Beyond ERP - towards
Dashboards of information
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•
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Decision Support / Monitoring
Information cost
Information overload
Push versus pull model
Concept of control room
– Analogy with process control or driving a car
– Focus on most important factors
CSF - Theory
• Definition:
Limited number of areas where satisfactory results will
ensure successful competitive performance for the
individual, the department or the firm
• Monitored on the basis of a set of measures specific standards that allow the calibration of
performance
• Measures can be soft or hard - ie: objective or
subjective
CSF method diagram
• Identification of a hierarchy of performance
measures that lead to identification of Critical
Factors and Issues that will determine a
The business mission statement
business’ success
The business vision statement
multiple business goals
multiple business objectives for each goal
multiple CSFs for each objective
Implementation:
multiple business objectives for each goal
multiple CSFs for each objective
Central Database - Data Warehouse
Data Preparation Layer / Data Mart
Common Interface
Dashboard
Indicators
KPI 1
KPI 2
KPI 3
KPI 4
Sources of CSFs
• Industry
• Competitive strategy and industry position
(leader / follower; big / small…)
• Environmental factors (eg: economic fluctuations
and national government policies)
• Temporal factors (temporary CSFs)
• Managerial position (more specific to one
manager)
Classification of CSFs
• Internal versus external
• Monitoring versus Building / Adapting
(eg: implementing of major corporate
plan)
• Evolution over time - eg: motor industry
+ / - of the CSF technique
• Small number of CSFs
• Managers normally aware of them - make them
explicit is possible
• Specific to firm / dept / manager
• But; not all CSFs are measurable at all (access
to data)
• Known CSFs may be trivial
• Time consuming to go beyond the obvious
• Will managers make time for CSF analysis?
Dashboards of information
• A CSF analysis can be turned into a
dashboard of info
• indication in real time of what is happening
• Concentration on the most important +
visual impact (e.g. colour coding)
• But data has to be very reliable and design
of interface must be good :
– three mile island
Some Problems with 3 mile
Island
• Layout of control not consistent with use of
indicators
• no consistency on where associated controls are
situated or how they operated
• layout of controls did not reflect layout of plant
• indicators and alarms were not sorted by degree of
importance
• no consistency in use of colour
• Cl: the layout of the dashboard and what indicators
represent (+ how they do it) requires much
attention
The Control Room
• Monitoring complex processes through
technology mediated systems
• Controlling without seeing directly
• Not completely similar to business
management
• But useful anyway to measure
performance in a specific and acccurate
fashion
Key issues for dashboard
development
• Limited attention - selection of indicators
(CSF)
• Accurate performance measurement methods (models) and data used
• Operator / user training - consensus /
awareness
• Dashboard layout - avoid confusion / be
consistent
Good Food Limited case study
Read and Prepare solutions
Discussion
Conclusions
Framework for dashboard
development
Question 1: Who will use this indicator?
Question 2: Can it be mapped out to a specific objective at a higher level?
Question 3: How frequently will managers need to monitor it?
Question 4: What calculation methods? What unit of measurement?
Question 5: What data source exists? What should be created?
Question 6: How detailed should the analysis be? How can the indicators be broken
down?
Question 7: What threshold values should be used to differentiate between adequate and
inadequate performance? What comparisons can be made to assess the company’s
performance?
Question 8: How can it be represented for maximum visual impact?
Question 9: What action must be taken when good or bad performance is
measured? Question 10: How will it be monitored / archived in the long term
Question 11: Is there any potential bias with the methods and data used for calculations?
What incentives may be given to organizational actors?
Overall method
• Rigorous procedures for reporting and
monitoring
• Set up a complete Budget framework
• Budget broken down per responsibility - e.g.
buyers give prices, production gives
productivity
• once a year = > budget put together
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–
–
expected levels are put proposed by each area
full report compiled (p/l for the year ahead)
negotiated with top management
final budget used to benchmark activity of the firm
General Indicators
• Focus on 3 key indicators compared with budget
makes it easier to analyse responsibilities:
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•
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– volume V (Vb for budget and Va for actuals)
– price P
– formula F
total variance = Va Pa Fa - Vb Pb Fb
volume variance = Va Pb Fb - Vb Pb Fb
= (Va-Vb) Pb Fb
Price variance = Va Pa Fa - Va Pb Fa
= (Pa - Pb) Va Fa
Formula variance = Va Pb Fa - Va Pb Fb
= (Fa - Fb) Va Pb
Analysing the general
indicators
• Volume variance :
– breakdown per product / market / week
– also per rep?
