IIoT AND BIG DATA ANALYTICS:

Transcription

1 IIoT AND BIG DATA ANALYTICS: Architecture Is Being Transformed lnsresearch.com

2 IIoT AND BIG DATA ANALYTICS: Section 1: IIoT: State of the Market... 3 Section 2: Understanding Digital Transformation... 8 Section 3: Adoption of IIoT Connectivity and Big Data Analytics Section 4: Building the Business Case and Recommended Actions lnsresearch.com

3 1 IIoT: State of the Market

4 4 Introduction The Industrial Internet of Things (IIoT) is a general term denoting the concept that standard Internet technologies are broadly applicable and will transform all areas of the industrial sector. Because of its expansive and transformative nature, the concept and term IIoT is starting to be used almost everywhere: manufacturers, automation vendors, enterprise application vendors, system integrators, management consultants, government sponsored consortia, and industrial companies as well. As an analyst firm, LNS Research is dedicated to helping all of these market players simplify, improve understanding, and more quickly capture the value of emerging technology. In early 2014 LNS began researching and advising companies on the IIoT, culminating in the 2015 research report: Smart Connected Operations: Capturing the Business Value of the IoT. In this work we gave the first, now broadly accepted, definition of the IIoT Platform, consisting of four main buckets of capabilities: Connectivity, Cloud, Big Data Analytics, and Application Development. We also put forth concepts of how this platform would enable new data and system architecture that would flatten existing hierarchies, provide data from anywhere to anywhere capabilities, and enable next-generation business applications. Since that time there have been dramatic moves by new start-ups and many incumbent vendors, including announcements of homegrown capabilities as well as mergers and acquisitions. All of these moves have confirmed the assertion that for the foreseeable future, the IIoT Platform space will be an ecosystem play that brings together both IT and OT vendors to enable new business models. In this new research, we will explore new survey data showing the increased market adoption of IIoT Platform capabilities and how these new technologies are transforming architectures today; not some unknown date in the future. We will also examine how the LNS Research Digital Transformation Framework can help industrial companies overcome IIoT challenges and get started now on the journey towards using Big Data Analytics to achieve a competitive advantage and better business results.

5 5 Research Demographics The data presented in this ebook represents over 300 completed surveys and was collected from the middle of 2015 to the middle of LNS Research deploys a social research model where our online format English language surveys are open to the general public. Companies participate in LNS Research surveys to gain access to the LNS Research library, meaning survey participants are research consumers as well. Each respondent is contacted with multiple s and phone calls and each response is reviewed by an LNS Research analyst for accuracy. The industry demographics of the survey largely match the broader demographics of the industrial landscape, with discrete being the largest segment, followed by process and batch industries. Our research also has a broad split across industries and company sizes. 15% 12% 28% 45% 41% 49% 10% 15% 48% 37% 2016 Metrics That Matter Survey GEOGRAPHY COLOR BY HQ LOCATION North America Europe Asia/Pacific Rest of World 2016 Metrics That Matter Survey REVENUE COLOR BY COMPANY REVENUE Small: Less than $250 Million Medium: $250 Million - $1 Billion Large: More than $1 Billion 2016 Metrics That Matter Survey INDUSTRY COLOR BY INDUSTRY Process Manufacturing Discrete Manufacturing Batch Manufacturing

6 6 IIoT Adoption Platform Technology Adoption A major change in the market from 2015 to 2016 was in regard to one of the biggest challenges limiting the adoption of IIoT technologies. In 2015, nearly half (44%) of companies did not know or understand the IIoT. In 2016 this number has reduced to 19%. Call it hype or call it hard work by many of the thought leaders in the space, but the majority of companies now understand what the IIoT is. In our 2015 research we showed that the IIoT market was largely still an early adopter market but would likely follow the typical adoption curve for major new technology innovations. We also stated three things would have to occur to move the market Please indicate how the IoT is impacting your business today toward mainstream adoption: Time would need to pass The market would have to better understand the technology Early adopters would have to prove results for easier business case validation Two of the three have occurred and it is now time for the market to prove the value and demonstrate the business case, which we will hopefully begin later in this ebook. Do not understand or know about IoT 19% 44% We are still investigating the impact 21% 33% We understand/are aware and see value to our operators/customers or both 18% 16% We understand and our customer demands are driving us We understand but see no impact at this time We understand and have already seen dramatic impact 13% 9% 8% 6% 8% 4% Do not understand IoT: 2015: 44% 2016: 19% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%

