I40 The Current Industrial Revolution
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What makes this the 4th industrial revolution? It's not just new technologies but all technologies working together as one system.
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I40 The Current Industrial Revolution
- 1. 1 Industry 4.0 – The Current Industrial Revolution
- 2. 2 Topics • What is I4.0 – A Brief History – What Makes This The 4th • What does I4.0 look like – The Flow of Economic Value – The Loops • Where to Begin – Business Priorities Don’t Change – Traditional Process Improvement – A Top Level Approach • Take-Aways
- 3. 3 A Brief History • The first industrial revolution was in the 18th century – Steam • The second industrial revolution was in the early 20th century – Electricity • The 3rd industrial revolution was in the mid to late 20th century – Electronics/IT/Automation • The 4th industrial revolution is in the early 21st century – Digitization, IoT, Big Data, Advanced Analytics (soon to be AI), Blockchain, and interoperability.
- 4. 4 What Makes This the 4th Revolution • Digitization – The electronic creation, communication, storage and use of data • IoT – An internet of all devices with an on and off switch • Big Data • Advanced Analytics (Soon to be AI) – Moving from data analytics to Artificial Intelligence • Interoperability: More than just an industrial internet – It consists of (among other things) Organizations, Architectures, & Standards • Blockchain – is a growing list of records, called blocks, which are linked using cryptography. Each block contains a cryptographic hash of the previous block,[6] a timestamp, and transaction data (generally represented as a merkle tree root hash). - Wikipedia
- 5. 5 What Makes This the 4th Revolution • It’s making much better use of the data being generated – Making relevant connections across the entire value chain • Data generation and usage potentially everywhere – Generating data at the right level to prevent noise • Taking automation to a whole new level – Automating decision making and the actions taken – At all levels of the enterprise and all spaces of the value chain – Machine to Product / Machine to Machine / Market to Supply Chain / Customers to Supply Chain / Customers to Market • It’s about transparency
- 6. 6 What Makes This the 4th Revolution • Technology is getting smaller and more integrated so it is starting to go unnoticed • I4.0 has the potential to connect every part of an economic value chain from potential markets to end of useful life. • What about Blockchains – Entire value chains protected from outside intrusion “I4.0 is realizing the full potential of all known technologies to optimize manual/physical efforts across the entire value chain through the combined effects of cyber systems.”
- 7. 7 Digitization • “…Converting [analog] data and information into a digital format.” – WhatIs.com • Starting to commonly use Exabyte in describing data needs – 1 Bit = Binary Digit 8 Bits = 1 Byte 1024 Bytes = 1 Kilobyte 1024 Kilobytes = 1 Megabyte 1024 Megabytes = 1 Gigabyte 1024 Gigabytes = 1 Terabyte 1024 Terabytes = 1 Petabyte 1024 Petabytes = 1 Exabyte • So is it digitization or digitalization? – One is conversion and the other is moving to a digital business – Source: “Digitization vs. Digitalization – Wordplay or World View?” May 11, 2016 by Jacqueline Prause Size of my hard drive
- 8. 8 The Internet of Things “Simply put, this is the concept of basically connecting any device with an on and off switch to the Internet (and/or to each other).” “A Simple Explanation Of 'The Internet Of Things‘” Jacob Morgan MAY 13, 2014 @ 12:05 AM FORBES.COM “At its core, IoT is simple: it’s about connecting devices over the internet, letting them talk to us, applications, and each other.” “What is the internet of things?” Nicole Kobie Wednesday 6 May 2015 theguardian.com Then there is NIALM: Non-Intrusive Appliance Load Monitoring. In conjunction with fine-grained appliance usage we can listen to ‘sound’ patterns to determine not only appliance usage but predict maintenance needs.
