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

Transcription

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

2 Cisco s Supply Chain Global. Complex. Diverse. 13 COUNTRIES 25+ LOCATIONS Diverse Portfolio Manufacturing Sites Mass production to highly configured Strategic Logistics Centers 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.

3 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 TOP *Report not published in 2006

4 The role of Data Analytics CUSTOMER INSIGHTS Distribution Channel Customer VISIBILITY & TRANSPARENCY Corporate Office Distribution Channel REAL TIME ANALYTICS Customer Distribution Channel Customer STRUCTURED & UNSTRUCTURED DATA FLOWS 2015 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 4

5 Data Analytics Driving New Levels of Operational Excellence ENHANCED CUSTOMER EXPERIENCE Distribution Channel Customer SPEED TO INFORMED DECISION MAKING Corporate Office Distribution Channel Customer PREDICTIVE RESOLUTION Distribution Channel Customer END TO END VALUE CHAIN 2015 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 5

6 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. C Cisco and/or its affiliates. All rights reserved. 6

7 Architecture to drive Operational Excellence & Customer Experience INTELLIGENCE ANALYTICS COLLABORATION DECISION SUPPORT Capital equipment expense Transformation costs Quality Forecast Customers Backlog Logistics Factories s Reven ue Cost / Payment Product Margin Human resources Capacity constraints Product Enablement Data Master Data Customer dissatisfaction / unhappiness Reinvest in Innovation 2015 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 7

8 Journey to an analytics driven supply chain Data Acquisition Technology & Tools Process & Governance Skills & Roles 2015 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 8

9 Survey Question: Which technologies can most change how you manage production over the next three years? Cisco Digital Manufacturer survey, [625 respondents] C Cisco and/or its affiliates. All rights reserved. 9

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

11 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 C Cisco and/or its affiliates. All rights reserved. Image source: Dailymail.co.uk 11

12 Digital Profile System Augment fleet-wide history Identify new patterns Monitor asset condition Feedback to design & operations every physical component with an identity in a secure cloud Predict future state Suggest process actions Capture learning's & scale Provide decision support 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 C Cisco and/or its affiliates. All rights reserved. 12

13 13 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 2015 Cisco and/or its affiliates. All rights reserved.

14 Through-life insight example: life sciences Analytics impact: Production insight Product Distribution Collaborative decision support C Cisco and/or its affiliates. All rights reserved. 14

15 Through-life insight example: electronics Analytics impact: Knowledge of install base, performance End of life strategy options Grey & Counterfeit market risk 2010 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 15

16 Through-life insight example: medical equipment Analytics impact: Clinical asset management Availability, scheduling New service offerings C Cisco and/or its affiliates. All rights reserved. 16

17 Through-life insight example: energy mgmt 1000 units installed with sensors at the factory site Real &me data monitoring of factory opera&onal systems - energy, H20, air pumps and HVAC 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 C Cisco and/or its affiliates. All rights reserved Cisco and/or its affiliates. All rights reserved. Cisco Confiden1al 17

18 Insight to re-think business models C Cisco and/or its affiliates. All rights reserved. 18

19 From Intelligent Assets to Digital Products Virtualising hardware function into the Cloud On-demand digital product inventory C Cisco and/or its affiliates. All rights reserved. 19

20 A few considerations for adoption 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 2010 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 20