7 7 Top IIoT Challenges Unfortunately, building the business case and securing the funding are the two top challenges facing IIoT technology adoption. Surprising to many, security concerns and technology scalability do not top the list. This result is likely due to some companies just looking to get pilot projects off the ground and not having tackled these technology issues yet. For other companies it is likely because they have already done the research and are confident that IoT technology is scalable to the industrial space. In either case it is clear that building an effective business case is the key to unlocking the potential of IIoT technology and Big Data Analytics. In the next several sections we will show how building the business case fits within the larger context of a Digital Transformation Framework, how it must support these objectives, and how it needs to be viewed as a journey rather than a singular, one-off decision. What are the top challenges your company faces in deploying IIoT technology? (N=269, all respondents) Funding Building a business case Understanding what IIoT is and how it applies to your business Security Standards Finding the right technology partner(s) Gaining insight from Big Data Developing new IIoT software applications Company culture Data gathering from legacy systems Product design and development complexity Hiring the right talent Executive support Scaling to 1,000s or 1,000,000s of devices 5% 8% 8% 14% 14% 13% 12% 16% 17% 22% 26% 25% 30% 32% 0% 5% 10% 15% 20% 25% 30% 35%

8 2 Understanding Digital Transformation

9 PAGE 9 Digital Transformation Framework Many industrial companies today are pursuing Digital Transformation initiatives. What many organizations are missing is a systematic approach to manage this transformation across all levels and functions of the organization. The LNS Research Digital Transformation Framework is designed to help industrial companies understand how to connect all of these simultaneous and interconnected initiatives. DIGITAL TRANSFORMATION FRAMEWORK STRATEGIC OBJECTIVES Reimagining Business Process and Service Delivery OPERATIONAL EXCELLENCE Realigning People, Process, and Technology ENERGY QUALITY OPERATIONS EHS APM OPERATIONAL ARCHITECTURE Managing IT-OT Convergence and Next- Gen IIoT Technology CONNECTIVITY CLOUD L5 IoT Enabled Governance and Planning Systems L4 IoT Enabled Business Systems Smart Connected Operations - IIoT Enabled L3 Production, Quality, Inventory, Maintenance BIG DATA ANALYTICS L2 L1 L0 Smart Connected Assets - IIoT Enabled Sensors, Instrumentation, Controls, Assets, and Materials APPLICATION DEVELOPMENT IIoT Enabled Next-Gen Systems BUSINESS CASE DEVELOPMENT Defining Immediate and Long Term ROI COSTS TOTAL YEAR 1 YEAR 2 YEAR 3 HARDWARE SOFTWARE LICENSING THIRD PARTY SOFTWARE APPLICATION SOFTWARE DOCUMENTATION & TRAINING MAINTENANCE INSTALLATION INTEGRATION LEGACY DATA LOADING PROJECT MANAGEMENT SUPPORT TOTAL: The LNS Research Digital Transformation Framework offers a systematic approach to undertaking simultaneous and interconnected IIoT initiatives SOLUTION SELECTION Eliminating Bias and Finding Long Term Partners Project Charter Team PLANNING BUSINESS CASE CEO/COO Business Leaders CDO, IT/OT Leaders Functional Managers, SMEs Business, IT/OT Practitioners DISCOVERY Research RFP Evaluation Pilot SELECTION

10 10 Digital Transformation Framework (Cont.) STRATEGIC OBJECTIVES: At the highest level industrial companies today need to be thinking about how many of these new technologies, like the IIoT, can disrupt and transform products, value chain business processes, and connected service delivery. At the strategic level, companies should be doing 5, 10, and even 20-year planning, and often these transformative visions are built around the competitive differentiators of the firm, the changing nature of service delivery, and existing models like Industrie 4.0, Smart Manufacturing, or Smart Connected Assets. OPERATIONAL EXCELLENCE: People, processes, and technology are the underpinnings of Operational Excellence initiatives, and these are typically owned by the business leaders in the organization. Leading companies today have developed maturity models to help set goals and growth plans for people, process, and technology capabilities along with metrics programs to evaluate performance across all areas of operations. Most companies have had Operational Excellence initiatives in some form or fashion for 10 years or more. Often these initiatives also incorporate the multiple management systems and continuous improvement capabilities of the firm, like Lean or Six Sigma. Moving forward, manufacturing companies need to continue to evolve Operational Excellence initiatives to not only be the continuous improvement engine of the company but also the driving force for innovation. Often this means moving to more of more of a lean, start-up mentality of fail often and fail fast, with pilot projects that have the potential of delivering much more than the typical 1%-2% benefits promised by most continuous improvement initiatives. OPERATIONAL ARCHITECTURE: Traditionally Enterprise Architecture has been owned by the IT organization and has been typically focused on establishing robust processes for evolving the enterprise application landscape and supporting IT stack. Separately, automation, corporate engineering, and/or advanced manufacturing (often now referred to as OT) owned the technology architecture for plant level technology. With the emergence of IIoT, LNS Research recommends industrial companies adopt an Operational Architecture approach that applies the formalized rigor and process of Enterprise Architecture to the entire IT-OT stack. For this to be effectively accomplished, industrial companies need to create supporting and collaborative groups that incorporate both IT and OT, and as the role of Chief Digital Officer emerges, the success of this new collaboration as a key part of his or her charter. BUSINESS CASE DEVELOPMENT: Often industrial companies begin business case development and solution selection without also thinking about the connection to broader Strategic Objectives, Operational Excellence, and Operational Architecture. Typically these business case development initiatives are successful when driven by deep subject matter experts that understand both the process and technology.

