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Round Table Orchestrating Demand with Big Data
EyeOn
JADS March 8th, 2018
Agenda – Round table orchestrating demand with big data
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13h00 Kick-off Emile van Geel
Scene setting André Vriens
Questionnaire findings Emile van Geel
14h30 Logitech case Roeland van den Berg
15h45 Heineken case Kalle Rasmussens
Wrap-up
17h00 Drinks!
3 15 March 2018
Introducing EyeOn
Consultancy & Implementation
Data Science& Solutions
Forecast Services
Develop & implement tailored planning processes that really work
Improve processes, tools & capabilities
Advance forecasting & planning specialists in their career
Turn data into insights and business value add
Develop & implement analytical models for decision support
Implement fast, innovative and scalable planning solutions
Deliver forecast and inventory management services
with state of the art models
EyeOn – Years Ahead in Planning & Forecasting
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A partnership with EyeOn : Full service offering to drive planning & business improvements
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PLANNING SPECIALISM
INDUSTRY EXPERTISE
DRIVE TO IMPLEMENT
ANALYTICS & TOOLING
PEOPLE FOCUSBUSINESS
IMPROVEMENTS
Processes Tools Organization
Operational experience in the industry
System independent Change management& hands-on mentality
EyeOn has a long hands-on experience in designing and implementing forecasting and planning processes in different industries
High Tech FMCG Process Life Science Marine & Offshore
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Other
Dedicated team of experts in forecasting techniques, data science & planning solutions
– Innovative supply chain planning solutions– Forecasting & supply chain planning processes & tooling– Advanced analytics, based on big data– Visualization & story telling
– Continuous innovation– Knowledge partnerships– Innovation labs
– Latest technologies– Cloud based planning solutions– Big data analytics– Supply chain simulation & optimization– Data storage
EyeOn DS2 – Data Science & Solutions
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Want to learn more?
Visit us at www.eyeon.nl or www.forecastservices.com
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Effective process design Planning excellence Accelerated improvement
– White papers – Benchmarks
– Masterclasses
– Fast forecast scan
– Inventory scan
9 15 March 2018
Scene setting
André Vriens
Managing Partner & Sr. Business Consultant
10 15 March 2018
Today’s world
Very volatile customer demand
Portfolio more complex than ever
Growing pressure on price & margins
Rapidly changing IT landscape
Increasingly more information on consumer level
War on talent
New technologies to enable us to sense & predict (before they know themselves) ?
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Shifts in supply chain thinking*
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Traditional Digital
Project definition Large projects with
known outcomes
Small projects that evolve
Funding Licensed deployments Cloud deployments
Type of data Structured transactional
data
Unstructured and
structured data
Data characteristics Data with low velocity Data with high velocity
Leadership Led by IT Led by the business
Process focus Inside-out Outside-in
Capabilities Better response Test and learn
Technologies Large, established
vendors
Small and evolving
technology sources
* Supply Chain Insights LLC Copyright © 2015
Now what?
21 15 March 2018
Real life cases
Data sharing “in the cloud” to control supply chains from head to tail
Cloud as chain enabler
Cloud solution with following functionalities:• Web portal with write access to
cloud database• Cloud database connected online
reporting environment• Scheduled automated data refresh• User-based view permissions
Information sharing in value chain
Change the storytelling game!
Alerts before issues arise
From a need for a data sharing tool to a cloud-based design to create insights for business
Tool solution
Extract the real business driver signals and use them in planning!
Decompose the signal Balance models & business intelligence
Use different signals:- Baseline- Promotions- Product introductions & phase out’s- Seasonality
Look at process timings
Analyse drivers and forecast value add
Modelling as starting pointProcess designed around
“who knows better”
Market driver based forecasting
Driver Based Forecasting
Regression Models use explanatory variables to forecast another variable, e.g. future demand
Advantages:
• Insight into relations & dependencies
• Allows what-if scenario’s
• Can exploit leading indicators
Application in planning
Create a reliable long term forecast at aggregated level in an volatile market environment
Quantifies Market Experience
Market Driver Based Forecasting use leading indicators to model the impact to demand
Reveal the drivers behind promotional sales to enable a more accurate promotion forecast and a better marketing mix
Revenue management
Shape demand and “get the actualsright!”
