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ORGANIZATION AND DSS
Approaches, Paths and results
Luca Nerozzi
Coop Alleanza 3.0
AGENDA
• What about DSS?
• The role of management
(and Innovation manager too)
• Approaches, paths and results:
some cases
• Conclusions
WHAT ABOUT DSS ?
• Spreadsheet
Business spend less
time to manage data,
more time to Analyze
Spend less time to
Analyze, more time
to think and decide
Reduce«Technicalityskill»
forenduser
• Data Base report
• BI SW & Tools
• Simulation platform:
• Integrated platform (& manage processes)
WHAT ABOUT DSS ?
Business spend less
time to manage data,
more time to analyze
Spend less time to
analyze, more time
to think and decide
Reduce«Technicalityskill»
forenduser
Complexity increases but you are
more able to enlarge, to
distribute, to extend and scan the
tree of knowledge with details
that immediately satisfy any
curiosity, doubts or deep
questions
Years
Agenda
• What about DSS?
• The role of management
(and Innovation manager too)
• Approaches, paths and results:
some cases
• Conclusions
THE ROLE OF MANAGEMENT
FROM manage people that
analyze for you
(and, first elaborate too)
TO manage people that think
about problems and discuss
about different point of view
(it’s simpler to create a lot of different
points of view)
Reduce«Technicalityskill»
forenduser
Years
THE ROLE OF MANAGEMENT
From understand what you
want to see
To knows what you can do and
the impact of details in
simulations and vision strategy.
Reduce«Technicalityskill»
forenduser
Years
THE ROLE OF
MANAGEMENT
Innovation Manager doesn’t mean only
innovation issue but it’s an holistic
vision that could be activated by the
evolution of technology and the power
of synthesis.
THE ROLE OF
MANAGEMENT
“I am a ‘Top’, I must remain with a ‘high
view’ to think, decide and manage at best”
(as if details were not really of the top managers but... "dirty stuff")
WHY ??
Details and vision strategies
are not in antithesis !!
SIMPLIFYING IS NOT TRIVIALIZING
• A «math» example:
Trivializing:
«Everything you divide by zero, will be infinitive»
In particular in this case that numerator remain «small»
into range -1, 1.
So result is:
SIMPLIFYING IS NOT TRIVIALIZING
• A «math» example Simplifying:
Knowledges
Someone must be knows
Taylor Series
Technical
competences
with «customer» view, vertical
skills with business approach to
understand which simplified
version could be solved by
anyone
Simplified version
that could be solved
by anyone
So result is:
SIMPLIFYING IS NOT TRIVIALIZING
Math example to summarise what?
• Trivializing is often «son of ignorance»
«Everything you divide by zero, will be infinitive»
• Details are not «dirty stuff» for a «top manager»
Would be interesting to define this «label»…
• Simplyfing of details are essential and needs people with knowledges and
technical competences with business view
DSS help their to spend more time to understand business requirements
SIMPLIFYING IS NOT TRIVIALIZING
Math example to summarise what?
• Experience are very important and make the difference but with the
“partnership” with DSS and their evolution
Think about you professional career: do you remember a «fail decision
episode» driven by too much confidence in experience only?
• Some DSS results (in particular in predictive and simulation systems) are
difficult to accept because challenging our beliefs.
AGENDA
• What about DSS?
• The role of management
(and Innovation manager too)
• Approaches, paths and results:
some cases
• Conclusions
APPROACHES, PATHS AND RESULTS
• Start with «hard operation activities»… are more understandable.
• Will arrive at «soft organization processes»:
– Now we are into a workload project (with the innovative approach that I showed at DSF
2017 «The Brick-Lego paradigma»)
– We will use DSS to support best organization processes decision
• Improve your competences about those issues during projects
• «Culture» and «mind-gap» are key success factors
APPROACHES, PATHS AND RESULTS
1. Routing Delivery Planner
to reduce transport costs
(best rate with
outsourcer
transportation company)
6. Internal Routing and
efficiency: optimizing
picking method
(double fork with
same or different
store)
2. Service Level and Key operation
driver to understand how
outsourcer can be more efficient
3. Logistics Management
(where and how to manage goods
into existing DC maintaining the
same service level)
4. Logistics Scenarioes
(how to modify delivery
and goods position
maintening service level)
5. Internal Routing
and efficiency:
optimizing Display
Scenarioes, Vision and
Strategies
Operative and support
daily activities
7. Automatization impacts:
right sizing with re-simulation
approach (total cost and
investment: internal and
transportation)
And
… START
AGAIN!