11 11 Digital Transformation Framework (Cont.) Identifying these experts can be a challenge, but often they are located in advanced manufacturing, hybrid IT/OT roles, are a leader within specific business functions, or are a technical fellow supporting the organization. Although these other areas of Digital Transformation do not need to be complete before a business case is started, they are interconnected. As such, it is important industrial companies do not view technology investments as a one-off business case but rather as a business case journey that aligns with Operational Architecture goals, depends on increasing Operational Excellence maturity, and supports long-term Strategic Objectives. It is also important to note that a strong business case will also incorporate risk-based principles into the decision making and explicitly look at no decision as an active choice. SOLUTION SELECTION: Often industrial companies view Digital Transformation upside down, starting with solution selection, which then drives all other portions of the framework, rather than vice versa. Again, with solution selection, it is important to put the activities within the context of the broader initiatives. Solution selection is never successful in a vacuum and when it is done in such a fashion, change management becomes an insurmountable challenge and adoption wanes. For success, build an effective solution selection process that is quantitative to eliminate bias and a team that incorporates all relevant portions of the organization, including IT, OT, and crossfunctional business leaders. Solution selection should always be viewed within the broader context of Digital Transformation initiatives; never in a vacuum

12 12 A New Model for Operational Architecture In moving to a new model of Operational Architecture, industrial organizations need to move to an expanded scope of Enterprise Architecture. This expanded scope should account for managing things with edge analytics and applications across the value chain of suppliers, internal operations, customers, and products. It should also span an application and analytics environment that includes cloud/ on-premise and time series/structured/unstructured data types. Upon careful inspection this expanded model should also incorporate the main components of the IIoT Platform: Connectivity, Cloud, Big Data Analytics, and Application Development. This expanded scope is also too broad to address the right level of detail for making meaningful architectural decisions across the enterprise. LNS Research recommends a three-level approach, where at Level 1 the entire scope is encompassed. LEVEL 1 OPERATIONAL ARCHITECTURE Big Data Analytics, Collaboration, and Mash-Up Apps ANALYTICS & APPS ANALYTICS & APPS ANALYTICS & APPS Applying an expanded scope to Operational Architecture that includes the IIoT Platform allows for the management of things across the value chain EDGE ANALYTICS AND APPLICATIONS Connectivity and Data Model EDGE ANALYTICS AND APPLICATIONS EDGE ANALYTICS AND APPLICATIONS EDGE ANALYTICS AND APPLICATIONS EDGE ANALYTICS AND APPLICATIONS EDGE ANALYTICS AND APPLICATIONS EDGE ANALYTICS AND APPLICATIONS EDGE ANALYTICS AND APPLICATIONS EDGE ANALYTICS AND APPLICATIONS SUPPLIERS OPERATIONS CUSTOMERS & PRODUCTS