- 9. 9 The Internet of Things • Not everything needs to generate data and not all data generated is useful. • There is a balance between generating enough data to for it to be useful at the right level and creating noise. – Does a plant manager need to know pressures, temperatures, clamp tonnage, cycle time variances of all the injection molding machines? Does the supervisor? • Generating data needs to fall in line within the guidelines of a measurement system model – Checkpoint / control point deployment
- 10. 10 Big Data • Doug Laney defined Big Data as the three Vs – Volume: Multiple sources – Velocity: The need for real-time dealing with torrents of data – Variety: Multiple formats; structured and unstructured • SAS considers 2 additional dimensions – Variability: Inconsistency with spiking – Complexity: Combination of the other dimensions • An information processing view of an organization provides insight into design optimization – “Organizational learning is the improvement in the knowledge base of the organization” – “Organizational learning involves four constructs: knowledge acquisition, information distribution, information interpretation and organization memory”
- 11. 11 Big Data In an IDC whitepaper Dan Vesset identifies the what, who and why for big data use: What: Operational Intelligence Exploration and Discovery Performance Management Who: Front-line Staff Analysts Manager Why: React Faster Anticipate Sooner Invest Smarter Source: Six Patterns of Big Data and Analytics Adoption: The Importance of the Information Architecture” Dan Vesset June 2015 – “When you just look at some basic statistics, like the fact that half of the world’s data was created just in the last ten months, meaning that half of the world’s data, in the history of mankind, was created in less than the last year.” - Bill Wiseman, ‘How Advanced Analytics Can Drive Productivity’ Podcast August 2016, McKinsey & Company
- 12. 12 Advanced Analytics I4.0 is currently at the advanced analytics stage using some automated business intelligence models. Machine learning is in its infancy but will have widespread applications soon. AI is in use in some isolated cases. • Data Analytics – Making sense of the numbers • Business Intelligence – Still a good deal of human interaction • Advanced Analytics – Scenario algorithms • Machine Learning – How can the machine adapt • Heuristics – available and representative • Artificial Intelligence – Intuition and tacit knowledge
- 13. 13 Advanced Analytics Firm, Division, Department, Or Process Inputs Outputs CustomersSuppliers Data Collection Process Measurement and Evaluation Systems Improvement Interventions & Techniques Action Data Massaging Process Manager Mgmt team Employees Upper Management Stakeholders, Board, HQ, etc. Measurement Data PortrayalPerception Control Loop Decision Adapted from: Planning and Measurement in Your Organization of the Future Sink/Tuttle 1989
- 14. 14 Advanced Analytics Suppliers CustomersOutputsProcessesInputs Innovation Quality of Work-Life Effectiveness Profitability/ Budgetability Productivity Efficiency Effectiveness = Actual Output / Expected Output Efficiency = Resources Expected to be Consumed / Resources Actually Consumed Productivity = Output / Input Policies / Procedures Adapted from: Planning and Measurement in Your Organization of the Future Sink/Tuttle 1989
- 15. 15 Human Intelligence Psychologist Howard Gardner believes there are 7: 1. Linguistic. 2. Logical-Mathematical. 3. Bodily-kinesthetic. 4. Spatial. 5. Musical. 6. Interpersonal. 7. Intrapersonal. Howard Gardner also believes there are 8 types of multiple intelligence
- 16. 16 Artificial Intelligence • 925 AI Patents • 908 Uses across 19 countries
- 17. 17 Interoperability • It’s foundational to I4.0 • Defined tiers that manage data, information, decisions and action • The Edge Tier • The Platform Tier • The Enterprise Tier • Open standards from OPC UA • It will facilitate the progression of advanced analytics to AI
- 20. 20 Interoperability Source: Industrial IoT/Industrie 4.0 Viewpoints, Industrial-iot.com/2015/06/the-industrial-internet-reference-architecture-first-impressions/
- 21. 21 The Flow of Economic Value Market Feedback Loop • How quickly and accurately can specifics of demand be acted upon and then tested for effect? – Frontline’s documentary: “The Merchants of Cool” • How can Advanced Analytics predict new trends • When will AI predict disruptors • The intersection of the MFL and SCCL is a great place for R&D and CEM to engage • Social media, opinion forums, website data, etc. • Economic indicators relevant to your industry – PPI, CPI, Employment Rate, Tobin’s Q, etc.