– source: budget / weekly sales
– who? Sales Director and reps + regional
supervisors + MD
– colour maps showing areas / markets
– threshold values determine colour
– volume and € figure
Analysing the general
indicators
• Price variance:
– breakdown per RM / component + labour (for each
–
–
–
–
category) [focus on most expensive]
buyers / production director + supervisors +personnel
director
source: budget figures + account payable / payroll
Monthly probably enough (changes don’t occur that
often)
tables for detail + exception reporting using icon
representing the factor that has high negative
variance
Analysing the general
indicators
• Formula variance:
– per product / per RM + labour
– source: stock issue dockets + production
sheets (sales too late) + labour hours
– some figures cannot be known exactly =>
use surrogate or estimate
– target: foremen, production staff and
director
– gauges, colour map of the factory,
exception lists
Monitoring Maintenance
• Imagine down time is increasing
• don’t know enough to fix the problem
(1) collect appropriate data on accidents:
– maintenance staff time sheets
– accident report for each problem - documented by
operators
– match both sources of data
(2) store it in a suitable DB
(3) analyse based on a number of CSF
(4) present analysis in computer dashboard
CSF analysis for the
maintenance
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Number of accidents per run (per unit / product)
Nature of accident (several categories to be found)
Location of accidents
Average duration of repair (for each assembly line)
Average duration of repair for each staff?
Average duration of repair for each type of accident
Mapping of when accidents happen
establish thresholds
Location (% of all accidents)
Stocks
Finished
Goods
Other
Areas:
2%
W3
Shipping
al 2
Main Corridor
10%
al 1
al 1
W2
WP2
storage
W1
WP1
cooling
al 2
al 1
al 2
Changing 3 - 3 - 15%
Rooms
Preparation ovens
and Related
Quality
Facilities
Control
Stocks
RM
5%
10%
8%
35%
3%
6%
Time spent (% of down time)
Stocks
Finished
Goods
Other
Areas:
2%
W3
Shipping
al 2
Main Corridor
5%
al 1
al 1
W2
WP2
storage
W1
WP1
cooling
al 2
Changing 3 - 3 - 8%
Rooms
and Related
Facilities
al 1
al 2
Preparation ovens
Quality
Control
Stocks
RM
5%
20%
41%
8%
3%
4%
When accidents happen
Number of Accidents per time period
30
20
10
0
6-7
7-8
8-9
9 - 10
10 - 11 11 - 12 12 - 14 14 - 15 15 - 16
Who does what?
Name
Job Title Nb Acc. Avg time Gravity
Steve
Maint.
Manager
Maint.
Staff
Maint.
Staff
App.
Martin
Bob
Mark
27
1 hour 25 4.5
35
1 hour
3
18
2 hours
3
20
1 hour
1
Analysing the types of
accidents
Time spent per acc. type
25%
20%
15%
40%
Proportion of acc. types
15%
45%
20%
20%
film jam
machine fault
accident
Op. Error
film jam
machine fault
accident
Op. Error
Conclusion on Maintenance
• Great potential for computerised solution
• Some added cost
• Focus on:
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–
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Actionable areas
Areas where scope for improvement
Communicate with staff
Use for improvement rather than finger pointing
• Evolution over time will point to policy decisions
Sales Returns
• Limited scope for computerised solutions
because no possibility of data capture (in this
case)
• Technical solution – surrogate what happens to
the product in a simulated environment – eg: a
fridge
• Holding samples of products over complete shelf
life at various temperatures
• Beyond product resistance – move to
reputational systems
Product Portfolio
• Little scope for computer support because
no data available
– No direct contact with customers
– Cannot really predict new product acceptance
with lag indicators
– Customers cannot tell you what they don’t
know!
• Use consumer panel – focus group(s)
Conclusions
• Dual approach on content and context
• Realise limitations of computerised
solutions when neither data nor model is
there
• Find surrogates when possible (data)
• Be creative in terms of activities that can
be pursued to learn more (models)
• Focus on delivering value rather than
software tools