8. Demand,
inventory and
replenishment
9. WorkLoad and
Store operation
Modelizing
(DSF ’17)
SOME CASES: 2. SERVICE LEVEL
• Simulation with different supports to increase transport saturation
– Half-height stackable plastic pallet crates.
– Re-design picking sequence lists
• Results:
– Up to 18% higher saturation level (with maximum height constrain)
– Reduce total mileage with same calendar
SOME CASES: 5. OPTIMIZING DISPLAY
• Simulation with optimized display (but fixed-picking position) and
measuring defects in order to:
– Category compatibilities of goods
– Weight and height constrain
– Rotation class
• Results:
– - 40% of Low defects
– - 58% of Medium defects
– - 71% of Serious defects
Result are versus manual
optimized display defined by
outsourcer expert of logistic
company
«Challenging our belief»
SOME CASES: 7. PICKING METHODS
New long forklift truck with weight detect system
Real problem: increase defects (probability of wrong goods position between
two different stores) ?
Simulation must be achievable
How z-pick Method can be combined with simultaneous different stores picking
(with long forklift)?
SOME CASES: 7. PICKING METHODS
Stores
Big
Stores
Small
Stores
Level of usage
% of «standard Long
forklift»
% of «weight detect
long forklift»
Stores association
also depends on
their delivery
calendar
SOME CASES: 7. PICKING METHODS
Interesting «investigation area»
APPROACHES, PATHS AND RESULTS: MISTAKES
• Approaches: detail level?
– Too fine  too time to obtain small better solution
– Too high  raw solution that could be «trivial»
Use simulation
You cannot improve simulation
without specific experience (in
diversified business too)
You must know what you can do with the simulation
You don’t have to know «how to do» the simulation
• Paths:
– Buy and install Arena SW
– Training to specialize dedicated person
AGENDA
• What about DSS?
• The role of management
(and Innovation manager too)
• Approaches, paths and results:
some cases
• Conclusions
CONCLUSIONS
• Improve your assimilation about those issues.
• DSS are not technology but are a «mentality»
• It’s ever important to know technicality but
now is simpler
(Really?? – Really!!)
• You can use DSS in operating activities or into
strategic approaches
(better if project is well defined and consider
all these two aspects)
CONCLUSIONS
• Change management is the key
success factor (and training too)
• Complexity increases, so simplifying
is a must.
Competences are fundamental: we
can spend more time to understand
what we can do and the impact of
details in simulations and vision
strategy.
CONCLUSIONS: THE SPHERE OF KNOWLEDGE
DSS help to enlarge vision,
think about different
approaches, improve
innovation, drive operating
activities.
CONCLUSIONS: THE SPHERE OF KNOWLEDGE
DSS help to enlarge vision,
think about different
approaches, improve
innovation, drive operating
activities.
Thanks !