LASSO offers a balancing act
Too few parameters results in lack of granularity and very rough results
Too many parameters results in overfitting: the model follows the history too well to predict the
future
Promo forecasting using regression requires a selection in suitable forecasting parameters
Application in planning
Select parameters with the most influence on sales during a promotion.
Examples:• Display type in the store• Price mechanism of the promo• Relation to holiday (X-mas, Easter)
• Web scraping methods• Sensing sentiments• Ratings and reviews
• Analysis to prepare scenario • Combining data sources
Point of Sale data
From reactive planning to pro-active demand shaping via demand sensing
OBJECTIVE
• Shaping of demand
• Distribution adjustment
• Promo adjustment
Business decisionInternal analysis collaboration
Integral visibility and combining different data sources
Freely available dataFast launch analysis
• Shape demand• Optimize margin
Sensing
Organise who should do what?
Focus on relevant adjustments only Different skills
Design the different roles, “the man of all crafts” doesn’t exist
Data scientist• Advanced
analytics, mathematics, machine learning
• Data fishing expeditions
• Curios and very analytical role
Business analyst• Within teams
supporting reporting needs, should be Visualization proficient
• Combine IT, analytics and domain knowledge.
• Functional reporting into data value team
Data engineer• Connecting data
sources• Data governance• Quality and control
roleArchitect
Build a data driven culture
Analytical skills and conceptual thinking, experience with structured problem solvingManaging large data sets
VSChallenge numbers at different
aggregation levels
Business / market knowledge
Communication skills
28 15 March 2018
Learnings
Orchestrate
Realize predictive business in a volatile market
Sense Plan Shape
Prescriptive
HOW?
"The important thing is not to stop questioning.
Curiosity has its own reason for existing."
Store that data!
Make the change
Build skills and enjoy!
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Collaborate
Agile – Fast response
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Response fast, just start!
How to make it work?
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1. Ask the right questions for your business!
2. Start collecting and storing data as of tomorrow
3. Build strong analytical skills – often centrally organized
4. Embed analytics in business processes & Collaborate with all relevant stakeholders
5. Remain agile, just start !
37 15 March 2018
QuestionnaireData driven demand management
Emile van Geel
Sr. Business Consultant
Questionnaire – main learnings
– Online survey via evalandgo.com
– Self assessment
– Benchmark
– 29 filled out surveys (...and growing)
• Consumer goods (retail, fast moving, electronics)
• others
– Personal feedback, story behind the survey
– Plenary feedback today!38
Find out what the current practice is of
analytics and big data in demand
management today and what it implicates
for the future
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Emile van Geel
André Vriens
FreekAertsen
Marcovan Alfen
TomVaessen
EdwardVersteijnen
40 15 March 2018
Key findings
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The EyeOn benchmark shows limited use of additional data sources!
...but...some sources didn’t even exist 10 years ago
Data sources used - details
– Widely collected PoS data
• Usage is still a journey for most companies
• Big differences between companies
• First steps made by retail and companies close to consumers42
Centrally available data
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Still a road ahead…
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Data only becomes information with correct analytics...
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Success factors for analytics
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Analytics skills ... growth potential
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Build strong analytical skills – often centrally organized
Business benefits
– Economies of scale by grouping analyst function
– Not biased by local successes / failures
Attract and retain talent
– Career path for analysts
– Easy to growing competences and standardizing processes
– Opportunity for challenging projects
– Data Ninjas
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DP & big data benchmark: 21% of companies have centralized analytics function
Centralization Analytics Function
Centralized Decentralized
Demand management as team sport
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Team sport .... and still growth potential
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Demand planning as a team sport – Demand Planner as orchestrator (local or central)
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Demand Planner
Baseline Forecast(stat4cast support) &
Orchestrating Demand
Enrichment
MarketingOther
Stakeholders(MT, Finance)
Sales
- Directions/guidelines based on budget/targets
- Short-term forecast plan on account level- Provide details on Key Account level (e.g.
promotions forecast)
- Input long-term promotional plan- Provide long-term view of the
forecast- Initialize launch plan (NPI)- Market research
Data driven insights
Splitted demand planner and analyst
split
unsplit
Key takeaways
• Big data era is coming
• New skills required
• Use fast insights for decision making in demand management
• Step by step (think big, start small, fail fast, scale fast)
• Start and learn!
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