for each and every process should lie with concerned individual, failure to maintain the same would initially subject him/her to warning and further failure to display accountability should subject him/her to severe proceedings.
2. One major root causes is missing – “Customer and Service Parts Return”. Mr. Saibal to share such cases where failure (increase in Inventory) is due to parts returned by either customer or service team. Once receipt of such cases, the root cause would be incorporated in ISHIKAWA and FMEA.
3. Inventory to be distributed to 4 territories instead of lying at Central stock so that the concerned territory should be responsible.
4. Territory Heads to be made accountable for the inventory of their respective territories and also should be responsible for the P&L Statement of the same. This would enable central parts team in effectively monitoring the parts stock at territory level. If required, resources from the central parts team would relocate across territories for effective monitoring and procurement of parts accordingly.
5. The project needs to be presented to SKC Sir and his opinions needs to be sought on further proceedings