This document outlines the scope and goals of a project to increase the inventory turn of Caterpillar spare parts. The current inventory turn is 2.31, below the budgeted goal of 4 for fiscal year 2015-2016. High inventory levels have resulted in approximately Rs. 12.63 crores in interest costs over the past 9 months. The goal of the project is to reduce average inventory from Rs. 100.29 crores to Rs. 66.6 crores in order to save Rs. 4.24 crores in interest costs by the end of fiscal year 2015-2016.

1. Increase Caterpillar Spare Parts
Inventory Turn (ITR)
A 6 SIGMA project

2. The Burning Platform
4
Module 3.0
Burning
Platform
The
Burning
Platform
The

3. 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 Stock
X3 = Consignment Stock
X4 = Protective Stock
X5 = Back to Back Ordering Process
X6 = Stock Order Process
X7 = 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 Agarwal
Process Owner - Saibal Mitra
Master Black Belt – Biswajit Mukherjee & Rajeev Kwatra
Black Belt – Krishnendu Chakraborty
Subject Matter Expert – Prodyot Haldar & Harish Awadhani.
Green Belts – Supriyo Majumdar, Niloy Ghosh, Sumit Sharma & Biswadip Mukherjee.
June July August October
W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4
Define
Measure
Analyse
Improve
Control
SeptemberFebruary March April May

4. 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

5. Parts Inventory & Supply Process – Functional Deployment Plan
Parts Inventory & Supply Process – Functional Deployment Map
PROCUREMENT
PROCESS
LOGISTICS WAREHOUSE
BACK 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
Yes
Yes
No
Yes
No
Start
Stop
All Issues
Resolved?
Yes
No

6. The Life Cycle of Parts Inventory
TemporaryStockParts
Exhaust Stock
Parts
Stocked
Parts
MadeStockParts
Non Stock Parts

7. 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.

8. SIPOC
Suppliers Input Process Steps Output Customers
Direct Customer Order
Commercial Terms & Conditions
Scope of Supply
Direct Customer Order
Debtors Outstanding
Credit days & Credit Limit
Manual Parts Requistion
Approved Purchase Order
Approved Purchase Requisition
Call / Demand Analysis
from Stocking Module
CAT/CIPL Invoices
Delivery Challan from branches
PDI Clearance Sheet
Parts Allocation to Customer
Order
Tax 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 Process
Delivery 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 Department
Stock 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/ SAP
Finance Module/
CSE/ PSSR/ Parts
C&L
Credit Lock Process

9. Input & Output Parameters
INPUT
INDICATORS
PROCESS
INDICATORS
OUTPUT
INDICATORS
• % 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

10. Measurement Plan
Performance 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

11. 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

12. 1050
99
90
50
10
1
840
99
90
50
10
1
432
99
90
50
10
1
531
99
90
50
10
1
210
99
90
50
10
1
East
Percent
South East North
North C entral Taratolla General O ffice
Mean 4.954
StDev 1.677
N 12
AD 1.583
P-Value <0.005
East
Mean 3.917
StDev 1.170
N 12
AD 1.140
P-Value <0.005
South East
Mean 2.862
StDev 0.3555
N 12
AD 0.252
P-Value 0.672
North
Mean 2.846
StDev 0.5588
N 12
AD 0.203
P-Value 0.838
North Central
Mean 1.069
StDev 0.3373
N 12
AD 0.892
Taratolla General Office
Normal - 95% CI
Territorial 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.

13. 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 4
Target 4
USL 5
Sample Mean 2.81677
Sample N 12
StDev (Within) 0.591867
StDev (O v erall) 0.60273
Process Data
C p 0.28
C PL -0.67
C PU 1.23
C pk -0.67
Pp 0.28
PPL -0.65
PPU 1.21
Ppk -0.65
C pm 0.00
O v erall C apability
Potential (Within) C apability
PPM < LSL 916666.67
PPM > USL 0.00
PPM Total 916666.67
O bserv ed Performance
PPM < LSL 977204.28
PPM > USL 112.69
PPM Total 977316.97
Exp. Within Performance
PPM < LSL 975184.09
PPM > USL 146.03
PPM Total 975330.13
Exp. O v erall Performance
Within
Overall
Process Capability of TIPL ITR

14. 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.

15. COS 80.67 49.39 36.33 30.53 24.13
Percent 36.5 22.3 16.4 13.8 10.9
Cum % 36.5 58.8 75.3 89.1 100.0
Territory
Taratolla
General O
ffice
North
Total
EastTotal
North
Central Total
South
East Total
250
200
150
100
50
0
100
80
60
40
20
0
Jan-Dec-14COS
Percent
Pareto Chart of Territorial Cost Of Sale
ITR 4.922 3.910 2.861 2.826 1.072
Percent 31.6 25.1 18.3 18.1 6.9
Cum % 31.6 56.6 75.0 93.1 100.0
Territory
Taratolla
General O
ffice
North
Central Total
North
Total
South
East Total
East Total
16
14
12
10
8
6
4
2
0
100
80
60
40
20
0
Jan-Dec-14ITR
Percent
Pareto Chart of Territorial ITR
Avg. Inventory 22.50 20.63 17.47 10.67 7.38
Percent 28.6 26.2 22.2 13.6 9.4
Cum % 28.6 54.8 77.1 90.6 100.0
Territory
East Total
North
Total
North
Central Total
South
East Total
Taratolla
General O
ffice
80
70
60
50
40
30
20
10
0
100
80
60
40
20
0
Jan-Dec-14Avg.Inventory
Percent
Pareto Chart of Territory Wise Jan-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.
.

