managing risk in supply chain operations_daniel_final

RESEARCH PAPER

MANAGING RISK IN SUPPLY CHAIN OPERATIONS

Course name: Global SCM and International Logistics

Professor: Troy Glassman

Student: Daniel Cywinski

April, 2016

Managing risk in Supply Chain operations Daniel Cywinski

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TABLE OF CONTENTS

1 INTRODUCTION..................................................................................................................4

2 LITERATURE REVIEW ......................................................................................................5

3 FMEA TECHNIQUE ............................................................................................................6

3.1 FMEA Steps...................................................................................................................6

3.2 Rating tables (occurrence, severity, detection) ........................................................7

3.3 Fuzzy terms ...................................................................................................................8

4 COMPANY INFORMATION ...............................................................................................9

5 DEFAULT FMEA ............................................................................................................... 11

6 FUZZY FMEA .................................................................................................................... 13

7 CONCLUSION................................................................................................................... 15

REFERENCES ..................................................................................................................... 16


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Abstract: Today’s business environment is characterised with constant changes,

expending markets and turbulences. Business operations are affected by external

and internal factors which are in some situations very hard for companies to identify

and to predict. In order to survive, companies are striving to achieve Excellency in

their operations, and managing risks is a very important part. Many techniques were

developed in order to help companies to identify and assess operational risks, so

they can be properly managed, minimized or removed. The one which is used very

often is focused on the analysis of possible failure mode and their effects on

operations - FMEA. Assessing risks in supply chain operations can be very

complicated, tricky and usually followed by uncertainty, ambiguity and even conflicts

between experts who are doing the assessment. To overcome these issues, this

paper is proposing the usage of fuzzy logic in the risk assessment process. Two

FMEA are tested on SC operations of one manufacturer and Distributer Company.

One default and one fuzzy FMEA. Final results and rankings of failure modes were

examined at the end and proposed some recommendations for improvement.

Key words: Risk assessment, Supply chain operations, Failure mode and Effect

Analysis, Fuzzy logic, Subjectivity and uncertainty.


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1 INTRODUCTION

Today’s business environment is characterised with constant changes, fast

expending markets and many turbulences. Business operations are affected by

external and internal factors which are in some situations very hard for companies to

identify and to predict their behaviour. To survive at the market, companies are

constantly looking for new ways to fulfil expectations of their stakeholders and to

acquire attention of other interested parties. Considering this, companies are striving

to achieve Excellency in their operations, and managing risks is a very important

part. Through time, many models, tools and techniques were developed in order to

help companies to identify and assess operational risks, so they can be properly

managed, minimized or removed. The one technique which is used very often now is

focused on the analysis of possible failure mode and their effects on operations.

FMEA (Failure Mode and Effects Analysis) represents structured, systematic and

proactive technique which can be used for failure analysis. It can be applied not only

on processes and their operations, but on the whole systems, development of new

products or services. Assessing risks in supply chain operations can be very

complicated, tricky and usually followed by uncertainty, ambiguity and even conflicts

between experts who are doing the assessment. To overcome these issues, fuzzy

logic can be used as one very powerful tool (Chang & Cheng, 2010). Because of

the ability to compute vagueness (Lalla et al., 2008), uncertainty and imprecision,

fuzzy logic finds its application in treatment of attributes and linguistic variables

(Jamshidi, 2003).

This paper aims to show the usage of FMEA (Failure Mode and Effects Analysis)

technique for assessing the risks in supply chain operations. To overcome

uncertainty and imprecision issues, when it comes to the expert assessments, fuzzy


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numbers are used as a modification to the “default” FMEA, following the steps and

suggestions proposed by (Liu et al., 2011). Following this introduction, literature

review will be presented in the next part of this paper. It will include the FMEA

technique application analysis and some authors’ propositions for FMEA

modification. In the third part, steps of the FMEA will be given with other necessary

elements for modified FMEA implementation. After that, all necessary data regarding

company and its supply chain operations will be provided in the fourth part. Two

FMEA will be conducted - one FMEA without modifications and one with Fuzzy

numbers included, as proposed by (Liu et al., 2011). This will be described step by

step, in parts five and six respectively. In the conclusion, results will be analysed and

compared and further research questions will be opened.

2 LITERATURE REVIEW

As stated at the beginning, identifying, assessing and managing risks in supply chain

operations is receiving a significant amount of attention nowadays. Considering

interconnection and correlation between processes, and the need for communication

and cooperation with many parties in the environment, it can be said that supply

chain is a very risky field (Coyle et al, 2013). Because of that, many researches

were conducted in order to properly investigate and briefly analyse the use of

different techniques for risk assessment. Through the analysis and comparison,

many authors (Stamatis, 2003; Carlson, 2012; Kumru & Kumru, 2013;)

highlighted strengths and weaknesses, identified issues and proposed combination

of other techniques to overcome them. One of the authors (Atwatera et al., 2014)

developed a risk assessment scorecard, using conjoint analysis, for motor carrier

firms to predict risks and their ability to answer constant changes of the environment.


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Similar, (Claypoola et al., 2014) developed a model which is based on the analysis

of product design, supply chain design and risk concurrently using Mixed Integer

Programming.

