Cambridge IfC Manufacturing Analytics1

Intelligent Assets Manufacturing Analytics Institute for Manufacturing, University of Cambridge 1st February 2016 Daniel Keely

Cisco’s Supply Chain Global. Complex. Diverse.

Manufacturing Sites

Strategic Logistics Centers

Diverse Portfolio

Mass production to highly

configured

30,000+ orderable items

20,000+ virtual team 3,200+ orders daily 220,000+ items shipped daily

700+ active suppliers 62,000 components

© 2015 Cisco and/or its affiliates. All rights reserved.

25+ LOCATIONS

13 COUNTRIES

Cisco Supply Chain Industry Recognition

1. Amazon 2. McDonalds 3. Unilever 4. Intel

5. Inditex

6. Cisco Systems 7. H&M 8. Samsung 9. Colgate - Palmolive

10. Nike

17

19 20

7

9 10

12 13 14 15 16

2

4

1

22

24 25

21

23

6

18

8

5

11

3

*Report not published in 2006

TOP 25

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The role of Data Analytics

Customer

Supplier

Distribution Channel


REAL TIME ANALYTICS

STRUCTURED & UNSTRUCTURED

DATA FLOWS

VISIBILITY & TRANSPARENCY

CUSTOMER INSIGHTS


Supplier

Supplier

Supplier

Supplier Supplier

Customer

Customer

Supplier

Corporate Office


Customer

Supplier




Supplier

Supplier

Supplier

Supplier Supplier

Customer

Customer

Supplier

Data Analytics Driving New Levels of Operational Excellence

Corporate Office

PREDICTIVE RESOLUTION

END TO END VALUE CHAIN

SPEED TO INFORMED DECISION MAKING

ENHANCED CUSTOMER

EXPERIENCE

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“If you went to bed last night as an industrial company, you’re going to wake up today as a software and analytics company” Jeff Immelt, CEO GE (talking about rapid adoption of IT into the manufacturing industry)

“the industrial world is changing dramatically, and those companies that make the best use of data will be the most successful.”


Architecture to drive Operational Excellence & Customer Experience

Reinvest in Innovation

ANALYTICS

Qua

lity

Fore

cast

Customers Backlog

Factories Logistics

Suppliers

Rev

enue

Prod

uct M

argi

n

Cos

t /

Paym

ent

Product Enablement Data Master Data

INTELLIGENCE COLLABORATION

DECISION SUPPORT Capital equipment expense

Transformation costs

Human resources

Capacity constraints

Customer dissatisfaction / unhappiness


Journey to an analytics driven supply chain

Data Acquisition

Technology & Tools

Process & Governance

Skills & Roles


Survey Question: Which technologies can most change how you manage production over the next three years? Cisco Digital Manufacturer survey, 2015. [625 respondents]

Promising technology for the factory and connected supply chain Traditional technological <-> Digital innovation Towards smarter assets


Creating Intelligent Assets

imagine if components, products and structures started telling us what they needed to remain safe, productive and efficient…

imagine if operational capabilities adapted based on this info…

Image source: Dailymail.co.uk


Monitor asset

condition

Augment fleet-wide

history

Identify new patterns

Predict future state

Suggest process actions

Provide decision support

Capture learning's &

scale

Feedback to design &

operations

every physical component with an identity in a secure cloud

“a rapid increase in the number of intelligent assets is reshaping the economy, and this development will create significant value” Intelligent Assets, World Economic Forum 2016

Digital Profile System

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A platform for taking advantage of intelligent assets using analytics to create new value

! Managing through-life digital profiles - giving physical objects a digital identity and ‘record of life’

! Sensing, data acquisition and ‘hyper aware’ analytics – insight into asset health & performance

! Visualisation and Collaboration – transforming decision support

! Process automation – dynamic optimisation of operations

! Open web objects representing physical assets: enabling business applications to tap into the new wealth of asset information


Analytics impact: §  Production insight §  Product Distribution §  Collaborative decision support

Through-life insight example: life sciences

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Through-life insight example: electronics

Analytics impact: •  Knowledge of install base,

performance

•  End of life strategy options

•  Grey & Counterfeit market risk


Through-life insight example: medical equipment

Analytics impact: §  Clinical asset management §  Availability, scheduling §  New service offerings


Real &me data monitoring of factory opera&onal systems -‐ energy, H20, air pumps and HVAC

1000 units installed with sensors at the factory site

Sensors and analy&cs measure, monitor and manage energy use to run

opera1ons during the lowest cost &mes

Energy management approach moves from “always on” to

“available when needed”

© 2015 Cisco and/or its affiliates. All rights reserved. Cisco Confiden1al

Through-life insight example: energy mgmt


Insight to re-think business models


From Intelligent Assets to Digital Products

Virtualising hardware function into the Cloud

On-demand digital product inventory

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•  Digitisation strategy to direct technology investments and transformation of

business models to capture the value of through-life data and intelligent assets

•  Through-life may challenge x-organisational focus and measures •  Architecture and platform able to take advantage of traditional technological &

digital innovation merger, and rapidly evolving technologies e.g. IoT, analytics, machine learning

•  Managing potential ownership, access or IP conflicts as perceived value is created through data collection and analysis

•  Creating win-win propositions to avoid customer push-back or legal barriers as a result of security, privacy or trust concerns

A few considerations for adoption

Documents

Cambridge IfC Manufacturing Analytics1