13 13 A New Model for Operational Architecture (Cont.) At the next level of detail, a particular element of the high level architecture should be driven into. For example, an organization s Level 2 Operational Architecture for structured data analytics and apps would largely map to the traditional scope of enterprise applications. When building this architecture, LNS Research recommends not LEVEL 2 OPERATIONAL ARCHITECTURE focusing on the traditional applications, such as ERP, PLM, MES, SCM, and CRM, but instead on the functional areas and map these to the corporate systems/management systems/value chain systems used across execution/planning/analytics. Then the different applications can be mapped to this model, not vice versa. Structured Data Applications and Analytics CORPORATE SYSTEMS - Defined by Sites and Organizational Structure ANALYTICS PLANNING HR, Procurement, Finance and Accounting, IT Management HR, Procurement, Finance and Accounting, IT Management EXECUTION HR, Procurement, Finance and Accounting, IT Management Big Data Analytics, Collaboration, and Mash-Up Apps MANAGEMENT SYSTEMS - Defined by Sites and Organizational Structure ANALYTICS & APPS ANALYTICS & APPS ANALYTICS & APPS ANALYTICS Quality, Environment, Health, Safety, Energy, Sustainability, Risk, Assets Connectivity and Data Model PLANNING Quality, Environment, Health, Safety, Energy, Sustainability, Risk, Assets EDGE ANALYTICS AND APPLICATIONS EDGE ANALYTICS EDGE ANALYTICS AND APPLICATIONS AND APPLICATIONS EDGE ANALYTICS AND APPLICATIONS EDGE ANALYTICS EDGE ANALYTICS AND APPLICATIONS AND APPLICATIONS EDGE ANALYTICS AND APPLICATIONS EDGE ANALYTICS EDGE ANALYTICS AND APPLICATIONS AND APPLICATIONS EXECUTION Quality, Environment, Health, Safety, Energy, Sustainability, Risk, Assets SUPPLIERS OPERATIONS CUSTOMERS & PRODUCTS VALUE CHAIN SYSTEMS - Defined by Sites and Organizational Structure ANALYTICS Marketing Sales Engineering Suppliers Asset Management Manufacturing Warehousing Distribution Retail Service PLANNING Marketing Sales Engineering Suppliers Asset Management Manufacturing Warehousing Distribution Retail Service EXECUTION Marketing Sales Engineering Suppliers Asset Management Manufacturing Warehousing Distribution Retail Service

14 14 A New Model for Operational Architecture (Cont.) Another important area of Operational Architecture that has not been traditionally managed in conjunction with enterprise applications is the Level 2 Operational Architecture for the edge analytics and connectivity across devices and assets in operations. At this level, industrial companies need to manage both the networking and automation infrastructure that supports security and the flow of data with context through the traditional control system hierarchy, as well as next generation IIoT protocols and gateways. This is an area where the most innovation and transformation is occurring. Many industrial companies fear that the movement towards IIoT technologies involves the movement to exclusive cloud and gateway use. However, for the vast majority of companies it will be a controlled and hybrid model for the foreseeable future, where information still flows in the traditional approach but also flows through all Firewall Plant Data Center / Application Server CELL 3 ENTERPRISE Drive these new flattened hierarchies. What this means is industrial companies will need a data and connectivity model that harmonizes across device, gateway, on-premise, and cloud. It also means that the plant floor, now as much as ever, needs a cost-effective, redundant, and fault-tolerant connectivity, compute, and storage environment that supports the move to IIoT. External DMZ/Firewall Core Switches Distribution Switch External DMZ/Firewall SMART CONNECTED DEVICES AND ASSETS CELL 4 Plant Data Center / Application Server Firewall Controller I/O Plant Data Center / Application Server SETUP AND CONFIGURATION SECURITY HARDWARE SETUP & INSTALLATION FUNCTIONS DATA CONNECTIVIT Y Control Gateway to Plant or IoT Network Mobile Device CELL 1 HMI Instrumen- CELL 2 tation PLANT PLANT DEMILITARIZED ZONE ENTERPRISE INTERNET

15 15 A New Model for Operational Architecture (Cont.) At the most detailed view, or Level 3 Operational Architecture, individual and specific elements of Level 2 will be integrated. Examples of this could include the specific pieces of functionality that are included within Manufacturing Operations Management (MOM) or the specific edge analytics, applications, security, device management, and communication protocols that are used for smart connected devices. SMART CONNECTED DEVICES SETUP AND CONFIGURATION SECURITY DATA MANUFACTURING OPERATIONS MANAGEMENT Future: Integration and Collaboration Platforms FUNCTIONS Enterprise Applications ESB, Standards MODULES/APPS: Scheduling, Dispatching MODULES/APPS: Time & Attendance, Training MODULES/APPS: Purchasing, Warehouse MODULES/APPS: EMI / OI, Reporting COMMON APPLICATION FUNCTIONALITY PROVIDED BY MOM PLATFORMS: Application Integration Security & Access Unified Asset & Production Model Unified Operations Database & Historian Global Deployment & Licensing Integrated Development Environment Collaboration & Workflow Visualization & Mobility CONFIGURATION: Platform Services, Modules/Apps MODULES/APPS: Execution, Tracking MODULES/APPS: Asset Tracking, MRO, RCM MODULES/APPS: OEE, Quality Standards, Proprietary Industrial Automation

