master thesis master of science in asset management...

How to reduce the amount of non – correct information in Salino

This paper may not be published or disclosed without the prior consent of the author.

MASTER THESIS

Master of Science In

Asset Management Control

HOW TO REDUCE THE AMOUNT OF NON-CORRECT INFORMATION IN SALINO

Tutor: Mr. Willem van ‗t Spijker PhD Student: Mr. Jorge Bosaans



ACKNOWLEDGEMENTS

First of all, I would like to thank Dr. John Stavenuiter, AMC Centre, UNAB, and Hogeschool Zeeland to allow me to get in touch with this amazing new body of knowledge: the asset management which has become my main field of interest because it encompasses several of the disciplines I have enjoyed with along my professional career, but now from a new and awe-inspiring approach. I also want to thank Dr. Willem van ‗t Spijker for giving me his valuable counseling and support during my research. My acknowledgements also go to the Chilean Navy for allowing me to pursue this research on its most valuable asset: the Fleet and its logistical management, which was converted in the laboratory during the research period. I deeply hope that the result of it serves to improve at least a little bit some logistic processes. I also want to thank the group of people at the Directorates who answered the questionnaires, the professionals who worked with me in the SALINO application and the team who participated in the interviews. Their time spent and their willingness to cooperate with me have comprised my gratitude. Without their valuable and enthusiastic participation this research could have not arrived to port. At last, but not the least, I want to thank my tribe: my wife Carmen for her permanent support and graciousness for allowing me to take away so many home hours, to my children Jorge Andrés, Juan Cristóbal and Sebastián because they have always been my inspiration and my motivational incentives in all my personal and professional challenges, and to my brother Juan who, with his particular sense of humor, always encouraged me to pursue my academic work.



CONTENT

Content Page Acknowledgement ………………………………………………………….. 2 Abstract …………………………………………………………………... 5 I Introduction ……………………………………………………………….. 5 The Problem ……………………………………………………………... 8 Research Map …………………………………………………………… 12 Conceptual Framework …………………………………………………. 13 Logistical Managerial …………………………………………………… 14

II Literature Review ………………………………………………………… 20 Asset management ……………………………………………………… 20 Data quality ………………………………………………………………. 28 Data as an asset ………………………………………………………… 30 Applying data quality principles to data ………………………………. 33 Data cleansing …………………………………………………………… 35

Business Intelligence (BI) ………………………………………………. 36

Salino ……………………………………………………………............. 38 III Methodology ……………………………………………………............... 43

IV Results …………………………………………………………................. 56 V Discussion ………………………………………………………………… 60 VI Conclusions ………………………………………………………………. 61 VII Recommendations ……………………………………………................ 62

VIII Cost – benefit analysis ………………………………………................ 63 66 IX Suggestion for further work ………………………………...................

X References …………………………………………………….................. 67 Bibliography ……………………………………………………………… 67

69 Web references …………………………………………………............ Documents ………………………………………………………............ 70

XI Appendices ……………………………………………………………….. 71 Appendix A: Survey 1 ……………………………………………............. 71 Appendix B: Survey 2 ……………………………………………............. 75 Appendix C: Application run ……………………………………………… 77 Appendix D: Face-to-face interview …………………………………….. 84 Appendix F: MINCOM Ellipse …………………………………………….. 85



LIST OF FIGURES

Figures Page Figure 1: Relations among the variables 11 Figure 2: Research map 12 Figure 3: CN Organization 13 Figure 4: Relationship among stakeholders 18 Figure 5: Flow of logistic information 19 Figure 6: Life cycle of an asset 21 Figure 7: Asset management objectives through LC 22 Figure 8: Conceptual design of an improved AMCS 23 Figure 9: The LCM-team 23 Figure 10: Example of Life cycle cost 24 Figure 11: Cost-effective structure 27 Figure 12: Hierarchical framework for data quality 30 Figure 13: Model of data policy 32 Figure 14: Steps for process management approach 34 Figure 15: Salino environment 40 Figure 16: Flow of information in Salino 42 Figure 17: Ishikawa Analysis Diagram 55 Figure 18: Data quality dimensions 56 Figure 19: Number of WO with errors 57 Figure 20: Rates of accuracy errors per ship 58 Figure 21: Relative occurrence of problems 59

LIST OF TABLES

Tables Page Table 1: Data quality dimensions 29 Table 2: Results of Survey 1 47 Table 3: Results of Survey 2 48 Table 4: Most mentioned registers 50 Table 5: Results of Application run 51 Table 6: Problems leading to errors 53 Table 7: Root causes for accuracy errors 54 Table 8: Problems that lead to errors 58



HOW TO REDUCE THE AMOUNT OF NON-CORRECT INFORMATION IN SALINO

Abstract The Navy implemented a powerful integrated information system that provides a means to collect valuable data generated throughout the entire Navy, which after being processed becomes useful information that supports the logistical managerial decision making process at several levels and stores historical data for future use. During a 12-year period of operation, some flaws have been detected. Now, when there is the commitment to achieve cost-effective management of assets, it is necessary to ensure the information delivered by the system be free of errors. The research determines the sources of main non-correct information, identifies its causes, and recommends actions to reduce the amount of errors, estimating the costs and benefits of its implementation. The good news about computers is that they do what you tell them to do. The bad news is that they do what you tell them to do. Ted Nelson

Keywords: Data quality – Information quality – asset management – life cycle - cost effectiveness

I Introduction

The Chilean Navy (CN), motivated by the recent renovation of the entire fleet and

the budgetary cuts, decided to improve its logistical managerial performance in

order to be able to manage its assets in the most cost-effective way (DGSA, 2008).

This decision was made after the close contact established with the navies

providers of the incorporated units, UKRN1 and RNLN2, when it was evident there

was a need for updating the processes of material logistics management to meet

the growing operational demand on the Fleet. This need came out because on the

one hand, the greater complexity of components of the units incorporated recently,

and the smaller life cycle of the higher technology components of the equipment on

1 United Kingdom Royal Navy

2 Royal Nederland Navy

http://www.brainyquote.com/quotes/quotes/t/tednelson184205.html



the other hand. This scenario obligates having personnel with updated knowledge

and skills to be able to analyze and manage, among others, the cost/effectiveness

rate of the assets during their entire life cycle.

In addition, this modernization pursues the objectives of managing staff knowledge

in the best way, possible streamline processes, applies good engineering practices

of systems engineering and adapts better to the new procedures of defense project

evaluation and resources allocation. (DGSA, 2008). The concepts stated in this

document constitute a big challenge.

The decision mentioned above was reinforced by the appointment of a high

ranking Officer as Project Leader of the Logistic Modernization Project,(DGSA,

2008).

The two paramount issues to achieve cost effective management control of the

assets are to control the systems effectiveness (SE) and their lifecycle cost

(LCC).(Stavenuiter, 2002).

To get control on SE and LCC of the assets, it is necessary to collect and process

an enormous amount of data that is generated all over the organization, then

process it to produce, store and deliver useful information to support the decision

making processes at different levels of the organization. To do this, the Navy

implemented an integrated information system called SALINO3, which is an ERP4

type of information system, which means it integrates internal and external

management information across the entire organization.

SALINO is defined in the Manual, which is an oficial document of the CN, as ―an

information system designed to support the administration of the material in an

integrated and online way, allowing automatic processes and enabling total

visibility of the assets, contributing to logistics decision making process‖(Manual de

SALINO, 1999,pp.4). This visibility considers the knowledge of place, status, cost,

and lifetime prediction of the naval material.

It was developed 12 years ago, on a Mincom Information Management System

MIMS – ELLIPSE software with a central database ORACLE. A more detailed

3 Sistema de Apoyo Logistico Naval=Naval Logistic Support System

4 Enterprise Resource Planning



description provided by SALINO management is given in Chapter II Literature

Review.

More than 10.000 users from different organizational levels are connected to the

system and perform around 2.500 transactions per day.(SIAMA5, 2011)

Through a DMZ6 which gives access to the OMEGA7 system of ASMAR8 and the

Naval Missions from abroad.

Even though SALINO is a powerful ERP that provides a means to collect data

generated throughout the entire Navy, which after being processed becomes

information that, among others, supports the decision making process of logistical

management at several levels and stores historical data for future use, some flaws

have been detected by the users of the information processed by the system,

mainly the Material, Maintenance and Technical Directorates (they are identified

and their functions described at I.c. Logistical Managerial) .

Some of these flaws are related to the amount of non-correct data entered into the

system, which has been detected by the Maintenance Information Service (SIAMA,

2009) when a revision of the data entered to the maintenance module was

performed. Before that, the Material Directorate gave instructions to reduce errors

in data entered in SALINO due to a data audit received by the DTIA9 (DGSA,

2007). Other types of flaws are procedure related: different types of costs are

wrongly charged. For instance, the electric power consumed by the units of the

fleet in Valparaiso is charged to the Supply Center which is the owner of the plant

that delivers the energy but not to the units that consume it (DGSA, 2009). Parts

used in the RxR10 process are charged to the DIRISNAV11 that manages the

process but not the unit that owned the installation (DIRISNAV, 2008). Fuel

consumed by units deployed to international missions is charged to the logistical

authority not to the unit. (DABA, 2009).

5 SIAMA: Servicio de Información Mantenimiento de la Armada=Maintenance Information Service

6 DMZ: Demilitary Zone= a physical or logical subnetwork that contains and exposes an organization's external

services to a larger untrusted network, usually the Internet.

7 OMEGA: the ERP of ASMAR.

8 ASMAR: Astilleros y Maetranzas de la Armada= the shipyard of the Chilean Navy.

9 DTIA: Direccion de Telecomunicaciones e Informatica de la Armada= CN TIC Directorate

10 RxR: Repair by replacement

11 DIRISNAV: Direccion de Ingeniería de Sistemas Navales= Naval Systems Engineering Directorate



Some other flaws are associated with the software: in the historical maintenance

information stored in the system the parts which failure has been the most frequent

cause of the installation failure are not highlighted, impeding the logistic managers

to take preventive actions on those specific parts (DABA, 2010).

Another type of flaw that is charged to the SALINO system is its lack of capability

to perform data mining which is the process of extracting patterns from the data

stored in it, to transform that data and information into business knowledge. (This

concept will be further explained in Chapter II Literature Review - Business

Intelligence).

Finally, a more generalized claim is that arising from the Technical Directorates

and it is related to the usefulness of the information coming out of SALINO that

they need to support their logistical managerial decisions. The users complain of

errors, incompleteness, and inconsistencies.(DGSA, 2009-1)

THE PROBLEM:

The Navy strongly needs to identify some relevant flaws in SALINO and find a way

to fix them in order to improve its logistical managerial decision making process to

manage its assets in a more cost – effective way (Directive DGSA, 2008).

Keeping this in mind, research was done to detect the flaws of SALINO, confirming

those mentioned above and searching for others, selecting those most relevant,

determining their causes and recommending appropriate actions to be taken in

order to improve the way in which SALINO supports the logistical managerial

decision making processes in the Chilean Navy.

To do this, the research question is:

How to reduce the non-correct information in SALINO?

In order to answer this question, the research was conducted according the

methodology described ahead, and answering the following sub questions:

1. - What is SALINO?



Its philosophy, structure, capabilities, functionalities, and general

characteristics had to be learned. To do this, the researcher attended a short

course on SALINO at the Academia Politécnica Naval (APN12) and obtained a

training account to become familiar with the system, its characteristics and

capabilities as well. In this activity it was very important to acquire the knowledge

of the system, but also it was important to be familiar with the environment that

people who enter data into SALINO are faced with in order to have a closer

experience about that feeling.

2. - What are the flaws of SALINO that produce the highest amount of

errors/misinformation of SALINO?

To answer this question the researcher had to corroborate the existence of

the flaws indicated previously searching at the Directorates that originated the

documents where the flaws are claimed (DGSA, 2009., SIAMA, 2009) and looked

for other still not identified. Then a selection was made in order to pick out the one

that was viewed as the most important for the users to support their decision

making processes.

