increase cat spare parts inventory turn (itr) v2.0

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


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



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


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


Allocated Stocks Data

Model Wise - What is the allocated quantity of stocks

SAP System



Protective Stocks Data

Model Wise - What is the Protected quantity of stocks

SAP System



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


Exhaust Stock and Non - Stocks Data

Model Wise - What is the Exaust Stock and Non Stock quantity of stocks



Dead Stock Data (If Any)

Model Wise - What is the Dead Stock and Non Stock quantity of stocks



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

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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

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10.1

Residual

Perc

ent

3020100

4

2

0

-2

-4

Fitted Value

Resi

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4.53.01.50.0-1.5-3.0

60

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30

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Freq

uenc

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1401301201101009080706050403020101

4

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Observation Order

Resi

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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

2

03.0

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)


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

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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

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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)


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)


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

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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