FMEA is a very popular technique for assessing risks in supply chain operations. It

provides a simple analysis procedure (Mozaffari et al., 2013) and it is very

structured and reliable method (Kostina et al., 2012). As stated by (Braglia, 2000),

FMEA stands as very useful and helpful techniques for managers. FMEA models

based on the usage of fuzzy logic and fuzzy numbers play a very important role

nowadays in risk management process. Authors (Braglia et al., 2003) combined

fuzzy TOPSIS model in order to improve reliability of the existing FMEA, the same as

(Song et al., 2014). Similar, (Chang & Cheng, 2011) used another fuzzy model

(DEMATEL) to better evaluate the risk of failure. (Chang et al., 2010) used

intuitionistic fuzzy set ranking technique to reprioritize previously calculated failure

modes. Since risk assessment is closely related to the decision making process,

(Hadi-Vencheh & Aghajani, 2013) used fuzzy group Multi Criteria Decision Making

approach to improve FMEA.

3 FMEA TECHNIQUE

For the purpose of this paper, default FMEA steps will be explained following the

description which can be found on American Society for Quality website (asq, 2004).

3.1 FMEA Steps

Steps for Failure Mode and Effects Analysis are:

Step 1 - Determine basic elements

- Identify the scope of FMEA (functions, processes, operations, activities); - Identify the purpose of the FMEA; - Assemble the project team;

Step 2 - For each operation list possible failure modes - If necessary, go back to the scope and rewrite the scope with more details ;


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- Failure modes should reflect the loss for the specific operation; Step 3 - Identify possible consequences for each of FM

- Consider consequences on current system, related systems, process and related processes, products, clients, reputation of company; - Helpful question: “What happens when this failure occurs?”

Step 4 - Determine severity rating - Helpful question: “How serious listed consequences are?”

- Severity is usually rated on a scale from 1 to 10 (meaning of the scale is given in the table 1

in next 3.2 part); Step 5 - Determine potential root causes - Use root cause analysis tools (5why, Tree diagram…..)

- Use experience and apply knowledge of the team members; Step 6 - Determine occurrence rating

- This rating estimates the probability of failure occurring during the lifetime of the chosen

scope; - Occurrence is usually rated on a scale from 1 to 10 (meaning of the scale is given in the table 2 in next 3.2 part);

Step 7 - Identify existing measures - Those are current process controls with the aim to prevent cause from happening, or reduce the likelihood that it will happen, or detect failure after the cause has already happened;

- Could be tests, procedures or mechanisms in place of the scope; Step 8 - Determine detection rating - Should be determined for each of the existing measures (controls);

- This rating estimates how well the controls can detect either the cause or its failure mode; - Detection is usually rated on a scale from 1 to 10 (meaning of the scale is given in the table 3 in next 3.2 part);

Step 9 - Calculate the risk priority number (RPN) - Equals Severity rating × Occurrence rating × Detection rating; Step 10 - Rank FM based on RPN

- Determine critical FM so they can be addressed with higher priority; Step 11 - Identify recommended actions

- Recommended actions should be identified with all proper elements (responsibilities, scope,

deadlines, and resources) Step 12 - Calculate new RPN

- After the completion of recommended actions, new S, D and O ratings need to be assessed

and new RPN calculated;

3.2 Rating tables (occurrence, severity, detection)

Traditional tables for the determination of Severity, Occurrence and Detection ratings

will be used, as also used by many other authors before, such as (Chin et al., 2008;

Chin et al., 2009; Wang et al., 2009). Rating tables are given following the FMEA

steps order.

Table 1. - Severity of a failure ratings

Rating Effect Severity of effect

10Hazardous without

warning

When a potential failure mode affects safe operation and/or involves

noncompliance with regulations without warning

9Hazardous with

warning

When a potential failure mode affects safe operation and/or involves

noncompliance with regulations with warning

8 Very High Inoperable state, with loss of primary function

7 High Operable state, but at reduced level of performance. Clients dissatisfied

6 Moderate Operable state. Clients experiences discomfort

5 Low Operable state, but with reduced level of performance. Clients dissatisfied

4 Very Low Very low effects on operations but can slightly effect on clients satisfaction

3 Minor Minor effects on operations

2 Very minor Very minor effects on operations

1 None No effect


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Table 2. - Occurrence of a failure ratings

Table 3. - Detection of a failure ratings

3.3 Fuzzy terms

Necessary fuzzy terms for Fuzzy FMEA now needs to be determined. In table 4,

fuzzy ratings for linguistic terms are given, as also used by (Liu et al., 2011).

Table 4. Fuzzy ratings for linguistic terms

Membership function for the linguistic terms is (Liu et al., 2011):

Figure 1. - Membership function for the linguistic terms

Rating Probability Failure rate

10 Very High: failure is almost inevitable ≥1/2

9 1/3

8 High: repeated failures 1/8

7 1/20

6 Moderate: occasional failures 1/80

5 1/400

4 1/2000

3 Low: relatively few failures 1/15000

2 1/150000

1 Remote: failure is unlikely 1/1500000

Rating Detection Criteria

10Absolutely

impossible

Design control will not and/or cannot detect a potential cause/mechanism and

subsequent failure mode; or there is no design control

9 Very remoteVery remote chance the design control will detect a potential cause/mechanism

and subsequent failure mode

8 RemoteRemote chance the design control will detect a potential cause/mechanism and

subsequent failure mode

7 Very lowVery Low chance the design control will detect a potential cause/mechanism


6 LowLow chance the design control will detect a potential cause/mechanism and


5 ModerateModerate chance the design control will detect a potential cause/mechanism


4 Moderately high Moderately high chance the design control will detect a potential

cause/mechanism and subsequent failure mode

3 HighHigh chance the design control will detect a potential cause/mechanism and


2 Very High Very High chance the design control will detect a potential cause/mechanism


1 Almost certain Design control will almost certainly detect a potential cause/mechanism and


Liguistic Occurrence Severity Detection Fuzzy numbers

Very low 1, 2 1, 2, 3, 4 1, 2 (0,0,1,2)