This was done, first by establishing what dimension or characteristic of the data is

more significant for the decision making process conducting a survey among

SALINO users at the Technical Directorates to sustain , on their own judgment, the

data dimension which is the most significant for the appropriate support to the

decision making process. Data dimension as defined by Wang, R and Strong, D.

1996 is a set of data quality attributes that represent a single aspect or construct of

data quality.

After this, a second survey was conducted, this time with the technician of the

Maintenance Information Service, to find out in what areas or modules of SALINO

have the greatest amount of errors in this data dimension.

Having this information at hand, an empiric measure was performed on a

significant amount of assets (five out of eight ships of the Fleet) to confirm or reject

the users perception about the flaws of SALINO. Using an application, developed

for this research by IT personnel of the Maintenance Information Service jointly

with the author, the exact amount of errors of SALINO was counted, in the module

12

APN: Academia Politecnica Naval = Naval Politechnical Academy



elected, over the total amount of current transactions that means transactions on

maintenance works open.

3. - What are the possible causes of the flaws?

From the flaws selected, the researcher determined the causes.

To do this, the author went to the source of the data entry process, the five ships

that participated in the measurement of errors. A combination of semi-structured

interview and in-depth group interviews were performed in order to come up with

the causes of the flaws.

The first set of semi-structured face-to-face individual interviews (Saunders, 2009)

with ten technicians pertaining to the ships where the data analyzed came from

(two per ship) that perform the data entry was intended to gather information about

their work environment and their relation to SALINO related tasks in order to

identify possible problems.

After all individual interviews were accomplished, a final in-depth group interview

was held with all the interviewees (minus three who could not attend) in order to

come up with the causes of errors, starting from the problems identified in the

previous activity. The Ishikawa (Fishbone) Diagram method and Brainstorming

techniques were used to establish the causes.

4. - How to remove or diminish these causes?

The researcher finalized with a recommendation of a set of actions to be

taken or changes to be implemented to reduce non-correct information in SALINO.

Actions oriented to facilitate the use of SALINO interface were analyzed in

brainstorming meetings with experts at the Maintenance Information Service and

SALINO Management Office. Action oriented to train and motivate the personnel

was elaborated by the author based on competences developed in Module 5

Motivation of the Master of Science in AMC13 course (Kwakernaack, 2009). Finally,

action oriented to detect in line errors was produced by the author based on the

application developed for this research.

A cost – benefit estimation of its implementation is presented. As learned in the

Master Course, where the core is how to achieve cost-effectiveness in assets

management, (Stavenuiter, 2002), the benefits of reducing or eliminating the

13

AMC: Asset Management Control



causes of errors obtained from the actions recommended must be analyzed in

front of their costs.

The relations among the variables are shown in the figure 1.

RELATIONS AMONG VARIABLES

Fig. 1 Relations among the variables of the research



Conceptual framework

In order to understand the logistical managerial decision making process within the

CN, it is necessary to describe first the whole organization structure and then the

entities directly associated to the asset management and how they interrelate

among them. The information that follows in these paragraphs was extracted from

the CN official website .

In the following figure the big picture of the organization is shown.

ORGANIGRAM OF THE CHILEAN NAVY

Fig. 3 CN Organization

The superior command of the organization is represented by the Commander in

Chief and the Staff. Five subordinate units report to this command:

1. The Operations Command concentrates more than the 85% of the capital

assets of the organization. It commands the Fleet, the Submarine

Command, Naval Aviation, the Marine Corps, and other smaller forces. It

corresponds to the Operator in the LCM14-team model of AMC15, explained

later.

14

LCM-team: Life Cycle Management - team

15 AMC: Asset Management Control

C in C &

STAFF

OPERATIONS COMMAND

FLEET

SS

NAVAL

AVIATION

MARINE

CORPS

MATERIAL

DIRECTORATE

SYS. ENG.

DIRECTORATE

R & D

DIRECTORATE

MAINTENANCE

DIRECTORATE

SUPPLY

DIRECTORATE

PERSONNEL

DIRECTORATE

HHRR

EDUCATION

DIRECTORATE

WELFARE

DIRECTORATE

HEALTH CARE

DIRECTORATE

FINANCE

DIRECTORATE

BUDGETING

DIRECTORATE

ACCOUNTING

DIRECTORATE

COAST

GUARDS

MARITIME OPS

DIRECTORATE

MARITIME INT

DIRECTORATE

ASMAR



The main logistic effort of the organization is focused whithin this command.

Here are the assets that allow reaching the organization its main goal: to

win the war at sea.

2. The Material Directorate is the design authority under the LCM-team model

of AMC. It embraces the Systems Engineering Directorate which leads the

maintenance phase of the assets, the Research & Development Directorate

that leads the design phase of the assets, the Maintenance directorate that

leads the execution of the planned maintenance at the shipyards, and the

Supply Directorate that leads the procurement processes.

3. The Personnel Directorate performs the selection, education, healthcare,

welfare, development and dismissal of personnel.

4. The Finance Directorate manages de financial assets of the CN.

5. The Coast Guard Directorate is the body of the CN, through which the state

of Chile oversees the compliance with the laws and international

agreements in force, to protect human life at sea, the environment, natural

resources and regulating the activities that develop in the aquatic area of its

jurisdiction, with the purpose to contribute to the development of the

maritime nation

6. In a customer – supplier relationship is ASMAR the repair and construction

shipyard of the CN. It is in charge with executing the second and third level

maintenance of the assets and it acts as the main contractor for the fourth

level maintenance carried out in the factory or third parties. It is the

Maintainer in the LCM-team model of AMC.

Logistical Managerial

During the operation/maintenance phase, which is the longest phase of the life

cycle of the asset, the following actors play important roles and their tasks and

interaction are relevant to accomplish the goal of achieving cost effective

management control of the capital assets.

The following text is based on the CN intranet description of the roles of the

members of the organization.



1. Material Directorate :

This is the logistical organization of the material at the highest level of the

institution, which has the mission to exercise the upper management of the

material and provide the material and technological resources to the Navy,

with the purpose of contributing to the fulfillment of its permanent mission. In

short:

a. Performs the long term planning of maintenance.

b. Authorizes alteration of systems

c. Assigns resources

2. Units Recuperation Directorate (DRUA16) (also mentioned as Maintenance

Directorate):

The DRUA manages the naval unit‘s recuperation and repair processes,

with the purpose of contributing to the fleet support and to optimize the use

of the CN resources. To do this:

It establishes the repair directives, works to be executed, and defines the

resources needed. (Planning)

It executes the planned repairs assuming the command of the unit. It gives

support during the period. (Execution)

It determines the materials needs managing the procurement in order to

ensure the timely availability. (Materials management).

It gathers information and experiences from repairs, analyzes them and

proposes recommendation to optimize the process.

In short:

a. Performs the short term maintenance planning.

b. Negotiates with ASMAR.

c. Manages maintenance resources.

d. Controls maintenance.

e. Acquires materials.

3. Technical Directorates:

Theses directorates are:

16

DRUA: Dirección de Recuperación de Unidades de la Armada= Units Recupation Directorate



The DIRISNAV manages the assets life cycle, serving as senior technical

authority. It manages the projects of replacements, alterations and

modifications; and in addition it manages the modernizations that

specifically are allocated by the DGSA, representing the institution in the

areas related to technology.

Advises and technical support to the Operational Command and technical

divisions. It manages the maintenance of 2nd, 3rd and 4th level of the

ammunition institutional. It advises the DGSA in the process of training and

technical training of the staff that operates and maintains the equipment and

systems. With the purpose of contributing to achieve the expected

availability in institutional planning, maximizing the reliability and operational

efficiency of the material and the naval systems assigned to their tuition by

the DGSA.

The DABA17 that manages the logistic functional element of supply, with the

purpose of delivering support and service of excellence to the naval forces

and to the land installations of the CN.

The DTIA has to ensure the timely processing and delivery of instructions

and information entered to the communications networks of the CN and the

availability and reliability of the systems and equipment associated. Counsel

to the Staff and CON18 in the construction and updating of the

communication planning. It is the superior technical authority on the ITC

material of the CN over its entire life cycle.

The SIAMA19 that has to program and control the implementation of the

planned maintenance system (SIMPLA) to all the maintainable material of

the organization, with the exception of the flight material of the Naval

Aviation Command, serves as administrator of the functional subsystems,

functional operations and maintenance of the SALINO system.

Disseminates in the Intranet the necessary information to optimize the

management of maintenance in the institution. Analyzes the quality of the

17

DABA: Dirección de Abastecimiento de la Armada = Supply Directorate

18 CON: Comando de Operaciones Navales = Naval Operations Command

19 SIAMA: Servicio de Información de Mantenimiento de la Armada =Maintenance Information Directorate



data of preventive and corrective maintenance entered by users into the

SALINO system.

Calculates and disseminates in the CN Intranet the value of the operational

availability of the naval units and the Marine Corps. Keep updated the

Material Configuration of the assets which means keeping control of the

location of every piece of equipment installed on board.

In short, these directorates:

a. Establish rules.

b. Authorize and prioritize work or projects.

c. Propose and assesses alterations.

d. Manage contracts and plans.

4. Ships – Operators:

They are the capital assets of the CN, and are the central part of our

concern and the subject of AMC efforts (Stavenuiter, 2002).

a. Project managers.

b. Performs first level maintenance.

c. Demands second and third level maintenance.

d. Controls safety.

e. Presents ship to trials.

5. ASMAR: This is the shipyard of the CN. Its main mission is to meet the

requirements of maintenance and naval construction of the CN. Additionally,

it can deliver the same services to the maritime market.

The company is organized in three industrial plants located in the ports of

Valparaiso, Talcahuano -the biggest infrastructure and capability - and

Punta Arenas. Its headquarters are located in Valparaiso. In short:

a. Performs second and third level maintenance.

b. Main contractor for fourth level maintenance.

c. Demands parts.

d. Controls quality and schedule.



6. Inspectorates (off Valparaiso). They are representative offices of DIRISNAV

in Talcahuano and Punta arenas.

a. Verifies rules compliance.

b. Represents technical directorates.

c. Performs diagnosis and proposes solutions.

d. Technical supports to ships.

At this time I would like to highlight a finding: the procurement of parts for

maintenance is performed by several Technical Directorates instead of ASMAR

which is the entity executing the maintenance actions. In my opinion, is where

there is quite an opportunity for improvement.

The following figure shows their actual relationships:

9

Stakeholders in the Maintenance Process

D.G.S.A.(Material Direct)

• Long term planning.

• Authorize alterations.

• Asign resources.

D.I.S.N. – D.A.B.A - D.T.I.A.

• Establish rules.

• Authorize &prioritize works.

• Propose/evaluate

alterations.

• Administer contracts &

planns.

• Material procurement.

D.R.U.A. (Maint. Direct)

• Short term planning.

• Negociate with

ASMAR.

• Manage resources.

• Control maintenancee

• Material procurement.

INSPECTORATES –(OFF Valpso Insp)

• Verify rules compliance.

• Represent Technical Directorates

• Diagnostic & propose solutions.

• Technical suppot to ships.

ASMAR

• Perform 2º&3ºlevel maint.

• Demand parts.

• Control quality &schedule.

SHIP- OPERATORS

• Project manager.

• Demand 2nd & 3r level maint.

• Perform 1est level maint.

• Manage resources.

• Control safety.

• Present ship for trials.

Fig. 4 Relationships among the main stakeholders in the maintenance process

In the previous figure it can be seen that Material Directorate is the superior

authority of the logistics actors, giving its output (long term planning, authorizations

and resources) to its executor branches: DRUA, and Technical Directorates. These

organizations relate to ASMAR, which is the entity that performs the maintenance,



directly in the case of DRUA and through the Inspectorates in the case of the rest

of the Technical Directorates.

The ship, which is at the center of the figure, also it is the central and main

stakeholder of the organization because it is the reason why the others exist.

The logistical information flows through the organization from the assets where

transactional data is generated and entered into Salino according to its rules. After

being processed and stored, it is delivered as logistic information to the Technical

Directorates where the logistic analysis and decision making process are

performed, then transferred to the Material Directorate from where ILS20 policy and

instructions emerge to achieve the needed cost – effective management of the

assets. This flow of information is shown in the next figure.