16 16 The Difference Between a Lot of Data and Big Data in Manufacturing LNS Research recommends taking a relatively generic IT view of what approximately half a terabyte of data per flight. What we have not had Big Data is and then applying the definition to the industrial space. to deal with is variety. All of this data has been relatively well structured process data stored as time series or transactional data stored as One definition that has received broad acceptance is the three V s of Big Data: structured data in enterprise applications. With the advent of the IIoT, data might include images, video, unstructured text, spectral (such as vibration), or other forms, such as Volume Velocity thermographic or sound. As all of these data types come together, Variety industrial companies will truly have to deal with Big Data in Manufacturing, which will bring together a whole new set of analytics opportu- In the industrial space we have typically had to deal with large volumes and velocity of data. According to Boeing, the 787 produces nities and challenges. BIG DATA Connectivity and Data Model

17 17 The Difference Between a Lot of Data and Big Data in Manufacturing (Cont.) As with defining Big Data, LNS Research also recommends taking However, as new solutions have emerged to manage Big Data, a relatively generic IT definition of analytics and applying it to the new analytics have also emerged that are mainly targeted toward industrial space. In the industrial space, even before Big Data, predictive and prescriptive analytics. To add confusion, even though companies were doing the full spectrum of analytics, including these tools were developed to analyze Big Data, they can be used descriptive, diagnostic, predictive, and prescriptive. Traditionally with any data set: small, large, or big. these analytics have been focused on analyzing structured and A common example of these new Big Data Analytics includes time series data to address the key drivers in industry: quality, production, assets, delivery, innovation, and more. generally data focused, where the traditional tools are model and Machine Learning, among many others. These new analytics are all Examples include: process specific, which adds to the challenges in bridging the gap Descriptive: Metrics and Scorecards for Overall Equipment between data scientists using Big Data Analytics and engineers using Effectiveness (OEE), On Time Delivery (OTD), Scrap, Mean traditional model based analytics. Time to Failure (MTTF) Next generation Prescriptive Analytics are really about moving Diagnostic: Reliability engineering, quality engineering, beyond choosing what to do next, and optimizing operations and enabling innovation. In the next section we will examine the adoption root cause analysis Predictive and Prescriptive: Modeling and simulation, of these tools and how industrial companies can hasten the value statistical process control, advanced process control captured from using them. BIG DATA ANALYTICS FRAMEWORK DESCRIPTIVE DIAGNOSTIC PREDICTIVE PRESCRIPTIVE What happened Why it happened What will happen What action to take

18 3 Adoption of IIoT Connectivity and Big Data Analytics

19 19 What Are the Top IIoT Use Cases of Today and Tomorrow? When considering adoption of IIoT and Big Data Analytics, it is informative to start with the use cases. When it comes to the IIoT use cases being pursued today, there is no single use case coveted by the majority of companies; instead, it is spread quite evenly across the traditional drivers in the industrial sector: energy, reliability, quality, production, etc. When comparing What are the top IIoT use cases your company is pursuing today? (N=252, all respondents) Remote monitoring Energy efficiency Asset reliability Quality improvement Production visibility Internet enabled products Business model transformation, e.g. selling capacity Asset and material tracking Traceability and serialization Customer access to information Improving safety Supplier visibility Improving environmental performance the use cases of today vs. one year from today there are some interesting insights. First, remote monitoring is top across both. Second, energy efficiency is viewed as low hanging fruit and something more likely to be pursued today than in a year. Finally, business transformation initiatives are viewed as a longer term use case and more likely to be pursued in a year from now rather than today. What are the top IIoT use cases your company will start pursuing in the next year? (N=249, all respondents) 29% Remote monitoring 26% 25% Asset reliability 23% Business model transformation, e.g. selling capacity 24% 22% 23% Asset and material tracking 21% 23% Quality monitoring 21% 22% Customer access to information 20% 19% Production visibility 19% 19% Energy efficiency 18% 17% Internet enabled products 18% 15% Traceability and serialization 15% 12% Supplier visibility 12% 6% Improving safety 8% Improving environmental performance 5% 5% 0% 5% 10% 15% 20% 25% 30% 35% 0% 5% 10% 15% 20% 25% 30%