Low 3, 4 5 3 (1,2,3,4)

Moderate 5, 6 6 4, 5 (3,4,6,7)

High 7, 8 7 6 (6,7,8,9)

Very High 9, 10 8, 9, 10 7, 8, 9, 10 (8,9,10,10)


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For the Defuzzification of the linguistic terms, next formula will be used:

]}[]{[]}[]{[

][][

1010

10

dadacbcb

cbcbhi

(1)

Where:

ih - Defuzzified number (rating of S, O, D)

10,10 ,, bbaa - Numbers of fuzzy number, given in brackets respectively

c - Minimum scale value, in this case zero

d - Maximum scale value, in this case 10

4 COMPANY INFORMATION

Based on previously conducted literature review and the analysis of the default and

modified FMEA models, the importance of proper risk identification and assessment

in supply chain operations is evident. Considering that, management of Intercorp

decided to conduct a project with the aim to identify and assess of risk in its supply

chain operations. Intercorp is an American manufacturing and distribution company

for paper and plastic household products. It was founded in Skokie in 1995, near

Chicago and up till now, it achieved constant every year growth. As a part of

company’s portfolio, there are two major groups of SKU’s - Paper group (paper

towels, handkerchiefs, napkins, tork paper, paper plates) and Plastic group (plastic

bags, stretch and aluminium foils, Plastic cutlery set). Number of clients now

exceeds 1000, and they are treated and served through two channels. Wholesalers

and retail (supermarkets, groceries restaurants, fast food shops, newspaper stands

and other retail stores). Intercorp now have 50 employees working on four site.

Central office as a major site 1, production facility with the warehouse as major site 2

and two locations with smaller cross dock warehouses. Transportation fleet consists

of 10 selling vehicles and 4 Lories in charged for wholesaler’s orders.


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So far in this year, Intercorp’s market share experienced enormous growth for

approximately 14%, caused by bigger and more frequent client’s demands.

Following that, operational errors and mistakes are more often and all this expanding

situation opened the possibility for many other operational risks to arise. Faced with

market share growth situation, director of the company decided to conduct risk

assessment project in supply chain operations for the start. This will give a company

more information about critical and risky areas and will help management to decide

in which direction to point up future investment of resources to properly manage

existing risks (minimize or remove). On monthly management meeting, it is decided

that Failure Mode and Effects Analysis will be most suitable to be conducted. Also, it

is decided that assessment should be initially conducted on several critical

operations using both regular and fuzzy numbers. After the comparison of the results

produced from Default and Fuzzy FMEA, more suitable analysis for Intercorp

organization will be chosen and conducted including all operations and activities in

supply chain, sales and support processes. As a project team leader, director named

Logistics manager. LM will participate in the assessments, will supervise project

tasks and FMEA steps respectively, and it will be responsible to deliver final project

results (risk matrixes) in defined deadline.

Before applying FMEA on chosen company, step 1 (determination of the basic

FMEA elements) will be defined here, since it is the same for both FMEA’s (default

and fuzzy). Four supply chain processes and their operations will represent a scope

of the analysis: 1. Inventory procurement (Forecasting, Ordering and General

Activities); 2. Warehousing (Receiving, Preparation and loading and Maintenance

Activities); 3. Transportation (Routing and Delivering); 4. Customer Service


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(Invoicing, Complaints handling and Price lists managing Activities). The purpose of

the analysis will be to identify, examine and assess risks for major and most frequent

failure modes in chosen operations. For this project, process owners will be in

charge for the assessment of failure modes for their specific operations together with

the Logistics Manager. So, the project team will be consisted of five people:

Procurement manager, Manager of Warehousing operations, Customer Service

Supervisor, Transportation Manager and Logistics Manager as a team leader. For

each of the rating, based on the assessments of LM and other manager, average

scores will be determined.

5 DEFAULT FMEA

Default FMEA will be applied considering previously determined elements (step 1).

Both logistic manager (LM) and process owners (PO) were giving the assessments

for the ratings and the average scores were determined.

Table 5. - Individual assessments

Average scores were used for all ratings. After applying all steps from 2 to 9,

following matrixes for four supply chain operations were created and given in tables.

LM PO Average LM PO Average LM PO Average

IPFM 1 8 8 8 2 4 3 3 1 2

IPFM 2 9 9 9 4 4 4 1 1 1

IPFM 3 8 4 6 1 3 2 1 1 1

WHFM 1 8 8 8 3 3 3 1 1 1

WHFM 2 8 8 8 1 3 2 4 2 3

WHFM 3 7 9 8 2 2 2 3 5 4

WHFM 4 10 6 8 3 1 2 1 1 1

TRFM 1 10 6 8 3 5 4 5 3 4

TRFM 2 7 9 8 4 2 3 2 6 4

TRFM 3 8 8 8 1 3 2 2 2 2

TRFM 4 8 10 9 2 2 2 4 2 3

TRFM 5 7 9 8 3 1 2 1 1 1

TRFM 6 6 10 8 1 1 1 4 4 4

CSFM 1 9 7 8 5 3 4 2 6 4

CSFM 2 6 10 8 1 3 2 1 1 1

CSFM 3 10 8 9 2 2 2 1 3 2

CSFM 4 8 8 8 2 4 3 1 3 2

Severity rating Occurrence rating Detection rating


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Table 6. - Inventory procurement risk assessment