LIFE CYCLE OF LOGISTIC INFORMATION

LOGISTIC INFORMATION

(PROCESSED DATA)

MATERIALADMINISTRATION

O. L. M.

ILS

Conceptual Map

DATA

CONSULTANTS

LOGÍSTICANALISIS

(TECHNICAL DIRECTORATES)

USERDATA ENTER

SALINOTABLES

ELIPSE

RULES FOR“ENTERING DATA”

MANGMNT

FLOW OF THE LOGISTIC INFORMATION

22

Fig 5 Flow of the logistic information trough the organization

20

ILS: Integrated Logistic Support



II Literature Review

Asset management

Capital assets as the ships of the Chilean Navy fleet are high-cost technical

systems which are capable of meeting operational requirements. (Stavenuiter,

2002). Going a little further into detail, we can say that these types of physical

assets (to differentiate them from the financial assets) also have some other

attributes that characterize them:

Have a high degree of complexity

Long life cycle

High cost of acquisition, operation, and maintenance.

Components with fast technical obsolescence.

Its disposal has significant cost.

Other major physical assets considered as capital assets are aircrafts, railways,

power generation plants, mining facilities, power distribution systems, and so on.

Asset management is defined as a management approach to manage all

processes (specify, design, produce, operate, maintain and dispose) needed to

achieve a capital asset capable to meet the operational need in the most effective

way for the customer/user. (Stavenuiter, 2002). If we add the concept of efficiency

to this definition we mean that the asset management must be performed at the

lowest cost.

The following figure obtained from the web site of EMA Inc. illustrates the concept

of lifecycle of an asset in a very complete form, because it starts from the very

beginning of the life cycle of an asset: the identification of the need. Most authors

define the start with the design, but prior to this it is required to identify the need of

an asset. This figure also incorporates the phase of planning which also includes

the budget required.

Additionally, the figure incorporates the phase modify/upgrade or modernize the

asset, which not many authors consider.



THE LIFE CYCLE OF AN ASSET

Fig. 6: The phases of the asset life cycle (EMA Inc web site)

The British Standard for Asset Management (PAS 51-1, 2008) defines asset

management as: ―systematic and coordinated activities and practices through

which an organization optimally and sustainably manages its assets and asset

systems, their associated performance, risks and expenditures over the lifecycle,

for the purpose of achieving its organizational strategic plan‖.

International Society for Engineering Asset Management (ISEAM) gives this

definition: ―The continuous process covering the whole lifecycle of an asset from

conceptual design through to construction/manufacture, operational use,

maintenance, rehabilitation and/or disposal. This definition lacks of the efficiency

concept (cost). (Amadi-Echendu et. al, 2010), (EAM Review Volume 1,2010).

Nicholas Hastings gives the following definition for asset management: ―it is the set

of activities associated with:

Identifying what assets are needed.

Identifying funding requirements.

Acquiring assets.

Providing logistic and maintenance support systems for assets.

Disposing or renewing assets,

So as to effectively and efficiently met the desired objectives‖.(Hastings, N., 2009)



These definitions imply that asset management is the management of the asset

during its whole life; that is, from the identification of the need, through

specification, purchasing/constructing, operating, maintaining and then managing

the consequences from the decision of refitting or replacing the asset before

decommissioning and disposal or recycling the components.

The four asset management objectives to be achieved are: (Stavenuiter, 2002 )

1. Specify system functionality taking into account the user needs which is the

starting point of the life cycle of the capital asset.

2. Acquire system functionality which implies all the acquisition phase that

starts with the design and ends with the construction phase.

3. Achieve cost – effectiveness. This objective is considered the most

important (Juran, 1998, Blanchard, 2008). Nevertheless, a system can only

demonstrate its cost- effectiveness during the operation phase.(Stavenuiter,

2002).

4. Last objective is justify phase out, because there are many environmental

and government regulations that have to be taken into account.

OBJECTIVES FROM NEED TO PHASE OUT

Fig 7 Asset management objectives through life cycle (Stavenuiter, 2002)

In order to achieve cost effective management control of a capital asset it would be

helpful to have an asset management control system (AMCS) than can be

applicable to any organization, to manage any asset, in any of their life cycles.

Stavenuiter (2002) proposes a conceptual design of an improved AMCS shown in

the figure:



ASSET MANAGEMENT CONTROL SYSTEM

Figure 8 Conceptual design of an improved AMCS (Stavenuiter, 2002)

This design, called the Life cycle management systems, LCM-systems, seeks the

optimization of management control and logistics support with respect to the

functionality of the technical asset.

In this model the LCM-team, a three-foot table, plays a fundamental role in the

asset management processes because each represents the entire set of

stakeholder interested in achieving cost effectiveness: operators, maintainers, and

design authority. In order to succeed they have to work coordinately.

Fig 9 The LCM-team (Stavenuiter, 2002)

Asset management is a structured program to optimize the life-cycle value of the

physical asset by reducing the total cost of ownership, while providing the required

level of service. To carry out this program it is necessar to implement an asset

management system to minimize the total cost of owning, operating, maintaining,

and replacing the asset, while ensuring reliable and uninterrupted delivery of

quality service. (Lin, S, 2006) et al.



LIFE CYCLE COST OF AN ASSET

Fig 10 Example of life-cycle costs elements contribution over the life cycle of an asset (Stavenuiter, 2002)

The above figure shows a fact that is often overlooked: that the major portion of the

total cost of ownership of an asset is maintenance cost rather than acquisition cost.

From the figure it can be understood that the ownership of this type of physical

asset implies management of enormous amounts of financial resources that are

spent by many actors in different departments of the organization, during a

significant period of time.

On the other hand, the goal is to manage the asset in the most cost-effective way,

which means to minimize the total cost and to try to reach the highest

effectiveness.

Since the system effectiveness can be defined as a function of performance (P),

availability (A), and dependability (D), or

SE = (P) (A) (D)

Where P constitutes the appropriate combination of system operational factors

(e.g., range, accuracy, speed); A refers to the probability the system will be

operable, when called upon, at the start of a given mission; and D refers to the

probability the system will successfully complete its mission, given that the system

is operational at the start of its mission. (Blanchard, 2008).



There are a huge amount of actors that participate to achieve the effectiveness

desired (operators, maintainers, logistic partakers and suppliers) which work has to

be coordinated and managed.

Cost – effective asset management aims to optimize utilization, increase output,

maximize availability, and lengthen lifespan, while simultaneously minimize costs

(Baskarada, 2006)

The process of asset management is sophisticated because it is an engineering

and planning process that requires a great amount of data that has to be collected

from many different parts of the organization, then it has to be properly processed,

and delivered timely to other substantial number of actors within the organization to

be used to support the decision making process at different levels of the

organization. Furthermore, the information has to be stored for many years in order

to identify long-term trends and for forensic purposes (Lin, S. et. Al., 2006)

Asset Management is not a new field that has suddenly come out of nowhere; we

have carried out asset management activities since we started utilizing capital

assets, such as power plants, ships, buildings, or any kind of production assets.

However, changes in technology and business environment mean that asset

management is more important that before and has a new focus, among other

reasons, because growing turbulence of markets, globalization and increasing

competition, pressure for higher profitability, so Asset Management has become a

new discipline and gradually has gained the attention in many places in the world

(Telli van der Lei, et al, 2012)

From the above, it becomes clear that to carry out a cost-effective asset

management it is necessary to have a strong and reliable asset management

system supported by an information system that is able to collect data from many

different places of the organization, process it, store it and deliver the obtained

information to be used at diverse levels of the organization to be a foundation for

the decision making process.



At this time it is convenient to state that strictly speaking there is a distinction

between two words that are usually used synonymously: data and information.

The Oxford dictionaries define Data as facts and statistics collected together for

reference or analysis and Information as data as processed, stored, or transmitted

by a computer.

In practice, managers differentiate information from data intuitively, and describe

information as data that has been processed. (Pippino, L. et. Al.2002)

For the purpose of this research we will make this distinction.

Moreover, we can distinguish three types of data: transactional data, analytical

data and master data,.

Transactional data (TD) are the elements that support the ongoing operations of an

organization. TD may include order management, purchasing, maintenance

activities. Rather than being the objects of a transaction such as customer or

product, TD is the describing data using time and numeric values.

In the realm of cost effective asset management, examples of transactional data

related to cost are all the resources spent in maintenance (materials, services, and

wages of maintainers). Transactional data related to performance are those to

calculate availability (up time and down time hours) like repair time, waiting time,

and research time.

Also related to performance are data to calculate reliability, like MTTF21, mission

time, number of failures; and availability like Up time (operation times) and down

time due to failures (waiting time + search time + repair time)

Analytical data (AD) are the numerical values, metrics, and measurements that

provide business intelligence and support organizational decision making. AD is

characterized as being the facts and numerical values in a dimensional model,

which is a data model referred to a dimension or category of information, for

example time, location, often used in data warehousing systems (Inmon, W. 2005).

Data warehouse is defined by Kimball, R. et al, 2011 as a copy of transaction data

specifically structured for query and analysis. A further explanation will be given

ahead.

21

MTTF: Mean time to failure



Generally, AD resides in fact tables surrounded by key dimensions like product,

account, location, and date/time. Nevertheless, AD are defined as the numerical

measurements rather than being the describing data.

Master data (MD) refers to the key organizational entities, are static and may

include data about products, configuration, employees, and suppliers. MD

represents the business entities over which the organization´s transactions are

executed. MD is typically persistent, non transactional data utilized by multiple

systems that defines the primary elements around which analytics are conducted.

(www.bi-insider.com)

Examples of master data in the realm of cost effective asset management are

configuration data (systems, installations, and components), cost of acquisition and

the base line for costs and performance.

As was mentioned before, in order to manage assets cost-effectively an enormous

amount of data – information has to be collected, processed, stored and delivered.

To illustrate this, the cost-effective structure given by Juran (1988) and presented

by Stavenuiter (2002) is appropriate to highlight some of the main items that

generate data in both system effectiveness and total (life cycle) costs. This

structure is shown in Fig.11.

COST-EFFECTIVE STRUCTURE

Fig 11 Cost-effectiveness structure according Juran, 1988 (Stavenuiter, 2002

http://www.bi/



Data Quality

Before talking about data quality, let us review the concept of quality.

According Juran, ―Quality‖ means freedom from deficiencies—freedom from errors

that requires doing work over again (reworking) or that result in field failures,

customer dissatisfaction, and customer claims, and so on.

Many researchers have proposed definitions for data quality and have identified its

dimensions (Lin, S. et. Al. 2006). But there is not a unique set of dimensions, so a

widely-accepted data quality definition is provided by Wang, R. and Strong, D.

1996, pp6, which is centered on the user needs: ―quality data are data that are fit

for use by the data consumer‖

From the literature it seems that there is not a universal definition of data quality,

neither is there an agreed framework of data quality dimensions

The four data quality dimensions more frequently mentioned are: accuracy,

completeness, timeliness, and consistency. (Lin, S. et. Al. 2006).

The dimension is defined by McGilvrey, 2008, ―As an aspect or feature of

information and a way to classify information and data quality needs‖, but a more

concise definition is the one given by Wang, R. and Strong, D.1996 : ―Dimension is

a set of data quality attributes that represent a single aspect or construct of data

quality‖.

After the literature review performed by the author, the identification of dimensions

made by Wang, R. and Strong, D. 1996, appears to be the most complete.

Moreover, they group the twenty dimensions into a hierarchical structure with four

categories of dimensions that encompass the rest of the dimensions facilitating the

understanding and communication with data consumers in doing further research

on this issue.



Data Quality Dimensions

Dimension Description

Believability The extent to which data are accepted as true, real, and credible.

Value-added The extent to which data are beneficial and provide advantages from their use.

Relevancy The extent to which data are applicable and helpful for the task at hands.

Accuracy The extent to which data are correct, reliable, and certified free of errors.