- 22. 22 The Flow of Economic Value Data from social media provides insight into not only trends but those disruptors that will take demand in a new direction Market Feedback Loop
- 23. 23 The Flow of Economic Value Market Feedback Loop Content provided by Manoranjith Pathekkara Managing Director at India LogicaMatrix
- 24. 24 The Flow of Economic Value • Suppliers, production, warehousing/cross-docking, and transportation to customers are all integrated providing transparency • Where a great deal of the focus for I4.0 has been • How flexible and responsive can the supply chain become – When I4.0 is fully realized does it mean that one company can be everything to everybody? Supply-Chain Coordination Loop
- 25. 25 The Flow of Economic Value • Additive manufacturing – Within the production line – Supplying the production line – In the maintenance department – At the end-user destination • Equipment and machine capabilities used to create process routes for each product family – Re-routing can be automatic and in real-time • Product specifications sampled to prevent quality issues – No defects are passed on to the next process • Raw material and WIP ordered/produced in real-time to assigned production line. Supply-Chain Coordination Loop
- 26. 26 The Flow of Economic Value • Shift level, plant level, division and corporate level dashboards are updated at relevant frequencies, real- time, instantaneously. • Worker attendance and skill levels are incorporated for predicted completion times – Useful for scheduling training – Supervisor apps are available for digitally recorded observations on worker performance • Orders, transportation and logistics are integrated with production to improve to 100% on-time and fill – Tracking customer demand versus negotiated delivery can be included Supply-Chain Coordination Loop
- 27. 27 The Flow of Economic Value • More than just a means to create better designs and better manage customer experience – We can learn far more about how products and services are actually used • How far can we extend the useful life? – Do version 2.0’s automatically cannibalize existing products? • Let’s not forget about the F/SL and MFL intersection – How product/services are actually used and potential markets for the varied uses/applications – And how does this influence the supply chain Field/Service Loop
- 28. 28 Where to Begin Business Priorities Don’t Change • Prioritize I4.0 to your business strategy – Know who you are to your customers – Don’t forget your purpose for being in business – Growth strategy • Build the brand – Improve position in favored markets – Adjacent spaces • Increase value to your customers – Price points – Convenience/availability – Quality – Productivity strategy • Improve cost structure • Improve asset utilization
- 29. 29 Where to Begin Traditional Process Improvement • Lean, Six-Sigma, BPR… still have a place • Process Improvement has created awareness and improved communication to and from processes so decision making can be made at the local level based on guidance/governance established at the corporate level. – In many cases this can now be automated
- 30. 30 Where to Begin A Top Level Approach • Document your enterprise level value stream • Choose a product family with a noticeable impact – Implementation will be along this family from customers/sourcing to delivery/end use • Develop a plan to formalize the processes digitally – Automate where you can, standardize processes where you can’t • Develop a plan to create an Edge Tier – Connect processes that drive key performance indicators • To each other and to an edge level dashboard – Connect to trusted partners • Suppliers to Inputs • Outputs to Customers
- 31. 31 Where to Begin A Top Level Approach • Develop a plan to create a platform tier – Multi-site coordination – Data analytics – Cloud access • Develop a plan to create an enterprise tier – Transparency throughout the value chain • Including customers and potential markets • Don’t forget to include economic indicators – Identify applications to automate strategic intent • Market feedback loop • Supply chain coordination loop • Field/Service Loop
- 32. 32 Take-Aways • Implementing I4.0 is affordable – Don’t need a capital investment in the beginning – Each step completed in the implementation plan should be an improvement • I4.0 is easy to implement – It’s a natural progression of the needed data and information to make better decisions faster • As with all improvement efforts the return on investment needs to be tracked – Begin with stating expectations in terms of growth and profitability in actual numbers
Editor's Notes
- Please feel free to ask clarifying question during the presentation I ask that your hold exploratory or in-depth questioning until the end.
- Where to begin: You are already doing part of it! Reminds of a AF Major who asked about improvements made before AFSO21 certification.
- Edison’s protégé went onto Chicago eventually establishing the first electrical grid in the U.S.
- I’ll go into more detail further in the presentation Organizations: Industrial Internet Consortium (IIC), Working Group for Industry 4.0, OPC Foundation, Object Management Group (OMG), W3C (WorldWide Web Consortium) Architectures: RAMI4.0 (Reference Architecture Model for Industry 4.0)[German], Industrial Internet Reference Architecture (IIRA)[U.S.], Draft ISO IoT RA, Web of Things Standards: ISA-88 batch standards, ISA-95 enterprise-control systems integration standards, OPC UA, IEC 6-1131-3, and PLCopen, OMG Data Distribution Service (DDS) Demonstration: SmartFactoryKL (http://dfki-3036.dfki.de/EN/index_e.php#) Working Groups: NAMUR {User Association of Automation Technology in Process Industries (19 Organizations)}, [OPC/OMG]
- Forward looking while taking into consideration all current & historical relevant facts.