Luca Nerozzi
luca.nerozzi@alleanza3-0.coop.it
luneroz@gmail.com

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Organization and DSS. Approaches, paths and results

  • 1. ORGANIZATION AND DSS Approaches, Paths and results Luca Nerozzi Coop Alleanza 3.0
  • 2. AGENDA • What about DSS? • The role of management (and Innovation manager too) • Approaches, paths and results: some cases • Conclusions
  • 3. WHAT ABOUT DSS ? • Spreadsheet Business spend less time to manage data, more time to Analyze Spend less time to Analyze, more time to think and decide Reduce«Technicalityskill» forenduser • Data Base report • BI SW & Tools • Simulation platform: • Integrated platform (& manage processes)
  • 4. WHAT ABOUT DSS ? Business spend less time to manage data, more time to analyze Spend less time to analyze, more time to think and decide Reduce«Technicalityskill» forenduser Complexity increases but you are more able to enlarge, to distribute, to extend and scan the tree of knowledge with details that immediately satisfy any curiosity, doubts or deep questions Years
  • 5. Agenda • What about DSS? • The role of management (and Innovation manager too) • Approaches, paths and results: some cases • Conclusions
  • 6. THE ROLE OF MANAGEMENT FROM manage people that analyze for you (and, first elaborate too) TO manage people that think about problems and discuss about different point of view (it’s simpler to create a lot of different points of view) Reduce«Technicalityskill» forenduser Years
  • 7. THE ROLE OF MANAGEMENT From understand what you want to see To knows what you can do and the impact of details in simulations and vision strategy. Reduce«Technicalityskill» forenduser Years
  • 8. THE ROLE OF MANAGEMENT Innovation Manager doesn’t mean only innovation issue but it’s an holistic vision that could be activated by the evolution of technology and the power of synthesis.
  • 9. THE ROLE OF MANAGEMENT “I am a ‘Top’, I must remain with a ‘high view’ to think, decide and manage at best” (as if details were not really of the top managers but... "dirty stuff") WHY ?? Details and vision strategies are not in antithesis !!
  • 10. SIMPLIFYING IS NOT TRIVIALIZING • A «math» example: Trivializing: «Everything you divide by zero, will be infinitive» In particular in this case that numerator remain «small» into range -1, 1. So result is:
  • 11. SIMPLIFYING IS NOT TRIVIALIZING • A «math» example Simplifying: Knowledges Someone must be knows Taylor Series Technical competences with «customer» view, vertical skills with business approach to understand which simplified version could be solved by anyone Simplified version that could be solved by anyone So result is:
  • 12. SIMPLIFYING IS NOT TRIVIALIZING Math example to summarise what? • Trivializing is often «son of ignorance» «Everything you divide by zero, will be infinitive» • Details are not «dirty stuff» for a «top manager» Would be interesting to define this «label»… • Simplyfing of details are essential and needs people with knowledges and technical competences with business view DSS help their to spend more time to understand business requirements
  • 13. SIMPLIFYING IS NOT TRIVIALIZING Math example to summarise what? • Experience are very important and make the difference but with the “partnership” with DSS and their evolution Think about you professional career: do you remember a «fail decision episode» driven by too much confidence in experience only? • Some DSS results (in particular in predictive and simulation systems) are difficult to accept because challenging our beliefs.
  • 14. AGENDA • What about DSS? • The role of management (and Innovation manager too) • Approaches, paths and results: some cases • Conclusions
  • 15. APPROACHES, PATHS AND RESULTS • Start with «hard operation activities»… are more understandable. • Will arrive at «soft organization processes»: – Now we are into a workload project (with the innovative approach that I showed at DSF 2017 «The Brick-Lego paradigma») – We will use DSS to support best organization processes decision • Improve your competences about those issues during projects • «Culture» and «mind-gap» are key success factors
  • 16. APPROACHES, PATHS AND RESULTS 1. Routing Delivery Planner to reduce transport costs (best rate with outsourcer transportation company) 6. Internal Routing and efficiency: optimizing picking method (double fork with same or different store) 2. Service Level and Key operation driver to understand how outsourcer can be more efficient 3. Logistics Management (where and how to manage goods into existing DC maintaining the same service level) 4. Logistics Scenarioes (how to modify delivery and goods position maintening service level) 5. Internal Routing and efficiency: optimizing Display Scenarioes, Vision and Strategies Operative and support daily activities 7. Automatization impacts: right sizing with re-simulation approach (total cost and investment: internal and transportation) And … START AGAIN! 8. Demand, inventory and replenishment 9. WorkLoad and Store operation Modelizing (DSF ’17)
  • 17. SOME CASES: 2. SERVICE LEVEL • Simulation with different supports to increase transport saturation – Half-height stackable plastic pallet crates. – Re-design picking sequence lists • Results: – Up to 18% higher saturation level (with maximum height constrain) – Reduce total mileage with same calendar
  • 18. SOME CASES: 5. OPTIMIZING DISPLAY • Simulation with optimized display (but fixed-picking position) and measuring defects in order to: – Category compatibilities of goods – Weight and height constrain – Rotation class • Results: – - 40% of Low defects – - 58% of Medium defects – - 71% of Serious defects Result are versus manual optimized display defined by outsourcer expert of logistic company «Challenging our belief»
  • 19. SOME CASES: 7. PICKING METHODS New long forklift truck with weight detect system Real problem: increase defects (probability of wrong goods position between two different stores) ? Simulation must be achievable How z-pick Method can be combined with simultaneous different stores picking (with long forklift)?