Interpretability The extent to which data are in appropriate language and units and the data

definitions are clear.

Easy of understanding The extent to which data are clear without ambiguity and easily comprehended.

Accessibility The extent to which data are available or easily and quickly retrievable.

Objectivity The extent to which data are unbiased (unprejudiced) and impartial.

Timeliness The extent to which the age of the data is appropriate for the task at hand.

Completeness The extent to which data are of sufficient breath, depth, and scope for the task at

hand.

Traceability The extent to which data are well documented, verifiable, and easily attributed to

a source.

Reputation The extent to which data are trusted or highly regarded in terms of their source

or content.

Representational

consistency

The extent to which data are always presented in the same format and are

compatible with previous data.

Cost-effectiveness The extent to which the cost of collecting appropriate data is reasonable.

Easy of operation The extent to which data are easily managed and manipulated (i.e., updated,

moved, aggregated, reproduced, customized).

Variety of data and

data sources

The extent to which data are available from several differing data sources

Concise The extent to which data are compactly represented without being overwhelming

(i.e., brief in presentation, yet complete and to the point).

Access security The extent to which access to data can be restricted and hence kept secure.

Appropriate amount of

data

The extent to which the quantity or volume of available data is appropriate.

Flexibility The extent to which data are expandable, adaptable, and easily applied to other

needs.

Table 1 Data quality Dimensions (Wang, R. and Strong, D., 1996)

As mentioned above, after field research they labeled the four categories of dimensions as

: accuracy the category that includes accuracy, objectivity, believability, and reputation;

relevancy the category that includes value-added, relevancy, timeliness, completeness,

and appropriate amount of data: representation the category that includes interpretability,

easy of understanding, representational consistency, concise representation; and

accessibility the category that includes accessibility and access security.



HIERARCHICAL FRAMEWORK FOR DATA QUALITY

Figure 12 A Conceptual hierarchical framework for data quality. From Wang,R. and Strong,D. 1996.

Data as an asset.

It is trite to hear that data is a critical asset in the information age. Also it is known

that the enormous amount of data acquired and stored by organizations is growing

by leaps and bounds. (Redman, 2004). Also it is very well known the phrase ―a

decision is not better than the quality of the data used to make that decision‖. Data

is a crucial input to decision making and planning. Data are ―the facts and figures‖

associated with every aspect of an organization nowadays and indeed of every

aspect of life in the information age.

Therefore, the value of the data for an organization is out of doubt.

The definition given by Investopedia.com for Asset: ―A resource with economic

value that an individual, corporation or country owns or controls with the

expectation that it will provide future benefit‖, which is appropriate for financial

assets and physical asset as well.

If we consider this definition, we can establish, without any doubt, that the data

managed within an organization is an asset and it should be treated accordingly.

But, what should be done for that? Redman 1995, proposes four simple but

powerful practices:



First, develop an inventory of data assets in a similar manner that it is done with

the other traditional assets of a company (employees, equipment).

The same way that someone is not able to imagine that an organization doesn´t

have the appropriate data to control its traditional assets: where are they located,

how many are they, which are their characteristics, we should think about data

accordingly. We should ask how much data do we have, where are they located,

how much are redundant, what are their quality level, what processes create the

data, how do we use the data.

Second, define what sort of asset data is. Most data is very dynamic, being

created, stored, processed, transmitted, used, and updated at very high rates. By

recognizing this dynamic nature of the data and the importance of the processes

for creating or acquiring the data, means that the processes are also a real asset.

Third, align responsibilities for data quality. If we focus on the highest level there

are three main activities related to data: acquire data, store data and using data. It

is advisable to establish a data policy in order to designate responsibility for each

activity.

Many actors within the organization are both data creators and data users. For

instance a maintainer is a user of master data of equipment (characteristics,

components) and at the same time is data creator of the maintenance activities

data (failures, time to repair, spares used). A model of data policy is shown in the

following figure:



EXAMPLE OF DATA POLICY

Fig. 13 Model Data Policy, from Redman 1995

Fourth, establish customer – supplier relationship for data. This technique is being

used in organizations highly committed with continuous improvement. These

partnerships work well for data sharing also.

But putting into practice this partnership is not always easy, so the first thing to do

is to give the supplier clear and operable definitions of what it is required. Data

supplier often has a primary function different of taking care of the data quality, so

it is important to educate them about the consequences of incorrect data and their

responsibilities.

Finally, nothing is free: to improve in data quality we must invest resources on it.



Applying quality principles to data

After we have revised the concepts of data quality and have stated that data are

assets, we should be able to apply general principles of quality to data.

We must apply to the data the same quality principles that traditionally have been

applied to products and services. From Juran, 1999, we know that quality means

―those features of products which meet customer needs and thereby provide

customer satisfaction and are free from deficiencies”. pp 2.2.

Keeping this in mind, we know that a product/service that satisfies the customer

needs better than the one of the competence is of higher quality, so the best

parameter to measure quality should be the customer satisfaction. Therefore, it is

mandatory to identify the customer and what does he/she needs.

There are three approaches to reach high quality applied to data:

1. Error detection and correction. The typical action is to inspect the product

for compliance of quality requisites. The easiest way is to inspect everything

and rejected items are discarded or reworked. This approach is quite

expensive. There are several techniques to detect poor quality data. For

instance, to improve data accuracy we must compare data with the real

world counterpart, but this technique does not lead to sustained

improvement because it doesn´t attack the causes of errors.

Another technique is the so called Data Base bashing that is used when

data is stored in two or more databases. Corresponding data are compared

and those that agree are assumed correct. This technique is easier and

cheaper than the previous one, especially because it can be performed

electronically. This technique also doesn‘t attack the causes of errors.

A third technique is called data editing and it consists in computerized

checking of data against constrains on the data. It is a validating action for

the value of the data at a given register.

These all three techniques are ineffective because they are performed

downstream from the creation of data values, either at storage or usage, so

the improvement you can get is not sustained.

2. Process management. The objective of process management is to detect

and get rid of the causes of errors. The steps to apply this approach are

shown in the following figure:



Process management approach

Figure 14. Steps for Process Management Approach. Redman 1995

The focus of this approach is on processes that create the data values and then

can identify the causes that produce the errors.

3. Process design. This approach focuses on making processes less inclined

to errors. In this approach is required the participation of IT people. The

redesign of the processes may include the incorporation of some automated

forms of entering data like bar code devices.

As quality systems develop, they rely less on inspection/rework, more on process

management and ultimately on process design. (Redman, 1995)



Data cleansing.

According to Maltetic, J. 2000, data (field) errors rates in organizations are around

5% or more (Error rate = number of erred fields/number of total fields), unless

extreme measures have been taken to avoid data errors. (Orr, K. 1998). Other

organizations are expected to have errors rates much higher (Mendoza, R., 2009).

Therefore, it is required to find a solution to clean the data that means to explore

the data sets searching for errors and correct them. To perform this task by hand is

practically impossible for any real data set, even though many companies spend

millions of dollars per year detecting and correcting errors this way. (Redman, T.

1996). Furthermore, being itself a manual process it is prone to error and

extremely expensive.

Data cleansing is the process of eliminating the errors and inconsistencies in data

and solving the object problem. (Galhardas, H. et. al., 1999).

Kimball, R., 1996, proposes a methodology for data cleansing breaking down the

process into six steps: elementizing, standardizing, verifying, matching, house

holding, and documenting.

But this approach, as the software tools for data cleansing offered by the main

companies in the marketplace for data cleansing are oriented to clean very large

customer address lists.

In a more general orientation Maltetic, J. and Marcus, A. 2000, state that a general

method for data cleansing should have these three phases:

Define and determine errors types.

Search and identify errors instances.

Correct the uncovered errors.

Each of these phases constitutes a complex problem in itself. To solve them, the

characteristics of the specific task at hand must be taken into account.



Business Intelligence BI

Even though the term Business Intelligence (BI) is quite new, the first BI systems

showed up almost fifty years ago (Power, 2004).The same systems we recognize

now as BI were also known as decision support or executive information systems

(EIS), but BI is much more than this two information systems (IS).

BI can provide decision makers with valuable information and knowledge by

leveraging a variety of sources of data, as well as structured and unstructured

information. The data and information could reside within or outside the

organization, could be obtained from multiple sources, and could be either

quantitative or qualitative (Sabherwal, R. and Becerra-Fernandez, I., 2011). Here it

is good to remember something said before in this papeer: information is data

processed. Information is a subset of data including only those data that possess

context, relevance, and purpose.

There are many definitions of BI, and the definition given by Nagask, 2004

describes it quite good:

“BI systems combine data gathering, data storage, and knowledge management

with analytical tools to present complex internal and competitive information to

planners and decision makers”.

Within this definition stands the idea that BI systems deliver actionable information

at the right time, where needed, and in the form needed to assist decision makers.

(Actionable information: provides data that can be used to make specific business

decisions. Actionable information is specific, consistent and credible. For example,

a report which shows trends in "employee retention" is important and interesting,

but not necessary actionable. However a dashboard or simple red/yellow/green

report which shows managers the turnover rate by department, accompanied by

the "top three reasons for leaving the company" is far more actionable)(Bersin,

2011)

There are many components of productive BI, but two of them have had great

recognition and application in the industry (Langseth and VIvatran, 2003): Data

Warehousing and Data Mining.



Data warehouse.

The data warehouse (DW) is an information tool that is based on a heap of

information, both detailed and summary, which comes from data that may be found

in databases and operational data from other external sources (data external to the

company itself or old data contained in another media type, etc.). It provides a

complete solution comprising a mix of hardware, software, knowledge of the

business, and system integration capabilities. (De Pablos, et. al.1998)

A DW is a database that contains data that has been cleansed and transformed

into informational form. (Informational data: data that is extracted from the

operational data and then transformed for end-user decision maker).

Other reason to separate the stored data in the warehouse from the operational

data bases is to avoid slow down the latter.

Data Mining.

Data mining (DM) is the practice of automatically searching large stores of data to

discover patterns and trends that go beyond simple analysis. Data mining uses

sophisticated mathematical algorithms to segment the data and evaluate the

probability of future events.(Oracle, 2008).

The key properties of data mining are:

Automatic discovery of patterns: by building models that use algorithms to

act on a set of data certain patterns can be discovered.

Prediction of likely outcomes: many forms of data mining are predictive. For

example, a model can predict availability of a system based on past failure

rate or spare consumption.

Creation of actionable information: data mining can derive actionable

information from large volume of data. For example, a car leasing agency

might use a model that identifies customer segment to design a promotion

on targeting high income customers.

Focus on large data sets and databases: this type of operations make sense

performed on large amount of data. It is not possible to infer trends based

in a few data.



Data mining is accomplished by building models that use an algorithm to act on a

set of data. Several forms of data mining are predictive. For example, a model

might predict failures of equipment based on the consumption of a specific spare.

Other forms of data mining identify natural groupings in the data. For example, a

model might identify the segment or group of all the equipment that have certain

future condition based on registered down time, spare consumption, and MTTR22.

Data can be mined whether it is stored in flat files, spreadsheets, database tables,

or some other storage format. The important criteria for the data are not the

storage format, but its applicability to the problem to be solved.

Proper data cleansing and preparation are very important for data mining, and a

data warehouse can facilitate these activities. However, a data warehouse will be

of no use if it does not contain the data that is needed to solve the given

problem.(Oracle, 2008).

As it can be seen, these tools are very valuable in the realm of assets

management because they can provide a powerful drive to managers to make

better decision since they are based on information of better quality.

SALINO

This is an ERP type information system which is defined as a system that is used

to manage and coordinate all the resources, information, and functions of a

business.

(Wikipedia).

This information was obtained from the SALINO Office and extracted from the

SALINO tutorial in the CN Intranet.

It was developed on the world class platform software MIMS Open Enterprise. This

software has been successfully installed in other navies like Australia, Canada,

and England. (MILU Sr. Consultants).

22

MTTR: Mean time to repair



It has also been installed in many mining plants like Mantos Blancos, Anaconda,

Disputada de Las Condes, Doña Inés de Collahuasi, and other firms, all of them

highly physical assets intensive companies.