- Seventeen different blockchain platforms available now: “17 blockchain platforms - a brief introduction” Published on April 7, 2017 Rohas Nagpal
- Common data storage is 1 million times larger than my terabyte hard drive Digitalization is putting tablets in the hands of doctors and nurses as well as high school/elementary school students
- Bain predicts that by 2020 annual revenues could exceed $470B for the IoT vendors selling the hardware, software and comprehensive solutions. McKinsey estimates the total IoT market size in 2015 was up to $900M, growing to $3.7B in 2020 attaining a 32.6% CAGR. General Electric predicts investment in the Industrial Internet of Things (IIoT) is expected to top $60 trillion during the next 15 years. IHS forecasts that the IoT market will grow from an installed base of 15.4 billion devices in 2015 to 30.7 billion devices in 2020 and 75.4 billion in 2025 SOURCE: https://www.forbes.com/sites/louiscolumbus/2016/11/27/roundup-of-internet-of-things-forecasts-and-market-estimates-2016/#3f1c1d26292d I bring up NIALM because devices are created and added to objects to collect data and connect to the internet. NIALM turns what was once thought noise into data. Digitization and the IoT, along with the Big Data created, left to their own devises will become chaotic and unmanageable. We need something like search engines, like google, are to the internet. We have helped organize the world wide web. We also need a way to add relevance and security.
- Very sophisticated sensors
- Grocery stores and POS data Big Data, Analytics sales to reach $187 billion by 2019: http://www.informationweek.com/big-data/big-data-analytics/big-data-analytics-sales-will-reach-$187-billion-by-2019/d/d-id/1325631
- This is where we start making better use of the data being generated
- Advanced analytics automates part of the process. People have a tendency to leap from data collection/data massaging to decisions/action. We can full automate the model.
- Advanced Analytics falls short because it can’t adjust quickly enough to initial conditions as the scope is widened past the boundaries of the enterprise.
- The older more established firms seem to be the leaders in developing and adopting AI AI is on it’s way but right now its best use is complimentary to people and processes. AutoNoMation According to ITEC 1010 York University: Major Branches of AI Perception – see, hear, feel Vision Systems Robotics Expert Systems – Inference Learning Natural Language Processing Neural Network – Human brain functioning; like turning feelings into words and vice versa
- The older more established firms seem to be the leaders in developing and adopting AI AI is on it’s way but right now its best use is complimentary to people and processes. AutoNoMation According to ITEC 1010 York University: Major Branches of AI Perception – see, hear, feel Vision Systems Robotics Expert Systems – Inference Learning Natural Language Processing Neural Network – Human brain functioning; like turning feelings into words and vice versa
- Edge – value generation level; Platform – supply chain level; Enterprise – value chain level OLE(Object Linking and Embedding) for Process Control Unified Architecture (OPC UA) Interoperability comes to the aid of advanced analytics but a higher level of automation is required to effectively incorporate all of the inputs; determining what is valid and what is noise, what is the significance of the relevant data, what is the decision, what actions are to be taken and when should the system act and can it act the way its needed.
- Interoperability is the foundation of I4.0 in that it supports the data being generated by the IoT, that data is efficiently stored, the information & decisions are communicated and acted upon
- Upper plane is the view of how the pieces fit together Security: Encryption where the data is generated, as it’s transmitted, and at the destination.
- The Lower Plane is a functional view of how the pieces interact.