20 20 IIoT Data Sources and Types Used Today Although many industrial companies are looking for IIoT technology to enable some previously unconsidered use case, as the data shows, most companies are pursuing already existing and previously unsolved problems instead. In many cases these are quality, manufacturing, and reliability issues that have plagued organizations for years. Examples of these specific use cases include: Dead on arrival quality issues that slip through the finished product and functional testing Engineering and manufacturing tolerances that are too tight or loose, allowing failures to the field or keeping good products from customers Unscheduled downtime occurring as an unknown failure due to systemic issues and relationships that are not immediately obvious, like component suppliers, manufacturing processes, environmental conditions, and customer use scenarios It is these earlier use cases that will allow for later transformation, like transitioning from selling assets to capacity and providing value added connected services. Fortunately, because of this use case progression, industrial companies can start slowly with the new data sources. As is shown in the below graph, most companies can do quite well just by collecting Manufacturing Execution System (MES) and Programmable Logic Controller (PLC) data to start. Then as maturity increases, they can add in data from smart devices. What information from the plant are you combining for Big Data Analytics? (N=30) MES, quality system, or other high level software Individual controllers (PLC) Complex equipment with embedded control Individual smart devices Other Individual dumb devices (sensors, switches, analogue readings) 13% 13% 13% 7% 47% 67% 0% 10% 20% 30% 40% 50% 60% 70%

21 21 Data Architecture Today Surprisingly, even though most companies have not yet moved to secondary system of sensors and connectivity. There are pros and start broadly collecting data from non-traditional data sources, cons to both and each will likely persist for the immediate future as already a third of companies are moving to a non-traditional, non-hierarchical approach to data flow. For just one example of the confusion, much of the data coming industrial companies experiment. Although companies are not ripping and replacing existing control from new sensor-to-gateway-to-cloud solutions is measuring data to information system hierarchies, many companies are beginning to points that are already collected within the control system but currently lack the context of the control system. But on the flip side, deploy gateway to cloud architectures, at least in a limited capacity, to begin delivering IIoT data to higher level enterprise applications these new sensor-based solutions are exclusively focused on delivering value from the new data coming from these sensors, deploy more and analytics packages. It remains to be seen if this information flowing through gateways quickly and easily than with existing automation solutions, and often and to the cloud will be enabled on the thing side of gateways provide a positive short-term ROI. with direct connections to existing automation equipment, or via a Today, how are you architecting the flow of IIoT data? (N=167, companies with IIoT initiatives) Through traditional control and information system architecture 34% Primarily through traditional control and information system architecture with some use of edge analytics, gateways, and cloud Even split between traditional control and information system architecture and use of edge analytics, IIoT gateways, and cloud Primarily use of edge analytics, IIoT gateways, and cloud with some use of traditional control and information system architecture Other 10% 8% 17% 28% Exclusive use of edge analytics, IIoT gateways, and cloud 4% 0% 5% 10% 15% 20% 25% 30% 35%

22 22 Data Connectivity and Data Ownership As new data sources and systems come online, emerging questions of data ownership and data sharing are becoming critical. One of the most important questions is who owns the new machine data? The maker of the asset/device, or the user? Interestingly, we see the market split on this point today, and there may not necessarily be a verdict or even a single answer any time soon. There are, however, a few points becoming clear. 1. When customers do not own the data, they prefer not to pay for the raw data coming back. Rather, they want to pay for the value services being delivered back to them that may only have been possible through data sharing. 2. Use of the machine matters. In scenarios where the use of the machine has no competitive differentiator, like in compressed air for example, data sharing and selling compressed air instead of compressors is not an issue. When the use of the machine does create competitive advantage, like with CNC machines or oil field service equipment, asset users are much more protective of data. Who owns the data coming from the machines you deliver to customers? (N=66, machine builders only) The customer owns the data, we are data custodians 50% We own the data and do not share raw data with customers We own the data but provide raw data access to customers 24% 29% 0% 10% 20% 30% 40% 50% 60%

23 23 Using Big Data Analytics One of the most surprising results from the survey came from the question asked regarding analytics expertise. The most common response, representing 40% of the market, was the belief that companies already had all the needed analytics expertise. From where does your company get or plan to get its analytics expertise? We have strong analytics teams that will not require much expansion 40% We use or will use large scale consulting companies with specialist industry knowledge Don t know - This is a potential stumbling block 17% 23% Don t know - We ll worry about this later 17% We plan to hire specialists in industrial analytics We will use expert consultants from our analytics software vendor(s) 10% 13% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Given the data shown above regarding the IIoT use cases, new data sources, and changing system architecture, it is unlikely this many industrial companies actually have the right degree of analytics expertise.