Since it is an integrated system it works with a unique catalog and unique

registers. These characteristics minimize identification problems and facilitate

global visibility of stocks.

It incorporates Integrated Technical Manuals in digital formats (CD23, ROM24, and

network) which facilitate information interchange between maintainers and material

supply systems. Having technical manuals within the system allows the users, not

just deal with stock numbers and descriptions of the items but also available are

drawings, pictures, assembly instructions, and so on.

It allows capturing 100% of the transactions in the maintenance and materials

areas avoiding the need of written reports between them. The procedure compels

to enter every maintenance activity, preventive and corrective, and every action on

material acquisition, so all the operational data produced must be entered into the

system.

It also allows measuring the logistical support effort since the acquisition of the

equipments, during operation and maintenance, until its disposal (the whole life

cycle). This is a consequence of the point stated previously, because the system is

able to trace all the resources oriented to support logistically the assets and their

subsystems. And this is a very important characteristic of the system, because it

would allow managing the assets since you only can manage what you can control

and you only can control what you can measure.

The Naval Intranet connects users all over the country and the commercial

missions abroad. The CN has implemented a robust and reliable Intranet managed

by DTIA which interconnects six LAN´s located in Iquique, Valparaiso, Con Con,

Santiago, Talcahuano, and Punta Arenas. LAN´s of Valparaiso and Talcahuano

are the biggest and strongest and are implemented on optical fiber rings. Through

a DMZ the Intranet connects with the Naval Mission at London and Washington

and also it gives access to OMEGA, the system of ASMAR. This communication

infrastructure allows the users to interact with SALINO from any unit of the CN.

23

CD = Compact disc

24 ROM = Read only memory



The next figure shows the way the LAN´s are interconnected

SALINO ENVIRONMENT

7

SALINO ENVIRONMENT

Local Area Network(VALPARAISO - VIÑA)

Materials & MaIntenance

USA UKLocal Area Network

(TALCAHUANO)

(PUNTA ARENAS)

(IQUIQUE)

(CON CON)FLEET - Molo

(P.M.) (P.W.)

Optical Fiber Ring

Integrated Technical Manuals

INTEGRATED SYSTEM

Darsena

INTERCAMBIO DIGITAL DE INFORMACION

Local Area Network

Optical Fiber Ring

Local Area Network

Local Area Network

Fig 15 SALINO environment (Adapted from SALINO tutorial)

In the previous figure, at the center, it is the Valparaiso LAN which is the main and

the broadest of all. It also encompasses Viña del Mar and it hosts the servers of

SALINO. The Fleet docked in Valparaiso at the sea breaker is part of this LAN.

To the right it is the Con Con LAN which encompasses the Naval Aviation and the

Marine Corps installations, located 40 kilometers north from Valparaiso.

At the bottom of the figure to the left it is Talcahuano LAN which is constructed on

a fiber optic ring. It is connected to ASMAR LAN represented by the big A, this way

SALINO communicates with OMEGA which is the ERP system of ASMAR.

The ships docked in Talcahuano at the basin are also connected to this LAN.

To the right, it is Punta Arenas LAN which encompasses the third Naval Zone

installations including units based in Puerto Williams located 3.500 kilometers

away from Valparaiso.

In the upper part of the figure, to the right and the left are represented the Naval

missions at London and Washington respectively, They are connected through a

DMZ to the servers in Valparaiso.



MIMS has four subsystems:

Maintenance and Operations Management

Materials Management

Finance Management (partially in Salino)

Human Resources Management (partially in Salino)

Other characteristics of MIMS Open Enterprises:

Client – Server architecture, which allows the user interact with the system and to

run its own applications, leaving to the server the tasks of processing the

information and managing the net.

Clients are in MS Windows environment. This facilitates the training and the use of

the system because this operating system is very familiar to the majority of the

users.

Easy access from PC connected to the CN LAN. This allows the users to interact

with SALINO not only from his/her workstation but also from any other connected

to the CN Intranet.

User access control (User RUT25 + password). It gives the required security

standard to grant access only to authorized users.

Every transaction registers who made it and who authorized it. This characteristic

is very important for forensic purposes.

User access to the system depends upon their profile (security). Not every user

have access to all the modules of SALINO, the profiles of everyone have been

designed so the users have access just to the information they need to know for

the proper accomplishment of their job.

It is structured on five Data Bases: Catalog, Suppliers, Equipments, Documents

Control, and Personnel. The Equipment DB is the central one because it contains

all the equipments (installations) of the assets controlled by the SALINO system.

The information flows across the organization from the user level where the data is

entered, until the upper level where operators use the information generated by the

system to support their decision making processes.

25

RUT = National identification number of a person in Chile (Rol único tributario)



HIERARCHICAL FLOW OF INFORMATION

7

HEAD DEPT.HEAD OF DEPT.

AU

THO

RIZ

E EX. OF

COMM. OF.EXECUTOR UNIT

EXECUTOR COMMAND DABA DISN CJE

CJA

ADMINISTER COMMAND

CON

DGSA

OF in ChargeOF MAT Of in Charge

Shop MaintainerDAT

A

FLOW AND VALIDATIONOF INFORMATION

Maintainer Maintainer

Fig16 Flow of information within Salino (adapted from SALINO tutorial)

This figure represents the flow of the information from its origin to the use at the

top of the organizational pyramid. The data is created and entered into the system

at the lowest level of the organization by the maintainers and technicians at the

shop, the validated in the next step by the material officer or officer in charge and

finally it is authorized at the third step of the user level by the head of department

or executive officer. This is the user level where the data information is created,

then it flows to the operators level where the information is used. The first step is

the Commanding officer following to the executor command represented by the

technical directorates finishing at the administrative command represented by the

Material Directorate and the organization command.



III Methodology

1. - The research started with a review of SALINO to learn its philosophy, structure,

capabilities, functionalities, and general characteristics. Then, a training account

was obtained to get access to the system.

In order to learn about the system the author attended a training course on

SALINO at the Academia Politécnica Naval (APN), between March and April 2012.

The SALINO module is part of a one semester course, Job Management, given to

students in second year of the Technical Mechanics specialty, in order to develop

the competences to act as SALINO users to the future graduates of APN.

This activity was very valuable for the author because it allowed him to acquire the

knowledge of the system, and also it was important to become familiar with the

environment that people who enter data into SALINO are faced with in order to

have a closer experience about that feeling.

2. - The AMC Master Course material and the bibliography were studied in order to

have the proper tools to accomplish the objective of the research which is how to

reduce the amount of non-correct information in SALINO. It was necessary to

review aspects on asset management, data quality, and data as an asset before to

start the field research. The existence of non-correct information in the system

means that the information delivered to the users doesn´t meet the user‘s needs

and this entails a data quality problem (Wang, R. and Strong, D. 1996), so it was

important to review aspects on data quality. A detailed revision of data

warehousing and data mining concepts was made because one the users blames

was the lack of this type of capabilities in SALINO.

3. - Research on the detected flaws to corroborate or deny their existence.

In the first phase, the research was initiated by studying the alleged existing flaws

detected in the previous phase of research (Bosaans, 2009-1) in order to verify

that they really exist.



Positive evidence was found to prove that they exist or existed. Some of them

were solved and others are waiting resolute authorization to implement the

remedy.

First alleged flaw: lack of visibility of cost of electric power consumed by the ships

when they are in Valparaiso docked to the breakwater, the entire consumption is

charged to the Supply Center which is the owner of the substation that delivers the

energy but not to the ship that consumes it (DGSA, 2009). Although this problem

deals with the lack of capability of measuring the electric power, users blames are

directed towards SALINO because the information delivered by it does not fit their

needs. A project was initiated (DOCA26, 2011) to install electrical equipment to

register the individual consumption of electricity of each ship in port in order to

reflect the cost incurred by everyone in this item. Five measurement equipment per

dock are proposed to be installed at the breakwater.

Second alleged flaw: Parts used in the RxR27 process are charged to DIRISNAV28

which manages the process but not the unit that owned the installation (DIRISNAV,

2008).

RxR is a procedure in which there exists, for some critical installations, a set of

spare installations in a ready-to-use condition stocked and administered by the

DIRISNAV. When an installation on board fails and the time for reparation exceeds

the span defined for that installation, is replaced for one of the stocked ready-to-

use installations. That is the reason why the cost of the repair is charged to

DIRISNAV which is the administrator of the equipments in this condition.

A proposal was made to Systems Engineering Directorate to change the procedure

that charges the costs of parts used to repair equipment in the RxR mode,

charging it to the owner instead of to the Directorate (SIAMA, 2010) in order to

reflect actual maintenance cost of the asset.

Third alleged flaw: Fuel consumed by units deployed on international missions is

charged to the logistical authority not to the unit. (DABA, 2009). This was fixed.

Now a credit is open to the unit for the fuel consumption (DABA, 2011).

26

DOCA: Departamento de Obras y Contrucciones de la Armada = Land Contrutions Department

27 RxR: Repair by replacement

28 DIRISNAV: Dirección de Ingeniería de Sistemas Navales= Naval Systems Engineering Directorate



Fourth alleged flaw: in the historical maintenance information stored in the system

the parts which failure has been the most frequent cause of the installation failure

are not highlighted, impeding the logistic managers to take preventive actions on

those specific parts (DABA, 2010). Applications to Salino have been made to

highlight failures of high frequency of occurrence to advice managers to perform

preventive actions on those equipments.(DGSA, 2012).

Fifth alleged flaw: SALINO lack of capability to perform data mining which is the

process of extracting patterns from the data stored in it, to transform that data into

business intelligence.(DGSA, 2009-1). During the upgrade of SALINO actually in

progress, it is considered a module of Business Intelligence (BI) in order to add the

capability of extracting patterns from the data stored in it, to transform the

information obtained from the data into knowledge.(DGSA, 2012)

Sixth and a more generalized claim is that this arises from the Technical

Directorates and it is related to the usefulness of the information coming out of

SALINO that they need in order to support their logistical managerial decisions.

The users complain errors in the information delivered by SALINO that they use in

their decision making processes.(DGSA, 2009)

From the statement of the problem we see that we face a case related to the

accomplishing of the requirements since the system is not delivering the

information in the way that the users need it.

So it was necessary to search not only in SALINO but also and especially with the

users because according to Wang, R. and Strong, D. 1996 p.p.6 ―data quality is

defined as data that are fit for use by data consumers‖.

As this part of the research consisted in a significant proportion in interaction with

users through surveys and interviews, the first step was to get the permission for

that.

This was, by far, the toughest part of the work due to the difficulty to gain access to

the information because most of it is classified. It took a lot more time than

expected.

Thanks to the author´s condition of Retired Officer and active service professor of

the APN, permission was granted to pursue the research, previous a presentation

to the DGSA of the scope of the work and the possible benefits for the

organization.



Then the DGSA instructed the technical directorates to appoint the personnel to

answer the surveys. The authorization was granted under the condition that the

names and ranks of the personnel who answered the surveys were not made

public and be with held by the author for custody for security reasons.

This condition had an additional benefit for the research: as the persons were told

their names and ranks wouldn´t be revealed, they were in a better frame of mind to

answer the questions and to be more sincere.

Prior to start the surveys three pilot tests were performed on the questionnaires to

be used in the surveys and interview.(Saunders, 2009). The test of the first survey

questionnaire was addressed to five technicians attending to training courses at

the APN, former members of Directorates, the test of the second survey

questionnaire was addressed to three technicians of the SIAMA, and the test of the

interview questionnaire was addressed to four technicians attending training

courses at APN, former crew members of ships of the Fleet.

These tests allowed to the author to refine the questionnaires to be sure that the

respondents did not have problems to answer the questions.

On the other hand these tests served the author to get a certain previous

evaluation of the questions validity.

This first survey was conducted to determine, according user opinion, which quality

data dimension is the most relevant for the quality of logistic information that they

use to support their decision making processes. The hierarchical data quality

dimension model proposed by Wang, R. and Strong, D. 1999 was used (Fig.12).

