Shaping the Future
Supply Chains In Practice
Networking Event
Data Driven SCM
Big Data for Big Breakthroughs
16th June 2015
Explore from 3 perspectives…
1.
2.
3.
2
Landscape overview
Enabling the data driven SC
Protecting the data driven SC
Matthew
Andy
Carsten
Join the discussion…
#SCinPractice
@WMGSupplyChain
3
Data-Driven Supply Chain Management:
Big Data for Big Breakthroughs
Matthew Burton, Partner
Warwick June, 2015
Most business leaders are familiar with analytics as a topic but uncertain on it’s definition and
application
The hype
►Current
Our advice
“buzz-word”
►Marketing
► Analytics
is about decision-making
in a complex, multi-connected world
hype and books
► Mathematical
models must be
pragmatic and feasible
► Technology
►Skewed
focus on software as the
route to creating value
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17 June 2015
alone only provides
enablement whilst analysis alone
does not guarantee results
Data-Driven Supply Chain Management: Big Data for Big Breakthroughs
81% agree that data should be at the heart of all decision making but only 31% have restructured to help do
this.
Source: EY and Nimbus Ninety Data Analytics survey
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17 June 2015
Data-Driven Supply Chain Management: Big Data for Big Breakthroughs
Most companies are still focused on backward looking analysis
BI and Analytics Maturity Model
Descriptive
Reporting
What happened?
Diagnostic
Analysis and
Visualisation
Why did it happen?
Predictive
Analytics
Where are we headed?
SC and Logistics Visual
control dashboards
Forecasting and automated
replenishment planning
Cost to Serve scenario
planning
Source: Gartner, EY research
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17 June 2015
What should we do in
future?
Future focused
Examples
Historic
focused
Traditional financial reporting
of supply chain costs and
performance
Prescriptive
Analytics
Data-Driven Supply Chain Management: Big Data for Big Breakthroughs
Integrated demand sensing
utilising promotion plans, pricing
and weather data and
integrated with supply and
capacity planning
Supply chains already generate their own Big Data without needing to wait for internet of everything
What are your sources of data?
Source: EY and Nimbus Ninety Data Analytics survey
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17 June 2015
Data-Driven Supply Chain Management: Big Data for Big Breakthroughs
Example: Supply chain synchronisation
Actual demand supply cycle – showing distinct lack of synchronisation despite heavy investment in tools
Revised demand supply cycle – synchronised planning parameters delivering stability with lower inventory
Source: EY case study
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17 June 2015
Data-Driven Supply Chain Management: Big Data for Big Breakthroughs
►9
Example: Hospital BI optimisation
Trend Monitoring
Forward View
• Trend
analysis for all
capacities within the
hospital eg theatre, beds,
surgeons
Optimisation
• Forecasting
of capacity
usage to identify resource
needs and bottlenecks
• Scenarios based on
different treatment cycles
Diagnostics
Forecasting
60
600
50
500
1400
• Creation
of capacity evelled
optimisation blueprint
• Calculation based on
unique algorithm
Optimisation
Trend resources
Forecast for planned CTC's
Needed
1200
40
400
30
300
20
200
10
100
1000
800
Count
10%
600
25%
50%
400
Estimated Forecast
75%
Agenda: 'ORTHOPEDIE A'
SubAgenda: '-- All --'
Appointment-Codes: 'DDNS', 'HNC', 'KNC', 'MNC', 'NC', 'NCK', 'NS', 'RNC', 'SNC',
'VNC'
05/10/2009
28/09/2009
21/09/2009
14/09/2009
07/09/2009
31/08/2009
24/08/2009
17/08/2009
10/08/2009
03/08/2009
27/07/2009
20/07/2009
13/07/2009
06/07/2009
29/06/2009
0
22/06/2009
0
90%
WIP Forecast
200
WIP External
WIP Scheduled
0
DynamicPlanner® Screenshots
Source: EY case study
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17 June 2015
Data-Driven Supply Chain Management: Big Data for Big Breakthroughs
►1
Ernst & Young LLP
Assurance | Tax | Transactions | Advisory
www.ey.com/uk
The UK firm Ernst & Young LLP is a limited liability
partnership registered in England and Wales
with registered number OC300001 and is a member firm
of Ernst & Young Global Limited.
Ernst & Young LLP, 1 More London Place, London SE1 2AF.
© Ernst & Young LLP 2015. Published in the UK.
All rights reserved.