24 24 Using Big Data Analytics (Cont.) What is more likely, as shown by this companion question, is that most industrial companies are just doing descriptive analytics on structured data sets rather than predictive and prescriptive analytics with Big Data. To help address this apparent lack of understanding, industrial companies need to invest in both the appropriate technology, but more importantly, process and training as well. Just as Six Sigma Algorithms used in analytics system and Lean were built into the fabric of continuous improvement initiatives and packaged for subject matter experts to use the appropriate financial modeling, process optimization, and variability reduction analytics without being a statistician, Big Data tools like Hadoop and Machine Learning need to be packaged and made accessible to industrial subject matter experts, not just data scientists. Trend analysis Data visualization 44% 59% Statistical distribution analysis 41% Statistical process control (SPC) Optimization 33% 41% Regression analysis 30% Predictive modeling 26% Material performance Correlation analysis 22% 22% Simulation 19% Condition based monitoring 19% Machine learning Data mining algorithms 11% 11% Sentiment analysis 7% 0% 10% 20% 30% 40% 50% 60% 70%

25 25 Enabling Smart Connected Assets and Smart Connected Operations In LNS Research s aforementioned ebook, Smart Connected Operations: Capturing the Business Value of the IoT, we first hypothe- map to operations instead of accounting models, and have A transformation of enterprise applications to more closely sized that IIoT Platform technologies would precipitate the flattening the ability to work flexibly with operational data and not just of the traditional hierarchical model. With this new research, we are structured transactional data. seeing the first quantitative evidence of this transformation occurring, and as such there will be several key changes including: The enablement of mash-up applications and analytics that can enable Big Data from anywhere to anywhere and support A transformation of control system hierarchy to move from one true end-to-end value chain processes. of distributed controllers and centralized control to truly distributed control with the enablement of Smart Connected Assets. A transformation of MES to become an orchestration and optimization platform for Smart Connected Operations, not simply an integration and analytics middleware layer for execution and compliance. SMART CONNECTED ENTERPRISE L5 L4 IIoT Enabled Next-Gen Systems MATERIALS AND SUPPLIERS L2 L3 PRODUCTS AND CUSTOMERS L1 L0

26 4 Building the Business Case and Recommended Actions

27 27 IIoT Challenges There is not an industrial company in business today that would not like easier access to operational data and the decision support tools to better address quality, production, and reliability issues. This is why for many it is counterintuitive that the top two challenges in IIoT adoption are not technical, but rather funding and business case development Funding Building a business case Understanding what IIoT is and how it applies to your business Security Standards Finding the right technology partner(s) Gaining insight from Big Data Developing new IIoT software applications Company culture Data gathering from legacy systems Product design and development complexity Hiring the right talent Executive support Scaling to 1,000s or 1,000,000s of devices The reason is a classic catch 22: before Big Data Analytics are implemented, companies cannot accurately predict their benefits. Likewise, without a comprehensive understanding of Big Data benefits, companies are reluctant to invest the time and resources on their implementation. What are the top challenges your company faces in deploying IIoT technology? (N=269, all respondents) 5% 8% 8% 14% 14% 13% 12% 16% 17% 22% 26% 25% 30% 32% 0% 5% 10% 15% 20% 25% 30% 35%

28 28 A Business Case Journey that Aligns to Strategic Objectives and Maturity To address this catch-22 companies should think of the Big Data Analytics investment as a journey that is based on Operational Excellence maturity, scope, and metrics rather than a one-off ROI calculation. Operational Excellence maturity is the driving factor for increasing the scope and value of the business case. LNS Research recommends using a 5-Level approach to quantifying maturity, where at the lowest, Ad Hoc Level companies are unable to meet the current and future demands of customers and at the highest, Market Leader level companies are able to define and transform markets, disrupting incumbents. The following matrix will allow companies to evaluate their current position based on their capabilities. INNOVATION LEADER Drives standards and expectations AGILE Evolved people, process, and technology across the enterprise HARMONIZED Flexibly unified at the organizational level CONTROLLED Repeatable within organizational, process, and/or technology boundaries AD HOC Unstandardized with significant variation L5 L4 L3 L2 L1 STRATEGY & EXECUTION LEADERSHIP & CULTURE ORGANIZATIONAL CAPABILITIES BUSINESS PROCESS EXCELLENCE TECHNOLOGY CAPABILITIES PERFORMANCE MANAGEMENT & KPIs AD HOC CON. PRO. AGI. Disconnected from corporate objectives Operational Excellence is a department rather than shared responsibility Operational Excellence distinct from corporate structure. Not in goals or incentives Disconnected and disparate Disconnected and disparate Non-role based, manual KPIs, disconnected from corporate goals INNOVATION LEADER Fully integrated with corporate objectives Operational Excellence is integral part of leadership and culture. Operational Excellence fully integrated into corporate structure Globally integrated and Harmonized. Fully embracing emerging capabilities Predictive, role-based, real-time metrics connected to corporate goals Predictive, role-based, real-time metrics connected to corporate goals