Table 7. - Warehousing risk assessment

Table 8. - Transportation risk assessment

Table 9. - Customer Service risk assessment

NoOperation/

Activity

Event - Failure

modeFM ID

Can lead to

(Risk description)

Consequences

(description)

Severity

ratingPossible causes

Occurrence

ratingExisting measures

Detection

ratingRPN

1 ForecastingBad forecast for the next

periodIPFM 1

excessive / insufficient

inventories

lost of sales/ opportunities

missed

Captured capital

Unsatisfied clients

Penalties

8

Bad inputs regarding

goals and capacity

limits

3

Procedure for forecasting

established; All staff trained and

supervised; Every forecast

double checked

2 48

2 Ordering

Delayed/long transport

from supplier to

warehouse

IPFM 2 Low inventory level

Can't fulfil clients

demands; Penalties;

Clients unsatisfaction

9

Orders not placed on

time; Delivery root

blocked/ changed

4

Procedure for ordering

established; Ordering time frames

arranged with supplier;

1 36

3 GeneralPlanner can't access

SAP (ERP)IPFM 3

Inability to work, delayed

operations

Disrespect of

deadlines and time

frames

6

Network not working;

Server down;

Computer broken;

2Preventive hardware and

software maintenance1 12

NoOperation/

Activity

Event - Failure

modeFM ID

Can lead to

(Risk description)

Consequences

(description)

Severity


Occurrence


Detection

ratingRPN

1 ReceivingWrong quantity entered

in SAPWHFM 1

Wrong information regarding

stock level; Out of stock;


Profit loss

Additional costs

8

Disregard of

Procedure; Wrong

quantity on receiving

documents;

3

Procedure for receiving goods

established; Receiving quantity

double checked every time;

1 24

Wrong quantity

separated from main

location to picking dock

WHFM 2Wrong quantity delivered to

client


Profit loss

Additional costs

8

Disregard of

Procedure; Wrong

picking quantity in

Hand device;

2

Procedure for preparation

established; Quantity on picking

dock double checked;

3 48

Wrong quantity loaded

in truck from picking

dock


client


Profit loss

Additional costs

8

Separated quantities

not grouped or labeled

adequately for specific

client;

2Procedure for loading in trucks

established;4 64

3 Maintenance

Picking equipment failure

(Hand Held device,

forklift, dock computers)

WHFM 4Inability to work, delayed

operations


Additional costs

Profit loss

8


Server down;

Internet down;

Equipment broken;

2Preventive picking equipment

maintenance;1 16

Preparation and

loading2

NoOperation/

Activity

Event - Failure

modeFM ID

Can lead to

(Risk description)

Consequences

(description)

Severity


Occurrence


Detection

ratingRPN

Incorrect clients details

(position, place of

delivery, opening

hours....)

TRFM 1Delay in delivery;

Inability to deliver;


Profit loss

Additional costs

Penalties

8

Clients details changes

not delivered to IT

department for update;

4

Established procedure - working

with clients; Conducted refreshing

trainings for sales staff;

4 128

Wrong input maps TRFM 2Delay in delivery;



Profit loss

Additional costs

Penalties

8Maps not updated on

regular basis;3

Established procedure for maps

and software updates;4 96

Routing software

unavailable/not workingTRFM 3

Inability to create route;

Inability to transfer data;


Profit loss

Additional costs

Penalties

8

Interruption of internet

connection and/or

server down

2Preventive IT equipment

maintenance;2 32

Crash accident (vehicle

involved)TRFM 4

Delay in delivery;


Damage of goods;


Profit loss

Additional costs

Penalties

9Drivers concentration

and attention down;2

Refreshing awareness trainings

for drivers;3 54

Vehicle breakdown TRFM 5Delay in delivery;



Profit loss

Additional costs

Penalties

8

Disregard of

Procedure for

preventive vehicle

maintenance;

Driver didn't report

error signals on

computer board;

2 Preventive vehicle maintenance; 1 16

Roadblock, natural

disasters (unforeseeable

circumstances)




Profit loss

Additional costs

Penalties

8Unforeseeable

circumstances1

Established procedure for drivers

in case of elementary disaster;4 32

Routing1

Delivering2

NoOperation/

Activity

Event - Failure

modeFM ID

Can lead to

(Risk description)

Consequences

(description)

Severity


Occurrence


Detection

ratingRPN

Wrong type/quantity of

goods on invoiceCSFM 1

Wrong goods delivered to

client

Clients

unsatisfaction;

Additional costs;

Profit loss;

Penalties;

8

CS staff manualy

entered order; Last

minute order changes;

4CS staff refreshing trainings

conducted periodicaly;4 128

CS staff can't access to

SAPCSFM 2

Invoices can't be created;

Delay in invoice formulation;

Delay in other

operations;

Clients

unsatisfaction;

Profit loss

Additional costs;

8


Server down;

Internet down;

2Preventive software and

hardware maintenance;1 16

2Complaints

handling

Clients complaint don't

have all information

(missing some

information)

CSFM 3 Complaint can't be processed

Clients

unsatisfactionl;