  • 20. SOME CASES: 7. PICKING METHODS Stores Big Stores Small Stores Level of usage % of «standard Long forklift» % of «weight detect long forklift» Stores association also depends on their delivery calendar
  • 21. SOME CASES: 7. PICKING METHODS Interesting «investigation area»
  • 22. APPROACHES, PATHS AND RESULTS: MISTAKES • Approaches: detail level? – Too fine  too time to obtain small better solution – Too high  raw solution that could be «trivial» Use simulation You cannot improve simulation without specific experience (in diversified business too) You must know what you can do with the simulation You don’t have to know «how to do» the simulation • Paths: – Buy and install Arena SW – Training to specialize dedicated person
  • 23. AGENDA • What about DSS? • The role of management (and Innovation manager too) • Approaches, paths and results: some cases • Conclusions
  • 24. CONCLUSIONS • Improve your assimilation about those issues. • DSS are not technology but are a «mentality» • It’s ever important to know technicality but now is simpler (Really?? – Really!!) • You can use DSS in operating activities or into strategic approaches (better if project is well defined and consider all these two aspects)
  • 25. CONCLUSIONS • Change management is the key success factor (and training too) • Complexity increases, so simplifying is a must. Competences are fundamental: we can spend more time to understand what we can do and the impact of details in simulations and vision strategy.
  • 26. CONCLUSIONS: THE SPHERE OF KNOWLEDGE DSS help to enlarge vision, think about different approaches, improve innovation, drive operating activities.
  • 27. CONCLUSIONS: THE SPHERE OF KNOWLEDGE DSS help to enlarge vision, think about different approaches, improve innovation, drive operating activities.

Editor's Notes

  1. Knowledges  esperto di simulazione Technincal competences  interno o esterno con orientamento ai processi e conoscenza del Business (sempre più le aziende IT stanno allargando il loro perimetro perché la conoscenza dei processi è elemento chiave, come il metodo (i.e. Agile), la parte di «consulenza» integrata.. Cmq VISIONE DI INSIEME: PROCESSI, STRUMENTI e ORGANIZZAZIONE vanno visti insieme.
  2. ESPERIENZA E DSS riducono il rischio… INSIEME. Semplificare non è banalizzare E spesso la banalizzazione è figlia di «ignoranza» Banalizzare (spesso anche «ignorare»): qualsiasi «cosa» diviso zero fa infinito. Semplificare (conoscendo la materia e passando anche da passaggi complessi) Lo stesso dicasi per le famose «medie» o le medie delle medie… ecc. Sicuramente questa è una platea evoluta ma sono certo che nella vostra esperienza professionale avrete in mente delle «cantonate» prese, anche da decisori importanti, proprio per una eccessiva «Confidenza» sulla propria esperienza e un oggettivo approccio alla banalizzazione D’altronde alcuni risultati ci mettono fortemente in discussione e si fatica, ancora, a crederci. D’altronde ci sono ancora situazioni dove avere un numero attendibile è «complicato» o per l’architettura o «oggettivamente complicato» (i.e. driver dinamici di produttività di cui parlerò dopo per il WorkLoad).
  3. WDLF: Nello stesso negozio per merceologie diverse, legato all’OdL del PV e alle sue dimensioni