systemic vulnerability analysis of a high-function atm
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Systemic Vulnerability Analysis of a High-Function ATM. Tina Chang Lindsay Wiseman Juthamas Choomlucksana. Overview of Contents. Introduction Background Motivation for Analysis Literature Review Objectives of Research. Overview of Contents. Introduction Background - PowerPoint PPT PresentationTRANSCRIPT
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SYSTEMIC VULNERABILITY ANALYSIS OF A HIGH-FUNCTION ATM
Tina ChangLindsay WisemanJuthamas Choomlucksana
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OVERVIEW OF CONTENTS Introduction
Background Motivation for Analysis Literature Review Objectives of Research
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OVERVIEW OF CONTENTS Introduction
Background Motivation for Analysis Literature Review Objectives of Research
Methodology IDEF0 HMSEM Workbook
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OVERVIEW OF CONTENTS Introduction
Background Motivation for Analysis Literature Review Objectives of Research
Methodology IDEF0 HMSEM Workbook
Recommendations
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BACKGROUND New high-function ATM is still in the design phase
Made assumptions in regards to the user interface due to the information being proprietary
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BACKGROUND New high-function ATM is still in the design
phase We had to make assumptions in regards to the
user interface due to the information being proprietary
High-function ATM Requires user to sign up as member to use
services Have their picture taken Scan their finger print Enter in personal information
Social security number, phone number, address, etc.
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BACKGROUND New high-function ATM is still in the design
phase We had to make assumptions in regards to the
user interface due to the information being proprietary
High function ATM User must sign up for the services
Scanning their personal identification Scanning their finger print Enter in personal information
Social security number, phone number, address, etc. The ATM will then verify the information to
accept or reject the application.
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MOTIVATION FOR ANALYSIS Human errors increase as automatic teller
machines become more sophisticated.
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MOTIVATION FOR ANALYSIS Human errors increase as automatic teller
machines become more sophisticated.
Leaves both long-time and new customers frustrated when they attempt to complete a wide range of transactions.
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MOTIVATION FOR ANALYSIS Human errors increase as automatic teller
machines become more sophisticated.
Leaves both long-time and new customers frustrated when they attempt to complete a wide range of transactions.
A thorough human engineering psychology analysis can give a product design-criterion to avoid these frustrations and help keep their customers satisfied.
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LITERATURE REVIEW (1) Zimmermann and Bridger (2000) developed
two ATM interfaces to compare the efficiency and error profiles in current use: Green monochrome Color display with newer displays and distinctly
different software
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LITERATURE REVIEW (1) Zimmermann and Bridger (2000) developed
two ATM interfaces to compare the efficiency and error profiles in ATM current use: Green monochrome Color display with newer displays and distinctly
different software Results showed withdrawals were the most
frequent transactions at ATMs by observing 300 members using ATMs in South Africa.
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LITERATURE REVIEW (2) In 2003, Chan and Khalid used automatic
speech recognition technology to investigate users’ usability and affective interaction with ATM.
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LITERATURE REVIEW (2) In 2003, Chan and Khalid used automatic
speech recognition technology to investigate users’ usability and affective interaction with ATM. All participants were required to be a current
ATM user or at least possess an ATM card.
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LITERATURE REVIEW (2) In 2003, Chan and Khalid used automatic
speech recognition technology to investigate users’ usability and affective interaction with ATM. All participants were required to be a current
ATM user or at least possess an ATM card. Participants performed two tasks, withdrawal and
balance enquiry, using a real-world ATM. Showed that automatics speech recognition
technology will not only improve system usability, but it also created a flexible interface in traditional ATM.
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LITERATURE REVIEW (2) In 2003, Chan and Khalid used automatic
speech recognition technology to investigate users’ usability and affective interaction with ATM. All participants were required to be a current ATM
user or at least possess an ATM card. Participants performed two tasks, withdrawal and
balance enquiry, using a real-world ATM. Showed that automatics speech recognition
technology will not only improve system usability, but it also created a flexible interface in traditional ATM.
Moreover, user emotions in terms of fun, joy and excitement should consider in design of affective user interface.
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LITERATURE REVIEW (3) As Norman (1988) addressed, a good user
interface can minimize the gap between the user’s knowledge and intentions with the system. Other perspectives to include in ATM design;
Age Usability by the blind Psychological attitudes to innovativeness and
computers (e.g., Adams and Thieben 1991, Mankze et al. 1992, Burgoyne et al. 1992)
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OBJECTIVES Create a complete model of all transactions
the customer can complete using this ATM
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OBJECTIVES Create a complete model of all transactions
the customer can complete using this ATM Compile a thorough list of human fallibilities
that can occur
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OBJECTIVES Create a complete model of all transactions
the customer can complete using this ATM Compile a thorough list of human fallibilities
that can occur Create a list of recommendations for ATM
design project manager and engineers to prevent these errors from occurring
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METHODOLOGY IDEF0 Model
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METHODOLOGY IDEF0 Model Human Machine System Engineering
Methodology (HMSEM) Workbook Informal List of Potential Errors
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METHODOLOGY IDEF0 Model Human Machine System Engineering
Methodology (HMSEM) Workbook Informal List of Potential Errors Task analysis
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METHODOLOGY IDEF0 Model Human Machine System Engineering
Methodology (HMSEM) Workbook Informal List of Potential Errors Task analysis Human Fallibility Identification and Remediation
Methodology (HFIRM) Analysis
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METHODOLOGY IDEF0 Model Human Machine System Engineering
Methodology (HMSEM) Workbook Informal List of Potential Errors Task analysis Human Fallibility Identification and Remediation
Methodology (HFIRM) Analysis Failure Modes and Effects Analysis (FMEA) analysis
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IDEF0 MODEL Used to represent all activities involved to
complete a wide variety of transactions from the customers’ perspective
Included all controls, mechanisms, inputs and outputs for every activity the customer will be involved it These mechanisms and controls were then
further evaluated to help create an informal list of potential human errors.