Bad reputation;

9

Disregard of

Procedure; Client didn't

provide all information

2

Procedure for complaints handling

established; All complaint

information needs to be entered in

electronic form;

2 36

3Managing Price

lists

Price list changes not

communicated on timeCSFM 4

Wroing invoicing;

Incorrect client's debt

information

Clients

unsatisfactionl;

Profit loss

Additional costs;

8

Disregard of

Procedure; Information

regarding changes not

received on time from

financial department;

3

Procedure for price list changes

communication established; CS

supervisor is checking possible

changes everyday with financial

departmen;

2 48

Invoicing1


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Based on given tables, Failure Modes are ranked by calculated RPN:

Rank 1 CSFM 1 - Wrong type/quantity of goods on invoice - 128 Rank 2 TRFM 1 - Incorrect clients details - 128 Rank 3 TRFM 2 - Wrong input maps - 96 Rank 4 WHFM 3 - Wrong quantity loaded in truck - 64 Rank 5 TRFM 4 - Crash accident (vehicle involved) - 54 Rank 6 CSFM 4 - Price list changes not communicated on time - 48 Rank 7 WHFM 2 - Wrong quantity separated from main - 48 Rank 8 IPFM 1 - Bad forecast for the next period - 48 Rank 9 CSFM 3 - Clients complaint don't have all information - 36 Rank 10 IPFM 2 - Delayed/long transport from supplier - 36 Rank 11 TRFM 3 - Routing software unavailable/not working - 32 Rank 12 TRFM 6 - Roadblock, natural disasters - 32 Rank 13 WHFM 1 - Wrong quantity entered in SAP - 24 Rank 14 CSFM 2 - CS staff can't access to SAP - 16 Rank 15 TRFM 5 - Vehicle breakdown - 16 Rank 16 WHFM 4 - Picking equipment failure - 16 Rank 17 IPFM 3 - Planner can't access SAP (ERP) - 12

According to the RPN’s and their rankings, it is evident that for some FM’s, ranks

can’t be assigned precisely, since they are the same. Because of that, before moving

to the final step and proposing recommendations for Intercorp to manage critical

risks, it is necessary to conduct more “precisely” and more “softly” FMEA.

6 FUZZY FMEA

Now, Fuzzy FMEA will be also applied on previously determined scope and

elements (step 1). Difference will be in parts where ratings are determined, where

fuzzy numbers from table 4 are applied instead. Following that, for calculation of

RPN (step 9), given formula (1) for defuzzification of fuzzy numbers will be used.

Average scores and matrixes with fuzzy numbers are given in the next tables.

Table 10. - Individual assessments with fuzzy numbers

LM PO Average LM PO Average LM PO Average

IPFM 1 (8,9,10,10) (8,9,10,10) (8,9,10,10) (0,0,1,2) (1,2,3,4) (0.5, 1, 2, 3) (1,2,3,4) (0,0,1,2) (0.5, 1, 2, 3)

IPFM 2 (8,9,10,10) (8,9,10,10) (8,9,10,10) (1,2,3,4) (1,2,3,4) (1, 2, 3, 4) (0,0,1,2) (0,0,1,2) (0, 0, 1, 2)

IPFM 3 (8,9,10,10) (0,0,1,2) (4, 4.5, 5.5, 6) (0,0,1,2) (1,2,3,4) (0.5, 1, 2, 3) (0,0,1,2) (0,0,1,2) (0, 0, 1, 2)

WHFM 1 (8,9,10,10) (8,9,10,10) (8,9,10,10) (1,2,3,4) (1,2,3,4) (1, 2, 3, 4) (0,0,1,2) (0,0,1,2) (0, 0, 1, 2)

WHFM 2 (8,9,10,10) (8,9,10,10) (8,9,10,10) (0,0,1,2) (1,2,3,4) (0.5, 1, 2, 3) (3,4,6,7) (0,0,1,2) (1.5, 2, 3.5, 4.5)

WHFM 3 (6,7,8,9) (8,9,10,10) (7, 8, 9, 9.5) (0,0,1,2) (0,0,1,2) (0, 0, 1, 2) (1,2,3,4) (3,4,6,7) (2, 3, 4.5, 5.5)

WHFM 4 (8,9,10,10) (3,4,6,7) (5.5, 6.5, 8, 8.5) (1,2,3,4) (0,0,1,2) (0.5, 1, 2, 3) (0,0,1,2) (0,0,1,2) (0, 0, 1, 2)

TRFM 1 (8,9,10,10) (3,4,6,7) (5.5, 6.5, 8, 8.5) (1,2,3,4) (3,4,6,7) (2, 3, 4.5, 5.5) (3,4,6,7) (1,2,3,4) (2, 3, 4.5, 5.5)

TRFM 2 (8,9,10,10) (8,9,10,10) (8,9,10,10) (1,2,3,4) (0,0,1,2) (0.5, 1, 2, 3) (0,0,1,2) (6,7,8,9) (3, 3.5, 4.5, 5.5)

TRFM 3 (8,9,10,10) (8,9,10,10) (8,9,10,10) (0,0,1,2) (1,2,3,4) (0.5, 1, 2, 3) (0,0,1,2) (0,0,1,2) (0, 0, 1, 2)

TRFM 4 (8,9,10,10) (8,9,10,10) (8,9,10,10) (0,0,1,2) (0,0,1,2) (0, 0, 1, 2) (3,4,6,7) (0,0,1,2) (1.5, 2, 3.5, 4.5)