16. Tem
p
Exhaust Value
Stock
in
Transit Value
Stock
at Vendor Value
Service
Stock
Value
SaleReturn
Stock
Value
SaleO
rder
Stock
Value
Protective
Value
Poisson
Value
Non
M
oving
Value
Excise
FG
Value
Consignm
ent Stock
Value
BlockStock-Scraping
Value
40
30
20
10
0
Stock Distribution
InventoryValue
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.
Tem
p
Exhaust Value
Stock
in
Transit Value
Stock
at Vendor Value
Service
Stock
Value
SaleReturn
Stock
Value
SaleO
rder
Stock
Value
Protective
Value
Poisson
Value
Non
M
oving
Value
Excise
FG
Value
Consignm
ent Stock
Value
BlockStock-Scraping
Value
35
30
25
20
15
10
5
0
Stock Distribution
InventoryMeanValue
Main Effects Plot for Stock Value by Stock Distribution
Data Means

17. 17 Marketing
One-way ANOVA: Stock Value versus Stock Distribution
Source DF SS MS F P
Stock Distribution 11 15000.28 1363.66 952.26 0.000
Error 132 189.03 1.43
Total 143 15189.31
S = 1.197 R-Sq = 98.76% R-Sq(adj) = 98.65%
Level N Mean StDev
BlockStock-Scraping Valu 12 0.080 0.261
Consignment Stock Value 12 5.748 1.657
Excise FG Value 12 2.552 0.706
Non Moving Value 12 12.459 1.176
Poisson Value 12 24.539 2.610
Protective Value 12 7.418 1.113
SaleOrder Stock Value 12 4.776 0.525
SaleReturn Stock Value 12 0.195 0.178
Service Stock Value 12 5.573 1.175
Stock at Vendor Value 12 0.230 0.098
Stock in Transit Value 12 2.058 0.508
Temp 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. 18 Marketing
Individual 95% CIs For Mean Based on
Pooled StDev
Level -+---------+---------+---------+--------
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.

19. 420-2-4
99.9
99
90
50
10
1
0.1
Residual
Percent
3020100
4
2
0
-2
-4
Fitted Value
Residual
4.53.01.50.0-1.5-3.0
60
45
30
15
0
Residual
Frequency
140
130
120
110
1009080706050403020101
4
2
0
-2
-4
Observation Order
Residual
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.

20. 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 0.160.080.00
40
20
0
10
5
0
3.0
1.5
0.0
10
5
08
4
04
2
0
3.0
1.5
0.0
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. 21 Marketing
Total Value Poisson Value Protective Value
Poisson 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. 22 Marketing
Temp Exhaust Val Nonmoving Value Consignment Stoc
Nonmoving 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 Va
Sale 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. 23 Marketing
The regression equation is
Total 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 VIF
Constant 0.5165 0.4134 1.25 0.217
Poisson Value 0.8915 0.1161 7.68 0.000 7.058
Protective Value 1.8477 0.2190 8.44 0.000 1.573
Temp Exhaust Value 1.0789 0.1849 5.83 0.000 7.554
Nonmoving Value 0.9504 0.2176 4.37 0.000 6.816
Consignment Stock Value 1.1875 0.1618 7.34 0.000 1.366
Service 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 P
Regression 6 3324.35 554.06 582.24 0.000
Residual Error 53 50.44 0.95
Total 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%

24. 3.01.50.0-1.5-3.0
99.9
99
90
50
10
1
0.1
Residual
Percent
40302010
3
2
1
0
-1
Fitted Value
Residual
3210-1
16
12
8
4
0
Residual
Frequency
605550454035302520151051
3
2
1
0
-1
Observation Order
Residual
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.

25. 3.01.50.0 210 1050 3.01.50.0 0.0100.0050.000 0.20.10.0 0.50.0-0.5
20
10
0
3.0
1.5
0.0
2
1
010
5
03.0
1.5
0.0
0.010
0.005
0.000
0.2
0.1
0.0
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.

26. Sum of Total Sum of Poisson Sum of Protective
Sum 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. 27 Marketing
Correlation of Individual Stock Values with Total Stock Value
(MARC)
Strong
Correlations
Exists.
Sum of Temp Exh Sum of Nonmovin Sum of Consignm
Sum 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 Service
Sum of Sale Ret *
*
Cell Contents: Pearson correlation
P-Value
* NOTE * All values in column are identical.

28. The regression equation is
Sum 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 VIF
Constant 0.00659 0.01814 0.36 0.718
Sum of Poisson Value 1.06448 0.02637 40.37 0.000 5.852
Sum of Protective Value 0.99658 0.07282 13.69 0.000 14.547
Sum of Temp Exhaust Value 1.00496 0.01773 56.69 0.000 14.662
Sum 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 P
Regression 4 1761.76 440.44 48584.40 0.000
Residual Error 55 0.50 0.01
Total 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%

29. 0.300.150.00-0.15-0.30
99.9
99
90
50
10
1
0.1
Residual
Percent
20151050
0.30
0.15
0.00
-0.15
-0.30
Fitted Value
Residual
0.240.120.00-0.12-0.24
40
30
20
10
0
Residual
Frequency
605550454035302520151051
0.30
0.15
0.00
-0.15
-0.30
Observation Order
Residual
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)

30. ISHIKAWA DIAGRAM

31. 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.
8
Inability 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.
8
Lack of System
Training/ System
Knowledge.
9 5 360
Parts Return Process
Parts not returned to
Principal
Inflated Inventory &
loss of revenue.
4 Parts Return Missed. 5 5 100
Ordering
Inventory Management
R
P
N
Process Function Potential Failure Mode
D
e
t
e
c
Potential Effect(s) of
Failure
S
e
v
Potential Cause(s)/
Mechanism(s) of Failure
O
c
c
u
r
Current
Process
Controls

32. 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

33. 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