JUNE 2015
Enabling and delivering
Analytics Capabilities
Concentra
Delivering operational improvements through the use of analytics
• An analytics company that focuses on delivering operational improvements
• Formed in 2008, a team from A.T. Kearney merged with a software development
house
• We are 85 people strong with our head office near London Bridge
Recent recognition for our products and solutions includes:
Confidential
| 13
Supply chains are becoming broader in scope, more complex
and more difficult to manage
Manufacturing
Tier 1 Impact
Warehouse
Replenishmen
t
Tier 2 Impact
Customer
Delivery
Customers
Operational Planning and Execution Systems are Integrated…. but
visibility of supply chain performance could be a lot better. Why is
this?
Confidential
| 14
The data and Information required tends to be cross-functional
or multi-system in nature and multi-time dimensional
• Optimise inventory levels by
analysing the flow of product
through the supply chain and
setting material and product
movement policies
HOLISTIC
SUPPLY CHAIN
MANAGEMENT
•
Design best-in-class business
processes and organisational
structures to turn the supply
chain function into a highperforming strategic asset
• Maximise product and
customer profitability by
modelling trade-offs
associated with price,
product portfolio diversity
and direct material
complexity
• Get new insights and
visibility of the activities
and costs associated with
converting materials to
finished goods and
delivering them to
customers
• Full end to end network
cost management
Analytics can add value and provide this visibility
Confidential
| 15
Demand Planning Process
Example dashboards
Confidential
| 16
Supply Planning Process
Example dashboards
Confidential
| 17
Inventory Planning Process
Example dashboards
• Levels
Confidential
| 18
Spend Analysis
Example dashboards
Confidential
| 19
Transaction Data
Flat file uploads
Planning systems
• Sales forecasts
• Inventory policies
• Planned production
• Planned stock movements
Confidential
Transactional ERP
• Sales
• Inventory
• Stock movements
• Production
| 20
Extending the data sources and the detail
Operational excellence, efficiency, process compliance
Data
Management
Analytic
s
Visualisatio
n
Scalable
Consistent
Develop a reliable
single data set with
latest information
that is structured to
provide fast
analysis.
Build reporting and
modelling tools to
answer questions
about the past,
present and future .
Create an
accessible
environment to
publish analysis and
share insights.
Secure
Standardised
INTERNET OF THINGS
MES Systems
Confidential
WMS Systems
Tags in Retail
Fast
| 21
Manufacturing Analytics and Excellence
Driving OEE Improvement
Confidential
| 22
Visualising and Analysing
VISUALISE AND ANALYSE
FLEXIBLE HIERARCHIES
CUSTOM DASHBOARDS
GRAPHS
INNOVATIVE VISUALISATIONS
Confidential
| 23
Then to extend visibility and understanding…
Sharing information and truly collaborating….cloud based
analytics
Confidential
| 24
Using Cloud based solutions to create industry level
information
• Industry data
• Single information
systems
• Supply Chain
partners – right
information to the
right people at the
right time
Protecting Data in Industry…
Confidential
| 25
Securing the Supply Chain
Supply Chains in Practice
Professor Carsten Maple
16 June 2015
WMG
Sharing Data through the Supply
Chain
"Sharing information with
suppliers is essential”
Information Security Forum
Securing the Supply Chain
• UK Security Breach Investigations Report
– 18% of attacks were through a business partner
• ISF Report
– 40 percent of the data-security breaches arise
from attacks on their suppliers
Risk Management Strategies
• Risk is commonly defined as the combination
of the likelihood or probability of an event
occurring and its impact or consequence.
The classical model for risk can be defined
as: Risk = Likelihood * Impact
Threat Actors
• A number of motivational factors exist and
have to be applied to each of the threat
actors, these motivations are:
•
•
•
•
•
Crime (incl. Financial)
Espionage (State & Industrial)
(H)activism
Terrorism
Warfare
Considerations when sharing data
in the supply chain
• What do you think?
Considerations when sharing data
in the supply chain
• How likely are your partners to be breached
due to the aforementioned threat actor
motivations?
• How do your partners manage your data and
theirs?
• What are you sharing? For how long?
Risk Management Strategies
• Reduce. Reduce probability of risk occurring
or the impact of the event should it occur.
• Contingency. Action is planned but only
implemented should risk occur.
• Transfer. Reduce financial impact of threat
(insurance or clauses in contract.)
• Accept. Take no action. Chosen because
risk has low probability and/or low impact.
Also if cost of action exceeds impact.
Risk Management Strategies
• Avoid. Action planned so no impact on the
project or probability of occurrence is zero.
Risk Management Strategies
• Avoid. Action planned so no impact on the
project or probability of occurrence is zero.
Is this really an option for your business?