TRFM 5 (8,9,10,10) (8,9,10,10) (8,9,10,10) (1,2,3,4) (0,0,1,2) (0.5, 1, 2, 3) (0,0,1,2) (0,0,1,2) (0, 0, 1, 2)

TRFM 6 (3,4,6,7) (8,9,10,10) (5.5, 6.5, 8, 8.5) (0,0,1,2) (0,0,1,2) (0, 0, 1, 2) (3,4,6,7) (3,4,6,7) (3, 4, 6, 7)

CSFM 1 (8,9,10,10) (6,7,8,9) (7, 8, 9, 9.5) (3,4,6,7) (1,2,3,4) (2, 3, 4.5, 5.5) (0,0,1,2) (6,7,8,9) (3, 3.5, 4.5, 5.5)

CSFM 2 (3,4,6,7) (8,9,10,10) (5.5, 6.5, 8, 8.5) (0,0,1,2) (1,2,3,4) (0.5, 1, 2, 3) (0,0,1,2) (0,0,1,2) (0, 0, 1, 2)

CSFM 3 (8,9,10,10) (8,9,10,10) (8,9,10,10) (0,0,1,2) (0,0,1,2) (0, 0, 1, 2) (0,0,1,2) (1,2,3,4) (0.5, 1, 2, 3)

CSFM 4 (8,9,10,10) (8,9,10,10) (8,9,10,10) (0,0,1,2) (1,2,3,4) (0.5, 1, 2, 3) (0,0,1,2) (1,2,3,4) (0.5, 1, 2, 3)

Severity rating Occurrence rating Detection rating


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Table 11. - Inventory procurement risk assessment

Table 12. - Warehousing risk assessment

Table 13. - Transportation risk assessment

Table 14. - Customer Service risk assessment

NoOperation/

Activity

Event - Failure

modeFM ID

Can lead to

(Risk description)

Consequences

(description)

Severity


Occurrence


Detection

ratingRPN

1 ForecastingBad forecast for the next

periodIPFM 1

excessive / insufficient

inventories

lost of sales/ opportunities

missed

Captured capital

Unsatisfied clients

Penalties

0.8696

Bad inputs regarding

goals and capacity

limits

0.2128

Procedure for forecasting

established; All staff trained and

supervised; Every forecast

double checked

0.2128 0.0394

2 Ordering

Delayed/long transport

from supplier to

warehouse

IPFM 2 Low inventory level

Can't fulfil clients

demands; Penalties;


0.8696

Orders not placed on

time; Delivery root

blocked/ changed

0.2917

Procedure for ordering

established; Ordering time frames

arranged with supplier;

0.1304 0.0331

3 GeneralPlanner can't access

SAP (ERP)IPFM 3

Inability to work, delayed

operations

Disrespect of

deadlines and time

frames

0.5Network not working;

Server down;

Computer broken;

0.2128Preventive hardware and

software maintenance0.1304 0.0139

NoOperation/

Activity

Event - Failure

modeFM ID

Can lead to

(Risk description)

Consequences

(description)

Severity


Occurrence


Detection

ratingRPN

1 ReceivingWrong quantity entered

in SAPWHFM 1

Wrong information regarding

stock level; Out of stock;


Profit loss

Additional costs

0.8696

Disregard of

Procedure; Wrong

quantity on receiving

documents;

0.2917

Procedure for receiving goods

established; Receiving quantity

double checked every time;

0.1304 0.0331

Wrong quantity

separated from main

location to picking dock


client


Profit loss

Additional costs

0.8696

Disregard of

Procedure; Wrong

picking quantity in

Hand device;

0.2128

Procedure for preparation

established; Quantity on picking

dock double checked;

0.3265 0.0604

Wrong quantity loaded

in truck from picking

dock


client


Profit loss

Additional costs

0.7872

Separated quantities

not grouped or labeled

adequately for specific

client;

0.1304Procedure for loading in trucks

established;0.4 0.0411

3 Maintenance

Picking equipment failure

(Hand Held device,

forklift, dock computers)

WHFM 4Inability to work, delayed

operations


Additional costs

Profit loss

0.6735


Server down;

Internet down;

Equipment broken;

0.2128Preventive picking equipment

maintenance;0.1304 0.0187

Preparation and

loading2

NoOperation/

Activity

Event - Failure

modeFM ID

Can lead to

(Risk description)

Consequences

(description)

Severity


Occurrence


Detection

ratingRPN

Incorrect clients details

(position, place of

delivery, opening

hours....)




Profit loss

Additional costs

Penalties

0.6735Clients details changes

not delivered to IT

department for update;

0.4000Established procedure - working

with clients; Conducted refreshing

trainings for sales staff;

0.4000 0.1077551

Wrong input maps TRFM 2Delay in delivery;



Profit loss

Additional costs

Penalties

0.8696Maps not updated on

regular basis;0.2128

Established procedure for maps

and software updates;0.4255 0.0787293

Routing software

unavailable/not workingTRFM 3

Inability to create route;

Inability to transfer data;


Profit loss

Additional costs

Penalties

0.8696Interruption of internet

connection and/or

server down

0.2128Preventive IT equipment

maintenance;0.1304 0.0241322

Crash accident (vehicle

involved)TRFM 4

Delay in delivery;


Damage of goods;