This survey was directed at 83 people pertaining to Technical Directorates

(Material, Systems engineering, Maintenance, Repairs, and Technical

Inspectorates) during May and July.

These persons are engineers and technicians who use the information delivered

by SALINO, analyze it and make decisions based on it. They also prepare reports

for higher ranking engineers to perform analysis and decision making of a higher

level. So they are the first line user of the information. That is the reason they were

chosen to answer the first survey.

The objective of the survey was to find out which is the most important quality data

dimension for the user to support their job.



It had a single question: ―After reviewing the description of the twenty basic data

quality dimension and the way they are grouped to conform four: accuracy,

relevancy, representational and accessibility, which of this is the most important to

support your decision making process?‖

Since the variable to be measured is an opinion, an investigative question was

used (Saunders, et. al,2007). The author´s interest was to find out which data

quality dimension is viewed by the users as the most important in order to focus his

research on the occurrences of errors in that dimension.

The search was narrowed to a single dimension because the time spent in

administrative paperwork to gain access to the information did not leave enough

time to broaden the scope.

Sent to them was the description of the twenty dimensions of the model and the

form they are grouped into four higher level dimensions: accuracy, relevancy,

representational and accessibility. They were asked to indicate which the most

important data quality dimension is, in order to support their decision making

processes. Only 64 answered the survey. The rest never gave an acceptable

excuse. The position of the participants, the questions and the result are shown in

the Appendix A and a summary is shown in the next table:

SEARCH OF THE MOST IMPORTANT DIMENSION

Table 2: Result of survey 1: Most important dimensions.

The purpose of this survey was to determine which data quality dimension was

more important for the users to support their decision making processes. Hence,



the work will focus in this dimension to continue the research in order to quantify its

frequency and the causes of its occurrence.

With this information the second phase of the research was started. This time the

search effort was oriented to find out more massive errors in the system and to try

to find a way to reduce them in order to improve the information that supports the

decision making process.

To do this, a second survey was conducted. This time the purpose of the survey

was to determine what part of Salino processes presented more accuracy errors.

The survey was directed to twenty technicians of the Maintenance Information

Service to identify in which of the logistics modules of Salino are found the higher

number of accuracy errors. The modules are: Work orders (WO), Task program

(TP), Group Work (GW), or Application Parts List (APL).

This Service was selected because it is the organization within the CN that revise

in a closer way the maintenance information generated by Salino.

The Job participants, the questions and the results of the survey are listed in the

Appendix D and a summary is shown in the table 3

SEARCH OF MODULE WITH THE HIGHEST NUMBER OF ERRORS

Table 3: Result of survey 2: Most frequent module with errors.

The objective of this survey was, as stated, to find out what part of Salino

processes presented more accuracy error, based on the opinion and experience of

the persons who use the information.



The survey had two questions:

Question one: - In your opinion, which module of Salino in the maintenance area

shows the highest amount of accuracy errors?

This investigative question (Saunders, et. al., 2007) was intended to find out the

opinion of the users about the module of SALINO that presents the highest number

of accuracy errors, in order to know where to perform the next step in the search

process which is the measurement.

Question two: Designate a register, in the module you chose, you think is the most

frequently erroneous, was intended to know about the users opinion what registers

within the module selected were wrongly filled more frequently. The reason for this

question was to know which registers to use in the application to be developed in

the next step to perform the measurement

This survey was also performed via Internet, between June and July.

The result of this survey stated widely (95% of the users opinion) that the module

of work orders was the one that showed the highest amount of accuracy errors

detected at Salino.

Even though, we need to be sure of the results of this survey and check the

opinions of the expert against the facts: the positive evidence, to measure the

actual number of errors.

To do this, an application was developed to run in Salino in order to verify and

quantify the information delivered by the experts.

In this application the seven most mentioned registers of the Work Order module

as showing errors most frequently were checked:



Table 4 : Most mentioned registers with errors

Register Relative

frequency

%

Work order status 5 25

Overscheduled 3 15

Equipment status versus failure effect

coherence

3 15

Has CN status? 2 10

Has task assigned? 2 10

Has text in the task? 2 10

Has extended text in the Work Order? 3 15

The application was developed by the author with the assistance of two ITC

technicians of the Maintenance Information Service (one systems analyst and one

programmer).

This application was run over the data of the WO currently open, which means

pertaining to maintenance activities in progress of five assets of the Fleet docked

at Valparaiso at the time of the search. (Due to the confidentiality of the information

the assets are identified as Case 1, Case 2, Case 3, Case 4 and Case 5).

The objective of this application was to measure the exact amount of accuracy

errors found in a specific module of SALINO at a given moment on real assets in

order to:

a) Measure and corroborate or deny the opinion of users.

b) Search for the causes on a validated real scenario.

The detailed result of the application is shown in Appendix C and a summary is

shown in the next table.



ERRORS IN OPEN WORK ORDERS

Table5: Result of application: Number of errors in open W.O.

With this surprising result, taking into consideration that data error rates in

organizations are around 5% (Orr,K., 1998), the next step in the research was to

try to find out the cause for the so high rate of errors.

A semi- structured face-to-face individual interview (Saunders, et. al., 2007) with

two technicians of each asset was performed. During these sessions the

interviewees were asked to explain: their job on board, the priority of them, the way

they relate with Salino, how friendly they find the procedure and the interface to

enter the data, and their knowledge about the use and importance of the data they

enter in Salino.

The objective of these interviews was to find out what do the people in charge of

entering the data into SALINO have to face when they perform their task, in order

to discover what aspects difficult their job or are seen as problematic so can lead

to commit error, as a way to come up with the root causes of the errors detected in

the search.

There were performed ten one-hour individual interviews with ten people (two per

asset measured with the SALINO application)

The questions asked were:

Question one: Explain your job on board: identify the tasks. Intended to know how

the person perceived their work (amount and relevance) and by listing their tasks



clarify all the activities they have to do. The answers showed they are used to

haveing their working day full of activities.

Question two: Prioritize them (five). Intended to know what priority had for each

interviwee the task of entering data into SALINO. No one mentioned it in the first

place, 2 in fourth place, 6 in fifth place and two didn‘t include it in the first five

priorities.

Question three: Describe how friendly or tough you find the prodedure and the

interface to enter the data in the W.O. module. Intended to obtain the opinion of the

interviewees about the friendliness or usability of the system when entering the

data into the W.O. module. Two answered they feel just ok with the task even it is

time consuming, the rest alleged the procedure is complex, time consuming, and it

has too many registers to fill.

Question four: What do you know about the use of the information generated by

the data you enter and how important do you think it is this data, Why? Intended to

know the interviewees opinion about the importance of the task they do and what

are consequences of performing the task in an unaccurate way. Most mentioned

problems leading to errors were lack of solid knowledge of the use and importance

of the data and too many registers to fill in (nine mentions). As a result of these

interviews a set of seven problems that could cause commiting errors was stated

.

This was done as follows: during the interviews, every time a problem that can lead

to commit errors raised or was mentioned, that problem was annotated.

In the case that the situation narrated in the answer involved a problem but was

not explicitly stated as one, the interviewer asked to the interviewee if he

recognized that situation as a problem. If so, it was annotated.

Problems detected were grouped in three categories: Organization related, System

related and People related.

The problems identified were:



Table 6: Problems leading to errors mentioned

Problem that can lead to cause errors Times

mentioned

% over total

problems

Category

Of Cause

Lack of time 7 14,3 Organization

Other jobs more important or more urgent 5 10,2 Organization

Procedure quite complex 8 16,3 System

Many registers to fill in 9 18,4 System

Too time consuming 7 14,3 System

Lack of knowledge about use and importance

of data.

9 18,4 People

Lack of training 4 8,1 People

One week after the interviews ended, an in-depth group interview was held.

The objective of this interview was twofold: first, to get a consensus about the

problems detected from previous interviews, and recognize them as causes of

errors entered in SALINO, eliminate those not agreed or add others not already

included; and second, based on this information (the causes agreed), find out the

so called root-causes, basic or underlying causes of errors through a group

discussion facilitated by the author who acted also as interviewer.

It was very difficult to get permission for this meeting and then to procure the time

frame in which the participants appointed could attend to it. But finally, it was held

on September on board.

To achieve the objective of the interview the Ishikawa (Fishbone) Diagram

technique was used. (Ishikawa, K., 1986), (Ishikawa, K., 1991)

We started with a ten-minute briefing to explain the subject and the method.

Then a blank diagram was presented on a flipchart to the interviewees. In the head

box was then written the Effect in the most concise form: Errors entered into the

W.O. module of SALINO.

The next step was to write down the categories selected in the previous interviews:

Organization, System and People.

The next step was to generate the causes in each category. This was done using

Brainstorming (Brainstorming: ―is a group or individual creativity technique by

which efforts are made to find a conclusion for a specific problem by gathering a

http://en.wikipedia.org/wiki/Creativity_technique



list of ideas spontaneously contributed by its member(s)‖. The term was

popularized by Alex Faickney Osborn in the 1953 book Applied Imagination)

(Wikipedia).There were generated seven causes already stated in the previous

interviews, no one was eliminated neither no one was added.

Then, following the same procedure fourteen sub causes were generated:

The sub causes generated are listed and explained in table 7

Table 7: Root causes for accuracy errors in WO module

Category Cause Sub cause Explanation

People Lack of training

Course too short

The course on SALINO at APN is a module within the subject Job Management and there are few hours dedicated to SALINO, so the competences acquired are not adequate.

People Lack of training

Lack of continuum training

There are no other courses , so the people just keep the skills on the modules they use most often and forgot the rest.

People Ignorance about use & importance of data

Poor motivation

Users are not informed properly about the use of the data and the tremendous impact that a wrong decision can cause because a non- correct information due to inaccurate data.

People Ignorance about use & importance of data

Poor communication

Users don´t have continuous information feedback about accuracy errors so they think they are doing well.

System Procedure too complex

Poor interface design

Register are not in the screen in a logical sequence they have to be filled, so the users have to go back and forth to enter the data.


Too many rules

There are many instructions to follow to correctly enter the data.


Authorization delays

People who authorize transactions use too much time to accomplish the task.

System Too many registers

Extended text There are almost one hundred registers to go through. There are many extended text that are to be filled according the user criteria. This slows down the process and as they don´t follow a standardization they lead to errors.

System Time consuming

Too much information

The amount of information to enter is so much that it doesn´t motivate to dedicate so much time.

System Time consuming

Navigation through screens

Users have to go through ten screens to enter the data, which is frustrating. To navigate the ship is priority

Organization Other tasks more important

Sailing During sailing periods, there is no time available for entering data into SALINO because other tasks have higher priority.

Organization Other tasks more important

Operations Perform operations is also a priority

Organization Lack of time Exercises Users have to participate in many exercises, at port and at sea, so the time left for administrative work is scarce.

Organization Lack of time Naval duties Also these activities demand too much time so the time to enter the data is little.

http://en.wikipedia.org/wiki/Alex_Faickney_Osborn



Then, with the same procedure the five most relevant causes were selected.

So the selected sub causes are:

a. Lack of continuum training

b. Poor motivation

c. Poor interface design

d. Extended text

e. Navigation through screens

They can be grouped in two:

a. People related: Lack of motivation – training

b. System related: procedure - interface too complex

The finished Ishikawa (Fishbone) Diagram is shown in Fig. 17.

CAUSES FOR ACCURACY ERRORS IN WO MODULE

Ishikawa (Fishbone) Analysis Diagram for Error Causesin SALINO

Issued at the in-depth group interview

Lack of training

Ignorance aboutUse & importance

Other tasks More important

Lack of time

Procedure toocomplex

Too manyregisters

Time consuming

ERRORS ENTERED

INW. O. OFSALINO

Poor interface design

Extended text

Too many rules

Course too short

Lack of continuumtraining

Poor motivation

Poor communication

Too much information

Navegation through screens

Operation

Sailing

Exercises

Naval duties

Authorizationdelays

Fig. 17 Causes for accuracy errors in WO module



IV Results.