- Mining for data from sources such as: social media; weather; economic indicators (PMI – Purchasing Manager’s Index, CPI, PPI, Currency valuation, Trade Balance, Tobin’s Q, Advance Retail Inventories, Business Inventories, Personal Income and Outlays, Monthly Wholesale Inventories, Advance Report Durable Goods, GDP, Employment, New Residential Construction) "It's one enclosed feedback loop," Rushkoff says. "Kids' culture and media culture are now one and the same, and it becomes impossible to tell which came first--the anger or the marketing of the anger." Estimated Dynamic Optimization (EDO) Model is a medium-scale New Keynesian dynamic stochastic general equilibrium (DSGE) model of the U.S. economy that has been used at the Federal Reserve Board since 2006. EDO includes greater disaggregation of U.S. domestic spending, notably housing and consumer durables. Another distinctive feature is the introduction of two production sectors, for fast- and slower-growing industries. EDO has recently been extended to include unemployment along the lines of Gali, Smets, and Wouters (NBER, 2011). It models the U.S. Economy showing effects on fast and slower growing industries. Optimizing yield management; price points for good, better, best; Quantify relationships; Market saturation
- Demand Sensing: 50% reduction in forecast errors, 20% increase in inventory accuracy. ‘…”aggregate level planning” using time-series methods often don’t achieve a Forecast Accuracy above 60% at item-location levels…’ Jonathan Cohen, ‘Can Demand Sensing Make That Big a Difference’ Point of Sale data: who is buying, when they are being, are trends increasing or decreasing, yield management insights, substitutes, new entrants, barriers Manufacturers have POS, social media, economic data, CEM, and traditional marketing techniques to better understand the market Credit card companies group your transactions for a convenient annual report for you but is it really for you?
- Demand Sensing: 50% reduction in forecast errors, 20% increase in inventory accuracy. ‘…”aggregate level planning” using time-series methods often don’t achieve a Forecast Accuracy above 60% at item-location levels…’ Jonathan Cohen, ‘Can Demand Sensing Make That Big a Difference’
- Robotics, automation, lights out factories & distribution centers, etc To answer the question of everything to everybody won’t be feasible due to intellectual property rights if nothing else.
- SCENARIO: Additive manufacturing used to produce maintenance parts, ASRS (automatic storage and retrieval system) used to pick parts and tools based on signal from equipment. Work order produced and messaged to production and assigned maintenance personnel with the scheduled downtime used if not an emergency.
- At one time a prevailing theory was that product life cycles were self-generated by the launch of new versions or new products; cannibalization of market share. In some cases this is still true but firms have used this to their advantage – software, cellphones, cars, Part of the newer versions is because they haven’t figured out how to make the new features yet. Even with design for serviceability – MRO consists more with replacing whole components. Manufacturers are enhancing products to automatically collect data in the field: Washers/Dryers; Refrigerators; Even clothing. – This helps avoid the embarrassment of highly publicized recalls
- Business priorities don’t change… at first. Attempting change in the middle of change can lead to chaos. Years of change management experience has taught me that people need time to practice together. I4.0 doesn’t replace business fundamentals. Porter’s 5 forces and the Productivity Frontier are still valid… just need to be viewed within the context of advanced technology I don’t know and It depends Two mantras I rely on to be able to help people. I4.0 may eliminate not knowing and determine on what depends Healthcare is one of the 3 sectors of early adopters of I4.0: Technology has dramatically increased healthcare from smart pacemakers to portable/personal biofeedback devices (blood pressure, blood glucose, blood O2 levels, etc. all devices that connect to a smart phone), surgical robots, etc. What GBS has discovered is the single greatest contributor to patient survival and recovery is the degree to which the patient takes responsibility for their care. Considering healthcare Effectiveness Data & Information (HEDIS) metrics (52 measures for Effectiveness alone) for an impaneled population.
- I’ve written several articles on where process improvement programs can mislead organizations. I4.0 if developed by the same mindset can mislead organizations again.
- Technologies and components of Industry 4.0 have been developing independent of each other for quite some time. There is a chance legacy systems will be created once a broader effort to align all the components into an interoperable platform is realized. We will see the larger, leading edge corporations driving the standardization; Siemens(MindSphere), IBM(Watson), SAP()
- Connectivity Security Advanced sensor technology Easily accessed data storage organized optimally All processes documented and eventually controlled Instant accessibility Check points and control points built into the right nodes within the system Best performing intuition and analytics developed into algorithms A strategy with which to effectively put all components of Industry 4.0 into a platform
- Manufacturing use to be the lagging adopter of new technology. Now they are one of the three largest sectors advancing technology. Organizations can have real-time, instantaneous tracking of projects to verify results to assumptions. When considering re-investment better decisions can be made. – Somewhat like ZBB.