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IDEF0 LEVEL A-0
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IDEF0 LEVEL A0
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INFORMAL LIST OF POTENTIAL ERRORS Created as we developed the IDEF0 Model Connected the list of errors to where they
could occur within the node list Examples
Customer forgets to take ATM card back after sign-out process
Customer fails to enter in correct PIN because of layout of keypad
Customer may choose the wrong transaction because of poor viewing conditions
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TASK ANALYSIS Fill in the following bits of information for ten
selected ‘youngest’ children Purpose/Value Added
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TASK ANALYSIS Fill in the following bits of information for ten
selected ‘youngest’ children Purpose/Value Added Location
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TASK ANALYSIS Fill in the following bits of information for ten
selected ‘youngest’ children Purpose/Value Added Location Frequency & Timing
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TASK ANALYSIS Fill in the following bits of information for ten
selected ‘youngest’ children Purpose/Value Added Location Frequency & Timing Environmental Conditions
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TASK ANALYSIS Fill in the following bits of information for ten
selected ‘youngest’ children Purpose/Value Added Location Frequency & Timing Environmental Conditions Information Requirements
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TASK ANALYSIS Fill in the following bits of information for ten
selected ‘youngest’ children Purpose/Value Added Location Frequency & Timing Environmental Conditions Information Requirements Sensory/Congitive/Motor Actions
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TASK ANALYSIS EXAMPLEA221: CHOOSE ACCOUNT TO WITHDRAW FROM
Purpose / Value Added Location Frequency
& TimingEnvironmental
ConditionsInformation Requirement
s
Sensory / Cognitive /
Motor Actions
Allow customer to identify which account to withdraw cash from.
Command window
1 per withdraw transaction
•Lighting, and weather condition surrounding ATM. •Poor lighting condition can make it difficult for customer to be able to read the command window. •Bright lighting condition can make it difficult for customer to be able to read the command window. •Poor weather condition can limit customer ability to read the command window.
1.Customers should know which accounts they are able to withdraw from. 2. Customers should know if they sufficient funds to withdraw.
1. Decision making which account to withdraw from.
2. Visual sensation to be able to read all account titles.
3. Customer must be able to reach the command window.
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Used the Human Fallibility Identification and Remediation Database (HFIRDB) to identify a formal list of human fallibilities for four of the ‘youngest children’
Continued our analysis using FMEA
HFIRM ANALYSIS
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FMEA ANALYSIS Completed analysis for all applicable human
fallibilities that were identified using the HFIRM analysis.
This helped us identify the following; (Other) Contributing Factor(s) Potential Failure Mode Potential Effects of Failure Mode Risk Priority Number (RPN)
RPN = Severity ∙ Probability ∙ Nondetectability
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FMEA ANALYSIS EXAMPLE:A221: CHOOSE ACCOUNT TO WITHDRAW FROM
HF_ID HumanFallibility Definition
(Other) Contributing Factor(s)
Potential Failure Mode
Potential Effects of
Failure Mode
Severity
Probability
Nondetectability
RPN
95 Word shape effect
Tendency for words comprised of mixed-
case letters to
be recognized as global shapes
and to be processed
quickly and
automatically when
presented as printed sentences.
mixed word format, size, case (words
shape inconsistent)
misreading, misinterpretati
on
selected the wrong account to
withdraw from
5 2 1 10
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RECOMMENDATIONS Make sure all features are physically
accessible by roughly 95% of adults Redundancy
Use lighted features on ATM to be activated when necessary as well as short video clips to show customer how to use all features
Color coding on command window and keypad Audio cues to give warning/aid
Proper lighting of the ATM Glare-free screen Light attached to ATM for camera use
Group common transactions
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QUESTIONS?
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REFERENCES Adams, A. S., and Thieben, K.A., (1991), “Automatic Teller
Machines and the Older Population”, Applied Ergonomics, 22, 85-90.
Burford, B. C., and Stanton, N. A., (1993), “ A User-centered Evaluation of a Simulated Adaptive Autoteller”, Contemporary Ergonomics, London, UK: Taylor and Francis Ltd, 117-122.
Chan, F. Y., and Khalid, H. M., (2003), “Is Talking to an Automated Teller Machine Natural and Fun?”, Ergonomics, 46, 1386-1407.
Mankze, J. M., Egan, D., H., Felix, D., and Krueger, H., (1998), “What Makes an Automated Teller Usable by Blind Users?”, Ergonomics, 41, 982-999.
Norman, D. A., (1998), “The Design of Everyday Things”, Now York, Doubleday.