First of all, alleged defects where demonstrated to be veracious, i.e., positive

evidence of their existence was obtained:

Lack of visibility of electric power costs of ships in Valparaiso is due to the lack of

individual measurement instruments.

Parts cost of equipments in RxR mode are not reflected in ship´s costs.

Costs of fuel consumption in international missions were not charged to the unit.

This is already fixed because now there is a credit added to the fuel account of the

ship equals to the amount delivered.

Historical maintenance information doesn´t discriminate parts with high rotation for

frequent failures of equipment. This problem existed but it has been solved through

applications developed.

The lack of capability to perform pattern extraction from the data stored is been

solved with the new updated version of the software to be installed that will add BI

capabilities: data warehousing and data mining.

The lack of usefulness of the information delivered by SALINO was corroborated

by searching on the system and the users of the organization.

Users of five directorates were asked their opinion about the most important data

quality dimension to support in proper form their decision making processes in their

jobs and 87,5 % of them answered that Accuracy is the most relevant, followed by

Relevancy with 7,8% Accessibility with 3,1% and Representational with 1,6%.

DATA QUALITY DIMENSION IMPORTANCE

Fig. 18 : Results of Survey 1 weighting the dimensions importance

87%

8% 3%2%

Data quality dimention

Accuracy Relevancy Accesability Representationa



In order to find out where the highest number of errors is, to focus the search

there, a survey to users of SIAMA showed that 95% of them said that the module

with highest amount of errors is Work Order (WO) and the rest opined that the

module Task Program has more accuracy errors. None of them thought that the

other modules (Group Work and APL) have more errors.

To confirm or deny this, an application was developed and run over the total WO

opened of five ships. The number of WO with errors is shown in the figure 19.

NUMBER OF WO WITH ACCURACY ERRORS

Fig. 19: Results of measurement: Amount of WO with errors

The rates of errors per ship are showed in the figure 20.

0

50

100

150

Case 1

Case 2

Case 3

Case 4

Case 5

128

23 26

6677

Amount of WO with errors

Amount of WO with errors



RATES OF ACCURACY ERRORS PER SHIP

Fig. 20: Results of measurement: Rates of errors

The work continued searching for the causes of this amount of errors.

The semi structured in-depth individual interviews held with the people who enter

the data established seven problems that lead to cause errors that are listed in the

table 8:

Table 8: Problems that lead to errors

Lack of time

Other jobs more important or more urgent

Procedure quite complex

Many registers to fill in

Too time consuming

Lack of knowledge about use and importance

of data.

Lack of training

The relative occurrence of each problem in the interview is shown in the figure 21

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

Case 1

Case 2

Case 3

Case 4

Case 5

39.9%

24.0%23.2%

40.2%

66.1%

Rate of errors

Rate of errors



Relative occurrence of problem

Fig 21: Results of individual interviews: problems leading to errors

Finally, in an in-depth group interview using a brainstorming technique, the

fourteen root causes of accuracy errors in the WO module of SALINO were

determined using the Ishikawa (Fishbone) Cause-Effect Method.

The five most relevant were selected to be:


b. Poor motivation


d. Extended text


They were grouped in two:

a. People related: Lack of motivation – training

b. System related: procedure - interface too complex

14.30%

10.20%

16.30%

18.40%

14.30%

18.40%

8.10%

Relative occurrence of problems

Lack of time

Other jobs more important or more urgent

Procedure quite complex

Many registers to fill in

Too time consuming

Lack of knowledge about use and importance of data.

Lack of training



V Discussion

Even though the lack of visibility in electric power costs for the ships in Valparaiso

exists, it is due an external problem of SALINO. And it has a way to be solved.

The parts cost in RxR mode of maintenance is a procedure problem because the

costs are wrongly charged to DIRISNAV instead of to the ship, so the operation

cost is affected.

The same situation occurred with the cost of fuel consumed in international

deployments which is already fixed.

Related to the SALINO capability to perform some kind of analysis on the data

stored to help the managers take preventive actions, the applications developed

are in the proper direction. Even though the implementation of the updated version

of the Ellipse software will add more capabilities of data analysis, the models have

to be constructed and the participation of users is crucial.

In order to try to narrow the scope of the research to be realistic with the time

available, users were asked for the most relevant data quality dimension for the

decision making process.

87,5% of the users surveyed chose accuracy as the most important aspect of the

data they used. The rest of the data quality dimensions felt far away from

accuracy: Relevancy got 7,8%, Accessibility 3,1% and Representational just 1,6%.

These indicated that the major efforts should be directed towards reducing or

ideally eliminating accuracy errors, because they are perceived as important to

support the user jobs.

When the search went to SIAMA to find out where the accuracy errors were and to

measure their occurrence it was found that 95% of the members of SIAMA

selected the WO module and only one person, equivalent to the 5%, opined Task

Program module. This is explicable because WO module is the broadest module

and the most used. The registers to be measured selected by the users obtained

very similar preferences (between 10% and 15%) and only register WO status

showed 25%, so it was decided to measure those registers.

The measurement performed gave results quite suppressive. The amount of errors

found was quite high if we consider some standards given in literature (Orr, K.,

1998).



The error rates measured indicate that we are far away from standards so

something has to be done to improve this situation.

In the search for causes of this high amount of errors there were detected the five

most relevant on the user opinions and they turned out to be:


b. Poor motivation


d. Extended text


All of them are feasible to reduce or eliminate. The author studied the subject and

came up with the solution proposed:

User related causes (a and b) have to be faced through training, oriented to

motivate and to deliver updated knowledge and skills on SALINO.

System related causes ( c, d, and e) have to be faced through a modification

project.

Both of them will be explained in the Recommendation Chapter.

VI Conclusions

The existence of flaws in Salino that produce non - correct information was proved:

there are errors in the data entered to the system. The most relevant type of error

in terms of frequency and importance was identified and it turned to be accuracy

errors. This type of error was viewed by the users of information the one that the

most affected them in their decision making processes.

The amount of accuracy errors was measured and it turned to be quite high

compared with standards.

The root causes were deducted from the interviews with the operators who enter

the data and can be summarized as: lack of motivation and training and the

difficulty in following complex procedures for entering the data in an interface with

a great amount of records to fill. Just to create a WO the user has to go through 15

screens.



VII Recommendations

The following actions are recommended:

a. To update and rationalize the rules for entering data to Salino

b. Simplify the interface, trying to automate more the task.

These two actions should be faced through a software development project that

after the classical phases of specifications requirements and analysis could

construct and implement a modernization to the system that was designed and

implemented almost twenty years ago. These improvements should update

procedures that show to be complex or that delay operation like to rationalize

authorization procedures. Also the interface has a lot of room for improvement. For

instance, the current WO module has fifteen screens through which the user has to

navigate. This could be replaced, with not too much effort, for only one screen with

ten or twelve tabs.

Another improvement to reduce the errors is to replace the extended text registers

by registers filled with predefined texts in tables.

A deep analysis should come up with improvements that could have a great

positive impact in the reduction of errors.

c. Train and motivate the personnel in charge of entering data. Ensure that

they are aware of the use and the importance of the information

generated by the data they enter as well as the consequences of errors

in the data entered.

These training activities have to have a double objective:

One objective is to refresh the knowledge of SALINO and to give the users the

necessary skills to operate it in a comfortable manner.

The other objective is a motivational objective: users have to be taught about the

role of SALINO in the asset management in the CN, It also should be highlighted

the importance of every single piece of data that is entered into the system and the

consequences of the errors.



d. Develop an application to detect in line the errors and inconsistencies in

the open WO, checking all the registers so they can be corrected on a

daily basis in order to keep the system as free of errors as possible.

This application should be similar to the one developed for this research but

covering all the modules (not only WO) all the registers and all the assets.

This should be an application to run batch at night to avoid slow down the system.

VIII Cost – benefit analysis

Objective: To perform a cost-benefit analysis of the implementation of the

recommended actions to diminish the amount of accuracy errors in the data

entered into the Salino system.

Specific actions to be taken :

a. To update and rationalize the rules for entering data.

b. Develop a modification to simplify the interface

c. Develop a training plan to deliver competencies and motivate

personnel.

d. Execute the training plan

e. Develop an application to detect erros on line.

Costs:

Action a: Not to be considered because there is a work in progres regarding

this issue when the system will be upgraded to a new version. Some

activities are considered in costs of Action b.

Action b:

40 person hours Analysis

120 person hours programming

20 person hours testing



Action c:

160 person hours an Expert in Salino and in motivational sciences

Action d:

30 courses, one week duration (40 hours) per semester. Action e:

40 person hours Analysis

120 person hours Programming

20 person hours testing

Person hour = $20.000 (Chilean pesos)

1 week course = 40 person hours + $200.000 course

material

Cost summary

Action Cost

Action a No cost

Action b $ 3.600.000

Action c $3.200.000

Action d $30.000.000

Action e $3.600.000

Total $40.400.000

Benefits: Action a: The rationalization of rules should diminish the amount of

errors in 10%

Action b: The simplification of the interface should diminish the

amount of errors in 10%

Action c: Do not produce any effect on errors



Action d: the training and motivation should diminish the amount of

errors in 30%.

Action e: The application should diminish the amount of errors in 20%

The implementation of the actions recommended should diminish the

amount of errors in 70%.

Given the enormous amount of errors detected, a decrese in a 70% is a

great contribution to the improvement of the quality of the information used

to support the logistical managerial decision making.

BONUS:

In addition to the enormous benefit described in the previous paragraphs, there is

another one related to the operational availability of the Fleet.

We know that the operational availability (Ao) defined as ―the probability that a

system or equipment, when used under stated conditions in an actual operational

environment, will operate satisfactorily when called upon‖ (Blanchard, 2008) and it

is expressed as

𝐴𝑜 =MTBM

MTBM +MDT

Where:

MTBM=mean time between maintenances

MDT= mean maintenance downtime

But the MDT is given by the sum of active maintenance time plus administrative

delay time plus logistical delay time. This last is the result, among others, for

waiting for spare parts to become available.

On the other hand, during the Seminar on Asset Maintenance held in 2009, two

lecturers stated that a significant incidence (not specified quantitatively at that time)

on the MDT due to lack of spare parts was caused for wrong data from SALINO.

(DGSA, 2009-1).



So the expected improvement in the data quality of SALINO will have necessarily a

repercussion in the diminishing of MDT and consequently in the increasing of Ao

and a positive impact on the operational readiness of the Fleet.

Due to the lack of time, it was not possible to quantify this correlation, but it is an

interesting topic for further research.

Summary: the implementation of the actions recommended will produce a

decrease of 70% in the total amount of errors in the data entered in Salino at a

total cost of $40.400.000. (Chilean pesos) (€62.800).

This is considered highly cost- effective.

IX Suggestion for further work

a. The correlation between the improvement of data quality in SALINO and the

improvement in the Operational Availability of the Fleet, as a result of the

diminishing of MDT, mentioned in the previous section should be a gripping

topic for further research.

b. During the research, especially during the activity of interviews, some other

problems were detected: there are missing registers in historic data.

Also it was found that several equipments that were replaced did not were

updated in the data base, so the data was not consistent.

Further research should be performed to investigate about performing data

cleansing or other techniques to ensure a better data/information quality.



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Documents

(SALINO) Manual de Salino (1999) Salino Manual

(DABA, 2009) Informe de Costos Operacionales Operational Costs Report

(DGSA, 2007) Resultado de auditoría de datos en Salino.

Salino data audit report.

(DGSA, 2008) Directiva DGSA Nº 400/10 de ago 2008.

Directive Material Directorate

(SIAMA, 2009) Informe de revisión de datos de SALINO

Salino data review report

(SIAMA, 2011) Informe anual de actividad de SALINO

Salino annual activity report

(DGSA, 2009) Informe estadístico de consumo de energía.

Statistical energy consumption repot

(DIRISNAV, 2008)

Análisis de gestión de RxR año 2007

RxR management analysis year 2007

DGSA, 2009-1)

Debriefing congreso de mantenimiento de activos.

Seminar on asset maintenance debriefing

(DABA, 2010) Segundo seminario para mejoramiento de stocks.