29 29 A Business Case Journey that Aligns to Strategic Objectives and Maturity (Cont.) When the business case is viewed as a journey, most industrial As maturity increases and initial cost reduction benefits are realized, the scope of the business case can increase and the types companies should begin on the cost side of the equation and within specific functional areas of operations. With limited maturity, there of metrics measured can move to being value based. As more and is not a shared vision of how productivity gains will drive actual more maturity is realized, industrial companies can more accurately financial benefits. By starting with a single area like quality, manufacturing efficiency, asset reliability, or energy usage, the need for ty gains and, ultimately, the achievement of strategic objectives like predict the economic benefits that will be realized from productivi- collaboration is minimized and cost reductions clearly go to the business model transformation or the entry to new markets. bottom line, eliminating uncertainty of real results. OPERATIONAL EXCELLENCE MATURITY $$$$$ 5 4 $$$$$ 3 $$$$$ 2 $$$$$ VALUE CENTER COST CENTER BUSINESS CASE JOURNEY METRICS Big Data* *Big Data Analytics, Diagnostic, Predictive, Prescriptive Value** **e.g. Revenue and Earnings Financial Operational 1 $$$$$ DEPARTMENT CROSS-FUNCTION BUSINESS CASE AND OBJECTIVE SCOPE EXECUTIVE Siloed

30 30 Recommended Actions IIoT Platform technologies are currently driving the most transformative period in the industrial sector over the past 40 years. As industrial executives attempt to establish high level strategic objectives, it is critical that a formalized and structured approach is taken to Digital Transformation that establishes an expanded view Operational Architecture and captures the value of Big Data Analytics. ESTABLISH A DIGITAL TRANSFORMATION FRAMEWORK Establish a Digital Transformation leader and new group responsible for a framework that connects and enables for change all levels and functions of the organization. Incorporate feedback loops at each stage of the journey and ensure that high level strategic objectives are aligned with Operational Excellence initiatives, system architectures, business cases, and solution selection. ESTABLISH AN OPERATIONAL ARCHITECTURE Without a formal Operational Architecture, your organization will not be able manage changing architectures based on new IIoT Platform technologies and capture the potential value of Big Data. Ensure that your organization s Operational Architecture includes a robust and flexible data and physical infrastructure model that can: o Tie together structured, semi-structured, and unstructured data o Manage IT and OT convergence o Support traditional descriptive and diagnostic analytics like dashboards, trend analysis, regression analysis, and more o Support next generation predictive and prescriptive analytics like machine learning IMPLEMENT A BUSINESS CASE JOURNEY FOR BIG DATA ANALYTICS Map your organization s business case journey for Big Data Analytics to the current and anticipated maturity of your Operational Excellence capabilities. At lower level of maturity you should focus on a narrow scope and cost focused benefits. At higher levels of maturity, we recommend you focus on a broader scope and value based metrics with direct connection to strategic objectives (but not necessarily short-term financial gains). Map the journey to align with an Operational Architecture that accounts for Big Data Analytics across operations. To improve Operational Excellence maturity, ensure you invest in systems and training to make Big Data Analytics accessible to existing subject matter experts, not just data scientists. CHOOSE AN INITIAL USE CASE FOR BIG DATA ANALYTICS THAT ALIGNS TO YOUR COMPANY S PAIN POINTS AND/OR COMPETITIVE DIFFERENTIATION Often these initial cases are for quality, manufacturing efficiency, or reliability. Quality is a great starting point because improved quality can drive both shortterm ROI through reduction in scrap and rework, but also long-term benefits for a differentiated customer experience and improved product design based on quality information coming from connected products.

31 31 IIoT AND BIG DATA ANALYTICS: Architecture Is Being Transformed Presented by: Connect: lnsresearch.com Author: Matthew Littlefield, President and Principal Analyst 2016 LNS Research.