Profit loss

Additional costs

Penalties

0.8696Drivers concentration

and attention down;0.1304

Refreshing awareness trainings

for drivers;0.3265 0.0370356

Vehicle breakdown TRFM 5Delay in delivery;



Profit loss

Additional costs

Penalties

0.8696

Disregard of

Procedure for

preventive vehicle

maintenance;

Driver didn't report

error signals on

computer board;

0.2128 Preventive vehicle maintenance; 0.1304 0.0241322

Roadblock, natural

disasters (unforeseeable

circumstances)




Profit loss

Additional costs

Penalties

0.6735Unforeseeable

circumstances0.1304

Established procedure for drivers

in case of elementary disaster;0.5000 0.0439219

Routing1

Delivering2

NoOperation/

Activity

Event - Failure

modeFM ID

Can lead to

(Risk description)

Consequences

(description)

Severity


Occurrence


Detection

ratingRPN

Wrong type/quantity of

goods on invoiceCSFM 1

Wrong goods delivered to

client

Clients

unsatisfaction;

Additional costs;

Profit loss;

Penalties;

0.7872CS staff manualy

entered order; Last

minute order changes;

0.4000CS staff refreshing trainings

conducted periodicaly;0.42553191 0.1339973

CS staff can't access to

SAPCSFM 2

Invoices can't be created;

Delay in invoice formulation;

Delay in other

operations;

Clients

unsatisfaction;

Profit loss

Additional costs;

0.6735


Server down;

Internet down;

0.2128Preventive software and

hardware maintenance;0.1304 0.0186902

2Complaints

handling

Clients complaint don't

have all information

(missing some

information)

CSFM 3 Complaint can't be processed

Clients

unsatisfactionl;

Bad reputation;

0.8696

Disregard of

Procedure; Client didn't

provide all information

0.1304

Procedure for complaints handling

established; All complaint

information needs to be entered in

electronic form;

0.2128 0.0241322

3Managing Price

lists

Price list changes not

communicated on timeCSFM 4

Wroing invoicing;

Incorrect client's debt

information

Clients

unsatisfactionl;

Profit loss

Additional costs;

0.8696

Disregard of

Procedure; Information

regarding changes not

received on time from

financial department;

0.2128

Procedure for price list changes

communication established; CS

supervisor is checking possible

changes everyday with financial

departmen;

0.2128 0.0393647

Invoicing1


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New rankings of Failure Modes by calculated RPN are:

Rank 1 CSFM 1 - Wrong type/quantity of goods on invoice - 0.1340 Rank 2 TRFM 1 - Incorrect clients details - 0.1078 Rank 3 TRFM 2 - Wrong input maps - 0.0787 Rank 4 WHFM 2 - Wrong quantity separated from main - 0.0604 Rank 5 TRFM 6 - Roadblock, natural disasters - 0.0439 Rank 6 WHFM 3 - Wrong quantity loaded in truck - 0.0411 Rank 7 CSFM 4 - Price list changes not communicated on time - 0.0394 Rank 8 IPFM 1 - Bad forecast for the next period - 0.0394 Rank 9 TRFM 4 - Crash accident (vehicle involved) - 0.0370 Rank 10 IPFM 2 - Delayed/long transport from supplier - 0.0331 Rank 11 WHFM 1 - Wrong quantity entered in SAP - 0.0331 Rank 12 CSFM 3 - Clients complaint don't have all information - 0.0241 Rank 13 TRFM 3 - Routing software unavailable/not working - 0.0241 Rank 14 TRFM 5 - Vehicle breakdown - 0.0241 Rank 15 CSFM 2 - CS staff can't access to SAP - 0.0187 Rank 16 WHFM 4 - Picking equipment failure - 0.0187 Rank 17 IPFM 3 - Planner can't access SAP (ERP) - 0.0139

According to those new ranking, first five critical FM can be determined, by according

to other RPN, some of them are again the same. Because of this, for the future

conduction of Fuzzy FMEA, weight factors for LM and PO need to be determined

and included into calculation.

7 CONCLUSION

Managing risks in supply chain operations is getting a huge everyday attention due

to its great importance for the company. Many internal and external factors are

affecting proper risk assessment, followed by uncertainty, subjectivity and ambiguity.

To overcome these issues, many risk assessment models were developed and

many techniques combined. The aim of this paper was to show the usage of FMEA

technique for the analysis of risks in SC operations. Demonstrated model is based

on the default FMEA model and the usage of fuzzy logic to overcome issues of

default FMEA and calculate overall RPN for identified Failure modes.

According to the final rankings, as results of the used model, several conclusions

can be made. In some situations, when using default FMEA, RPN for failure modes


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can be equal, rankings can’t be done correctly and manager’s decision on which

critical risks to focus resources can be harder. On the other hand, if fuzzy numbers

are applied for assessments, there can also be some difficulties in final ranking

determination. Because of all this, recommendation is related to the usage of weight

criteria for each of the person who is in charge for the assessment. Also, since there

is a certain distance between marks which were given by LM and OP, another

recommendation will be to calculate consensus between them, using some of the

available techniques (such as Simple Dependencies of Attributes). Consensus rate

can also be considered in ranking part. In case of Intercorp, this would be for

Logistics Manager (as a project team leader) and process owners (team members).

Since this was an initial project, it can be said that purpose is fulfilled. Now, to

conduct risk assessment in all processes, Intercorp should apply proposed

recommendations, assign weight factors to LM and OP, and calculate consensus

between their assessments.