Second seminar on stock improvement.

DOCA, 2011) Anteproyecto modificación distribución eléctrica molo.

Modification of electrical distribution at sea breaker Project

SIAMA, 2010) Proposición de modificación de Procedimiento.

Procedure modification proposition

(DABA, 2011) Informe de costos operacionales. Operational cost report

(DGSA, 2011) Solicita considerar Magister en AMC como curso regular.

Request to consider Master in AMC course as regular postgraduate course

(DGSA, 2012) Plan de actualización de versión de SALINO

Version update plan for Salino



XI Appendices Appendix A Survey 1 Objective: to determine the most important data quality dimension from the point of view of the decision makers. Participants: Engineers and technicians of the Material and Technical Directorates. Question: After reviewing the description of the twenty basic data quality dimension and the way they are grouped to conform four: accuracy, relevancy, representational and accessibility, which of this is the most important to support your decision making process? Material delivered:

Description of the twenty data quality dimensions

Dimension Description

Believability The extent to which data are accepted as true, real, and credible.

Value-added The extent to which data are beneficial and provide advantages from their use.

Relevancy The extent to which data are applicable and helpful for the task at hands.

Accuracy The extent to which data are correct, reliable, and certified free of errors.

Interpretability The extent to which data are in appropriate language and units and the data

definitions are clear.

Easy of understanding The extent to which data are clear without ambiguity and easily comprehended.

Accessibility The extent to which data are available or easily and quickly retrievable.

Objectivity The extent to which data are unbiased (unprejudiced) and impartial.

Timeliness The extent to which the age of the data is appropriate for the task at hand.

Completeness The extent to which data are of sufficient breath, depth, and scope for the task at

hand.

Traceability The extent to which data are well documented, verifiable, and easily attributed to

a source.

Reputation The extent to which data are trusted or highly regarded in terms of their source

or content.

Representational

consistency

The extent to which data are always presented in the same format and are

compatible with previous data.

Cost-effectiveness The extent to which the cost of collecting appropriate data is reasonable.

Easy of operation The extent to which data are easily managed and manipulated (i.e., updated,

moved, aggregated, reproduced, customized).

Variety of data and

data sources

The extent to which data are available from several differing data sources

Concise The extent to which data are compactly represented without being overwhelming

(i.e., brief in presentation, yet complete and to the point).

Access security The extent to which access to data can be restricted and hence kept secure.

Appropriate amount of

data

The extent to which the quantity or volume of available data is appropriate.

Flexibility The extent to which data are expandable, adaptable, and easily applied to other

needs.



Grouped data quality dimensions

Result of the survey

Job position Professional

level Most important

dimension

Material directorate

Operative Planning Engineer Accuracy

Financial planning Engineer Accuracy

Projects & contracts Engineer Accuracy

Head Div 210 Technician Accuracy



Head operations dept. Engineer Accuracy

Naval material div Engineer Accuracy

Naval Material assistant Technician Accuracy

Configuration control Engineer Accuracy

Finances dept. Engineer Accuracy

Management control div Technician Accuracy

Supply div Technician Relevancy

Monetary resources Engineer Accuracy

Non monetary resources Engineer Accessibility

Techn. Directorate coord. Technician Accuracy

Budgeting div Engineer Accuracy

Budget assistant Technician Relevancy

Insurance div Technician Accuracy

Transport div Technician Accuracy

Acquisition portal Technician Relevancy

Systems Engineering Directorate

Head Frigate project L-M Engineer Accuracy

Asistant Frigate proj L-M Technician Accuracy

Head Frigate project 22 Engineer Accuracy

Asistant Frigate project 22 Technician Accuracy



Head marit unit proj Engineer Accuracy

Assist. Maritime unit proj Technician Accuracy

Head Av. & M C proj Engineer Accuracy

Assit. Av & MC proj Technician Accuracy

Monitor LPC, LPM Technician Accuracy

Monitor LSG Technician Accuracy

Monitor LEP Technician Accuracy

Weapon system div Engineer Accuracy

TP & A/S war Technician Accuracy

Long term planning maint. Technician Accuracy

Fire control &Sonar div Engineer Representational

MC weapons Technician Accuracy

Maritime units weapons Technician Accuracy

Electronics & Nav div Engineer Accuracy

Electronic war div Technician Accuracy

MAE & ECM div Technician Accuracy

Radars div Technician Accuracy

Command & Control div Engineer Accessibility

Units Recuperations

Systems analysis 1 Technician Accuracy



Data Link Engineer Accuracy

Diesel plan Technician Accuracy

Gas turbines 1 Engineer Accuracy

Technician Technician Accuracy

Electricity program Technician Relevancy

Aux machinery program Technician Accuracy

Missiles program Engineer Accuracy

Boilers program Technician Accuracy

Automatic control Technician Accuracy

Stability div Technician Accuracy

Alterations div Engineer Accuracy

Platform area div Technician Accuracy

Finance div Technician Accuracy

Inspectorate

Artillery div Technician Accuracy

Digital documentation Engineer Relevancy

Telecommunication div Technician Accuracy

Maintenance Directorate

Weapons planning Technician Accuracy

Engineering planning Engineer Accuracy

Submarines planning Technician Accuracy

Maritime units planning Engineer Accuracy



Summary of the results



Appendix B: Survey 2 Objective: To find out what module of Salino in the maintenance area shows the highest number of accuracy errors. Participants: Engineers and technicians of the Maintenance Information Directorate. Questionnaire: 1.- In your opinion, which module of Salino in the maintenance area shows the highest amount of accuracy errors? 2.- Designate a register , in the module you chose, you think is the most frequently erroneous.

Results of the survey

Job position Professional level

Module Register

Head planning dept. Engineer Work Order WO Status

Ass. Plan. dept Technician Work Order Equip stat/f.ef

Head oper. Dept Engineer Work Order Has text ext

Ass. Oper. Dept Technician Work Order Overscheduled

Artillery area Engineer Work Order Has text

Maneuvering area Technician Work Order WO Status

Navigation area Technician Work Order WO Status

Electronic area Engineer Work Order Has task asig.

Telecomm. Area Technician Work Order Has text ext

Project mngr. Technician Work Order Overscheduled

Scorpene proj Engineer Work Order WO Status

Electricity area Technician Work Order Has text ext.

DC area Technician Work Order Has text

Propulsion area Technician Work Order Equip stat/f.ef

MC area Technician Work Order Has CN status

Control Dept. Engineer Work Order Has CN Status

Finance Dept. Technician Task Program

Equip stat/f.ef

Frigates area Technician Work Order WO Status

Submarines area Engineer Work Order Overscheduled



Summary of the Results

Registers most refered

Work order status

Overscheduled

Equipment status/failure effect

Has CN Status?

Has task assigned?

Has text?

Has extended text?



Appendix C: Application run

Objective: To detect how many errors exist in seven selected registers of the Work Orders open. Participants: Five ships of the Fleet Registered checked: Work order staus, Overscheduled, equipment status/failure effect, has CN status?, has task asssigned?, has text?, has extended text?

CASE 1

CASE Nº 1 OPEN W.O.: 321 AMOUNT OF ERRORS:128

Type of failure

Work Order Status

End of reapir due

Equipment status/ Failure effect

Errors in entry data or modification of W.O.

Has task assigned?

Has text in the task?

Has extended text inthe W.O.?

Has CN status?



CASE 2


Type of failure

Work Order Status

End of reapir due



Has task assigned?



Has CN status?



CASE 3


CASE 4

CASE Nº 4 OPEN W.O. : 164 AMOUNT OF ERRORS : 66

Type of failure

Work Order Status

End of reapir due



Has task assigned?



Has CN status?



CASE 5

CASE Nº 5 OPEN W.O.: 115 AMOUNT OF ERRORS : 76

Type of failure

Work Order Status

End of reapir due



Has task assigned?



Has CN status?



Appendix D: Face-to-face interviews Objective: To find out the root causes of the great amount of accuracy errors found in the process of entering data in the W.O. module of Salino. Participants: Two operators of each ship investigated with application developed by the researcher. Question aked :

1. Explain your job on board: identify the tasks. 2. Prioritize them. 3. Describe how friendly or tough you find the prodedure and the

interface to enter the data in the W.O. module. 4. What do you know about the use of the information generated by the

data you enter and how important do you think it is this data, Why?

Job position Professional level Ship

Power generation Technician Case 1

Navigation system Technician Case 1

Propulsion system Technician Case 2

Power generation Technician Case 2

A/A weapons Technician Case 3


Auxiliary machinery Technician Case 4

Data link system Technician Case 4

Auxiliary machinery Technician Case 5


Problems detected

1.- Lack of time, too many things to do.

2.- Other jobs more important or mor urgent

3.- The procedure is quite complex

4.- Many registrers to fill in.

5.- Too time consuming

6.- Lack of knowledge about the use and importance of data

7.- Lack of training

Problems 3, 4,and 5 are highlighted because a generalized claim was : ― the

amount of data to be enered to the system is too high, there are too many registers to be filled in, and the way to classify is to bothersome‖

The interviewees said that, after a determined limit, due to the lack of time,

they opt for leaving registers empy by selecting ―accept‖ or ―Cancel‖ in the default options.



Appendix F: Mincom Ellipse

Mincom Ellipse is a fully integrated Enterprise Asset Management (EAM) application suite providing complete visibility and management of assets to the capital-intensive industries of Mining, Utilities, Defense, Transportation and Government.

Mincom Ellipse enables world's best practice in the management of assets, work, logistics, financials and human resources, empowering asset-intensive organizations to respond faster and make better decisions that directly impact the bottom line by:

Increasing asset utilization Reducing operating costs Delivering quality products and services Meeting regulatory standards for audit compliance Ensuring supply chain availability and visibility Allocating skilled resources appropriately Mitigating Risk Improving productivity.

Mincom Ellipse's world-class modules of Asset and Work Management, Supply Chain Management, Financial Management and Human Resource Management are an integrated foundation, delivered from a Service Oriented Architecture (SOA) platform. These foundation modules are complemented by business analytics, advanced maintenance and planning and mobile workforce solutions, offering the broadest functionality and the most robust solution in the Enterprise Asset Management (EAM) market today.

Mincom used elements within SALINO Configuration: - MIMS Vu - MIMS Extender - LinkOne GTi - MIMS open enterprise

Mincom LinkOne

Mincom LinkOne is software for creating graphical catalogues that helps users find the right part, first time, every time. A world leader in graphical content creation, distribution and access, Mincom LinkOne has the unmatched power to link graphics with parts lists and rich text to easily produce electronic parts catalogs (EPC) and graphical Interactive Electronic Technical Manuals (IETM).

Mincom LinkOne Version 5, released on the 30th of November 2006, takes the strength of the 13 years of development and understanding Mincom has built, and provides a flexible solution for Asset Maintainers and Original Equipment Manufacturers.

http://www.mincom.com/



Mincom LinkOne's superior publishing, distribution, and viewing system makes all forms of electronic parts and service information available at the touch of a key via the internet, intranet, or CD. This assists our customers in the areas of:

Asset Management Inventory Control Supply Chain Management Process Manufacturing Material Management

MIMS open enterprise MIMS = Mincom Information Management System (MIMS) This is an Open Enterprise product. MIMS Open Enterprise 4.3 features technology and functionality improvements that enhance Mincom's ability to deliver total EAM solutions MIMS Open Enterprise is made by Mincom, a Brisbane, Australia-based business solutions assembler in the enterprise asset management software, services and solutions market. MIMS is designed for capital-intensive industries with work and maintenance management, inventory control and/or costing requirements.

MIMS Vu Report Tool MIMS Extender The MIMS/Extender is a user-friendly GUI environment that allows customers to rapidly develop and



customize front-end applications, and includes extensive samples, templates and documentation. MIMS/Extender was developed over the Inprise Corporation's Delphi product. The business object connector SDK enables customers to access MIMS Open Enterprise data using applications such as Microsoft Office, Excel, Project and Access, and display that information in a format familiar to them. The Business Object Connector uses industry standard interfaces such as COM, DCOM, CORBA and C++.