This paper is focused on the proposition for the upgrade of “default” FMEA, and

treats fuzzy logic as a one possibility for upgrade next to many more available

techniques (Rough set theory, Intuitive sets, Grey analysis, Fault tree analysis). It

doesn’t explain in more mathematical and detailed way fuzzy logic and numbers

which were used. Instead of that, it could serve as an example how one theory can

be applied and used to improve existing model. For more detailed explanation of

fuzzy logic, used references can be consulted.

REFERENCES

1. Chang, K.H., Cheng, C.H. (2010). A risk assessment methodology using

intuitionistic fuzzy set in FMEA. International Journal of Syst. Sci. 41, pp. 1457–1471;


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2. Lalla, M., Facchinetti, G., Mastroleo, G. (2008). Vagueness evaluation of the crisp output in a fuzzy inference system. Fuzzy Sets and Systems, 159, pp.3297–3312;

3. Jamshidi, M. (2003). Tools for Intelligent Control: Fuzzy Controllers, Neural Networks and Genetic Algorithms. Philosophical Transactions: Mathematical,

Physical and Engineering Sciences, 361(1809), pp.1781-1808;

4. Liu, H-C., Liu, L., Bian, Q-H., Lin, Q-L., Dong, N. and Xu, P-C. (2011). Failure mode and effects analysis using fuzzy evidential reasoning approach and grey

theory. Expert Systems with Applications, 38, pp. 4403 - 4415;

5. Coyle, J. J., Langley, Jr., C. J., Novack, R. A., & Gibson, B. A. (2013). Supply

Chain Management - A Logistics Perspective (9th Ed.). Mason, OH: Cengage Learning;

6. Stamatis, D.H. (2003). Failure mode and effect analysis: FMEA from theory to

execution. ASQ Quality, Press Pub, Milwaukee, Wisconsin;

7. Carlson, C.S. (2012). Effective FMEAs: Achieving Safe, Reliable, and Economical

Products and Processes Using Failure Mode and Effects Analysis. Wiley, Hoboken, New Jersey;

8. Kumru, M., Kumru, P.Y. (2013). Fuzzy FMEA application to improve purchasing

process in a public hospital. Applied Soft Computing, 13, pp.721-733;

9. Atwatera, C., Gopalan, R., Lancionia, R., Hunta, J. (2014). Measuring supply

chain risk: Predicting motor carriers’ ability to withstand disruptive environmental change using conjoint analysis. Transportation Research Part C: Emerging Technologies, 48, pp. 360–378;

10. Claypoola, E., Normana, A.B., Needyb, K.L. (2014). Modelling risk in a Design for Supply Chain problem. Computers & Industrial Engineering, 78, pp. 44–54;

11. Mozaffari, F., Eidi, A., Mohammadi, L., Alavi, Z. (2013) Implementation of FMEA to improve the reliability of GEO satellite payload. In: Reliability and Maintainability Symposium (RAMS), 2013 Proceedings-Annual, IEEE, 1–6;

12. Kostina, M., Karaulova, T., Sahno, J., Maleki, M. (2012). Reliability estimation for manufacturing processes. J. Achiev. Mater. Manuf. Eng. 51, pp. 7–13;

13. Braglia, M. (2000). MAFMA: multi-attribute failure mode analysis. Int. J. Quality Reliability Manage. 17, pp. 1017–1033;

14. Braglia, M., Frosolini, M., Montanari, R. (2003). Fuzzy TOPSIS approach for

failure mode, effects and criticality analysis. Q Reliability Eng. Int. 19, pp. 425–443;

15. Song, W., Ming, X., Wu, Z., Zhu, B. (2014). A rough TOPSIS approach for failure

mode and effects analysis in uncertain environments. Qual. Reliab. Eng. Int. 30, pp. 473–486;

16. Chang, K.-H., Cheng, C.-H. (2011). Evaluating the risk of failure using the fuzzy

OWA and DEMATEL method. J. Intell. Manuf. 22, pp. 113–129;


of 18

17. Chang, K.-H., Cheng, C.-H., Chang, Y.-C. (2010). Reprioritization of failures in a silane supply system using an intuitionistic fuzzy set ranking technique. Soft.

Comput. 14, pp. 285–298;

18. Hadi-Vencheh, A., Aghajani, M. (2013) Failure mode and effects analysis: A

fuzzy group MCDM approach. J. Soft Comput. Appl. 2013, pp. 1–14;

19. American Society for Quality. (2004). Failure Mode and Effects Analysis. Excerpted from Nancy R. Tague’s The Quality Toolbox, Second Edition, ASQ Quality

Press, pages 236–240. Accessed on April 10th 2016 from: http://asq.org/learn-about-quality/process-analysis-tools/overview/fmea.html;

20. Chin, K. S., Chan, A., & Yang, J. B. (2008). Development of a fuzzy FMEA based product design system. International Journal of Advanced Manufacturing Technology, 36, pp. 633–649;

21. Chin, K. S., Wang, Y. M., Ka Kwai Poon, G., & Yang, J. B. (2009). Failure mode and effects analysis using a group-based evidential reasoning approach. Computers

& Operations Research, 36, pp. 1768–1779;

22. Wang, Y. M., Chin, K. S., Ka Kwai Poon, G., & Yang, J. B. (2009). Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric

mean. Expert Systems with Applications, 36, pp. 1195–1207;

http://asq.org/learn-about-quality/process-analysis-tools/overview/fmea.html

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